diff --git a/.gitignore b/.gitignore index c2648945..de160688 100644 --- a/.gitignore +++ b/.gitignore @@ -35,3 +35,6 @@ nltk_data/ tika-server*.jar* cl100k_base.tiktoken libssl*.deb + +sandbox/lib/seccomp_python/target +sandbox/lib/seccomp_nodejs/target diff --git a/api/app/__init__.py b/api/app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 185d746c..3e7db8cb 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -3,9 +3,10 @@ import platform from datetime import timedelta from urllib.parse import quote -from app.core.config import settings from celery import Celery +from app.core.config import settings + # 创建 Celery 应用实例 # broker: 任务队列（使用 Redis DB 0） # backend: 结果存储（使用 Redis DB 10） @@ -67,11 +68,11 @@ celery_app.conf.update( 'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'}, 'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'}, - # Beat/periodic tasks → document_tasks queue (prefork worker) - 'app.tasks.workspace_reflection_task': {'queue': 'document_tasks'}, - 'app.tasks.regenerate_memory_cache': {'queue': 'document_tasks'}, - 'app.tasks.run_forgetting_cycle_task': {'queue': 'document_tasks'}, - 'app.controllers.memory_storage_controller.search_all': {'queue': 'document_tasks'}, + # Beat/periodic tasks → periodic_tasks queue (dedicated periodic worker) + 'app.tasks.workspace_reflection_task': {'queue': 'periodic_tasks'}, + 'app.tasks.regenerate_memory_cache': {'queue': 'periodic_tasks'}, + 'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'}, + 'app.controllers.memory_storage_controller.search_all': {'queue': 'periodic_tasks'}, }, ) @@ -79,40 +80,40 @@ celery_app.conf.update( celery_app.autodiscover_tasks(['app']) # Celery Beat schedule for periodic tasks -memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS) -memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS) -workspace_reflection_schedule = timedelta(seconds=30) # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME -forgetting_cycle_schedule = timedelta(hours=24) # 每24小时运行一次遗忘周期 +# memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS) +# memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS) +# workspace_reflection_schedule = timedelta(seconds=30) # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME +# forgetting_cycle_schedule = timedelta(hours=24) # 每24小时运行一次遗忘周期 # 构建定时任务配置 -beat_schedule_config = { - "run-workspace-reflection": { - "task": "app.tasks.workspace_reflection_task", - "schedule": workspace_reflection_schedule, - "args": (), - }, - "regenerate-memory-cache": { - "task": "app.tasks.regenerate_memory_cache", - "schedule": memory_cache_regeneration_schedule, - "args": (), - }, - "run-forgetting-cycle": { - "task": "app.tasks.run_forgetting_cycle_task", - "schedule": forgetting_cycle_schedule, - "kwargs": { - "config_id": None, # 使用默认配置，可以通过环境变量配置 - }, - }, -} +# beat_schedule_config = { +# "run-workspace-reflection": { +# "task": "app.tasks.workspace_reflection_task", +# "schedule": workspace_reflection_schedule, +# "args": (), +# }, +# "regenerate-memory-cache": { +# "task": "app.tasks.regenerate_memory_cache", +# "schedule": memory_cache_regeneration_schedule, +# "args": (), +# }, +# "run-forgetting-cycle": { +# "task": "app.tasks.run_forgetting_cycle_task", +# "schedule": forgetting_cycle_schedule, +# "kwargs": { +# "config_id": None, # 使用默认配置，可以通过环境变量配置 +# }, +# }, +# } # 如果配置了默认工作空间ID，则添加记忆总量统计任务 -if settings.DEFAULT_WORKSPACE_ID: - beat_schedule_config["write-total-memory"] = { - "task": "app.controllers.memory_storage_controller.search_all", - "schedule": memory_increment_schedule, - "kwargs": { - "workspace_id": settings.DEFAULT_WORKSPACE_ID, - }, - } +# if settings.DEFAULT_WORKSPACE_ID: +# beat_schedule_config["write-total-memory"] = { +# "task": "app.controllers.memory_storage_controller.search_all", +# "schedule": memory_increment_schedule, +# "kwargs": { +# "workspace_id": settings.DEFAULT_WORKSPACE_ID, +# }, +# } -celery_app.conf.beat_schedule = beat_schedule_config +# celery_app.conf.beat_schedule = beat_schedule_config diff --git a/api/app/controllers/__init__.py b/api/app/controllers/__init__.py index 3701f14d..765ef967 100644 --- a/api/app/controllers/__init__.py +++ b/api/app/controllers/__init__.py @@ -45,6 +45,7 @@ from . import ( home_page_controller, memory_perceptual_controller, memory_working_controller, + ontology_controller, ) # 创建管理端 API 路由器 @@ -90,5 +91,6 @@ manager_router.include_router(implicit_memory_controller.router) manager_router.include_router(memory_perceptual_controller.router) manager_router.include_router(memory_working_controller.router) manager_router.include_router(file_storage_controller.router) +manager_router.include_router(ontology_controller.router) __all__ = ["manager_router"] diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index 3b4e5a25..71e6e7ca 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -454,7 +454,8 @@ async def draft_run( user_id=payload.user_id or str(current_user.id), variables=payload.variables, storage_type=storage_type, - user_rag_memory_id=user_rag_memory_id + user_rag_memory_id=user_rag_memory_id, + files=payload.files # 传递多模态文件 ): yield event @@ -475,7 +476,8 @@ async def draft_run( "app_id": str(app_id), "message_length": len(payload.message), "has_conversation_id": bool(payload.conversation_id), - "has_variables": bool(payload.variables) + "has_variables": bool(payload.variables), + "has_files": bool(payload.files) } ) @@ -490,7 +492,8 @@ async def draft_run( user_id=payload.user_id or str(current_user.id), variables=payload.variables, storage_type=storage_type, - user_rag_memory_id=user_rag_memory_id + user_rag_memory_id=user_rag_memory_id, + files=payload.files # 传递多模态文件 ) logger.debug( @@ -872,3 +875,44 @@ async def update_workflow_config( workspace_id = current_user.current_workspace_id cfg = app_service.update_workflow_config(db, app_id=app_id, data=payload, workspace_id=workspace_id) return success(data=WorkflowConfigSchema.model_validate(cfg)) + + +@router.get("/{app_id}/statistics", summary="应用统计数据") +@cur_workspace_access_guard() +def get_app_statistics( + app_id: uuid.UUID, + start_date: int, + end_date: int, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """获取应用统计数据 + + Args: + app_id: 应用ID + start_date: 开始时间戳（毫秒） + end_date: 结束时间戳（毫秒） + + Returns: + - daily_conversations: 每日会话数统计 + - total_conversations: 总会话数 + - daily_new_users: 每日新增用户数 + - total_new_users: 总新增用户数 + - daily_api_calls: 每日API调用次数 + - total_api_calls: 总API调用次数 + - daily_tokens: 每日token消耗 + - total_tokens: 总token消耗 + """ + workspace_id = current_user.current_workspace_id + + from app.services.app_statistics_service import AppStatisticsService + stats_service = AppStatisticsService(db) + + result = stats_service.get_app_statistics( + app_id=app_id, + workspace_id=workspace_id, + start_date=start_date, + end_date=end_date + ) + + return success(data=result) diff --git a/api/app/controllers/emotion_config_controller.py b/api/app/controllers/emotion_config_controller.py index 76450d8a..b1630ee6 100644 --- a/api/app/controllers/emotion_config_controller.py +++ b/api/app/controllers/emotion_config_controller.py @@ -7,11 +7,13 @@ Routes: GET /memory/config/emotion - 获取情绪引擎配置 POST /memory/config/emotion - 更新情绪引擎配置 """ +import uuid from fastapi import APIRouter, Depends, Query, HTTPException, status from pydantic import BaseModel, Field -from typing import Optional +from typing import Optional, Union from sqlalchemy.orm import Session +from uuid import UUID from app.core.response_utils import success from app.dependencies import get_current_user @@ -20,6 +22,7 @@ from app.schemas.response_schema import ApiResponse from app.services.emotion_config_service import EmotionConfigService from app.core.logging_config import get_api_logger from app.db import get_db +from app.utils.config_utils import resolve_config_id # 获取API专用日志器 api_logger = get_api_logger() @@ -32,11 +35,11 @@ router = APIRouter( class EmotionConfigQuery(BaseModel): """情绪配置查询请求模型""" - config_id: int = Field(..., description="配置ID") + config_id: UUID = Field(..., description="配置ID") class EmotionConfigUpdate(BaseModel): """情绪配置更新请求模型""" - config_id: int = Field(..., description="配置ID") + config_id: Union[uuid.UUID, int, str]= Field(..., description="配置ID") emotion_enabled: bool = Field(..., description="是否启用情绪提取") emotion_model_id: Optional[str] = Field(None, description="情绪分析专用模型ID") emotion_extract_keywords: bool = Field(..., description="是否提取情绪关键词") @@ -45,7 +48,7 @@ class EmotionConfigUpdate(BaseModel): @router.get("/read_config", response_model=ApiResponse) def get_emotion_config( - config_id: int = Query(..., description="配置ID"), + config_id: UUID|int = Query(..., description="配置ID"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ): @@ -78,7 +81,7 @@ def get_emotion_config( f"用户 {current_user.username} 请求获取情绪配置", extra={"config_id": config_id} ) - + config_id=resolve_config_id(config_id, db) # 初始化服务 config_service = EmotionConfigService(db) @@ -157,6 +160,7 @@ def update_emotion_config( } } """ + config.config_id=resolve_config_id(config.config_id, db) try: api_logger.info( f"用户 {current_user.username} 请求更新情绪配置", diff --git a/api/app/controllers/emotion_controller.py b/api/app/controllers/emotion_controller.py index 154a3928..cd199aa7 100644 --- a/api/app/controllers/emotion_controller.py +++ b/api/app/controllers/emotion_controller.py @@ -53,7 +53,7 @@ async def get_emotion_tags( api_logger.info( f"用户 {current_user.username} 请求获取情绪标签统计", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "emotion_type": request.emotion_type, "start_date": request.start_date, "end_date": request.end_date, @@ -63,7 +63,7 @@ async def get_emotion_tags( # 调用服务层 data = await emotion_service.get_emotion_tags( - end_user_id=request.group_id, + end_user_id=request.end_user_id, emotion_type=request.emotion_type, start_date=request.start_date, end_date=request.end_date, @@ -73,7 +73,7 @@ async def get_emotion_tags( api_logger.info( "情绪标签统计获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "total_count": data.get("total_count", 0), "tags_count": len(data.get("tags", [])) } @@ -84,7 +84,7 @@ async def get_emotion_tags( except Exception as e: api_logger.error( f"获取情绪标签统计失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -105,7 +105,7 @@ async def get_emotion_wordcloud( api_logger.info( f"用户 {current_user.username} 请求获取情绪词云数据", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "emotion_type": request.emotion_type, "limit": request.limit } @@ -113,7 +113,7 @@ async def get_emotion_wordcloud( # 调用服务层 data = await emotion_service.get_emotion_wordcloud( - end_user_id=request.group_id, + end_user_id=request.end_user_id, emotion_type=request.emotion_type, limit=request.limit ) @@ -121,7 +121,7 @@ async def get_emotion_wordcloud( api_logger.info( "情绪词云数据获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "total_keywords": data.get("total_keywords", 0) } ) @@ -131,7 +131,7 @@ async def get_emotion_wordcloud( except Exception as e: api_logger.error( f"获取情绪词云数据失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -159,21 +159,21 @@ async def get_emotion_health( api_logger.info( f"用户 {current_user.username} 请求获取情绪健康指数", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "time_range": request.time_range } ) # 调用服务层 data = await emotion_service.calculate_emotion_health_index( - end_user_id=request.group_id, + end_user_id=request.end_user_id, time_range=request.time_range ) api_logger.info( "情绪健康指数获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "health_score": data.get("health_score", 0), "level": data.get("level", "未知") } @@ -186,7 +186,7 @@ async def get_emotion_health( except Exception as e: api_logger.error( f"获取情绪健康指数失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -206,7 +206,7 @@ async def get_emotion_suggestions( """获取个性化情绪建议（从缓存读取） Args: - request: 包含 group_id 和可选的 config_id + request: 包含 end_user_id 和可选的 config_id db: 数据库会话 current_user: 当前用户 @@ -217,22 +217,22 @@ async def get_emotion_suggestions( api_logger.info( f"用户 {current_user.username} 请求获取个性化情绪建议（缓存）", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "config_id": request.config_id } ) # 从缓存获取建议 data = await emotion_service.get_cached_suggestions( - end_user_id=request.group_id, + end_user_id=request.end_user_id, db=db ) if data is None: # 缓存不存在或已过期 api_logger.info( - f"用户 {request.group_id} 的建议缓存不存在或已过期", - extra={"group_id": request.group_id} + f"用户 {request.end_user_id} 的建议缓存不存在或已过期", + extra={"end_user_id": request.end_user_id} ) return fail( BizCode.NOT_FOUND, @@ -243,7 +243,7 @@ async def get_emotion_suggestions( api_logger.info( "个性化建议获取成功（缓存）", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "suggestions_count": len(data.get("suggestions", [])) } ) @@ -253,7 +253,7 @@ async def get_emotion_suggestions( except Exception as e: api_logger.error( f"获取个性化建议失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( diff --git a/api/app/controllers/file_storage_controller.py b/api/app/controllers/file_storage_controller.py index c28ffe6c..1a7e8ad2 100644 --- a/api/app/controllers/file_storage_controller.py +++ b/api/app/controllers/file_storage_controller.py @@ -310,7 +310,7 @@ async def get_file_url( try: if permanent: # Generate permanent URL (no expiration check) - server_url = f"http://{settings.SERVER_IP}:8000/api" + server_url = settings.FILE_LOCAL_SERVER_URL url = f"{server_url}/storage/permanent/{file_id}" return success( data={ diff --git a/api/app/controllers/implicit_memory_controller.py b/api/app/controllers/implicit_memory_controller.py index a53290e2..96e437d6 100644 --- a/api/app/controllers/implicit_memory_controller.py +++ b/api/app/controllers/implicit_memory_controller.py @@ -122,10 +122,10 @@ def validate_confidence_threshold(threshold: float) -> None: raise ValueError("confidence_threshold must be between 0.0 and 1.0") -@router.get("/preferences/{user_id}", response_model=ApiResponse) +@router.get("/preferences/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_preference_tags( - user_id: str, + end_user_id: str, confidence_threshold: float = Query(0.5, ge=0.0, le=1.0, description="Minimum confidence threshold"), tag_category: Optional[str] = Query(None, description="Filter by tag category"), start_date: Optional[datetime] = Query(None, description="Filter start date"), @@ -137,7 +137,7 @@ async def get_preference_tags( Get user preference tags from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID confidence_threshold: Minimum confidence score (0.0-1.0) tag_category: Optional category filter start_date: Optional start date filter @@ -146,20 +146,20 @@ async def get_preference_tags( Returns: List of preference tags from cache """ - api_logger.info(f"Preference tags requested for user: {user_id} (from cache)") + api_logger.info(f"Preference tags requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -192,17 +192,17 @@ async def get_preference_tags( filtered_preferences.append(pref) - api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {user_id} (from cache)") + api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {end_user_id} (from cache)") return success(data=filtered_preferences, msg="偏好标签获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "偏好标签获取", user_id) + return handle_implicit_memory_error(e, "偏好标签获取", end_user_id) -@router.get("/portrait/{user_id}", response_model=ApiResponse) +@router.get("/portrait/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_dimension_portrait( - user_id: str, + end_user_id: str, include_history: bool = Query(False, description="Include historical trends"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) @@ -211,26 +211,26 @@ async def get_dimension_portrait( Get user's four-dimension personality portrait from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID include_history: Whether to include historical trend data (ignored for cached data) Returns: Four-dimension personality portrait from cache """ - api_logger.info(f"Dimension portrait requested for user: {user_id} (from cache)") + api_logger.info(f"Dimension portrait requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -240,17 +240,17 @@ async def get_dimension_portrait( # Extract portrait from cache portrait = cached_profile.get("portrait", {}) - api_logger.info(f"Dimension portrait retrieved for user: {user_id} (from cache)") + api_logger.info(f"Dimension portrait retrieved for user: {end_user_id} (from cache)") return success(data=portrait, msg="四维画像获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "四维画像获取", user_id) + return handle_implicit_memory_error(e, "四维画像获取", end_user_id) -@router.get("/interest-areas/{user_id}", response_model=ApiResponse) +@router.get("/interest-areas/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_interest_area_distribution( - user_id: str, + end_user_id: str, include_trends: bool = Query(False, description="Include trend analysis"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) @@ -259,26 +259,26 @@ async def get_interest_area_distribution( Get user's interest area distribution from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID include_trends: Whether to include trend analysis data (ignored for cached data) Returns: Interest area distribution from cache """ - api_logger.info(f"Interest area distribution requested for user: {user_id} (from cache)") + api_logger.info(f"Interest area distribution requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -288,17 +288,17 @@ async def get_interest_area_distribution( # Extract interest areas from cache interest_areas = cached_profile.get("interest_areas", {}) - api_logger.info(f"Interest area distribution retrieved for user: {user_id} (from cache)") + api_logger.info(f"Interest area distribution retrieved for user: {end_user_id} (from cache)") return success(data=interest_areas, msg="兴趣领域分布获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "兴趣领域分布获取", user_id) + return handle_implicit_memory_error(e, "兴趣领域分布获取", end_user_id) -@router.get("/habits/{user_id}", response_model=ApiResponse) +@router.get("/habits/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_behavior_habits( - user_id: str, + end_user_id: str, confidence_level: Optional[str] = Query(None, regex="^(high|medium|low)$", description="Filter by confidence level"), frequency_pattern: Optional[str] = Query(None, regex="^(daily|weekly|monthly|seasonal|occasional|event_triggered)$", description="Filter by frequency pattern"), time_period: Optional[str] = Query(None, regex="^(current|past)$", description="Filter by time period"), @@ -309,7 +309,7 @@ async def get_behavior_habits( Get user's behavioral habits from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID confidence_level: Filter by confidence level (high, medium, low) frequency_pattern: Filter by frequency pattern (daily, weekly, monthly, seasonal, occasional, event_triggered) time_period: Filter by time period (current, past) @@ -317,20 +317,20 @@ async def get_behavior_habits( Returns: List of behavioral habits from cache """ - api_logger.info(f"Behavior habits requested for user: {user_id} (from cache)") + api_logger.info(f"Behavior habits requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -368,11 +368,11 @@ async def get_behavior_habits( filtered_habits.append(habit) - api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {user_id} (from cache)") + api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {end_user_id} (from cache)") return success(data=filtered_habits, msg="行为习惯获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "行为习惯获取", user_id) + return handle_implicit_memory_error(e, "行为习惯获取", end_user_id) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 78a5771f..61b16d9e 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -125,7 +125,7 @@ async def write_server( Write service endpoint - processes write operations synchronously Args: - user_input: Write request containing message and group_id + user_input: Write request containing message and end_user_id Returns: Response with write operation status @@ -160,19 +160,18 @@ async def write_server( api_logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储") storage_type = 'neo4j' - api_logger.info(f"Write service requested for group {user_input.group_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") + api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") try: - # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - result = await memory_agent_service.write_memory( - user_input.group_id, - messages_list, # 传递结构化消息列表 + user_input.end_user_id, + messages_list, config_id, db, storage_type, user_rag_memory_id ) + return success(data=result, msg="写入成功") except BaseException as e: # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup @@ -196,7 +195,7 @@ async def write_server_async( Async write service endpoint - enqueues write processing to Celery Args: - user_input: Write request containing message and group_id + user_input: Write request containing message and end_user_id Returns: Task ID for tracking async operation @@ -226,10 +225,10 @@ async def write_server_async( try: # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - + task = celery_app.send_task( "app.core.memory.agent.write_message", - args=[user_input.group_id, messages_list, config_id, storage_type, user_rag_memory_id] + args=[user_input.end_user_id, messages_list, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Write task queued: {task.id}") @@ -255,16 +254,14 @@ async def read_server( - "2": Direct answer based on context Args: - user_input: Read request with message, history, search_switch, and group_id + user_input: Read request with message, history, search_switch, and end_user_id Returns: Response with query answer """ config_id = user_input.config_id workspace_id = current_user.current_workspace_id - api_logger.info(f"Read service: workspace_id={workspace_id}, config_id={config_id}") - # 获取 storage_type，如果为 None 则使用默认值 storage_type = workspace_service.get_workspace_storage_type( db=db, workspace_id=workspace_id, @@ -279,12 +276,13 @@ async def read_server( name="USER_RAG_MERORY", workspace_id=workspace_id ) - if knowledge: user_rag_memory_id = str(knowledge.id) + if knowledge: + user_rag_memory_id = str(knowledge.id) - api_logger.info(f"Read service: group={user_input.group_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}") + api_logger.info(f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}") try: result = await memory_agent_service.read_memory( - user_input.group_id, + user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch, @@ -295,17 +293,20 @@ async def read_server( ) if str(user_input.search_switch) == "2": retrieve_info = result['answer'] - history = await SessionService(store).get_history(user_input.group_id, user_input.group_id, user_input.group_id) + history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id, user_input.end_user_id) query = user_input.message - + # 调用 memory_agent_service 的方法生成最终答案 result['answer'] = await memory_agent_service.generate_summary_from_retrieve( + end_user_id=user_input.end_user_id, retrieve_info=retrieve_info, history=history, query=query, config_id=config_id, db=db ) + if "信息不足，无法回答" in result['answer']: + result['answer']=retrieve_info return success(data=result, msg="回复对话消息成功") except BaseException as e: # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup @@ -403,7 +404,7 @@ async def read_server_async( try: task = celery_app.send_task( "app.core.memory.agent.read_message", - args=[user_input.group_id, user_input.message, user_input.history, user_input.search_switch, + args=[user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Read task queued: {task.id}") @@ -447,7 +448,7 @@ async def get_read_task_result( return success( data={ "result": task_result.get("result"), - "group_id": task_result.get("group_id"), + "end_user_id": task_result.get("end_user_id"), "elapsed_time": task_result.get("elapsed_time"), "task_id": task_id }, @@ -524,7 +525,7 @@ async def get_write_task_result( return success( data={ "result": task_result.get("result"), - "group_id": task_result.get("group_id"), + "end_user_id": task_result.get("end_user_id"), "elapsed_time": task_result.get("elapsed_time"), "task_id": task_id }, @@ -578,16 +579,16 @@ async def status_type( Determine the type of user message (read or write) Args: - user_input: Request containing user message and group_id + user_input: Request containing user message and end_user_id Returns: Type classification result """ - api_logger.info(f"Status type check requested for group {user_input.group_id}") + api_logger.info(f"Status type check requested for group {user_input.end_user_id}") try: # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - + # 将消息列表转换为字符串用于分类 # 只取最后一条用户消息进行分类 last_user_message = "" @@ -595,11 +596,11 @@ async def status_type( if msg.get('role') == 'user': last_user_message = msg.get('content', '') break - + if not last_user_message: # 如果没有用户消息，使用所有消息的内容 last_user_message = " ".join([msg.get('content', '') for msg in messages_list]) - + result = await memory_agent_service.classify_message_type( last_user_message, user_input.config_id, @@ -624,7 +625,7 @@ async def get_knowledge_type_stats_api( 会对缺失类型补 0，返回字典形式。 可选按状态过滤。 - 知识库类型根据当前用户的 current_workspace_id 过滤 - - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (group_id) 过滤 + - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤 - 如果用户没有当前工作空间或未提供 end_user_id，对应的统计返回 0 """ api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}") @@ -697,7 +698,7 @@ async def get_user_profile_api( current_user: User = Depends(get_current_user) ): """ - 获取工作空间下Popular Memory Tags，包含： + 获取用户详情，包含： - name: 用户名字（直接使用 end_user_id） - tags: 3个用户特征标签（从语句和实体中LLM总结） - hot_tags: 4个热门记忆标签 diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index e03c1846..88684a39 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -49,63 +49,134 @@ async def get_workspace_end_users( current_user: User = Depends(get_current_user), ): """ - 获取工作空间的宿主列表 + 获取工作空间的宿主列表（高性能优化版本 v2） - 返回格式与原 memory_list 接口中的 end_users 字段相同， - 并包含每个用户的记忆配置信息（memory_config_id 和 memory_config_name） + 优化策略： + 1. 批量查询 end_users（一次查询而非循环） + 2. 并发查询所有用户的记忆数量（Neo4j） + 3. RAG 模式使用批量查询（一次 SQL） + 4. 只返回必要字段减少数据传输 + 5. 添加短期缓存减少重复查询 + 6. 并发执行配置查询和记忆数量查询 + + 返回格式： + { + "end_user": {"id": "uuid", "other_name": "名称"}, + "memory_num": {"total": 数量}, + "memory_config": {"memory_config_id": "id", "memory_config_name": "名称"} + } """ + import asyncio + import json + from app.aioRedis import aio_redis_get, aio_redis_set + workspace_id = current_user.current_workspace_id + + # 尝试从缓存获取（30秒缓存） + cache_key = f"end_users:workspace:{workspace_id}" + try: + cached_data = await aio_redis_get(cache_key) + if cached_data: + api_logger.info(f"从缓存获取宿主列表: workspace_id={workspace_id}") + return success(data=json.loads(cached_data), msg="宿主列表获取成功") + except Exception as e: + api_logger.warning(f"Redis 缓存读取失败: {str(e)}") + # 获取当前空间类型 current_workspace_type = memory_dashboard_service.get_current_workspace_type(db, workspace_id, current_user) api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表") + + # 获取 end_users（已优化为批量查询） end_users = memory_dashboard_service.get_workspace_end_users( db=db, workspace_id=workspace_id, current_user=current_user ) - - # 批量获取所有用户的记忆配置信息（优化：一次查询而非 N 次） - end_user_ids = [str(user.id) for user in end_users] - memory_configs_map = {} - if end_user_ids: + if not end_users: + api_logger.info("工作空间下没有宿主") + # 缓存空结果，避免重复查询 try: - memory_configs_map = get_end_users_connected_configs_batch(end_user_ids, db) + await aio_redis_set(cache_key, json.dumps([]), expire=30) + except Exception as e: + api_logger.warning(f"Redis 缓存写入失败: {str(e)}") + return success(data=[], msg="宿主列表获取成功") + + end_user_ids = [str(user.id) for user in end_users] + + # 并发执行两个独立的查询任务 + async def get_memory_configs(): + """获取记忆配置（在线程池中执行同步查询）""" + try: + return await asyncio.to_thread( + get_end_users_connected_configs_batch, + end_user_ids, db + ) except Exception as e: api_logger.error(f"批量获取记忆配置失败: {str(e)}") - # 失败时使用空字典，不影响其他数据返回 + return {} + async def get_memory_nums(): + """获取记忆数量""" + if current_workspace_type == "rag": + # RAG 模式：批量查询 + try: + chunk_map = await asyncio.to_thread( + memory_dashboard_service.get_users_total_chunk_batch, + end_user_ids, db, current_user + ) + return {uid: {"total": count} for uid, count in chunk_map.items()} + except Exception as e: + api_logger.error(f"批量获取 RAG chunk 数量失败: {str(e)}") + return {uid: {"total": 0} for uid in end_user_ids} + + elif current_workspace_type == "neo4j": + # Neo4j 模式：并发查询（带并发限制） + # 使用信号量限制并发数，避免大量用户时压垮 Neo4j + MAX_CONCURRENT_QUERIES = 10 + semaphore = asyncio.Semaphore(MAX_CONCURRENT_QUERIES) + + async def get_neo4j_memory_num(end_user_id: str): + async with semaphore: + try: + return await memory_storage_service.search_all(end_user_id) + except Exception as e: + api_logger.error(f"获取用户 {end_user_id} Neo4j 记忆数量失败: {str(e)}") + return {"total": 0} + + memory_nums_list = await asyncio.gather(*[get_neo4j_memory_num(uid) for uid in end_user_ids]) + return {end_user_ids[i]: memory_nums_list[i] for i in range(len(end_user_ids))} + + return {uid: {"total": 0} for uid in end_user_ids} + + # 并发执行配置查询和记忆数量查询 + memory_configs_map, memory_nums_map = await asyncio.gather( + get_memory_configs(), + get_memory_nums() + ) + + # 构建结果（优化：使用列表推导式） result = [] for end_user in end_users: - memory_num = {} - if current_workspace_type == "neo4j": - # EndUser 是 Pydantic 模型，直接访问属性而不是使用 .get() - memory_num = await memory_storage_service.search_all(str(end_user.id)) - elif current_workspace_type == "rag": - memory_num = { - "total":memory_dashboard_service.get_current_user_total_chunk(str(end_user.id), db, current_user) - } - - # 从批量查询结果中获取配置信息 user_id = str(end_user.id) - memory_config_info = memory_configs_map.get(user_id, { - "memory_config_id": None, - "memory_config_name": None - }) - - # 只保留需要的字段，移除 error 字段（如果有） - memory_config = { - "memory_config_id": memory_config_info.get("memory_config_id"), - "memory_config_name": memory_config_info.get("memory_config_name") - } - - result.append( - { - 'end_user': end_user, - 'memory_num': memory_num, - 'memory_config': memory_config + config_info = memory_configs_map.get(user_id, {}) + result.append({ + 'end_user': { + 'id': user_id, + 'other_name': end_user.other_name + }, + 'memory_num': memory_nums_map.get(user_id, {"total": 0}), + 'memory_config': { + "memory_config_id": config_info.get("memory_config_id"), + "memory_config_name": config_info.get("memory_config_name") } - ) - + }) + + # 写入缓存（30秒过期） + try: + await aio_redis_set(cache_key, json.dumps(result), expire=30) + except Exception as e: + api_logger.warning(f"Redis 缓存写入失败: {str(e)}") + api_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return success(data=result, msg="宿主列表获取成功") diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index ca628d0c..2b5ef72f 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -11,6 +11,7 @@ """ from typing import Optional +from uuid import UUID from fastapi import APIRouter, Depends from sqlalchemy.orm import Session @@ -33,7 +34,7 @@ from app.schemas.memory_storage_schema import ( ) from app.schemas.response_schema import ApiResponse from app.services.memory_forget_service import MemoryForgetService - +from app.utils.config_utils import resolve_config_id # 获取API专用日志器 api_logger = get_api_logger() @@ -83,7 +84,8 @@ async def trigger_forgetting_cycle( connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") - + config_id = resolve_config_id((config_id), db) + if config_id is None: api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None") @@ -106,7 +108,7 @@ async def trigger_forgetting_cycle( # 调用服务层执行遗忘周期 report = await forget_service.trigger_forgetting_cycle( db=db, - group_id=end_user_id, # 服务层方法的参数名是 group_id + end_user_id=end_user_id, # 服务层方法的参数名是 end_user_id max_merge_batch_size=payload.max_merge_batch_size, min_days_since_access=payload.min_days_since_access, config_id=config_id @@ -128,7 +130,7 @@ async def trigger_forgetting_cycle( @router.get("/read_config", response_model=ApiResponse) async def read_forgetting_config( - config_id: int, + config_id: UUID|int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -157,6 +159,7 @@ async def read_forgetting_config( ) try: + config_id=resolve_config_id(config_id, db) # 调用服务层读取配置 config = forget_service.read_forgetting_config(db=db, config_id=config_id) @@ -194,6 +197,8 @@ async def update_forgetting_config( ApiResponse: 包含更新结果的响应 """ workspace_id = current_user.current_workspace_id + payload.config_id=resolve_config_id((payload.config_id), db) + # 检查用户是否已选择工作空间 if workspace_id is None: @@ -236,7 +241,7 @@ async def update_forgetting_config( @router.get("/stats", response_model=ApiResponse) async def get_forgetting_stats( - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -246,7 +251,7 @@ async def get_forgetting_stats( 返回知识层节点统计、激活值分布等信息。 Args: - group_id: 组ID（即 end_user_id，可选） + end_user_id: 组ID（即 end_user_id，可选） current_user: 当前用户 db: 数据库会话 @@ -254,26 +259,25 @@ async def get_forgetting_stats( ApiResponse: 包含统计信息的响应 """ workspace_id = current_user.current_workspace_id - # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - - # 如果提供了 group_id，通过它获取 config_id + # 如果提供了 end_user_id，通过它获取 config_id config_id = None - if group_id: + if end_user_id: try: from app.services.memory_agent_service import get_end_user_connected_config - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") + config_id = resolve_config_id(config_id, db) if config_id is None: - api_logger.warning(f"终端用户 {group_id} 未关联记忆配置") - return fail(BizCode.INVALID_PARAMETER, f"终端用户 {group_id} 未关联记忆配置", "memory_config_id is None") + api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") + return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None") - api_logger.debug(f"通过 group_id={group_id} 获取到 config_id={config_id}") + api_logger.debug(f"通过 end_user_id={end_user_id} 获取到 config_id={config_id}") except ValueError as e: api_logger.warning(f"获取终端用户配置失败: {str(e)}") return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError") @@ -283,14 +287,14 @@ async def get_forgetting_stats( api_logger.info( f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: " - f"group_id={group_id}, config_id={config_id}" + f"end_user_id={end_user_id}, config_id={config_id}" ) try: # 调用服务层获取统计信息 stats = await forget_service.get_forgetting_stats( db=db, - group_id=group_id, + end_user_id=end_user_id, config_id=config_id ) @@ -324,7 +328,7 @@ async def get_forgetting_curve( ApiResponse: 包含遗忘曲线数据的响应 """ workspace_id = current_user.current_workspace_id - + request.config_id = resolve_config_id((request.config_id), db) # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间") diff --git a/api/app/controllers/memory_perceptual_controller.py b/api/app/controllers/memory_perceptual_controller.py index 5154c763..44750808 100644 --- a/api/app/controllers/memory_perceptual_controller.py +++ b/api/app/controllers/memory_perceptual_controller.py @@ -27,27 +27,27 @@ router = APIRouter( ) -@router.get("/{group_id}/count", response_model=ApiResponse) +@router.get("/{end_user_id}/count", response_model=ApiResponse) def get_memory_count( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve perceptual memory statistics for a user group. Args: - group_id: ID of the user group (usually end_user_id in this context) + end_user_id: ID of the user group (usually end_user_id in this context) current_user: Current authenticated user db: Database session Returns: ApiResponse: Response containing memory count statistics """ - api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - count_stats = service.get_memory_count(group_id) + count_stats = service.get_memory_count(end_user_id) api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}") @@ -57,37 +57,37 @@ def get_memory_count( ) except Exception as e: - api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch memory statistics: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch memory statistics", ) -@router.get("/{group_id}/last_visual", response_model=ApiResponse) +@router.get("/{end_user_id}/last_visual", response_model=ApiResponse) def get_last_visual_memory( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent VISION-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest visual memory """ - api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest visual memory: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - visual_memory = service.get_latest_visual_memory(group_id) + visual_memory = service.get_latest_visual_memory(end_user_id) if visual_memory is None: - api_logger.info(f"No visual memory found: group_id={group_id}") + api_logger.info(f"No visual memory found: end_user_id={end_user_id}") return success( data=None, msg="No visual memory available" @@ -101,37 +101,37 @@ def get_last_visual_memory( ) except Exception as e: - api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest visual memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest visual memory", ) -@router.get("/{group_id}/last_listen", response_model=ApiResponse) +@router.get("/{end_user_id}/last_listen", response_model=ApiResponse) def get_last_memory_listen( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent AUDIO-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest audio memory """ - api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest audio memory: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - audio_memory = service.get_latest_audio_memory(group_id) + audio_memory = service.get_latest_audio_memory(end_user_id) if audio_memory is None: - api_logger.info(f"No audio memory found: group_id={group_id}") + api_logger.info(f"No audio memory found: end_user_id={end_user_id}") return success( data=None, msg="No audio memory available" @@ -145,38 +145,38 @@ def get_last_memory_listen( ) except Exception as e: - api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest audio memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest audio memory", ) -@router.get("/{group_id}/last_text", response_model=ApiResponse) +@router.get("/{end_user_id}/last_text", response_model=ApiResponse) def get_last_text_memory( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent TEXT-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest text memory """ - api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest text memory: user={current_user.username}, end_user_id={end_user_id}") try: # 调用服务层获取最近的文本记忆 service = MemoryPerceptualService(db) - text_memory = service.get_latest_text_memory(group_id) + text_memory = service.get_latest_text_memory(end_user_id) if text_memory is None: - api_logger.info(f"No text memory found: group_id={group_id}") + api_logger.info(f"No text memory found: end_user_id={end_user_id}") return success( data=None, msg="No text memory available" @@ -190,16 +190,16 @@ def get_last_text_memory( ) except Exception as e: - api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest text memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest text memory", ) -@router.get("/{group_id}/timeline", response_model=ApiResponse) +@router.get("/{end_user_id}/timeline", response_model=ApiResponse) def get_memory_time_line( - group_id: uuid.UUID, + end_user_id: uuid.UUID, perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"), page: int = Query(1, ge=1, description="页码"), page_size: int = Query(10, ge=1, le=100, description="每页大小"), @@ -209,7 +209,7 @@ def get_memory_time_line( """Retrieve a timeline of perceptual memories for a user group. Args: - group_id: ID of the user group + end_user_id: ID of the user group perceptual_type: Optional filter for perceptual type page: Page number for pagination page_size: Number of items per page @@ -221,7 +221,7 @@ def get_memory_time_line( """ api_logger.info( f"Fetching perceptual memory timeline: user={current_user.username}, " - f"group_id={group_id}, type={perceptual_type}, page={page}" + f"end_user_id={end_user_id}, type={perceptual_type}, page={page}" ) try: @@ -232,7 +232,7 @@ def get_memory_time_line( ) service = MemoryPerceptualService(db) - timeline_data = service.get_time_line(group_id, query) + timeline_data = service.get_time_line(end_user_id, query) api_logger.info( f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, " @@ -246,7 +246,7 @@ def get_memory_time_line( except Exception as e: api_logger.error( - f"Failed to fetch perceptual memory timeline: group_id={group_id}, " + f"Failed to fetch perceptual memory timeline: end_user_id={end_user_id}, " f"error={str(e)}" ) return fail( diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index abd50a33..8d5408f1 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -1,6 +1,7 @@ import asyncio import time import uuid +from uuid import UUID from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine.self_reflexion import ( @@ -11,7 +12,7 @@ from app.core.response_utils import success from app.db import get_db from app.dependencies import get_current_user from app.models.user_model import User -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_reflection_schemas import Memory_Reflection from app.services.memory_reflection_service import ( @@ -24,6 +25,8 @@ from fastapi import APIRouter, Depends, HTTPException, status,Header from sqlalchemy import text from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + load_dotenv() api_logger = get_api_logger() @@ -42,15 +45,15 @@ async def save_reflection_config( """Save reflection configuration to data_comfig table""" try: config_id = request.config_id + config_id = resolve_config_id(config_id, db) if not config_id: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="缺少必需参数: config_id" ) - api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}") - data_config = DataConfigRepository.update_reflection_config( + memory_config = MemoryConfigRepository.update_reflection_config( db, config_id=config_id, enable_self_reflexion=request.reflection_enabled, @@ -63,17 +66,17 @@ async def save_reflection_config( ) db.commit() - db.refresh(data_config) + db.refresh(memory_config) reflection_result={ - "config_id": data_config.config_id, - "enable_self_reflexion": data_config.enable_self_reflexion, - "iteration_period": data_config.iteration_period, - "reflexion_range": data_config.reflexion_range, - "baseline": data_config.baseline, - "reflection_model_id": data_config.reflection_model_id, - "memory_verify": data_config.memory_verify, - "quality_assessment": data_config.quality_assessment} + "config_id": memory_config.config_id, + "enable_self_reflexion": memory_config.enable_self_reflexion, + "iteration_period": memory_config.iteration_period, + "reflexion_range": memory_config.reflexion_range, + "baseline": memory_config.baseline, + "reflection_model_id": memory_config.reflection_model_id, + "memory_verify": memory_config.memory_verify, + "quality_assessment": memory_config.quality_assessment} return success(data=reflection_result, msg="反思配置成功") @@ -98,7 +101,7 @@ async def start_workspace_reflection( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """Activate the reflection function for all matching applications in the workspace""" + """启动工作空间中所有匹配应用的反思功能""" workspace_id = current_user.current_workspace_id reflection_service = MemoryReflectionService(db) @@ -107,42 +110,55 @@ async def start_workspace_reflection( service = WorkspaceAppService(db) result = service.get_workspace_apps_detailed(workspace_id) - reflection_results = [] - for data in result['apps_detailed_info']: - if data['data_configs'] == []: + # 跳过没有配置的应用 + if not data['memory_configs']: + api_logger.debug(f"应用 {data['id']} 没有memory_configs，跳过") continue - + releases = data['releases'] - data_configs = data['data_configs'] + memory_configs = data['memory_configs'] end_users = data['end_users'] - - for base, config, user in zip(releases, data_configs, end_users): - # 安全地转换为整数，处理空字符串和None的情况 - print(base['config']) - try: - base_config = int(base['config']) if base['config'] else 0 - config_id = int(config['config_id']) if config['config_id'] else 0 - except (ValueError, TypeError): - api_logger.warning(f"无效的配置ID: base['config']={base.get('config')}, config['config_id']={config.get('config_id')}") + + # 为每个配置和用户组合执行反思 + for config in memory_configs: + config_id_str = str(config['config_id']) + + # 找到匹配此配置的所有release + matching_releases = [r for r in releases if str(r['config']) == config_id_str] + + if not matching_releases: + api_logger.debug(f"配置 {config_id_str} 没有匹配的release") continue - - if base_config == config_id and base['app_id'] == user['app_id']: - # 调用反思服务 - api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") - - reflection_result = await reflection_service.start_text_reflection( - config_data=config, - end_user_id=user['id'] - ) - - reflection_results.append({ - "app_id": base['app_id'], - "config_id": config['config_id'], - "end_user_id": user['id'], - "reflection_result": reflection_result - }) + + # 为每个用户执行反思 + for user in end_users: + api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}") + + try: + reflection_result = await reflection_service.start_text_reflection( + config_data=config, + end_user_id=user['id'] + ) + + reflection_results.append({ + "app_id": data['id'], + "config_id": config_id_str, + "end_user_id": user['id'], + "reflection_result": reflection_result + }) + except Exception as e: + api_logger.error(f"用户 {user['id']} 反思失败: {str(e)}") + reflection_results.append({ + "app_id": data['id'], + "config_id": config_id_str, + "end_user_id": user['id'], + "reflection_result": { + "status": "错误", + "message": f"反思失败: {str(e)}" + } + }) return success(data=reflection_results, msg="反思配置成功") @@ -156,17 +172,20 @@ async def start_workspace_reflection( @router.get("/reflection/configs") async def start_reflection_configs( - config_id: int, + config_id: uuid.UUID|int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """通过config_id查询data_config表中的反思配置信息""" + """通过config_id查询memory_config表中的反思配置信息""" + config_id = resolve_config_id(config_id, db) try: + config_id=resolve_config_id(config_id,db) api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - result = DataConfigRepository.query_reflection_config_by_id(db, config_id) + result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) + memory_config_id = resolve_config_id(result.config_id, db) # 构建返回数据 reflection_config = { - "config_id": result.config_id, + "config_id": memory_config_id, "reflection_enabled": result.enable_self_reflexion, "reflection_period_in_hours": result.iteration_period, "reflexion_range": result.reflexion_range, @@ -191,7 +210,7 @@ async def start_reflection_configs( @router.get("/reflection/run") async def reflection_run( - config_id: int, + config_id: UUID|int, language_type: str = Header(default="zh", alias="X-Language-Type"), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), @@ -199,9 +218,9 @@ async def reflection_run( """Activate the reflection function for all matching applications in the workspace""" api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - - # 使用DataConfigRepository查询反思配置 - result = DataConfigRepository.query_reflection_config_by_id(db, config_id) + config_id = resolve_config_id(config_id, db) + # 使用MemoryConfigRepository查询反思配置 + result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) if not result: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, diff --git a/api/app/controllers/memory_storage_controller.py b/api/app/controllers/memory_storage_controller.py index f4175923..ae372d3b 100644 --- a/api/app/controllers/memory_storage_controller.py +++ b/api/app/controllers/memory_storage_controller.py @@ -1,5 +1,6 @@ import os from typing import Optional +from uuid import UUID from app.core.error_codes import BizCode from app.core.logging_config import get_api_logger @@ -34,6 +35,8 @@ from fastapi import APIRouter, Depends from fastapi.responses import StreamingResponse from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + # Get API logger api_logger = get_api_logger() @@ -140,7 +143,6 @@ def create_config( db: Session = Depends(get_db), ) -> dict: workspace_id = current_user.current_workspace_id - # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试创建配置但未选择工作空间") @@ -160,12 +162,12 @@ def create_config( @router.delete("/delete_config", response_model=ApiResponse) # 删除数据库中的内容（按配置名称） def delete_config( - config_id: str, + config_id: UUID|int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: workspace_id = current_user.current_workspace_id - + config_id=resolve_config_id(config_id, db) # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试删除配置但未选择工作空间") @@ -187,7 +189,7 @@ def update_config( db: Session = Depends(get_db), ) -> dict: workspace_id = current_user.current_workspace_id - + payload.config_id = resolve_config_id(payload.config_id, db) # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试更新配置但未选择工作空间") @@ -210,7 +212,7 @@ def update_config_extracted( db: Session = Depends(get_db), ) -> dict: workspace_id = current_user.current_workspace_id - + payload.config_id = resolve_config_id(payload.config_id, db) # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试更新提取配置但未选择工作空间") @@ -232,12 +234,12 @@ def update_config_extracted( @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除 def read_config_extracted( - config_id: str, + config_id: UUID | int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: workspace_id = current_user.current_workspace_id - + config_id = resolve_config_id(config_id, db) # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试读取提取配置但未选择工作空间") @@ -285,6 +287,7 @@ async def pilot_run( f"Pilot run requested: config_id={payload.config_id}, " f"dialogue_text_length={len(payload.dialogue_text)}" ) + payload.config_id = resolve_config_id(payload.config_id, db) svc = DataConfigService(db) return StreamingResponse( svc.pilot_run_stream(payload), @@ -420,15 +423,95 @@ async def get_hot_memory_tags_api( db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ) -> dict: - api_logger.info(f"Hot memory tags requested for current_user: {current_user.id}") + """ + 获取热门记忆标签（带Redis缓存） + + 缓存策略： + - 缓存键：workspace_id + limit + - 过期时间：5分钟（300秒） + - 缓存命中：~50ms + - 缓存未命中：~600-800ms（取决于LLM速度） + """ + workspace_id = current_user.current_workspace_id + + # 构建缓存键 + cache_key = f"hot_memory_tags:{workspace_id}:{limit}" + + api_logger.info(f"Hot memory tags requested for workspace: {workspace_id}, limit: {limit}") + try: + # 尝试从Redis缓存获取 + from app.aioRedis import aio_redis_get, aio_redis_set + import json + + cached_result = await aio_redis_get(cache_key) + if cached_result: + api_logger.info(f"Cache hit for key: {cache_key}") + try: + data = json.loads(cached_result) + return success(data=data, msg="查询成功（缓存）") + except json.JSONDecodeError: + api_logger.warning(f"Failed to parse cached data, will refresh") + + # 缓存未命中，执行查询 + api_logger.info(f"Cache miss for key: {cache_key}, executing query") result = await analytics_hot_memory_tags(db, current_user, limit) + + # 写入缓存（过期时间：5分钟） + # 注意：result是列表，需要转换为JSON字符串 + try: + cache_data = json.dumps(result, ensure_ascii=False) + await aio_redis_set(cache_key, cache_data, expire=300) + api_logger.info(f"Cached result for key: {cache_key}") + except Exception as cache_error: + # 缓存写入失败不影响主流程 + api_logger.warning(f"Failed to cache result: {str(cache_error)}") + return success(data=result, msg="查询成功") + except Exception as e: api_logger.error(f"Hot memory tags failed: {str(e)}") return fail(BizCode.INTERNAL_ERROR, "热门标签查询失败", str(e)) +@router.delete("/analytics/hot_memory_tags/cache", response_model=ApiResponse) +async def clear_hot_memory_tags_cache( + current_user: User = Depends(get_current_user), + ) -> dict: + """ + 清除热门标签缓存 + + 用于： + - 手动刷新数据 + - 调试和测试 + - 数据更新后立即生效 + """ + workspace_id = current_user.current_workspace_id + + api_logger.info(f"Clear hot memory tags cache requested for workspace: {workspace_id}") + + try: + from app.aioRedis import aio_redis_delete + + # 清除所有limit的缓存（常见的limit值） + cleared_count = 0 + for limit in [5, 10, 15, 20, 30, 50]: + cache_key = f"hot_memory_tags:{workspace_id}:{limit}" + result = await aio_redis_delete(cache_key) + if result: + cleared_count += 1 + api_logger.info(f"Cleared cache for key: {cache_key}") + + return success( + data={"cleared_count": cleared_count}, + msg=f"成功清除 {cleared_count} 个缓存" + ) + + except Exception as e: + api_logger.error(f"Clear cache failed: {str(e)}") + return fail(BizCode.INTERNAL_ERROR, "清除缓存失败", str(e)) + + @router.get("/analytics/recent_activity_stats", response_model=ApiResponse) async def get_recent_activity_stats_api( current_user: User = Depends(get_current_user), diff --git a/api/app/controllers/memory_working_controller.py b/api/app/controllers/memory_working_controller.py index dfd64044..e5de3c04 100644 --- a/api/app/controllers/memory_working_controller.py +++ b/api/app/controllers/memory_working_controller.py @@ -20,18 +20,18 @@ router = APIRouter( ) -@router.get("/{group_id}/count", response_model=ApiResponse) +@router.get("/{end_user_id}/count", response_model=ApiResponse) def get_memory_count( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): pass -@router.get("/{group_id}/conversations", response_model=ApiResponse) +@router.get("/{end_user_id}/conversations", response_model=ApiResponse) def get_conversations( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -39,7 +39,7 @@ def get_conversations( Retrieve all conversations for the current user in a specific group. Args: - group_id (UUID): The group identifier. + end_user_id (UUID): The group identifier. current_user (User, optional): The authenticated user. db (Session, optional): SQLAlchemy session. @@ -53,7 +53,7 @@ def get_conversations( """ conversation_service = ConversationService(db) conversations = conversation_service.get_user_conversations( - group_id + end_user_id ) return success(data=[ { @@ -63,7 +63,7 @@ def get_conversations( ], msg="get conversations success") -@router.get("/{group_id}/messages", response_model=ApiResponse) +@router.get("/{end_user_id}/messages", response_model=ApiResponse) def get_messages( conversation_id: uuid.UUID, current_user: User = Depends(get_current_user), @@ -100,7 +100,7 @@ def get_messages( return success(data=messages, msg="get conversation history success") -@router.get("/{group_id}/detail", response_model=ApiResponse) +@router.get("/{end_user_id}/detail", response_model=ApiResponse) async def get_conversation_detail( conversation_id: uuid.UUID, current_user: User = Depends(get_current_user), diff --git a/api/app/controllers/model_controller.py b/api/app/controllers/model_controller.py index 42d59664..83753744 100644 --- a/api/app/controllers/model_controller.py +++ b/api/app/controllers/model_controller.py @@ -3,15 +3,17 @@ from sqlalchemy.orm import Session from typing import Optional import uuid - +from app.core.error_codes import BizCode +from app.core.exceptions import BusinessException from app.db import get_db from app.dependencies import get_current_user -from app.models.models_model import ModelProvider, ModelType +from app.models.models_model import ModelProvider, ModelType, LoadBalanceStrategy from app.models.user_model import User +from app.repositories.model_repository import ModelConfigRepository from app.schemas import model_schema from app.core.response_utils import success from app.schemas.response_schema import ApiResponse, PageData -from app.services.model_service import ModelConfigService, ModelApiKeyService +from app.services.model_service import ModelConfigService, ModelApiKeyService, ModelBaseService from app.core.logging_config import get_api_logger # 获取API专用日志器 @@ -24,24 +26,83 @@ router = APIRouter( @router.get("/type", response_model=ApiResponse) def get_model_types(): - return success(msg="获取模型类型成功", data=list(ModelType)) @router.get("/provider", response_model=ApiResponse) def get_model_providers(): - return success(msg="获取模型提供商成功", data=list(ModelProvider)) + providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE] + return success(msg="获取模型提供商成功", data=providers) + +@router.get("/strategy", response_model=ApiResponse) +def get_model_strategies(): + return success(msg="获取模型策略成功", data=list(LoadBalanceStrategy)) @router.get("", response_model=ApiResponse) def get_model_list( - type: Optional[str] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), - provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"), + type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), + provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"), + is_active: Optional[bool] = Query(None, description="激活状态筛选"), + is_public: Optional[bool] = Query(None, description="公开状态筛选"), + search: Optional[str] = Query(None, description="搜索关键词"), + page: int = Query(1, ge=1, description="页码"), + pagesize: int = Query(10, ge=1, le=100, description="每页数量"), + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """ + 获取模型配置列表 + + 支持多个 type 参数： + - 单个：?type=LLM + - 多个（逗号分隔）：?type=LLM,EMBEDDING + - 多个（重复参数）：?type=LLM&type=EMBEDDING + """ + api_logger.info( + f"获取模型配置列表请求: type={type}, provider={provider}, page={page}, pagesize={pagesize}, tenant_id={current_user.tenant_id}") + + try: + # 解析 type 参数（支持逗号分隔） + type_list = [] + if type is not None: + flat_type = [] + for item in type: + split_items = [t.strip() for t in item.split(',') if t.strip()] + flat_type.extend(split_items) + + unique_flat_type = list(dict.fromkeys(flat_type)) + type_list = [ModelType(t.lower()) for t in unique_flat_type] + + api_logger.error(f"获取模型type_list: {type_list}") + query = model_schema.ModelConfigQuery( + type=type_list, + provider=provider, + is_active=is_active, + is_public=is_public, + search=search, + page=page, + pagesize=pagesize + ) + + api_logger.debug(f"开始获取模型配置列表: {query.dict()}") + result_orm = ModelConfigService.get_model_list(db=db, query=query, tenant_id=current_user.tenant_id) + result = PageData.model_validate(result_orm) + api_logger.info(f"模型配置列表获取成功: 总数={result.page.total}, 当前页={len(result.items)}") + return success(data=result, msg="模型配置列表获取成功") + except Exception as e: + api_logger.error(f"获取模型配置列表失败: {str(e)}") + raise + + +@router.get("/new", response_model=ApiResponse) +def get_model_list_new( + type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), + provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于ModelConfig)"), is_active: Optional[bool] = Query(None, description="激活状态筛选"), is_public: Optional[bool] = Query(None, description="公开状态筛选"), search: Optional[str] = Query(None, description="搜索关键词"), - page: int = Query(1, ge=1, description="页码"), - pagesize: int = Query(10, ge=1, le=100, description="每页数量"), + is_composite: Optional[bool] = Query(None, description="组合模型筛选"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) ): @@ -53,36 +114,127 @@ def get_model_list( - 多个（逗号分隔）：?type=LLM,EMBEDDING - 多个（重复参数）：?type=LLM&type=EMBEDDING """ - api_logger.info(f"获取模型配置列表请求: type={type}, provider={provider}, page={page}, pagesize={pagesize}, tenant_id={current_user.tenant_id}") + api_logger.info(f"获取模型配置列表请求: type={type}, provider={provider}, tenant_id={current_user.tenant_id}") try: # 解析 type 参数（支持逗号分隔） - type_list = None - if type: - type_values = [t.strip() for t in type.split(',')] - type_list = [model_schema.ModelType(t.lower()) for t in type_values if t] + type_list = [] + if type is not None: + flat_type = [] + for item in type: + split_items = [t.strip() for t in item.split(',') if t.strip()] + flat_type.extend(split_items) + + unique_flat_type = list(dict.fromkeys(flat_type)) + type_list = [ModelType(t.lower()) for t in unique_flat_type] - api_logger.error(f"获取模型type_list: {type_list}") - query = model_schema.ModelConfigQuery( + api_logger.info(f"获取模型type_list: {type_list}") + query = model_schema.ModelConfigQueryNew( type=type_list, provider=provider, is_active=is_active, is_public=is_public, - search=search, - page=page, - pagesize=pagesize + is_composite=is_composite, + search=search ) - api_logger.debug(f"开始获取模型配置列表: {query.dict()}") - result_orm = ModelConfigService.get_model_list(db=db, query=query, tenant_id=current_user.tenant_id) - result = PageData.model_validate(result_orm) - api_logger.info(f"模型配置列表获取成功: 总数={result.page.total}, 当前页={len(result.items)}") + api_logger.debug(f"开始获取模型配置列表: {query.model_dump()}") + result = ModelConfigService.get_model_list_new(db=db, query=query, tenant_id=current_user.tenant_id) + api_logger.info(f"模型配置列表获取成功: 分组数={len(result)}, 总模型数={sum(len(item['models']) for item in result)}") return success(data=result, msg="模型配置列表获取成功") except Exception as e: api_logger.error(f"获取模型配置列表失败: {str(e)}") raise +@router.get("/model_plaza", response_model=ApiResponse) +def get_model_plaza_list( + type: Optional[ModelType] = Query(None, description="模型类型"), + provider: Optional[ModelProvider] = Query(None, description="供应商"), + is_official: Optional[bool] = Query(None, description="是否官方模型"), + is_deprecated: Optional[bool] = Query(None, description="是否弃用"), + search: Optional[str] = Query(None, description="搜索关键词"), + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """模型广场查询接口（按供应商分组）""" + + query = model_schema.ModelBaseQuery( + type=type, + provider=provider, + is_official=is_official, + is_deprecated=is_deprecated, + search=search + ) + result = ModelBaseService.get_model_base_list(db=db, query=query, tenant_id=current_user.tenant_id) + return success(data=result, msg="模型广场列表获取成功") + + +@router.get("/model_plaza/{model_base_id}", response_model=ApiResponse) +def get_model_base_by_id( + model_base_id: uuid.UUID, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取基础模型详情""" + + result = ModelBaseService.get_model_base_by_id(db=db, model_base_id=model_base_id) + return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型获取成功") + + +@router.post("/model_plaza", response_model=ApiResponse) +def create_model_base( + data: model_schema.ModelBaseCreate, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """创建基础模型""" + + result = ModelBaseService.create_model_base(db=db, data=data) + return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型创建成功") + + +@router.put("/model_plaza/{model_base_id}", response_model=ApiResponse) +def update_model_base( + model_base_id: uuid.UUID, + data: model_schema.ModelBaseUpdate, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """更新基础模型""" + + # 不允许更改type类型 + if data.type is not None or data.provider is not None: + raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER) + + result = ModelBaseService.update_model_base(db=db, model_base_id=model_base_id, data=data) + return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型更新成功") + + +@router.delete("/model_plaza/{model_base_id}", response_model=ApiResponse) +def delete_model_base( + model_base_id: uuid.UUID, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """删除基础模型""" + + ModelBaseService.delete_model_base(db=db, model_base_id=model_base_id) + return success(msg="基础模型删除成功") + + +@router.post("/model_plaza/{model_base_id}/add", response_model=ApiResponse) +def add_model_from_plaza( + model_base_id: uuid.UUID, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """从模型广场添加模型到模型列表""" + + result = ModelBaseService.add_model_from_plaza(db=db, model_base_id=model_base_id, tenant_id=current_user.tenant_id) + return success(data=model_schema.ModelConfig.model_validate(result), msg="模型添加成功") + + @router.get("/{model_id}", response_model=ApiResponse) def get_model_by_id( model_id: uuid.UUID, @@ -138,6 +290,73 @@ async def create_model( raise +@router.post("/composite", response_model=ApiResponse) +async def create_composite_model( + model_data: model_schema.CompositeModelCreate, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """ + 创建组合模型 + + - 绑定一个或多个现有的 API Key + - 所有 API Key 必须来自非组合模型 + - 所有 API Key 关联的模型类型必须与组合模型类型一致 + """ + api_logger.info(f"创建组合模型请求: {model_data.name}, 用户: {current_user.username}, tenant_id={current_user.tenant_id}") + + try: + result_orm = await ModelConfigService.create_composite_model(db=db, model_data=model_data, tenant_id=current_user.tenant_id) + api_logger.info(f"组合模型创建成功: {result_orm.name} (ID: {result_orm.id})") + + result = model_schema.ModelConfig.model_validate(result_orm) + return success(data=result, msg="组合模型创建成功") + except Exception as e: + api_logger.error(f"创建组合模型失败: {model_data.name} - {str(e)}") + raise + + +@router.put("/composite/{model_id}", response_model=ApiResponse) +async def update_composite_model( + model_id: uuid.UUID, + model_data: model_schema.CompositeModelCreate, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """更新组合模型""" + api_logger.info(f"更新组合模型请求: model_id={model_id}, 用户: {current_user.username}") + + try: + if model_data.type is not None: + raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER) + result_orm = await ModelConfigService.update_composite_model(db=db, model_id=model_id, model_data=model_data, tenant_id=current_user.tenant_id) + api_logger.info(f"组合模型更新成功: {result_orm.name} (ID: {model_id})") + + result = model_schema.ModelConfig.model_validate(result_orm) + return success(data=result, msg="组合模型更新成功") + except Exception as e: + api_logger.error(f"更新组合模型失败: model_id={model_id} - {str(e)}") + raise + + +@router.delete("/composite/{model_id}", response_model=ApiResponse) +def delete_composite_model( + model_id: uuid.UUID, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """删除组合模型""" + api_logger.info(f"删除组合模型请求: model_id={model_id}, 用户: {current_user.username}") + + try: + ModelConfigService.delete_model(db=db, model_id=model_id, tenant_id=current_user.tenant_id) + api_logger.info(f"组合模型删除成功: model_id={model_id}") + return success(msg="组合模型删除成功") + except Exception as e: + api_logger.error(f"删除组合模型失败: model_id={model_id} - {str(e)}") + raise + + @router.put("/{model_id}", response_model=ApiResponse) def update_model( model_id: uuid.UUID, @@ -214,6 +433,53 @@ def get_model_api_keys( raise +@router.post("/provider/apikeys", response_model=ApiResponse) +async def create_model_api_key_by_provider( + api_key_data: model_schema.ModelApiKeyCreateByProvider, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """ + 根据供应商为所有匹配的模型创建API Key + """ + api_logger.info(f"创建API Key请求: provider={api_key_data.provider}, 用户: {current_user.username}") + + try: + # 根据tenant_id和provider筛选model_config_id列表 + model_config_ids = api_key_data.model_config_ids + if not model_config_ids: + model_config_ids = ModelConfigRepository.get_model_config_ids_by_provider( + db=db, + tenant_id=current_user.tenant_id, + provider=api_key_data.provider + ) + + if not model_config_ids: + raise BusinessException(f"未找到供应商 {api_key_data.provider} 的模型配置", BizCode.MODEL_NOT_FOUND) + + # 构造schema并调用service + create_data = model_schema.ModelApiKeyCreateByProvider( + provider=api_key_data.provider, + api_key=api_key_data.api_key, + api_base=api_key_data.api_base, + description=api_key_data.description, + config=api_key_data.config, + is_active=api_key_data.is_active, + priority=api_key_data.priority, + model_config_ids=model_config_ids + ) + created_keys, failed_models = await ModelApiKeyService.create_api_key_by_provider(db=db, data=create_data) + + api_logger.info(f"API Key创建成功: 关联{len(created_keys)}个模型") + # result_list = [model_schema.ModelApiKey.model_validate(key) for key in created_keys] + result = "API Key已存在" if len(created_keys) == 0 and len(failed_models) == 0 else \ + f"成功为 {len(created_keys)} 个模型创建API Key, 失败模型列表{failed_models}" + return success(data=result, msg=f"成功为 {len(created_keys)} 个模型创建API Key") + except Exception as e: + api_logger.error(f"创建API Key失败: {str(e)}") + raise + + @router.post("/{model_id}/apikeys", response_model=ApiResponse, status_code=status.HTTP_201_CREATED) async def create_model_api_key( model_id: uuid.UUID, @@ -228,11 +494,12 @@ async def create_model_api_key( try: # 设置模型配置ID - api_key_data.model_config_id = model_id + api_key_data.model_config_ids = [model_id] api_logger.debug(f"开始创建模型API Key: {api_key_data.model_name}") - result = await ModelApiKeyService.create_api_key(db=db, api_key_data=api_key_data) - api_logger.info(f"模型API Key创建成功: {result.model_name} (ID: {result.id})") + result_orm = await ModelApiKeyService.create_api_key(db=db, api_key_data=api_key_data) + api_logger.info(f"模型API Key创建成功: {result_orm.model_name} (ID: {result_orm.id})") + result = model_schema.ModelApiKey.model_validate(result_orm) return success(data=result, msg="模型API Key创建成功") except Exception as e: api_logger.error(f"创建模型API Key失败: {api_key_data.model_name} - {str(e)}") @@ -334,5 +601,3 @@ async def validate_model_config( return success(data=model_schema.ModelValidateResponse(**result), msg="验证完成") - - diff --git a/api/app/controllers/ontology_controller.py b/api/app/controllers/ontology_controller.py new file mode 100644 index 00000000..1cf8e64e --- /dev/null +++ b/api/app/controllers/ontology_controller.py @@ -0,0 +1,964 @@ +"""本体提取API控制器 + +本模块提供本体提取系统的RESTful API端点。 + +Endpoints: + POST /api/memory/ontology/extract - 提取本体类 + POST /api/memory/ontology/export - 导出OWL文件 + POST /api/memory/ontology/scene - 创建本体场景 + PUT /api/memory/ontology/scene/{scene_id} - 更新本体场景 + DELETE /api/memory/ontology/scene/{scene_id} - 删除本体场景 + GET /api/memory/ontology/scene/{scene_id} - 获取单个场景 + GET /api/memory/ontology/scenes - 获取场景列表 + POST /api/memory/ontology/class - 创建本体类型 + PUT /api/memory/ontology/class/{class_id} - 更新本体类型 + DELETE /api/memory/ontology/class/{class_id} - 删除本体类型 + GET /api/memory/ontology/class/{class_id} - 获取单个类型 + GET /api/memory/ontology/classes - 获取类型列表 +""" + +import logging +import tempfile +from typing import Dict, Optional + +from fastapi import APIRouter, Depends, HTTPException, Header +from sqlalchemy.orm import Session + +from app.core.error_codes import BizCode +from app.core.logging_config import get_api_logger +from app.core.response_utils import fail, success +from app.db import get_db +from app.dependencies import get_current_user +from app.models.user_model import User +from app.services.memory_base_service import Translation_English +from app.core.memory.models.ontology_models import OntologyClass +from typing import List +from app.schemas.ontology_schemas import ( + ExportRequest, + ExportResponse, + ExtractionRequest, + ExtractionResponse, + SceneCreateRequest, + SceneUpdateRequest, + SceneResponse, + SceneListResponse, + ClassCreateRequest, + ClassUpdateRequest, + ClassResponse, + ClassListResponse, +) +from app.schemas.response_schema import ApiResponse +from app.services.ontology_service import OntologyService +from app.core.memory.llm_tools.openai_client import OpenAIClient +from app.core.memory.utils.validation.owl_validator import OWLValidator +from app.services.model_service import ModelConfigService + + +api_logger = get_api_logger() +logger = logging.getLogger(__name__) + +router = APIRouter( + prefix="/memory/ontology", + tags=["Ontology"], +) + + +async def translate_ontology_classes( + classes: List[OntologyClass], + model_id: str +) -> List[OntologyClass]: + """翻译本体类列表 + + 将本体类的中文字段翻译为英文，包括： + - name_chinese: 中文名称 + - description: 描述 + - examples: 示例列表 + + Args: + classes: 本体类列表 + model_id: LLM模型ID，用于翻译 + + Returns: + List[OntologyClass]: 翻译后的本体类列表 + """ + translated_classes = [] + + for ontology_class in classes: + # 创建类的副本，避免修改原对象 + translated_class = ontology_class.model_copy(deep=True) + + # 翻译 name_chinese 字段 + if translated_class.name_chinese: + try: + translated_class.name_chinese = await Translation_English( + model_id, + translated_class.name_chinese + ) + except Exception as e: + logger.warning(f"Failed to translate name_chinese: {e}") + # 保留原文 + + # 翻译 description 字段 + if translated_class.description: + try: + translated_class.description = await Translation_English( + model_id, + translated_class.description + ) + except Exception as e: + logger.warning(f"Failed to translate description: {e}") + # 保留原文 + + # 翻译 examples 列表 + if translated_class.examples: + translated_examples = [] + for example in translated_class.examples: + try: + translated_example = await Translation_English( + model_id, + example + ) + translated_examples.append(translated_example) + except Exception as e: + logger.warning(f"Failed to translate example: {e}") + translated_examples.append(example) # 保留原文 + translated_class.examples = translated_examples + + translated_classes.append(translated_class) + + return translated_classes + + +def _get_ontology_service( + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user), + llm_id: str = None +) -> OntologyService: + """获取OntologyService实例的依赖注入函数 + + 指定的llm_id获取LLM配置,创建OpenAIClient和OntologyService实例。 + + Args: + db: 数据库会话 + current_user: 当前用户 + llm_id: 可选的LLM模型ID,如果提供则使用指定模型,否则使用工作空间默认模型 + + Returns: + OntologyService: 本体提取服务实例 + + Raises: + HTTPException: 如果无法获取LLM配置 + """ + try: + import uuid + + # 必须提供llm_id + if not llm_id: + logger.error(f"llm_id is required but not provided - user: {current_user.id}") + raise HTTPException( + status_code=400, + detail="必须提供llm_id参数" + ) + + logger.info(f"Using specified LLM model: {llm_id}") + + # 验证llm_id格式 + try: + model_id = uuid.UUID(llm_id) + except ValueError: + logger.error(f"Invalid llm_id format: {llm_id}") + raise HTTPException( + status_code=400, + detail="无效的LLM模型ID格式" + ) + + # 获取指定的模型配置 + try: + model_config = ModelConfigService.get_model_by_id(db=db, model_id=model_id) + except Exception as e: + logger.error(f"Model {llm_id} not found: {str(e)}") + raise HTTPException( + status_code=400, + detail=f"找不到指定的LLM模型: {llm_id}" + ) + + # 检查是否为组合模型 + if hasattr(model_config, 'is_composite') and model_config.is_composite: + logger.error(f"Model {llm_id} is a composite model, which is not supported for ontology extraction") + raise HTTPException( + status_code=400, + detail="本体提取不支持使用组合模型，请选择单个模型" + ) + + # 验证模型配置了API密钥 + if not model_config.api_keys: + logger.error(f"Model {llm_id} has no API key configuration") + raise HTTPException( + status_code=400, + detail="指定的LLM模型没有配置API密钥" + ) + + api_key_config = model_config.api_keys[0] + + logger.info( + f"Using specified model - user: {current_user.id}, " + f"model_id: {llm_id}, model_name: {api_key_config.model_name}" + ) + + # 创建模型配置对象 + from app.core.models.base import RedBearModelConfig + + llm_model_config = RedBearModelConfig( + model_name=api_key_config.model_name, + provider=model_config.provider if hasattr(model_config, 'provider') else "openai", + api_key=api_key_config.api_key, + base_url=api_key_config.api_base, + max_retries=3, + timeout=60.0 + ) + + # 创建OpenAI客户端 + llm_client = OpenAIClient(model_config=llm_model_config) + + # 创建OntologyService + service = OntologyService(llm_client=llm_client, db=db) + + logger.debug( + f"OntologyService created successfully - " + f"user: {current_user.id}, model: {api_key_config.model_name}" + ) + + return service + + except HTTPException: + raise + except Exception as e: + logger.error(f"Failed to create OntologyService: {str(e)}", exc_info=True) + raise HTTPException( + status_code=500, + detail=f"创建本体提取服务失败: {str(e)}" + ) + + +@router.post("/extract", response_model=ApiResponse) +async def extract_ontology( + request: ExtractionRequest, + language_type: str = Header(default="zh", alias="X-Language-Type"), + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """提取本体类 + + 从场景描述中提取符合OWL规范的本体类。 + 提取结果仅返回给前端，不会自动保存到数据库。 + 前端可以从返回结果中选择需要的类型，然后调用 /class 接口创建类型。 + 支持中英文切换，通过 X-Language-Type Header 指定语言。 + + Args: + request: 提取请求,包含scenario、domain、llm_id和scene_id + language_type: 语言类型，'zh'（中文）或 'en'（英文），默认 'zh' + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含提取结果的响应 + + Response format: + { + "code": 200, + "msg": "本体提取成功", + "data": { + "classes": [ + { + "id": "147d9db50b524a9e909e01a753d3acdd", + "name": "Patient", + "name_chinese": "患者", + "description": "在医疗机构中接受诊疗、护理或健康管理的个体", + "examples": ["糖尿病患者", "术后康复患者", "门诊初诊患者"], + "parent_class": null, + "entity_type": "Person", + "domain": "Healthcare" + }, + ... + ], + "domain": "Healthcare", + "extracted_count": 7 + } + } + """ + api_logger.info( + f"Ontology extraction requested by user {current_user.id}, " + f"scenario_length={len(request.scenario)}, " + f"domain={request.domain}, " + f"llm_id={request.llm_id}, " + f"scene_id={request.scene_id}, " + f"language_type={language_type}" + ) + + try: + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建OntologyService实例,传入llm_id + service = _get_ontology_service( + db=db, + current_user=current_user, + llm_id=request.llm_id + ) + + # 调用服务层执行提取，传入scene_id和workspace_id + result = await service.extract_ontology( + scenario=request.scenario, + domain=request.domain, + scene_id=request.scene_id, + workspace_id=workspace_id + ) + + # ===== 新增：翻译逻辑 ===== + # 如果需要英文，则翻译数据 + if language_type != 'zh': + api_logger.info(f"Translating extraction result to English") + + # 翻译 classes 列表 + result.classes = await translate_ontology_classes( + result.classes, + request.llm_id + ) + + # 翻译 domain 字段 + if result.domain: + try: + result.domain = await Translation_English( + request.llm_id, + result.domain + ) + except Exception as e: + logger.warning(f"Failed to translate domain: {e}") + # 保留原文 + # ===== 翻译逻辑结束 ===== + + # 构建响应 + response = ExtractionResponse( + classes=result.classes, + domain=result.domain, + extracted_count=len(result.classes) + ) + + api_logger.info( + f"Ontology extraction completed, extracted {len(result.classes)} classes, " + f"saved to scene {request.scene_id}, language={language_type}" + ) + + return success(data=response.model_dump(), msg="本体提取成功") + + except ValueError as e: + # 验证错误 (400) + api_logger.warning(f"Validation error in extraction: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + # 运行时错误 (500) + api_logger.error(f"Runtime error in extraction: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "本体提取失败", str(e)) + + except Exception as e: + # 未知错误 (500) + api_logger.error(f"Unexpected error in extraction: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "本体提取失败", str(e)) + + +@router.post("/export", response_model=ApiResponse) +async def export_owl( + request: ExportRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """导出OWL文件 + + 将提取的本体类导出为OWL文件,支持多种格式。 + 导出操作不需要LLM,只使用OWL验证器和Owlready2库。 + + Args: + request: 导出请求,包含classes、format和include_metadata + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含OWL文件内容的响应 + + Supported formats: + - rdfxml: 标准OWL RDF/XML格式(完整) + - turtle: Turtle格式(可读性好) + - ntriples: N-Triples格式(简单) + - json: JSON格式(简化,只包含类信息) + + Response format: + { + "code": 200, + "msg": "OWL文件导出成功", + "data": { + "owl_content": "...", + "format": "rdfxml", + "classes_count": 7 + } + } + """ + api_logger.info( + f"OWL export requested by user {current_user.id}, " + f"classes_count={len(request.classes)}, " + f"format={request.format}, " + f"include_metadata={request.include_metadata}" + ) + + try: + # 验证格式 + valid_formats = ["rdfxml", "turtle", "ntriples", "json"] + if request.format not in valid_formats: + api_logger.warning(f"Invalid export format: {request.format}") + return fail( + BizCode.BAD_REQUEST, + "不支持的导出格式", + f"format必须是以下之一: {', '.join(valid_formats)}" + ) + + # JSON格式直接导出,不需要OWL验证 + if request.format == "json": + owl_validator = OWLValidator() + owl_content = owl_validator.export_to_owl( + world=None, + format="json", + classes=request.classes + ) + + response = ExportResponse( + owl_content=owl_content, + format=request.format, + classes_count=len(request.classes) + ) + + api_logger.info( + f"JSON export completed, content_length={len(owl_content)}" + ) + + return success(data=response.model_dump(), msg="OWL文件导出成功") + + # 创建临时文件路径 + with tempfile.NamedTemporaryFile( + mode='w', + suffix='.owl', + delete=False + ) as tmp_file: + output_path = tmp_file.name + + # 导出操作不需要LLM,直接使用OWL验证器 + owl_validator = OWLValidator() + + # 验证本体类 + logger.debug("Validating ontology classes") + is_valid, errors, world = owl_validator.validate_ontology_classes( + classes=request.classes, + ) + + if not is_valid: + logger.warning( + f"OWL validation found {len(errors)} issues during export: {errors}" + ) + # 继续导出,但记录警告 + + if not world: + error_msg = "Failed to create OWL world for export" + logger.error(error_msg) + return fail(BizCode.INTERNAL_ERROR, "创建OWL世界失败", error_msg) + + # 导出OWL文件 + logger.info(f"Exporting to {request.format} format") + owl_content = owl_validator.export_to_owl( + world=world, + output_path=output_path, + format=request.format, + classes=request.classes + ) + + # 构建响应 + response = ExportResponse( + owl_content=owl_content, + format=request.format, + classes_count=len(request.classes) + ) + + api_logger.info( + f"OWL export completed, format={request.format}, " + f"content_length={len(owl_content)}" + ) + + return success(data=response.model_dump(), msg="OWL文件导出成功") + + except ValueError as e: + # 验证错误 (400) + api_logger.warning(f"Validation error in export: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + # 运行时错误 (500) + api_logger.error(f"Runtime error in export: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "OWL文件导出失败", str(e)) + + except Exception as e: + # 未知错误 (500) + api_logger.error(f"Unexpected error in export: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "OWL文件导出失败", str(e)) + + +# ==================== 本体场景管理接口 ==================== + +@router.post("/scene", response_model=ApiResponse) +async def create_scene( + request: SceneCreateRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """创建本体场景 + + 在当前工作空间下创建新的本体场景。 + + Args: + request: 场景创建请求 + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含创建的场景信息 + """ + api_logger.info( + f"Scene creation requested by user {current_user.id}, " + f"name={request.scene_name}" + ) + + try: + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建OntologyService实例（不需要LLM） + from app.core.memory.llm_tools.openai_client import OpenAIClient + from app.core.models.base import RedBearModelConfig + + # 创建一个空的LLM配置（场景管理不需要LLM） + dummy_config = RedBearModelConfig( + model_name="dummy", + provider="openai", + api_key="dummy", + base_url="https://api.openai.com/v1" + ) + llm_client = OpenAIClient(model_config=dummy_config) + service = OntologyService(llm_client=llm_client, db=db) + + # 调用服务层创建场景 + scene = service.create_scene( + scene_name=request.scene_name, + scene_description=request.scene_description, + workspace_id=workspace_id + ) + + # 构建响应 + # 动态计算 type_num + type_num = len(scene.classes) if scene.classes else 0 + + response = SceneResponse( + scene_id=scene.scene_id, + scene_name=scene.scene_name, + scene_description=scene.scene_description, + type_num=type_num, + workspace_id=scene.workspace_id, + created_at=scene.created_at, + updated_at=scene.updated_at, + classes_count=type_num + ) + + api_logger.info(f"Scene created successfully: {scene.scene_id}") + + return success(data=response.model_dump(), msg="场景创建成功") + + except ValueError as e: + api_logger.warning(f"Validation error in scene creation: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in scene creation: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "场景创建失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in scene creation: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "场景创建失败", str(e)) + + +@router.put("/scene/{scene_id}", response_model=ApiResponse) +async def update_scene( + scene_id: str, + request: SceneUpdateRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """更新本体场景 + + 更新指定场景的信息，只能更新当前工作空间下的场景。 + + Args: + scene_id: 场景ID + request: 场景更新请求 + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含更新后的场景信息 + """ + api_logger.info( + f"Scene update requested by user {current_user.id}, " + f"scene_id={scene_id}" + ) + + try: + from uuid import UUID + + # 验证UUID格式 + try: + scene_uuid = UUID(scene_id) + except ValueError: + api_logger.warning(f"Invalid scene_id format: {scene_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式") + + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建OntologyService实例 + from app.core.memory.llm_tools.openai_client import OpenAIClient + from app.core.models.base import RedBearModelConfig + + dummy_config = RedBearModelConfig( + model_name="dummy", + provider="openai", + api_key="dummy", + base_url="https://api.openai.com/v1" + ) + llm_client = OpenAIClient(model_config=dummy_config) + service = OntologyService(llm_client=llm_client, db=db) + + # 调用服务层更新场景 + scene = service.update_scene( + scene_id=scene_uuid, + scene_name=request.scene_name, + scene_description=request.scene_description, + workspace_id=workspace_id + ) + + # 构建响应 + # 动态计算 type_num + type_num = len(scene.classes) if scene.classes else 0 + + response = SceneResponse( + scene_id=scene.scene_id, + scene_name=scene.scene_name, + scene_description=scene.scene_description, + type_num=type_num, + workspace_id=scene.workspace_id, + created_at=scene.created_at, + updated_at=scene.updated_at, + classes_count=type_num + ) + + api_logger.info(f"Scene updated successfully: {scene_id}") + + return success(data=response.model_dump(), msg="场景更新成功") + + except ValueError as e: + api_logger.warning(f"Validation error in scene update: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in scene update: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "场景更新失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in scene update: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "场景更新失败", str(e)) + + +@router.delete("/scene/{scene_id}", response_model=ApiResponse) +async def delete_scene( + scene_id: str, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """删除本体场景 + + 删除指定场景及其所有关联类型，只能删除当前工作空间下的场景。 + + Args: + scene_id: 场景ID + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 删除结果 + """ + api_logger.info( + f"Scene deletion requested by user {current_user.id}, " + f"scene_id={scene_id}" + ) + + try: + from uuid import UUID + + # 验证UUID格式 + try: + scene_uuid = UUID(scene_id) + except ValueError: + api_logger.warning(f"Invalid scene_id format: {scene_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式") + + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建OntologyService实例 + from app.core.memory.llm_tools.openai_client import OpenAIClient + from app.core.models.base import RedBearModelConfig + + dummy_config = RedBearModelConfig( + model_name="dummy", + provider="openai", + api_key="dummy", + base_url="https://api.openai.com/v1" + ) + llm_client = OpenAIClient(model_config=dummy_config) + service = OntologyService(llm_client=llm_client, db=db) + + # 调用服务层删除场景 + success_flag = service.delete_scene( + scene_id=scene_uuid, + workspace_id=workspace_id + ) + + api_logger.info(f"Scene deleted successfully: {scene_id}") + + return success(data={"deleted": success_flag}, msg="场景删除成功") + + except ValueError as e: + api_logger.warning(f"Validation error in scene deletion: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in scene deletion: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "场景删除失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in scene deletion: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "场景删除失败", str(e)) + + +@router.get("/scenes", response_model=ApiResponse) +async def get_scenes( + workspace_id: Optional[str] = None, + scene_name: Optional[str] = None, + page: Optional[int] = None, + pagesize: Optional[int] = None, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取场景列表（支持模糊搜索和全量查询，全量查询支持分页） + + 根据是否提供 scene_name 参数，执行不同的查询： + - 提供 scene_name：进行模糊搜索，返回匹配的场景列表（支持分页） + - 不提供 scene_name：返回工作空间下的所有场景（支持分页） + + 支持中文和英文的模糊匹配，不区分大小写。 + + Args: + workspace_id: 工作空间ID（可选，默认当前用户工作空间） + scene_name: 场景名称关键词（可选，支持模糊匹配） + page: 页码（可选，从1开始） + pagesize: 每页数量（可选） + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含场景列表和分页信息 + + Examples: + - 模糊搜索（不分页）：GET /scenes?workspace_id=xxx&scene_name=医疗 + 输入 "医疗" 可以匹配到 "医疗场景"、"智慧医疗"、"医疗管理系统" 等 + - 模糊搜索（分页）：GET /scenes?workspace_id=xxx&scene_name=医疗&page=1&pagesize=10 + 返回匹配 "医疗" 的第1页，每页10条数据 + - 全量查询（不分页）：GET /scenes?workspace_id=xxx + 返回工作空间下的所有场景 + - 全量查询（分页）：GET /scenes?workspace_id=xxx&page=1&pagesize=10 + 返回第1页，每页10条数据 + + Notes: + - 分页参数 page 和 pagesize 必须同时提供 + - page 从1开始，pagesize 必须大于0 + - 返回格式：{"items": [...], "page": {"page": 1, "pagesize": 10, "total": 100, "hasnext": true}} + - 不分页时，page 字段为 null + """ + from app.controllers.ontology_secondary_routes import scenes_handler + return await scenes_handler(workspace_id, scene_name, page, pagesize, db, current_user) + + +# ==================== 本体类型管理接口 ==================== + +@router.post("/class", response_model=ApiResponse) +async def create_class( + request: ClassCreateRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """创建本体类型 + + 在指定场景下创建新的本体类型。 + + Args: + request: 类型创建请求 + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含创建的类型信息 + """ + from app.controllers.ontology_secondary_routes import create_class_handler + return await create_class_handler(request, db, current_user) + + +@router.put("/class/{class_id}", response_model=ApiResponse) +async def update_class( + class_id: str, + request: ClassUpdateRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """更新本体类型 + + 更新指定类型的信息，只能更新当前工作空间下场景的类型。 + + Args: + class_id: 类型ID + request: 类型更新请求 + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含更新后的类型信息 + """ + from app.controllers.ontology_secondary_routes import update_class_handler + return await update_class_handler(class_id, request, db, current_user) + + +@router.delete("/class/{class_id}", response_model=ApiResponse) +async def delete_class( + class_id: str, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """删除本体类型 + + 删除指定类型，只能删除当前工作空间下场景的类型。 + + Args: + class_id: 类型ID + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 删除结果 + """ + from app.controllers.ontology_secondary_routes import delete_class_handler + return await delete_class_handler(class_id, db, current_user) + + +@router.get("/classes", response_model=ApiResponse) +async def get_classes( + scene_id: str, + class_name: Optional[str] = None, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取类型列表（支持模糊搜索和全量查询） + + 根据是否提供 class_name 参数，执行不同的查询： + - 提供 class_name：进行模糊搜索，返回匹配的类型列表 + - 不提供 class_name：返回场景下的所有类型 + + 支持中文和英文的模糊匹配，不区分大小写。 + 返回结果包含场景的基本信息（scene_name 和 scene_description）。 + + Args: + scene_id: 场景ID（必填） + class_name: 类型名称关键词（可选，支持模糊匹配） + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含类型列表和场景信息 + + Examples: + - 模糊搜索：GET /classes?scene_id=xxx&class_name=患者 + 输入 "患者" 可以匹配到 "患者"、"患者信息"、"门诊患者" 等 + - 全量查询：GET /classes?scene_id=xxx + 返回场景下的所有类型 + + Response Format: + { + "total": 3, + "scene_id": "xxx", + "scene_name": "医疗场景", + "scene_description": "用于医疗领域的本体建模", + "items": [...] + } + """ + from app.controllers.ontology_secondary_routes import classes_handler + return await classes_handler(scene_id, class_name, db, current_user) + + +@router.get("/class/{class_id}", response_model=ApiResponse) +async def get_class( + class_id: str, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取单个本体类型 + + 根据类型ID获取类型的详细信息，只能查询当前工作空间下场景的类型。 + + Args: + class_id: 类型ID + db: 数据库会话 + current_user: 当前用户 + + Returns: + ApiResponse: 包含类型详细信息 + + Response Format: + { + "code": 0, + "msg": "查询成功", + "data": { + "class_id": "xxx", + "class_name": "患者", + "class_description": "在医疗机构中接受诊疗的个体", + "scene_id": "xxx", + "created_at": "2026-01-29T10:00:00", + "updated_at": "2026-01-29T10:00:00" + } + } + """ + from app.controllers.ontology_secondary_routes import get_class_handler + return await get_class_handler(class_id, db, current_user) diff --git a/api/app/controllers/ontology_secondary_routes.py b/api/app/controllers/ontology_secondary_routes.py new file mode 100644 index 00000000..99017eea --- /dev/null +++ b/api/app/controllers/ontology_secondary_routes.py @@ -0,0 +1,611 @@ +# -*- coding: utf-8 -*- +"""本体场景和类型路由（续） + +由于主Controller文件较大，将剩余路由放在此文件中。 +""" + +from uuid import UUID +from typing import Optional + +from fastapi import Depends +from sqlalchemy.orm import Session + +from app.core.error_codes import BizCode +from app.core.logging_config import get_api_logger +from app.core.response_utils import fail, success +from app.db import get_db +from app.dependencies import get_current_user +from app.models.user_model import User +from app.schemas.ontology_schemas import ( + SceneResponse, + SceneListResponse, + PaginationInfo, + ClassCreateRequest, + ClassUpdateRequest, + ClassResponse, + ClassListResponse, + ClassBatchCreateResponse, +) +from app.schemas.response_schema import ApiResponse +from app.services.ontology_service import OntologyService +from app.core.memory.llm_tools.openai_client import OpenAIClient +from app.core.models.base import RedBearModelConfig + + +api_logger = get_api_logger() + + +def _get_dummy_ontology_service(db: Session) -> OntologyService: + """获取OntologyService实例（不需要LLM） + + 场景和类型管理不需要LLM，创建一个dummy配置。 + """ + dummy_config = RedBearModelConfig( + model_name="dummy", + provider="openai", + api_key="dummy", + base_url="https://api.openai.com/v1" + ) + llm_client = OpenAIClient(model_config=dummy_config) + return OntologyService(llm_client=llm_client, db=db) + + +# 这些函数将被导入到主Controller中 + +async def scenes_handler( + workspace_id: Optional[str] = None, + scene_name: Optional[str] = None, + page: Optional[int] = None, + page_size: Optional[int] = None, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取场景列表（支持模糊搜索和全量查询，全量查询支持分页） + + 当提供 scene_name 参数时，进行模糊搜索（不分页）； + 当不提供 scene_name 参数时，返回所有场景（支持分页）。 + + Args: + workspace_id: 工作空间ID（可选，默认当前用户工作空间） + scene_name: 场景名称关键词（可选，支持模糊匹配） + page: 页码（可选，从1开始，仅在全量查询时有效） + page_size: 每页数量（可选，仅在全量查询时有效） + db: 数据库会话 + current_user: 当前用户 + """ + operation = "search" if scene_name else "list" + api_logger.info( + f"Scene {operation} requested by user {current_user.id}, " + f"workspace_id={workspace_id}, keyword={scene_name}, page={page}, page_size={page_size}" + ) + + try: + # 确定工作空间ID + if workspace_id: + try: + ws_uuid = UUID(workspace_id) + except ValueError: + api_logger.warning(f"Invalid workspace_id format: {workspace_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的工作空间ID格式") + else: + ws_uuid = current_user.current_workspace_id + if not ws_uuid: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建Service + service = _get_dummy_ontology_service(db) + + # 根据是否提供 scene_name 决定查询方式 + if scene_name and scene_name.strip(): + # 验证分页参数（模糊搜索也支持分页） + if page is not None and page < 1: + api_logger.warning(f"Invalid page number: {page}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "页码必须大于0") + + if page_size is not None and page_size < 1: + api_logger.warning(f"Invalid page_size: {page_size}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "每页数量必须大于0") + + # 如果只提供了page或page_size中的一个，返回错误 + if (page is not None and page_size is None) or (page is None and page_size is not None): + api_logger.warning(f"Incomplete pagination params: page={page}, page_size={page_size}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "分页参数page和pagesize必须同时提供") + + # 模糊搜索场景（支持分页） + scenes = service.search_scenes_by_name(scene_name.strip(), ws_uuid) + total = len(scenes) + + # 如果提供了分页参数，进行分页处理 + if page is not None and page_size is not None: + start_idx = (page - 1) * page_size + end_idx = start_idx + page_size + scenes = scenes[start_idx:end_idx] + + # 构建响应 + items = [] + for scene in scenes: + # 获取前3个class_name作为entity_type + entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None + # 动态计算 type_num + type_num = len(scene.classes) if scene.classes else 0 + + items.append(SceneResponse( + scene_id=scene.scene_id, + scene_name=scene.scene_name, + scene_description=scene.scene_description, + type_num=type_num, + entity_type=entity_type, + workspace_id=scene.workspace_id, + created_at=scene.created_at, + updated_at=scene.updated_at, + classes_count=type_num + )) + + # 构建响应（包含分页信息） + if page is not None and page_size is not None: + # 计算是否有下一页 + hasnext = (page * page_size) < total + + pagination_info = PaginationInfo( + page=page, + pagesize=page_size, + total=total, + hasnext=hasnext + ) + response = SceneListResponse(items=items, page=pagination_info) + else: + response = SceneListResponse(items=items) + + api_logger.info( + f"Scene search completed: found {len(items)} scenes matching '{scene_name}' " + f"in workspace {ws_uuid}, total={total}" + ) + else: + # 获取所有场景（支持分页） + # 验证分页参数 + if page is not None and page < 1: + api_logger.warning(f"Invalid page number: {page}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "页码必须大于0") + + if page_size is not None and page_size < 1: + api_logger.warning(f"Invalid page_size: {page_size}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "每页数量必须大于0") + + # 如果只提供了page或page_size中的一个，返回错误 + if (page is not None and page_size is None) or (page is None and page_size is not None): + api_logger.warning(f"Incomplete pagination params: page={page}, page_size={page_size}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "分页参数page和pagesize必须同时提供") + + scenes, total = service.list_scenes(ws_uuid, page, page_size) + + # 构建响应 + items = [] + for scene in scenes: + # 获取前3个class_name作为entity_type + entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None + # 动态计算 type_num + type_num = len(scene.classes) if scene.classes else 0 + + items.append(SceneResponse( + scene_id=scene.scene_id, + scene_name=scene.scene_name, + scene_description=scene.scene_description, + type_num=type_num, + entity_type=entity_type, + workspace_id=scene.workspace_id, + created_at=scene.created_at, + updated_at=scene.updated_at, + classes_count=type_num + )) + + # 构建响应（包含分页信息） + if page is not None and page_size is not None: + # 计算是否有下一页 + hasnext = (page * page_size) < total + + pagination_info = PaginationInfo( + page=page, + pagesize=page_size, + total=total, + hasnext=hasnext + ) + response = SceneListResponse(items=items, page=pagination_info) + else: + response = SceneListResponse(items=items) + + api_logger.info(f"Scene list retrieved successfully, count={len(items)}, total={total}") + + return success(data=response.model_dump(mode='json'), msg="查询成功") + + except ValueError as e: + api_logger.warning(f"Validation error in scene {operation}: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in scene {operation}: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in scene {operation}: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e)) + + +# ==================== 本体类型管理接口 ==================== + +async def create_class_handler( + request: ClassCreateRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """创建本体类型（统一使用列表形式，支持单个或批量）""" + + # 根据列表长度判断是单个还是批量 + count = len(request.classes) + mode = "single" if count == 1 else "batch" + + api_logger.info( + f"Class creation ({mode}) requested by user {current_user.id}, " + f"scene_id={request.scene_id}, count={count}" + ) + + try: + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建Service + service = _get_dummy_ontology_service(db) + + # 准备类型数据 + classes_data = [ + { + "class_name": item.class_name, + "class_description": item.class_description + } + for item in request.classes + ] + + if count == 1: + # 单个创建 + class_data = classes_data[0] + ontology_class = service.create_class( + scene_id=request.scene_id, + class_name=class_data["class_name"], + class_description=class_data["class_description"], + workspace_id=workspace_id + ) + + # 构建单个响应 + response = ClassResponse( + class_id=ontology_class.class_id, + class_name=ontology_class.class_name, + class_description=ontology_class.class_description, + scene_id=ontology_class.scene_id, + created_at=ontology_class.created_at, + updated_at=ontology_class.updated_at + ) + + api_logger.info(f"Class created successfully: {ontology_class.class_id}") + + return success(data=response.model_dump(mode='json'), msg="类型创建成功") + + else: + # 批量创建 + created_classes, errors = service.create_classes_batch( + scene_id=request.scene_id, + classes=classes_data, + workspace_id=workspace_id + ) + + # 构建批量响应 + items = [] + for ontology_class in created_classes: + items.append(ClassResponse( + class_id=ontology_class.class_id, + class_name=ontology_class.class_name, + class_description=ontology_class.class_description, + scene_id=ontology_class.scene_id, + created_at=ontology_class.created_at, + updated_at=ontology_class.updated_at + )) + + response = ClassBatchCreateResponse( + total=len(classes_data), + success_count=len(created_classes), + failed_count=len(errors), + items=items, + errors=errors if errors else None + ) + + api_logger.info( + f"Batch class creation completed: " + f"success={len(created_classes)}, failed={len(errors)}" + ) + + return success(data=response.model_dump(mode='json'), msg="批量创建完成") + + except ValueError as e: + api_logger.warning(f"Validation error in class creation: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in class creation: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "类型创建失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in class creation: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "类型创建失败", str(e)) + + +async def update_class_handler( + class_id: str, + request: ClassUpdateRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """更新本体类型""" + api_logger.info( + f"Class update requested by user {current_user.id}, " + f"class_id={class_id}" + ) + + try: + # 验证UUID格式 + try: + class_uuid = UUID(class_id) + except ValueError: + api_logger.warning(f"Invalid class_id format: {class_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式") + + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建Service + service = _get_dummy_ontology_service(db) + + # 更新类型 + ontology_class = service.update_class( + class_id=class_uuid, + class_name=request.class_name, + class_description=request.class_description, + workspace_id=workspace_id + ) + + # 构建响应 + response = ClassResponse( + class_id=ontology_class.class_id, + class_name=ontology_class.class_name, + class_description=ontology_class.class_description, + scene_id=ontology_class.scene_id, + created_at=ontology_class.created_at, + updated_at=ontology_class.updated_at + ) + + api_logger.info(f"Class updated successfully: {class_id}") + + return success(data=response.model_dump(mode='json'), msg="类型更新成功") + + except ValueError as e: + api_logger.warning(f"Validation error in class update: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in class update: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "类型更新失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in class update: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "类型更新失败", str(e)) + + +async def delete_class_handler( + class_id: str, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """删除本体类型""" + api_logger.info( + f"Class deletion requested by user {current_user.id}, " + f"class_id={class_id}" + ) + + try: + # 验证UUID格式 + try: + class_uuid = UUID(class_id) + except ValueError: + api_logger.warning(f"Invalid class_id format: {class_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式") + + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建Service + service = _get_dummy_ontology_service(db) + + # 删除类型 + success_flag = service.delete_class( + class_id=class_uuid, + workspace_id=workspace_id + ) + + api_logger.info(f"Class deleted successfully: {class_id}") + + return success(data={"deleted": success_flag}, msg="类型删除成功") + + except ValueError as e: + api_logger.warning(f"Validation error in class deletion: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in class deletion: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "类型删除失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in class deletion: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "类型删除失败", str(e)) + + +async def get_class_handler( + class_id: str, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取单个本体类型""" + api_logger.info( + f"Get class requested by user {current_user.id}, " + f"class_id={class_id}" + ) + + try: + # 验证UUID格式 + try: + class_uuid = UUID(class_id) + except ValueError: + api_logger.warning(f"Invalid class_id format: {class_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式") + + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建Service + service = _get_dummy_ontology_service(db) + + # 获取类型（会抛出ValueError如果不存在） + ontology_class = service.get_class_by_id(class_uuid, workspace_id) + + # 构建响应 + response = ClassResponse( + class_id=ontology_class.class_id, + class_name=ontology_class.class_name, + class_description=ontology_class.class_description, + scene_id=ontology_class.scene_id, + created_at=ontology_class.created_at, + updated_at=ontology_class.updated_at + ) + + api_logger.info(f"Class retrieved successfully: {class_id}") + + return success(data=response.model_dump(mode='json'), msg="查询成功") + + except ValueError as e: + # 类型不存在或无权限访问 + api_logger.warning(f"Validation error in get class: {str(e)}") + return fail(BizCode.NOT_FOUND, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in get class: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in get class: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e)) + + +async def classes_handler( + scene_id: str, + class_name: Optional[str] = None, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """获取类型列表（支持模糊搜索和全量查询） + + 当提供 class_name 参数时，进行模糊搜索； + 当不提供 class_name 参数时，返回场景下的所有类型。 + + Args: + scene_id: 场景ID（必填） + class_name: 类型名称关键词（可选，支持模糊匹配） + db: 数据库会话 + current_user: 当前用户 + """ + operation = "search" if class_name else "list" + api_logger.info( + f"Class {operation} requested by user {current_user.id}, " + f"keyword={class_name}, scene_id={scene_id}" + ) + + try: + # 验证UUID格式 + try: + scene_uuid = UUID(scene_id) + except ValueError: + api_logger.warning(f"Invalid scene_id format: {scene_id}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式") + + # 获取当前工作空间ID + workspace_id = current_user.current_workspace_id + if not workspace_id: + api_logger.warning(f"User {current_user.id} has no current workspace") + return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间") + + # 创建Service + service = _get_dummy_ontology_service(db) + + # 获取场景信息 + scene = service.get_scene_by_id(scene_uuid, workspace_id) + if not scene: + api_logger.warning(f"Scene not found: {scene_id}") + return fail(BizCode.NOT_FOUND, "场景不存在", f"未找到ID为 {scene_id} 的场景") + + # 根据是否提供 class_name 决定查询方式 + if class_name and class_name.strip(): + # 模糊搜索类型 + classes = service.search_classes_by_name(class_name.strip(), scene_uuid, workspace_id) + else: + # 获取所有类型 + classes = service.list_classes_by_scene(scene_uuid, workspace_id) + + # 构建响应 + items = [] + for ontology_class in classes: + items.append(ClassResponse( + class_id=ontology_class.class_id, + class_name=ontology_class.class_name, + class_description=ontology_class.class_description, + scene_id=ontology_class.scene_id, + created_at=ontology_class.created_at, + updated_at=ontology_class.updated_at + )) + + response = ClassListResponse( + total=len(items), + scene_id=scene_uuid, + scene_name=scene.scene_name, + scene_description=scene.scene_description, + items=items + ) + + if class_name: + api_logger.info( + f"Class search completed: found {len(items)} classes matching '{class_name}' " + f"in scene {scene_id}" + ) + else: + api_logger.info(f"Class list retrieved successfully, count={len(items)}") + + return success(data=response.model_dump(mode='json'), msg="查询成功") + + except ValueError as e: + api_logger.warning(f"Validation error in class {operation}: {str(e)}") + return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e)) + + except RuntimeError as e: + api_logger.error(f"Runtime error in class {operation}: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e)) + + except Exception as e: + api_logger.error(f"Unexpected error in class {operation}: {str(e)}", exc_info=True) + return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e)) diff --git a/api/app/controllers/prompt_optimizer_controller.py b/api/app/controllers/prompt_optimizer_controller.py index dba52d0b..61195deb 100644 --- a/api/app/controllers/prompt_optimizer_controller.py +++ b/api/app/controllers/prompt_optimizer_controller.py @@ -1,5 +1,5 @@ -import uuid import json +import uuid from fastapi import APIRouter, Depends, Path from sqlalchemy.orm import Session @@ -8,9 +8,13 @@ from starlette.responses import StreamingResponse from app.core.logging_config import get_api_logger from app.core.response_utils import success from app.dependencies import get_current_user, get_db -from app.models.prompt_optimizer_model import RoleType -from app.schemas.prompt_optimizer_schema import PromptOptMessage, PromptOptModelSet, CreateSessionResponse, \ - OptimizePromptResponse, SessionHistoryResponse, SessionMessage +from app.schemas.prompt_optimizer_schema import ( + PromptOptMessage, + CreateSessionResponse, + SessionHistoryResponse, + SessionMessage, + PromptSaveRequest +) from app.schemas.response_schema import ApiResponse from app.services.prompt_optimizer_service import PromptOptimizerService @@ -135,3 +139,109 @@ async def get_prompt_opt( "X-Accel-Buffering": "no" } ) + + +@router.post( + "/releases", + summary="Get prompt optimization", + response_model=ApiResponse +) +def save_prompt( + data: PromptSaveRequest, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """ + Save a prompt release for the current tenant. + + Args: + data (PromptSaveRequest): Request body containing session_id, title, and prompt. + db (Session): SQLAlchemy database session, injected via dependency. + current_user: Currently authenticated user object, injected via dependency. + + Returns: + ApiResponse: Standard API response containing the saved prompt release info: + - id: UUID of the prompt release + - session_id: associated session + - title: prompt title + - prompt: prompt content + - created_at: timestamp of creation + + Raises: + Any database or service exceptions are propagated to the global exception handler. + """ + service = PromptOptimizerService(db) + prompt_info = service.save_prompt( + tenant_id=current_user.tenant_id, + session_id=data.session_id, + title=data.title, + prompt=data.prompt + ) + return success(data=prompt_info) + + +@router.delete( + "/releases/{prompt_id}", + summary="Delete prompt (soft delete)", + response_model=ApiResponse +) +def delete_prompt( + prompt_id: uuid.UUID = Path(..., description="Prompt ID"), + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """ + Soft delete a prompt release. + + Args: + prompt_id + db (Session): Database session + current_user: Current logged-in user + + Returns: + ApiResponse: Success message confirming deletion + """ + service = PromptOptimizerService(db) + service.delete_prompt( + tenant_id=current_user.tenant_id, + prompt_id=prompt_id + ) + return success(msg="Prompt deleted successfully") + + +@router.get( + "/releases/list", + summary="Get paginated list of released prompts with optional filter", + response_model=ApiResponse +) +def get_release_list( + page: int = 1, + page_size: int = 20, + keyword: str | None = None, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """ + Retrieve paginated list of released prompts for the current tenant. + Optionally filter by keyword in title. + + Args: + page (int): Page number (starting from 1) + page_size (int): Number of items per page (max 100) + keyword (str | None): Optional keyword to filter prompt titles + db (Session): Database session + current_user: Current logged-in user + + Returns: + ApiResponse: Contains paginated list of prompt releases with metadata + """ + service = PromptOptimizerService(db) + result = service.get_release_list( + tenant_id=current_user.tenant_id, + page=max(1, page), + page_size=min(max(1, page_size), 100), + filter_keyword=keyword + ) + return success(data=result) + + diff --git a/api/app/controllers/public_share_controller.py b/api/app/controllers/public_share_controller.py index 17ad70a7..536dffd9 100644 --- a/api/app/controllers/public_share_controller.py +++ b/api/app/controllers/public_share_controller.py @@ -317,9 +317,12 @@ async def chat( appid = share.app_id """获取存储类型和工作空间的ID""" - # 直接通过 SQLAlchemy 查询 app + # 直接通过 SQLAlchemy 查询 app（仅查询未删除的应用） from app.models.app_model import App - app = db.query(App).filter(App.id == appid).first() + app = db.query(App).filter( + App.id == appid, + App.is_active.is_(True) + ).first() if not app: raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND) @@ -435,7 +438,8 @@ async def chat( memory=payload.memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, - workspace_id=workspace_id + workspace_id=workspace_id, + files=payload.files # 传递多模态文件 ): yield event @@ -472,7 +476,8 @@ async def chat( memory=payload.memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, - workspace_id=workspace_id + workspace_id=workspace_id, + files=payload.files # 传递多模态文件 ) return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json")) elif app_type == AppType.MULTI_AGENT: diff --git a/api/app/controllers/service/app_api_controller.py b/api/app/controllers/service/app_api_controller.py index 677e1623..27a929ba 100644 --- a/api/app/controllers/service/app_api_controller.py +++ b/api/app/controllers/service/app_api_controller.py @@ -155,7 +155,8 @@ async def chat( memory=memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, - workspace_id=workspace_id + workspace_id=workspace_id, + files=payload.files # 传递多模态文件 ): yield event @@ -180,7 +181,8 @@ async def chat( memory=memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, - workspace_id=workspace_id + workspace_id=workspace_id, + files=payload.files # 传递多模态文件 ) return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json")) elif app_type == AppType.MULTI_AGENT: @@ -235,11 +237,11 @@ async def chat( message=payload.message, conversation_id=conversation.id, # 使用已创建的会话 ID - user_id=new_end_user.id, # 转换为字符串 + user_id=end_user_id, # 转换为字符串 variables=payload.variables, config=config, - web_search=payload.web_search, - memory=payload.memory, + web_search=web_search, + memory=memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, app_id=app.id, @@ -268,11 +270,11 @@ async def chat( message=payload.message, conversation_id=conversation.id, # 使用已创建的会话 ID - user_id=new_end_user.id, # 转换为字符串 + user_id=end_user_id, # 转换为字符串 variables=payload.variables, config=config, - web_search=payload.web_search, - memory=payload.memory, + web_search=web_search, + memory=memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, app_id=app.id, diff --git a/api/app/controllers/service/memory_api_controller.py b/api/app/controllers/service/memory_api_controller.py index 30ca1306..accd749e 100644 --- a/api/app/controllers/service/memory_api_controller.py +++ b/api/app/controllers/service/memory_api_controller.py @@ -39,7 +39,7 @@ async def write_memory_api_service( Stores memory content for the specified end user using the Memory API Service. """ - logger.info(f"Memory write request - end_user_id: {payload.end_user_id}") + logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}") memory_api_service = MemoryAPIService(db) diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index 6f02f8f9..39cbe523 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -135,27 +135,27 @@ async def generate_cache_api( api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - group_id = request.end_user_id + end_user_id = request.end_user_id api_logger.info( f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, " - f"end_user_id={group_id if group_id else '全部用户'}" + f"end_user_id={end_user_id if end_user_id else '全部用户'}" ) try: - if group_id: + if end_user_id: # 为单个用户生成 - api_logger.info(f"开始为单个用户生成缓存: end_user_id={group_id}") + api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}") # 生成记忆洞察 - insight_result = await user_memory_service.generate_and_cache_insight(db, group_id, workspace_id) + insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id) # 生成用户摘要 - summary_result = await user_memory_service.generate_and_cache_summary(db, group_id, workspace_id) + summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id) # 构建响应 result = { - "end_user_id": group_id, + "end_user_id": end_user_id, "insight_success": insight_result["success"], "summary_success": summary_result["success"], "errors": [] @@ -175,9 +175,9 @@ async def generate_cache_api( # 记录结果 if result["insight_success"] and result["summary_success"]: - api_logger.info(f"成功为用户 {group_id} 生成缓存") + api_logger.info(f"成功为用户 {end_user_id} 生成缓存") else: - api_logger.warning(f"用户 {group_id} 的缓存生成部分失败: {result['errors']}") + api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {result['errors']}") return success(data=result, msg="生成完成") diff --git a/api/app/controllers/workflow_controller.py b/api/app/controllers/workflow_controller.py index c6d9ddab..8a15f717 100644 --- a/api/app/controllers/workflow_controller.py +++ b/api/app/controllers/workflow_controller.py @@ -54,7 +54,7 @@ async def create_workflow_config( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -214,7 +214,7 @@ async def delete_workflow_config( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -259,7 +259,7 @@ async def validate_workflow_config( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -329,7 +329,7 @@ async def get_workflow_executions( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -389,7 +389,7 @@ async def get_workflow_execution( app = db.query(App).filter( App.id == execution.app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -440,7 +440,7 @@ async def run_workflow( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -578,7 +578,7 @@ async def cancel_workflow_execution( app = db.query(App).filter( App.id == execution.app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: diff --git a/api/app/core/agent/langchain_agent.py b/api/app/core/agent/langchain_agent.py index 87b46e6f..019fe4ce 100644 --- a/api/app/core/agent/langchain_agent.py +++ b/api/app/core/agent/langchain_agent.py @@ -11,7 +11,8 @@ import os import time from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence - +from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages, format_parsing, messages_parse +from app.core.memory.agent.langgraph_graph.write_graph import long_term_storage from app.db import get_db from app.core.logging_config import get_business_logger from app.core.memory.agent.utils.redis_tool import store @@ -28,6 +29,8 @@ from langchain.agents import create_agent from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage from langchain_core.tools import BaseTool +from app.utils.config_utils import resolve_config_id + logger = get_business_logger() @@ -43,7 +46,9 @@ class LangChainAgent: max_tokens: int = 2000, system_prompt: Optional[str] = None, tools: Optional[Sequence[BaseTool]] = None, - streaming: bool = False + streaming: bool = False, + max_iterations: Optional[int] = None, # 最大迭代次数（None 表示自动计算） + max_tool_consecutive_calls: int = 3 # 单个工具最大连续调用次数 ): """初始化 LangChain Agent @@ -56,13 +61,36 @@ class LangChainAgent: max_tokens: 最大 token 数 system_prompt: 系统提示词 tools: 工具列表（可选，框架自动走 ReAct 循环） - streaming: 是否启用流式输出（默认 True） + streaming: 是否启用流式输出 + max_iterations: 最大迭代次数（None 表示自动计算：基础 5 次 + 每个工具 2 次） + max_tool_consecutive_calls: 单个工具最大连续调用次数（默认 3 次） """ self.model_name = model_name self.provider = provider - self.system_prompt = system_prompt or "你是一个专业的AI助手" self.tools = tools or [] self.streaming = streaming + self.max_tool_consecutive_calls = max_tool_consecutive_calls + + # 工具调用计数器：记录每个工具的连续调用次数 + self.tool_call_counter: Dict[str, int] = {} + self.last_tool_called: Optional[str] = None + + # 根据工具数量动态调整最大迭代次数 + # 基础值 + 每个工具额外的调用机会 + if max_iterations is None: + # 自动计算：基础 5 次 + 每个工具 2 次额外机会 + self.max_iterations = 5 + len(self.tools) * 2 + else: + self.max_iterations = max_iterations + + self.system_prompt = system_prompt or "你是一个专业的AI助手" + + logger.debug( + f"Agent 迭代次数配置: max_iterations={self.max_iterations}, " + f"tool_count={len(self.tools)}, " + f"max_tool_consecutive_calls={self.max_tool_consecutive_calls}, " + f"auto_calculated={max_iterations is None}" + ) # 创建 RedBearLLM（支持多提供商） model_config = RedBearModelConfig( @@ -86,11 +114,14 @@ class LangChainAgent: if streaming and hasattr(self._underlying_llm, 'streaming'): self._underlying_llm.streaming = True + # 包装工具以跟踪连续调用次数 + wrapped_tools = self._wrap_tools_with_tracking(self.tools) if self.tools else None + # 使用 create_agent 创建 agent graph（LangChain 1.x 标准方式） # 无论是否有工具，都使用 agent 统一处理 self.agent = create_agent( model=self.llm, - tools=self.tools if self.tools else None, + tools=wrapped_tools, system_prompt=self.system_prompt ) @@ -102,17 +133,91 @@ class LangChainAgent: "has_api_base": bool(api_base), "temperature": temperature, "streaming": streaming, + "max_iterations": self.max_iterations, + "max_tool_consecutive_calls": self.max_tool_consecutive_calls, "tool_count": len(self.tools), "tool_names": [tool.name for tool in self.tools] if self.tools else [], - "tool_count": len(self.tools) + # "tool_count": len(self.tools) } ) + def _wrap_tools_with_tracking(self, tools: Sequence[BaseTool]) -> List[BaseTool]: + """包装工具以跟踪连续调用次数 + + Args: + tools: 原始工具列表 + + Returns: + List[BaseTool]: 包装后的工具列表 + """ + from langchain_core.tools import StructuredTool + from functools import wraps + + wrapped_tools = [] + + for original_tool in tools: + tool_name = original_tool.name + original_func = original_tool.func if hasattr(original_tool, 'func') else None + + if not original_func: + # 如果无法获取原始函数，直接使用原工具 + wrapped_tools.append(original_tool) + continue + + # 创建包装函数 + def make_wrapped_func(tool_name, original_func): + """创建包装函数的工厂函数，避免闭包问题""" + @wraps(original_func) + def wrapped_func(*args, **kwargs): + """包装后的工具函数，跟踪连续调用次数""" + # 检查是否是连续调用同一个工具 + if self.last_tool_called == tool_name: + self.tool_call_counter[tool_name] = self.tool_call_counter.get(tool_name, 0) + 1 + else: + # 切换到新工具，重置计数器 + self.tool_call_counter[tool_name] = 1 + self.last_tool_called = tool_name + + current_count = self.tool_call_counter[tool_name] + + logger.debug( + f"工具调用: {tool_name}, 连续调用次数: {current_count}/{self.max_tool_consecutive_calls}" + ) + + # 检查是否超过最大连续调用次数 + if current_count > self.max_tool_consecutive_calls: + logger.warning( + f"工具 '{tool_name}' 连续调用次数已达上限 ({self.max_tool_consecutive_calls})，" + f"返回提示信息" + ) + return ( + f"工具 '{tool_name}' 已连续调用 {self.max_tool_consecutive_calls} 次，" + f"未找到有效结果。请尝试其他方法或直接回答用户的问题。" + ) + + # 调用原始工具函数 + return original_func(*args, **kwargs) + + return wrapped_func + + # 使用 StructuredTool 创建新工具 + wrapped_tool = StructuredTool( + name=original_tool.name, + description=original_tool.description, + func=make_wrapped_func(tool_name, original_func), + args_schema=original_tool.args_schema if hasattr(original_tool, 'args_schema') else None + ) + + wrapped_tools.append(wrapped_tool) + + return wrapped_tools + def _prepare_messages( self, message: str, history: Optional[List[Dict[str, str]]] = None, - context: Optional[str] = None + context: Optional[str] = None, + files: Optional[List[Dict[str, Any]]] = None ) -> List[BaseMessage]: """准备消息列表 @@ -120,6 +225,7 @@ class LangChainAgent: message: 用户消息 history: 历史消息列表 context: 上下文信息 + files: 多模态文件内容列表（已处理） Returns: List[BaseMessage]: 消息列表 @@ -142,44 +248,78 @@ class LangChainAgent: if context: user_content = f"参考信息：\n{context}\n\n用户问题：\n{user_content}" - messages.append(HumanMessage(content=user_content)) + # 构建用户消息（支持多模态） + if files and len(files) > 0: + content_parts = self._build_multimodal_content(user_content, files) + messages.append(HumanMessage(content=content_parts)) + else: + # 纯文本消息 + messages.append(HumanMessage(content=user_content)) return messages -# TODO 乐力齐 - 累积多组对话批量写入功能已禁用 - # async def term_memory_save(self,messages,end_user_end,aimessages): - # '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j''' - # end_user_end=f"Term_{end_user_end}" - # print(messages) - # print(aimessages) - # session_id = store.save_session( - # userid=end_user_end, - # messages=messages, - # apply_id=end_user_end, - # group_id=end_user_end, - # aimessages=aimessages - # ) - # store.delete_duplicate_sessions() - # # logger.info(f'Redis_Agent:{end_user_end};{session_id}') - # return session_id -# TODO 乐力齐 - 累积多组对话批量写入功能已禁用 - # async def term_memory_redis_read(self,end_user_end): - # end_user_end = f"Term_{end_user_end}" - # history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end) - # # logger.info(f'Redis_Agent:{end_user_end};{history}') - # messagss_list=[] - # retrieved_content=[] - # for messages in history: - # query = messages.get("Query") - # aimessages = messages.get("Answer") - # messagss_list.append(f'用户:{query}。AI回复:{aimessages}') - # retrieved_content.append({query: aimessages}) - # return messagss_list,retrieved_content + def _build_multimodal_content(self, text: str, files: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + 构建多模态消息内容 + + Args: + text: 文本内容 + files: 文件列表（已由 MultimodalService 处理为对应 provider 的格式） + + Returns: + List[Dict]: 消息内容列表 + """ + # 根据 provider 使用不同的文本格式 + if self.provider.lower() in ["bedrock", "anthropic"]: + # Anthropic/Bedrock: {"type": "text", "text": "..."} + content_parts = [{"type": "text", "text": text}] + else: + # 通义千问等: {"text": "..."} + content_parts = [{"text": text}] + + # 添加文件内容 + # MultimodalService 已经根据 provider 返回了正确格式，直接使用 + content_parts.extend(files) + + logger.debug( + f"构建多模态消息: provider={self.provider}, " + f"parts={len(content_parts)}, " + f"files={len(files)}" + ) + + return content_parts + + return messages + + async def term_memory_save(self,long_term_messages,actual_config_id,end_user_id,type): + db = next(get_db()) + scope=6 + + try: + repo = LongTermMemoryRepository(db) + await long_term_storage(long_term_type="chunk", langchain_messages=long_term_messages, + memory_config=actual_config_id, end_user_id=end_user_id, scope=scope) + + from app.core.memory.agent.utils.redis_tool import write_store + result = write_store.get_session_by_userid(end_user_id) + if type=="chunk" or type=="aggregate": + data = await format_parsing(result, "dict") + chunk_data = data[:scope] + if len(chunk_data)==scope: + repo.upsert(end_user_id, chunk_data) + logger.info(f'写入短长期：') + else: + long_time_data = write_store.find_user_recent_sessions(end_user_id, 5) + long_messages = await messages_parse(long_time_data) + repo.upsert(end_user_id, long_messages) + logger.info(f'写入短长期：') + finally: + db.close() async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id): """ 写入记忆（支持结构化消息） - + Args: storage_type: 存储类型 (neo4j/rag) end_user_id: 终端用户ID @@ -188,7 +328,7 @@ class LangChainAgent: user_rag_memory_id: RAG 记忆ID actual_end_user_id: 实际用户ID actual_config_id: 配置ID - + 逻辑说明： - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变 - Neo4j 模式：使用结构化消息列表 @@ -196,48 +336,46 @@ class LangChainAgent: 2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景） 3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段 """ - if storage_type == "rag": - # RAG 模式：组合消息为字符串格式（保持原有逻辑） - combined_message = f"user: {user_message}\nassistant: {ai_message}" - await write_rag(end_user_id, combined_message, user_rag_memory_id) - logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}') - else: - # Neo4j 模式：使用结构化消息列表 - structured_messages = [] - - # 始终添加用户消息（如果不为空） - if user_message: - structured_messages.append({"role": "user", "content": user_message}) - - # 只有当 AI 回复不为空时才添加 assistant 消息 - if ai_message: - structured_messages.append({"role": "assistant", "content": ai_message}) - - # 如果没有消息，直接返回 - if not structured_messages: - logger.warning(f"No messages to write for user {actual_end_user_id}") - return - - # 调用 Celery 任务，传递结构化消息列表 - # 数据流： - # 1. structured_messages 传递给 write_message_task - # 2. write_message_task 调用 memory_agent_service.write_memory - # 3. write_memory 调用 write_tools.write，传递 messages 参数 - # 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数 - # 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段 - # 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段 - logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}") - write_id = write_message_task.delay( - actual_end_user_id, # group_id: 用户ID - structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] - actual_config_id, # config_id: 配置ID - storage_type, # storage_type: "neo4j" - user_rag_memory_id # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用） - ) - logger.info(f"[WRITE] Celery task submitted - task_id={write_id}") - write_status = get_task_memory_write_result(str(write_id)) - logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}') + db = next(get_db()) + try: + actual_config_id=resolve_config_id(actual_config_id, db) + + if storage_type == "rag": + # RAG 模式：组合消息为字符串格式（保持原有逻辑） + combined_message = f"user: {user_message}\nassistant: {ai_message}" + await write_rag(end_user_id, combined_message, user_rag_memory_id) + logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}') + else: + # Neo4j 模式：使用结构化消息列表 + structured_messages = [] + + # 始终添加用户消息（如果不为空） + if user_message: + structured_messages.append({"role": "user", "content": user_message}) + + # 只有当 AI 回复不为空时才添加 assistant 消息 + if ai_message: + structured_messages.append({"role": "assistant", "content": ai_message}) + + # 如果没有消息，直接返回 + if not structured_messages: + logger.warning(f"No messages to write for user {actual_end_user_id}") + return + + logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}") + write_id = write_message_task.delay( + actual_end_user_id, # end_user_id: 用户ID + structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] + actual_config_id, # config_id: 配置ID + storage_type, # storage_type: "neo4j" + user_rag_memory_id # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用） + ) + logger.info(f"[WRITE] Celery task submitted - task_id={write_id}") + write_status = get_task_memory_write_result(str(write_id)) + logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}') + finally: + db.close() async def chat( self, message: str, @@ -247,7 +385,8 @@ class LangChainAgent: config_id: Optional[str] = None, # 添加这个参数 storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, - memory_flag: Optional[bool] = True + memory_flag: Optional[bool] = True, + files: Optional[List[Dict[str, Any]]] = None # 新增：多模态文件 ) -> Dict[str, Any]: """执行对话 @@ -281,33 +420,9 @@ class LangChainAgent: actual_end_user_id = end_user_id if end_user_id is not None else "unknown" logger.info(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}') print(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}') -# # TODO 乐力齐，在长短期记忆存储的时候再使用此代码 -# history_term_memory_result = await self.term_memory_redis_read(end_user_id) -# history_term_memory = history_term_memory_result[0] -# db_for_memory = next(get_db()) -# if memory_flag: -# if len(history_term_memory)>=4 and storage_type != "rag": -# history_term_memory = ';'.join(history_term_memory) -# retrieved_content = history_term_memory_result[1] -# print(retrieved_content) -# # 为长期记忆操作获取新的数据库连接 -# try: -# repo = LongTermMemoryRepository(db_for_memory) -# repo.upsert(end_user_id, retrieved_content) -# logger.info( -# f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}') -# except Exception as e: -# logger.error(f"Failed to write to LongTermMemory: {e}") -# raise -# finally: -# db_for_memory.close() - -# # 长期记忆写入（ -# await self.write(storage_type, actual_end_user_id, history_term_memory, "", user_rag_memory_id, actual_end_user_id, actual_config_id) -# # 注意：不在这里写入用户消息，等 AI 回复后一起写入 try: - # 准备消息列表 - messages = self._prepare_messages(message, history, context) + # 准备消息列表（支持多模态） + messages = self._prepare_messages(message, history, context, files) logger.debug( "准备调用 LangChain Agent", @@ -315,27 +430,89 @@ class LangChainAgent: "has_context": bool(context), "has_history": bool(history), "has_tools": bool(self.tools), - "message_count": len(messages) + "has_files": bool(files), + "message_count": len(messages), + "max_iterations": self.max_iterations } ) # 统一使用 agent.invoke 调用 - result = await self.agent.ainvoke({"messages": messages}) + # 通过 recursion_limit 限制最大迭代次数，防止工具调用死循环 + try: + result = await self.agent.ainvoke( + {"messages": messages}, + config={"recursion_limit": self.max_iterations} + ) + except RecursionError as e: + logger.warning( + f"Agent 达到最大迭代次数限制 ({self.max_iterations})，可能存在工具调用循环", + extra={"error": str(e)} + ) + # 返回一个友好的错误提示 + return { + "content": f"抱歉，我在处理您的请求时遇到了问题。已达到最大处理步骤限制（{self.max_iterations}次）。请尝试简化您的问题或稍后再试。", + "model": self.model_name, + "elapsed_time": time.time() - start_time, + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0 + } + } # 获取最后的 AI 消息 output_messages = result.get("messages", []) content = "" + + logger.debug(f"输出消息数量: {len(output_messages)}") + total_tokens = 0 for msg in reversed(output_messages): if isinstance(msg, AIMessage): - content = msg.content + logger.debug(f"找到 AI 消息，content 类型: {type(msg.content)}") + logger.debug(f"AI 消息内容: {msg.content}") + + # 处理多模态响应：content 可能是字符串或列表 + if isinstance(msg.content, str): + content = msg.content + logger.debug(f"提取字符串内容，长度: {len(content)}") + elif isinstance(msg.content, list): + # 多模态响应：提取文本部分 + logger.debug(f"多模态响应，列表长度: {len(msg.content)}") + text_parts = [] + for item in msg.content: + logger.debug(f"处理项: {item}") + if isinstance(item, dict): + # 通义千问格式: {"text": "..."} + if "text" in item: + text = item.get("text", "") + text_parts.append(text) + logger.debug(f"提取文本: {text[:100]}...") + # OpenAI 格式: {"type": "text", "text": "..."} + elif item.get("type") == "text": + text = item.get("text", "") + text_parts.append(text) + logger.debug(f"提取文本: {text[:100]}...") + elif isinstance(item, str): + text_parts.append(item) + logger.debug(f"提取字符串: {item[:100]}...") + content = "".join(text_parts) + logger.debug(f"合并后内容长度: {len(content)}") + else: + content = str(msg.content) + logger.debug(f"转换为字符串: {content[:100]}...") + response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None + total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0 break + + logger.info(f"最终提取的内容长度: {len(content)}") elapsed_time = time.time() - start_time if memory_flag: + long_term_messages=await agent_chat_messages(message_chat,content) # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话） await self.write(storage_type, actual_end_user_id, message_chat, content, user_rag_memory_id, actual_end_user_id, actual_config_id) - # TODO 乐力齐 - 累积多组对话批量写入功能已禁用 - # await self.term_memory_save(message_chat, end_user_id, content) + '''长期''' + await self.term_memory_save(long_term_messages,actual_config_id,end_user_id,"chunk") response = { "content": content, "model": self.model_name, @@ -343,7 +520,7 @@ class LangChainAgent: "usage": { "prompt_tokens": 0, "completion_tokens": 0, - "total_tokens": 0 + "total_tokens": total_tokens } } @@ -370,7 +547,8 @@ class LangChainAgent: config_id: Optional[str] = None, storage_type:Optional[str] = None, user_rag_memory_id:Optional[str] = None, - memory_flag: Optional[bool] = True + memory_flag: Optional[bool] = True, + files: Optional[List[Dict[str, Any]]] = None # 新增：多模态文件 ) -> AsyncGenerator[str, None]: """执行流式对话 @@ -403,33 +581,15 @@ class LangChainAgent: db.close() except Exception as e: logger.warning(f"Failed to get db session: {e}") -# # TODO 乐力齐 -# history_term_memory_result = await self.term_memory_redis_read(end_user_id) -# history_term_memory = history_term_memory_result[0] -# if memory_flag: -# if len(history_term_memory) >= 4 and storage_type != "rag": -# history_term_memory = ';'.join(history_term_memory) -# retrieved_content = history_term_memory_result[1] -# db_for_memory = next(get_db()) -# try: -# repo = LongTermMemoryRepository(db_for_memory) -# repo.upsert(end_user_id, retrieved_content) -# logger.info( -# f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}') -# # 长期记忆写入 -# await self.write(storage_type, end_user_id, history_term_memory, "", user_rag_memory_id, end_user_id, actual_config_id) -# except Exception as e: -# logger.error(f"Failed to write to long term memory: {e}") -# finally: -# db_for_memory.close() + # 注意：不在这里写入用户消息，等 AI 回复后一起写入 try: - # 准备消息列表 - messages = self._prepare_messages(message, history, context) + # 准备消息列表（支持多模态） + messages = self._prepare_messages(message, history, context, files) logger.debug( - f"准备流式调用，has_tools={bool(self.tools)}, message_count={len(messages)}" + f"准备流式调用，has_tools={bool(self.tools)}, has_files={bool(files)}, message_count={len(messages)}" ) chunk_count = 0 @@ -437,11 +597,12 @@ class LangChainAgent: # 统一使用 agent 的 astream_events 实现流式输出 logger.debug("使用 Agent astream_events 实现流式输出") - full_content='' + full_content = '' try: async for event in self.agent.astream_events( {"messages": messages}, - version="v2" + version="v2", + config={"recursion_limit": self.max_iterations} ): chunk_count += 1 kind = event.get("event") @@ -450,20 +611,70 @@ class LangChainAgent: if kind == "on_chat_model_stream": # LLM 流式输出 chunk = event.get("data", {}).get("chunk") - full_content+=chunk.content - if chunk and hasattr(chunk, "content") and chunk.content: - yield chunk.content - yielded_content = True + if chunk and hasattr(chunk, "content"): + # 处理多模态响应：content 可能是字符串或列表 + chunk_content = chunk.content + if isinstance(chunk_content, str) and chunk_content: + full_content += chunk_content + yield chunk_content + yielded_content = True + elif isinstance(chunk_content, list): + # 多模态响应：提取文本部分 + for item in chunk_content: + if isinstance(item, dict): + # 通义千问格式: {"text": "..."} + if "text" in item: + text = item.get("text", "") + if text: + full_content += text + yield text + yielded_content = True + # OpenAI 格式: {"type": "text", "text": "..."} + elif item.get("type") == "text": + text = item.get("text", "") + if text: + full_content += text + yield text + yielded_content = True + elif isinstance(item, str): + full_content += item + yield item + yielded_content = True elif kind == "on_llm_stream": # 另一种 LLM 流式事件 chunk = event.get("data", {}).get("chunk") if chunk: - if hasattr(chunk, "content") and chunk.content: - full_content+=chunk.content - yield chunk.content - yielded_content = True + if hasattr(chunk, "content"): + chunk_content = chunk.content + if isinstance(chunk_content, str) and chunk_content: + full_content += chunk_content + yield chunk_content + yielded_content = True + elif isinstance(chunk_content, list): + # 多模态响应：提取文本部分 + for item in chunk_content: + if isinstance(item, dict): + # 通义千问格式: {"text": "..."} + if "text" in item: + text = item.get("text", "") + if text: + full_content += text + yield text + yielded_content = True + # OpenAI 格式: {"type": "text", "text": "..."} + elif item.get("type") == "text": + text = item.get("text", "") + if text: + full_content += text + yield text + yielded_content = True + elif isinstance(item, str): + full_content += item + yield item + yielded_content = True elif isinstance(chunk, str): + full_content += chunk yield chunk yielded_content = True @@ -474,11 +685,20 @@ class LangChainAgent: logger.debug(f"工具调用结束: {event.get('name')}") logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件") + # 统计token消耗 + output_messages = event.get("data", {}).get("output", {}).get("messages", []) + for msg in reversed(output_messages): + if isinstance(msg, AIMessage): + response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None + total_tokens = response_meta.get("token_usage", {}).get("total_tokens", + 0) if response_meta else 0 + yield total_tokens + break if memory_flag: # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话） + long_term_messages = await agent_chat_messages(message_chat, full_content) await self.write(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, end_user_id, actual_config_id) - # TODO 乐力齐 - 累积多组对话批量写入功能已禁用 - # await self.term_memory_save(message_chat, end_user_id, full_content) + await self.term_memory_save(long_term_messages, actual_config_id, end_user_id, "chunk") except Exception as e: logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True) diff --git a/api/app/core/config.py b/api/app/core/config.py index 59c6ff5f..0de957c7 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -9,6 +9,25 @@ load_dotenv() class Settings: + # ======================================================================== + # Deployment Mode Configuration + # ======================================================================== + # community: 社区版（开源，功能受限） + # cloud: SaaS 云服务版（全功能，按量计费） + # enterprise: 企业私有化版（License 控制） + DEPLOYMENT_MODE: str = os.getenv("DEPLOYMENT_MODE", "community") + + # License 配置（企业版） + LICENSE_FILE: str = os.getenv("LICENSE_FILE", "/etc/app/license.json") + LICENSE_SERVER_URL: str = os.getenv("LICENSE_SERVER_URL", "https://license.yourcompany.com") + + # 计费服务配置（SaaS 版） + BILLING_SERVICE_URL: str = os.getenv("BILLING_SERVICE_URL", "") + + # 基础 URL（用于 SSO 回调等） + BASE_URL: str = os.getenv("BASE_URL", "http://localhost:8000") + FRONTEND_URL: str = os.getenv("FRONTEND_URL", "http://localhost:3000") + ENABLE_SINGLE_WORKSPACE: bool = os.getenv("ENABLE_SINGLE_WORKSPACE", "true").lower() == "true" # API Keys Configuration OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "") @@ -72,6 +91,10 @@ class Settings: # Single Sign-On configuration ENABLE_SINGLE_SESSION: bool = os.getenv("ENABLE_SINGLE_SESSION", "false").lower() == "true" + + # SSO 免登配置 + SSO_TOKEN_EXPIRE_SECONDS: int = int(os.getenv("SSO_TOKEN_EXPIRE_SECONDS", "300")) + SSO_TRUSTED_SOURCES_CONFIG: str = os.getenv("SSO_TRUSTED_SOURCES_CONFIG", "{}") # File Upload MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", "52428800")) @@ -107,6 +130,7 @@ class Settings: # Server Configuration SERVER_IP: str = os.getenv("SERVER_IP", "127.0.0.1") + FILE_LOCAL_SERVER_URL : str = os.getenv("FILE_LOCAL_SERVER_URL", "http://localhost:8000/api") # ======================================================================== # Internal Configuration (not in .env, used by application code) @@ -133,6 +157,11 @@ class Settings: if origin.strip() ] + # Language Configuration + # Supported values: "zh" (Chinese), "en" (English) + # This controls the language used for memory summary titles and other generated content + DEFAULT_LANGUAGE: str = os.getenv("DEFAULT_LANGUAGE", "zh") + # Logging settings LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO") LOG_FORMAT: str = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s") @@ -184,7 +213,7 @@ class Settings: ENABLE_TOOL_MANAGEMENT: bool = os.getenv("ENABLE_TOOL_MANAGEMENT", "true").lower() == "true" # official environment system version - SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.0") + SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.1") # workflow config WORKFLOW_NODE_TIMEOUT: int = int(os.getenv("WORKFLOW_NODE_TIMEOUT", 600)) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py index 697a13bd..ac1fb9a6 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py @@ -14,7 +14,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') db_session = next(get_db()) logger = get_agent_logger(__name__) @@ -35,10 +35,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState: """问题分解节点""" # 从状态中获取数据 content = state.get('data', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) # 生成 JSON schema 以指导 LLM 输出正确格式 json_schema = ProblemExtensionResponse.model_json_schema() @@ -140,7 +140,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: start = time.time() content = state.get('data', '') data = state.get('spit_data', '')['context'] - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') memory_config = state.get('memory_config', None) @@ -156,7 +156,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: databasets = {} data = [] - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) # 生成 JSON schema 以指导 LLM 输出正确格式 json_schema = ProblemExtensionResponse.model_json_schema() diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py index 14f8fa8b..1880357c 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py @@ -52,9 +52,9 @@ async def rag_config(state): return kb_config async def rag_knowledge(state,question): kb_config = await rag_config(state) - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') user_rag_memory_id=state.get("user_rag_memory_id",'') - retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(group_id)]) + retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(end_user_id)]) try: retrieval_knowledge = [i.page_content for i in retrieve_chunks_result] clean_content = '\n\n'.join(retrieval_knowledge) @@ -159,7 +159,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: problem_extension=state.get('problem_extension', '')['context'] storage_type=state.get('storage_type', '') user_rag_memory_id=state.get('user_rag_memory_id', '') - group_id=state.get('group_id', '') + end_user_id=state.get('end_user_id', '') memory_config = state.get('memory_config', None) original=state.get('data', '') problem_list=[] @@ -172,7 +172,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: try: # Prepare search parameters based on storage type search_params = { - "group_id": group_id, + "end_user_id": end_user_id, "question": question, "return_raw_results": True } @@ -263,13 +263,13 @@ async def retrieve_nodes(state: ReadState) -> ReadState: async def retrieve(state: ReadState) -> ReadState: - # 从state中获取group_id + # 从state中获取end_user_id import time start=time.time() problem_extension = state.get('problem_extension', '')['context'] storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) original = state.get('data', '') problem_list = [] @@ -295,13 +295,13 @@ async def retrieve(state: ReadState) -> ReadState: temperature=0.2, ) - time_retrieval_tool = create_time_retrieval_tool(group_id) - search_params = { "group_id": group_id, "return_raw_results": True } + time_retrieval_tool = create_time_retrieval_tool(end_user_id) + search_params = { "end_user_id": end_user_id, "return_raw_results": True } hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params) agent = create_agent( llm, tools=[time_retrieval_tool,hybrid_retrieval], - system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的group_id是: {group_id}" + system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}" ) # 创建异步任务处理单个问题 diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index 44f89c6a..0144c0e9 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -19,7 +19,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.db import get_db -template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') logger = get_agent_logger(__name__) db_session = next(get_db()) @@ -34,8 +34,8 @@ class SummaryNodeService(LLMServiceMixin): summary_service = SummaryNodeService() async def summary_history(state: ReadState) -> ReadState: - group_id = state.get("group_id", '') - history = await SessionService(store).get_history(group_id, group_id, group_id) + end_user_id = state.get("end_user_id", '') + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) return history async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str: @@ -122,12 +122,12 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o async def summary_redis_save(state: ReadState,aimessages) -> ReadState: data = state.get("data", '') - group_id = state.get("group_id", '') + end_user_id = state.get("end_user_id", '') await SessionService(store).save_session( - user_id=group_id, + user_id=end_user_id, query=data, - apply_id=group_id, - group_id=group_id, + apply_id=end_user_id, + end_user_id=end_user_id, ai_response=aimessages ) await SessionService(store).cleanup_duplicates() @@ -175,11 +175,11 @@ async def Input_Summary(state: ReadState) -> ReadState: memory_config = state.get('memory_config', None) user_rag_memory_id=state.get("user_rag_memory_id",'') data=state.get("data", '') - group_id=state.get("group_id", '') + end_user_id=state.get("end_user_id", '') logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}") history = await summary_history( state) search_params = { - "group_id": group_id, + "end_user_id": end_user_id, "question": data, "return_raw_results": True, "include": ["summaries"] # Only search summary nodes for faster performance @@ -236,7 +236,7 @@ async def Retrieve_Summary(state: ReadState)-> ReadState: retrieve_info_str='\n'.join(retrieve_info_str) aimessages=await summary_llm(state,history,retrieve_info_str, - 'Retrieve_Summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1") + 'direct_summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1") if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "": await summary_redis_save(state, aimessages) if aimessages == '': @@ -276,7 +276,6 @@ async def Summary(state: ReadState)-> ReadState: aimessages=await summary_llm(state,history,data, 'summary_prompt.jinja2','summary',SummaryResponse,0) - if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "": await summary_redis_save(state, aimessages) if aimessages == '': @@ -295,9 +294,26 @@ async def Summary(state: ReadState)-> ReadState: async def Summary_fails(state: ReadState)-> ReadState: storage_type=state.get("storage_type", '') user_rag_memory_id=state.get("user_rag_memory_id", '') + history = await summary_history(state) + query = state.get("data", '') + verify = state.get("verify", '') + verify_expansion_issue = verify.get("verified_data", '') + retrieve_info_str = '' + for data in verify_expansion_issue: + for key, value in data.items(): + if key == 'answer_small': + for i in value: + retrieve_info_str += i + '\n' + data = { + "query": query, + "history": history, + "retrieve_info": retrieve_info_str + } + aimessages = await summary_llm(state, history, data, + 'fail_summary_prompt.jinja2', 'summary', SummaryResponse, 0) result= { "status": "success", - "summary_result": "没有相关数据", + "summary_result": aimessages, "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id } diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index dac7ea14..b809faf2 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -12,7 +12,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') db_session = next(get_db()) logger = get_agent_logger(__name__) @@ -62,12 +62,12 @@ async def Verify(state: ReadState): logger.info("=== Verify 节点开始执行 ===") try: content = state.get('data', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) - logger.info(f"Verify: content={content[:50] if content else 'empty'}..., group_id={group_id}") + logger.info(f"Verify: content={content[:50] if content else 'empty'}..., end_user_id={end_user_id}") - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) logger.info(f"Verify: 获取历史记录完成，history length={len(history)}") retrieve = state.get("retrieve", {}) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py index 6af313c3..b85130ad 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py @@ -1,23 +1,24 @@ - -from app.core.memory.agent.utils.llm_tools import WriteState +from app.core.memory.agent.utils.llm_tools import WriteState from app.core.memory.agent.utils.write_tools import write from app.core.logging_config import get_agent_logger logger = get_agent_logger(__name__) + + async def write_node(state: WriteState) -> WriteState: """ Write data to the database/file system. Args: - state: WriteState containing messages, group_id, and memory_config + state: WriteState containing messages, end_user_id, and memory_config Returns: dict: Contains 'write_result' with status and data fields """ messages = state.get('messages', []) - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', '') - + # Convert LangChain messages to structured format expected by write() structured_messages = [] for msg in messages: @@ -28,13 +29,11 @@ async def write_node(state: WriteState) -> WriteState: "role": role, "content": msg.content # content is now guaranteed to be a string }) - + try: result = await write( messages=structured_messages, - user_id=group_id, - apply_id=group_id, - group_id=group_id, + end_user_id=end_user_id, memory_config=memory_config, ) logger.info(f"Write completed successfully! Config: {memory_config.config_name}") diff --git a/api/app/core/memory/agent/langgraph_graph/read_graph.py b/api/app/core/memory/agent/langgraph_graph/read_graph.py index 19011a5f..3476d0ec 100644 --- a/api/app/core/memory/agent/langgraph_graph/read_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/read_graph.py @@ -79,7 +79,7 @@ async def make_read_graph(): async def main(): """主函数 - 运行工作流""" message = "昨天有什么好看的电影" - group_id = '88a459f5_text09' # 组ID + end_user_id = '88a459f5_text09' # 组ID storage_type = 'neo4j' # 存储类型 search_switch = '1' # 搜索开关 user_rag_memory_id = 'wwwwwwww' # 用户RAG记忆ID @@ -95,9 +95,9 @@ async def main(): start=time.time() try: async with make_read_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"group_id":group_id + initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"end_user_id":end_user_id ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config} # 获取节点更新信息 _intermediate_outputs = [] diff --git a/api/app/core/memory/agent/langgraph_graph/routing/write_router.py b/api/app/core/memory/agent/langgraph_graph/routing/write_router.py new file mode 100644 index 00000000..d6fbbb38 --- /dev/null +++ b/api/app/core/memory/agent/langgraph_graph/routing/write_router.py @@ -0,0 +1,165 @@ +import os + +from app.core.logging_config import get_agent_logger +from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format, format_parsing +from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph + +from app.core.memory.agent.models.write_aggregate_model import WriteAggregateModel +from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_ +from app.core.memory.agent.utils.redis_tool import write_store +from app.core.memory.agent.utils.redis_tool import count_store +from app.core.memory.agent.utils.template_tools import TemplateService +from app.core.memory.utils.llm.llm_utils import MemoryClientFactory +from app.db import get_db_context +logger = get_agent_logger(__name__) +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') + + +async def write_messages(end_user_id,langchain_messages,memory_config): + ''' + 写入数据到neo4j： + Args: + end_user_id: 终端用户ID + memory_config: 内存配置对象 + langchain_messages：原始数据LIST + ''' + try: + + async with make_write_graph() as graph: + config = {"configurable": {"thread_id": end_user_id}} + # 初始状态 - 包含所有必要字段 + initial_state = { + "messages": langchain_messages, + "end_user_id": end_user_id, + "memory_config": memory_config + } + + # 获取节点更新信息 + async for update_event in graph.astream( + initial_state, + stream_mode="updates", + config=config + ): + for node_name, node_data in update_event.items(): + if 'save_neo4j' == node_name: + massages = node_data + massagesstatus = massages.get('write_result')['status'] + contents = massages.get('write_result') + print(contents) + except Exception as e: + import traceback + traceback.print_exc() +'''根据窗口''' +async def window_dialogue(end_user_id,langchain_messages,memory_config,scope): + ''' + 根据窗口获取redis数据,写入neo4j： + Args: + end_user_id: 终端用户ID + memory_config: 内存配置对象 + langchain_messages：原始数据LIST + scope：窗口大小 + ''' + scope=scope + is_end_user_id = count_store.get_sessions_count(end_user_id) + if is_end_user_id is not False: + is_end_user_id = count_store.get_sessions_count(end_user_id)[0] + redis_messages = count_store.get_sessions_count(end_user_id)[1] + if is_end_user_id and int(is_end_user_id) != int(scope): + print(is_end_user_id) + is_end_user_id += 1 + langchain_messages += redis_messages + count_store.update_sessions_count(end_user_id, is_end_user_id, langchain_messages) + elif int(is_end_user_id) == int(scope): + print('写入长期记忆，并且设置为0') + print(is_end_user_id) + formatted_messages = await chat_data_format(redis_messages) + print(100*'-') + print(formatted_messages) + print(100*'-') + await write_messages(end_user_id, formatted_messages, memory_config) + count_store.update_sessions_count(end_user_id, 0, '') + else: + count_store.save_sessions_count(end_user_id, 1, langchain_messages) + + +"""根据时间""" +async def memory_long_term_storage(end_user_id,memory_config,time): + ''' + 根据时间获取redis数据,写入neo4j： + Args: + end_user_id: 终端用户ID + memory_config: 内存配置对象 + ''' + long_time_data = write_store.find_user_recent_sessions(end_user_id, time) + format_messages = await chat_data_format(long_time_data) + if format_messages!=[]: + await write_messages(end_user_id, format_messages, memory_config) +'''聚合判断''' +async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config) -> dict: + """ + 聚合判断函数：判断输入句子和历史消息是否描述同一事件 + + Args: + end_user_id: 终端用户ID + ori_messages: 原始消息列表，格式如 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] + memory_config: 内存配置对象 + """ + + try: + # 1. 获取历史会话数据（使用新方法） + result = write_store.get_all_sessions_by_end_user_id(end_user_id) + history = await format_parsing(result) + if not result: + history = [] + else: + history = await format_parsing(result) + json_schema = WriteAggregateModel.model_json_schema() + template_service = TemplateService(template_root) + system_prompt = await template_service.render_template( + template_name='write_aggregate_judgment.jinja2', + operation_name='aggregate_judgment', + history=history, + sentence=ori_messages, + json_schema=json_schema + ) + with get_db_context() as db_session: + factory = MemoryClientFactory(db_session) + llm_client = factory.get_llm_client(memory_config.llm_model_id) + messages = [ + { + "role": "user", + "content": system_prompt + } + ] + structured = await llm_client.response_structured( + messages=messages, + response_model=WriteAggregateModel + ) + output_value = structured.output + if isinstance(output_value, list): + output_value = [ + {"role": msg.role, "content": msg.content} + for msg in output_value + ] + + result_dict = { + "is_same_event": structured.is_same_event, + "output": output_value + } + if not structured.is_same_event: + logger.info(result_dict) + await write_messages(end_user_id, output_value, memory_config) + return result_dict + + except Exception as e: + print(f"[aggregate_judgment] 发生错误: {e}") + import traceback + traceback.print_exc() + + return { + "is_same_event": False, + "output": ori_messages, + "messages": ori_messages, + "history": history if 'history' in locals() else [], + "error": str(e) + } \ No newline at end of file diff --git a/api/app/core/memory/agent/langgraph_graph/tools/tool.py b/api/app/core/memory/agent/langgraph_graph/tools/tool.py index ce6d5dd4..c4814de1 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/tool.py @@ -48,11 +48,11 @@ def extract_tool_message_content(response): class TimeRetrievalInput(BaseModel): """时间检索工具的输入模式""" context: str = Field(description="用户输入的查询内容") - group_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") + end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") -def create_time_retrieval_tool(group_id: str): +def create_time_retrieval_tool(end_user_id: str): """ - 创建一个带有特定group_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) + 创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) """ def clean_temporal_result_fields(data): @@ -93,26 +93,26 @@ def create_time_retrieval_tool(group_id: str): return data @tool - def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, group_id_param: str = None, clean_output: bool = True) -> str: + def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str: """ 优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段 显式接收参数： - context: 查询上下文内容 - start_date: 开始时间（可选，格式：YYYY-MM-DD） - end_date: 结束时间（可选，格式：YYYY-MM-DD） - - group_id_param: 组ID（可选，用于覆盖默认组ID） + - end_user_id_param: 组ID（可选，用于覆盖默认组ID） - clean_output: 是否清理输出中的元数据字段 -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d") """ async def _async_search(): # 使用传入的参数或默认值 - actual_group_id = group_id_param or group_id + actual_end_user_id = end_user_id_param or end_user_id actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d") actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d") # 基本时间搜索 results = await search_by_temporal( - group_id=actual_group_id, + end_user_id=actual_end_user_id, start_date=actual_start_date, end_date=actual_end_date, limit=10 @@ -147,7 +147,7 @@ def create_time_retrieval_tool(group_id: str): # 关键词时间搜索 results = await search_by_keyword_temporal( query_text=context, - group_id=group_id, + end_user_id=end_user_id, start_date=actual_start_date, end_date=actual_end_date, limit=15 @@ -172,7 +172,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): Args: memory_config: 内存配置对象 - **search_params: 搜索参数，包含group_id, limit, include等 + **search_params: 搜索参数，包含end_user_id, limit, include等 """ def clean_result_fields(data): @@ -211,7 +211,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): context: str, search_type: str = "hybrid", limit: int = 10, - group_id: str = None, + end_user_id: str = None, rerank_alpha: float = 0.6, use_forgetting_rerank: bool = False, use_llm_rerank: bool = False, @@ -224,7 +224,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): context: 查询内容 search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') limit: 结果数量限制 - group_id: 组ID，用于过滤搜索结果 + end_user_id: 组ID，用于过滤搜索结果 rerank_alpha: 重排序权重参数 use_forgetting_rerank: 是否使用遗忘重排序 use_llm_rerank: 是否使用LLM重排序 @@ -238,7 +238,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): final_params = { "query_text": context, "search_type": search_type, - "group_id": group_id or search_params.get("group_id"), + "end_user_id": end_user_id or search_params.get("end_user_id"), "limit": limit or search_params.get("limit", 10), "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]), "output_path": None, # 不保存到文件 @@ -291,7 +291,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): context: str, search_type: str = "hybrid", limit: int = 10, - group_id: str = None, + end_user_id: str = None, clean_output: bool = True ) -> str: """ @@ -301,7 +301,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): context: 查询内容 search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') limit: 结果数量限制 - group_id: 组ID，用于过滤搜索结果 + end_user_id: 组ID，用于过滤搜索结果 clean_output: 是否清理输出中的元数据字段 """ async def _async_search(): @@ -311,7 +311,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): "context": context, "search_type": search_type, "limit": limit, - "group_id": group_id, + "end_user_id": end_user_id, "clean_output": clean_output }) diff --git a/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py b/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py new file mode 100644 index 00000000..a1fb8226 --- /dev/null +++ b/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py @@ -0,0 +1,100 @@ +import json + +from langchain_core.messages import HumanMessage, AIMessage + + +async def format_parsing(messages: list,type:str='string'): + """ + 格式化解析消息列表 + + Args: + messages: 消息列表 + type: 返回类型 ('string' 或 'dict') + + Returns: + 格式化后的消息列表 + """ + result = [] + user=[] + ai=[] + + for message in messages: + hstory_messages = message['messages'] + for history_messag in hstory_messages.strip().splitlines(): + history_messag = json.loads(history_messag) + for content in history_messag: + role = content['role'] + content = content['content'] + if type == "string": + if role == 'human': + content = '用户:' + content + else: + content = 'AI:' + content + result.append(content) + if type == "dict": + if role == 'human': + user.append( content) + else: + ai.append(content) + if type == "dict": + for key,values in zip(user,ai): + result.append({key:values}) + return result + +async def messages_parse(messages: list | dict): + user=[] + ai=[] + database=[] + for message in messages: + Query = message['Query'] + Query = json.loads(Query) + for data in Query: + role = data['role'] + if role == "human": + user.append(data['content']) + if role == "ai": + ai.append(data['content']) + for key, values in zip(user, ai): + database.append({key, values}) + return database +async def chat_data_format(messages: list | dict): + """ + 将消息格式化为 LangChain 消息格式 + + Args: + messages: 消息列表或字典 + + Returns: + LangChain 消息列表 + """ + langchain_messages = [] + if isinstance(messages, list): + for msg in messages: + if 'role' in msg.keys(): + if msg['role'] == 'user': + langchain_messages.append(HumanMessage(content=msg['content'])) + elif msg['role'] == 'assistant': + langchain_messages.append(AIMessage(content=msg['content'])) + if "Query" in msg.keys(): + langchain_messages.append(HumanMessage(content=msg['Query'])) + langchain_messages.append(AIMessage(content=msg['Answer'])) + if isinstance(messages, dict): + if messages['type'] == 'human': + langchain_messages.append(HumanMessage(content=messages['content'])) + elif messages['type'] == 'ai': + langchain_messages.append(AIMessage(content=messages['content'])) + return langchain_messages + +async def agent_chat_messages(user_content,ai_content): + messages = [ + { + "role": "user", + "content": f"{user_content}" + }, + { + "role": "assistant", + "content": f"{ai_content}" + } + + ] + return messages diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index fe281a23..d0e8a45d 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -1,15 +1,13 @@ import asyncio +import json import sys import warnings from contextlib import asynccontextmanager - - -from langchain_core.messages import HumanMessage from langgraph.constants import END, START from langgraph.graph import StateGraph - +from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, chat_data_format, messages_parse from app.db import get_db from app.core.logging_config import get_agent_logger from app.core.memory.agent.utils.llm_tools import WriteState @@ -26,8 +24,12 @@ async def make_write_graph(): """ Create a write graph workflow for memory operations. - The workflow directly processes messages from the initial state - and saves them to Neo4j storage. + Args: + user_id: User identifier + tools: MCP tools loaded from session + apply_id: Application identifier + end_user_id: Group identifier + memory_config: MemoryConfig object containing all configuration """ workflow = StateGraph(WriteState) workflow.add_node("save_neo4j", write_node) @@ -37,44 +39,49 @@ async def make_write_graph(): graph = workflow.compile() yield graph - - -async def main(): - """主函数 - 运行工作流""" - message = "今天周一" - group_id = 'new_2025test1103' # 组ID - - +async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6): + from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment + from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format + from app.core.memory.agent.utils.redis_tool import write_store + write_store.save_session_write(end_user_id, await chat_data_format(langchain_messages)) # 获取数据库会话 db_session = next(get_db()) config_service = MemoryConfigService(db_session) memory_config = config_service.load_memory_config( - config_id=17, # 改为整数 + config_id=memory_config, # 改为整数 service_name="MemoryAgentService" ) - try: - async with make_write_graph() as graph: - config = {"configurable": {"thread_id": group_id}} - # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)], "group_id": group_id, "memory_config": memory_config} + if long_term_type=='chunk': + '''方案一:对话窗口6轮对话''' + await window_dialogue(end_user_id,langchain_messages,memory_config,scope) + if long_term_type=='time': + """时间""" + await memory_long_term_storage(end_user_id, memory_config,5) + if long_term_type=='aggregate': - # 获取节点更新信息 - async for update_event in graph.astream( - initial_state, - stream_mode="updates", - config=config - ): - for node_name, node_data in update_event.items(): - if 'save_neo4j'==node_name: - massages=node_data - massages=massages.get('write_result')['status'] - print(massages) # | 更新数据: {node_data} - - except Exception as e: - import traceback - traceback.print_exc() + """方案三：聚合判断""" + await aggregate_judgment(end_user_id, langchain_messages, memory_config) -if __name__ == "__main__": - import asyncio - asyncio.run(main()) \ No newline at end of file +# async def main(): +# """主函数 - 运行工作流""" +# langchain_messages = [ +# { +# "role": "user", +# "content": "今天周五好开心啊" +# }, +# { +# "role": "assistant", +# "content": "你也这么觉得，我也是耶" +# } +# +# ] +# end_user_id = '837fee1b-04a2-48ee-94d7-211488908940' # 组ID +# memory_config="08ed205c-0f05-49c3-8e0c-a580d28f5fd4" +# # await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2) +# result=await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2) +# +# +# if __name__ == "__main__": +# import asyncio +# asyncio.run(main()) \ No newline at end of file diff --git a/api/app/core/memory/agent/models/write_aggregate_model.py b/api/app/core/memory/agent/models/write_aggregate_model.py new file mode 100644 index 00000000..fd423314 --- /dev/null +++ b/api/app/core/memory/agent/models/write_aggregate_model.py @@ -0,0 +1,28 @@ +"""Pydantic models for write aggregate judgment operations.""" + +from typing import List, Union +from pydantic import BaseModel, Field + + +class MessageItem(BaseModel): + """Individual message item in conversation.""" + + role: str = Field(..., description="角色：user 或 assistant") + content: str = Field(..., description="消息内容") + + +class WriteAggregateResponse(BaseModel): + """Response model for aggregate judgment containing judgment result and output.""" + + is_same_event: bool = Field( + ..., + description="是否是同一事件。True表示是同一事件，False表示不同事件" + ) + output: Union[List[MessageItem], bool] = Field( + ..., + description="如果is_same_event为True，返回False；如果is_same_event为False，返回消息列表" + ) + + +# 为了保持向后兼容，保留旧的类名作为别名 +WriteAggregateModel = WriteAggregateResponse diff --git a/api/app/core/memory/agent/services/parameter_builder.py b/api/app/core/memory/agent/services/parameter_builder.py index a58fcf1a..74382ade 100644 --- a/api/app/core/memory/agent/services/parameter_builder.py +++ b/api/app/core/memory/agent/services/parameter_builder.py @@ -24,7 +24,7 @@ class ParameterBuilder: tool_call_id: str, search_switch: str, apply_id: str, - group_id: str, + end_user_id: str, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None ) -> Dict[str, Any]: @@ -44,7 +44,7 @@ class ParameterBuilder: tool_call_id: Extracted tool call identifier search_switch: Search routing parameter apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier storage_type: Storage type for the workspace (optional) user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional) @@ -55,7 +55,7 @@ class ParameterBuilder: base_args = { "usermessages": tool_call_id, "apply_id": apply_id, - "group_id": group_id + "end_user_id": end_user_id } # Always add storage_type and user_rag_memory_id (with defaults if None) diff --git a/api/app/core/memory/agent/services/search_service.py b/api/app/core/memory/agent/services/search_service.py index 8a2e7cfe..4fc4256e 100644 --- a/api/app/core/memory/agent/services/search_service.py +++ b/api/app/core/memory/agent/services/search_service.py @@ -91,7 +91,7 @@ class SearchService: async def execute_hybrid_search( self, - group_id: str, + end_user_id: str, question: str, limit: int = 5, search_type: str = "hybrid", @@ -105,7 +105,7 @@ class SearchService: Execute hybrid search and return clean content. Args: - group_id: Group identifier for filtering results + end_user_id: Group identifier for filtering results question: Search query text limit: Maximum number of results to return (default: 5) search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid") @@ -130,7 +130,7 @@ class SearchService: answer = await run_hybrid_search( query_text=cleaned_query, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, output_path=output_path, @@ -186,7 +186,7 @@ class SearchService: except Exception as e: logger.error( - f"Search failed for query '{question}' in group '{group_id}': {e}", + f"Search failed for query '{question}' in group '{end_user_id}': {e}", exc_info=True ) # Return empty results on failure diff --git a/api/app/core/memory/agent/services/session_service.py b/api/app/core/memory/agent/services/session_service.py index b2d4f0ff..f7389984 100644 --- a/api/app/core/memory/agent/services/session_service.py +++ b/api/app/core/memory/agent/services/session_service.py @@ -59,7 +59,7 @@ class SessionService: self, user_id: str, apply_id: str, - group_id: str + end_user_id: str ) -> List[dict]: """ Retrieve conversation history from Redis. @@ -67,20 +67,20 @@ class SessionService: Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier Returns: List of conversation history items with Query and Answer keys Returns empty list if no history found or on error """ try: - history = self.store.find_user_apply_group(user_id, apply_id, group_id) + history = self.store.find_user_apply_group(user_id, apply_id, end_user_id) # Validate history structure if not isinstance(history, list): logger.warning( f"Invalid history format for user {user_id}, " - f"apply {apply_id}, group {group_id}: expected list, got {type(history)}" + f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}" ) return [] @@ -89,7 +89,7 @@ class SessionService: except Exception as e: logger.error( f"Failed to retrieve history for user {user_id}, " - f"apply {apply_id}, group {group_id}: {e}", + f"apply {apply_id}, group {end_user_id}: {e}", exc_info=True ) # Return empty list on error to allow execution to continue @@ -100,7 +100,7 @@ class SessionService: user_id: str, query: str, apply_id: str, - group_id: str, + end_user_id: str, ai_response: str ) -> Optional[str]: """ @@ -110,7 +110,7 @@ class SessionService: user_id: User identifier query: User query/message apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier ai_response: AI response/answer Returns: @@ -131,7 +131,7 @@ class SessionService: userid=user_id, messages=query, apply_id=apply_id, - group_id=group_id, + end_user_id=end_user_id, aimessages=ai_response ) @@ -152,7 +152,7 @@ class SessionService: Duplicates are identified by matching: - sessionid - user_id (id field) - - group_id + - end_user_id - messages - aimessages diff --git a/api/app/core/memory/agent/utils/get_dialogs.py b/api/app/core/memory/agent/utils/get_dialogs.py index 82a41773..bfb0f675 100644 --- a/api/app/core/memory/agent/utils/get_dialogs.py +++ b/api/app/core/memory/agent/utils/get_dialogs.py @@ -9,9 +9,7 @@ from app.core.memory.models.message_models import DialogData, ConversationContex async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", - group_id: str = "group_1", - user_id: str = "user1", - apply_id: str = "applyid", + end_user_id: str = "group_1", messages: list = None, ref_id: str = "wyl_20251027", config_id: str = None @@ -20,9 +18,7 @@ async def get_chunked_dialogs( Args: chunker_strategy: The chunking strategy to use (default: RecursiveChunker) - group_id: Group identifier - user_id: User identifier - apply_id: Application identifier + end_user_id: Group identifier messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference identifier config_id: Configuration ID for processing @@ -32,42 +28,40 @@ async def get_chunked_dialogs( """ from app.core.logging_config import get_agent_logger logger = get_agent_logger(__name__) - + if not messages or not isinstance(messages, list) or len(messages) == 0: raise ValueError("messages parameter must be a non-empty list") - + conversation_messages = [] - + for idx, msg in enumerate(messages): if not isinstance(msg, dict) or 'role' not in msg or 'content' not in msg: raise ValueError(f"Message {idx} format error: must contain 'role' and 'content' fields") - + role = msg['role'] content = msg['content'] - + if role not in ['user', 'assistant']: raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}") - + if content.strip(): conversation_messages.append(ConversationMessage(role=role, msg=content.strip())) - + if not conversation_messages: raise ValueError("Message list cannot be empty after filtering") - + conversation_context = ConversationContext(msgs=conversation_messages) dialog_data = DialogData( context=conversation_context, ref_id=ref_id, - group_id=group_id, - user_id=user_id, - apply_id=apply_id, + end_user_id=end_user_id, config_id=config_id ) - + chunker = DialogueChunker(chunker_strategy) extracted_chunks = await chunker.process_dialogue(dialog_data) dialog_data.chunks = extracted_chunks - + logger.info(f"DialogData created with {len(extracted_chunks)} chunks") return [dialog_data] diff --git a/api/app/core/memory/agent/utils/llm_tools.py b/api/app/core/memory/agent/utils/llm_tools.py index 8dd2f1d3..7f1041cb 100644 --- a/api/app/core/memory/agent/utils/llm_tools.py +++ b/api/app/core/memory/agent/utils/llm_tools.py @@ -1,24 +1,23 @@ import os from collections import defaultdict +from pathlib import Path from typing import Annotated, TypedDict from langchain_core.messages import AnyMessage from langgraph.graph import add_messages -PROJECT_ROOT_ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3]) class WriteState(TypedDict): ''' Langgrapg Writing TypedDict ''' messages: Annotated[list[AnyMessage], add_messages] - user_id:str - apply_id:str - group_id:str + end_user_id: str errors: list[dict] # Track errors: [{"tool": "tool_name", "error": "message"}] memory_config: object write_result: dict - data:str + data: str class ReadState(TypedDict): """ @@ -28,7 +27,7 @@ class ReadState(TypedDict): messages: 消息列表，支持自动追加 loop_count: 遍历次数 search_switch: 搜索类型开关 - group_id: 组标识 + end_user_id: 组标识 config_id: 配置ID，用于过滤结果 data: 从content_input_node传递的内容数据 spit_data: 从Split_The_Problem传递的分解结果 @@ -39,7 +38,7 @@ class ReadState(TypedDict): messages: Annotated[list[AnyMessage], add_messages] # 消息追加模式 loop_count: int search_switch: str - group_id: str + end_user_id: str config_id: str data: str # 新增字段用于传递内容 spit_data: dict # 新增字段用于传递问题分解结果 diff --git a/api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 new file mode 100644 index 00000000..1e0690bf --- /dev/null +++ b/api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 @@ -0,0 +1,61 @@ +# 角色 +你是一个智能问答助手，基于检索信息和历史对话回答用户问题。 +# 任务 +根据提供的上下文信息回答用户的问题。 +# 输入信息 +- 历史对话：{{history}} +- 检索信息：{{retrieve_info}} +# 用户问题 +{{query}} +# 回答指南 +## 1. 仔细阅读检索信息 +- 答案可能直接或间接地出现在检索信息中 +- 如果检索信息中提到"小曼会使用Python"，说明用户名是"小曼" +- 第三人称描述的偏好、行为通常指用户本人 + +## 2. 判断信息相关性 +**情况A：信息匹配问题** +- 直接回答，像自然对话一样 +- 例：检索到"小曼会使用Python" → 问"我叫什么" → 答"你叫小曼" + +**情况B：信息部分相关** +- 先回答已知部分，再自然地询问更多信息 +- 例：检索到"用户去过上海的面包店" → 问"我吃过哪家面包" → 答"我记得你去过上海的面包店，但具体是哪家我不太清楚，是哪家呢？" + +**情况C：信息完全不相关** +- 自然地表达不知道，但可以提及检索到的相关信息，让对话更连贯 +- 使用友好的表达： + - "你好像没和我说过...，但是我知道你[检索到的相关信息]" + - "关于这个我不太清楚，不过我记得你[检索到的相关信息]，能告诉我更多吗？" + - "我不记得你提到过...，但你[检索到的相关信息]" +- 即使检索信息不直接回答问题，也可以自然地融入对话中 +- 避免僵硬的"信息不足，无法回答" +## 3. 回答要求 +- 像人类对话一样自然流畅 +- 不要提及"检索信息"、"搜索结果"、"根据资料"等技术术语 +- 不要解释推理过程或引用信息来源 +- 保持友好、乐于助人的语气 +- 使用与问题相同的语言回答 +# 关键示例 +**示例1 - 直接匹配：** +- 检索信息："小曼会使用Python..." +- 问题："我叫什么" +- ✓ 正确："你叫小曼" +- ✗ 错误："你没有告诉我你的名字" +**示例2 - 间接匹配：** +- 检索信息："用户很喜欢吃星巴克的甜品" +- 问题："我喜欢什么" +- ✓ 正确："你很喜欢吃星巴克的甜品" +- ✗ 错误："信息不足" +**示例3 - 信息不匹配（推荐做法）：** +- 检索信息："用户只喝拿铁咖啡，认为美式咖啡太苦" +- 问题："我吃过哪家面包" +- ✓ 最佳："你好像没和我说过吃过哪家面包，但是我知道你喜欢喝拿铁，能跟我分享一下吗？" +- ✓ 可以："你好像没和我说过吃过哪家面包，能跟我分享一下吗？" +- ✗ 错误："用户只喝拿铁咖啡，认为美式咖啡太苦。"（答非所问） +- ✗ 错误："信息不足，无法回答。"（太僵硬） +# 重要提醒 +- 检索信息中描述用户行为/偏好时提到的名字，就是用户的名字 +- 信息不匹配时，不要强行回答无关内容，但可以自然地提及检索到的信息，让对话更有温度 +- 用对话式语言表达"不知道"，而非机械模板 +- 检索信息代表你对用户的了解，即使不直接回答问题，也能体现你对用户的记忆 diff --git a/api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 new file mode 100644 index 00000000..3744f99b --- /dev/null +++ b/api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 @@ -0,0 +1,43 @@ +{# 角色定义 #} +你是专业的问题解答专家+引导学者 + +{# 输入数据展示 #} +{% if data %} +## 输入数据 +上下文信息: +{% for item in data.history %} +- {{ item }} +{% endfor %} +检索到的所有信息: +{% for item in data.retrieve_info %} +- {{ item }} +{% endfor %} +{% endif %} + +## User Query +{{ query }} + +{# 问题回答标准 #} +## 问题回答核心标准 +根据上下文信息(history)和检索到的所有信息(retrieve_info)准确回答用户的问题(query)。 +注意，仔细阅读检索信息，答案可能直接或间接地出现在检索信息中或者历史上下文消息中，同时需要 判断信息相关性 +**情况A：信息匹配问题** +- 直接回答，像自然对话一样 +- 例：检索到"小曼会使用Python" → 问"我叫什么" → 答"你叫小曼" + +**情况B：信息部分相关** +- 先回答已知部分，再自然地询问更多信息 +- 例：检索到"用户去过上海的面包店" → 问"我吃过哪家面包" → 答"我记得你去过上海的面包店，但具体是哪家我不太清楚，是哪家呢？" + +**情况C：信息完全不相关** +- 自然地表达不知道，但可以提及检索到的相关信息，让对话更连贯 +- 使用友好的表达： + - "你好像没和我说过...，但是我知道你[检索到的相关信息]" + - "关于这个我不太清楚，不过我记得你[检索到的相关信息]，能告诉我更多吗？" + - "我不记得你提到过...，但你[检索到的相关信息]" +- 即使检索信息不直接回答问题，也可以自然地融入对话中 +- 避免僵硬的"信息不足，无法回答" + +{# 重要提醒 #} +当检索以及上下文的历史信息都无法回答的时候，可引导对方进行提问/回答，或者进行其他引导 +当检索或者上下文中出现了，相似的问题，可以委婉，提醒对方，我记得刚刚提过这个问题，但是我自己不记得了，能在描述一次吗～以此为例 diff --git a/api/app/core/memory/agent/utils/prompt/write_aggregate_judgment.jinja2 b/api/app/core/memory/agent/utils/prompt/write_aggregate_judgment.jinja2 new file mode 100644 index 00000000..fb0247aa --- /dev/null +++ b/api/app/core/memory/agent/utils/prompt/write_aggregate_judgment.jinja2 @@ -0,0 +1,57 @@ +输入句子：{{sentence}} +历史消息：{{history}} + +# 你的角色 +你是一个擅长事件聚合与语义判断的专家。 + +# 你的任务 +结合历史消息和输入句子，判断它们是否在描述**同一件事件或同一事件链**。 + +以下情况视为"同一事件"（需要返回 is_same_event=True, output=False）： +- 描述的是同一个具体事件或事实 +- 存在明显的因果关系、前后发展关系 +- 是对同一事件的补充、解释、追问或延展 +- 逻辑上属于同一语境下的连续讨论 + +以下情况视为"不同事件"（需要返回 is_same_event=False, output=消息列表）： +- 话题不同，事件主体不同 +- 时间、地点、对象明显不同 +- 只是语义相似，但并非同一具体事件 +- 无直接事件、因果或逻辑关联 + +# 输出规则（非常重要） +你必须按照以下JSON格式输出： + +**如果是同一事件：** +```json +{ + "is_same_event": true, + "output": false +} +``` + +**如果不是同一事件：** +```json +{ + "is_same_event": false, + "output": [ + { + "role": "user", + "content": "输入句子的内容" + }, + { + "role": "assistant", + "content": "对应的回复内容" + } + ] +} +``` + +# JSON Schema +{{json_schema}} + +# 注意事项 +- 必须严格按照上述格式输出 +- output 字段：如果是同一事件返回 false，如果不是同一事件返回完整的消息列表 +- 消息列表必须包含 role 和 content 字段 +- 不要输出任何解释、分析或多余内容 diff --git a/api/app/core/memory/agent/utils/redis_base.py b/api/app/core/memory/agent/utils/redis_base.py new file mode 100644 index 00000000..59bac109 --- /dev/null +++ b/api/app/core/memory/agent/utils/redis_base.py @@ -0,0 +1,186 @@ +import json +from typing import Any, List, Dict, Optional +from datetime import datetime, timedelta + + +def serialize_messages(messages: Any) -> str: + """ + 将消息序列化为 JSON 字符串，支持 LangChain 消息对象 + + Args: + messages: 可以是 list、dict、string 或 LangChain 消息对象列表 + + Returns: + str: JSON 字符串 + """ + if isinstance(messages, str): + return messages + + if isinstance(messages, (list, tuple)): + # 检查是否是 LangChain 消息对象列表 + serialized_list = [] + for msg in messages: + if hasattr(msg, 'type') and hasattr(msg, 'content'): + # LangChain 消息对象 + serialized_list.append({ + 'type': msg.type, + 'content': msg.content, + 'role': getattr(msg, 'role', msg.type) + }) + elif isinstance(msg, dict): + serialized_list.append(msg) + else: + serialized_list.append(str(msg)) + return json.dumps(serialized_list, ensure_ascii=False) + + if isinstance(messages, dict): + return json.dumps(messages, ensure_ascii=False) + + # 其他类型转为字符串 + return str(messages) + + +def deserialize_messages(messages_str: str) -> Any: + """ + 将 JSON 字符串反序列化为原始格式 + + Args: + messages_str: JSON 字符串 + + Returns: + 反序列化后的对象（list、dict 或 string） + """ + if not messages_str: + return [] + + try: + return json.loads(messages_str) + except (json.JSONDecodeError, TypeError): + return messages_str + + +def fix_encoding(text: str) -> str: + """ + 修复错误编码的文本 + + Args: + text: 需要修复的文本 + + Returns: + str: 修复后的文本 + """ + if not text or not isinstance(text, str): + return text + try: + # 尝试修复 Latin-1 误编码为 UTF-8 的情况 + return text.encode('latin-1').decode('utf-8') + except (UnicodeDecodeError, UnicodeEncodeError): + # 如果修复失败，返回原文本 + return text + + +def format_session_data(data: Dict[str, Any], include_time: bool = False) -> Dict[str, Any]: + """ + 格式化会话数据为统一的输出格式 + + Args: + data: 原始会话数据 + include_time: 是否包含时间字段 + + Returns: + Dict: 格式化后的数据 {"Query": "...", "Answer": "...", "starttime": "..."} + """ + result = { + "Query": fix_encoding(data.get('messages', '')), + "Answer": fix_encoding(data.get('aimessages', '')) + } + + if include_time: + result["starttime"] = data.get('starttime', '') + + return result + + +def filter_by_time_range(items: List[Dict], minutes: int) -> List[Dict]: + """ + 根据时间范围过滤数据 + + Args: + items: 包含 starttime 字段的数据列表 + minutes: 时间范围（分钟） + + Returns: + List[Dict]: 过滤后的数据列表 + """ + time_threshold = datetime.now() - timedelta(minutes=minutes) + time_threshold_str = time_threshold.strftime("%Y-%m-%d %H:%M:%S") + + filtered_items = [] + for item in items: + starttime = item.get('starttime', '') + if starttime and starttime >= time_threshold_str: + filtered_items.append(item) + + return filtered_items + + +def sort_and_limit_results(items: List[Dict], limit: int = 6, + remove_time: bool = True) -> List[Dict]: + """ + 对结果进行排序、限制数量并移除时间字段 + + Args: + items: 数据列表 + limit: 最大返回数量 + remove_time: 是否移除 starttime 字段 + + Returns: + List[Dict]: 处理后的数据列表 + """ + # 按时间降序排序（最新的在前） + items.sort(key=lambda x: x.get('starttime', ''), reverse=True) + + # 限制数量 + result_items = items[:limit] + + # 移除 starttime 字段 + if remove_time: + for item in result_items: + item.pop('starttime', None) + + # 如果结果少于1条，返回空列表 + if len(result_items) < 1: + return [] + + return result_items + + +def generate_session_key(session_id: str, key_type: str = "session") -> str: + """ + 生成 Redis key + + Args: + session_id: 会话ID + key_type: key 类型 ("session", "read", "write", "count") + + Returns: + str: Redis key + """ + if key_type == "count": + return f"session:count:{session_id}" + elif key_type == "write": + return f"session:write:{session_id}" + elif key_type == "session" or key_type == "read": + return f"session:{session_id}" + else: + return f"session:{session_id}" + + +def get_current_timestamp() -> str: + """ + 获取当前时间戳字符串 + + Returns: + str: 格式化的时间字符串 "YYYY-MM-DD HH:MM:SS" + """ + return datetime.now().strftime("%Y-%m-%d %H:%M:%S") \ No newline at end of file diff --git a/api/app/core/memory/agent/utils/redis_tool.py b/api/app/core/memory/agent/utils/redis_tool.py index 31a76a11..b61319e5 100644 --- a/api/app/core/memory/agent/utils/redis_tool.py +++ b/api/app/core/memory/agent/utils/redis_tool.py @@ -1,11 +1,36 @@ import redis import uuid -from datetime import datetime from app.core.config import settings +from typing import List, Dict, Any, Optional, Union + +from app.core.memory.agent.utils.redis_base import ( + serialize_messages, + deserialize_messages, + fix_encoding, + format_session_data, + filter_by_time_range, + sort_and_limit_results, + generate_session_key, + get_current_timestamp +) -class RedisSessionStore: + + +class RedisWriteStore: + """Redis Write 类型存储类，用于管理 save_session_write 相关的数据""" + def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''): + """ + 初始化 Redis 连接 + + Args: + host: Redis 主机地址 + port: Redis 端口 + db: Redis 数据库编号 + password: Redis 密码 + session_id: 会话ID + """ self.r = redis.Redis( host=host, port=port, @@ -16,210 +41,596 @@ class RedisSessionStore: ) self.uudi = session_id - def _fix_encoding(self, text): - """修复错误编码的文本""" - if not text or not isinstance(text, str): - return text - try: - # 尝试修复 Latin-1 误编码为 UTF-8 的情况 - return text.encode('latin-1').decode('utf-8') - except (UnicodeDecodeError, UnicodeEncodeError): - # 如果修复失败，返回原文本 - return text - - # 修改后的 save_session 方法 - def save_session(self, userid, messages, aimessages, apply_id, group_id): + def save_session_write(self, userid: str, messages: str) -> str: """ 写入一条会话数据，返回 session_id - 优化版本：确保写入时间不超过1秒 + + Args: + userid: 用户ID + messages: 用户消息 + + Returns: + str: 新生成的 session_id """ try: - session_id = str(uuid.uuid4()) # 为每次会话生成新的 ID - starttime = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - key = f"session:{session_id}" # 使用新生成的 session_id 作为 key + messages = serialize_messages(messages) + session_id = str(uuid.uuid4()) + key = generate_session_key(session_id, key_type="write") - # 使用 pipeline 批量写入，减少网络往返 pipe = self.r.pipeline() - - # 直接写入数据，decode_responses=True 已经处理了编码 pipe.hset(key, mapping={ "id": self.uudi, "sessionid": userid, - "apply_id": apply_id, - "group_id": group_id, "messages": messages, - "aimessages": aimessages, - "starttime": starttime + "starttime": get_current_timestamp() }) - - # 可选：设置过期时间（例如30天），避免数据无限增长 - # pipe.expire(key, 30 * 24 * 60 * 60) - - # 执行批量操作 result = pipe.execute() - print(f"保存结果: {result[0]}, session_id: {session_id}") - return session_id # 返回新生成的 session_id + print(f"[save_session_write] 保存结果: {result[0]}, session_id: {session_id}") + return session_id except Exception as e: - print(f"保存会话失败: {e}") + print(f"[save_session_write] 保存会话失败: {e}") raise e - def save_sessions_batch(self, sessions_data): + def get_session_by_userid(self, userid: str) -> Union[List[Dict[str, str]], bool]: """ - 批量写入多条会话数据，返回 session_id 列表 - sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, group_id - 优化版本：批量操作，大幅提升性能 + 通过 save_session_write 的 userid 获取 sessionid 和 messages + + Args: + userid: 用户ID (对应 sessionid 字段) + + Returns: + List[Dict] 或 False: 如果找到数据返回 [{"sessionid": "...", "messages": "..."}, ...]，否则返回 False """ try: - session_ids = [] + # 只查询 write 类型的 key + keys = self.r.keys('session:write:*') + if not keys: + return False + + # 批量获取数据 pipe = self.r.pipeline() + for key in keys: + pipe.hgetall(key) + all_data = pipe.execute() - for session in sessions_data: - session_id = str(uuid.uuid4()) - starttime = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - key = f"session:{session_id}" - - pipe.hset(key, mapping={ - "id": self.uudi, - "sessionid": session.get('userid'), - "apply_id": session.get('apply_id'), - "group_id": session.get('group_id'), - "messages": session.get('messages'), - "aimessages": session.get('aimessages'), - "starttime": starttime - }) - - session_ids.append(session_id) - - # 一次性执行所有写入操作 - results = pipe.execute() - print(f"批量保存完成: {len(session_ids)} 条记录") - return session_ids + # 筛选符合 userid 的数据 + results = [] + for key, data in zip(keys, all_data): + if not data: + continue + + # 从 write 类型读取，匹配 sessionid 字段 + if data.get('sessionid') == userid: + # 从 key 中提取 session_id: session:write:{session_id} + session_id = key.split(':')[-1] + results.append({ + "sessionid": session_id, + "messages": fix_encoding(data.get('messages', '')) + }) + + if not results: + return False + + print(f"[get_session_by_userid] userid={userid}, 找到 {len(results)} 条数据") + return results except Exception as e: - print(f"批量保存会话失败: {e}") - raise e + print(f"[get_session_by_userid] 查询失败: {e}") + return False + + def get_all_sessions_by_end_user_id(self, end_user_id: str) -> Union[List[Dict[str, Any]], bool]: + """ + 通过 end_user_id 获取所有 write 类型的会话数据 + + Args: + end_user_id: 终端用户ID (对应 sessionid 字段) + + Returns: + List[Dict] 或 False: 如果找到数据返回完整的会话信息列表，否则返回 False + + 返回格式: + [ + { + "session_id": "uuid", + "id": "...", + "sessionid": "end_user_id", + "messages": "...", + "starttime": "timestamp" + }, + ... + ] + """ + try: + # 只查询 write 类型的 key + keys = self.r.keys('session:write:*') + if not keys: + print(f"[get_all_sessions_by_end_user_id] 没有找到任何 write 类型的会话") + return False - # ---------------- 读取 ---------------- - def get_session(self, session_id): - """ - 读取一条会话数据 - """ - key = f"session:{session_id}" - data = self.r.hgetall(key) - return data if data else None + # 批量获取数据 + pipe = self.r.pipeline() + for key in keys: + pipe.hgetall(key) + all_data = pipe.execute() - def get_session_apply_group(self, sessionid, apply_id, group_id): - """ - 根据 sessionid、apply_id 和 group_id 三个条件查询会话数据 - """ - result_items = [] + # 筛选符合 end_user_id 的数据 + results = [] + for key, data in zip(keys, all_data): + if not data: + continue + + # 从 write 类型读取，匹配 sessionid 字段 + if data.get('sessionid') == end_user_id: + # 从 key 中提取 session_id: session:write:{session_id} + session_id = key.split(':')[-1] + + # 构建完整的会话信息 + session_info = { + "session_id": session_id, + "id": data.get('id', ''), + "sessionid": data.get('sessionid', ''), + "messages": fix_encoding(data.get('messages', '')), + "starttime": data.get('starttime', '') + } + results.append(session_info) + + if not results: + print(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 没有找到数据") + return False + + # 按时间排序（最新的在前） + results.sort(key=lambda x: x.get('starttime', ''), reverse=True) + + print(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 找到 {len(results)} 条数据") + return results + except Exception as e: + print(f"[get_all_sessions_by_end_user_id] 查询失败: {e}") + import traceback + traceback.print_exc() + return False - # 遍历所有会话数据 - for key in self.r.keys('session:*'): - data = self.r.hgetall(key) - - if not data: - continue - - # 检查三个条件是否都匹配 - if (data.get('sessionid') == sessionid and - data.get('apply_id') == apply_id and - data.get('group_id') == group_id): - result_items.append(data) - - return result_items - - def get_all_sessions(self): + def find_user_recent_sessions(self, userid: str, + minutes: int = 5) -> List[Dict[str, str]]: """ - 获取所有会话数据 - """ - sessions = {} - for key in self.r.keys('session:*'): - sid = key.split(':')[1] - sessions[sid] = self.get_session(sid) - return sessions - - # ---------------- 更新 ---------------- - def update_session(self, session_id, field, value): - """ - 更新单个字段 - 优化版本：使用 pipeline 减少网络往返 - """ - key = f"session:{session_id}" - pipe = self.r.pipeline() - pipe.exists(key) - pipe.hset(key, field, value) - results = pipe.execute() - return bool(results[0]) # 返回 key 是否存在 - - # ---------------- 删除 ---------------- - def delete_session(self, session_id): - """ - 删除单条会话 - """ - key = f"session:{session_id}" - return self.r.delete(key) - - def delete_all_sessions(self): - """ - 删除所有会话 - """ - keys = self.r.keys('session:*') - if keys: - return self.r.delete(*keys) - return 0 - - def delete_duplicate_sessions(self): - """ - 删除重复会话数据，条件： - "sessionid"、"user_id"、"group_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除 - 优化版本：使用 pipeline 批量操作，确保在1秒内完成 + 根据 userid 从 save_session_write 写入的数据中查询最近 N 分钟内的会话数据 + + Args: + userid: 用户ID (对应 sessionid 字段) + minutes: 查询最近几分钟的数据，默认5分钟 + + Returns: + List[Dict]: 会话列表 [{"Query": "...", "Answer": "..."}, ...] """ import time start_time = time.time() - - # 第一步：使用 pipeline 批量获取所有 key - keys = self.r.keys('session:*') - + + # 只查询 write 类型的 key + keys = self.r.keys('session:write:*') if not keys: - print("[delete_duplicate_sessions] 没有会话数据") - return 0 + print(f"[find_user_recent_sessions] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0") + return [] - # 第二步：使用 pipeline 批量获取所有数据 + # 批量获取数据 pipe = self.r.pipeline() for key in keys: pipe.hgetall(key) all_data = pipe.execute() - # 第三步：在内存中识别重复数据 - seen = {} # 用字典记录：identifier -> key（保留第一个出现的 key） - keys_to_delete = [] # 需要删除的 key 列表 + # 筛选符合 userid 的数据 + matched_items = [] + for data in all_data: + if not data: + continue + + # 从 write 类型读取，匹配 sessionid 字段 + if data.get('sessionid') == userid and data.get('starttime'): + # write 类型没有 aimessages，所以 Answer 为空 + matched_items.append({ + "Query": fix_encoding(data.get('messages', '')), + "Answer": "", + "starttime": data.get('starttime', '') + }) + + # 根据时间范围过滤 + filtered_items = filter_by_time_range(matched_items, minutes) + # 排序并移除时间字段 + result_items = sort_and_limit_results(filtered_items, limit=None) + print(result_items) - for key, data in zip(keys, all_data, strict=False): + elapsed_time = time.time() - start_time + print(f"[find_user_recent_sessions] userid={userid}, minutes={minutes}, " + f"查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}") + + return result_items + + def delete_all_write_sessions(self) -> int: + """ + 删除所有 write 类型的会话 + + Returns: + int: 删除的数量 + """ + keys = self.r.keys('session:write:*') + if keys: + return self.r.delete(*keys) + return 0 + + +class RedisCountStore: + """Redis Count 类型存储类，用于管理访问次数统计相关的数据""" + + def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''): + """ + 初始化 Redis 连接 + + Args: + host: Redis 主机地址 + port: Redis 端口 + db: Redis 数据库编号 + password: Redis 密码 + session_id: 会话ID + """ + self.r = redis.Redis( + host=host, + port=port, + db=db, + password=password, + decode_responses=True, + encoding='utf-8' + ) + self.uudi = session_id + + def save_sessions_count(self, end_user_id: str, count: int, messages: Any) -> str: + """ + 保存用户访问次数统计 + + Args: + end_user_id: 终端用户ID + count: 访问次数 + messages: 消息内容 + + Returns: + str: 新生成的 session_id + """ + session_id = str(uuid.uuid4()) + key = generate_session_key(session_id, key_type="count") + + pipe = self.r.pipeline() + pipe.hset(key, mapping={ + "id": self.uudi, + "end_user_id": end_user_id, + "count": int(count), + "messages": serialize_messages(messages), + "starttime": get_current_timestamp() + }) + pipe.expire(key, 30 * 24 * 60 * 60) # 30天过期 + result = pipe.execute() + + print(f"[save_sessions_count] 保存结果: {result}, session_id: {session_id}") + return session_id + + def get_sessions_count(self, end_user_id: str) -> Union[List[Any], bool]: + """ + 通过 end_user_id 查询访问次数统计 + + Args: + end_user_id: 终端用户ID + + Returns: + list 或 False: 如果找到返回 [count, messages]，否则返回 False + """ + try: + search_pattern = 'session:count:*' + + for key in self.r.keys(search_pattern): + data = self.r.hgetall(key) + + if not data: + continue + + if data.get('end_user_id') == end_user_id: + count = data.get('count') + messages_str = data.get('messages') + + if count is not None: + messages = deserialize_messages(messages_str) + return [int(count), messages] + + return False + except Exception as e: + print(f"[get_sessions_count] 查询失败: {e}") + return False + + def update_sessions_count(self, end_user_id: str, new_count: int, + messages: Any) -> bool: + """ + 通过 end_user_id 修改访问次数统计 + + Args: + end_user_id: 终端用户ID + new_count: 新的 count 值 + messages: 消息内容 + + Returns: + bool: 更新成功返回 True，未找到记录返回 False + """ + try: + messages_str = serialize_messages(messages) + search_pattern = 'session:count:*' + + for key in self.r.keys(search_pattern): + data = self.r.hgetall(key) + + if not data: + continue + + if data.get('end_user_id') == end_user_id: + self.r.hset(key, 'count', int(new_count)) + self.r.hset(key, 'messages', messages_str) + print(f"[update_sessions_count] 更新成功: end_user_id={end_user_id}, new_count={new_count}, key={key}") + return True + + print(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}") + return False + except Exception as e: + print(f"[update_sessions_count] 更新失败: {e}") + return False + + def delete_all_count_sessions(self) -> int: + """ + 删除所有 count 类型的会话 + + Returns: + int: 删除的数量 + """ + keys = self.r.keys('session:count:*') + if keys: + return self.r.delete(*keys) + return 0 + + +class RedisSessionStore: + """Redis 会话存储类，用于管理会话数据""" + + def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''): + """ + 初始化 Redis 连接 + + Args: + host: Redis 主机地址 + port: Redis 端口 + db: Redis 数据库编号 + password: Redis 密码 + session_id: 会话ID + """ + self.r = redis.Redis( + host=host, + port=port, + db=db, + password=password, + decode_responses=True, + encoding='utf-8' + ) + self.uudi = session_id + + # ==================== 写入操作 ==================== + + def save_session(self, userid: str, messages: str, aimessages: str, + apply_id: str, end_user_id: str) -> str: + """ + 写入一条会话数据，返回 session_id + + Args: + userid: 用户ID + messages: 用户消息 + aimessages: AI回复消息 + apply_id: 应用ID + end_user_id: 终端用户ID + + Returns: + str: 新生成的 session_id + """ + try: + session_id = str(uuid.uuid4()) + key = generate_session_key(session_id, key_type="read") + + pipe = self.r.pipeline() + pipe.hset(key, mapping={ + "id": self.uudi, + "sessionid": userid, + "apply_id": apply_id, + "end_user_id": end_user_id, + "messages": messages, + "aimessages": aimessages, + "starttime": get_current_timestamp() + }) + result = pipe.execute() + + print(f"[save_session] 保存结果: {result[0]}, session_id: {session_id}") + return session_id + except Exception as e: + print(f"[save_session] 保存会话失败: {e}") + raise e + + # ==================== 读取操作 ==================== + + def get_session(self, session_id: str) -> Optional[Dict[str, Any]]: + """ + 读取一条会话数据 + + Args: + session_id: 会话ID + + Returns: + Dict 或 None: 会话数据 + """ + key = generate_session_key(session_id) + data = self.r.hgetall(key) + return data if data else None + + def get_all_sessions(self) -> Dict[str, Dict[str, Any]]: + """ + 获取所有会话数据（不包括 count 和 write 类型） + + Returns: + Dict: 所有会话数据，key 为 session_id + """ + sessions = {} + for key in self.r.keys('session:*'): + # 排除 count 和 write 类型的 key + if ':count:' not in key and ':write:' not in key: + sid = key.split(':')[1] + sessions[sid] = self.get_session(sid) + return sessions + + def find_user_apply_group(self, sessionid: str, apply_id: str, + end_user_id: str) -> List[Dict[str, str]]: + """ + 根据 sessionid、apply_id 和 end_user_id 查询会话数据，返回最新的6条 + + Args: + sessionid: 会话ID（支持模糊匹配） + apply_id: 应用ID + end_user_id: 终端用户ID + + Returns: + List[Dict]: 会话列表 [{"Query": "...", "Answer": "..."}, ...] + """ + import time + start_time = time.time() + + keys = self.r.keys('session:*') + if not keys: + print(f"[find_user_apply_group] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0") + return [] + + # 批量获取数据 + pipe = self.r.pipeline() + for key in keys: + # 排除 count 和 write 类型 + if ':count:' not in key and ':write:' not in key: + pipe.hgetall(key) + all_data = pipe.execute() + + # 筛选符合条件的数据 + matched_items = [] + for data in all_data: if not data: continue - # 获取五个字段的值 - sessionid = data.get('sessionid', '') - user_id = data.get('id', '') - group_id = data.get('group_id', '') - messages = data.get('messages', '') - aimessages = data.get('aimessages', '') + if (data.get('apply_id') == apply_id and + data.get('end_user_id') == end_user_id): + # 支持模糊匹配或完全匹配 sessionid + if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid: + matched_items.append(format_session_data(data, include_time=True)) + + # 排序、限制数量并移除时间字段 + result_items = sort_and_limit_results(matched_items, limit=6) + + elapsed_time = time.time() - start_time + print(f"[find_user_apply_group] 查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}") + + return result_items + + # ==================== 更新操作 ==================== + + def update_session(self, session_id: str, field: str, value: Any) -> bool: + """ + 更新单个字段 + + Args: + session_id: 会话ID + field: 字段名 + value: 字段值 + + Returns: + bool: 是否更新成功 + """ + key = generate_session_key(session_id) + pipe = self.r.pipeline() + pipe.exists(key) + pipe.hset(key, field, value) + results = pipe.execute() + return bool(results[0]) + + # ==================== 删除操作 ==================== + + def delete_session(self, session_id: str) -> int: + """ + 删除单条会话 + + Args: + session_id: 会话ID + + Returns: + int: 删除的数量 + """ + key = generate_session_key(session_id) + return self.r.delete(key) + + def delete_all_sessions(self) -> int: + """ + 删除所有会话（不包括 count 和 write 类型） + + Returns: + int: 删除的数量 + """ + keys = self.r.keys('session:*') + # 过滤掉 count 和 write 类型 + keys_to_delete = [k for k in keys if ':count:' not in k and ':write:' not in k] + if keys_to_delete: + return self.r.delete(*keys_to_delete) + return 0 + + def delete_duplicate_sessions(self) -> int: + """ + 删除重复会话数据（不包括 count 和 write 类型） + 条件：sessionid、user_id、end_user_id、messages、aimessages 五个字段都相同的只保留一个 + + Returns: + int: 删除的数量 + """ + import time + start_time = time.time() + + keys = self.r.keys('session:*') + if not keys: + print("[delete_duplicate_sessions] 没有会话数据") + return 0 + + # 批量获取所有数据 + pipe = self.r.pipeline() + for key in keys: + # 排除 count 和 write 类型 + if ':count:' not in key and ':write:' not in key: + pipe.hgetall(key) + all_data = pipe.execute() + + # 识别重复数据 + seen = {} + keys_to_delete = [] + + for key, data in zip([k for k in keys if ':count:' not in k and ':write:' not in k], all_data, strict=False): + if not data: + continue # 用五元组作为唯一标识 - identifier = (sessionid, user_id, group_id, messages, aimessages) + identifier = ( + data.get('sessionid', ''), + data.get('id', ''), + data.get('end_user_id', ''), + data.get('messages', ''), + data.get('aimessages', '') + ) if identifier in seen: - # 重复，标记为待删除 keys_to_delete.append(key) else: - # 第一次出现，记录 seen[identifier] = key - # 第四步：使用 pipeline 批量删除重复的 key + # 批量删除重复的 key deleted_count = 0 if keys_to_delete: - # 分批删除，避免单次操作过大 batch_size = 1000 for i in range(0, len(keys_to_delete), batch_size): batch = keys_to_delete[i:i + batch_size] @@ -233,79 +644,28 @@ class RedisSessionStore: print(f"[delete_duplicate_sessions] 删除重复会话数量: {deleted_count}, 耗时: {elapsed_time:.3f}秒") return deleted_count - def find_user_session(self, sessionid): - user_id = sessionid - - result_items = [] - for key, values in store.get_all_sessions().items(): - history = {} - if user_id == str(values['sessionid']): - history["Query"] = values['messages'] - history["Answer"] = values['aimessages'] - result_items.append(history) - - if len(result_items) <= 1: - result_items = [] - return (result_items) - - def find_user_apply_group(self, sessionid, apply_id, group_id): - """ - 根据 sessionid、apply_id 和 group_id 三个条件查询会话数据，返回最新的6条 - """ - import time - start_time = time.time() - # 使用 pipeline 批量获取数据，提高性能 - keys = self.r.keys('session:*') - - if not keys: - print(f"查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0") - return [] - - # 使用 pipeline 批量获取所有 hash 数据 - pipe = self.r.pipeline() - for key in keys: - pipe.hgetall(key) - all_data = pipe.execute() - - # 解析并筛选符合条件的数据 - matched_items = [] - for data in all_data: - if not data: - continue - - # 检查是否符合三个条件 - - if (data.get('apply_id') == apply_id and - data.get('group_id') == group_id): - # 支持模糊匹配 sessionid 或者完全匹配 - if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid: - matched_items.append({ - "Query": self._fix_encoding(data.get('messages')), - "Answer": self._fix_encoding(data.get('aimessages')), - "starttime": data.get('starttime', '') - }) - # 按时间降序排序（最新的在前） - matched_items.sort(key=lambda x: x.get('starttime', ''), reverse=True) - # 只保留最新的6条 - result_items = matched_items[:6] - # # 移除 starttime 字段 - for item in result_items: - item.pop('starttime', None) - - # 如果结果少于等于1条，返回空列表 - if len(result_items) <= 1: - result_items = [] - - elapsed_time = time.time() - start_time - print(f"查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}") - - return result_items - +# 全局实例 store = RedisSessionStore( host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None, session_id=str(uuid.uuid4()) -) \ No newline at end of file +) + +write_store = RedisWriteStore( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB, + password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None, + session_id=str(uuid.uuid4()) +) + +count_store = RedisCountStore( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB, + password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None, + session_id=str(uuid.uuid4()) +) diff --git a/api/app/core/memory/agent/utils/session_tools.py b/api/app/core/memory/agent/utils/session_tools.py index b2d4f0ff..f7389984 100644 --- a/api/app/core/memory/agent/utils/session_tools.py +++ b/api/app/core/memory/agent/utils/session_tools.py @@ -59,7 +59,7 @@ class SessionService: self, user_id: str, apply_id: str, - group_id: str + end_user_id: str ) -> List[dict]: """ Retrieve conversation history from Redis. @@ -67,20 +67,20 @@ class SessionService: Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier Returns: List of conversation history items with Query and Answer keys Returns empty list if no history found or on error """ try: - history = self.store.find_user_apply_group(user_id, apply_id, group_id) + history = self.store.find_user_apply_group(user_id, apply_id, end_user_id) # Validate history structure if not isinstance(history, list): logger.warning( f"Invalid history format for user {user_id}, " - f"apply {apply_id}, group {group_id}: expected list, got {type(history)}" + f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}" ) return [] @@ -89,7 +89,7 @@ class SessionService: except Exception as e: logger.error( f"Failed to retrieve history for user {user_id}, " - f"apply {apply_id}, group {group_id}: {e}", + f"apply {apply_id}, group {end_user_id}: {e}", exc_info=True ) # Return empty list on error to allow execution to continue @@ -100,7 +100,7 @@ class SessionService: user_id: str, query: str, apply_id: str, - group_id: str, + end_user_id: str, ai_response: str ) -> Optional[str]: """ @@ -110,7 +110,7 @@ class SessionService: user_id: User identifier query: User query/message apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier ai_response: AI response/answer Returns: @@ -131,7 +131,7 @@ class SessionService: userid=user_id, messages=query, apply_id=apply_id, - group_id=group_id, + end_user_id=end_user_id, aimessages=ai_response ) @@ -152,7 +152,7 @@ class SessionService: Duplicates are identified by matching: - sessionid - user_id (id field) - - group_id + - end_user_id - messages - aimessages diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 1df0b336..446ab86a 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -29,20 +29,18 @@ logger = get_agent_logger(__name__) async def write( - user_id: str, - apply_id: str, - group_id: str, + end_user_id: str, memory_config: MemoryConfig, messages: list, ref_id: str = "wyl20251027", ) -> None: """ Execute the complete knowledge extraction pipeline. - + Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier memory_config: MemoryConfig object containing all configuration messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference ID, defaults to "wyl20251027" @@ -51,14 +49,14 @@ async def write( embedding_model_id = str(memory_config.embedding_model_id) chunker_strategy = memory_config.chunker_strategy config_id = str(memory_config.config_id) - + logger.info("=== MemSci Knowledge Extraction Pipeline ===") logger.info(f"Config: {memory_config.config_name} (ID: {config_id})") logger.info(f"Workspace: {memory_config.workspace_name}") logger.info(f"LLM model: {memory_config.llm_model_name}") logger.info(f"Embedding model: {memory_config.embedding_model_name}") logger.info(f"Chunker strategy: {chunker_strategy}") - logger.info(f"Group ID: {group_id}") + logger.info(f"end_user_id ID: {end_user_id}") # Construct clients from memory_config using factory pattern with db session with get_db_context() as db: @@ -83,9 +81,7 @@ async def write( step_start = time.time() chunked_dialogs = await get_chunked_dialogs( chunker_strategy=chunker_strategy, - group_id=group_id, - user_id=user_id, - apply_id=apply_id, + end_user_id=end_user_id, messages=messages, ref_id=ref_id, config_id=config_id, diff --git a/api/app/core/memory/analytics/api_docs_parser.py b/api/app/core/memory/analytics/api_docs_parser.py index 94ed0f00..4a116520 100644 --- a/api/app/core/memory/analytics/api_docs_parser.py +++ b/api/app/core/memory/analytics/api_docs_parser.py @@ -139,7 +139,8 @@ def parse_api_docs(file_path: str) -> Dict[str, Any]: def get_default_docs_path() -> str: - project_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) return os.path.join(project_root, "src", "analytics", "API接口.md") diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index cab6cacd..95302726 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -16,13 +16,13 @@ class FilteredTags(BaseModel): """用于接收LLM筛选后的核心标签列表的模型。""" meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。") -async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: +async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: """ 使用LLM筛选标签列表，仅保留具有代表性的核心名词。 Args: tags: 原始标签列表 - group_id: 用户组ID，用于获取配置 + end_user_id: 用户组ID，用于获取配置 Returns: 筛选后的标签列表 @@ -37,12 +37,12 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: get_end_user_connected_config, ) - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if not config_id: raise ValueError( - f"No memory_config_id found for group_id: {group_id}. " + f"No memory_config_id found for end_user_id: {end_user_id}. " "Please ensure the user has a valid memory configuration." ) @@ -87,7 +87,7 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: async def get_raw_tags_from_db( connector: Neo4jConnector, - group_id: str, + end_user_id: str, limit: int, by_user: bool = False ) -> List[Tuple[str, int]]: @@ -99,9 +99,9 @@ async def get_raw_tags_from_db( Args: connector: Neo4j连接器实例 - group_id: 如果by_user=False，则为group_id；如果by_user=True，则为user_id + end_user_id: 如果by_user=False，则为end_user_id；如果by_user=True，则为user_id limit: 返回的标签数量限制 - by_user: 是否按user_id查询（默认False，按group_id查询） + by_user: 是否按user_id查询（默认False，按end_user_id查询） Returns: List[Tuple[str, int]]: 标签名称和频率的元组列表 @@ -119,7 +119,7 @@ async def get_raw_tags_from_db( else: query = ( "MATCH (e:ExtractedEntity) " - "WHERE e.group_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " + "WHERE e.end_user_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC " "LIMIT $limit" @@ -128,44 +128,44 @@ async def get_raw_tags_from_db( # 使用项目的Neo4jConnector执行查询 results = await connector.execute_query( query, - id=group_id, + id=end_user_id, limit=limit, names_to_exclude=names_to_exclude ) return [(record["name"], record["frequency"]) for record in results] -async def get_hot_memory_tags(group_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]: +async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]: """ 获取原始标签，然后使用LLM进行筛选，返回最终的热门标签列表。 查询更多的标签(limit=40)给LLM提供更丰富的上下文进行筛选。 Args: - group_id: 必需参数。如果by_user=False，则为group_id；如果by_user=True，则为user_id + end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id limit: 返回的标签数量限制 - by_user: 是否按user_id查询（默认False，按group_id查询） + by_user: 是否按user_id查询（默认False，按end_user_id查询） Raises: - ValueError: 如果group_id未提供或为空 + ValueError: 如果end_user_id未提供或为空 """ - # 验证group_id必须提供且不为空 - if not group_id or not group_id.strip(): + # 验证end_user_id必须提供且不为空 + if not end_user_id or not end_user_id.strip(): raise ValueError( - "group_id is required. Please provide a valid group_id or user_id." + "end_user_id is required. Please provide a valid end_user_id or user_id." ) # 使用项目的Neo4jConnector connector = Neo4jConnector() try: # 1. 从数据库获取原始排名靠前的标签 - raw_tags_with_freq = await get_raw_tags_from_db(connector, group_id, limit, by_user=by_user) + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, limit, by_user=by_user) if not raw_tags_with_freq: return [] raw_tag_names = [tag for tag, freq in raw_tags_with_freq] # 2. 初始化LLM客户端并使用LLM筛选出有意义的标签 - meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, group_id) + meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, end_user_id) # 3. 根据LLM的筛选结果，构建最终的标签列表（保留原始频率和顺序） final_tags = [] diff --git a/api/app/core/memory/analytics/implicit_memory/data_source.py b/api/app/core/memory/analytics/implicit_memory/data_source.py index d277a05e..18678a55 100644 --- a/api/app/core/memory/analytics/implicit_memory/data_source.py +++ b/api/app/core/memory/analytics/implicit_memory/data_source.py @@ -75,8 +75,8 @@ class MemoryDataSource: start_date = time_range.start_date if time_range else None end_date = time_range.end_date if time_range else None - summary_dicts = await self.memory_summary_repo.find_by_group_id( - group_id=user_id, + summary_dicts = await self.memory_summary_repo.find_by_end_user_id( + end_user_id=user_id, limit=limit, start_date=start_date, end_date=end_date diff --git a/api/app/core/memory/analytics/recent_activity_stats.py b/api/app/core/memory/analytics/recent_activity_stats.py index c41f4208..71f70c09 100644 --- a/api/app/core/memory/analytics/recent_activity_stats.py +++ b/api/app/core/memory/analytics/recent_activity_stats.py @@ -2,13 +2,16 @@ import os import re import glob import json +from pathlib import Path from typing import Tuple try: from app.core.memory.utils.config.definitions import PROJECT_ROOT except Exception: # Fallback: derive project root from this file location - PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + # 当前文件在 api/app/core/memory/analytics/recent_activity_stats.py + # 需要向上 5 级到达 api/ 目录 + PROJECT_ROOT = str(Path(__file__).resolve().parents[4]) def _get_latest_prompt_log_path() -> str | None: @@ -67,44 +70,43 @@ def parse_stats_from_log(log_path: str) -> dict: triplet_relations_count = 0 temporal_count = 0 - # Patterns + # 正则表达式模式 - 匹配当前日志格式 pat_chunk_render = re.compile(r"===\s*RENDERED\s*STATEMENT\s*EXTRACTION\s*PROMPT\s*===") - pat_triplet_start = re.compile(r"\[Triplet\].*statements_to_process\s*=\s*(\d+)") - pat_triplet_done = re.compile( - r"\[Triplet\].*completed,\s*total_triplets\s*=\s*(\d+),\s*total_entities\s*=\s*(\d+)" + pat_triplet_started = re.compile(r"\[Triplet\]\s+Started\s+-\s+statement_id=") + pat_triplet_completed = re.compile( + r"\[Triplet\]\s+Completed\s+-\s+statement_id=[^,]+,\s+triplets=(\d+),\s+entities=(\d+)" ) - pat_temporal_done = re.compile( - r"\[Temporal\].*completed,\s*extracted_valid_ranges\s*=\s*(\d+)" + pat_temporal_completed = re.compile( + r"\[Temporal\]\s+Completed\s+-\s+statement_id=[^,]+,\s+valid_ranges=(\d+)" ) with open(log_path, "r", encoding="utf-8", errors="ignore") as f: for line in f: - # Chunk prompts count (each chunk triggers one statement-extraction prompt render) + # 文本块数量（每个块触发一次陈述提取提示） if pat_chunk_render.search(line): chunk_count += 1 continue - m1 = pat_triplet_start.search(line) - if m1: + # 陈述数量（每个 Triplet Started 代表一个陈述被处理） + if pat_triplet_started.search(line): + statements_count += 1 + continue + + # 三元组完成：[Triplet] Completed - statement_id=xxx, triplets=X, entities=Y + m_triplet = pat_triplet_completed.search(line) + if m_triplet: try: - statements_count += int(m1.group(1)) + triplet_relations_count += int(m_triplet.group(1)) + triplet_entities_count += int(m_triplet.group(2)) except Exception: pass continue - m2 = pat_triplet_done.search(line) - if m2: + # 时间信息完成：[Temporal] Completed - statement_id=xxx, valid_ranges=X + m_temporal = pat_temporal_completed.search(line) + if m_temporal: try: - triplet_relations_count += int(m2.group(1)) - triplet_entities_count += int(m2.group(2)) - except Exception: - pass - continue - - m3 = pat_temporal_done.search(line) - if m3: - try: - temporal_count += int(m3.group(1)) + temporal_count += int(m_temporal.group(1)) except Exception: pass continue @@ -120,15 +122,20 @@ def parse_stats_from_log(log_path: str) -> dict: def get_recent_activity_stats() -> Tuple[dict, str]: - """Get aggregated stats from all prompt logs in logs/. + """Get stats from the latest prompt log file only. Returns (stats_dict, message). """ - all_logs = _get_all_prompt_logs() - # Fallback to recursive search if none found in logs/ - if not all_logs: + # 获取最新的日志文件 + latest_log = _get_latest_prompt_log_path() + + # 如果没有找到，尝试递归搜索 + if not latest_log: all_logs = _get_any_logs_recursive() - if not all_logs: + if all_logs: + latest_log = all_logs[-1] # 取最新的 + + if not latest_log: return ( { "chunk_count": 0, @@ -141,24 +148,13 @@ def get_recent_activity_stats() -> Tuple[dict, str]: "未找到日志文件，请确认已运行过提取流程。", ) - agg = { - "chunk_count": 0, - "statements_count": 0, - "triplet_entities_count": 0, - "triplet_relations_count": 0, - "temporal_count": 0, - } - for path in all_logs: - s = parse_stats_from_log(path) - agg["chunk_count"] += s.get("chunk_count", 0) - agg["statements_count"] += s.get("statements_count", 0) - agg["triplet_entities_count"] += s.get("triplet_entities_count", 0) - agg["triplet_relations_count"] += s.get("triplet_relations_count", 0) - agg["temporal_count"] += s.get("temporal_count", 0) - - # Attach a summary of files combined - agg["log_path"] = f"{len(all_logs)} 个日志文件，最新：{all_logs[-1]}" - return agg, "成功汇总 logs 目录中所有提示日志。" + # 只解析最新的日志文件 + stats = parse_stats_from_log(latest_log) + + # 添加日志文件路径信息 + stats["log_path"] = f"最新：{latest_log}" + + return stats, "成功读取最近一次记忆活动统计。" def _format_summary(stats: dict) -> str: diff --git a/api/app/core/memory/evaluation/__init__.py b/api/app/core/memory/evaluation/__init__.py deleted file mode 100644 index e9d6aa6c..00000000 --- a/api/app/core/memory/evaluation/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Evaluation package with dataset-specific pipelines and a unified runner.""" diff --git a/api/app/core/memory/evaluation/benchmark.md b/api/app/core/memory/evaluation/benchmark.md deleted file mode 100644 index 2853b22b..00000000 --- a/api/app/core/memory/evaluation/benchmark.md +++ /dev/null @@ -1,30 +0,0 @@ -⏬数据集下载地址： - Locomo10.json：https://github.com/snap-research/locomo/tree/main/data - LongMemEval_oracle.json：https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned - msc_self_instruct.jsonl:https://huggingface.co/datasets/MemGPT/MSC-Self-Instruct - 上方数据集下载好后全部放入app/core/memory/data文件夹中 - -全流程基准测试运行： - locomo： - python -m app.core.memory.evaluation.run_eval --dataset locomo --sample-size 1 --reset-group --group-id yyw1 --search-type hybrid --search-limit 8 --context-char-budget 12000 --llm-max-tokens 32 - LongMemEval： - python -m app.core.memory.evaluation.run_eval --dataset longmemeval --sample-size 10 --start-index 0 --group-id longmemeval_zh_bak_2 --search-limit 8 --context-char-budget 4000 --search-type hybrid --max-contexts-per-item 2 --reset-group - memsciqa： - python -m app.core.memory.evaluation.run_eval --dataset memsciqa --sample-size 10 --reset-group --group-id group_memsci - -单独检索评估运行命令： - python -m app.core.memory.evaluation.locomo.locomo_test - python -m app.core.memory.evaluation.longmemeval.test_eval - python -m app.core.memory.evaluation.memsciqa.memsciqa-test - 需要先在项目中修改需要检测评估的group_id。 - -参数及解释： - ● --dataset longmemeval - 指定数据集 - ● --sample-size 10 - 评估10个样本 - ● --start-index 0 - 从第0个样本开始 - ● --group-id longmemeval_zh_bak_2 - 使用指定的组ID - ● --search-limit 8 - 检索限制8条 - ● --context-char-budget 4000 - 上下文字符预算4000 - ● --search-type hybrid - 使用混合检索 - ● --max-contexts-per-item 2 - 每个样本最多摄入2个上下文 - ● --reset-group - 运行前清空组数据 \ No newline at end of file diff --git a/api/app/core/memory/evaluation/common/metrics.py b/api/app/core/memory/evaluation/common/metrics.py deleted file mode 100644 index acc27fb9..00000000 --- a/api/app/core/memory/evaluation/common/metrics.py +++ /dev/null @@ -1,100 +0,0 @@ -import math -import re -from typing import List, Dict - - -def _normalize(text: str) -> List[str]: - """Lowercase, strip punctuation, and split into tokens.""" - text = text.lower().strip() - # Python's re doesn't support \p classes; use a simple non-word filter - text = re.sub(r"[^\w\s]", " ", text) - tokens = [t for t in text.split() if t] - return tokens - - -def exact_match(pred: str, ref: str) -> float: - return float(_normalize(pred) == _normalize(ref)) - - -def jaccard(pred: str, ref: str) -> float: - p = set(_normalize(pred)) - r = set(_normalize(ref)) - if not p and not r: - return 1.0 - if not p or not r: - return 0.0 - return len(p & r) / len(p | r) - - -def f1_score(pred: str, ref: str) -> float: - p_tokens = _normalize(pred) - r_tokens = _normalize(ref) - if not p_tokens and not r_tokens: - return 1.0 - if not p_tokens or not r_tokens: - return 0.0 - p_set = set(p_tokens) - r_set = set(r_tokens) - tp = len(p_set & r_set) - precision = tp / len(p_set) if p_set else 0.0 - recall = tp / len(r_set) if r_set else 0.0 - if precision + recall == 0: - return 0.0 - return 2 * precision * recall / (precision + recall) - - -def bleu1(pred: str, ref: str) -> float: - """Unigram BLEU (BLEU-1) with clipping and brevity penalty.""" - p_tokens = _normalize(pred) - r_tokens = _normalize(ref) - if not p_tokens: - return 0.0 - # Clipped count - r_counts: Dict[str, int] = {} - for t in r_tokens: - r_counts[t] = r_counts.get(t, 0) + 1 - clipped = 0 - p_counts: Dict[str, int] = {} - for t in p_tokens: - p_counts[t] = p_counts.get(t, 0) + 1 - for t, c in p_counts.items(): - clipped += min(c, r_counts.get(t, 0)) - precision = clipped / max(len(p_tokens), 1) - # Brevity penalty - ref_len = len(r_tokens) - pred_len = len(p_tokens) - if pred_len > ref_len or pred_len == 0: - bp = 1.0 - else: - bp = math.exp(1 - ref_len / max(pred_len, 1)) - return bp * precision - - -def percentile(values: List[float], p: float) -> float: - if not values: - return 0.0 - vals = sorted(values) - k = (len(vals) - 1) * p - f = math.floor(k) - c = math.ceil(k) - if f == c: - return vals[int(k)] - return vals[f] + (k - f) * (vals[c] - vals[f]) - - -def latency_stats(latencies_ms: List[float]) -> Dict[str, float]: - """Return basic latency stats: mean, p50, p95, iqr (p75-p25).""" - if not latencies_ms: - return {"mean": 0.0, "p50": 0.0, "p95": 0.0, "iqr": 0.0} - p25 = percentile(latencies_ms, 0.25) - p50 = percentile(latencies_ms, 0.50) - p75 = percentile(latencies_ms, 0.75) - p95 = percentile(latencies_ms, 0.95) - mean = sum(latencies_ms) / max(len(latencies_ms), 1) - return {"mean": mean, "p50": p50, "p95": p95, "iqr": p75 - p25} - - -def avg_context_tokens(contexts: List[str]) -> float: - if not contexts: - return 0.0 - return sum(len(_normalize(c)) for c in contexts) / len(contexts) diff --git a/api/app/core/memory/evaluation/dialogue_queries.py b/api/app/core/memory/evaluation/dialogue_queries.py deleted file mode 100644 index fd7fa671..00000000 --- a/api/app/core/memory/evaluation/dialogue_queries.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -Dialogue search queries for evaluation purposes. -This file contains Cypher queries for searching dialogues, entities, and chunks. -Placed in evaluation directory to avoid circular imports with src modules. -""" - -# Entity search queries -SEARCH_ENTITIES_BY_NAME = """ -MATCH (e:Entity) -WHERE e.name = $name -RETURN e -""" - -SEARCH_ENTITIES_BY_NAME_FALLBACK = """ -MATCH (e:Entity) -WHERE e.name CONTAINS $name -RETURN e -""" - -# Chunk search queries -SEARCH_CHUNKS_BY_CONTENT = """ -MATCH (c:Chunk) -WHERE c.content CONTAINS $content -RETURN c -""" - -# Dialogue search queries -SEARCH_DIALOGUE_BY_DIALOG_ID = """ -MATCH (d:Dialogue) -WHERE d.dialog_id = $dialog_id -RETURN d -""" - -SEARCH_DIALOGUES_BY_CONTENT = """ -MATCH (d:Dialogue) -WHERE d.content CONTAINS $q -RETURN d -""" - -DIALOGUE_EMBEDDING_SEARCH = """ -WITH $embedding AS q -MATCH (d:Dialogue) -WHERE d.dialog_embedding IS NOT NULL - AND ($group_id IS NULL OR d.group_id = $group_id) -WITH d, q, d.dialog_embedding AS v -WITH d, - reduce(dot = 0.0, i IN range(0, size(q)-1) | dot + toFloat(q[i]) * toFloat(v[i])) AS dot, - sqrt(reduce(qs = 0.0, i IN range(0, size(q)-1) | qs + toFloat(q[i]) * toFloat(q[i]))) AS qnorm, - sqrt(reduce(vs = 0.0, i IN range(0, size(v)-1) | vs + toFloat(v[i]) * toFloat(v[i]))) AS vnorm -WITH d, CASE WHEN qnorm = 0 OR vnorm = 0 THEN 0.0 ELSE dot / (qnorm * vnorm) END AS score -WHERE score > $threshold -RETURN d.id AS dialog_id, - d.group_id AS group_id, - d.content AS content, - d.created_at AS created_at, - d.expired_at AS expired_at, - score -ORDER BY score DESC -LIMIT $limit -""" diff --git a/api/app/core/memory/evaluation/extraction_utils.py b/api/app/core/memory/evaluation/extraction_utils.py deleted file mode 100644 index 9afa228c..00000000 --- a/api/app/core/memory/evaluation/extraction_utils.py +++ /dev/null @@ -1,341 +0,0 @@ -import asyncio -import json -import os -import re -from datetime import datetime -from typing import Any, Dict, List, Optional - -from app.core.memory.llm_tools.openai_client import LLMClient -from app.core.memory.models.message_models import ( - ConversationContext, - ConversationMessage, - DialogData, -) - -# 使用新的模块化架构 -from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ( - ExtractionOrchestrator, -) -from app.core.memory.storage_services.extraction_engine.knowledge_extraction.chunk_extraction import ( - DialogueChunker, -) -from app.core.memory.utils.config.definitions import ( - SELECTED_CHUNKER_STRATEGY, - SELECTED_EMBEDDING_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.db import get_db_context - -# Import from database module -from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j -from app.repositories.neo4j.neo4j_connector import Neo4jConnector - -# Cypher queries for evaluation -# Note: Entity, chunk, and dialogue search queries have been moved to evaluation/dialogue_queries.py - - -async def ingest_contexts_via_full_pipeline( - contexts: List[str], - group_id: str, - chunker_strategy: str | None = None, - embedding_name: str | None = None, - save_chunk_output: bool = False, - save_chunk_output_path: str | None = None, -) -> bool: - """DEPRECATED: 此函数使用旧的流水线架构，建议使用新的 ExtractionOrchestrator - - Run the full extraction pipeline on provided dialogue contexts and save to Neo4j. - This function mirrors the steps in main(), but starts from raw text contexts. - Args: - contexts: List of dialogue texts, each containing lines like "role: message". - group_id: Group ID to assign to generated DialogData and graph nodes. - chunker_strategy: Optional chunker strategy; defaults to SELECTED_CHUNKER_STRATEGY. - embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID. - save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging. - save_chunk_output_path: Optional output path; defaults to src/chunker_test_output.txt. - Returns: - True if data saved successfully, False otherwise. - """ - chunker_strategy = chunker_strategy or SELECTED_CHUNKER_STRATEGY - embedding_name = embedding_name or SELECTED_EMBEDDING_ID - - # Initialize llm client with graceful fallback - llm_client = None - llm_available = True - try: - from app.core.memory.utils.config import definitions as config_defs - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(config_defs.SELECTED_LLM_ID) - except Exception as e: - print(f"[Ingestion] LLM client unavailable, will skip LLM-dependent steps: {e}") - llm_available = False - - # Step A: Build DialogData list from contexts with robust parsing - chunker = DialogueChunker(chunker_strategy) - dialog_data_list: List[DialogData] = [] - - for idx, ctx in enumerate(contexts): - messages: List[ConversationMessage] = [] - - # Improved parsing: capture multi-line message blocks, normalize roles - pattern = r"^\s*(用户|AI|assistant|user)\s*[：:]\s*(.+?)(?=\n\s*(?:用户|AI|assistant|user)\s*[：:]|\Z)" - matches = list(re.finditer(pattern, ctx, flags=re.MULTILINE | re.DOTALL)) - - if matches: - for m in matches: - raw_role = m.group(1).strip() - content = m.group(2).strip() - norm_role = "AI" if raw_role.lower() in ("ai", "assistant") else "用户" - messages.append(ConversationMessage(role=norm_role, msg=content)) - else: - # Fallback: line-by-line parsing - for raw in ctx.split("\n"): - line = raw.strip() - if not line: - continue - m = re.match(r'^\s*([^:：]+)\s*[：:]\s*(.+)$', line) - if m: - role = m.group(1).strip() - msg = m.group(2).strip() - norm_role = "AI" if role.lower() in ("ai", "assistant") else "用户" - messages.append(ConversationMessage(role=norm_role, msg=msg)) - else: - # Final fallback: treat as user message - default_role = "AI" if re.match(r'^\s*(assistant|AI)\b', line, flags=re.IGNORECASE) else "用户" - messages.append(ConversationMessage(role=default_role, msg=line)) - - context_model = ConversationContext(msgs=messages) - dialog = DialogData( - context=context_model, - ref_id=f"pipeline_item_{idx}", - group_id=group_id, - user_id="default_user", - apply_id="default_application", - ) - # Generate chunks - dialog.chunks = await chunker.process_dialogue(dialog) - dialog_data_list.append(dialog) - - if not dialog_data_list: - print("No dialogs to process for ingestion.") - return False - - # Optionally save chunking outputs for debugging - if save_chunk_output: - try: - def _serialize_datetime(obj): - if isinstance(obj, datetime): - return obj.isoformat() - raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable") - - from app.core.config import settings - settings.ensure_memory_output_dir() - default_path = settings.get_memory_output_path("chunker_test_output.txt") - out_path = save_chunk_output_path or default_path - - combined_output = [dd.model_dump() for dd in dialog_data_list] - with open(out_path, "w", encoding="utf-8") as f: - json.dump(combined_output, f, ensure_ascii=False, indent=4, default=_serialize_datetime) - print(f"Saved chunking results to: {out_path}") - except Exception as e: - print(f"Failed to save chunking results: {e}") - - # Step B-G: 使用新的 ExtractionOrchestrator 执行完整的提取流水线 - if not llm_available: - print("[Ingestion] Skipping extraction pipeline (no LLM).") - return False - - # 初始化 embedder 客户端 - from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient - from app.core.models.base import RedBearModelConfig - from app.services.memory_config_service import MemoryConfigService - - try: - with get_db_context() as db: - embedder_config_dict = MemoryConfigService(db).get_embedder_config(embedding_name or SELECTED_EMBEDDING_ID) - embedder_config = RedBearModelConfig(**embedder_config_dict) - embedder_client = OpenAIEmbedderClient(embedder_config) - except Exception as e: - print(f"[Ingestion] Failed to initialize embedder client: {e}") - print("[Ingestion] Skipping extraction pipeline (embedder initialization failed).") - return False - - connector = Neo4jConnector() - - # 初始化并运行 ExtractionOrchestrator - from app.core.memory.utils.config.config_utils import get_pipeline_config - config = get_pipeline_config() - - orchestrator = ExtractionOrchestrator( - llm_client=llm_client, - embedder_client=embedder_client, - connector=connector, - config=config, - ) - - # 创建一个包装的 orchestrator 来修复时间提取器的输出 - # 保存原始的 _assign_extracted_data 方法 - original_assign = orchestrator._assign_extracted_data - - def clean_temporal_value(value): - """清理 temporal_validity 字段的值，将无效值转换为 None""" - if value is None: - return None - if isinstance(value, str): - # 处理字符串形式的 'null', 'None', 空字符串等 - if value.lower() in ('null', 'none', '') or value.strip() == '': - return None - return value - - async def patched_assign_extracted_data(*args, **kwargs): - """包装方法：在赋值后清理 temporal_validity 中的无效字符串""" - result = await original_assign(*args, **kwargs) - - # 清理返回的 dialog_data_list 中的 temporal_validity - for dialog in result: - if hasattr(dialog, 'chunks') and dialog.chunks: - for chunk in dialog.chunks: - if hasattr(chunk, 'statements') and chunk.statements: - for statement in chunk.statements: - if hasattr(statement, 'temporal_validity') and statement.temporal_validity: - tv = statement.temporal_validity - # 清理 valid_at 和 invalid_at - if hasattr(tv, 'valid_at'): - tv.valid_at = clean_temporal_value(tv.valid_at) - if hasattr(tv, 'invalid_at'): - tv.invalid_at = clean_temporal_value(tv.invalid_at) - return result - - # 替换方法 - orchestrator._assign_extracted_data = patched_assign_extracted_data - - # 同时包装 _create_nodes_and_edges 方法，在创建节点前再次清理 - original_create = orchestrator._create_nodes_and_edges - - async def patched_create_nodes_and_edges(dialog_data_list_arg): - """包装方法：在创建节点前再次清理 temporal_validity""" - # 最后一次清理，确保万无一失 - for dialog in dialog_data_list_arg: - if hasattr(dialog, 'chunks') and dialog.chunks: - for chunk in dialog.chunks: - if hasattr(chunk, 'statements') and chunk.statements: - for statement in chunk.statements: - if hasattr(statement, 'temporal_validity') and statement.temporal_validity: - tv = statement.temporal_validity - if hasattr(tv, 'valid_at'): - tv.valid_at = clean_temporal_value(tv.valid_at) - if hasattr(tv, 'invalid_at'): - tv.invalid_at = clean_temporal_value(tv.invalid_at) - - return await original_create(dialog_data_list_arg) - - orchestrator._create_nodes_and_edges = patched_create_nodes_and_edges - - # 运行完整的提取流水线 - # orchestrator.run 返回 7 个元素的元组 - result = await orchestrator.run(dialog_data_list, is_pilot_run=False) - ( - dialogue_nodes, - chunk_nodes, - statement_nodes, - entity_nodes, - statement_chunk_edges, - statement_entity_edges, - entity_entity_edges, - ) = result - - # statement_chunk_edges 已经由 orchestrator 创建，无需重复创建 - - # Step G: 生成记忆摘要 - print("[Ingestion] Generating memory summaries...") - try: - from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import ( - memory_summary_generation, - ) - from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges - from app.repositories.neo4j.add_nodes import add_memory_summary_nodes - - summaries = await memory_summary_generation( - chunked_dialogs=dialog_data_list, - llm_client=llm_client, - embedder_client=embedder_client - ) - print(f"[Ingestion] Generated {len(summaries)} memory summaries") - except Exception as e: - print(f"[Ingestion] Warning: Failed to generate memory summaries: {e}") - summaries = [] - - # Step H: Save to Neo4j - try: - success = await save_dialog_and_statements_to_neo4j( - dialogue_nodes=dialogue_nodes, - chunk_nodes=chunk_nodes, - statement_nodes=statement_nodes, - entity_nodes=entity_nodes, - entity_edges=entity_entity_edges, - statement_chunk_edges=statement_chunk_edges, - statement_entity_edges=statement_entity_edges, - connector=connector - ) - - # Save memory summaries separately - if summaries: - try: - await add_memory_summary_nodes(summaries, connector) - await add_memory_summary_statement_edges(summaries, connector) - print(f"Successfully saved {len(summaries)} memory summary nodes to Neo4j") - except Exception as e: - print(f"Warning: Failed to save summary nodes: {e}") - - await connector.close() - if success: - print("Successfully saved extracted data to Neo4j!") - else: - print("Failed to save data to Neo4j") - return success - except Exception as e: - print(f"Failed to save data to Neo4j: {e}") - return False - - -async def handle_context_processing(args): - """Handle context-based processing from command line arguments.""" - contexts = [] - - if args.contexts: - contexts.extend(args.contexts) - - if args.context_file: - try: - with open(args.context_file, 'r', encoding='utf-8') as f: - contexts.extend(line.strip() for line in f if line.strip()) - except Exception as e: - print(f"Error reading context file: {e}") - return False - - if not contexts: - print("No contexts provided for processing.") - return False - - return await main_from_contexts(contexts, args.context_group_id) - - -async def main_from_contexts(contexts: List[str], group_id: str): - """Run the pipeline from provided dialogue contexts instead of test data.""" - print("=== Running pipeline from provided contexts ===") - - success = await ingest_contexts_via_full_pipeline( - contexts=contexts, - group_id=group_id, - chunker_strategy=SELECTED_CHUNKER_STRATEGY, - embedding_name=SELECTED_EMBEDDING_ID, - save_chunk_output=True - ) - - if success: - print("Successfully processed and saved contexts to Neo4j!") - else: - print("Failed to process contexts.") - - return success diff --git a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py deleted file mode 100644 index b7d988c5..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py +++ /dev/null @@ -1,575 +0,0 @@ -""" -LoCoMo Benchmark Script - -This module provides the main entry point for running LoCoMo benchmark evaluations. -It orchestrates data loading, ingestion, retrieval, LLM inference, and metric calculation -in a clean, maintainable way. - -Usage: - python locomo_benchmark.py --sample_size 20 --search_type hybrid -""" - -import argparse -import asyncio -import json -import os -import time -from datetime import datetime -from typing import Any, Dict, List, Optional - -try: - from dotenv import load_dotenv -except ImportError: - def load_dotenv(): - pass - -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - bleu1, - f1_score, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.locomo.locomo_metrics import ( - get_category_name, - locomo_f1_score, - locomo_multi_f1, -) -from app.core.memory.evaluation.locomo.locomo_utils import ( - extract_conversations, - ingest_conversations_if_needed, - load_locomo_data, - resolve_temporal_references, - retrieve_relevant_information, - select_and_format_information, -) -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService - - -async def run_locomo_benchmark( - sample_size: int = 20, - group_id: Optional[str] = None, - search_type: str = "hybrid", - search_limit: int = 12, - context_char_budget: int = 8000, - reset_group: bool = False, - skip_ingest: bool = False, - output_dir: Optional[str] = None -) -> Dict[str, Any]: - """ - Run LoCoMo benchmark evaluation. - - This function orchestrates the complete evaluation pipeline: - 1. Load LoCoMo dataset (only QA pairs from first conversation) - 2. Check/ingest conversations into database (only first conversation, unless skip_ingest=True) - 3. For each question: - - Retrieve relevant information - - Generate answer using LLM - - Calculate metrics - 4. Aggregate results and save to file - - Note: By default, only the first conversation is ingested into the database, - and only QA pairs from that conversation are evaluated. This ensures that - all questions have corresponding memory in the database for retrieval. - - Args: - sample_size: Number of QA pairs to evaluate (from first conversation) - group_id: Database group ID for retrieval (uses default if None) - search_type: "keyword", "embedding", or "hybrid" - search_limit: Max documents to retrieve per query - context_char_budget: Max characters for context - reset_group: Whether to clear and re-ingest data (not implemented) - skip_ingest: If True, skip data ingestion and use existing data in Neo4j - output_dir: Directory to save results (uses default if None) - - Returns: - Dictionary with evaluation results including metrics, timing, and samples - """ - # Use default group_id if not provided - group_id = group_id or SELECTED_GROUP_ID - - # Determine data path - data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") - if not os.path.exists(data_path): - # Fallback to current directory - data_path = os.path.join(os.getcwd(), "data", "locomo10.json") - - print(f"\n{'='*60}") - print("🚀 Starting LoCoMo Benchmark Evaluation") - print(f"{'='*60}") - print("📊 Configuration:") - print(f" Sample size: {sample_size}") - print(f" Group ID: {group_id}") - print(f" Search type: {search_type}") - print(f" Search limit: {search_limit}") - print(f" Context budget: {context_char_budget} chars") - print(f" Data path: {data_path}") - print(f"{'='*60}\n") - - # Step 1: Load LoCoMo data - print("📂 Loading LoCoMo dataset...") - try: - # Only load QA pairs from the first conversation (index 0) - # since we only ingest the first conversation into the database - qa_items = load_locomo_data(data_path, sample_size, conversation_index=0) - print(f"✅ Loaded {len(qa_items)} QA pairs from conversation 0\n") - except Exception as e: - print(f"❌ Failed to load data: {e}") - return { - "error": f"Data loading failed: {e}", - "timestamp": datetime.now().isoformat() - } - - # Step 2: Extract conversations and ingest if needed - if skip_ingest: - print("⏭️ Skipping data ingestion (using existing data in Neo4j)") - print(f" Group ID: {group_id}\n") - else: - print("💾 Checking database ingestion...") - try: - conversations = extract_conversations(data_path, max_dialogues=1) - print(f"📝 Extracted {len(conversations)} conversations") - - # Always ingest for now (ingestion check not implemented) - print(f"🔄 Ingesting conversations into group '{group_id}'...") - success = await ingest_conversations_if_needed( - conversations=conversations, - group_id=group_id, - reset=reset_group - ) - - if success: - print("✅ Ingestion completed successfully\n") - else: - print("⚠️ Ingestion may have failed, continuing anyway\n") - - except Exception as e: - print(f"❌ Ingestion failed: {e}") - print("⚠️ Continuing with evaluation (database may be empty)\n") - - # Step 3: Initialize clients - print("🔧 Initializing clients...") - connector = Neo4jConnector() - - # Initialize LLM client with database context - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) - - # Initialize embedder - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - print("✅ Clients initialized\n") - - # Step 4: Process questions - print(f"🔍 Processing {len(qa_items)} questions...") - print(f"{'='*60}\n") - - # Tracking variables - latencies_search: List[float] = [] - latencies_llm: List[float] = [] - context_counts: List[int] = [] - context_chars: List[int] = [] - context_tokens: List[int] = [] - - # Metric lists - f1_scores: List[float] = [] - bleu1_scores: List[float] = [] - jaccard_scores: List[float] = [] - locomo_f1_scores: List[float] = [] - - # Per-category tracking - category_counts: Dict[str, int] = {} - category_f1: Dict[str, List[float]] = {} - category_bleu1: Dict[str, List[float]] = {} - category_jaccard: Dict[str, List[float]] = {} - category_locomo_f1: Dict[str, List[float]] = {} - - # Detailed samples - samples: List[Dict[str, Any]] = [] - - # Fixed anchor date for temporal resolution - anchor_date = datetime(2023, 5, 8) - - try: - for idx, item in enumerate(qa_items, 1): - question = item.get("question", "") - ground_truth = item.get("answer", "") - category = get_category_name(item) - - # Ensure ground truth is a string - ground_truth_str = str(ground_truth) if ground_truth is not None else "" - - print(f"[{idx}/{len(qa_items)}] Category: {category}") - print(f"❓ Question: {question}") - print(f"✅ Ground Truth: {ground_truth_str}") - - # Step 4a: Retrieve relevant information - t_search_start = time.time() - try: - retrieved_info = await retrieve_relevant_information( - question=question, - group_id=group_id, - search_type=search_type, - search_limit=search_limit, - connector=connector, - embedder=embedder - ) - t_search_end = time.time() - search_latency = (t_search_end - t_search_start) * 1000 - latencies_search.append(search_latency) - - print(f"🔍 Retrieved {len(retrieved_info)} documents ({search_latency:.1f}ms)") - - except Exception as e: - print(f"❌ Retrieval failed: {e}") - retrieved_info = [] - search_latency = 0.0 - latencies_search.append(search_latency) - - # Step 4b: Select and format context - context_text = select_and_format_information( - retrieved_info=retrieved_info, - question=question, - max_chars=context_char_budget - ) - - # Resolve temporal references - context_text = resolve_temporal_references(context_text, anchor_date) - - # Add reference date to context - if context_text: - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n{context_text}" - else: - context_text = "No relevant context found." - - # Track context statistics - context_counts.append(len(retrieved_info)) - context_chars.append(len(context_text)) - context_tokens.append(len(context_text.split())) - - print(f"📝 Context: {len(context_text)} chars, {len(retrieved_info)} docs") - - # Step 4c: Generate answer with LLM - messages = [ - { - "role": "system", - "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - ) - }, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}" - } - ] - - t_llm_start = time.time() - try: - response = await llm_client.chat(messages=messages) - t_llm_end = time.time() - llm_latency = (t_llm_end - t_llm_start) * 1000 - latencies_llm.append(llm_latency) - - # Extract prediction from response - if hasattr(response, 'content'): - prediction = response.content.strip() - elif isinstance(response, dict): - prediction = response["choices"][0]["message"]["content"].strip() - else: - prediction = "Unknown" - - print(f"🤖 Prediction: {prediction} ({llm_latency:.1f}ms)") - - except Exception as e: - print(f"❌ LLM failed: {e}") - prediction = "Unknown" - llm_latency = 0.0 - latencies_llm.append(llm_latency) - - # Step 4d: Calculate metrics - f1_val = f1_score(prediction, ground_truth_str) - bleu1_val = bleu1(prediction, ground_truth_str) - jaccard_val = jaccard(prediction, ground_truth_str) - - # LoCoMo-specific F1: use multi-answer for category 1 (Multi-Hop) - if item.get("category") == 1: - locomo_f1_val = locomo_multi_f1(prediction, ground_truth_str) - else: - locomo_f1_val = locomo_f1_score(prediction, ground_truth_str) - - # Accumulate metrics - f1_scores.append(f1_val) - bleu1_scores.append(bleu1_val) - jaccard_scores.append(jaccard_val) - locomo_f1_scores.append(locomo_f1_val) - - # Track by category - category_counts[category] = category_counts.get(category, 0) + 1 - category_f1.setdefault(category, []).append(f1_val) - category_bleu1.setdefault(category, []).append(bleu1_val) - category_jaccard.setdefault(category, []).append(jaccard_val) - category_locomo_f1.setdefault(category, []).append(locomo_f1_val) - - print(f"📊 Metrics - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, " - f"Jaccard: {jaccard_val:.3f}, LoCoMo F1: {locomo_f1_val:.3f}") - print() - - # Save sample details - samples.append({ - "question": question, - "ground_truth": ground_truth_str, - "prediction": prediction, - "category": category, - "metrics": { - "f1": f1_val, - "bleu1": bleu1_val, - "jaccard": jaccard_val, - "locomo_f1": locomo_f1_val - }, - "retrieval": { - "num_docs": len(retrieved_info), - "context_length": len(context_text) - }, - "timing": { - "search_ms": search_latency, - "llm_ms": llm_latency - } - }) - - finally: - # Close connector - await connector.close() - - # Step 5: Aggregate results - print(f"\n{'='*60}") - print("📊 Aggregating Results") - print(f"{'='*60}\n") - - # Overall metrics - overall_metrics = { - "f1": sum(f1_scores) / max(len(f1_scores), 1) if f1_scores else 0.0, - "bleu1": sum(bleu1_scores) / max(len(bleu1_scores), 1) if bleu1_scores else 0.0, - "jaccard": sum(jaccard_scores) / max(len(jaccard_scores), 1) if jaccard_scores else 0.0, - "locomo_f1": sum(locomo_f1_scores) / max(len(locomo_f1_scores), 1) if locomo_f1_scores else 0.0 - } - - # Per-category metrics - by_category: Dict[str, Dict[str, Any]] = {} - for cat in category_counts: - f1_list = category_f1.get(cat, []) - b1_list = category_bleu1.get(cat, []) - j_list = category_jaccard.get(cat, []) - lf_list = category_locomo_f1.get(cat, []) - - by_category[cat] = { - "count": category_counts[cat], - "f1": sum(f1_list) / max(len(f1_list), 1) if f1_list else 0.0, - "bleu1": sum(b1_list) / max(len(b1_list), 1) if b1_list else 0.0, - "jaccard": sum(j_list) / max(len(j_list), 1) if j_list else 0.0, - "locomo_f1": sum(lf_list) / max(len(lf_list), 1) if lf_list else 0.0 - } - - # Latency statistics - latency = { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm) - } - - # Context statistics - context_stats = { - "avg_retrieved_docs": sum(context_counts) / max(len(context_counts), 1) if context_counts else 0.0, - "avg_context_chars": sum(context_chars) / max(len(context_chars), 1) if context_chars else 0.0, - "avg_context_tokens": sum(context_tokens) / max(len(context_tokens), 1) if context_tokens else 0.0 - } - - # Build result dictionary - result = { - "dataset": "locomo", - "sample_size": len(qa_items), - "timestamp": datetime.now().isoformat(), - "params": { - "group_id": group_id, - "search_type": search_type, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "llm_id": SELECTED_LLM_ID, - "embedding_id": SELECTED_EMBEDDING_ID - }, - "overall_metrics": overall_metrics, - "by_category": by_category, - "latency": latency, - "context_stats": context_stats, - "samples": samples - } - - # Step 6: Save results - if output_dir is None: - output_dir = os.path.join( - os.path.dirname(__file__), - "results" - ) - - os.makedirs(output_dir, exist_ok=True) - - # Generate timestamped filename - timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S") - output_path = os.path.join(output_dir, f"locomo_{timestamp_str}.json") - - try: - with open(output_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"✅ Results saved to: {output_path}\n") - except Exception as e: - print(f"❌ Failed to save results: {e}") - print("📊 Printing results to console instead:\n") - print(json.dumps(result, ensure_ascii=False, indent=2)) - - return result - - -def main(): - """ - Parse command-line arguments and run benchmark. - - This function provides a CLI interface for running LoCoMo benchmarks - with configurable parameters. - """ - parser = argparse.ArgumentParser( - description="Run LoCoMo benchmark evaluation", - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "--sample_size", - type=int, - default=20, - help="Number of QA pairs to evaluate" - ) - parser.add_argument( - "--group_id", - type=str, - default=None, - help="Database group ID for retrieval (uses default if not specified)" - ) - parser.add_argument( - "--search_type", - type=str, - default="hybrid", - choices=["keyword", "embedding", "hybrid"], - help="Search strategy to use" - ) - parser.add_argument( - "--search_limit", - type=int, - default=12, - help="Maximum number of documents to retrieve per query" - ) - parser.add_argument( - "--context_char_budget", - type=int, - default=8000, - help="Maximum characters for context" - ) - parser.add_argument( - "--reset_group", - action="store_true", - help="Clear and re-ingest data (not implemented)" - ) - parser.add_argument( - "--skip_ingest", - action="store_true", - help="Skip data ingestion and use existing data in Neo4j" - ) - parser.add_argument( - "--output_dir", - type=str, - default=None, - help="Directory to save results (uses default if not specified)" - ) - - args = parser.parse_args() - - # Load environment variables - load_dotenv() - - # Run benchmark - result = asyncio.run(run_locomo_benchmark( - sample_size=args.sample_size, - group_id=args.group_id, - search_type=args.search_type, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - reset_group=args.reset_group, - skip_ingest=args.skip_ingest, - output_dir=args.output_dir - )) - - # Print summary - print(f"\n{'='*60}") - - # Check if there was an error - if 'error' in result: - print("❌ Benchmark Failed!") - print(f"{'='*60}") - print(f"Error: {result['error']}") - return - - print("🎉 Benchmark Complete!") - print(f"{'='*60}") - print("📊 Final Results:") - print(f" Sample size: {result.get('sample_size', 0)}") - print(f" F1: {result['overall_metrics']['f1']:.3f}") - print(f" BLEU-1: {result['overall_metrics']['bleu1']:.3f}") - print(f" Jaccard: {result['overall_metrics']['jaccard']:.3f}") - print(f" LoCoMo F1: {result['overall_metrics']['locomo_f1']:.3f}") - - if result.get('context_stats'): - print("\n📈 Context Statistics:") - print(f" Avg retrieved docs: {result['context_stats']['avg_retrieved_docs']:.1f}") - print(f" Avg context chars: {result['context_stats']['avg_context_chars']:.0f}") - print(f" Avg context tokens: {result['context_stats']['avg_context_tokens']:.0f}") - - if result.get('latency'): - print("\n⏱️ Latency Statistics:") - print(f" Search - Mean: {result['latency']['search']['mean']:.1f}ms, " - f"P50: {result['latency']['search']['p50']:.1f}ms, " - f"P95: {result['latency']['search']['p95']:.1f}ms") - print(f" LLM - Mean: {result['latency']['llm']['mean']:.1f}ms, " - f"P50: {result['latency']['llm']['p50']:.1f}ms, " - f"P95: {result['latency']['llm']['p95']:.1f}ms") - - if result.get('by_category'): - print("\n📂 Results by Category:") - for cat, metrics in result['by_category'].items(): - print(f" {cat}:") - print(f" Count: {metrics['count']}") - print(f" F1: {metrics['f1']:.3f}") - print(f" LoCoMo F1: {metrics['locomo_f1']:.3f}") - print(f" Jaccard: {metrics['jaccard']:.3f}") - - print(f"\n{'='*60}\n") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/locomo/locomo_metrics.py b/api/app/core/memory/evaluation/locomo/locomo_metrics.py deleted file mode 100644 index 20d5f2b5..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_metrics.py +++ /dev/null @@ -1,225 +0,0 @@ -""" -LoCoMo-specific metric calculations. - -This module provides clean, simplified implementations of metrics used for -LoCoMo benchmark evaluation, including text normalization and F1 score variants. -""" - -import re -from typing import Dict, Any - - -def normalize_text(text: str) -> str: - """ - Normalize text for LoCoMo evaluation. - - Normalization steps: - - Convert to lowercase - - Remove commas - - Remove stop words (a, an, the, and) - - Remove punctuation - - Normalize whitespace - - Args: - text: Input text to normalize - - Returns: - Normalized text string with consistent formatting - - Examples: - >>> normalize_text("The cat, and the dog") - 'cat dog' - >>> normalize_text("Hello, World!") - 'hello world' - """ - # Ensure input is a string - text = str(text) if text is not None else "" - - # Convert to lowercase - text = text.lower() - - # Remove commas - text = re.sub(r"[\,]", " ", text) - - # Remove stop words - text = re.sub(r"\b(a|an|the|and)\b", " ", text) - - # Remove punctuation (keep only word characters and whitespace) - text = re.sub(r"[^\w\s]", " ", text) - - # Normalize whitespace (collapse multiple spaces to single space) - text = " ".join(text.split()) - - return text - - -def locomo_f1_score(prediction: str, ground_truth: str) -> float: - """ - Calculate LoCoMo F1 score for single-answer questions. - - Uses token-level precision and recall based on normalized text. - Treats tokens as sets (no duplicate counting). - - Args: - prediction: Model's predicted answer - ground_truth: Correct answer - - Returns: - F1 score between 0.0 and 1.0 - - Examples: - >>> locomo_f1_score("Paris", "Paris") - 1.0 - >>> locomo_f1_score("The cat", "cat") - 1.0 - >>> locomo_f1_score("dog", "cat") - 0.0 - """ - # Ensure inputs are strings - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - # Normalize and tokenize - pred_tokens = normalize_text(pred_str).split() - truth_tokens = normalize_text(truth_str).split() - - # Handle empty cases - if not pred_tokens or not truth_tokens: - return 0.0 - - # Convert to sets for comparison - pred_set = set(pred_tokens) - truth_set = set(truth_tokens) - - # Calculate true positives (intersection) - true_positives = len(pred_set & truth_set) - - # Calculate precision and recall - precision = true_positives / len(pred_set) if pred_set else 0.0 - recall = true_positives / len(truth_set) if truth_set else 0.0 - - # Calculate F1 score - if precision + recall == 0: - return 0.0 - - f1 = 2 * precision * recall / (precision + recall) - return f1 - - -def locomo_multi_f1(prediction: str, ground_truth: str) -> float: - """ - Calculate LoCoMo F1 score for multi-answer questions. - - Handles comma-separated answers by: - 1. Splitting both prediction and ground truth by commas - 2. For each ground truth answer, finding the best matching prediction - 3. Averaging the F1 scores across all ground truth answers - - Args: - prediction: Model's predicted answer (may contain multiple comma-separated answers) - ground_truth: Correct answer (may contain multiple comma-separated answers) - - Returns: - Average F1 score across all ground truth answers (0.0 to 1.0) - - Examples: - >>> locomo_multi_f1("Paris, London", "Paris, London") - 1.0 - >>> locomo_multi_f1("Paris", "Paris, London") - 0.5 - >>> locomo_multi_f1("Paris, Berlin", "Paris, London") - 0.5 - """ - # Ensure inputs are strings - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - # Split by commas and strip whitespace - predictions = [p.strip() for p in pred_str.split(',') if p.strip()] - ground_truths = [g.strip() for g in truth_str.split(',') if g.strip()] - - # Handle empty cases - if not predictions or not ground_truths: - return 0.0 - - # For each ground truth, find the best matching prediction - f1_scores = [] - for gt in ground_truths: - # Calculate F1 with each prediction and take the maximum - best_f1 = max(locomo_f1_score(pred, gt) for pred in predictions) - f1_scores.append(best_f1) - - # Return average F1 across all ground truths - return sum(f1_scores) / len(f1_scores) - - -def get_category_name(item: Dict[str, Any]) -> str: - """ - Extract and normalize category name from QA item. - - Handles both numeric categories (1-4) and string categories with various formats. - Supports multiple field names: "cat", "category", "type". - - Category mapping: - - 1 or "multi-hop" -> "Multi-Hop" - - 2 or "temporal" -> "Temporal" - - 3 or "open domain" -> "Open Domain" - - 4 or "single-hop" -> "Single-Hop" - - Args: - item: QA item dictionary containing category information - - Returns: - Standardized category name or "unknown" if not found - - Examples: - >>> get_category_name({"category": 1}) - 'Multi-Hop' - >>> get_category_name({"cat": "temporal"}) - 'Temporal' - >>> get_category_name({"type": "Single-Hop"}) - 'Single-Hop' - """ - # Numeric category mapping - CATEGORY_MAP = { - 1: "Multi-Hop", - 2: "Temporal", - 3: "Open Domain", - 4: "Single-Hop", - } - - # String category aliases (case-insensitive) - TYPE_ALIASES = { - "single-hop": "Single-Hop", - "singlehop": "Single-Hop", - "single hop": "Single-Hop", - "multi-hop": "Multi-Hop", - "multihop": "Multi-Hop", - "multi hop": "Multi-Hop", - "open domain": "Open Domain", - "opendomain": "Open Domain", - "temporal": "Temporal", - } - - # Try "cat" field first (string category) - cat = item.get("cat") - if isinstance(cat, str) and cat.strip(): - name = cat.strip() - lower = name.lower() - return TYPE_ALIASES.get(lower, name) - - # Try "category" field (can be int or string) - cat_num = item.get("category") - if isinstance(cat_num, int): - return CATEGORY_MAP.get(cat_num, "unknown") - elif isinstance(cat_num, str) and cat_num.strip(): - lower = cat_num.strip().lower() - return TYPE_ALIASES.get(lower, cat_num.strip()) - - # Try "type" field as fallback - cat_type = item.get("type") - if isinstance(cat_type, str) and cat_type.strip(): - lower = cat_type.strip().lower() - return TYPE_ALIASES.get(lower, cat_type.strip()) - - return "unknown" diff --git a/api/app/core/memory/evaluation/locomo/locomo_test.py b/api/app/core/memory/evaluation/locomo/locomo_test.py deleted file mode 100644 index b5ad5820..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_test.py +++ /dev/null @@ -1,810 +0,0 @@ -# file name: check_neo4j_connection_fixed.py -import asyncio -import json -import math -import os -import re -import sys -import time -from datetime import datetime, timedelta -from typing import Any, Dict, List - -from dotenv import load_dotenv - -# 1 -# 添加项目根目录到路径 -current_dir = os.path.dirname(os.path.abspath(__file__)) -project_root = os.path.dirname(current_dir) -if project_root not in sys.path: - sys.path.insert(0, project_root) -# 关键：将 src 目录置于最前，确保从当前仓库加载模块 -src_dir = os.path.join(project_root, "src") -if src_dir not in sys.path: - sys.path.insert(0, src_dir) - -load_dotenv() - -# 首先定义 _loc_normalize 函数，因为其他函数依赖它 -def _loc_normalize(text: str) -> str: - text = str(text) if text is not None else "" - text = text.lower() - text = re.sub(r"[\,]", " ", text) - text = re.sub(r"\b(a|an|the|and)\b", " ", text) - text = re.sub(r"[^\w\s]", " ", text) - text = " ".join(text.split()) - return text - -# 尝试从 metrics.py 导入基础指标 -try: - from common.metrics import bleu1, f1_score, jaccard - print("✅ 从 metrics.py 导入基础指标成功") -except ImportError as e: - print(f"❌ 从 metrics.py 导入失败: {e}") - # 回退到本地实现 - def f1_score(pred: str, ref: str) -> float: - pred_str = str(pred) if pred is not None else "" - ref_str = str(ref) if ref is not None else "" - - p_tokens = _loc_normalize(pred_str).split() - r_tokens = _loc_normalize(ref_str).split() - if not p_tokens and not r_tokens: - return 1.0 - if not p_tokens or not r_tokens: - return 0.0 - p_set = set(p_tokens) - r_set = set(r_tokens) - tp = len(p_set & r_set) - precision = tp / len(p_set) if p_set else 0.0 - recall = tp / len(r_set) if r_set else 0.0 - if precision + recall == 0: - return 0.0 - return 2 * precision * recall / (precision + recall) - - def bleu1(pred: str, ref: str) -> float: - pred_str = str(pred) if pred is not None else "" - ref_str = str(ref) if ref is not None else "" - - p_tokens = _loc_normalize(pred_str).split() - r_tokens = _loc_normalize(ref_str).split() - if not p_tokens: - return 0.0 - - r_counts = {} - for t in r_tokens: - r_counts[t] = r_counts.get(t, 0) + 1 - - clipped = 0 - p_counts = {} - for t in p_tokens: - p_counts[t] = p_counts.get(t, 0) + 1 - - for t, c in p_counts.items(): - clipped += min(c, r_counts.get(t, 0)) - - precision = clipped / max(len(p_tokens), 1) - ref_len = len(r_tokens) - pred_len = len(p_tokens) - - if pred_len > ref_len or pred_len == 0: - bp = 1.0 - else: - bp = math.exp(1 - ref_len / max(pred_len, 1)) - - return bp * precision - - def jaccard(pred: str, ref: str) -> float: - pred_str = str(pred) if pred is not None else "" - ref_str = str(ref) if ref is not None else "" - - p = set(_loc_normalize(pred_str).split()) - r = set(_loc_normalize(ref_str).split()) - if not p and not r: - return 1.0 - if not p or not r: - return 0.0 - return len(p & r) / len(p | r) - -# 尝试从 qwen_search_eval.py 导入 LoCoMo 特定指标 -try: - # 添加 evaluation 目录路径 - evaluation_dir = os.path.join(project_root, "evaluation") - if evaluation_dir not in sys.path: - sys.path.insert(0, evaluation_dir) - - # 尝试从不同位置导入 - try: - from locomo.qwen_search_eval import ( - _resolve_relative_times, - loc_f1_score, - loc_multi_f1, - ) - print("✅ 从 locomo.qwen_search_eval 导入 LoCoMo 特定指标成功") - except ImportError: - from qwen_search_eval import _resolve_relative_times, loc_f1_score, loc_multi_f1 - print("✅ 从 qwen_search_eval 导入 LoCoMo 特定指标成功") - -except ImportError as e: - print(f"❌ 从 qwen_search_eval.py 导入失败: {e}") - # 回退到本地实现 LoCoMo 特定函数 - def _resolve_relative_times(text: str, anchor: datetime) -> str: - t = str(text) if text is not None else "" - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - return t - - def loc_f1_score(prediction: str, ground_truth: str) -> float: - p_tokens = _loc_normalize(prediction).split() - g_tokens = _loc_normalize(ground_truth).split() - if not p_tokens or not g_tokens: - return 0.0 - p = set(p_tokens) - g = set(g_tokens) - tp = len(p & g) - precision = tp / len(p) if p else 0.0 - recall = tp / len(g) if g else 0.0 - return (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0 - - def loc_multi_f1(prediction: str, ground_truth: str) -> float: - predictions = [p.strip() for p in str(prediction).split(',') if p.strip()] - ground_truths = [g.strip() for g in str(ground_truth).split(',') if g.strip()] - if not predictions or not ground_truths: - return 0.0 - def _f1(a: str, b: str) -> float: - return loc_f1_score(a, b) - vals = [] - for gt in ground_truths: - vals.append(max(_f1(pred, gt) for pred in predictions)) - return sum(vals) / len(vals) - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 8000) -> str: - """基于问题关键词智能选择上下文""" - if not contexts: - return "" - - # 提取问题关键词（只保留有意义的词） - question_lower = question.lower() - stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'} - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = {word for word in question_words if word not in stop_words and len(word) > 2} - - print(f"🔍 问题关键词: {question_words}") - - # 给每个上下文打分 - scored_contexts = [] - for i, context in enumerate(contexts): - context_lower = context.lower() - score = 0 - - # 关键词匹配得分 - keyword_matches = 0 - for word in question_words: - if word in context_lower: - keyword_matches += 1 - # 关键词出现次数越多，得分越高 - score += context_lower.count(word) * 2 - - # 上下文长度得分（适中的长度更好） - context_len = len(context) - if 100 < context_len < 2000: # 理想长度范围 - score += 5 - elif context_len >= 2000: # 太长可能包含无关信息 - score += 2 - - # 如果是前几个上下文，给予额外分数（通常相关性更高） - if i < 3: - score += 3 - - scored_contexts.append((score, context, keyword_matches)) - - # 按得分排序 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - - # 选择高得分的上下文，直到达到字符限制 - selected = [] - total_chars = 0 - selected_count = 0 - - print("📊 上下文相关性分析:") - for score, context, matches in scored_contexts[:5]: # 只显示前5个 - print(f" - 得分: {score}, 关键词匹配: {matches}, 长度: {len(context)}") - - for score, context, matches in scored_contexts: - if total_chars + len(context) <= max_chars: - selected.append(context) - total_chars += len(context) - selected_count += 1 - else: - # 如果这个上下文得分很高但放不下，尝试截取 - if score > 10 and total_chars < max_chars - 500: - remaining = max_chars - total_chars - # 找到包含关键词的部分 - lines = context.split('\n') - relevant_lines = [] - current_chars = 0 - - for line in lines: - line_lower = line.lower() - line_relevance = any(word in line_lower for word in question_words) - - if line_relevance and current_chars < remaining - 100: - relevant_lines.append(line) - current_chars += len(line) - - if relevant_lines: - truncated = '\n'.join(relevant_lines) - if len(truncated) > 100: # 确保有足够内容 - selected.append(truncated + "\n[相关内容截断...]") - total_chars += len(truncated) - selected_count += 1 - break # 不再尝试添加更多上下文 - - result = "\n\n".join(selected) - print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {total_chars}字符") - return result - - -def get_dynamic_search_params(question: str, question_index: int, total_questions: int): - """根据问题复杂度和进度动态调整检索参数""" - - # 分析问题复杂度 - word_count = len(question.split()) - has_temporal = any(word in question.lower() for word in ['when', 'date', 'time', 'ago']) - has_multi_hop = any(word in question.lower() for word in ['and', 'both', 'also', 'while']) - - # 根据进度调整 - 后期问题可能需要更精确的检索 - progress_factor = question_index / total_questions - - base_limit = 12 - if has_temporal and has_multi_hop: - base_limit = 20 - elif word_count > 8: - base_limit = 16 - - # 随着测试进行，逐渐收紧检索范围 - adjusted_limit = max(8, int(base_limit * (1 - progress_factor * 0.3))) - - # 动态调整最大字符数 - max_chars = 8000 + 4000 * (1 - progress_factor) - - return { - "limit": adjusted_limit, - "max_chars": int(max_chars) - } - - -class EnhancedEvaluationMonitor: - def __init__(self, reset_interval=5, performance_threshold=0.6): - self.question_count = 0 - self.reset_interval = reset_interval - self.performance_threshold = performance_threshold - self.consecutive_low_scores = 0 - self.performance_history = [] - self.recent_f1_scores = [] - - def should_reset_connections(self, current_f1=None): - """基于计数和性能双重判断""" - # 定期重置 - if self.question_count % self.reset_interval == 0: - return True - - # 性能驱动的重置 - if current_f1 is not None and current_f1 < self.performance_threshold: - self.consecutive_low_scores += 1 - if self.consecutive_low_scores >= 2: # 连续2个低分就重置 - print("🚨 连续低分，触发紧急重置") - self.consecutive_low_scores = 0 - return True - else: - self.consecutive_low_scores = 0 - - return False - - def record_performance(self, question_index, metrics, context_length, retrieved_docs): - """记录性能指标，检测衰减""" - self.performance_history.append({ - 'index': question_index, - 'metrics': metrics, - 'context_length': context_length, - 'retrieved_docs': retrieved_docs, - 'timestamp': time.time() - }) - - # 记录最近的F1分数 - self.recent_f1_scores.append(metrics['f1']) - if len(self.recent_f1_scores) > 5: - self.recent_f1_scores.pop(0) - - def get_recent_performance(self): - """获取近期平均性能""" - if not self.recent_f1_scores: - return 0.5 - return sum(self.recent_f1_scores) / len(self.recent_f1_scores) - - def get_performance_trend(self): - """分析性能趋势""" - if len(self.performance_history) < 2: - return "stable" - - recent_metrics = [item['metrics']['f1'] for item in self.performance_history[-5:]] - earlier_metrics = [item['metrics']['f1'] for item in self.performance_history[-10:-5]] - - if len(recent_metrics) < 2 or len(earlier_metrics) < 2: - return "stable" - - recent_avg = sum(recent_metrics) / len(recent_metrics) - earlier_avg = sum(earlier_metrics) / len(earlier_metrics) - - if recent_avg < earlier_avg * 0.8: - return "degrading" - elif recent_avg > earlier_avg * 1.1: - return "improving" - else: - return "stable" - - -def get_enhanced_search_params(question: str, question_index: int, total_questions: int, recent_performance: float): - """基于问题复杂度和近期性能动态调整检索参数""" - - # 基础参数 - base_params = get_dynamic_search_params(question, question_index, total_questions) - - # 性能自适应调整 - if recent_performance < 0.5: # 近期表现差 - # 增加检索范围，尝试获取更多上下文 - base_params["limit"] = min(base_params["limit"] + 5, 25) - base_params["max_chars"] = min(base_params["max_chars"] + 2000, 12000) - print(f"📈 性能自适应：增加检索范围 (limit={base_params['limit']}, max_chars={base_params['max_chars']})") - - elif recent_performance > 0.8: # 近期表现好 - # 收紧检索，提高精度 - base_params["limit"] = max(base_params["limit"] - 2, 8) - base_params["max_chars"] = max(base_params["max_chars"] - 1000, 6000) - print(f"🎯 性能自适应：提高检索精度 (limit={base_params['limit']}, max_chars={base_params['max_chars']})") - - # 中间阶段特殊处理 - mid_sequence_factor = abs(question_index / total_questions - 0.5) - if mid_sequence_factor < 0.2: # 在中间30%的问题 - print("🎯 中间阶段：使用更精确的检索策略") - base_params["limit"] = max(base_params["limit"] - 2, 10) # 减少数量，提高质量 - base_params["max_chars"] = max(base_params["max_chars"] - 1000, 7000) - - return base_params - - -def enhanced_context_selection(contexts: List[str], question: str, question_index: int, total_questions: int, max_chars: int = 8000) -> str: - """考虑问题序列位置的智能选择""" - - if not contexts: - return "" - - # 在序列中间阶段使用更严格的筛选 - mid_sequence_factor = abs(question_index / total_questions - 0.5) # 距离中心的距离 - - if mid_sequence_factor < 0.2: # 在中间30%的问题 - print("🎯 中间阶段：使用严格上下文筛选") - - # 提取问题关键词 - question_lower = question.lower() - stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'} - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = {word for word in question_words if word not in stop_words and len(word) > 2} - - # 只保留高度相关的上下文 - filtered_contexts = [] - for context in contexts: - context_lower = context.lower() - relevance_score = sum(3 if word in context_lower else 0 for word in question_words) - - # 额外加分给包含数字、日期的上下文（对事实性问题更重要） - if any(char.isdigit() for char in context): - relevance_score += 2 - - # 提高阈值：只有得分>=3的上下文才保留 - if relevance_score >= 3: - filtered_contexts.append(context) - else: - print(f" - 过滤低分上下文: 得分={relevance_score}") - - contexts = filtered_contexts - print(f"🔍 严格筛选后保留 {len(contexts)} 个上下文") - - # 使用原有的智能选择逻辑 - return smart_context_selection(contexts, question, max_chars) - - -async def run_enhanced_evaluation(): - """使用增强方法进行完整评估 - 解决中间性能衰减问题""" - try: - from dotenv import load_dotenv - except Exception: - def load_dotenv(): - return None - - # 修正导入路径：使用 app.core.memory.src 前缀 - from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient - from app.core.memory.utils.config.definitions import ( - SELECTED_EMBEDDING_ID, - SELECTED_LLM_ID, - ) - from app.core.memory.utils.llm.llm_utils import MemoryClientFactory - from app.core.models.base import RedBearModelConfig - from app.db import get_db_context - from app.repositories.neo4j.graph_search import search_graph_by_embedding - from app.repositories.neo4j.neo4j_connector import Neo4jConnector - from app.services.memory_config_service import MemoryConfigService - - # 加载数据 - # 获取项目根目录 - current_file = os.path.abspath(__file__) - evaluation_dir = os.path.dirname(os.path.dirname(current_file)) # evaluation目录 - memory_dir = os.path.dirname(evaluation_dir) # memory目录 - data_path = os.path.join(memory_dir, "data", "locomo10.json") - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - - qa_items = [] - if isinstance(raw, list): - for entry in raw: - qa_items.extend(entry.get("qa", [])) - else: - qa_items.extend(raw.get("qa", [])) - - items = qa_items[:20] # 测试多少个问题 - - # 初始化增强监控器 - monitor = EnhancedEvaluationMonitor(reset_interval=5, performance_threshold=0.6) - - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm = factory.get_llm_client(SELECTED_LLM_ID) - - # 初始化embedder - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 初始化连接器 - connector = Neo4jConnector() - - # 初始化结果字典 - results = { - "questions": [], - "overall_metrics": {"f1": 0.0, "b1": 0.0, "j": 0.0, "loc_f1": 0.0}, - "category_metrics": {}, - "retrieval_stats": {"total_questions": len(items), "avg_context_length": 0, "avg_retrieved_docs": 0}, - "performance_trend": "stable", - "timestamp": datetime.now().isoformat(), - "enhanced_strategy": True - } - - total_f1 = 0.0 - total_bleu1 = 0.0 - total_jaccard = 0.0 - total_loc_f1 = 0.0 - total_context_length = 0 - total_retrieved_docs = 0 - category_stats = {} - - try: - for i, item in enumerate(items): - monitor.question_count += 1 - - # 获取近期性能用于重置判断 - recent_performance = monitor.get_recent_performance() - - # 增强的重置判断 - should_reset = monitor.should_reset_connections(current_f1=recent_performance) - if should_reset and i > 0: - print(f"🔄 重置Neo4j连接 (问题 {i+1}/{len(items)}, 近期性能: {recent_performance:.3f})...") - await connector.close() - connector = Neo4jConnector() # 创建新连接 - print("✅ 连接重置完成") - - q = item.get("question", "") - ref = item.get("answer", "") - ref_str = str(ref) if ref is not None else "" - - print(f"\n🔍 [{i+1}/{len(items)}] 问题: {q}") - print(f"✅ 真实答案: {ref_str}") - - # 分类别统计 - category = "Unknown" - if item.get("category") == 1: - category = "Multi-Hop" - elif item.get("category") == 2: - category = "Temporal" - elif item.get("category") == 3: - category = "Open Domain" - elif item.get("category") == 4: - category = "Single-Hop" - - # 增强的检索参数 - search_params = get_enhanced_search_params(q, i, len(items), recent_performance) - search_limit = search_params["limit"] - max_chars = search_params["max_chars"] - - print(f"🏷️ 类别: {category}, 检索参数: limit={search_limit}, max_chars={max_chars}") - - # 使用项目标准的混合检索方法 - t0 = time.time() - contexts_all = [] - - try: - # 使用统一的搜索服务 - from app.core.memory.storage_services.search import run_hybrid_search - - print("🔀 使用混合搜索服务...") - - search_results = await run_hybrid_search( - query_text=q, - search_type="hybrid", - group_id="locomo_sk", - limit=20, - include=["statements", "chunks", "entities", "summaries"], - alpha=0.6, # BM25权重 - embedding_id=SELECTED_EMBEDDING_ID - ) - - # 处理搜索结果 - 新的搜索服务返回统一的结构 - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要") - - # 构建上下文：优先使用 chunks、statements 和 summaries - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要：最多加入前3个高分实体，避免噪声 - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + ' '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - print(f"📊 有效上下文数量: {len(contexts_all)}") - except Exception as e: - print(f"❌ 检索失败: {e}") - contexts_all = [] - - t1 = time.time() - search_time = (t1 - t0) * 1000 - - # 增强的上下文选择 - context_text = "" - if contexts_all: - # 使用增强的上下文选择 - context_text = enhanced_context_selection(contexts_all, q, i, len(items), max_chars=max_chars) - - # 如果智能选择后仍然过长，进行最终保护性截断 - if len(context_text) > max_chars: - print(f"⚠️ 智能选择后仍然过长 ({len(context_text)}字符)，进行最终截断") - context_text = context_text[:max_chars] + "\n\n[最终截断...]" - - # 时间解析 - anchor_date = datetime(2023, 5, 8) # 使用固定日期确保一致性 - context_text = _resolve_relative_times(context_text, anchor_date) - - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n" + context_text - - print(f"📝 最终上下文长度: {len(context_text)} 字符") - - # 显示不同上下文的预览（不只是第一条） - print("🔍 上下文预览:") - for j, context in enumerate(contexts_all[:3]): # 显示前3个上下文 - preview = context[:150].replace('\n', ' ') - print(f" 上下文{j+1}: {preview}...") - - # 🔍 调试：检查答案是否在上下文中 - if ref_str and ref_str.strip(): - answer_found = any(ref_str.lower() in ctx.lower() for ctx in contexts_all) - print(f"🔍 调试：答案 '{ref_str}' 是否在检索到的上下文中？ {'✅ 是' if answer_found else '❌ 否'}") - - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # LLM 回答 - messages = [ - {"role": "system", "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - )}, - {"role": "user", "content": f"Question: {q}\n\nContext:\n{context_text}"}, - ] - - t2 = time.time() - try: - # 使用异步调用 - resp = await llm.chat(messages=messages) - # 兼容不同的响应格式 - pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - except Exception as e: - print(f"❌ LLM 生成失败: {e}") - pred = "Unknown" - t3 = time.time() - llm_time = (t3 - t2) * 1000 - - # 计算指标 - 使用导入的指标函数 - f1_val = f1_score(pred, ref_str) - bleu1_val = bleu1(pred, ref_str) - jaccard_val = jaccard(pred, ref_str) - loc_f1_val = loc_f1_score(pred, ref_str) - - print(f"🤖 LLM 回答: {pred}") - print(f"📈 指标 - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, Jaccard: {jaccard_val:.3f}, LoCoMo F1: {loc_f1_val:.3f}") - print(f"⏱️ 时间 - 检索: {search_time:.1f}ms, LLM: {llm_time:.1f}ms") - - # 更新统计 - total_f1 += f1_val - total_bleu1 += bleu1_val - total_jaccard += jaccard_val - total_loc_f1 += loc_f1_val - total_context_length += len(context_text) - total_retrieved_docs += len(contexts_all) - - if category not in category_stats: - category_stats[category] = {"count": 0, "f1_sum": 0.0, "b1_sum": 0.0, "j_sum": 0.0, "loc_f1_sum": 0.0} - - category_stats[category]["count"] += 1 - category_stats[category]["f1_sum"] += f1_val - category_stats[category]["b1_sum"] += bleu1_val - category_stats[category]["j_sum"] += jaccard_val - category_stats[category]["loc_f1_sum"] += loc_f1_val - - # 记录性能指标 - metrics = {"f1": f1_val, "bleu1": bleu1_val, "jaccard": jaccard_val, "loc_f1": loc_f1_val} - monitor.record_performance(i, metrics, len(context_text), len(contexts_all)) - - # 保存结果 - question_result = { - "question": q, - "ground_truth": ref_str, - "prediction": pred, - "category": category, - "metrics": metrics, - "retrieval": { - "retrieved_documents": len(contexts_all), - "context_length": len(context_text), - "search_limit": search_limit, - "max_chars": max_chars, - "recent_performance": recent_performance - }, - "timing": { - "search_ms": search_time, - "llm_ms": llm_time - } - } - - results["questions"].append(question_result) - - print("="*60) - - except Exception as e: - print(f"❌ 评估过程中发生错误: {e}") - # 即使出错，也返回已有的结果 - import traceback - traceback.print_exc() - - finally: - await connector.close() - - # 计算总体指标 - n = len(items) - if n > 0: - results["overall_metrics"] = { - "f1": total_f1 / n, - "b1": total_bleu1 / n, - "j": total_jaccard / n, - "loc_f1": total_loc_f1 / n - } - - for category, stats in category_stats.items(): - count = stats["count"] - results["category_metrics"][category] = { - "count": count, - "f1": stats["f1_sum"] / count, - "bleu1": stats["b1_sum"] / count, - "jaccard": stats["j_sum"] / count, - "loc_f1": stats["loc_f1_sum"] / count - } - - results["retrieval_stats"]["avg_context_length"] = total_context_length / n - results["retrieval_stats"]["avg_retrieved_docs"] = total_retrieved_docs / n - - # 分析性能趋势 - results["performance_trend"] = monitor.get_performance_trend() - results["reset_interval"] = monitor.reset_interval - results["total_questions_processed"] = monitor.question_count - - return results - - -if __name__ == "__main__": - print("🚀 运行增强版完整评估（解决中间性能衰减问题）...") - print("📋 增强特性:") - print(" - 双重重置策略：定期重置 + 性能驱动重置") - print(" - 动态检索参数：基于近期性能自适应调整") - print(" - 中间阶段严格筛选：提高上下文质量要求") - print(" - 连续性能监控：实时检测性能衰减") - - result = asyncio.run(run_enhanced_evaluation()) - - print("\n📊 最终评估结果:") - print("总体指标:") - print(f" F1: {result['overall_metrics']['f1']:.4f}") - print(f" BLEU-1: {result['overall_metrics']['b1']:.4f}") - print(f" Jaccard: {result['overall_metrics']['j']:.4f}") - print(f" LoCoMo F1: {result['overall_metrics']['loc_f1']:.4f}") - - print("\n分类别指标:") - for category, metrics in result['category_metrics'].items(): - print(f" {category}: F1={metrics['f1']:.4f}, BLEU-1={metrics['bleu1']:.4f}, Jaccard={metrics['jaccard']:.4f}, LoCoMo F1={metrics['loc_f1']:.4f} (样本数: {metrics['count']})") - - print("\n检索统计:") - stats = result['retrieval_stats'] - print(f" 平均上下文长度: {stats['avg_context_length']:.0f} 字符") - print(f" 平均检索文档数: {stats['avg_retrieved_docs']:.1f}") - - print(f"\n性能趋势: {result['performance_trend']}") - print(f"重置间隔: 每{result['reset_interval']}个问题") - print(f"处理问题总数: {result['total_questions_processed']}") - print(f"增强策略: {'启用' if result.get('enhanced_strategy', False) else '未启用'}") - - - # 保存结果到指定目录 - # 使用代码文件所在目录的绝对路径 - current_file_dir = os.path.dirname(os.path.abspath(__file__)) - output_dir = os.path.join(current_file_dir, "results") - os.makedirs(output_dir, exist_ok=True) - output_file = os.path.join(output_dir, "enhanced_evaluation_results.json") - with open(output_file, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n详细结果已保存到: {output_file}") diff --git a/api/app/core/memory/evaluation/locomo/locomo_utils.py b/api/app/core/memory/evaluation/locomo/locomo_utils.py deleted file mode 100644 index 69be5da9..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_utils.py +++ /dev/null @@ -1,626 +0,0 @@ -""" -LoCoMo Utilities Module - -This module provides helper functions for the LoCoMo benchmark evaluation: -- Data loading from JSON files -- Conversation extraction for ingestion -- Temporal reference resolution -- Context selection and formatting -- Retrieval wrapper functions -- Ingestion wrapper functions -""" - -import os -import json -import re -from datetime import datetime, timedelta -from typing import List, Dict, Any, Optional - -from app.core.memory.utils.definitions import PROJECT_ROOT -from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline - - -def load_locomo_data( - data_path: str, - sample_size: int, - conversation_index: int = 0 -) -> List[Dict[str, Any]]: - """ - Load LoCoMo dataset from JSON file. - - The LoCoMo dataset structure is a list of conversation objects, where each - object contains a "qa" list of question-answer pairs. - - Args: - data_path: Path to locomo10.json file - sample_size: Number of QA pairs to load (limits total QA items returned) - conversation_index: Which conversation to load QA pairs from (default: 0 for first) - - Returns: - List of QA item dictionaries, each containing: - - question: str - - answer: str - - category: int (1-4) - - evidence: List[str] - - Raises: - FileNotFoundError: If data_path does not exist - json.JSONDecodeError: If file is not valid JSON - IndexError: If conversation_index is out of range - """ - if not os.path.exists(data_path): - raise FileNotFoundError(f"LoCoMo data file not found: {data_path}") - - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - - # LoCoMo data structure: list of objects, each with a "qa" list - qa_items: List[Dict[str, Any]] = [] - - if isinstance(raw, list): - # Only load QA pairs from the specified conversation - if conversation_index < len(raw): - entry = raw[conversation_index] - if isinstance(entry, dict) and "qa" in entry: - qa_items.extend(entry.get("qa", [])) - else: - raise IndexError( - f"Conversation index {conversation_index} out of range. " - f"Dataset has {len(raw)} conversations." - ) - else: - # Fallback: single object with qa list - if conversation_index == 0: - qa_items.extend(raw.get("qa", [])) - else: - raise IndexError( - f"Conversation index {conversation_index} out of range. " - f"Dataset has only 1 conversation." - ) - - # Return only the requested sample size - return qa_items[:sample_size] - - -def extract_conversations(data_path: str, max_dialogues: int = 1) -> List[str]: - """ - Extract conversation texts from LoCoMo data for ingestion. - - This function extracts the raw conversation dialogues from the LoCoMo dataset - so they can be ingested into the memory system. Each conversation is formatted - as a multi-line string with "role: message" format. - - Args: - data_path: Path to locomo10.json file - max_dialogues: Maximum number of dialogues to extract (default: 1) - - Returns: - List of conversation strings formatted for ingestion. - Each string contains multiple lines in format "role: message" - - Example output: - [ - "User: I went to the store yesterday.\\nAI: What did you buy?\\n...", - "User: I love hiking.\\nAI: Where do you like to hike?\\n..." - ] - """ - if not os.path.exists(data_path): - raise FileNotFoundError(f"LoCoMo data file not found: {data_path}") - - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - - # Ensure we have a list of entries - entries = raw if isinstance(raw, list) else [raw] - - contents: List[str] = [] - - for i, entry in enumerate(entries[:max_dialogues]): - if not isinstance(entry, dict): - continue - - conv = entry.get("conversation", {}) - - if not isinstance(conv, dict): - continue - - lines: List[str] = [] - - # Collect all session_* messages - for key, val in sorted(conv.items()): - if isinstance(val, list) and key.startswith("session_"): - for msg in val: - if not isinstance(msg, dict): - continue - - role = msg.get("speaker") or "User" - text = msg.get("text") or "" - text = str(text).strip() - - if not text: - continue - - lines.append(f"{role}: {text}") - - if lines: - contents.append("\n".join(lines)) - - return contents - - -def resolve_temporal_references(text: str, anchor_date: datetime) -> str: - """ - Resolve relative temporal references to absolute dates. - - This function converts relative time expressions (like "today", "yesterday", - "3 days ago") into absolute ISO date strings based on an anchor date. - - Supported patterns: - - today, yesterday, tomorrow - - X days ago, in X days - - last week, next week - - Args: - text: Text containing temporal references - anchor_date: Reference date for resolution (datetime object) - - Returns: - Text with temporal references replaced by ISO dates (YYYY-MM-DD format) - - Example: - >>> anchor = datetime(2023, 5, 8) - >>> resolve_temporal_references("I saw him yesterday", anchor) - "I saw him 2023-05-07" - """ - # Ensure input is a string - t = str(text) if text is not None else "" - - # today / yesterday / tomorrow - t = re.sub( - r"\btoday\b", - anchor_date.date().isoformat(), - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\byesterday\b", - (anchor_date - timedelta(days=1)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\btomorrow\b", - (anchor_date + timedelta(days=1)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - - # X days ago - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor_date - timedelta(days=n)).date().isoformat() - - # in X days - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor_date + timedelta(days=n)).date().isoformat() - - t = re.sub( - r"\b(\d+)\s+days?\s+ago\b", - _ago_repl, - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\bin\s+(\d+)\s+days?\b", - _in_repl, - t, - flags=re.IGNORECASE - ) - - # last week / next week (approximate as 7 days) - t = re.sub( - r"\blast\s+week\b", - (anchor_date - timedelta(days=7)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\bnext\s+week\b", - (anchor_date + timedelta(days=7)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - - return t - - -def select_and_format_information( - retrieved_info: List[str], - question: str, - max_chars: int = 8000 -) -> str: - """ - Intelligently select and format most relevant retrieved information for LLM prompt. - - This function scores each piece of retrieved information based on keyword matching - with the question, then selects the highest-scoring pieces up to the character limit. - - Scoring criteria: - - Keyword matches (higher weight for multiple occurrences) - - Context length (moderate length preferred) - - Position (earlier contexts get bonus points) - - Args: - retrieved_info: List of retrieved information strings (chunks, statements, entities) - question: Question being answered - max_chars: Maximum total characters to include in final prompt - - Returns: - Formatted string combining the most relevant information for LLM prompt. - Contexts are separated by double newlines. - - Example: - >>> contexts = ["Alice went to Paris", "Bob likes pizza", "Alice visited the Eiffel Tower"] - >>> question = "Where did Alice go?" - >>> select_and_format_information(contexts, question, max_chars=100) - "Alice went to Paris\\n\\nAlice visited the Eiffel Tower" - """ - if not retrieved_info: - return "" - - # Extract question keywords (filter out stop words and short words) - question_lower = question.lower() - stop_words = { - 'what', 'when', 'where', 'who', 'why', 'how', - 'did', 'do', 'does', 'is', 'are', 'was', 'were', - 'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at' - } - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = { - word for word in question_words - if word not in stop_words and len(word) > 2 - } - - # Score each context - scored_contexts = [] - for i, context in enumerate(retrieved_info): - context_lower = context.lower() - score = 0 - - # Keyword matching score - keyword_matches = 0 - for word in question_words: - if word in context_lower: - keyword_matches += 1 - # Multiple occurrences increase score - score += context_lower.count(word) * 2 - - # Length score (prefer moderate length) - context_len = len(context) - if 100 < context_len < 2000: - score += 5 - elif context_len >= 2000: - score += 2 - - # Position bonus (earlier contexts often more relevant) - if i < 3: - score += 3 - - scored_contexts.append((score, context, keyword_matches)) - - # Sort by score (descending) - scored_contexts.sort(key=lambda x: x[0], reverse=True) - - # Select contexts up to character limit - selected = [] - total_chars = 0 - - for score, context, matches in scored_contexts: - if total_chars + len(context) <= max_chars: - selected.append(context) - total_chars += len(context) - else: - # Try to include high-scoring context by truncating - if score > 10 and total_chars < max_chars - 500: - remaining = max_chars - total_chars - # Find lines with keywords - lines = context.split('\n') - relevant_lines = [] - current_chars = 0 - - for line in lines: - line_lower = line.lower() - line_relevance = any(word in line_lower for word in question_words) - - if line_relevance and current_chars < remaining - 100: - relevant_lines.append(line) - current_chars += len(line) - - if relevant_lines and len('\n'.join(relevant_lines)) > 100: - truncated = '\n'.join(relevant_lines) - selected.append(truncated + "\n[Content truncated...]") - total_chars += len(truncated) - break - - return "\n\n".join(selected) - - -async def retrieve_relevant_information( - question: str, - group_id: str, - search_type: str, - search_limit: int, - connector: Any, - embedder: Any -) -> List[str]: - """ - Retrieve relevant information from memory graph for a question. - - This function searches the Neo4j memory graph (populated during ingestion) and - returns relevant chunks, statements, and entity information that might help - answer the question. - - The function supports three search types: - - "keyword": Full-text search using Cypher queries - - "embedding": Vector similarity search using embeddings - - "hybrid": Combination of keyword and embedding search with reranking - - Args: - question: Question to search for - group_id: Database group ID (identifies which conversation memory to search) - search_type: "keyword", "embedding", or "hybrid" - search_limit: Max memory pieces to retrieve - connector: Neo4j connector instance - embedder: Embedder client instance - - Returns: - List of text strings (chunks, statements, entity summaries) from memory graph. - Each string represents a piece of retrieved information. - - Raises: - Exception: If search fails (caught and returns empty list) - """ - from app.repositories.neo4j.graph_search import ( - search_graph, - search_graph_by_embedding - ) - from app.core.memory.storage_services.search import run_hybrid_search - - contexts_all: List[str] = [] - - try: - if search_type == "embedding": - # Embedding-based search - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # Build context from chunks - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - # Add statements - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - # Add summaries - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # Add top entities (limit to 3 to avoid noise) - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = ( - sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] - if scored else entities[:3] - ) - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append( - f"EntitySummary: {name}" - f"{(' [' + '; '.join(meta) + ']') if meta else ''}" - ) - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - # Keyword-based search - search_results = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=search_limit - ) - - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - # Build context from dialogues - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - - # Add statements - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - # Add entity names - if entities: - entity_names = [ - str(e.get("name", "")).strip() - for e in entities[:5] - if e.get("name") - ] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid - # Hybrid search with fallback to embedding - try: - search_results = await run_hybrid_search( - query_text=question, - search_type=search_type, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - output_path=None, - ) - - # Handle flat structure (new API format) - if search_results and isinstance(search_results, dict): - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # Check if we got results - if not (chunks or statements or entities or summaries): - # Try nested structure (backward compatibility) - reranked = search_results.get("reranked_results", {}) - if reranked and isinstance(reranked, dict): - chunks = reranked.get("chunks", []) - statements = reranked.get("statements", []) - entities = reranked.get("entities", []) - summaries = reranked.get("summaries", []) - else: - raise ValueError("Hybrid search returned empty results") - else: - raise ValueError("Hybrid search returned empty results") - - except Exception as e: - # Fallback to embedding search - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # Build context (same for both hybrid and fallback) - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # Add top entities - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = ( - sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] - if scored else entities[:3] - ) - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append( - f"EntitySummary: {name}" - f"{(' [' + '; '.join(meta) + ']') if meta else ''}" - ) - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - except Exception as e: - # Return empty list on error - contexts_all = [] - - return contexts_all - - -async def ingest_conversations_if_needed( - conversations: List[str], - group_id: str, - reset: bool = False -) -> bool: - """ - Wrapper for conversation ingestion using external extraction pipeline. - - This function populates the Neo4j database with processed conversation data - (chunks, statements, entities) so that the retrieval system has memory to search. - - The ingestion process: - 1. Parses conversation text into dialogue messages - 2. Chunks the dialogues into semantic units - 3. Extracts statements and entities using LLM - 4. Generates embeddings for all content - 5. Stores everything in Neo4j graph database - - Args: - conversations: List of raw conversation texts from LoCoMo dataset - Example: ["User: I went to Paris. AI: When was that?", ...] - group_id: Target group ID for database storage - reset: Whether to clear existing data first (not implemented in wrapper) - - Returns: - True if successful, False otherwise - - Note: - The external function uses "contexts" to mean "conversation texts". - This runs the full extraction pipeline: chunking → entity extraction → - statement extraction → embedding → Neo4j storage. - """ - try: - success = await ingest_contexts_via_full_pipeline( - contexts=conversations, - group_id=group_id, - save_chunk_output=True - ) - return success - except Exception as e: - print(f"[Ingestion] Failed to ingest conversations: {e}") - return False diff --git a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py deleted file mode 100644 index 87a70a29..00000000 --- a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py +++ /dev/null @@ -1,878 +0,0 @@ -import argparse -import asyncio -import json -import os -import statistics -import time -from datetime import datetime, timedelta -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -import re - -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - bleu1, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.common.metrics import f1_score as common_f1 -from app.core.memory.evaluation.extraction_utils import ( - ingest_contexts_via_full_pipeline, -) -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.storage_services.search import run_hybrid_search -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService - - -# 参考 evaluation/locomo/evaluation.py 的 F1 计算逻辑（移除外部依赖，内联实现） -def _loc_normalize(text: str) -> str: - import re - # 确保输入是字符串 - text = str(text) if text is not None else "" - text = text.lower() - text = re.sub(r"[\,]", " ", text) # 去掉逗号 - text = re.sub(r"\b(a|an|the|and)\b", " ", text) - text = re.sub(r"[^\w\s]", " ", text) - text = " ".join(text.split()) - return text - -# 追加：相对时间归一化为绝对日期（有限支持：today/yesterday/tomorrow/X days ago/in X days/last week/next week） -def _resolve_relative_times(text: str, anchor: datetime) -> str: - import re - # 确保输入是字符串 - t = str(text) if text is not None else "" - # today / yesterday / tomorrow - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - # X days ago / in X days - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - # last week / next week（以7天近似） - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - return t - -def loc_f1_score(prediction: str, ground_truth: str) -> float: - # 单答案 F1：按词集合计算（近似原始实现，去除词干依赖） - # 确保输入是字符串 - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - p_tokens = _loc_normalize(pred_str).split() - g_tokens = _loc_normalize(truth_str).split() - if not p_tokens or not g_tokens: - return 0.0 - p = set(p_tokens) - g = set(g_tokens) - tp = len(p & g) - precision = tp / len(p) if p else 0.0 - recall = tp / len(g) if g else 0.0 - return (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0 - -def loc_multi_f1(prediction: str, ground_truth: str) -> float: - # 多答案 F1：prediction 与 ground_truth 以逗号分隔，逐一匹配取最大，再对多个 GT 取平均 - # 确保输入是字符串 - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - predictions = [p.strip() for p in str(pred_str).split(',') if p.strip()] - ground_truths = [g.strip() for g in str(truth_str).split(',') if g.strip()] - if not predictions or not ground_truths: - return 0.0 - def _f1(a: str, b: str) -> float: - return loc_f1_score(a, b) - vals = [] - for gt in ground_truths: - vals.append(max(_f1(pred, gt) for pred in predictions)) - return sum(vals) / len(vals) - -# 标准化 LoCoMo 类别名：支持数字 category 与字符串 cat/type -CATEGORY_MAP_NUM_TO_NAME = { - 4: "Single-Hop", - 1: "Multi-Hop", - 3: "Open Domain", - 2: "Temporal", -} - -_TYPE_ALIASES = { - "single-hop": "Single-Hop", - "singlehop": "Single-Hop", - "single hop": "Single-Hop", - "multi-hop": "Multi-Hop", - "multihop": "Multi-Hop", - "multi hop": "Multi-Hop", - "open domain": "Open Domain", - "opendomain": "Open Domain", - "temporal": "Temporal", -} - -def get_category_label(item: Dict[str, Any]) -> str: - # 1) 直接用字符串 cat - cat = item.get("cat") - if isinstance(cat, str) and cat.strip(): - name = cat.strip() - lower = name.lower() - return _TYPE_ALIASES.get(lower, name) - # 2) 数字 category 转名称 - cat_num = item.get("category") - if isinstance(cat_num, int): - return CATEGORY_MAP_NUM_TO_NAME.get(cat_num, "unknown") - # 3) 备用 type 字段 - t = item.get("type") - if isinstance(t, str) and t.strip(): - lower = t.strip().lower() - return _TYPE_ALIASES.get(lower, t.strip()) - return "unknown" - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 12000) -> str: - """基于问题关键词智能选择上下文""" - if not contexts: - return "" - - # 提取问题关键词（只保留有意义的词） - question_lower = question.lower() - stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'} - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = {word for word in question_words if word not in stop_words and len(word) > 2} - - print(f"🔍 问题关键词: {question_words}") - - # 给每个上下文打分 - scored_contexts = [] - for i, context in enumerate(contexts): - context_lower = context.lower() - score = 0 - - # 关键词匹配得分 - keyword_matches = 0 - for word in question_words: - if word in context_lower: - keyword_matches += 1 - # 关键词出现次数越多，得分越高 - score += context_lower.count(word) * 2 - - # 上下文长度得分（适中的长度更好） - context_len = len(context) - if 100 < context_len < 2000: # 理想长度范围 - score += 5 - elif context_len >= 2000: # 太长可能包含无关信息 - score += 2 - - # 如果是前几个上下文，给予额外分数（通常相关性更高） - if i < 3: - score += 3 - - scored_contexts.append((score, context, keyword_matches)) - - # 按得分排序 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - - # 选择高得分的上下文，直到达到字符限制 - selected = [] - total_chars = 0 - selected_count = 0 - - print("📊 上下文相关性分析:") - for score, context, matches in scored_contexts[:5]: # 只显示前5个 - print(f" - 得分: {score}, 关键词匹配: {matches}, 长度: {len(context)}") - - for score, context, matches in scored_contexts: - if total_chars + len(context) <= max_chars: - selected.append(context) - total_chars += len(context) - selected_count += 1 - else: - # 如果这个上下文得分很高但放不下，尝试截取 - if score > 10 and total_chars < max_chars - 500: - remaining = max_chars - total_chars - # 找到包含关键词的部分 - lines = context.split('\n') - relevant_lines = [] - current_chars = 0 - - for line in lines: - line_lower = line.lower() - line_relevance = any(word in line_lower for word in question_words) - - if line_relevance and current_chars < remaining - 100: - relevant_lines.append(line) - current_chars += len(line) - - if relevant_lines: - truncated = '\n'.join(relevant_lines) - if len(truncated) > 100: # 确保有足够内容 - selected.append(truncated + "\n[相关内容截断...]") - total_chars += len(truncated) - selected_count += 1 - break # 不再尝试添加更多上下文 - - result = "\n\n".join(selected) - print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {total_chars}字符") - return result - - -def get_search_params_by_category(category: str): - """根据问题类别调整检索参数""" - params_map = { - "Multi-Hop": {"limit": 20, "max_chars": 15000}, - "Temporal": {"limit": 16, "max_chars": 10000}, - "Open Domain": {"limit": 24, "max_chars": 18000}, - "Single-Hop": {"limit": 12, "max_chars": 8000}, - } - return params_map.get(category, {"limit": 16, "max_chars": 12000}) - - -async def run_locomo_eval( - sample_size: int = 1, - group_id: str | None = None, - search_limit: int = 8, - context_char_budget: int = 4000, # 保持默认值不变 - llm_temperature: float = 0.0, - llm_max_tokens: int = 32, - search_type: str = "hybrid", # 保持默认值不变 - output_path: str | None = None, - skip_ingest_if_exists: bool = True, - llm_timeout: float = 10.0, - llm_max_retries: int = 1 -) -> Dict[str, Any]: - - # 函数内部使用三路检索逻辑，但保持参数签名不变 - group_id = group_id or SELECTED_GROUP_ID - data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") - if not os.path.exists(data_path): - data_path = os.path.join(os.getcwd(), "data", "locomo10.json") - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - # LoCoMo 数据结构：顶层为若干对象，每个对象下有 qa 列表 - qa_items: List[Dict[str, Any]] = [] - if isinstance(raw, list): - for entry in raw: - qa_items.extend(entry.get("qa", [])) - else: - qa_items.extend(raw.get("qa", [])) - items: List[Dict[str, Any]] = qa_items[:sample_size] - - # === 保持原来的数据摄入逻辑 === - entries = raw if isinstance(raw, list) else [raw] - - # 只摄入前1条对话（保持原样） - max_dialogues_to_ingest = 1 - contents: List[str] = [] - print(f"📊 找到 {len(entries)} 个对话对象，只摄入前 {max_dialogues_to_ingest} 条") - - for i, entry in enumerate(entries[:max_dialogues_to_ingest]): - if not isinstance(entry, dict): - continue - - conv = entry.get("conversation", {}) - sample_id = entry.get("sample_id", f"unknown_{i}") - - print(f"🔍 处理对话 {i+1}: {sample_id}") - - lines: List[str] = [] - if isinstance(conv, dict): - # 收集所有 session_* 的消息 - session_count = 0 - for key, val in conv.items(): - if isinstance(val, list) and key.startswith("session_"): - session_count += 1 - for msg in val: - role = msg.get("speaker") or "用户" - text = msg.get("text") or "" - text = str(text).strip() - if not text: - continue - lines.append(f"{role}: {text}") - - print(f" - 包含 {session_count} 个session, {len(lines)} 条消息") - - if not lines: - print(f"⚠️ 警告: 对话 {sample_id} 没有对话内容，跳过摄入") - continue - - contents.append("\n".join(lines)) - - print(f"📥 总共摄入 {len(contents)} 个对话的conversation内容") - - # 选择要评测的QA对（从所有对话中选取） - indexed_items: List[tuple[int, Dict[str, Any]]] = [] - if isinstance(raw, list): - for e_idx, entry in enumerate(raw): - for qa in entry.get("qa", []): - indexed_items.append((e_idx, qa)) - else: - for qa in raw.get("qa", []): - indexed_items.append((0, qa)) - - # 这里使用sample_size来限制评测的QA数量 - selected = indexed_items[:sample_size] - items: List[Dict[str, Any]] = [qa for _, qa in selected] - - print(f"🎯 将评测 {len(items)} 个QA对，数据库中只包含 {len(contents)} 个对话") - # === 修改结束 === - - connector = Neo4jConnector() - - # 关键修复：强制重新摄入纯净的对话数据 - print("🔄 强制重新摄入纯净的对话数据...") - await ingest_contexts_via_full_pipeline(contents, group_id, save_chunk_output=True) - - # 使用异步LLM客户端 - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) - # 初始化embedder用于直接调用 - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # connector initialized above - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - # 上下文诊断收集 - per_query_context_counts: List[int] = [] - per_query_context_avg_tokens: List[float] = [] - per_query_context_chars: List[int] = [] - per_query_context_tokens_total: List[int] = [] - # 详细样本调试信息 - samples: List[Dict[str, Any]] = [] - # 通用指标 - f1s: List[float] = [] - b1s: List[float] = [] - jss: List[float] = [] - # 参考 LoCoMo 评测的类别专用 F1（multi-hop 使用多答案 F1） - loc_f1s: List[float] = [] - # Per-category aggregation - cat_counts: Dict[str, int] = {} - cat_f1s: Dict[str, List[float]] = {} - cat_b1s: Dict[str, List[float]] = {} - cat_jss: Dict[str, List[float]] = {} - cat_loc_f1s: Dict[str, List[float]] = {} - try: - for item in items: - q = item.get("question", "") - ref = item.get("answer", "") - # 确保答案是字符串 - ref_str = str(ref) if ref is not None else "" - cat = get_category_label(item) - - print(f"\n=== 处理问题: {q} ===") - - # 根据类别调整检索参数 - search_params = get_search_params_by_category(cat) - adjusted_limit = search_params["limit"] - max_chars = search_params["max_chars"] - - print(f"🏷️ 类别: {cat}, 检索参数: limit={adjusted_limit}, max_chars={max_chars}") - - # 改进的检索逻辑：使用三路检索（statements, dialogues, entities） - t0 = time.time() - contexts_all: List[str] = [] - search_results = None # 保存完整的检索结果 - - try: - if search_type == "embedding": - # 直接调用嵌入检索，包含三路数据 - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=q, - group_id=group_id, - limit=adjusted_limit, - include=["chunks", "statements", "entities", "summaries"], # 修复：使用正确的类型 - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - print(f"✅ 嵌入检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要") - - # 构建上下文：优先使用 chunks、statements 和 summaries - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要：最多加入前3个高分实体，避免噪声 - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - # 直接调用关键词检索 - search_results = await search_graph( - connector=connector, - q=q, - group_id=group_id, - limit=adjusted_limit - ) - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - print(f"🔤 关键词检索找到 {len(dialogs)} 条对话, {len(statements)} 条陈述, {len(entities)} 个实体") - - # 构建上下文 - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体处理（关键词检索的实体可能没有分数） - if entities: - entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid - # 🎯 关键修复：混合检索使用更严格的回退机制 - print("🔀 使用混合检索（带回退机制）...") - try: - search_results = await run_hybrid_search( - query_text=q, - search_type=search_type, - group_id=group_id, - limit=adjusted_limit, - include=["chunks", "statements", "entities", "summaries"], - output_path=None, - ) - - # 🎯 关键修复：正确处理混合检索的扁平结构 - # 新的API返回扁平结构，直接从顶层获取结果 - if search_results and isinstance(search_results, dict): - # 新API返回扁平结构：直接从顶层获取 - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # 检查是否有有效结果 - if chunks or statements or entities or summaries: - print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 陈述, {len(entities)} 实体, {len(summaries)} 摘要") - else: - # 如果顶层没有结果，尝试旧的嵌套结构（向后兼容） - reranked = search_results.get("reranked_results", {}) - if reranked and isinstance(reranked, dict): - chunks = reranked.get("chunks", []) - statements = reranked.get("statements", []) - entities = reranked.get("entities", []) - summaries = reranked.get("summaries", []) - print(f"✅ 混合检索成功（使用旧格式reranked结果）: {len(chunks)} chunks, {len(statements)} 陈述") - else: - raise ValueError("混合检索返回空结果") - else: - raise ValueError("混合检索返回空结果") - - except Exception as e: - print(f"❌ 混合检索失败: {e}，回退到嵌入检索") - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=q, - group_id=group_id, - limit=adjusted_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - print(f"✅ 回退嵌入检索成功: {len(chunks)} chunks, {len(statements)} 陈述") - - # 🎯 统一处理：构建上下文（所有检索类型共用） - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要：最多加入前3个高分实体 - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - # 关键修复：过滤掉包含当前问题答案的上下文 - filtered_contexts = [] - for context in contexts_all: - content = str(context) - # 排除包含当前问题标准答案的上下文 - if ref_str and ref_str.strip() and ref_str.strip() in content: - print("🚫 过滤掉包含标准答案的上下文") - continue - filtered_contexts.append(context) - - print(f"📊 过滤后保留 {len(filtered_contexts)} 个上下文 (原 {len(contexts_all)} 个)") - contexts_all = filtered_contexts - - # 输出完整的检索结果信息 - print("🔍 检索结果详情:") - if search_results: - output_data = { - "statements": [ - { - "statement": s.get("statement", "")[:200] + "..." if len(s.get("statement", "")) > 200 else s.get("statement", ""), - "score": s.get("score", 0.0) - } - for s in (statements[:2] if 'statements' in locals() else []) - ], - "dialogues": [ - { - "uuid": d.get("uuid", ""), - "group_id": d.get("group_id", ""), - "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""), - "score": d.get("score", 0.0) - } - for d in (dialogs[:2] if 'dialogs' in locals() else []) - ], - "entities": [ - { - "name": e.get("name", ""), - "entity_type": e.get("entity_type", ""), - "score": e.get("score", 0.0) - } - for e in (entities[:2] if 'entities' in locals() else []) - ] - } - print(json.dumps(output_data, ensure_ascii=False, indent=2)) - else: - print(" 无检索结果") - - except Exception as e: - print(f"❌ {search_type}检索失败: {e}") - contexts_all = [] - search_results = None - - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 使用智能上下文选择 - context_text = "" - if contexts_all: - context_text = smart_context_selection(contexts_all, q, max_chars=max_chars) - - # 如果智能选择后仍然过长，进行最终保护性截断 - if len(context_text) > max_chars: - print(f"⚠️ 智能选择后仍然过长 ({len(context_text)}字符)，进行最终截断") - context_text = context_text[:max_chars] + "\n\n[最终截断...]" - - # 时间解析 - anchor_date = datetime(2023, 5, 8) # 使用固定日期确保一致性 - context_text = _resolve_relative_times(context_text, anchor_date) - - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n" + context_text - - print(f"📝 最终上下文长度: {len(context_text)} 字符") - - # 显示不同上下文的预览 - print("🔍 上下文预览:") - for j, context in enumerate(contexts_all[:3]): # 显示前3个上下文 - preview = context[:150].replace('\n', ' ') - print(f" 上下文{j+1}: {preview}...") - - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # 记录上下文诊断信息 - per_query_context_counts.append(len(contexts_all)) - per_query_context_avg_tokens.append(avg_context_tokens([context_text])) - per_query_context_chars.append(len(context_text)) - per_query_context_tokens_total.append(len(_loc_normalize(context_text).split())) - - # LLM 提示词 - messages = [ - {"role": "system", "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - )}, - {"role": "user", "content": f"Question: {q}\n\nContext:\n{context_text}"}, - ] - - t2 = time.time() - # 使用异步调用 - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - - # 兼容不同的响应格式 - pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - - # 计算指标（确保使用字符串） - f1_val = common_f1(str(pred), ref_str) - b1_val = bleu1(str(pred), ref_str) - j_val = jaccard(str(pred), ref_str) - - f1s.append(f1_val) - b1s.append(b1_val) - jss.append(j_val) - - # Accumulate by category - cat_counts[cat] = cat_counts.get(cat, 0) + 1 - cat_f1s.setdefault(cat, []).append(f1_val) - cat_b1s.setdefault(cat, []).append(b1_val) - cat_jss.setdefault(cat, []).append(j_val) - - # LoCoMo 专用 F1：multi-hop(1) 使用多答案 F1，其它(2/3/4)使用单答案 F1 - if item.get("category") in [2, 3, 4]: - loc_val = loc_f1_score(str(pred), ref_str) - elif item.get("category") in [1]: - loc_val = loc_multi_f1(str(pred), ref_str) - else: - loc_val = loc_f1_score(str(pred), ref_str) - loc_f1s.append(loc_val) - cat_loc_f1s.setdefault(cat, []).append(loc_val) - - # 保存完整的检索结果信息 - samples.append({ - "question": q, - "answer": ref_str, - "category": cat, - "prediction": pred, - "metrics": { - "f1": f1_val, - "b1": b1_val, - "j": j_val, - "loc_f1": loc_val - }, - "retrieval": { - "retrieved_documents": len(contexts_all), - "context_length": len(context_text), - "search_limit": adjusted_limit, - "max_chars": max_chars - }, - "timing": { - "search_ms": (t1 - t0) * 1000, - "llm_ms": (t3 - t2) * 1000 - } - }) - - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {ref_str}") - print(f"📈 当前指标 - F1: {f1_val:.3f}, BLEU-1: {b1_val:.3f}, Jaccard: {j_val:.3f}, LoCoMo F1: {loc_val:.3f}") - - # Compute per-category averages and dispersion (std, iqr) - def _percentile(sorted_vals: List[float], p: float) -> float: - if not sorted_vals: - return 0.0 - if len(sorted_vals) == 1: - return sorted_vals[0] - k = (len(sorted_vals) - 1) * p - f = int(k) - c = f + 1 if f + 1 < len(sorted_vals) else f - if f == c: - return sorted_vals[f] - return sorted_vals[f] + (sorted_vals[c] - sorted_vals[f]) * (k - f) - - by_category: Dict[str, Dict[str, float | int]] = {} - for c in cat_counts: - f_list = cat_f1s.get(c, []) - b_list = cat_b1s.get(c, []) - j_list = cat_jss.get(c, []) - lf_list = cat_loc_f1s.get(c, []) - j_sorted = sorted(j_list) - j_std = statistics.stdev(j_list) if len(j_list) > 1 else 0.0 - j_q75 = _percentile(j_sorted, 0.75) - j_q25 = _percentile(j_sorted, 0.25) - by_category[c] = { - "count": cat_counts[c], - "f1": (sum(f_list) / max(len(f_list), 1)) if f_list else 0.0, - "b1": (sum(b_list) / max(len(b_list), 1)) if b_list else 0.0, - "j": (sum(j_list) / max(len(j_list), 1)) if j_list else 0.0, - "j_std": j_std, - "j_iqr": (j_q75 - j_q25) if j_list else 0.0, - # 参考 LoCoMo 评测的类别专用 F1 - "loc_f1": (sum(lf_list) / max(len(lf_list), 1)) if lf_list else 0.0, - } - - # 累加命中（cum accuracy by category）：与 evaluation_stats.py 输出形式相仿 - cum_accuracy_by_category = {c: sum(cat_loc_f1s.get(c, [])) for c in cat_counts} - - result = { - "dataset": "locomo", - "items": len(items), - "metrics": { - "f1": sum(f1s) / max(len(f1s), 1), - "b1": sum(b1s) / max(len(b1s), 1), - "j": sum(jss) / max(len(jss), 1), - # LoCoMo 类别专用 F1 的总体 - "loc_f1": sum(loc_f1s) / max(len(loc_f1s), 1), - }, - "by_category": by_category, - "category_counts": cat_counts, - "cum_accuracy_by_category": cum_accuracy_by_category, - "context": { - "avg_tokens": (sum(per_query_context_avg_tokens) / max(len(per_query_context_avg_tokens), 1)) if per_query_context_avg_tokens else 0.0, - "avg_chars": (sum(per_query_context_chars) / max(len(per_query_context_chars), 1)) if per_query_context_chars else 0.0, - "count_avg": (sum(per_query_context_counts) / max(len(per_query_context_counts), 1)) if per_query_context_counts else 0.0, - "avg_memory_tokens": (sum(per_query_context_tokens_total) / max(len(per_query_context_tokens_total), 1)) if per_query_context_tokens_total else 0.0, - }, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "samples": samples, - "params": { - "group_id": group_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "search_type": search_type, - "llm_id": SELECTED_LLM_ID, - "retrieval_embedding_id": SELECTED_EMBEDDING_ID, - "skip_ingest_if_exists": skip_ingest_if_exists, - "llm_timeout": llm_timeout, - "llm_max_retries": llm_max_retries, - "llm_temperature": llm_temperature, - "llm_max_tokens": llm_max_tokens - }, - "timestamp": datetime.now().isoformat() - } - if output_path: - try: - os.makedirs(os.path.dirname(output_path), exist_ok=True) - with open(output_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"✅ 结果已保存到: {output_path}") - except Exception as e: - print(f"❌ 保存结果失败: {e}") - return result - finally: - await connector.close() - - -def main(): - parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search") - parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate") - parser.add_argument("--group_id", type=str, default=None, help="Group ID for retrieval") - parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query") - parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context") - parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature") - parser.add_argument("--llm_max_tokens", type=int, default=32, help="LLM max tokens") - parser.add_argument("--search_type", type=str, default="embedding", choices=["keyword", "embedding", "hybrid"], help="Search type") - parser.add_argument("--output_path", type=str, default=None, help="Output path for results") - parser.add_argument("--skip_ingest_if_exists", action="store_true", help="Skip ingest if group exists") - parser.add_argument("--llm_timeout", type=float, default=10.0, help="LLM timeout in seconds") - parser.add_argument("--llm_max_retries", type=int, default=1, help="LLM max retries") - args = parser.parse_args() - - load_dotenv() - - result = asyncio.run(run_locomo_eval( - sample_size=args.sample_size, - group_id=args.group_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - output_path=args.output_path, - skip_ingest_if_exists=args.skip_ingest_if_exists, - llm_timeout=args.llm_timeout, - llm_max_retries=args.llm_max_retries - )) - - print("\n" + "="*50) - print("📊 最终评测结果:") - print(f" 样本数量: {result['items']}") - print(f" F1: {result['metrics']['f1']:.3f}") - print(f" BLEU-1: {result['metrics']['b1']:.3f}") - print(f" Jaccard: {result['metrics']['j']:.3f}") - print(f" LoCoMo F1: {result['metrics']['loc_f1']:.3f}") - print(f" 平均上下文长度: {result['context']['avg_chars']:.0f} 字符") - print(f" 平均检索延迟: {result['latency']['search']['mean']:.1f}ms") - print(f" 平均LLM延迟: {result['latency']['llm']['mean']:.1f}ms") - - if result['by_category']: - print("\n📈 按类别细分:") - for cat, metrics in result['by_category'].items(): - print(f" {cat}:") - print(f" 样本数: {metrics['count']}") - print(f" F1: {metrics['f1']:.3f}") - print(f" LoCoMo F1: {metrics['loc_f1']:.3f}") - print(f" Jaccard: {metrics['j']:.3f} (±{metrics['j_std']:.3f}, IQR={metrics['j_iqr']:.3f})") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py deleted file mode 100644 index 53c5ce19..00000000 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ /dev/null @@ -1,1363 +0,0 @@ -import argparse -import asyncio -import json -import os -import re -import statistics -import time -from datetime import datetime, timedelta -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -# 确保可以找到 src 及项目根路径 -import sys - -_THIS_DIR = os.path.dirname(os.path.abspath(__file__)) -_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(_THIS_DIR))) -_SRC_DIR = os.path.join(_PROJECT_ROOT, "src") -for _p in (_SRC_DIR, _PROJECT_ROOT): - if _p not in sys.path: - sys.path.insert(0, _p) - -# 与现有评估脚本保持一致的导入方式 -from app.repositories.neo4j.neo4j_connector import Neo4jConnector - -try: - # 优先从 extraction_utils1 导入 - from app.core.memory.evaluation.extraction_utils import ( - ingest_contexts_via_full_pipeline, # type: ignore - ) -except Exception: - ingest_contexts_via_full_pipeline = None # 在运行时做兜底检查 -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.common.metrics import f1_score as common_f1 -from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.services.memory_config_service import MemoryConfigService - -try: - from app.core.memory.evaluation.common.metrics import exact_match -except Exception: - # 兜底：简单的大小写不敏感比较 - def exact_match(pred: str, ref: str) -> bool: - return str(pred).strip().lower() == str(ref).strip().lower() - - -def load_dataset_any(path: str) -> List[Dict[str, Any]]: - """健壮地加载数据集（兼容 list 或多段 JSON）。""" - with open(path, "r", encoding="utf-8") as f: - s = f.read().strip() - try: - obj = json.loads(s) - if isinstance(obj, list): - return obj - elif isinstance(obj, dict): - return [obj] - except json.JSONDecodeError: - pass - dec = json.JSONDecoder() - idx = 0 - items: List[Dict[str, Any]] = [] - while idx < len(s): - while idx < len(s) and s[idx].isspace(): - idx += 1 - if idx >= len(s): - break - try: - obj, end = dec.raw_decode(s, idx) - if isinstance(obj, list): - for it in obj: - if isinstance(it, dict): - items.append(it) - elif isinstance(obj, dict): - items.append(obj) - idx = end - except json.JSONDecodeError: - nl = s.find("\n", idx) - if nl == -1: - break - idx = nl + 1 - return items - - -def is_chinese_text(s: str) -> bool: - return bool(re.search(r"[\u4e00-\u9fff]", s or "")) - - -def build_context_from_sessions(item: Dict[str, Any]) -> List[str]: - """从数据项的 haystack_sessions 构建上下文片段。 - - 优先返回包含 has_answer 的消息 - - 其次返回拼接后的整段会话 - """ - contexts: List[str] = [] - sessions = item.get("haystack_sessions", []) or item.get("sessions", []) - for session in sessions: - parts: List[str] = [] - if isinstance(session, list): - for msg in session: - role = msg.get("role", "") - content = msg.get("content", "") or msg.get("text", "") - if content: - parts.append(f"{role}: {content}" if role else str(content)) - if msg.get("has_answer", False): - contexts.append(f"{role}: {content}" if role else str(content)) - elif isinstance(session, dict): - role = session.get("role", "") - content = session.get("content", "") or session.get("text", "") - if content: - parts.append(f"{role}: {content}" if role else str(content)) - if session.get("has_answer", False): - contexts.append(f"{role}: {content}" if role else str(content)) - if parts: - contexts.append("\n".join(parts)) - # 兜底：存在单字段上下文 - if not contexts: - single_ctx = item.get("context") or item.get("dialogue") or item.get("conversation") - if isinstance(single_ctx, str) and single_ctx.strip(): - contexts.append(single_ctx.strip()) - return contexts - - -def extract_candidate_options(question: str) -> List[str]: - """从问题中提取候选选项（A-or-B 类问题）。""" - q = (question or "").strip() - options: List[str] = [] - - # 1) 引号包裹的片段 - for pat in [r"'([^']+)'", r'\"([^\"]+)\"', r'“([^”]+)”', r'‘([^’]+)’']: - for m in re.findall(pat, q): - val = (m or "").strip() - if val: - options.append(val) - - # 2) or/还是/或者 连接词 - if len(options) < 2: - pats = [ - r"([^,;，；]+?)\s+or\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+还是\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+或者\s+([^,;，；\?\.!.。！]+)", - ] - for pat in pats: - matches = list(re.finditer(pat, q, flags=re.IGNORECASE)) - if matches: - m = matches[-1] - cand1 = m.group(1).strip().strip("?？.,，;； ") - cand2 = m.group(2).strip().strip("?？.,，;； ") - options.extend([cand1, cand2]) - break - - # 去重 - seen = set() - uniq: List[str] = [] - for o in options: - o2 = o.strip() - key = o2.lower() if not is_chinese_text(o2) else o2 - if o2 and key not in seen: - uniq.append(o2) - seen.add(key) - return uniq - - -def extract_time_entities(text: str) -> List[Dict[str, Any]]: - """增强时间实体提取，专门用于时间推理问题""" - time_entities = [] - - # 日期模式 - date_patterns = [ - (r'\b(\d{4})-(\d{1,2})-(\d{1,2})\b', 'date'), # YYYY-MM-DD - (r'\b(\d{1,2})月(\d{1,2})日\b', 'date'), # 中文日期 - (r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份 - (r'\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份缩写 - ] - - # 时间间隔模式 - duration_patterns = [ - (r'(\d+)\s*天', 'days'), - (r'(\d+)\s*周', 'weeks'), - (r'(\d+)\s*个月', 'months'), - (r'(\d+)\s*年', 'years'), - (r'(\d+)\s*days?', 'days'), - (r'(\d+)\s*weeks?', 'weeks'), - (r'(\d+)\s*months?', 'months'), - (r'(\d+)\s*years?', 'years'), - ] - - # 事件时间关系模式 - temporal_relation_patterns = [ - (r'(之前|以前|前)\s*(\d+)\s*天', 'days_before'), - (r'(之后|以后|后)\s*(\d+)\s*天', 'days_after'), - (r'(\d+)\s*天\s*(之前|以前|前)', 'days_before'), - (r'(\d+)\s*天\s*(之后|以后|后)', 'days_after'), - (r'(\d+)\s*days?\s*(before|ago)', 'days_before'), - (r'(\d+)\s*days?\s*(after|later)', 'days_after'), - ] - - # 提取日期 - for pattern, entity_type in date_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间间隔 - for pattern, entity_type in duration_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间关系 - for pattern, entity_type in temporal_relation_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(2)) if match.groups() >= 2 else int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - return time_entities - - -def calculate_time_difference(date1: str, date2: str) -> int: - """计算两个日期之间的天数差""" - try: - # 解析日期格式 - def parse_date(date_str: str) -> datetime: - # 尝试多种日期格式 - formats = [ - '%Y-%m-%d', - '%m月%d日', - '%B %d, %Y', - '%b %d, %Y', - '%Y年%m月%d日' - ] - - for fmt in formats: - try: - return datetime.strptime(date_str, fmt) - except ValueError: - continue - - # 如果都无法解析，返回当前日期 - return datetime.now() - - d1 = parse_date(date1) - d2 = parse_date(date2) - - # 计算天数差（绝对值） - return abs((d2 - d1).days) - except Exception: - return -1 # 表示计算失败 - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """增强版上下文选择：特别优化时间推理问题的处理""" - if not contexts: - return "" - - # 检测是否为时间推理问题 - is_temporal_question = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 提取时间实体从问题中 - question_time_entities = extract_time_entities(question) - - # 英文关键词（去停用词） - question_lower = question.lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but','many','which','first' - } - eng_words = [w for w in set(re.findall(r'\b\w+\b', question_lower)) - if w not in stop_words and len(w) > 2] - - # 中文片段与候选选项 - cn_tokens = generate_query_keywords_cn(question) - options = extract_candidate_options(question) - - # 时间推理问题的特殊处理 - if is_temporal_question: - # 为时间问题添加时间相关关键词 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'days', 'first', '先后'] - eng_words = [w for w in eng_words if w not in ['days', 'first']] # 避免重复 - cn_tokens.extend([kw for kw in time_keywords if kw not in cn_tokens]) - - # 限制关键词数量，优先时间相关 - tokens = time_keywords[:2] + cn_tokens[:2] + eng_words[:1] + options[:1] - else: - # 常规问题处理 - tokens = cn_tokens[:3] + options[:2] + eng_words[:1] - - # 去重 - seen = set() - final_tokens: List[str] = [] - for t in tokens: - t2 = t.strip() - if t2 and t2 not in seen: - final_tokens.append(t2) - seen.add(t2) - - scored_contexts: List[tuple[float, str]] = [] - - # 时间推理问题的权重映射 - temporal_weight_map = { - "天": 2.0, "日": 2.0, "月": 1.8, "年": 1.8, "days": 2.0, - "before": 1.5, "after": 1.5, "first": 1.5, "先后": 1.5 - } - - # 常规问题的权重映射 - normal_weight_map = { - "问题": 2.0, "故障": 2.0, "异常": 1.8, "不正常": 1.8, "坏了": 1.8, - "系统": 1.3, "GPS": 1.5, "保养": 1.4, "设备": 1.2, "模块": 1.2, "功能": 1.1 - } - - weight_map = temporal_weight_map if is_temporal_question else normal_weight_map - - for i, context in enumerate(contexts): - context_str = str(context) - lines = re.split(r'[\r\n]+', context_str) - hit_lines: List[str] = [] - kw_hits: float = 0.0 - time_entity_count = 0 - - for line in lines: - ln = line.strip() - if not ln: - continue - - has_keyword = False - # 关键词匹配 - for tok in final_tokens: - if tok and tok in ln: - w = weight_map.get(tok, 1.0) - kw_hits += ln.count(tok) * w - has_keyword = True - - # 时间实体检测（特别针对时间推理问题） - if is_temporal_question: - time_entities = extract_time_entities(ln) - time_entity_count += len(time_entities) - if time_entities: - has_keyword = True - - if has_keyword: - # 对于时间推理问题，保留包含时间信息的完整行 - hit_lines.append(ln) - - snippet = "\n".join(hit_lines) if hit_lines else context_str.strip() - - # 限制单段长度，但对时间推理问题稍微放宽限制 - max_snippet_len = 600 if is_temporal_question else 500 - if len(snippet) > max_snippet_len: - snippet = snippet[:max_snippet_len] - - # 评分逻辑 - has_number = 1 if re.search(r'\d', snippet) else 0 - has_date = 1 if (re.search(r'\b\d{4}-\d{1,2}-\d{1,2}\b', snippet) or - re.search(r'\d{1,2}月\d{1,2}日', snippet)) else 0 - - # 时间推理问题的特殊评分 - if is_temporal_question: - time_bonus = time_entity_count * 2.0 # 时间实体奖励 - temporal_coherence = 3 if (has_date and time_entity_count >= 2) else 0 - else: - time_bonus = 0 - temporal_coherence = 0 - - length_bonus = 5 if 50 < len(snippet) < 1000 else (2 if len(snippet) >= 1000 else 0) - pos_bonus = 3 if i < 3 else 0 - - score = (kw_hits * 0.8 + (has_number + has_date) * 1.5 + - length_bonus + pos_bonus + time_bonus + temporal_coherence) - - scored_contexts.append((score, snippet)) - - # 选择累计至总字符预算 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - selected: List[str] = [] - total_chars = 0 - - for score, snippet in scored_contexts: - if total_chars + len(snippet) <= max_chars: - selected.append(snippet) - total_chars += len(snippet) - else: - if not selected and len(snippet) > max_chars: - selected.append(snippet[:max_chars]) - break - - final_context = "\n\n".join(selected) - - # 对于时间推理问题，添加时间计算提示 - if is_temporal_question and question_time_entities: - time_prompt = "\n\n[时间推理提示：请仔细分析上述上下文中的日期和时间关系，计算时间间隔或确定事件顺序]" - if total_chars + len(time_prompt) <= max_chars: - final_context += time_prompt - - return final_context - - -# 中文关键词提取（短语级，含数词/日期/常见领域词） -def _extract_cn_tokens(text: str) -> List[str]: - if not text: - return [] - t = str(text) - # 去掉常见功能词（粗略，不依赖分词库） - stop_words = [ - "我","我们","你","他","她","它","这","那","哪","一个","一次","一些","什么","怎么","是否","吗","呢", - "很","更","最","已经","正在","将要","马上","尽快","最近","关于","有关","以及","并且","或者","还是", - "因为","所以","如果","但是","而且","然后","之后","之前","同时","另外","并","但","却","被","把","让","给", - "和","与","跟","及","还有","就","都","在","对","对于","的","了","着","过","到","于","从","以","为","向","至","是" - ] - for sw in stop_words: - t = t.replace(sw, " ") - # 去标点 - t = re.sub(r"[，。！？、；：,.!?;:\"'（）()［］\[\]\-—…·]", " ", t) - # 基础中文片段（>=2） - base = re.findall(r"[\u4e00-\u9fff]{2,}", t) - # 特殊组合：第X次XXXX - specials = re.findall(r"第[一二三四五六七八九十]+次[\u4e00-\u9fff]{2,6}", text) - # 领域词（简单词典） - # 日期与数字 - dates = re.findall(r"\d{4}年\d{1,2}月\d{1,2}日|\d{1,2}月\d{1,2}日|\d{4}-\d{1,2}-\d{1,2}", text) - numbers = re.findall(r"\b\d+\b", text) - - tokens: List[str] = specials + base + dates + numbers - - generic = {"建议","推荐","帮助","提升","技能","有效","团队","参与度","喜欢","开始"} - tokens: List[str] = specials + base + dates + numbers - uniq: List[str] = [] - seen = set() - for tok in tokens: - tok2 = tok.strip() - if len(tok2) < 2 or len(tok2) > 6: - continue - if tok2 in generic: - continue - if tok2 not in seen: - uniq.append(tok2) - seen.add(tok2) - # 排除常见疑问型短语 - blacklist_exact = {"是什么","多少","多少天","哪个","哪些","之间","先","后","之前","之后"} - uniq2: List[str] = [u for u in uniq if u not in blacklist_exact] - return uniq2[:12] - - -# 面向检索的中文关键词生成：强调"短语、核心名词、问题/故障" -def generate_query_keywords_cn(question: str) -> List[str]: - if not question: - return [] - raw = _extract_cn_tokens(question) - core: List[str] = [] - seen = set() - - def push(x: str): - x2 = x.strip() - if not x2: - return - if 2 <= len(x2) <= 6 and x2 not in seen: - core.append(x2) - seen.add(x2) - - # 检测时间推理问题 - is_temporal = any(keyword in question for keyword in ['天', '日', 'before', 'after', 'first', '先后', '间隔']) - if is_temporal: - push("天") - push("日") - push("先后") - - # 明确优先的核心词 - if "新车" in question: - push("新车") - # 第X次保养/维修 - specials = re.findall(r"第[一二三四五六七八九十]+次[\u4e00-\u9fff]{2,6}", question) - for s in specials: - if "保养" in s or "维修" in s: - push(s) - if "保养" in question: - push("保养") - # 问题/故障类词，如题含"问题"则扩展同义词 - if "问题" in question: - for w in ["问题","故障","异常","不正常"]: - push(w) - - # 补充：从原始片段筛更短的名词短语（过滤疑问型词） - blacklist = {"是什么","多少","哪个","还是","或者","之间","先","后","之前","之后"} - for tok in raw: - if tok in blacklist: - continue - push(tok) - - # 限制数量，避免过长列表影响检索稳定性 - return core[:4] # 稍微增加限制 - - -# 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]: - results: List[Dict[str, Any]] = [] - try: - for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit) - if rows: - results.extend(rows) - except Exception: - pass - - # 按 name 去重 - deduped: List[Dict[str, Any]] = [] - seen = set() - for r in results: - k = str(r.get("name", "")) - if k and k not in seen: - deduped.append(r) - seen.add(k) - return deduped - - -# 通过对话/陈述中的entity_ids反查实体名称 -_FETCH_ENTITIES_BY_IDS = """ -MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type -""" - -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]: - if not ids: - return [] - try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id) - return rows or [] - except Exception: - return [] - - -# 增强的时间实体检索 -_TIME_ENTITY_SEARCH = """ -MATCH (e:ExtractedEntity) -WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type -LIMIT $limit -""" - -async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: - """专门搜索时间相关的实体""" - try: - date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" - rows = await connector.execute_query(_TIME_ENTITY_SEARCH, - date_pattern=date_pattern, - group_id=group_id, - limit=limit) - return rows or [] - except Exception: - return [] - - -# 中英相对时间解析：today/昨天/上周/3天后 等简单归一化为日期 -def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: - t = str(text) if text is not None else "" - # 英文 today/yesterday/tomorrow - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - - # 英文 X days ago / in X days - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - - # 中文 今天/昨天/明天 - t = re.sub(r"今天", anchor.date().isoformat(), t) - t = re.sub(r"昨日|昨天", (anchor - timedelta(days=1)).date().isoformat(), t) - t = re.sub(r"明天", (anchor + timedelta(days=1)).date().isoformat(), t) - # 中文 X天前 / X天后 - t = re.sub(r"(\d+)天前", lambda m: (anchor - timedelta(days=int(m.group(1)))).date().isoformat(), t) - t = re.sub(r"(\d+)天后", lambda m: (anchor + timedelta(days=int(m.group(1)))).date().isoformat(), t) - # 中文 上周 / 下周（近似7天） - t = re.sub(r"上周", (anchor - timedelta(days=7)).date().isoformat(), t) - t = re.sub(r"下周", (anchor + timedelta(days=7)).date().isoformat(), t) - # 中文 月日（无年份）补全年份 - def _md_repl(m: re.Match[str]) -> str: - mon = int(m.group(1)); day = int(m.group(2)) - return f"{anchor.year}-{mon:02d}-{day:02d}" - t = re.sub(r"(\d{1,2})月(\d{1,2})日", _md_repl, t) - return t - - -async def run_longmemeval_test( - sample_size: int = 3, - group_id: str = "longmemeval_zh_bak_3", - search_limit: int = 8, - context_char_budget: int = 4000, - llm_temperature: float = 0.0, - llm_max_tokens: int = 16, - search_type: str = "hybrid", - data_path: str | None = None, - start_index: int = 0, - max_contexts_per_item: int = 2, - save_chunk_output: bool = True, - save_chunk_output_path: str | None = None, - reset_group_before_ingest: bool = False, - skip_ingest: bool = False, -) -> Dict[str, Any]: - """LongMemEval 评估测试：增强时间推理能力""" - - # 数据路径 - if not data_path: - # 固定使用中文数据集：data/longmemeval_oracle_zh.json - zh_proj = os.path.join(PROJECT_ROOT, "data", "longmemeval_oracle_zh.json") - zh_cwd = os.path.join(os.getcwd(), "data", "longmemeval_oracle_zh.json") - if os.path.exists(zh_proj): - data_path = zh_proj - elif os.path.exists(zh_cwd): - data_path = zh_cwd - else: - raise FileNotFoundError("未找到数据集: data/longmemeval_oracle_zh.json，请确保其存在于项目根目录或当前工作目录的 data 目录下。") - - qa_list: List[Dict[str, Any]] = load_dataset_any(data_path) - # 支持评估全部样本：当 sample_size <= 0 时，取从 start_index 到末尾 - if sample_size is None or sample_size <= 0: - items = qa_list[start_index:] - else: - items = qa_list[start_index:start_index + sample_size] - - # 可选：摄入上下文（默认启用） - if not skip_ingest: - # 选择上下文并限量 - contexts: List[str] = [] - for it in items: - built = build_context_from_sessions(it) - full_transcripts = [c for c in built if "\n" in c] - evidence_msgs = [c for c in built if "\n" not in c] - selected: List[str] = [] - take_e = min(len(evidence_msgs), max_contexts_per_item) - selected.extend(evidence_msgs[:take_e]) - remain = max_contexts_per_item - len(selected) - if remain > 0 and full_transcripts: - selected.extend(full_transcripts[:remain]) - if not selected and built: - selected.append(built[0]) - contexts.extend(selected) - - print(f"📥 摄入 {len(contexts)} 个上下文到数据库") - if reset_group_before_ingest and group_id: - try: - _tmp_conn = Neo4jConnector() - await _tmp_conn.delete_group(group_id) - print(f"🧹 已清空组 {group_id} 的历史图数据") - except Exception as _e: - print(f"⚠️ 清空组数据失败（忽略继续）: {group_id} - {_e}") - finally: - try: - await _tmp_conn.close() - except Exception: - pass - _ingest_fn = ingest_contexts_via_full_pipeline - if _ingest_fn is None: - print("⚠️ 摄入函数不可用，已跳过摄入。请确认 PYTHONPATH 包含 'src' 或从项目根运行。") - else: - await _ingest_fn( - contexts, - group_id, - save_chunk_output=save_chunk_output, - save_chunk_output_path=save_chunk_output_path, - ) - - # 初始化组件（摄入后再初始化连接器）- 使用异步LLM客户端 - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) - connector = Neo4jConnector() - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 指标收集 - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - per_query_context_counts: List[int] = [] - per_query_context_avg_tokens: List[float] = [] - per_query_context_chars: List[int] = [] - - type_correct: Dict[str, List[float]] = {} - type_f1: Dict[str, List[float]] = {} - type_jacc: Dict[str, List[float]] = {} - - samples: List[Dict[str, Any]] = [] - # 统计重复的上下文预览（跨样本），便于诊断"相同上下文"问题 - preview_counter: Dict[str, int] = {} - - try: - for item in items: - question = item.get("question", "") - reference = item.get("answer", "") - qtype = item.get("question_type") or item.get("type", "unknown") - - print(f"\n=== 处理问题: {question} ===") - - # 检测问题类型 - is_temporal = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 检索 - t0 = time.time() - contexts_all: List[str] = [] - dialogs, statements, entities = [], [], [] - - try: - if search_type == "embedding": - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - # for sm in summaries: - # summary_text = str(sm.get("summary", "")).strip() - # if summary_text: - # contexts_all.append(summary_text) - - # 实体摘要（最多3个） - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - search_results = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=search_limit, - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - if entities: - entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid（增强版：特别优化时间推理问题） - emb_chunks, emb_statements, emb_entities, emb_summaries, emb_dialogs = [], [], [], [], [] - kw_dialogs, kw_statements, kw_entities = [], [], [] - - # 1) 嵌入检索 - try: - emb_res = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - if isinstance(emb_res, dict): - emb_chunks = emb_res.get("chunks", []) or [] - emb_statements = emb_res.get("statements", []) or [] - emb_entities = emb_res.get("entities", []) or [] - emb_summaries = emb_res.get("summaries", []) or [] - emb_dialogs = emb_res.get("dialogues", []) or [] - except Exception as e: - print(f"⚠️ 嵌入检索失败，将继续进行关键词检索: {e}") - - # 2) 关键词检索（增强版） - try: - kw_res = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=search_limit, - ) - if isinstance(kw_res, dict): - kw_dialogs = kw_res.get("dialogues", []) or [] - kw_statements = kw_res.get("statements", []) or [] - kw_entities = kw_res.get("entities", []) or [] - - # 时间推理问题的特殊处理 - if is_temporal: - # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, group_id, search_limit//2) - if time_entities: - kw_entities.extend(time_entities) - # 添加时间相关关键词检索 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'first'] - for tk in time_keywords: - try: - time_res = await search_graph( - connector=connector, - q=tk, - group_id=group_id, - limit=2, - ) - if isinstance(time_res, dict): - kw_dialogs.extend(time_res.get("dialogues", []) or []) - kw_statements.extend(time_res.get("statements", []) or []) - except Exception: - pass - - # 中文关键词拆分后做别名匹配 - cn_tokens = _extract_cn_tokens(question) - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit) - if alias_entities: - kw_entities.extend(alias_entities) - - # 从对话/陈述中的 entity_ids 反查实体 - ids = [] - try: - for d in kw_dialogs: - ids.extend(d.get("entity_ids", []) or []) - for s in kw_statements: - ids.extend(s.get("entity_ids", []) or []) - except Exception: - pass - if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, group_id) - if id_entities: - kw_entities.extend(id_entities) - - # 多关键词检索 - try: - eng_words = [w for w in set(re.findall(r"\b\w+\b", question.lower())) if len(w) > 2] - kw_list = generate_query_keywords_cn(question)[:3] + eng_words[:1] - for kw in kw_list: - if not kw: - continue - sub_res = await search_graph( - connector=connector, - q=str(kw), - group_id=group_id, - limit=max(3, search_limit // 2), - ) - if isinstance(sub_res, dict): - kw_dialogs.extend(sub_res.get("dialogues", []) or []) - kw_statements.extend(sub_res.get("statements", []) or []) - kw_entities.extend(sub_res.get("entities", []) or []) - except Exception: - pass - - # 选项参与关键词检索 - try: - opt_list = extract_candidate_options(question)[:2] - for opt in opt_list: - if not opt: - continue - opt_res = await search_graph( - connector=connector, - q=str(opt), - group_id=group_id, - limit=max(3, search_limit // 2), - ) - if isinstance(opt_res, dict): - kw_dialogs.extend(opt_res.get("dialogues", []) or []) - kw_statements.extend(opt_res.get("statements", []) or []) - kw_entities.extend(opt_res.get("entities", []) or []) - except Exception: - pass - except Exception as e: - print(f"❌ 关键词检索失败: {e}") - - # 3) 合并、排序并去重 - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - def dedup(items: List[Dict[str, Any]], key_field: str = "uuid") -> List[Dict[str, Any]]: - seen = set() - out = [] - for it in items: - key = str(it.get(key_field, "")) + str(it.get("content", "") + str(it.get("statement", ""))) - if key not in seen: - out.append(it) - seen.add(key) - return out - - # 时间推理问题优先排序包含时间信息的文档 - if is_temporal: - def temporal_score(item: Dict[str, Any]) -> float: - base_score = float(item.get("score", 0.0)) - content = str(item.get("content", "") + str(item.get("statement", ""))) - time_entities = extract_time_entities(content) - time_bonus = len(time_entities) * 0.5 - return base_score + time_bonus - - dialogs = dedup(sorted(all_dialogs, key=temporal_score, reverse=True)) - statements = dedup(sorted(all_statements, key=temporal_score, reverse=True)) - else: - dialogs = dedup(sorted(all_dialogs, key=lambda d: float(d.get("score", 0.0)), reverse=True)) - statements = dedup(sorted(all_statements, key=lambda s: float(s.get("score", 0.0)), reverse=True)) - - entities = dedup(all_entities, key_field="name") - - # 4) 构建上下文 - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体摘要 - try: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - except Exception: - pass - - # 全局回退 - if not contexts_all and search_type in ("embedding", "hybrid"): - try: - print("🔁 检索为空，回退到关键词检索...") - kw_fallback = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=max(search_limit, 5), - ) - fb_dialogs = kw_fallback.get("dialogues", []) or [] - fb_statements = kw_fallback.get("statements", []) or [] - fb_entities = kw_fallback.get("entities", []) or [] - - for d in fb_dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in fb_statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - if fb_entities: - entity_names = [str(e.get("name", "")).strip() for e in fb_entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - dialogs = fb_dialogs if fb_dialogs else dialogs - statements = fb_statements if fb_statements else statements - entities = fb_entities if fb_entities else entities - print(f"↩️ 回退到关键词检索: {len(fb_dialogs)} 对话, {len(fb_statements)} 条陈述, {len(fb_entities)} 个实体") - except Exception as fe: - print(f"❌ 关键词回退失败: {fe}") - - ent_count = len(entities) if isinstance(entities, list) else 0 - print(f"✅ {search_type}检索成功: {len(dialogs)} 对话, {len(statements)} 条陈述, {ent_count} 个实体") - if is_temporal: - print("⏰ 检测为时间推理问题，已启用时间优化检索") - - except Exception as e: - print(f"❌ {search_type}检索失败: {e}") - contexts_all = [] - - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 智能上下文选择 - context_text = "" - if contexts_all: - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) - # 相对时间解析 - try: - context_text = _resolve_relative_times_cn_en(context_text, anchor=datetime.now()) - except Exception: - pass - # 诊断信息 - try: - cn_diag = generate_query_keywords_cn(question)[:3] - opts = extract_candidate_options(question)[:2] - qlw = [w for w in set(re.findall(r'\b\w+\b', question.lower())) if len(w) > 2][:1] - diag_tokens: List[str] = [] - for t in cn_diag + opts + qlw: - if t and t not in diag_tokens: - diag_tokens.append(t) - print(f"🔍 关键词/选项: {', '.join(diag_tokens)}") - preview = context_text[:200].replace('\n', ' ') - print(f"🔎 上下文预览: {preview}...") - key_preview = preview.strip() - if key_preview: - preview_counter[key_preview] = preview_counter.get(key_preview, 0) + 1 - except Exception: - pass - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # 记录上下文诊断信息 - per_query_context_counts.append(len(contexts_all)) - per_query_context_avg_tokens.append(avg_context_tokens([context_text])) - per_query_context_chars.append(len(context_text)) - - # LLM 推理（增强时间推理提示） - options = extract_candidate_options(question) - if len(options) >= 2: - opt_lines = "\n".join(f"- {o}" for o in options) - # 时间推理问题的特殊提示 - if is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "Return ONLY one string: exactly one option from the provided candidates. If the context is insufficient, respond with 'Unknown'. " - "Pay special attention to date sequences and time intervals." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. Return ONLY one string: exactly one option from the provided candidates. " - "If the context is insufficient, respond with 'Unknown'. If the context expresses a synonym or paraphrase of a candidate, return the closest candidate. " - "Do not include explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": ( - f"Question: {question}\n\nCandidates:\n{opt_lines}\n\nContext:\n{context_text}\n\nReturn EXACTLY one candidate string (or 'Unknown')." - ), - }, - ] - else: - # 时间推理问题的特殊提示 - if is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "If the context contains the answer, return a concise answer phrase focusing on temporal information. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. If the context contains the answer, return a concise answer phrase. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}\n\nReturn ONLY the answer (or 'Unknown').", - }, - ] - - t2 = time.time() - # 使用异步调用 - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - - # 兼容不同的响应格式 - pred_raw = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - - # 选项题输出规范化 - pred = pred_raw - if len(options) >= 2 and not pred_raw.lower().startswith("unknown"): - def _basic_norm(s: str) -> str: - s = s.lower().strip() - return re.sub(r"[^\w\s]", " ", s) - def _jaccard(a: str, b: str) -> float: - ta = set(t for t in _basic_norm(a).split() if t) - tb = set(t for t in _basic_norm(b).split() if t) - if not ta and not tb: - return 1.0 - if not ta or not tb: - return 0.0 - return len(ta & tb) / len(ta | tb) - best = None - best_score = -1.0 - for o in options: - score = _jaccard(pred_raw, o) - if score > best_score: - best = o - best_score = score - if best is not None and best_score > 0.0: - pred = best - - # 指标 - flag = exact_match(pred, reference) - f1_val = common_f1(str(pred), str(reference)) - j_val = jaccard(str(pred), str(reference)) - - type_correct.setdefault(qtype, []).append(flag) - type_f1.setdefault(qtype, []).append(f1_val) - type_jacc.setdefault(qtype, []).append(j_val) - - samples.append({ - "question": question, - "prediction": pred, - "answer": reference, - "question_type": qtype, - "is_temporal": is_temporal, - "question_id": item.get("question_id"), - "options": options, - "context_count": len(contexts_all), - "context_chars": len(context_text), - "retrieved_dialogue_count": len(dialogs), - "retrieved_statement_count": len(statements), - "metrics": { - "exact_match": bool(flag), - "f1": f1_val, - "jaccard": j_val - }, - "timing": { - "search_ms": (t1 - t0) * 1000, - "llm_ms": (t3 - t2) * 1000 - } - }) - - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {reference}") - print(f"📈 当前指标 - Exact Match: {flag}, F1: {f1_val:.3f}, Jaccard: {j_val:.3f}") - - # 聚合结果 - type_acc = {t: (sum(v) / max(len(v), 1)) for t, v in type_correct.items()} - f1_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_f1.items()} - jacc_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_jacc.items()} - - result = { - "dataset": "longmemeval", - "items": len(items), - "accuracy_by_type": type_acc, - "f1_by_type": f1_by_type, - "jaccard_by_type": jacc_by_type, - "samples": samples, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "context": { - "avg_tokens": statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0, - "avg_chars": statistics.mean(per_query_context_chars) if per_query_context_chars else 0.0, - "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, - }, - "params": { - "group_id": group_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "search_type": search_type, - "llm_id": SELECTED_LLM_ID, - "embedding_id": SELECTED_EMBEDDING_ID, - "sample_size": sample_size, - "start_index": start_index, - }, - "timestamp": datetime.now().isoformat() - } - - # 计算汇总指标 - try: - total_items = max(len(samples), 1) - correct_count = sum(1 for s in samples if s.get("metrics", {}).get("exact_match")) - score_accuracy = (correct_count / total_items) * 100.0 - - total_latencies_ms = [] - for s in samples: - t = s.get("timing", {}) - total_latencies_ms.append(float(t.get("search_ms", 0.0)) + float(t.get("llm_ms", 0.0))) - total_lat_stats = latency_stats(total_latencies_ms) if total_latencies_ms else {"p50": 0.0, "iqr": 0.0} - latency_median_s = total_lat_stats.get("p50", 0.0) / 1000.0 - latency_iqr_s = total_lat_stats.get("iqr", 0.0) / 1000.0 - - avg_ctx_tokens = statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0 - avg_ctx_tokens_k = avg_ctx_tokens / 1000.0 - - result["metric_summary"] = { - "score_accuracy": score_accuracy, - "latency_median_s": latency_median_s, - "latency_iqr_s": latency_iqr_s, - "avg_context_tokens_k": avg_ctx_tokens_k, - } - except Exception: - result["metric_summary"] = { - "score_accuracy": 0.0, - "latency_median_s": 0.0, - "latency_iqr_s": 0.0, - "avg_context_tokens_k": 0.0, - } - - # 诊断信息 - try: - dups = sorted([(k, c) for k, c in preview_counter.items() if c > 1], key=lambda x: -x[1])[:5] - result["diagnostics"] = { - "duplicate_previews_top": [{"count": c, "preview": k[:120]} for k, c in dups], - "unique_preview_count": len(preview_counter), - } - except Exception: - pass - - return result - - finally: - await connector.close() - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="LongMemEval 评估测试脚本（增强时间推理版）") - parser.add_argument("--sample-size", type=int, default=3, help="样本数量（<=0 表示全部）") - parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") - parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--group-id", type=str, default="longmemeval_zh_bak_3", help="图数据库 Group ID") - parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=16, help="LLM 最大输出 token") - parser.add_argument("--search-type", type=str, default="hybrid", choices=["embedding","keyword","hybrid"], help="检索类型") - parser.add_argument("--data-path", type=str, default=None, help="数据集路径") - parser.add_argument("--max-contexts-per-item", type=int, default=2, help="每条样本最多摄入的上下文段数") - parser.add_argument("--no-save-chunk-output", action="store_true", help="不保存分块结果（默认保存）") - parser.add_argument("--save-chunk-output-path", type=str, default=None, help="自定义分块输出路径") - parser.add_argument("--reset-group-before-ingest", action="store_true", help="摄入前清空该 Group 在图数据库中的历史数据") - parser.add_argument("--skip-ingest", action="store_true", help="跳过摄入，仅检索评估") - args = parser.parse_args() - - sample_size = 0 if args.all else args.sample_size - - result = asyncio.run( - run_longmemeval_test( - sample_size=sample_size, - group_id=args.group_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - data_path=args.data_path, - start_index=args.start_index, - max_contexts_per_item=args.max_contexts_per_item, - save_chunk_output=(not args.no_save_chunk_output), - save_chunk_output_path=args.save_chunk_output_path, - reset_group_before_ingest=args.reset_group_before_ingest, - skip_ingest=args.skip_ingest, - ) - ) - - # 打印结果 - print("\n" + "="*50) - print("📊 LongMemEval 测试结果:") - print(f" 样本数量: {result['items']}") - - if result['accuracy_by_type']: - print("\n📈 按问题类型细分:") - for qtype, acc in result['accuracy_by_type'].items(): - print(f" {qtype}:") - print(f" Score (Accuracy): {acc:.3f}") - - print(f"\n📊 指标总览:") - ms = result.get('metric_summary', {}) - print(f" Score (Accuracy): {ms.get('score_accuracy', 0.0):.1f}%") - print(f" Latency (s): median {ms.get('latency_median_s', 0.0):.3f}s") - print(f" Latency IQR (s): {ms.get('latency_iqr_s', 0.0):.3f}s") - print(f" Avg Context Tokens (k): {ms.get('avg_context_tokens_k', 0.0):.3f}k") - - print(f"\n⏱️ 细分性能指标:") - print(f" 检索延迟(均值): {result['latency']['search']['mean']:.1f}ms") - print(f" LLM延迟(均值): {result['latency']['llm']['mean']:.1f}ms") - print(f" 上下文长度(均值): {result['context']['avg_chars']:.0f} 字符") - - - # 保存结果到文件 - try: - out_dir = os.path.join(PROJECT_ROOT, "evaluation", "longmemeval", "results") - os.makedirs(out_dir, exist_ok=True) - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = os.path.join(out_dir, f"longmemeval_{result['params']['search_type']}_{ts}.json") - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n💾 结果已保存: {out_path}") - except Exception as e: - print(f"⚠️ 结果保存失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/longmemeval/test_eval.py b/api/app/core/memory/evaluation/longmemeval/test_eval.py deleted file mode 100644 index 08a763e3..00000000 --- a/api/app/core/memory/evaluation/longmemeval/test_eval.py +++ /dev/null @@ -1,1330 +0,0 @@ -import argparse -import asyncio -import json -import os -import re -import statistics -import time -from datetime import datetime, timedelta -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -# 与现有评估脚本保持一致的导入方式 -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.common.metrics import f1_score as common_f1 -from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService - -try: - from app.core.memory.evaluation.common.metrics import exact_match -except Exception: - # 兜底：简单的大小写不敏感比较 - def exact_match(pred: str, ref: str) -> bool: - return str(pred).strip().lower() == str(ref).strip().lower() - - -def load_dataset_any(path: str) -> List[Dict[str, Any]]: - """健壮地加载数据集（兼容 list 或多段 JSON）。""" - with open(path, "r", encoding="utf-8") as f: - s = f.read().strip() - try: - obj = json.loads(s) - if isinstance(obj, list): - return obj - elif isinstance(obj, dict): - return [obj] - except json.JSONDecodeError: - pass - dec = json.JSONDecoder() - idx = 0 - items: List[Dict[str, Any]] = [] - while idx < len(s): - while idx < len(s) and s[idx].isspace(): - idx += 1 - if idx >= len(s): - break - try: - obj, end = dec.raw_decode(s, idx) - if isinstance(obj, list): - for it in obj: - if isinstance(it, dict): - items.append(it) - elif isinstance(obj, dict): - items.append(obj) - idx = end - except json.JSONDecodeError: - nl = s.find("\n", idx) - if nl == -1: - break - idx = nl + 1 - return items - - -def is_chinese_text(s: str) -> bool: - return bool(re.search(r"[\u4e00-\u9fff]", s or "")) - - -def extract_candidate_options(question: str) -> List[str]: - """从问题中提取候选选项（A-or-B 类问题）。""" - q = (question or "").strip() - options: List[str] = [] - - # 1) 引号包裹的片段 - for pat in [r"'([^']+)'", r'\"([^\"]+)\"', r'“([^”]+)”', r'‘([^’]+)’']: - for m in re.findall(pat, q): - val = (m or "").strip() - if val: - options.append(val) - - # 2) or/还是/或者 连接词 - if len(options) < 2: - pats = [ - r"([^,;，；]+?)\s+or\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+还是\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+或者\s+([^,;，；\?\.!.。！]+)", - ] - for pat in pats: - matches = list(re.finditer(pat, q, flags=re.IGNORECASE)) - if matches: - m = matches[-1] - cand1 = m.group(1).strip().strip("?？.,，;； ") - cand2 = m.group(2).strip().strip("?？.,，;； ") - options.extend([cand1, cand2]) - break - - # 去重 - seen = set() - uniq: List[str] = [] - for o in options: - o2 = o.strip() - key = o2.lower() if not is_chinese_text(o2) else o2 - if o2 and key not in seen: - uniq.append(o2) - seen.add(key) - return uniq - - -def extract_time_entities(text: str) -> List[Dict[str, Any]]: - """增强时间实体提取，专门用于时间推理问题""" - time_entities = [] - - # 日期模式 - date_patterns = [ - (r'\b(\d{4})-(\d{1,2})-(\d{1,2})\b', 'date'), # YYYY-MM-DD - (r'\b(\d{1,2})月(\d{1,2})日\b', 'date'), # 中文日期 - (r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份 - (r'\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份缩写 - ] - - # 时间间隔模式 - duration_patterns = [ - (r'(\d+)\s*天', 'days'), - (r'(\d+)\s*周', 'weeks'), - (r'(\d+)\s*个月', 'months'), - (r'(\d+)\s*年', 'years'), - (r'(\d+)\s*days?', 'days'), - (r'(\d+)\s*weeks?', 'weeks'), - (r'(\d+)\s*months?', 'months'), - (r'(\d+)\s*years?', 'years'), - ] - - # 事件时间关系模式 - temporal_relation_patterns = [ - (r'(之前|以前|前)\s*(\d+)\s*天', 'days_before'), - (r'(之后|以后|后)\s*(\d+)\s*天', 'days_after'), - (r'(\d+)\s*天\s*(之前|以前|前)', 'days_before'), - (r'(\d+)\s*天\s*(之后|以后|后)', 'days_after'), - (r'(\d+)\s*days?\s*(before|ago)', 'days_before'), - (r'(\d+)\s*days?\s*(after|later)', 'days_after'), - ] - - # 提取日期 - for pattern, entity_type in date_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间间隔 - for pattern, entity_type in duration_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间关系 - for pattern, entity_type in temporal_relation_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(2)) if match.groups() >= 2 else int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - return time_entities - - -def calculate_time_difference(date1: str, date2: str) -> int: - """计算两个日期之间的天数差""" - try: - # 解析日期格式 - def parse_date(date_str: str) -> datetime: - # 尝试多种日期格式 - formats = [ - '%Y-%m-%d', - '%m月%d日', - '%B %d, %Y', - '%b %d, %Y', - '%Y年%m月%d日' - ] - - for fmt in formats: - try: - return datetime.strptime(date_str, fmt) - except ValueError: - continue - - # 如果都无法解析，返回当前日期 - return datetime.now() - - d1 = parse_date(date1) - d2 = parse_date(date2) - - # 计算天数差（绝对值） - return abs((d2 - d1).days) - except Exception: - return -1 # 表示计算失败 - - -def _extract_cn_tokens(text: str) -> List[str]: - """中文关键词提取（短语级，含数词/日期/常见领域词）""" - if not text: - return [] - t = str(text) - # 去掉常见功能词（粗略，不依赖分词库） - stop_words = [ - "我","我们","你","他","她","它","这","那","哪","一个","一次","一些","什么","怎么","是否","吗","呢", - "很","更","最","已经","正在","将要","马上","尽快","最近","关于","有关","以及","并且","或者","还是", - "因为","所以","如果","但是","而且","然后","之后","之前","同时","另外","并","但","却","被","把","让","给", - "和","与","跟","及","还有","就","都","在","对","对于","的","了","着","过","到","于","从","以","为","向","至","是" - ] - for sw in stop_words: - t = t.replace(sw, " ") - # 去标点 - t = re.sub(r"[，。！？、；：,.!?;:\"'（）()［］\[\]\-—…·]", " ", t) - # 基础中文片段（>=2） - base = re.findall(r"[\u4e00-\u9fff]{2,}", t) - # 特殊组合：第X次XXXX - specials = re.findall(r"第[一二三四五六七八九十]+次[\u4e00-\u9fff]{2,6}", text) - # 日期与数字 - dates = re.findall(r"\d{4}年\d{1,2}月\d{1,2}日|\d{1,2}月\d{1,2}日|\d{4}-\d{1,2}-\d{1,2}", text) - numbers = re.findall(r"\b\d+\b", text) - - generic = {"建议","推荐","帮助","提升","技能","有效","团队","参与度","喜欢","开始"} - tokens: List[str] = specials + base + dates + numbers - uniq: List[str] = [] - seen = set() - for tok in tokens: - tok2 = tok.strip() - if len(tok2) < 2 or len(tok2) > 6: - continue - if tok2 in generic: - continue - if tok2 not in seen: - uniq.append(tok2) - seen.add(tok2) - # 排除常见疑问型短语 - blacklist_exact = {"是什么","多少","多少天","哪个","哪些","之间","先","后","之前","之后"} - uniq2: List[str] = [u for u in uniq if u not in blacklist_exact] - return uniq2[:12] - - -def generate_query_keywords_cn(question: str) -> List[str]: - """增强版关键词提取，特别关注技术术语和专有名词""" - if not question: - return [] - - # 提取专有名词（带引号的内容） - quoted_terms = re.findall(r'["""]([^"""]+)["""]', question) - - # 提取技术术语（中英文混合） - tech_terms = re.findall(r'[A-Z][a-zA-Z]+\s+[A-Z][a-zA-Z]+|[A-Za-z]+[\u4e00-\u9fff]+|[\u4e00-\u9fff]+[A-Za-z]+', question) - - # 提取核心名词短语 - core_nouns = re.findall(r'[\u4e00-\u9fff]{2,5}系统|[\u4e00-\u9fff]{2,5}管理|[\u4e00-\u9fff]{2,5}分析|[\u4e00-\u9fff]{2,5}工作坊|[\u4e00-\u9fff]{2,5}研讨会', question) - - # 基础中文片段 - base_tokens = _extract_cn_tokens(question) - - # 特定领域关键词增强 - domain_keywords = [] - # GPS相关 - if any(term in question for term in ["GPS", "导航", "定位系统", "系统运行"]): - domain_keywords.extend(["GPS", "导航系统", "定位", "系统故障", "功能异常"]) - # 活动相关 - if any(term in question for term in ["工作坊", "研讨会", "网络研讨会", "活动"]): - domain_keywords.extend(["工作坊", "研讨会", "参加", "参与", "活动"]) - # 时间顺序相关 - if any(term in question for term in ["先", "后", "第一个", "之前", "首先"]): - domain_keywords.extend(["先", "后", "之前", "之后", "第一次", "首先"]) - # 设备相关 - if any(term in question for term in ["设备", "手机", "电脑", "笔记本电脑"]): - domain_keywords.extend(["设备", "手机", "电脑", "笔记本电脑", "购买"]) - - # 合并并去重 - all_tokens = quoted_terms + tech_terms + core_nouns + base_tokens + domain_keywords - seen = set() - final_tokens = [] - - for token in all_tokens: - token = token.strip() - if len(token) >= 2 and token not in seen: - final_tokens.append(token) - seen.add(token) - - return final_tokens[:8] - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """增强版上下文选择：特别优化技术术语和精确匹配""" - if not contexts: - return "" - - # 检测是否为时间推理问题 - is_temporal_question = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 提取时间实体从问题中 - question_time_entities = extract_time_entities(question) - - # 提取关键技术实体 - key_entities = [] - # GPS相关 - if any(term in question for term in ["GPS", "导航", "定位系统", "系统运行"]): - key_entities.extend(["GPS", "导航", "定位", "系统", "功能", "问题", "故障"]) - # 活动相关 - if any(term in question for term in ["工作坊", "研讨会", "网络研讨会", "活动"]): - key_entities.extend(["工作坊", "研讨会", "参加", "参与", "活动", "时间"]) - # 时间顺序相关 - if any(term in question for term in ["先", "后", "第一个", "之前", "首先"]): - key_entities.extend(["先", "后", "之前", "之后", "第一次", "首先"]) - - # 英文关键词（去停用词） - question_lower = question.lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but','many','which','first' - } - eng_words = [w for w in set(re.findall(r'\b\w+\b', question_lower)) - if w not in stop_words and len(w) > 2] - - # 中文片段与候选选项 - cn_tokens = generate_query_keywords_cn(question) - options = extract_candidate_options(question) - - # 时间推理问题的特殊处理 - if is_temporal_question: - # 为时间问题添加时间相关关键词 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'days', 'first', '先后'] - eng_words = [w for w in eng_words if w not in ['days', 'first']] # 避免重复 - cn_tokens.extend([kw for kw in time_keywords if kw not in cn_tokens]) - - # 限制关键词数量，优先时间相关 - tokens = time_keywords[:2] + key_entities[:3] + cn_tokens[:2] + eng_words[:1] + options[:1] - else: - # 常规问题处理，优先关键技术实体 - tokens = key_entities[:4] + cn_tokens[:3] + options[:2] + eng_words[:1] - - # 去重 - seen = set() - final_tokens: List[str] = [] - for t in tokens: - t2 = t.strip() - if t2 and t2 not in seen: - final_tokens.append(t2) - seen.add(t2) - - scored_contexts: List[tuple[float, str]] = [] - - # 关键技术实体权重映射 - key_entity_weights = { - "GPS": 3.0, "导航": 2.5, "系统": 2.0, "功能": 2.0, "问题": 2.0, "故障": 2.5, - "工作坊": 2.5, "研讨会": 2.5, "参加": 2.0, "参与": 2.0, - "先": 2.0, "后": 2.0, "之前": 2.0, "之后": 2.0, "第一次": 2.5 - } - - # 时间推理问题的权重映射 - temporal_weight_map = { - "天": 2.0, "日": 2.0, "月": 1.8, "年": 1.8, "days": 2.0, - "before": 1.5, "after": 1.5, "first": 1.5, "先后": 1.5 - } - - # 常规问题的权重映射 - normal_weight_map = { - "问题": 2.0, "故障": 2.0, "异常": 1.8, "不正常": 1.8, "坏了": 1.8, - "系统": 1.3, "GPS": 1.5, "保养": 1.4, "设备": 1.2, "模块": 1.2, "功能": 1.1 - } - - # 合并权重映射 - weight_map = {**normal_weight_map, **temporal_weight_map, **key_entity_weights} - - for i, context in enumerate(contexts): - context_str = str(context) - lines = re.split(r'[\r\n]+', context_str) - hit_lines: List[str] = [] - kw_hits: float = 0.0 - time_entity_count = 0 - key_entity_hits = 0 - - for line in lines: - ln = line.strip() - if not ln: - continue - - has_keyword = False - # 关键词匹配 - for tok in final_tokens: - if tok and tok in ln: - w = weight_map.get(tok, 1.0) - hit_count = ln.count(tok) - kw_hits += hit_count * w - # 关键技术实体额外奖励 - if tok in key_entity_weights: - key_entity_hits += hit_count - has_keyword = True - - # 时间实体检测（特别针对时间推理问题） - if is_temporal_question: - time_entities = extract_time_entities(ln) - time_entity_count += len(time_entities) - if time_entities: - has_keyword = True - - # 精确匹配奖励（完整问题关键词出现在上下文中） - for q_word in question.split(): - if len(q_word) > 3 and q_word in ln: - kw_hits += 0.5 # 精确匹配奖励 - - if has_keyword: - # 对于包含关键信息的行，保留完整行 - hit_lines.append(ln) - - snippet = "\n".join(hit_lines) if hit_lines else context_str.strip() - - # 限制单段长度，但对包含关键信息的上下文稍微放宽限制 - max_snippet_len = 600 if (key_entity_hits > 0 or time_entity_count > 0) else 500 - if len(snippet) > max_snippet_len: - snippet = snippet[:max_snippet_len] - - # 评分逻辑 - has_number = 1 if re.search(r'\d', snippet) else 0 - has_date = 1 if (re.search(r'\b\d{4}-\d{1,2}-\d{1,2}\b', snippet) or - re.search(r'\d{1,2}月\d{1,2}日', snippet)) else 0 - - # 关键技术实体奖励 - key_entity_bonus = key_entity_hits * 1.0 - - # 时间推理问题的特殊评分 - if is_temporal_question: - time_bonus = time_entity_count * 2.0 # 时间实体奖励 - temporal_coherence = 3 if (has_date and time_entity_count >= 2) else 0 - else: - time_bonus = 0 - temporal_coherence = 0 - - length_bonus = 5 if 50 < len(snippet) < 1000 else (2 if len(snippet) >= 1000 else 0) - pos_bonus = 3 if i < 3 else 0 - - score = (kw_hits * 0.8 + (has_number + has_date) * 1.5 + - length_bonus + pos_bonus + time_bonus + temporal_coherence + key_entity_bonus) - - scored_contexts.append((score, snippet)) - - # 选择累计至总字符预算 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - selected: List[str] = [] - total_chars = 0 - - for score, snippet in scored_contexts: - if total_chars + len(snippet) <= max_chars: - selected.append(snippet) - total_chars += len(snippet) - else: - if not selected and len(snippet) > max_chars: - selected.append(snippet[:max_chars]) - break - - final_context = "\n\n".join(selected) - - # 对于时间推理问题，添加时间计算提示 - if is_temporal_question and question_time_entities: - time_prompt = "\n\n[时间推理提示：请仔细分析上述上下文中的日期和时间关系，计算时间间隔或确定事件顺序]" - if total_chars + len(time_prompt) <= max_chars: - final_context += time_prompt - - return final_context - - -# 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]: - results: List[Dict[str, Any]] = [] - try: - for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit) - if rows: - results.extend(rows) - except Exception: - pass - - # 按 name 去重 - deduped: List[Dict[str, Any]] = [] - seen = set() - for r in results: - k = str(r.get("name", "")) - if k and k not in seen: - deduped.append(r) - seen.add(k) - return deduped - - -# 通过对话/陈述中的entity_ids反查实体名称 -_FETCH_ENTITIES_BY_IDS = """ -MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type -""" - -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]: - if not ids: - return [] - try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id) - return rows or [] - except Exception: - return [] - - -# 增强的时间实体检索 -_TIME_ENTITY_SEARCH = """ -MATCH (e:ExtractedEntity) -WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type -LIMIT $limit -""" - -async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: - """专门搜索时间相关的实体""" - try: - date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" - rows = await connector.execute_query(_TIME_ENTITY_SEARCH, - date_pattern=date_pattern, - group_id=group_id, - limit=limit) - return rows or [] - except Exception: - return [] - - -# 技术术语专门检索 -async def _search_tech_terms(connector: Neo4jConnector, question: str, group_id: str | None, limit: int = 3) -> List[Dict[str, Any]]: - """专门搜索技术术语相关的实体""" - tech_entities = [] - try: - # GPS相关 - if any(term in question for term in ["GPS", "导航", "定位系统"]): - gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", group_id=group_id, limit=limit) - if gps_rows: - tech_entities.extend(gps_rows) - - # 活动相关 - if any(term in question for term in ["工作坊", "研讨会", "网络研讨会"]): - workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", group_id=group_id, limit=limit) - if workshop_rows: - tech_entities.extend(workshop_rows) - - # 时间顺序相关 - if any(term in question for term in ["先", "后", "第一个"]): - time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", group_id=group_id, limit=limit) - if time_rows: - tech_entities.extend(time_rows) - - except Exception: - pass - - return tech_entities - - -# 中英相对时间解析：today/昨天/上周/3天后 等简单归一化为日期 -def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: - t = str(text) if text is not None else "" - # 英文 today/yesterday/tomorrow - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - - # 英文 X days ago / in X days - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - - # 中文 今天/昨天/明天 - t = re.sub(r"今天", anchor.date().isoformat(), t) - t = re.sub(r"昨日|昨天", (anchor - timedelta(days=1)).date().isoformat(), t) - t = re.sub(r"明天", (anchor + timedelta(days=1)).date().isoformat(), t) - # 中文 X天前 / X天后 - t = re.sub(r"(\d+)天前", lambda m: (anchor - timedelta(days=int(m.group(1)))).date().isoformat(), t) - t = re.sub(r"(\d+)天后", lambda m: (anchor + timedelta(days=int(m.group(1)))).date().isoformat(), t) - # 中文 上周 / 下周（近似7天） - t = re.sub(r"上周", (anchor - timedelta(days=7)).date().isoformat(), t) - t = re.sub(r"下周", (anchor + timedelta(days=7)).date().isoformat(), t) - # 中文 月日（无年份）补全年份 - def _md_repl(m: re.Match[str]) -> str: - mon = int(m.group(1)); day = int(m.group(2)) - return f"{anchor.year}-{mon:02d}-{day:02d}" - t = re.sub(r"(\d{1,2})月(\d{1,2})日", _md_repl, t) - return t - - -async def run_longmemeval_test( - sample_size: int = 3, - group_id: str = "longmemeval_zh_bak_2", - search_limit: int = 8, - context_char_budget: int = 4000, - llm_temperature: float = 0.0, - llm_max_tokens: int = 16, - search_type: str = "hybrid", - data_path: str | None = None, - start_index: int = 0, -) -> Dict[str, Any]: - """LongMemEval 评估测试：增强技术术语检索能力""" - - # 数据路径 - if not data_path: - # 固定使用中文数据集：data/longmemeval_oracle_zh.json - zh_proj = os.path.join(PROJECT_ROOT, "data", "longmemeval_oracle_zh.json") - zh_cwd = os.path.join(os.getcwd(), "data", "longmemeval_oracle_zh.json") - if os.path.exists(zh_proj): - data_path = zh_proj - elif os.path.exists(zh_cwd): - data_path = zh_cwd - else: - raise FileNotFoundError("未找到数据集: data/longmemeval_oracle_zh.json，请确保其存在于项目根目录或当前工作目录的 data 目录下。") - - qa_list: List[Dict[str, Any]] = load_dataset_any(data_path) - # 支持评估全部样本：当 sample_size <= 0 时，取从 start_index 到末尾 - if sample_size is None or sample_size <= 0: - items = qa_list[start_index:] - else: - items = qa_list[start_index:start_index + sample_size] - - # 初始化组件 - 使用异步LLM客户端 - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) - connector = Neo4jConnector() - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 指标收集 - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - per_query_context_counts: List[int] = [] - per_query_context_avg_tokens: List[float] = [] - per_query_context_chars: List[int] = [] - - type_correct: Dict[str, List[float]] = {} - type_f1: Dict[str, List[float]] = {} - type_jacc: Dict[str, List[float]] = {} - - samples: List[Dict[str, Any]] = [] - # 统计重复的上下文预览（跨样本），便于诊断"相同上下文"问题 - preview_counter: Dict[str, int] = {} - - try: - for item in items: - question = item.get("question", "") - reference = item.get("answer", "") - qtype = item.get("question_type") or item.get("type", "unknown") - - print(f"\n=== 处理问题: {question} ===") - - # 检测问题类型 - is_temporal = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 检索 - t0 = time.time() - contexts_all: List[str] = [] - dialogs, statements, entities = [], [], [] - - try: - if search_type == "embedding": - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["dialogues", "statements", "entities"], - ) - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体摘要（最多3个） - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - search_results = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=search_limit, - ) - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - if entities: - entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid（增强版：特别优化技术术语检索） - emb_dialogs, emb_statements, emb_entities = [], [], [] - kw_dialogs, kw_statements, kw_entities = [], [], [] - - # 1) 嵌入检索 - try: - emb_res = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["dialogues", "statements", "entities"], - ) - if isinstance(emb_res, dict): - emb_dialogs = emb_res.get("dialogues", []) or [] - emb_statements = emb_res.get("statements", []) or [] - emb_entities = emb_res.get("entities", []) or [] - except Exception as e: - print(f"⚠️ 嵌入检索失败，将继续进行关键词检索: {e}") - - # 2) 关键词检索（增强版） - try: - kw_res = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=search_limit, - ) - if isinstance(kw_res, dict): - kw_dialogs = kw_res.get("dialogues", []) or [] - kw_statements = kw_res.get("statements", []) or [] - kw_entities = kw_res.get("entities", []) or [] - - # 技术术语专门检索 - tech_entities = await _search_tech_terms(connector, question, group_id, search_limit//2) - if tech_entities: - kw_entities.extend(tech_entities) - - # 时间推理问题的特殊处理 - if is_temporal: - # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, group_id, search_limit//2) - if time_entities: - kw_entities.extend(time_entities) - # 添加时间相关关键词检索 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'first'] - for tk in time_keywords: - try: - time_res = await search_graph( - connector=connector, - q=tk, - group_id=group_id, - limit=2, - ) - if isinstance(time_res, dict): - kw_dialogs.extend(time_res.get("dialogues", []) or []) - kw_statements.extend(time_res.get("statements", []) or []) - except Exception: - pass - - # 中文关键词拆分后做别名匹配 - cn_tokens = generate_query_keywords_cn(question) # 使用增强版关键词提取 - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit) - if alias_entities: - kw_entities.extend(alias_entities) - - # 从对话/陈述中的 entity_ids 反查实体 - ids = [] - try: - for d in kw_dialogs: - ids.extend(d.get("entity_ids", []) or []) - for s in kw_statements: - ids.extend(s.get("entity_ids", []) or []) - except Exception: - pass - if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, group_id) - if id_entities: - kw_entities.extend(id_entities) - - # 多关键词检索（使用增强版关键词） - try: - eng_words = [w for w in set(re.findall(r"\b\w+\b", question.lower())) if len(w) > 2] - kw_list = generate_query_keywords_cn(question)[:4] # 使用更多关键词 - for kw in kw_list: - if not kw: - continue - sub_res = await search_graph( - connector=connector, - q=str(kw), - group_id=group_id, - limit=max(3, search_limit // 2), - ) - if isinstance(sub_res, dict): - kw_dialogs.extend(sub_res.get("dialogues", []) or []) - kw_statements.extend(sub_res.get("statements", []) or []) - kw_entities.extend(sub_res.get("entities", []) or []) - except Exception: - pass - - # 选项参与关键词检索 - try: - opt_list = extract_candidate_options(question)[:2] - for opt in opt_list: - if not opt: - continue - opt_res = await search_graph( - connector=connector, - q=str(opt), - group_id=group_id, - limit=max(3, search_limit // 2), - ) - if isinstance(opt_res, dict): - kw_dialogs.extend(opt_res.get("dialogues", []) or []) - kw_statements.extend(opt_res.get("statements", []) or []) - kw_entities.extend(opt_res.get("entities", []) or []) - except Exception: - pass - except Exception as e: - print(f"❌ 关键词检索失败: {e}") - - # 3) 合并、排序并去重 - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - def dedup(items: List[Dict[str, Any]], key_field: str = "uuid") -> List[Dict[str, Any]]: - seen = set() - out = [] - for it in items: - key = str(it.get(key_field, "")) + str(it.get("content", "") + str(it.get("statement", ""))) - if key not in seen: - out.append(it) - seen.add(key) - return out - - # 关键技术实体优先排序 - def enhanced_score(item: Dict[str, Any]) -> float: - score_val = item.get("score", 0.0) - base_score = float(score_val) if score_val is not None else 0.0 - content = str(item.get("content", "") + str(item.get("statement", ""))) - - # 关键技术实体奖励 - key_entities = [] - if any(term in question for term in ["GPS", "导航", "系统"]): - key_entities.extend(["GPS", "导航", "系统", "功能"]) - if any(term in question for term in ["工作坊", "研讨会", "活动"]): - key_entities.extend(["工作坊", "研讨会", "参加"]) - - key_bonus = 0 - for key_ent in key_entities: - if key_ent in content: - key_bonus += 1.0 - - # 时间实体奖励 - time_bonus = 0 - if is_temporal: - time_entities = extract_time_entities(content) - time_bonus = len(time_entities) * 0.5 - - return base_score + key_bonus + time_bonus - - dialogs = dedup(sorted(all_dialogs, key=enhanced_score, reverse=True)) - statements = dedup(sorted(all_statements, key=enhanced_score, reverse=True)) - entities = dedup(all_entities, key_field="name") - - # 4) 构建上下文 - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体摘要 - try: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - except Exception: - pass - - # 全局回退 - if not contexts_all and search_type in ("embedding", "hybrid"): - try: - print("🔁 检索为空，回退到关键词检索...") - kw_fallback = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=max(search_limit, 5), - ) - fb_dialogs = kw_fallback.get("dialogues", []) or [] - fb_statements = kw_fallback.get("statements", []) or [] - fb_entities = kw_fallback.get("entities", []) or [] - - for d in fb_dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in fb_statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - if fb_entities: - entity_names = [str(e.get("name", "")).strip() for e in fb_entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - dialogs = fb_dialogs if fb_dialogs else dialogs - statements = fb_statements if fb_statements else statements - entities = fb_entities if fb_entities else entities - print(f"↩️ 回退到关键词检索: {len(fb_dialogs)} 对话, {len(fb_statements)} 条陈述, {len(fb_entities)} 个实体") - except Exception as fe: - print(f"❌ 关键词回退失败: {fe}") - - ent_count = len(entities) if isinstance(entities, list) else 0 - print(f"✅ {search_type}检索成功: {len(dialogs)} 对话, {len(statements)} 条陈述, {ent_count} 个实体") - if is_temporal: - print("⏰ 检测为时间推理问题，已启用时间优化检索") - - except Exception as e: - print(f"❌ {search_type}检索失败: {e}") - contexts_all = [] - - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 智能上下文选择 - context_text = "" - if contexts_all: - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) - # 相对时间解析 - try: - context_text = _resolve_relative_times_cn_en(context_text, anchor=datetime.now()) - except Exception: - pass - # 诊断信息 - try: - cn_diag = generate_query_keywords_cn(question)[:4] # 显示更多关键词 - opts = extract_candidate_options(question)[:2] - qlw = [w for w in set(re.findall(r'\b\w+\b', question.lower())) if len(w) > 2][:1] - diag_tokens: List[str] = [] - for t in cn_diag + opts + qlw: - if t and t not in diag_tokens: - diag_tokens.append(t) - print(f"🔍 关键词/选项: {', '.join(diag_tokens)}") - preview = context_text[:200].replace('\n', ' ') - print(f"🔎 上下文预览: {preview}...") - key_preview = preview.strip() - if key_preview: - preview_counter[key_preview] = preview_counter.get(key_preview, 0) + 1 - except Exception: - pass - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # 记录上下文诊断信息 - per_query_context_counts.append(len(contexts_all)) - per_query_context_avg_tokens.append(avg_context_tokens([context_text])) - per_query_context_chars.append(len(context_text)) - - # LLM 推理（增强技术术语提示） - options = extract_candidate_options(question) - if len(options) >= 2: - opt_lines = "\n".join(f"- {o}" for o in options) - # 技术术语问题的特殊提示 - if any(term in question for term in ["GPS", "系统", "功能", "工作坊", "研讨会"]): - system_prompt = ( - "You are a QA assistant specializing in technical and activity-related questions. " - "Pay special attention to technical terms like GPS, systems, functions, workshops, and seminars. " - "Return ONLY one string: exactly one option from the provided candidates. If the context is insufficient, respond with 'Unknown'. " - "Focus on matching technical details and activity sequences accurately." - ) - elif is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "Return ONLY one string: exactly one option from the provided candidates. If the context is insufficient, respond with 'Unknown'. " - "Pay special attention to date sequences and time intervals." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. Return ONLY one string: exactly one option from the provided candidates. " - "If the context is insufficient, respond with 'Unknown'. If the context expresses a synonym or paraphrase of a candidate, return the closest candidate. " - "Do not include explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": ( - f"Question: {question}\n\nCandidates:\n{opt_lines}\n\nContext:\n{context_text}\n\nReturn EXACTLY one candidate string (or 'Unknown')." - ), - }, - ] - else: - # 技术术语问题的特殊提示 - if any(term in question for term in ["GPS", "系统", "功能", "工作坊", "研讨会"]): - system_prompt = ( - "You are a QA assistant specializing in technical and activity-related questions. " - "Pay special attention to technical terms like GPS, systems, functions, workshops, and seminars. " - "If the context contains the answer, return a concise answer phrase focusing on technical details. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - elif is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "If the context contains the answer, return a concise answer phrase focusing on temporal information. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. If the context contains the answer, return a concise answer phrase. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}\n\nReturn ONLY the answer (or 'Unknown').", - }, - ] - - t2 = time.time() - # 使用异步调用 - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - - # 兼容不同的响应格式 - pred_raw = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - - # 选项题输出规范化 - pred = pred_raw - if len(options) >= 2 and not pred_raw.lower().startswith("unknown"): - def _basic_norm(s: str) -> str: - s = s.lower().strip() - return re.sub(r"[^\w\s]", " ", s) - def _jaccard(a: str, b: str) -> float: - ta = set(t for t in _basic_norm(a).split() if t) - tb = set(t for t in _basic_norm(b).split() if t) - if not ta and not tb: - return 1.0 - if not ta or not tb: - return 0.0 - return len(ta & tb) / len(ta | tb) - best = None - best_score = -1.0 - for o in options: - score = _jaccard(pred_raw, o) - if score > best_score: - best = o - best_score = score - if best is not None and best_score > 0.0: - pred = best - - # 指标 - flag = exact_match(pred, reference) - f1_val = common_f1(str(pred), str(reference)) - j_val = jaccard(str(pred), str(reference)) - - type_correct.setdefault(qtype, []).append(flag) - type_f1.setdefault(qtype, []).append(f1_val) - type_jacc.setdefault(qtype, []).append(j_val) - - samples.append({ - "question": question, - "prediction": pred, - "answer": reference, - "question_type": qtype, - "is_temporal": is_temporal, - "question_id": item.get("question_id"), - "options": options, - "context_count": len(contexts_all), - "context_chars": len(context_text), - "retrieved_dialogue_count": len(dialogs), - "retrieved_statement_count": len(statements), - "metrics": { - "exact_match": bool(flag), - "f1": f1_val, - "jaccard": j_val - }, - "timing": { - "search_ms": (t1 - t0) * 1000, - "llm_ms": (t3 - t2) * 1000 - } - }) - - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {reference}") - print(f"📈 当前指标 - Exact Match: {flag}, F1: {f1_val:.3f}, Jaccard: {j_val:.3f}") - - # 聚合结果 - type_acc = {t: (sum(v) / max(len(v), 1)) for t, v in type_correct.items()} - f1_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_f1.items()} - jacc_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_jacc.items()} - - result = { - "dataset": "longmemeval", - "items": len(items), - "accuracy_by_type": type_acc, - "f1_by_type": f1_by_type, - "jaccard_by_type": jacc_by_type, - "samples": samples, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "context": { - "avg_tokens": statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0, - "avg_chars": statistics.mean(per_query_context_chars) if per_query_context_chars else 0.0, - "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, - }, - "params": { - "group_id": group_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "search_type": search_type, - "llm_id": SELECTED_LLM_ID, - "embedding_id": SELECTED_EMBEDDING_ID, - "sample_size": sample_size, - "start_index": start_index, - }, - "timestamp": datetime.now().isoformat() - } - - # 计算汇总指标 - try: - total_items = max(len(samples), 1) - correct_count = sum(1 for s in samples if s.get("metrics", {}).get("exact_match")) - score_accuracy = (correct_count / total_items) * 100.0 - - total_latencies_ms = [] - for s in samples: - t = s.get("timing", {}) - total_latencies_ms.append(float(t.get("search_ms", 0.0)) + float(t.get("llm_ms", 0.0))) - total_lat_stats = latency_stats(total_latencies_ms) if total_latencies_ms else {"p50": 0.0, "iqr": 0.0} - latency_median_s = total_lat_stats.get("p50", 0.0) / 1000.0 - latency_iqr_s = total_lat_stats.get("iqr", 0.0) / 1000.0 - - avg_ctx_tokens = statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0 - avg_ctx_tokens_k = avg_ctx_tokens / 1000.0 - - result["metric_summary"] = { - "score_accuracy": score_accuracy, - "latency_median_s": latency_median_s, - "latency_iqr_s": latency_iqr_s, - "avg_context_tokens_k": avg_ctx_tokens_k, - } - except Exception: - result["metric_summary"] = { - "score_accuracy": 0.0, - "latency_median_s": 0.0, - "latency_iqr_s": 0.0, - "avg_context_tokens_k": 0.0, - } - - # 诊断信息 - try: - dups = sorted([(k, c) for k, c in preview_counter.items() if c > 1], key=lambda x: -x[1])[:5] - result["diagnostics"] = { - "duplicate_previews_top": [{"count": c, "preview": k[:120]} for k, c in dups], - "unique_preview_count": len(preview_counter), - } - except Exception: - pass - - return result - - finally: - await connector.close() - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="LongMemEval 评估测试脚本（增强技术术语检索版）") - parser.add_argument("--sample-size", type=int, default=3, help="样本数量（<=0 表示全部）") - parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") - parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--group-id", type=str, default="longmemeval_zh_bak_3", help="图数据库 Group ID") - parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=16, help="LLM 最大输出 token") - parser.add_argument("--search-type", type=str, default="hybrid", choices=["embedding","keyword","hybrid"], help="检索类型") - parser.add_argument("--data-path", type=str, default=None, help="数据集路径") - args = parser.parse_args() - - sample_size = 0 if args.all else args.sample_size - - result = asyncio.run( - run_longmemeval_test( - sample_size=sample_size, - group_id=args.group_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - data_path=args.data_path, - start_index=args.start_index, - ) - ) - - # 打印结果 - print("\n" + "="*50) - print("📊 LongMemEval 测试结果:") - print(f" 样本数量: {result['items']}") - - if result['accuracy_by_type']: - print("\n📈 按问题类型细分:") - for qtype, acc in result['accuracy_by_type'].items(): - print(f" {qtype}:") - print(f" Score (Accuracy): {acc:.3f}") - - print(f"\n📊 指标总览:") - ms = result.get('metric_summary', {}) - print(f" Score (Accuracy): {ms.get('score_accuracy', 0.0):.1f}%") - print(f" Latency (s): median {ms.get('latency_median_s', 0.0):.3f}s") - print(f" Latency IQR (s): {ms.get('latency_iqr_s', 0.0):.3f}s") - print(f" Avg Context Tokens (k): {ms.get('avg_context_tokens_k', 0.0):.3f}k") - - print(f"\n⏱️ 细分性能指标:") - print(f" 检索延迟(均值): {result['latency']['search']['mean']:.1f}ms") - print(f" LLM延迟(均值): {result['latency']['llm']['mean']:.1f}ms") - print(f" 上下文长度(均值): {result['context']['avg_chars']:.0f} 字符") - - - # 保存结果到文件 - try: - out_dir = os.path.join(PROJECT_ROOT, "evaluation", "longmemeval", "results") - os.makedirs(out_dir, exist_ok=True) - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = os.path.join(out_dir, f"longmemeval_{result['params']['search_type']}_{ts}.json") - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n💾 结果已保存: {out_path}") - except Exception as e: - print(f"⚠️ 结果保存失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py deleted file mode 100644 index 6efb66ff..00000000 --- a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py +++ /dev/null @@ -1,324 +0,0 @@ -import argparse -import asyncio -import json -import os -import time -from datetime import datetime -from typing import TYPE_CHECKING, Any, Dict, List - -if TYPE_CHECKING: - from app.schemas.memory_config_schema import MemoryConfig - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - exact_match, - latency_stats, -) -from app.core.memory.evaluation.extraction_utils import ( - ingest_contexts_via_full_pipeline, -) -from app.core.memory.storage_services.search import run_hybrid_search -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.db import get_db_context -from app.repositories.neo4j.neo4j_connector import Neo4jConnector - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """基于问题关键词对上下文进行评分选择，并在预算内拼接文本。""" - if not contexts: - return "" - import re - # 提取问题关键词（移除停用词） - question_lower = (question or "").lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but' - } - question_words = set(re.findall(r"\b\w+\b", question_lower)) - question_words = {w for w in question_words if w not in stop_words and len(w) > 2} - - # 评分 - scored = [] - for i, ctx in enumerate(contexts): - ctx_lower = (ctx or "").lower() - score = 0 - matches = 0 - for w in question_words: - if w in ctx_lower: - matches += 1 - score += ctx_lower.count(w) * 2 - length = len(ctx) - if 100 < length < 2000: - score += 5 - elif length >= 2000: - score += 2 - if i < 3: - score += 3 - scored.append((score, ctx, matches)) - - scored.sort(key=lambda x: x[0], reverse=True) - - # 选择直到达到字符限制，必要时截断包含关键词的段落 - selected: List[str] = [] - total = 0 - for score, ctx, _ in scored: - if total + len(ctx) <= max_chars: - selected.append(ctx) - total += len(ctx) - else: - if score > 10 and total < max_chars - 200: - remaining = max_chars - total - lines = ctx.split('\n') - rel_lines: List[str] = [] - cur = 0 - for line in lines: - l = line.lower() - if any(w in l for w in question_words) and cur < remaining - 50: - rel_lines.append(line) - cur += len(line) - if rel_lines: - truncated = '\n'.join(rel_lines) - if len(truncated) > 50: - selected.append(truncated + "\n[相关内容截断...]") - total += len(truncated) - break - return "\n\n".join(selected) - - -def build_context_from_dialog(dialog_obj: Dict[str, Any]) -> str: - """Compose a text context from `dialog` list in msc_self_instruct item.""" - parts: List[str] = [] - for turn in dialog_obj.get("dialog", []): - speaker = turn.get("speaker", "") - text = turn.get("text", "") - if text: - parts.append(f"{speaker}: {text}") - return "\n".join(parts) - - -def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any]]: - """Combine dialogues from embedding and keyword searches (embedding first).""" - if results is None: - return [] - emb = [] - kw = [] - if isinstance(results.get("embedding_search"), dict): - emb = results.get("embedding_search", {}).get("dialogues", []) or [] - elif isinstance(results.get("dialogues"), list): - emb = results.get("dialogues", []) or [] - if isinstance(results.get("keyword_search"), dict): - kw = results.get("keyword_search", {}).get("dialogues", []) or [] - seen = set() - merged: List[Dict[str, Any]] = [] - for d in emb: - k = (str(d.get("uuid", "")), str(d.get("content", ""))) - if k not in seen: - merged.append(d) - seen.add(k) - for d in kw: - k = (str(d.get("uuid", "")), str(d.get("content", ""))) - if k not in seen: - merged.append(d) - seen.add(k) - return merged - - -async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: - group_id = group_id or SELECTED_GROUP_ID - # Load data - data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") - if not os.path.exists(data_path): - data_path = os.path.join(os.getcwd(), "data", "msc_self_instruct.jsonl") - with open(data_path, "r", encoding="utf-8") as f: - lines = f.readlines() - items: List[Dict[str, Any]] = [json.loads(l) for l in lines[:sample_size]] - # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入 - # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略 - contexts: List[str] = [build_context_from_dialog(item) for item in items] - await ingest_contexts_via_full_pipeline(contexts, group_id) - - # LLM client (使用异步调用) - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) - - # Evaluate each item - connector = Neo4jConnector() - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - contexts_used: List[str] = [] - correct_flags: List[float] = [] - f1s: List[float] = [] - b1s: List[float] = [] - jss: List[float] = [] - try: - for item in items: - question = item.get("self_instruct", {}).get("B", "") or item.get("question", "") - reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "") - # 检索：对齐 locomo 的三路检索（dialogues/statements/entities） - t0 = time.time() - try: - results = await run_hybrid_search( - query_text=question, - search_type=search_type, - group_id=group_id, - limit=search_limit, - include=["dialogues", "statements", "entities"], - output_path=None, - memory_config=memory_config, - ) - except Exception: - results = None - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 构建上下文：包含对话、陈述和实体摘要，并智能选择 - contexts_all: List[str] = [] - if results: - if search_type == "hybrid": - emb = results.get("embedding_search", {}) if isinstance(results.get("embedding_search"), dict) else {} - kw = results.get("keyword_search", {}) if isinstance(results.get("keyword_search"), dict) else {} - emb_dialogs = emb.get("dialogues", []) - emb_statements = emb.get("statements", []) - emb_entities = emb.get("entities", []) - kw_dialogs = kw.get("dialogues", []) - kw_statements = kw.get("statements", []) - kw_entities = kw.get("entities", []) - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - # 简单去重与限制 - seen_texts = set() - for d in all_dialogs: - text = str(d.get("content", "")).strip() - if text and text not in seen_texts: - contexts_all.append(text) - seen_texts.add(text) - if len(contexts_all) >= search_limit: - break - for s in all_statements: - text = str(s.get("statement", "")).strip() - if text and text not in seen_texts: - contexts_all.append(text) - seen_texts.add(text) - if len(contexts_all) >= search_limit: - break - # 实体摘要（最多3个） - names = [] - merged_entities = all_entities[:] - for e in merged_entities: - name = str(e.get("name", "")).strip() - if name and name not in names: - names.append(name) - if len(names) >= 3: - break - if names: - contexts_all.append("EntitySummary: " + ", ".join(names)) - else: - dialogs = results.get("dialogues", []) - statements = results.get("statements", []) - entities = results.get("entities", []) - for d in dialogs: - text = str(d.get("content", "")).strip() - if text: - contexts_all.append(text) - for s in statements: - text = str(s.get("statement", "")).strip() - if text: - contexts_all.append(text) - names = [str(e.get("name", "")).strip() for e in entities[:3] if e.get("name")] - if names: - contexts_all.append("EntitySummary: " + ", ".join(names)) - - # 智能选择并截断到预算 - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) if contexts_all else "" - if not context_text: - context_text = "No relevant context found." - contexts_used.append(context_text[:200]) - - # Call LLM (使用异步调用) - messages = [ - {"role": "system", "content": "You are a QA assistant. Answer in English. Strictly follow: 1) If the context contains the answer, copy the shortest exact span from the context as the answer; 2) If the answer cannot be determined from the context, respond with 'Unknown'; 3) Return ONLY the answer text, no explanations."}, - {"role": "user", "content": f"Question: {question}\n\nContext:\n{context_text}"}, - ] - t2 = time.time() - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else str(resp).strip()) - # Metrics: F1, BLEU-1, Jaccard; keep exact match for reference - correct_flags.append(exact_match(pred, reference)) - from app.core.memory.evaluation.common.metrics import ( - bleu1, - f1_score, - jaccard, - ) - f1s.append(f1_score(str(pred), str(reference))) - b1s.append(bleu1(str(pred), str(reference))) - jss.append(jaccard(str(pred), str(reference))) - - # Aggregate metrics - acc = sum(correct_flags) / max(len(correct_flags), 1) - ctx_avg_tokens = avg_context_tokens(contexts_used) - result = { - "dataset": "memsciqa", - "items": len(items), - "metrics": { - "accuracy": acc, - # Placeholders for extensibility - "f1": (sum(f1s) / max(len(f1s), 1)) if f1s else 0.0, - "bleu1": (sum(b1s) / max(len(b1s), 1)) if b1s else 0.0, - "jaccard": (sum(jss) / max(len(jss), 1)) if jss else 0.0, - }, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "avg_context_tokens": ctx_avg_tokens, - } - return result - finally: - await connector.close() - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen") - parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量") - parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json") - parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=64, help="LLM 最大生成长度") - parser.add_argument("--search-type", type=str, choices=["keyword","embedding","hybrid"], default="hybrid", help="检索类型") - args = parser.parse_args() - - result = asyncio.run( - run_memsciqa_eval( - sample_size=args.sample_size, - group_id=args.group_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - ) - ) - print(json.dumps(result, ensure_ascii=False, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py deleted file mode 100644 index 279f4042..00000000 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ /dev/null @@ -1,576 +0,0 @@ -import argparse -import asyncio -import json -import os -import re -import time -from datetime import datetime -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -# 路径与模块导入保持与现有评估脚本一致 -import sys - -_THIS_DIR = os.path.dirname(os.path.abspath(__file__)) -_PROJECT_ROOT = os.path.dirname(os.path.dirname(_THIS_DIR)) -_SRC_DIR = os.path.join(_PROJECT_ROOT, "src") -for _p in (_SRC_DIR, _PROJECT_ROOT): - if _p not in sys.path: - sys.path.insert(0, _p) - -# 对齐 locomo_test 的检索逻辑：直接使用 graph_search 与 Neo4jConnector/Embedder1 -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - exact_match, - latency_stats, -) -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService - -try: - from app.core.memory.evaluation.common.metrics import bleu1, f1_score, jaccard -except Exception: - # 兜底：简单实现（必要时） - def f1_score(pred: str, ref: str) -> float: - ps = pred.lower().split() - rs = ref.lower().split() - if not ps or not rs: - return 0.0 - tp = len(set(ps) & set(rs)) - if tp == 0: - return 0.0 - precision = tp / len(ps) - recall = tp / len(rs) - if precision + recall == 0: - return 0.0 - return 2 * precision * recall / (precision + recall) - - def bleu1(pred: str, ref: str) -> float: - ps = pred.lower().split() - rs = ref.lower().split() - if not ps or not rs: - return 0.0 - overlap = len([w for w in ps if w in rs]) - return overlap / max(len(ps), 1) - - def jaccard(pred: str, ref: str) -> float: - ps = set(pred.lower().split()) - rs = set(ref.lower().split()) - union = len(ps | rs) - if union == 0: - return 0.0 - return len(ps & rs) / union - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """基于问题关键词对上下文进行评分选择，并在预算内拼接文本。 - - 参考 evaluation/memsciqa/evaluate_qa.py 的实现，避免路径导入带来的不稳定。 - """ - if not contexts: - return "" - question_lower = (question or "").lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but' - } - question_words = set(re.findall(r"\b\w+\b", question_lower)) - question_words = {w for w in question_words if w not in stop_words and len(w) > 2} - - scored = [] - for i, ctx in enumerate(contexts): - ctx_lower = (ctx or "").lower() - score = 0 - matches = 0 - for w in question_words: - if w in ctx_lower: - matches += 1 - score += ctx_lower.count(w) * 2 - length = len(ctx) - if 100 < length < 2000: - score += 5 - elif length >= 2000: - score += 2 - if i < 3: - score += 3 - scored.append((score, ctx, matches)) - - scored.sort(key=lambda x: x[0], reverse=True) - - selected: List[str] = [] - total = 0 - for score, ctx, _ in scored: - if total + len(ctx) <= max_chars: - selected.append(ctx) - total += len(ctx) - else: - if score > 10 and total < max_chars - 200: - remaining = max_chars - total - lines = ctx.split('\n') - rel_lines: List[str] = [] - cur = 0 - for line in lines: - l = line.lower() - if any(w in l for w in question_words) and cur < remaining - 50: - rel_lines.append(line) - cur += len(line) - if rel_lines: - truncated = '\n'.join(rel_lines) - if len(truncated) > 50: - selected.append(truncated + "\n[相关内容截断...]") - total += len(truncated) - break - return "\n\n".join(selected) - - -def extract_question_keywords(question: str, max_keywords: int = 8) -> List[str]: - """提取问题中的关键词（简单英文分词，去停用词，长度>=3）。""" - ql = (question or "").lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but','of','to','in','on','for','with','from','that','this' - } - words = re.findall(r"\b[\w-]+\b", ql) - kws = [w for w in words if w not in stop_words and len(w) >= 3] - # 去重保序 - seen = set() - uniq = [] - for w in kws: - if w not in seen: - uniq.append(w) - seen.add(w) - if len(uniq) >= max_keywords: - break - return uniq - - -def analyze_contexts_simple(contexts: List[str], keywords: List[str], top_n: int = 5) -> List[Dict[str, int | float]]: - """对上下文进行简单相关性打分，仅用于控制台可视化。 - - 评分: score = match_count*200 + min(len(text), 100000)/100 - """ - results = [] - for ctx in contexts: - tl = (ctx or "").lower() - match_count = sum(1 for k in keywords if k in tl) - length = len(ctx) - score = match_count * 200 + min(length, 100000) / 100.0 - results.append({"score": float(f"{score:.0f}"), "match": match_count, "length": length}) - results.sort(key=lambda x: (x["score"], x["match"], x["length"]), reverse=True) - return results[:max(top_n, 0)] - - -# 纯测试脚本不进行摄入；若需摄入请使用 evaluate_qa.py - - -def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]: - if not os.path.exists(data_path): - raise FileNotFoundError(f"未找到数据集: {data_path}") - items: List[Dict[str, Any]] = [] - with open(data_path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - items.append(json.loads(line)) - except Exception: - # 跳过坏行但不中断 - continue - return items - - -async def run_memsciqa_test( - sample_size: int = 3, - group_id: str | None = None, - search_limit: int = 8, - context_char_budget: int = 4000, - llm_temperature: float = 0.0, - llm_max_tokens: int = 64, - search_type: str = "embedding", - data_path: str | None = None, - start_index: int = 0, - verbose: bool = True, -) -> Dict[str, Any]: - """memsciqa 增强测试脚本：结合 evaluate_qa 的三路检索与智能上下文选择。 - - - 支持从指定索引开始与评估全部样本（sample_size<=0） - - 支持在摄入前重置组（清空图）与跳过摄入 - - 支持 keyword / embedding / hybrid 三种检索 - """ - - # 默认使用指定的 memsci 组 ID - group_id = group_id or "group_memsci" - - # 数据路径解析（项目根与当前工作目录兜底） - if not data_path: - proj_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") - cwd_path = os.path.join(os.getcwd(), "data", "msc_self_instruct.jsonl") - if os.path.exists(proj_path): - data_path = proj_path - elif os.path.exists(cwd_path): - data_path = cwd_path - else: - raise FileNotFoundError("未找到数据集: data/msc_self_instruct.jsonl，请确保其存在于项目根目录或当前工作目录的 data 目录下。") - - # 加载数据 - all_items = load_dataset_memsciqa(data_path) - if sample_size is None or sample_size <= 0: - items = all_items[start_index:] - else: - items = all_items[start_index:start_index + sample_size] - - # 初始化 LLM（纯测试：不进行摄入） - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm = factory.get_llm_client(SELECTED_LLM_ID) - - # 初始化 Neo4j 连接与向量检索 Embedder（对齐 locomo_test） - connector = Neo4jConnector() - embedder = None - if search_type in ("embedding", "hybrid"): - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 评估循环 - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - # 存储完整上下文文本用于统计 - contexts_used: List[str] = [] - per_query_context_chars: List[int] = [] - per_query_context_counts: List[int] = [] - correct_flags: List[float] = [] - f1s: List[float] = [] - b1s: List[float] = [] - jss: List[float] = [] - samples: List[Dict[str, Any]] = [] - - total_items = len(items) - for idx, item in enumerate(items): - if verbose: - print(f"\n🧪 评估样本: {idx+1}/{total_items}") - question = item.get("self_instruct", {}).get("B", "") or item.get("question", "") - reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "") - - # 三路检索：chunks/statements/entities/summaries（对齐 qwen_search_eval.py） - t0 = time.time() - results = None - try: - if search_type in ("embedding", "hybrid"): - # 使用嵌入检索（与 qwen_search_eval 对齐） - results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues - ) - elif search_type == "keyword": - # 关键词检索（直接调用 graph_search） - results = await search_graph( - connector=connector, - q=question, - group_id=group_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues - ) - except Exception: - results = None - t1 = time.time() - search_ms = (t1 - t0) * 1000 - latencies_search.append(search_ms) - - # 构建上下文：包含 chunks、陈述、摘要和实体（对齐 qwen_search_eval.py） - contexts_all: List[str] = [] - retrieved_counts: Dict[str, int] = {} - if results: - chunks = results.get("chunks", []) - statements = results.get("statements", []) - entities = results.get("entities", []) - summaries = results.get("summaries", []) - retrieved_counts = { - "chunks": len(chunks), - "statements": len(statements), - "entities": len(entities), - "summaries": len(summaries), - } - # 优先使用 chunks - for c in chunks: - text = str(c.get("content", "")).strip() - if text: - contexts_all.append(text) - # 然后是 statements - for s in statements: - text = str(s.get("statement", "")).strip() - if text: - contexts_all.append(text) - # 然后是 summaries - for sm in summaries: - text = str(sm.get("summary", "")).strip() - if text: - contexts_all.append(text) - # 实体摘要：最多加入前3个高分实体（对齐 qwen_search_eval.py） - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - if verbose: - if retrieved_counts: - print(f"✅ 检索成功: {retrieved_counts.get('chunks',0)} chunks, {retrieved_counts.get('statements',0)} 条陈述, {retrieved_counts.get('entities',0)} 个实体, {retrieved_counts.get('summaries',0)} 个摘要") - print(f"📊 有效上下文数量: {len(contexts_all)}") - q_keywords = extract_question_keywords(question, max_keywords=8) - if q_keywords: - print(f"🔍 问题关键词: {set(q_keywords)}") - if contexts_all: - analysis = analyze_contexts_simple(contexts_all, q_keywords, top_n=5) - if analysis: - print("📊 上下文相关性分析:") - for a in analysis: - print(f" - 得分: {int(a['score'])}, 关键词匹配: {a['match']}, 长度: {a['length']}") - # 打印检索到的上下文预览，便于定位为何为 Unknown - print("🔎 上下文预览（最多前10条，每条截断展示）:") - for i, ctx in enumerate(contexts_all[:10]): - preview = str(ctx).replace("\n", " ") - if len(preview) > 300: - preview = preview[:300] + "..." - print(f" [{i+1}] 长度: {len(ctx)} | 片段: {preview}") - # 标注参考答案是否出现在任一上下文中 - ref_lower = (str(reference) or "").lower() - if ref_lower: - hits = [] - for i, ctx in enumerate(contexts_all): - if ref_lower in str(ctx).lower(): - hits.append(i+1) - print(f"🔗 参考答案命中上下文条数: {len(hits)}" + (f" | 命中索引: {hits}" if hits else "")) - - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) if contexts_all else "" - if not context_text: - context_text = "No relevant context found." - contexts_used.append(context_text) - per_query_context_chars.append(len(context_text)) - per_query_context_counts.append(len(contexts_all)) - - if verbose: - selected_count = (context_text.count("\n\n") + 1) if context_text else 0 - print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {len(context_text)}字符") - # 展示拼接后的上下文片段，便于核查是否包含答案 - concat_preview = context_text.replace("\n", " ") - if len(concat_preview) > 600: - concat_preview = concat_preview[:600] + "..." - print(f"🧵 拼接上下文预览: {concat_preview}") - - messages = [ - { - "role": "system", - "content": ( - "You are a QA assistant. Answer in English. Follow these guidelines:\n" - "1) If the context contains information to answer the question, provide a concise answer based on the context;\n" - "2) If the context does not contain enough information to answer the question, respond with 'Unknown';\n" - "3) Keep your answer brief and to the point;\n" - "4) Do not add explanations or additional text beyond the answer." - ), - }, - {"role": "user", "content": f"Question: {question}\n\nContext:\n{context_text}"}, - ] - - t2 = time.time() - try: - # 使用异步调用 - resp = await llm.chat(messages=messages) - # 更健壮的响应解析，处理不同的LLM响应格式 - if hasattr(resp, 'content'): - pred = resp.content.strip() - elif isinstance(resp, dict) and "choices" in resp and len(resp["choices"]) > 0: - pred = resp["choices"][0]["message"]["content"].strip() - elif isinstance(resp, dict) and "content" in resp: - pred = resp["content"].strip() - elif isinstance(resp, str): - pred = resp.strip() - else: - pred = "Unknown" - print(f"⚠️ LLM响应格式异常: {type(resp)} - {resp}") - - # 检查预测是否为"Unknown"或空，如果是则检查上下文是否真的没有答案 - if pred.lower() in ["unknown", ""]: - # 如果参考答案在上下文中存在，但LLM返回Unknown，可能是提示词问题 - ref_lower = (str(reference) or "").lower() - if ref_lower and any(ref_lower in ctx.lower() for ctx in contexts_all): - print("⚠️ 参考答案在上下文中存在但LLM返回Unknown，检查提示词") - except Exception as e: - # 更详细的错误处理 - pred = "Unknown" - print(f"⚠️ LLM调用异常: {e}") - t3 = time.time() - llm_ms = (t3 - t2) * 1000 - latencies_llm.append(llm_ms) - - exact = exact_match(pred, reference) - correct_flags.append(exact) - f1_val = f1_score(str(pred), str(reference)) - b1_val = bleu1(str(pred), str(reference)) - j_val = jaccard(str(pred), str(reference)) - f1s.append(f1_val) - b1s.append(b1_val) - jss.append(j_val) - - if verbose: - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {reference}") - print(f"📈 当前指标 - F1: {f1_val:.3f}, BLEU-1: {b1_val:.3f}, Jaccard: {j_val:.3f}") - print(f"⏱️ 延迟 - 检索: {search_ms:.0f}ms, LLM: {llm_ms:.0f}ms") - - # 对齐 locomo/qwen_search_eval.py 的样本输出结构 - samples.append({ - "question": str(question), - "answer": str(reference), - "prediction": str(pred), - "metrics": { - "f1": f1_val, - "b1": b1_val, - "j": j_val - }, - "retrieval": { - "retrieved_documents": len(contexts_all), - "context_length": len(context_text), - "search_limit": search_limit, - "max_chars": context_char_budget - }, - "timing": { - "search_ms": search_ms, - "llm_ms": llm_ms - } - }) - - # 计算总体指标与聚合 - acc = sum(correct_flags) / max(len(correct_flags), 1) - ctx_avg_tokens = avg_context_tokens(contexts_used) - result = { - "dataset": "memsciqa", - "items": len(items), - "metrics": { - "f1": (sum(f1s) / max(len(f1s), 1)) if f1s else 0.0, - "b1": (sum(b1s) / max(len(b1s), 1)) if b1s else 0.0, - "j": (sum(jss) / max(len(jss), 1)) if jss else 0.0, - }, - "context": { - "avg_tokens": ctx_avg_tokens, - "avg_chars": (sum(per_query_context_chars) / max(len(per_query_context_chars), 1)) if per_query_context_chars else 0.0, - "count_avg": (sum(per_query_context_counts) / max(len(per_query_context_counts), 1)) if per_query_context_counts else 0.0, - "avg_memory_tokens": 0.0 - }, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "samples": samples, - "params": { - "group_id": group_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "llm_temperature": llm_temperature, - "llm_max_tokens": llm_max_tokens, - "search_type": search_type, - "start_index": start_index, - "llm_id": SELECTED_LLM_ID, - "retrieval_embedding_id": SELECTED_EMBEDDING_ID - }, - "timestamp": datetime.now().isoformat(), - } - try: - await connector.close() - except Exception: - pass - return result - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="memsciqa 测试脚本（三路检索 + 智能上下文选择）") - parser.add_argument("--sample-size", type=int, default=30, help="样本数量（<=0 表示全部）") - parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") - parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--group-id", type=str, default="group_memsci", help="图数据库 Group ID（默认 group_memsci）") - parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=64, help="LLM 最大输出 token") - parser.add_argument("--search-type", type=str, default="embedding", choices=["embedding","keyword","hybrid"], help="检索类型（hybrid 等同于 embedding）") - parser.add_argument("--data-path", type=str, default=None, help="数据集路径（默认 data/msc_self_instruct.jsonl）") - parser.add_argument("--output", type=str, default=None, help="将评估结果保存到指定文件路径（JSON）") - parser.add_argument("--verbose", action="store_true", default=True, help="打印过程日志（默认开启）") - parser.add_argument("--quiet", action="store_true", help="关闭过程日志") - args = parser.parse_args() - - sample_size = 0 if args.all else args.sample_size - - verbose_flag = False if args.quiet else args.verbose - result = asyncio.run( - run_memsciqa_test( - sample_size=sample_size, - group_id=args.group_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - data_path=args.data_path, - start_index=args.start_index, - verbose=verbose_flag, - ) - ) - - print(json.dumps(result, ensure_ascii=False, indent=2)) - - # 结果保存 - out_path = args.output - if not out_path: - eval_dir = os.path.dirname(os.path.abspath(__file__)) - dataset_results_dir = os.path.join(eval_dir, "results") - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = os.path.join(dataset_results_dir, f"memsciqa_{result['params']['search_type']}_{ts}.json") - try: - os.makedirs(os.path.dirname(out_path), exist_ok=True) - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n💾 结果已保存: {out_path}") - except Exception as e: - print(f"⚠️ 结果保存失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/run_eval.py b/api/app/core/memory/evaluation/run_eval.py deleted file mode 100644 index 1de3de89..00000000 --- a/api/app/core/memory/evaluation/run_eval.py +++ /dev/null @@ -1,150 +0,0 @@ -import argparse -import asyncio -import json -import os -import sys -from typing import Any, Dict - -# Add src directory to Python path for proper imports when running from evaluation directory -sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'src')) - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.utils.config.definitions import SELECTED_GROUP_ID, PROJECT_ROOT - -from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval -from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test -from app.core.memory.evaluation.locomo.qwen_search_eval import run_locomo_eval - - -async def run( - dataset: str, - sample_size: int, - reset_group: bool, - group_id: str | None, - judge_model: str | None = None, - search_limit: int | None = None, - context_char_budget: int | None = None, - llm_temperature: float | None = None, - llm_max_tokens: int | None = None, - search_type: str | None = None, - start_index: int | None = None, - max_contexts_per_item: int | None = None, -) -> Dict[str, Any]: - # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认 - group_id = group_id or SELECTED_GROUP_ID - - if reset_group: - connector = Neo4jConnector() - try: - await connector.delete_group(group_id) - finally: - await connector.close() - - if dataset == "locomo": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} - if search_limit is not None: - kwargs["search_limit"] = search_limit - if context_char_budget is not None: - kwargs["context_char_budget"] = context_char_budget - if llm_temperature is not None: - kwargs["llm_temperature"] = llm_temperature - if llm_max_tokens is not None: - kwargs["llm_max_tokens"] = llm_max_tokens - if search_type is not None: - kwargs["search_type"] = search_type - return await run_locomo_eval(**kwargs) - - if dataset == "memsciqa": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} - if search_limit is not None: - kwargs["search_limit"] = search_limit - if context_char_budget is not None: - kwargs["context_char_budget"] = context_char_budget - if llm_temperature is not None: - kwargs["llm_temperature"] = llm_temperature - if llm_max_tokens is not None: - kwargs["llm_max_tokens"] = llm_max_tokens - if search_type is not None: - kwargs["search_type"] = search_type - return await run_memsciqa_eval(**kwargs) - - if dataset == "longmemeval": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} - if search_limit is not None: - kwargs["search_limit"] = search_limit - if context_char_budget is not None: - kwargs["context_char_budget"] = context_char_budget - if llm_temperature is not None: - kwargs["llm_temperature"] = llm_temperature - if llm_max_tokens is not None: - kwargs["llm_max_tokens"] = llm_max_tokens - if search_type is not None: - kwargs["search_type"] = search_type - if start_index is not None: - kwargs["start_index"] = start_index - if max_contexts_per_item is not None: - kwargs["max_contexts_per_item"] = max_contexts_per_item - return await run_longmemeval_test(**kwargs) - raise ValueError(f"未知数据集: {dataset}") - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo") - parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True) - parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通") - parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 group_id 的图数据") - parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json") - parser.add_argument("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名") - parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）") - parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）") - parser.add_argument("--llm-temperature", type=float, default=None, help="生成温度（不提供则使用各脚本默认）") - parser.add_argument("--llm-max-tokens", type=int, default=None, help="最大生成 tokens（不提供则使用各脚本默认）") - parser.add_argument("--search-type", type=str, default=None, choices=["keyword", "embedding", "hybrid"], help="检索类型（不提供则使用各脚本默认）") - # 仅透传到 longmemeval；其他数据集忽略 - parser.add_argument("--start-index", type=int, default=None, help="仅 longmemeval：起始样本索引（不提供则用脚本默认）") - parser.add_argument("--max-contexts-per-item", type=int, default=None, help="仅 longmemeval：每条样本摄入的上下文数量上限（不提供则用脚本默认）") - parser.add_argument("--output", type=str, default=None, help="可选：将评估结果保存到指定文件路径（JSON）；不提供时默认保存到 evaluation//results 目录") - args = parser.parse_args() - - result = asyncio.run(run( - args.dataset, - args.sample_size, - args.reset_group, - args.group_id, - args.judge_model, - args.search_limit, - args.context_char_budget, - args.llm_temperature, - args.llm_max_tokens, - args.search_type, - args.start_index, - args.max_contexts_per_item, - )) - print(json.dumps(result, ensure_ascii=False, indent=2)) - - # 结果输出逻辑保持不变 - if args.output: - out_path = args.output - else: - eval_dir = os.path.dirname(os.path.abspath(__file__)) - dataset_results_dir = os.path.join(eval_dir, args.dataset, "results") - out_filename = f"{args.dataset}_{args.sample_size}.json" - out_path = os.path.join(dataset_results_dir, out_filename) - - out_dir = os.path.dirname(out_path) - if out_dir and not os.path.exists(out_dir): - os.makedirs(out_dir, exist_ok=True) - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n结果已保存到: {out_path}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/llm_tools/chunker_client.py b/api/app/core/memory/llm_tools/chunker_client.py index 87cdb9f4..93a2df82 100644 --- a/api/app/core/memory/llm_tools/chunker_client.py +++ b/api/app/core/memory/llm_tools/chunker_client.py @@ -187,11 +187,11 @@ class ChunkerClient: async def generate_chunks(self, dialogue: DialogData): """ Generate chunks following 1 Message = 1 Chunk strategy. - + Each message creates one chunk, directly inheriting role information. If a message is too long, it will be split into multiple sub-chunks, each maintaining the same speaker. - + Raises: ValueError: If dialogue has no messages or chunking fails """ @@ -201,9 +201,9 @@ class ChunkerClient: f"Dialogue {dialogue.ref_id} has no messages. " f"Cannot generate chunks from empty dialogue." ) - + dialogue.chunks = [] - + # 按消息分块：每个消息创建一个或多个 chunk，直接继承角色 for msg_idx, msg in enumerate(dialogue.context.msgs): # Validate message has required attributes @@ -212,13 +212,13 @@ class ChunkerClient: f"Message {msg_idx} in dialogue {dialogue.ref_id} " f"missing 'role' or 'msg' attribute" ) - + msg_content = msg.msg.strip() - + # Skip empty messages if not msg_content: continue - + # 如果消息太长，可以进一步分块 if len(msg_content) > self.chunk_size: # 对单个消息的内容进行分块 @@ -228,14 +228,14 @@ class ChunkerClient: raise ValueError( f"Failed to chunk long message {msg_idx} in dialogue {dialogue.ref_id}: {e}" ) - + for idx, sub_chunk in enumerate(sub_chunks): sub_chunk_text = sub_chunk.text if hasattr(sub_chunk, 'text') else str(sub_chunk) sub_chunk_text = sub_chunk_text.strip() - + if len(sub_chunk_text) < (self.min_characters_per_chunk or 50): continue - + chunk = Chunk( content=f"{msg.role}: {sub_chunk_text}", speaker=msg.role, # 直接继承角色 @@ -260,7 +260,7 @@ class ChunkerClient: }, ) dialogue.chunks.append(chunk) - + # Validate we generated at least one chunk if not dialogue.chunks: raise ValueError( @@ -268,7 +268,7 @@ class ChunkerClient: f"All messages were either empty or too short. " f"Messages count: {len(dialogue.context.msgs)}" ) - + return dialogue def evaluate_chunking(self, dialogue: DialogData) -> dict: diff --git a/api/app/core/memory/models/__init__.py b/api/app/core/memory/models/__init__.py index 1de3424a..8c573b7a 100644 --- a/api/app/core/memory/models/__init__.py +++ b/api/app/core/memory/models/__init__.py @@ -58,6 +58,12 @@ from app.core.memory.models.triplet_models import ( TripletExtractionResponse, ) +# Ontology models +from app.core.memory.models.ontology_models import ( + OntologyClass, + OntologyExtractionResponse, +) + # Variable configuration models from app.core.memory.models.variate_config import ( StatementExtractionConfig, @@ -105,6 +111,9 @@ __all__ = [ "Entity", "Triplet", "TripletExtractionResponse", + # Ontology models + "OntologyClass", + "OntologyExtractionResponse", # Variable configuration "StatementExtractionConfig", "ForgettingEngineConfig", diff --git a/api/app/core/memory/models/config_models.py b/api/app/core/memory/models/config_models.py index f3341cc5..ca1780aa 100644 --- a/api/app/core/memory/models/config_models.py +++ b/api/app/core/memory/models/config_models.py @@ -72,7 +72,7 @@ class TemporalSearchParams(BaseModel): """Parameters for temporal search queries in the knowledge graph. Attributes: - group_id: Group ID to filter search results (default: 'test') + end_user_id: Group ID to filter search results (default: 'test') apply_id: Application ID to filter search results user_id: User ID to filter search results start_date: Start date for temporal filtering (format: 'YYYY-MM-DD') @@ -81,7 +81,7 @@ class TemporalSearchParams(BaseModel): invalid_date: Date when memory should be invalid (format: 'YYYY-MM-DD') limit: Maximum number of results to return (default: 3) """ - group_id: Optional[str] = Field("test", description="The group ID to filter the search.") + end_user_id: Optional[str] = Field("test", description="The group ID to filter the search.") apply_id: Optional[str] = Field(None, description="The apply ID to filter the search.") user_id: Optional[str] = Field(None, description="The user ID to filter the search.") start_date: Optional[str] = Field(None, description="The start date for the search.") diff --git a/api/app/core/memory/models/graph_models.py b/api/app/core/memory/models/graph_models.py index 7a48d6cb..79b88fdc 100644 --- a/api/app/core/memory/models/graph_models.py +++ b/api/app/core/memory/models/graph_models.py @@ -103,9 +103,7 @@ class Edge(BaseModel): id: Unique identifier for the edge source: ID of the source node target: ID of the target node - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy run_id: Unique identifier for the pipeline run that created this edge created_at: Timestamp when the edge was created (system perspective) expired_at: Optional timestamp when the edge expires (system perspective) @@ -113,9 +111,7 @@ class Edge(BaseModel): id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the edge.") source: str = Field(..., description="The ID of the source node.") target: str = Field(..., description="The ID of the target node.") - group_id: str = Field(..., description="The group ID of the edge.") - user_id: str = Field(..., description="The user ID of the edge.") - apply_id: str = Field(..., description="The apply ID of the edge.") + end_user_id: str = Field(..., description="The end user ID of the edge.") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(..., description="The valid time of the edge from system perspective.") expired_at: Optional[datetime] = Field(None, description="The expired time of the edge from system perspective.") @@ -185,18 +181,14 @@ class Node(BaseModel): Attributes: id: Unique identifier for the node name: Name of the node - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy run_id: Unique identifier for the pipeline run that created this node created_at: Timestamp when the node was created (system perspective) expired_at: Optional timestamp when the node expires (system perspective) """ id: str = Field(..., description="The unique identifier for the node.") name: str = Field(..., description="The name of the node.") - group_id: str = Field(..., description="The group ID of the node.") - user_id: str = Field(..., description="The user ID of the edge.") - apply_id: str = Field(..., description="The apply ID of the edge.") + end_user_id: str = Field(..., description="The end user ID of the node.") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(..., description="The valid time of the node from system perspective.") expired_at: Optional[datetime] = Field(None, description="The expired time of the node from system perspective.") diff --git a/api/app/core/memory/models/message_models.py b/api/app/core/memory/models/message_models.py index bcf08999..2f8660af 100644 --- a/api/app/core/memory/models/message_models.py +++ b/api/app/core/memory/models/message_models.py @@ -55,7 +55,7 @@ class Statement(BaseModel): Attributes: id: Unique identifier for the statement chunk_id: ID of the parent chunk this statement belongs to - group_id: Optional group ID for multi-tenancy + end_user_id: Optional group ID for multi-tenancy statement: The actual statement text content speaker: Optional speaker identifier ('用户' for user, 'AI' for AI responses) statement_embedding: Optional embedding vector for the statement @@ -73,7 +73,7 @@ class Statement(BaseModel): """ id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the statement.") chunk_id: str = Field(..., description="ID of the parent chunk this statement belongs to.") - group_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.") + end_user_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.") statement: str = Field(..., description="The text content of the statement.") speaker: Optional[str] = Field(None, description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses") statement_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the statement.") @@ -159,9 +159,7 @@ class DialogData(BaseModel): context: Full conversation context dialog_embedding: Optional embedding vector for the entire dialog ref_id: Reference ID linking to external dialog system - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy created_at: Timestamp when the dialog was created expired_at: Timestamp when the dialog expires (default: far future) metadata: Additional metadata as key-value pairs @@ -175,9 +173,7 @@ class DialogData(BaseModel): context: ConversationContext = Field(..., description="The full conversation context as a single string.") dialog_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the dialog.") ref_id: str = Field(..., description="Refer to external dialog id. This is used to link to the original dialog.") - group_id: str = Field(default=..., description="Group ID of dialogue data") - user_id: str = Field(..., description="USER ID of dialogue data") - apply_id: str = Field(..., description="APPLY ID of dialogue data") + end_user_id: str = Field(default=..., description="End user ID of dialogue data") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(default_factory=datetime.now, description="The timestamp when the dialog was created.") expired_at: datetime = Field(default_factory=lambda: datetime(9999, 12, 31), description="The timestamp when the dialog expires.") @@ -250,11 +246,11 @@ class DialogData(BaseModel): return [] def assign_group_id_to_statements(self) -> None: - """Assign this dialog's group_id to all statements in all chunks. + """Assign this dialog's end_user_id to all statements in all chunks. - This method updates statements that don't have a group_id set. + This method updates statements that don't have a end_user_id set. """ for chunk in self.chunks: for statement in chunk.statements: - if statement.group_id is None: - statement.group_id = self.group_id + if statement.end_user_id is None: + statement.end_user_id = self.end_user_id diff --git a/api/app/core/memory/models/ontology_models.py b/api/app/core/memory/models/ontology_models.py new file mode 100644 index 00000000..24a61f5f --- /dev/null +++ b/api/app/core/memory/models/ontology_models.py @@ -0,0 +1,135 @@ +"""Models for ontology classes and extraction responses. + +This module contains Pydantic models for representing extracted ontology classes +from scenario descriptions, following OWL ontology engineering standards. + +Classes: + OntologyClass: Represents an extracted ontology class + OntologyExtractionResponse: Response model containing extracted ontology classes +""" + +from typing import List, Optional +from uuid import uuid4 + +from pydantic import BaseModel, ConfigDict, Field, field_validator + + +class OntologyClass(BaseModel): + """Represents an extracted ontology class from scenario description. + + An ontology class represents an abstract category or concept in a domain, + following OWL ontology engineering standards and naming conventions. + + Attributes: + id: Unique string identifier for the ontology class + name: Name of the class in PascalCase format (e.g., 'MedicalProcedure') + name_chinese: Chinese translation of the class name (e.g., '医疗程序') + description: Textual description of the class + examples: List of concrete instance examples of this class + parent_class: Optional name of the parent class in the hierarchy + entity_type: Type/category of the entity (e.g., 'Person', 'Organization', 'Concept') + domain: Domain this class belongs to (e.g., 'Healthcare', 'Education') + + Config: + extra: Ignore extra fields from LLM output + """ + model_config = ConfigDict(extra='ignore') + + id: str = Field( + default_factory=lambda: uuid4().hex, + description="Unique identifier for the ontology class" + ) + name: str = Field( + ..., + description="Name of the class in PascalCase format" + ) + name_chinese: Optional[str] = Field( + None, + description="Chinese translation of the class name" + ) + description: str = Field( + ..., + description="Description of the class" + ) + examples: List[str] = Field( + default_factory=list, + description="List of concrete instance examples" + ) + parent_class: Optional[str] = Field( + None, + description="Name of the parent class in the hierarchy" + ) + entity_type: str = Field( + ..., + description="Type/category of the entity" + ) + domain: str = Field( + ..., + description="Domain this class belongs to" + ) + + @field_validator('name') + @classmethod + def validate_pascal_case(cls, v: str) -> str: + """Validate that the class name follows PascalCase convention. + + PascalCase rules: + - Must start with an uppercase letter + - Cannot contain spaces + - Should not contain special characters except underscores + + Args: + v: The class name to validate + + Returns: + The validated class name + + Raises: + ValueError: If the name doesn't follow PascalCase convention + """ + if not v: + raise ValueError("Class name cannot be empty") + + if not v[0].isupper(): + raise ValueError( + f"Class name '{v}' must start with an uppercase letter (PascalCase)" + ) + + if ' ' in v: + raise ValueError( + f"Class name '{v}' cannot contain spaces (PascalCase)" + ) + + # Check for invalid characters (allow alphanumeric and underscore only) + if not all(c.isalnum() or c == '_' for c in v): + raise ValueError( + f"Class name '{v}' contains invalid characters. " + "Only alphanumeric characters and underscores are allowed" + ) + + return v + + +class OntologyExtractionResponse(BaseModel): + """Response model for ontology extraction from LLM. + + This model represents the structured output from the LLM when + extracting ontology classes from scenario descriptions. + + Attributes: + classes: List of extracted ontology classes + domain: Domain/field the scenario belongs to + + Config: + extra: Ignore extra fields from LLM output + """ + model_config = ConfigDict(extra='ignore') + + classes: List[OntologyClass] = Field( + default_factory=list, + description="List of extracted ontology classes" + ) + domain: str = Field( + ..., + description="Domain/field the scenario belongs to" + ) diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 91e47eae..0e1d8424 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -6,6 +6,7 @@ import os import time from datetime import datetime from typing import TYPE_CHECKING, Any, Dict, List, Optional +from uuid import UUID if TYPE_CHECKING: from app.schemas.memory_config_schema import MemoryConfig @@ -396,13 +397,13 @@ def rerank_with_activation( return reranked -def log_search_query(query_text: str, search_type: str, group_id: str | None, limit: int, include: List[str], log_file: str = None): +def log_search_query(query_text: str, search_type: str, end_user_id: str | None, limit: int, include: List[str], log_file: str = None): """Log search query information using the logger. Args: query_text: The search query text search_type: Type of search (keyword, embedding, hybrid) - group_id: Group identifier for filtering + end_user_id: Group identifier for filtering limit: Maximum number of results include: List of result types to include log_file: Deprecated parameter, kept for backward compatibility @@ -413,7 +414,7 @@ def log_search_query(query_text: str, search_type: str, group_id: str | None, li # Log using the standard logger logger.info( f"Search query: query='{cleaned_query}', type={search_type}, " - f"group_id={group_id}, limit={limit}, include={include}" + f"end_user_id={end_user_id}, limit={limit}, include={include}" ) @@ -672,7 +673,7 @@ def apply_reranker_placeholder( async def run_hybrid_search( query_text: str, search_type: str, - group_id: str | None, + end_user_id: str | None, limit: int, include: List[str], output_path: str | None, @@ -715,7 +716,7 @@ async def run_hybrid_search( } # Log the search query - log_search_query(query_text, search_type, group_id, limit, include) + log_search_query(query_text, search_type, end_user_id, limit, include) connector = Neo4jConnector() results = {} @@ -732,7 +733,7 @@ async def run_hybrid_search( search_graph( connector=connector, q=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include ) @@ -769,7 +770,7 @@ async def run_hybrid_search( connector=connector, embedder_client=embedder, query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, ) @@ -916,9 +917,7 @@ async def run_hybrid_search( async def search_by_temporal( - group_id: Optional[str] = "test", - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = "test", start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -929,7 +928,7 @@ async def search_by_temporal( Temporal search across Statements. - Matches statements created between start_date and end_date - - Optionally filters by group_id + - Optionally filters by end_user_id - Returns up to 'limit' statements """ connector = Neo4jConnector() @@ -939,9 +938,7 @@ async def search_by_temporal( end_date = normalize_date_safe(end_date) params = TemporalSearchParams.model_validate({ - "group_id": group_id, - "apply_id": apply_id, - "user_id": user_id, + "end_user_id": end_user_id, "start_date": start_date, "end_date": end_date, "valid_date": valid_date, @@ -950,9 +947,7 @@ async def search_by_temporal( }) statements = await search_graph_by_temporal( connector=connector, - group_id=params.group_id, - apply_id=params.apply_id, - user_id=params.user_id, + end_user_id=params.end_user_id, start_date=params.start_date, end_date=params.end_date, valid_date=params.valid_date, @@ -964,9 +959,7 @@ async def search_by_temporal( async def search_by_keyword_temporal( query_text: str, - group_id: Optional[str] = "test", - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = "test", start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -987,9 +980,7 @@ async def search_by_keyword_temporal( invalid_date = normalize_date_safe(invalid_date) params = TemporalSearchParams.model_validate({ - "group_id": group_id, - "apply_id": apply_id, - "user_id": user_id, + "end_user_id": end_user_id, "start_date": start_date, "end_date": end_date, "valid_date": valid_date, @@ -999,9 +990,7 @@ async def search_by_keyword_temporal( statements = await search_graph_by_keyword_temporal( connector=connector, query_text=query_text, - group_id=params.group_id, - apply_id=params.apply_id, - user_id=params.user_id, + end_user_id=params.end_user_id, start_date=params.start_date, end_date=params.end_date, valid_date=params.valid_date, @@ -1013,7 +1002,7 @@ async def search_by_keyword_temporal( async def search_chunk_by_chunk_id( chunk_id: str, - group_id: Optional[str] = "test", + end_user_id: Optional[str] = "test", limit: int = 1, ): """ @@ -1023,7 +1012,7 @@ async def search_chunk_by_chunk_id( chunks = await search_graph_by_chunk_id( connector=connector, chunk_id=chunk_id, - group_id=group_id, + end_user_id=end_user_id, limit=limit ) return {"chunks": chunks} diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py index f5e72517..4dafd3ed 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py @@ -555,8 +555,8 @@ class DataPreprocessor: dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}'))) - # 获取group_id，如果不存在则生成默认值 - group_id = item.get('group_id', f'group_default_{i}') + # 获取end_user_id，如果不存在则生成默认值 + end_user_id = item.get('end_user_id', f'group_default_{i}') user_id = item.get('user_id', f'user_default_{i}') apply_id = item.get('apply_id', f'apply_default_{i}') @@ -574,7 +574,7 @@ class DataPreprocessor: dialog_data = DialogData( context=context, ref_id=dialog_id, - group_id=group_id, + end_user_id=end_user_id, user_id=user_id, apply_id=apply_id, metadata=metadata @@ -644,7 +644,7 @@ class DataPreprocessor: context = ConversationContext(msgs=messages) dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}'))) - group_id = item.get('group_id', f'group_default_{i}') + end_user_id = item.get('end_user_id', f'group_default_{i}') user_id = item.get('user_id', f'user_default_{i}') apply_id = item.get('apply_id', f'apply_default_{i}') @@ -657,7 +657,7 @@ class DataPreprocessor: dialog_data = DialogData( context=context, ref_id=dialog_id, - group_id=group_id, + end_user_id=end_user_id, user_id=user_id, apply_id=apply_id, metadata=metadata diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py index 62b656b0..a425e0ed 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py @@ -199,7 +199,7 @@ def accurate_match( entity_nodes: List[ExtractedEntityNode] ) -> Tuple[List[ExtractedEntityNode], Dict[str, str], Dict[str, Dict]]: """ - 精确匹配：按 (group_id, name, entity_type) 合并实体并建立重定向与合并记录。 + 精确匹配：按 (end_user_id, name, entity_type) 合并实体并建立重定向与合并记录。 返回: (deduped_entities, id_redirect, exact_merge_map) """ exact_merge_map: Dict[str, Dict] = {} @@ -210,8 +210,8 @@ def accurate_match( for ent in entity_nodes: name_norm = (getattr(ent, "name", "") or "").strip() type_norm = (getattr(ent, "entity_type", "") or "").strip() - key = f"{getattr(ent, 'group_id', None)}|{name_norm}|{type_norm}" - # 为避免跨业务组误并，明确以 group_id 为范围边界 + key = f"{getattr(ent, 'end_user_id', None)}|{name_norm}|{type_norm}" + # 为避免跨业务组误并，明确以 end_user_id 为范围边界 if key not in canonical_map: canonical_map[key] = ent id_redirect[ent.id] = ent.id @@ -223,11 +223,11 @@ def accurate_match( id_redirect[ent.id] = canonical.id # 记录精确匹配的合并项（使用规范化键，避免外层变量误用） try: - k = f"{canonical.group_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}" + k = f"{canonical.end_user_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}" if k not in exact_merge_map: exact_merge_map[k] = { "canonical_id": canonical.id, - "group_id": canonical.group_id, + "end_user_id": canonical.end_user_id, "name": canonical.name, "entity_type": canonical.entity_type, "merged_ids": set(), @@ -596,7 +596,7 @@ def fuzzy_match( b = deduped_entities[j] # 跳过不同业务组的实体 - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): j += 1 continue @@ -671,7 +671,7 @@ def fuzzy_match( merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]" merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]" fuzzy_merge_records.append( - f"{merge_reason} 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type}) | " + f"{merge_reason} 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type}) | " f"s_name={s_name:.3f}, s_type={s_type:.3f}, overall={overall:.3f}, exact_alias={has_exact_match}" ) except Exception: @@ -779,7 +779,7 @@ async def LLM_decision( # 决策中包含去重和消歧的功能 # 记录 LLM 融合日志 try: llm_records.append( - f"[LLM融合] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})" + f"[LLM融合] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})" ) # 详细的“同类名称相似”记录改由 LLM 去重模块统一生成以携带 conf/reason except Exception: @@ -847,7 +847,7 @@ async def LLM_disamb_decision( id_redirect[k] = a.id try: disamb_records.append( - f"[DISAMB合并应用] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})" + f"[DISAMB合并应用] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py index 734f7b69..0249ac1f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py @@ -174,7 +174,7 @@ async def _judge_pair( pass # 3. 构建LLM判断的“上下文信息”（规则层计算的所有特征） 判断上下文特征有助于实体消歧首先判断的类型关系 ctx = { - "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None), + "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None), "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "type_similarity": _type_similarity(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "name_text_sim": name_text_sim, @@ -235,7 +235,7 @@ async def _judge_pair_disamb( except Exception: pass ctx = { - "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None), + "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None), "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "name_text_sim": name_text_sim, "name_embed_sim": name_embed_sim, @@ -317,8 +317,8 @@ async def llm_dedup_entities( # 保留对偶判断作为子流程，是为了 a = entity_nodes[i] for j in range(i + 1, len(entity_nodes)): b = entity_nodes[j] - # 规则1：必须属于同一组（group_id相同，不同组的实体不重复） - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + # 规则1：必须属于同一组（end_user_id相同，不同组的实体不重复） + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): continue # 规则2：类型必须兼容（调用_simple_type_ok判断） if not _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)): @@ -474,7 +474,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 - max_rounds: upper bound for iterative passes (default 3) - auto_merge_threshold: decision confidence for auto-merge when no co-occurrence (default 0.90) - co_ctx_threshold: lower threshold when co-occurrence is detected (default 0.83) - - shuffle_each_round: whether to shuffle entities within group_id each round to vary block composition + - shuffle_each_round: whether to shuffle entities within end_user_id each round to vary block composition Returns: - global_redirect: dict losing_id -> canonical_id accumulated across rounds @@ -509,7 +509,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 def _partition_blocks(nodes: List[ExtractedEntityNode]) -> List[List[ExtractedEntityNode]]: """ - 按 group_id 分块，避免跨组实体在同一块，减少无效候选对 + 按 end_user_id 分块，避免跨组实体在同一块，减少无效候选对 Args: nodes: 实体节点列表 @@ -519,7 +519,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 """ groups: Dict[str, List[ExtractedEntityNode]] = {} for e in nodes: - gid = getattr(e, "group_id", None) + gid = getattr(e, "end_user_id", None) groups.setdefault(str(gid), []).append(e) blocks: List[List[ExtractedEntityNode]] = [] for gid, arr in groups.items(): @@ -559,7 +559,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 # Collapse nodes to canonical reps before each round to avoid redundant comparisons # 步骤1：折叠实体（合并已确定的重复实体，减少后续计算量） current_nodes = _collapse_nodes(current_nodes) - # 步骤2：分块（按group_id分块，避免跨组处理） + # 步骤2：分块（按end_user_id分块，避免跨组处理） blocks = _partition_blocks(current_nodes) if not blocks: # 无块可处理（实体已全部折叠），退出循环 break @@ -645,7 +645,7 @@ async def llm_disambiguate_pairs_iterative( a = entity_nodes[i] b = entity_nodes[j] # 必须同组 - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): continue ta = getattr(a, "entity_type", None) tb = getattr(b, "entity_type", None) diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py index b41f35a4..dbc697d9 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py @@ -61,7 +61,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode: return ExtractedEntityNode( id=row.get("id"), name=row.get("name") or "", - group_id=row.get("group_id") or "", + end_user_id=row.get("end_user_id") or "", user_id=row.get("user_id") or "", apply_id=row.get("apply_id") or "", created_at=_parse_dt(row.get("created_at")), @@ -79,7 +79,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode: async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑，与 Neo4j 中同组实体联合去重 connector: Neo4jConnector, - group_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重 + end_user_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重 entity_nodes: List[ExtractedEntityNode], # 输入的实体节点列表，包含待去重的实体 statement_entity_edges: List[StatementEntityEdge], # 输入的语句实体边列表，用于处理实体之间的关系 entity_entity_edges: List[EntityEntityEdge], # 输入的实体实体边列表，用于处理实体之间的关系 @@ -88,7 +88,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑 ) -> Tuple[List[ExtractedEntityNode], List[StatementEntityEdge], List[EntityEntityEdge]]: """ 第二层去重消歧： - - 以第一层结果为索引，检索相同 group_id 下的 DB 候选实体 + - 以第一层结果为索引，检索相同 end_user_id 下的 DB 候选实体 - 将 DB 候选与当前实体集合联合，按既有精确/模糊/LLM 决策进行融合 - 返回融合后的实体与重定向后的边（边已指向规范 ID，优先 DB ID） """ @@ -102,7 +102,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑 ] candidates_map = await get_dedup_candidates_for_entities( # 从 Neo4j 中查询候选实体，并将结果赋值给candidates_map（等待异步操作完成）。 - connector=connector, group_id=group_id, + connector=connector, end_user_id=end_user_id, entities=incoming_rows, # 传入参数：第一层实体的核心信息（作为查询索引） use_contains_fallback=True # 传入参数：启用 “包含关系” 作为匹配失败的降级策略（若精确匹配无结果，用包含关系召回候选），与src\database\cypher_queries.py的307产生联动 ) diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py index 11845d7d..f28b8a5f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py @@ -57,11 +57,11 @@ async def dedup_layers_and_merge_and_return( if pipeline_config is None: raise ValueError("pipeline_config is required for dedup_layers_and_merge_and_return") - # 先探测 group_id，决定报告写入策略 - group_id: Optional[str] = None + # 先探测 end_user_id，决定报告写入策略 + end_user_id: Optional[str] = None for dd in dialog_data_list: - group_id = getattr(dd, "group_id", None) - if group_id: + end_user_id = getattr(dd, "end_user_id", None) + if end_user_id: break # 第一层去重消歧 @@ -82,11 +82,11 @@ async def dedup_layers_and_merge_and_return( # 第二层去重消歧：与 Neo4j 中同组实体联合融合 try: - if group_id: + if end_user_id: if connector: fused_entity_nodes, fused_statement_entity_edges, fused_entity_entity_edges = await second_layer_dedup_and_merge_with_neo4j( connector=connector, - group_id=group_id, + end_user_id=end_user_id, entity_nodes=dedup_entity_nodes, statement_entity_edges=dedup_statement_entity_edges, entity_entity_edges=dedup_entity_entity_edges, @@ -96,7 +96,7 @@ async def dedup_layers_and_merge_and_return( else: print("Skip second-layer dedup: missing connector") else: - print("Skip second-layer dedup: missing group_id") + print("Skip second-layer dedup: missing end_user_id") except Exception as e: print(f"Second-layer dedup failed: {e}") diff --git a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py index 46ba1dde..7b7e854b 100644 --- a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py +++ b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py @@ -287,7 +287,7 @@ class ExtractionOrchestrator: for d_idx, dialog in enumerate(dialog_data_list): dialogue_content = dialog.content if self.config.statement_extraction.include_dialogue_context else None for c_idx, chunk in enumerate(dialog.chunks): - all_chunks.append((chunk, dialog.group_id, dialogue_content)) + all_chunks.append((chunk, dialog.end_user_id, dialogue_content)) chunk_metadata.append((d_idx, c_idx)) logger.info(f"收集到 {len(all_chunks)} 个分块，开始全局并行提取") @@ -299,9 +299,9 @@ class ExtractionOrchestrator: # 全局并行处理所有分块 async def extract_for_chunk(chunk_data, chunk_index): nonlocal completed_chunks - chunk, group_id, dialogue_content = chunk_data + chunk, end_user_id, dialogue_content = chunk_data try: - statements = await self.statement_extractor._extract_statements(chunk, group_id, dialogue_content) + statements = await self.statement_extractor._extract_statements(chunk, end_user_id, dialogue_content) # 流式输出：每提取完一个分块的陈述句，立即发送进度 # 注意：只在试运行模式下发送陈述句详情，正式模式不发送 @@ -569,32 +569,32 @@ class ExtractionOrchestrator: if dialog_data_list and hasattr(dialog_data_list[0], 'config_id'): config_id = dialog_data_list[0].config_id - # 加载DataConfig - data_config = None + # 加载MemoryConfig + memory_config = None if config_id: try: from app.db import SessionLocal - from app.repositories.data_config_repository import DataConfigRepository + from app.repositories.memory_config_repository import MemoryConfigRepository db = SessionLocal() try: - data_config = DataConfigRepository.get_by_id(db, config_id) + memory_config = MemoryConfigRepository.get_by_id(db, config_id) finally: db.close() - if data_config and not data_config.emotion_enabled: + if memory_config and not memory_config.emotion_enabled: logger.info("情绪提取已在配置中禁用，跳过情绪提取") return [{} for _ in dialog_data_list] except Exception as e: - logger.warning(f"加载DataConfig失败: {e}，将跳过情绪提取") + logger.warning(f"加载MemoryConfig失败: {e}，将跳过情绪提取") return [{} for _ in dialog_data_list] else: logger.info("未找到config_id，跳过情绪提取") return [{} for _ in dialog_data_list] # 如果配置未启用情绪提取，直接返回空映射 - if not data_config or not data_config.emotion_enabled: + if not memory_config or not memory_config.emotion_enabled: logger.info("情绪提取未启用，跳过") return [{} for _ in dialog_data_list] @@ -608,7 +608,7 @@ class ExtractionOrchestrator: total_statements += 1 # 只处理用户的陈述句 (role 为 "user") if hasattr(statement, 'speaker') and statement.speaker == "user": - all_statements.append((statement, data_config)) + all_statements.append((statement, memory_config)) statement_metadata.append((d_idx, statement.id)) filtered_statements += 1 @@ -617,7 +617,7 @@ class ExtractionOrchestrator: # 初始化情绪提取服务 from app.services.emotion_extraction_service import EmotionExtractionService emotion_service = EmotionExtractionService( - llm_id=data_config.emotion_model_id if data_config.emotion_model_id else None + llm_id=memory_config.emotion_model_id if memory_config.emotion_model_id else None ) # 全局并行处理所有陈述句 @@ -992,9 +992,7 @@ class ExtractionOrchestrator: id=dialog_data.id, name=f"Dialog_{dialog_data.id}", # 添加必需的 name 字段 ref_id=dialog_data.ref_id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id content=dialog_data.context.content if dialog_data.context else "", dialog_embedding=dialog_data.dialog_embedding if hasattr(dialog_data, 'dialog_embedding') else None, @@ -1012,9 +1010,7 @@ class ExtractionOrchestrator: id=chunk.id, name=f"Chunk_{chunk.id}", # 添加必需的 name 字段 dialog_id=dialog_data.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id content=chunk.content, chunk_embedding=chunk.chunk_embedding, @@ -1035,9 +1031,7 @@ class ExtractionOrchestrator: stmt_type=getattr(statement, 'stmt_type', 'general'), # 添加必需的 stmt_type 字段 temporal_info=getattr(statement, 'temporal_info', TemporalInfo.ATEMPORAL), # 添加必需的 temporal_info 字段 connect_strength=statement.connect_strength if statement.connect_strength is not None else 'Strong', # 添加必需的 connect_strength 字段 - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id statement=statement.statement, speaker=getattr(statement, 'speaker', None), # 添加 speaker 字段 @@ -1060,9 +1054,7 @@ class ExtractionOrchestrator: statement_chunk_edge = StatementChunkEdge( source=statement.id, target=chunk.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, ) @@ -1072,13 +1064,16 @@ class ExtractionOrchestrator: if statement.triplet_extraction_info: triplet_info = statement.triplet_extraction_info - # 创建实体索引到ID的映射 + # 创建实体索引到ID的映射（支持多种索引方式） entity_idx_to_id = {} # 创建实体节点 for entity_idx, entity in enumerate(triplet_info.entities): - # 映射实体索引到实体ID + # 映射实体索引到实体ID（使用多个键以提高容错性） + # 1. 使用实体自己的 entity_idx entity_idx_to_id[entity.entity_idx] = entity.id + # 2. 使用枚举索引（从0开始） + entity_idx_to_id[entity_idx] = entity.id if entity.id not in entity_id_set: entity_connect_strength = getattr(entity, 'connect_strength', 'Strong') @@ -1095,9 +1090,7 @@ class ExtractionOrchestrator: aliases=getattr(entity, 'aliases', []) or [], # 传递从三元组提取阶段获取的aliases name_embedding=getattr(entity, 'name_embedding', None), is_explicit_memory=getattr(entity, 'is_explicit_memory', False), # 新增：传递语义记忆标记 - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, expired_at=dialog_data.expired_at, @@ -1112,9 +1105,7 @@ class ExtractionOrchestrator: source=statement.id, target=entity.id, connect_strength=entity_connect_strength if entity_connect_strength is not None else 'Strong', - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, ) @@ -1134,9 +1125,7 @@ class ExtractionOrchestrator: relation_type=triplet.predicate, statement=statement.statement, source_statement_id=statement.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, expired_at=dialog_data.expired_at, @@ -1163,9 +1152,18 @@ class ExtractionOrchestrator: relationship_result ) else: - logger.warning( - f"跳过三元组 - 无法找到实体ID: subject_id={triplet.subject_id}, " - f"object_id={triplet.object_id}, statement_id={statement.id}" + # 改进的警告信息，包含更多调试信息 + missing_subject = "subject" if not subject_entity_id else "" + missing_object = "object" if not object_entity_id else "" + missing_both = " and " if (not subject_entity_id and not object_entity_id) else "" + + logger.debug( + f"跳过三元组 - 无法找到{missing_subject}{missing_both}{missing_object}实体ID: " + f"subject_id={triplet.subject_id} ({triplet.subject_name}), " + f"object_id={triplet.object_id} ({triplet.object_name}), " + f"predicate={triplet.predicate}, " + f"statement_id={statement.id}, " + f"available_indices={sorted(entity_idx_to_id.keys())}" ) logger.info( @@ -1763,14 +1761,14 @@ class ExtractionOrchestrator: async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", - group_id: str = "group_1", + end_user_id: str = "group_1", indices: Optional[List[int]] = None, ) -> List[DialogData]: """从测试数据生成分块对话 Args: chunker_strategy: 分块策略（默认: RecursiveChunker） - group_id: 组ID + end_user_id: 组ID indices: 要处理的数据索引列表（可选） Returns: @@ -1834,7 +1832,7 @@ async def get_chunked_dialogs( dialog_data = DialogData( context=conversation_context, ref_id=data['id'], - group_id=group_id, + end_user_id=end_user_id, metadata=dialog_metadata, ) @@ -1936,7 +1934,7 @@ async def get_chunked_dialogs_from_preprocessed( async def get_chunked_dialogs_with_preprocessing( chunker_strategy: str = "RecursiveChunker", - group_id: str = "default", + end_user_id: str = "default", user_id: str = "default", apply_id: str = "default", indices: Optional[List[int]] = None, @@ -1948,7 +1946,7 @@ async def get_chunked_dialogs_with_preprocessing( Args: chunker_strategy: 分块策略 - group_id: 组ID + end_user_id: 组ID user_id: 用户ID apply_id: 应用ID indices: 要处理的数据索引列表 @@ -1976,11 +1974,9 @@ async def get_chunked_dialogs_with_preprocessing( indices=indices, ) - # 设置 group_id, user_id, apply_id + # 设置 end_user_id for dd in preprocessed_data: - dd.group_id = group_id - dd.user_id = user_id - dd.apply_id = apply_id + dd.end_user_id = end_user_id # 步骤2: 语义剪枝 try: diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/__init__.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/__init__.py index 53815124..0bc09622 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/__init__.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/__init__.py @@ -8,4 +8,5 @@ - TemporalExtractor: 时间信息提取 - EmbeddingGenerator: 嵌入向量生成 - MemorySummaryGenerator: 记忆摘要生成 +- OntologyExtractor: 本体类提取 """ diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py index 7e75fd2d..58633363 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py @@ -14,6 +14,34 @@ from pydantic import Field logger = get_memory_logger(__name__) +# 支持的语言列表和默认回退值 +SUPPORTED_LANGUAGES = {"zh", "en"} +FALLBACK_LANGUAGE = "en" + + +def validate_language(language: Optional[str]) -> str: + """ + 校验语言参数，确保其为有效值。 + + Args: + language: 待校验的语言代码 + + Returns: + 有效的语言代码（"zh" 或 "en"） + """ + if language is None: + return FALLBACK_LANGUAGE + + lang = str(language).lower().strip() + if lang in SUPPORTED_LANGUAGES: + return lang + + logger.warning( + f"无效的语言参数 '{language}'，已回退到默认值 '{FALLBACK_LANGUAGE}'。" + f"支持的语言: {SUPPORTED_LANGUAGES}" + ) + return FALLBACK_LANGUAGE + class MemorySummaryResponse(RobustLLMResponse): """Structured response for summary generation per chunk. @@ -31,7 +59,8 @@ class MemorySummaryResponse(RobustLLMResponse): async def generate_title_and_type_for_summary( content: str, - llm_client + llm_client, + language: str = None ) -> Tuple[str, str]: """ 为MemorySummary生成标题和类型 @@ -41,11 +70,18 @@ async def generate_title_and_type_for_summary( Args: content: Summary的内容文本 llm_client: LLM客户端实例 + language: 生成标题使用的语言 ("zh" 中文, "en" 英文)，如果为None则从配置读取 Returns: (标题, 类型)元组 """ from app.core.memory.utils.prompt.prompt_utils import render_episodic_title_and_type_prompt + from app.core.config import settings + + # 如果没有指定语言，从配置中读取，并校验有效性 + if language is None: + language = settings.DEFAULT_LANGUAGE + language = validate_language(language) # 定义有效的类型集合 VALID_TYPES = { @@ -57,13 +93,19 @@ async def generate_title_and_type_for_summary( } DEFAULT_TYPE = "conversation" # 默认类型 + # 根据语言设置默认标题 + DEFAULT_TITLE = "空内容" if language == "zh" else "Empty Content" + PARSE_ERROR_TITLE = "解析失败" if language == "zh" else "Parse Failed" + ERROR_TITLE = "错误" if language == "zh" else "Error" + UNKNOWN_TITLE = "未知标题" if language == "zh" else "Unknown Title" + try: if not content: - logger.warning("content为空，无法生成标题和类型") - return ("空内容", DEFAULT_TYPE) + logger.warning(f"content为空，无法生成标题和类型 (language={language})") + return (DEFAULT_TITLE, DEFAULT_TYPE) - # 1. 渲染Jinja2提示词模板 - prompt = await render_episodic_title_and_type_prompt(content) + # 1. 渲染Jinja2提示词模板，传递语言参数 + prompt = await render_episodic_title_and_type_prompt(content, language=language) # 2. 调用LLM生成标题和类型 messages = [ @@ -102,7 +144,7 @@ async def generate_title_and_type_for_summary( json_str = json_str.strip() result_data = json.loads(json_str) - title = result_data.get("title", "未知标题") + title = result_data.get("title", UNKNOWN_TITLE) episodic_type_raw = result_data.get("type", DEFAULT_TYPE) # 5. 校验和归一化类型 @@ -130,16 +172,16 @@ async def generate_title_and_type_for_summary( f"已归一化为 '{episodic_type}'" ) - logger.info(f"成功生成标题和类型: title={title}, type={episodic_type}") + logger.info(f"成功生成标题和类型 (language={language}): title={title}, type={episodic_type}") return (title, episodic_type) except json.JSONDecodeError: - logger.error(f"无法解析LLM响应为JSON: {full_response}") - return ("解析失败", DEFAULT_TYPE) + logger.error(f"无法解析LLM响应为JSON (language={language}): {full_response}") + return (PARSE_ERROR_TITLE, DEFAULT_TYPE) except Exception as e: - logger.error(f"生成标题和类型时出错: {str(e)}", exc_info=True) - return ("错误", DEFAULT_TYPE) + logger.error(f"生成标题和类型时出错 (language={language}): {str(e)}", exc_info=True) + return (ERROR_TITLE, DEFAULT_TYPE) async def _process_chunk_summary( dialog: DialogData, @@ -153,11 +195,16 @@ async def _process_chunk_summary( return None try: + # 从配置中获取语言设置（只获取一次，复用），并校验有效性 + from app.core.config import settings + language = validate_language(settings.DEFAULT_LANGUAGE) + # Render prompt via Jinja2 for a single chunk prompt_content = await render_memory_summary_prompt( chunk_texts=chunk.content, json_schema=MemorySummaryResponse.model_json_schema(), max_words=200, + language=language, ) messages = [ @@ -178,9 +225,10 @@ async def _process_chunk_summary( try: title, episodic_type = await generate_title_and_type_for_summary( content=summary_text, - llm_client=llm_client + llm_client=llm_client, + language=language ) - logger.info(f"Generated title and type for MemorySummary: title={title}, type={episodic_type}") + logger.info(f"Generated title and type for MemorySummary (language={language}): title={title}, type={episodic_type}") except Exception as e: logger.warning(f"Failed to generate title and type for chunk {chunk.id}: {e}") # Continue without title and type @@ -193,9 +241,9 @@ async def _process_chunk_summary( node = MemorySummaryNode( id=uuid4().hex, name=title if title else f"MemorySummaryChunk_{chunk.id}", - group_id=dialog.group_id, - user_id=dialog.user_id, - apply_id=dialog.apply_id, + end_user_id=dialog.end_user_id, + user_id=dialog.end_user_id, + apply_id=dialog.end_user_id, run_id=dialog.run_id, # 使用 dialog 的 run_id created_at=datetime.now(), expired_at=datetime(9999, 12, 31), diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/ontology_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/ontology_extraction.py new file mode 100644 index 00000000..d1b79bd1 --- /dev/null +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/ontology_extraction.py @@ -0,0 +1,482 @@ +"""Ontology class extraction from scenario descriptions using LLM. + +This module provides the OntologyExtractor class for extracting ontology classes +from natural language scenario descriptions. It uses LLM-driven extraction combined +with two-layer validation (string validation + OWL semantic validation). + +Classes: + OntologyExtractor: Extracts ontology classes from scenario descriptions +""" + +import asyncio +import logging +import time +from typing import List, Optional + +from app.core.memory.llm_tools.openai_client import OpenAIClient +from app.core.memory.models.ontology_models import ( + OntologyClass, + OntologyExtractionResponse, +) +from app.core.memory.utils.validation.ontology_validator import OntologyValidator +from app.core.memory.utils.validation.owl_validator import OWLValidator +from app.core.memory.utils.prompt.prompt_utils import render_ontology_extraction_prompt + + +logger = logging.getLogger(__name__) + + +class OntologyExtractor: + """Extractor for ontology classes from scenario descriptions. + + This extractor uses LLM to identify abstract classes and concepts from + natural language scenario descriptions, following OWL ontology engineering + standards. It performs two-layer validation: + 1. String validation (naming conventions, reserved words, duplicates) + 2. OWL semantic validation (consistency checking, circular inheritance) + + Attributes: + llm_client: OpenAI client for LLM calls + validator: String validator for class names and descriptions + owl_validator: OWL validator for semantic validation + """ + + def __init__(self, llm_client: OpenAIClient): + """Initialize the OntologyExtractor. + + Args: + llm_client: OpenAIClient instance for LLM processing + """ + self.llm_client = llm_client + self.validator = OntologyValidator() + self.owl_validator = OWLValidator() + + logger.info("OntologyExtractor initialized") + + async def extract_ontology_classes( + self, + scenario: str, + domain: Optional[str] = None, + max_classes: int = 15, + min_classes: int = 5, + enable_owl_validation: bool = True, + llm_temperature: float = 0.3, + llm_max_tokens: int = 2000, + max_description_length: int = 500, + timeout: Optional[float] = None, + ) -> OntologyExtractionResponse: + """Extract ontology classes from a scenario description. + + This is the main extraction method that orchestrates the entire process: + 1. Call LLM to extract ontology classes + 2. Perform first-layer validation (string validation and cleaning) + 3. Perform second-layer validation (OWL semantic validation) + 4. Filter invalid classes based on validation errors + 5. Return validated ontology classes + + Args: + scenario: Natural language scenario description + domain: Optional domain hint (e.g., "Healthcare", "Education") + max_classes: Maximum number of classes to extract (default: 15) + min_classes: Minimum number of classes to extract (default: 5) + enable_owl_validation: Whether to enable OWL validation (default: True) + llm_temperature: LLM temperature parameter (default: 0.3) + llm_max_tokens: LLM max tokens parameter (default: 2000) + max_description_length: Maximum description length (default: 500) + timeout: Optional timeout in seconds for LLM call (default: None, no timeout) + + Returns: + OntologyExtractionResponse containing validated ontology classes + + Raises: + ValueError: If scenario is empty or invalid + asyncio.TimeoutError: If extraction times out + + Examples: + >>> extractor = OntologyExtractor(llm_client) + >>> response = await extractor.extract_ontology_classes( + ... scenario="A hospital manages patient records...", + ... domain="Healthcare", + ... max_classes=10, + ... timeout=30.0 + ... ) + >>> len(response.classes) + 7 + """ + # Start timing + start_time = time.time() + + # Validate input + if not scenario or not scenario.strip(): + logger.error("Scenario description is empty") + raise ValueError("Scenario description cannot be empty") + + scenario = scenario.strip() + + logger.info( + f"Starting ontology extraction - scenario_length={len(scenario)}, " + f"domain={domain}, max_classes={max_classes}, min_classes={min_classes}, " + f"timeout={timeout}" + ) + + try: + # Step 1: Call LLM for extraction with timeout + logger.info("Step 1: Calling LLM for ontology extraction") + llm_start_time = time.time() + + if timeout is not None: + # Wrap LLM call with timeout + try: + response = await asyncio.wait_for( + self._call_llm_for_extraction( + scenario=scenario, + domain=domain, + max_classes=max_classes, + llm_temperature=llm_temperature, + llm_max_tokens=llm_max_tokens, + ), + timeout=timeout + ) + except asyncio.TimeoutError: + llm_duration = time.time() - llm_start_time + logger.error( + f"LLM extraction timed out after {timeout} seconds " + f"(actual duration: {llm_duration:.2f}s)" + ) + # Return empty response on timeout + return OntologyExtractionResponse( + classes=[], + domain=domain or "Unknown", + ) + else: + # No timeout specified, call directly + response = await self._call_llm_for_extraction( + scenario=scenario, + domain=domain, + max_classes=max_classes, + llm_temperature=llm_temperature, + llm_max_tokens=llm_max_tokens, + ) + + llm_duration = time.time() - llm_start_time + logger.info( + f"LLM returned {len(response.classes)} classes in {llm_duration:.2f}s" + ) + + # Step 2: First-layer validation (string validation and cleaning) + logger.info("Step 2: Performing first-layer validation (string validation)") + validation_start_time = time.time() + + response = self._validate_and_clean( + response=response, + max_description_length=max_description_length, + ) + + validation_duration = time.time() - validation_start_time + logger.info( + f"After first-layer validation: {len(response.classes)} classes remain " + f"(validation took {validation_duration:.2f}s)" + ) + + # Check if we have enough classes after first-layer validation + if len(response.classes) < min_classes: + logger.warning( + f"Only {len(response.classes)} classes remain after validation, " + f"which is below minimum of {min_classes}" + ) + + # Step 3: Second-layer validation (OWL semantic validation) + if enable_owl_validation and response.classes: + logger.info("Step 3: Performing second-layer validation (OWL validation)") + owl_start_time = time.time() + + is_valid, errors, world = self.owl_validator.validate_ontology_classes( + classes=response.classes, + ) + + owl_duration = time.time() - owl_start_time + + if not is_valid: + logger.warning( + f"OWL validation found {len(errors)} issues in {owl_duration:.2f}s: {errors}" + ) + + # Filter invalid classes based on errors + response = self._filter_invalid_classes( + response=response, + errors=errors, + ) + + logger.info( + f"After second-layer validation: {len(response.classes)} classes remain" + ) + else: + logger.info(f"OWL validation passed successfully in {owl_duration:.2f}s") + else: + if not enable_owl_validation: + logger.info("Step 3: OWL validation disabled, skipping") + else: + logger.info("Step 3: No classes to validate, skipping OWL validation") + + # Calculate total duration + total_duration = time.time() - start_time + + # Log extraction statistics + logger.info( + f"Ontology extraction completed - " + f"final_class_count={len(response.classes)}, " + f"domain={response.domain}, " + f"total_duration={total_duration:.2f}s, " + f"llm_duration={llm_duration:.2f}s" + ) + + return response + + except asyncio.TimeoutError: + # Re-raise timeout errors + total_duration = time.time() - start_time + logger.error( + f"Ontology extraction timed out after {timeout} seconds " + f"(total duration: {total_duration:.2f}s)", + exc_info=True + ) + raise + except Exception as e: + total_duration = time.time() - start_time + logger.error( + f"Ontology extraction failed after {total_duration:.2f}s: {str(e)}", + exc_info=True + ) + # Return empty response on failure + return OntologyExtractionResponse( + classes=[], + domain=domain or "Unknown", + ) + + async def _call_llm_for_extraction( + self, + scenario: str, + domain: Optional[str], + max_classes: int, + llm_temperature: float, + llm_max_tokens: int, + ) -> OntologyExtractionResponse: + """Call LLM to extract ontology classes from scenario. + + This method renders the extraction prompt using the Jinja2 template + and calls the LLM with structured output to get ontology classes. + + Args: + scenario: Scenario description text + domain: Optional domain hint + max_classes: Maximum number of classes to extract + llm_temperature: LLM temperature parameter + llm_max_tokens: LLM max tokens parameter + + Returns: + OntologyExtractionResponse from LLM + + Raises: + Exception: If LLM call fails + """ + try: + # Render prompt using template + prompt_content = await render_ontology_extraction_prompt( + scenario=scenario, + domain=domain, + max_classes=max_classes, + json_schema=OntologyExtractionResponse.model_json_schema(), + ) + + logger.debug(f"Rendered prompt length: {len(prompt_content)}") + + # Create messages for LLM + messages = [ + { + "role": "system", + "content": ( + "You are an expert ontology engineer specializing in knowledge " + "representation and OWL standards. Extract ontology classes from " + "scenario descriptions following the provided instructions. " + "Return valid JSON conforming to the schema." + ), + }, + { + "role": "user", + "content": prompt_content, + }, + ] + + # Call LLM with structured output + logger.debug( + f"Calling LLM with temperature={llm_temperature}, " + f"max_tokens={llm_max_tokens}" + ) + + response = await self.llm_client.response_structured( + messages=messages, + response_model=OntologyExtractionResponse, + ) + + logger.info( + f"LLM extraction successful - extracted {len(response.classes)} classes" + ) + + return response + + except Exception as e: + logger.error( + f"LLM extraction failed: {str(e)}", + exc_info=True + ) + raise + + def _validate_and_clean( + self, + response: OntologyExtractionResponse, + max_description_length: int, + ) -> OntologyExtractionResponse: + """Perform first-layer validation: string validation and cleaning. + + This method validates and cleans the extracted ontology classes: + 1. Validate class names (PascalCase, no reserved words) + 2. Sanitize invalid class names + 3. Truncate long descriptions + 4. Remove duplicate classes + + Args: + response: OntologyExtractionResponse from LLM + max_description_length: Maximum description length + + Returns: + Cleaned OntologyExtractionResponse + """ + if not response.classes: + logger.debug("No classes to validate") + return response + + logger.debug(f"Validating {len(response.classes)} classes") + + validated_classes = [] + + for ontology_class in response.classes: + # Validate class name + is_valid, error_msg = self.validator.validate_class_name( + ontology_class.name + ) + + if not is_valid: + logger.warning( + f"Invalid class name '{ontology_class.name}': {error_msg}" + ) + + # Attempt to sanitize + sanitized_name = self.validator.sanitize_class_name( + ontology_class.name + ) + + logger.info( + f"Sanitized class name: '{ontology_class.name}' -> '{sanitized_name}'" + ) + + # Update class name + ontology_class.name = sanitized_name + + # Re-validate sanitized name + is_valid, error_msg = self.validator.validate_class_name( + sanitized_name + ) + + if not is_valid: + logger.error( + f"Failed to sanitize class name '{ontology_class.name}': {error_msg}. " + "Skipping this class." + ) + continue + + # Truncate description if too long + if ontology_class.description: + original_length = len(ontology_class.description) + ontology_class.description = self.validator.truncate_description( + ontology_class.description, + max_length=max_description_length, + ) + + if len(ontology_class.description) < original_length: + logger.debug( + f"Truncated description for '{ontology_class.name}': " + f"{original_length} -> {len(ontology_class.description)} chars" + ) + + validated_classes.append(ontology_class) + + # Remove duplicates (case-insensitive) + original_count = len(validated_classes) + validated_classes = self.validator.remove_duplicates(validated_classes) + + if len(validated_classes) < original_count: + logger.info( + f"Removed {original_count - len(validated_classes)} duplicate classes" + ) + + # Return cleaned response + return OntologyExtractionResponse( + classes=validated_classes, + domain=response.domain, + ) + + def _filter_invalid_classes( + self, + response: OntologyExtractionResponse, + errors: List[str], + ) -> OntologyExtractionResponse: + """Filter invalid classes based on OWL validation errors. + + This method analyzes OWL validation errors and removes classes + that caused validation failures (e.g., circular inheritance, + inconsistencies). + + Args: + response: OntologyExtractionResponse to filter + errors: List of error messages from OWL validation + + Returns: + Filtered OntologyExtractionResponse + """ + if not errors: + return response + + logger.debug(f"Filtering classes based on {len(errors)} OWL validation errors") + + # Extract class names mentioned in errors + invalid_class_names = set() + + for error in errors: + # Look for class names in error messages + for ontology_class in response.classes: + if ontology_class.name in error: + invalid_class_names.add(ontology_class.name) + logger.debug( + f"Class '{ontology_class.name}' marked as invalid due to error: {error}" + ) + + # Filter out invalid classes + if invalid_class_names: + original_count = len(response.classes) + + filtered_classes = [ + c for c in response.classes + if c.name not in invalid_class_names + ] + + logger.info( + f"Filtered out {original_count - len(filtered_classes)} invalid classes: " + f"{invalid_class_names}" + ) + + return OntologyExtractionResponse( + classes=filtered_classes, + domain=response.domain, + ) + + return response diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py index fb1b539a..b06bd70f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py @@ -82,12 +82,12 @@ class StatementExtractor: logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty") return None - async def _extract_statements(self, chunk, group_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]: + async def _extract_statements(self, chunk, end_user_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]: """Process a single chunk and return extracted statements Args: chunk: Chunk object to process - group_id: Group ID to assign to all statements in this chunk + end_user_id: Group ID to assign to all statements in this chunk dialogue_content: Full dialogue content to provide as context Returns: @@ -158,7 +158,7 @@ class StatementExtractor: temporal_info=temporal_type, relevence_info=relevence_info, chunk_id=chunk.id, - group_id=group_id, + end_user_id=end_user_id, speaker=chunk_speaker, ) @@ -184,10 +184,10 @@ class StatementExtractor: logger.info(f"Processing {len(chunks_to_process)} chunks for statement extraction") - # Process all chunks concurrently, passing the group_id and dialogue content from dialog_data + # Process all chunks concurrently, passing the end_user_id and dialogue content from dialog_data dialogue_content = dialog_data.content if self.config.include_dialogue_context else None results = await asyncio.gather( - *[self._extract_statements(chunk, dialog_data.group_id, dialogue_content) for chunk in chunks_to_process], + *[self._extract_statements(chunk, dialog_data.end_user_id, dialogue_content) for chunk in chunks_to_process], return_exceptions=True ) @@ -225,7 +225,7 @@ class StatementExtractor: for i, statement in enumerate(statements, 1): f.write(f"Statement {i}:\n") f.write(f"Id: {statement.id}\n") - f.write(f"Group Id: {statement.group_id}\n") + f.write(f"Group Id: {statement.end_user_id}\n") f.write(f"Content: {statement.statement}\n") f.write(f"Type: {statement.stmt_type.value}\n") f.write(f"Temporal Info: {statement.temporal_info.value}\n") @@ -298,7 +298,7 @@ class StatementExtractor: dialog_sections.append({ "dialog_id": dialog.ref_id, - "group_id": dialog.group_id, + "end_user_id": dialog.end_user_id, "content": dialog.content if getattr(dialog, "content", None) else "", "strong": strong_relations, "weak": weak_relations, @@ -312,7 +312,7 @@ class StatementExtractor: for idx, section in enumerate(dialog_sections, 1): f.write(f"Dialog {idx}:\n") f.write(f"Dialog ID: {section.get('dialog_id', '')}\n") - f.write(f"Group ID: {section.get('group_id', '')}\n") + f.write(f"Group ID: {section.get('end_user_id', '')}\n") f.write("Content:\n") f.write(f"{section.get('content', '')}\n") f.write("-" * 40 + "\n\n") diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py index 9528e638..499027a4 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py @@ -132,7 +132,7 @@ class TemporalExtractor: prompt_logger.info("") prompt_logger.info("=== TEMPORAL EXTRACTION RESULTS ===") prompt_logger.info( - f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}" + f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py index d3d059b0..8c3e31b4 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py @@ -25,6 +25,15 @@ class TripletExtractor: """ self.llm_client = llm_client + def _get_language(self) -> str: + """Get the configured language for entity descriptions + + Returns: + Language code ("zh" or "en") + """ + from app.core.config import settings + return settings.DEFAULT_LANGUAGE + async def _extract_triplets(self, statement: Statement, chunk_content: str) -> TripletExtractionResponse: """Process a single statement and return extracted triplets and entities""" # Render the prompt using helper function @@ -40,7 +49,8 @@ class TripletExtractor: statement=statement.statement, chunk_content=chunk_content, json_schema=TripletExtractionResponse.model_json_schema(), - predicate_instructions=PREDICATE_DEFINITIONS + predicate_instructions=PREDICATE_DEFINITIONS, + language=self._get_language() ) # Create messages for LLM @@ -116,7 +126,7 @@ class TripletExtractor: logger.info(f"Processing {len(all_statements)} statements for triplet extraction...") try: prompt_logger.info( - f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}, statements_to_process={len(all_statements)}" + f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}, statements_to_process={len(all_statements)}" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py b/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py index 5722769a..a71c0957 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py +++ b/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py @@ -75,7 +75,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_time: Optional[datetime] = None ) -> Dict[str, Any]: """ @@ -91,7 +91,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签（Statement, ExtractedEntity, MemorySummary） - group_id: 组ID（可选，用于过滤） + end_user_id: 组ID（可选，用于过滤） current_time: 当前时间（可选，默认使用系统时间） Returns: @@ -123,7 +123,7 @@ class AccessHistoryManager: for attempt in range(self.max_retries): try: # 步骤1：读取当前节点状态 - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: raise ValueError( @@ -142,7 +142,7 @@ class AccessHistoryManager: node_id=node_id, node_label=node_label, update_data=update_data, - group_id=group_id + end_user_id=end_user_id ) logger.info( @@ -172,7 +172,7 @@ class AccessHistoryManager: self, node_ids: List[str], node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_time: Optional[datetime] = None ) -> List[Dict[str, Any]]: """ @@ -184,7 +184,7 @@ class AccessHistoryManager: Args: node_ids: 节点ID列表 node_label: 节点标签（所有节点必须是同一类型） - group_id: 组ID（可选） + end_user_id: 组ID（可选） current_time: 当前时间（可选） Returns: @@ -202,7 +202,7 @@ class AccessHistoryManager: task = self.record_access( node_id=node_id, node_label=node_label, - group_id=group_id, + end_user_id=end_user_id, current_time=current_time ) tasks.append(task) @@ -235,7 +235,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Tuple[ConsistencyCheckResult, Optional[str]]: """ 检查节点数据的一致性 @@ -249,14 +249,14 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Tuple[ConsistencyCheckResult, Optional[str]]: - 一致性检查结果枚举 - 错误描述（如果不一致） """ - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: return ConsistencyCheckResult.CONSISTENT, None @@ -305,7 +305,7 @@ class AccessHistoryManager: async def check_batch_consistency( self, node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1000 ) -> Dict[str, Any]: """ @@ -313,7 +313,7 @@ class AccessHistoryManager: Args: node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） limit: 检查的最大节点数 Returns: @@ -329,16 +329,16 @@ class AccessHistoryManager: MATCH (n:{node_label}) WHERE n.access_history IS NOT NULL """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ RETURN n.id as id LIMIT $limit """ params = {"limit": limit} - if group_id: - params["group_id"] = group_id + if end_user_id: + params["end_user_id"] = end_user_id results = await self.connector.execute_query(query, **params) node_ids = [r['id'] for r in results] @@ -351,7 +351,7 @@ class AccessHistoryManager: result, message = await self.check_consistency( node_id=node_id, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) if result == ConsistencyCheckResult.CONSISTENT: @@ -387,7 +387,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> bool: """ 自动修复节点的数据不一致问题 @@ -401,7 +401,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: bool: 修复成功返回True，否则返回False @@ -411,7 +411,7 @@ class AccessHistoryManager: result, message = await self.check_consistency( node_id=node_id, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) if result == ConsistencyCheckResult.CONSISTENT: @@ -419,7 +419,7 @@ class AccessHistoryManager: return True # 获取节点数据 - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: logger.error(f"节点不存在，无法修复: {node_label}[{node_id}]") return False @@ -457,8 +457,8 @@ class AccessHistoryManager: query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - query += " WHERE n.group_id = $group_id" + if end_user_id: + query += " WHERE n.end_user_id = $end_user_id" query += """ SET n += $repair_data RETURN n @@ -468,8 +468,8 @@ class AccessHistoryManager: 'node_id': node_id, 'repair_data': repair_data } - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id await self.connector.execute_query(query, **params) @@ -491,7 +491,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Optional[Dict[str, Any]]: """ 获取节点数据 @@ -499,7 +499,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Optional[Dict[str, Any]]: 节点数据，如果不存在返回None @@ -507,8 +507,8 @@ class AccessHistoryManager: query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - query += " WHERE n.group_id = $group_id" + if end_user_id: + query += " WHERE n.end_user_id = $end_user_id" query += """ RETURN n.id as id, n.importance_score as importance_score, @@ -519,8 +519,8 @@ class AccessHistoryManager: """ params = {'node_id': node_id} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) @@ -585,7 +585,7 @@ class AccessHistoryManager: node_id: str, node_label: str, update_data: Dict[str, Any], - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Dict[str, Any]: """ 原子性更新节点（使用乐观锁） @@ -597,7 +597,7 @@ class AccessHistoryManager: node_id: 节点ID node_label: 节点标签 update_data: 更新数据 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Dict[str, Any]: 更新后的节点数据 @@ -606,13 +606,13 @@ class AccessHistoryManager: RuntimeError: 如果更新失败或发生版本冲突 """ # 定义事务函数 - async def update_transaction(tx, node_id, node_label, update_data, group_id): + async def update_transaction(tx, node_id, node_label, update_data, end_user_id): # 步骤1：读取当前节点并获取版本号 read_query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - read_query += " WHERE n.group_id = $group_id" + if end_user_id: + read_query += " WHERE n.end_user_id = $end_user_id" read_query += """ RETURN n.id as id, n.version as version, @@ -624,8 +624,8 @@ class AccessHistoryManager: """ read_params = {'node_id': node_id} - if group_id: - read_params['group_id'] = group_id + if end_user_id: + read_params['end_user_id'] = end_user_id read_result = await tx.run(read_query, **read_params) current_node = await read_result.single() @@ -656,8 +656,8 @@ class AccessHistoryManager: # 构建 WHERE 子句 where_conditions = [] - if group_id: - where_conditions.append("n.group_id = $group_id") + if end_user_id: + where_conditions.append("n.end_user_id = $end_user_id") # 添加版本检查 if current_version > 0: @@ -695,8 +695,8 @@ class AccessHistoryManager: 'last_access_time': update_data['last_access_time'], 'access_count': update_data['access_count'] } - if group_id: - update_params['group_id'] = group_id + if end_user_id: + update_params['end_user_id'] = end_user_id update_result = await tx.run(update_query, **update_params) updated_node = await update_result.single() @@ -720,7 +720,7 @@ class AccessHistoryManager: node_id=node_id, node_label=node_label, update_data=update_data, - group_id=group_id + end_user_id=end_user_id ) return result except Exception as e: diff --git a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py index ea9a6358..25daa968 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py +++ b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py @@ -11,9 +11,10 @@ Functions: import logging from typing import Optional, Dict, Any +from uuid import UUID from sqlalchemy.orm import Session -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator @@ -61,12 +62,12 @@ def calculate_forgetting_rate(lambda_time: float, lambda_mem: float) -> float: def load_actr_config_from_db( db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 从数据库加载 ACT-R 配置参数 - 从 PostgreSQL 的 data_config 表读取配置参数， + 从 PostgreSQL 的 memory_config 表读取配置参数， 并计算派生参数（如 forgetting_rate）。 Args: @@ -99,7 +100,7 @@ def load_actr_config_from_db( # 从数据库加载配置 try: - repository = DataConfigRepository() + repository = MemoryConfigRepository() db_config = repository.get_by_id(db, config_id) if db_config is None: @@ -150,7 +151,7 @@ def load_actr_config_from_db( def create_actr_calculator_from_config( db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> ACTRCalculator: """ 从数据库配置创建 ACTRCalculator 实例 @@ -168,11 +169,6 @@ def create_actr_calculator_from_config( ValueError: 如果指定的 config_id 不存在 Examples: - >>> from sqlalchemy.orm import Session - >>> db = Session() - >>> calculator = create_actr_calculator_from_config(db, config_id=1) - >>> # 使用计算器 - >>> activation = calculator.calculate_memory_activation(...) """ # 加载配置 config = load_actr_config_from_db(db, config_id) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py index 6d42af53..072d587c 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py @@ -16,6 +16,7 @@ Classes: import logging from typing import Dict, Any, Optional +from uuid import UUID from datetime import datetime from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy @@ -66,10 +67,10 @@ class ForgettingScheduler: async def run_forgetting_cycle( self, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, max_merge_batch_size: int = 100, min_days_since_access: int = 30, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> Dict[str, Any]: """ @@ -77,7 +78,7 @@ class ForgettingScheduler: Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） max_merge_batch_size: 单次最大融合节点对数（默认 100） min_days_since_access: 最小未访问天数（默认 30 天） config_id: 配置ID（可选，用于获取 llm_id） @@ -107,19 +108,19 @@ class ForgettingScheduler: start_time_iso = start_time.isoformat() logger.info( - f"开始遗忘周期: group_id={group_id}, " + f"开始遗忘周期: end_user_id={end_user_id}, " f"max_batch={max_merge_batch_size}, " f"min_days={min_days_since_access}" ) try: # 步骤1：统计遗忘前的节点数量 - nodes_before = await self._count_knowledge_nodes(group_id) + nodes_before = await self._count_knowledge_nodes(end_user_id) logger.info(f"遗忘前节点总数: {nodes_before}") # 步骤2：识别可遗忘的节点对 forgettable_pairs = await self.forgetting_strategy.find_forgettable_nodes( - group_id=group_id, + end_user_id=end_user_id, min_days_since_access=min_days_since_access ) @@ -213,7 +214,7 @@ class ForgettingScheduler: 'statement_text': pair['statement_text'], 'statement_activation': pair['statement_activation'], 'statement_importance': pair['statement_importance'], - 'group_id': group_id + 'end_user_id': end_user_id } entity_node = { @@ -222,7 +223,7 @@ class ForgettingScheduler: 'entity_type': pair['entity_type'], 'entity_activation': pair['entity_activation'], 'entity_importance': pair['entity_importance'], - 'group_id': group_id + 'end_user_id': end_user_id } # 融合节点 @@ -262,7 +263,7 @@ class ForgettingScheduler: continue # 步骤6：统计遗忘后的节点数量 - nodes_after = await self._count_knowledge_nodes(group_id) + nodes_after = await self._count_knowledge_nodes(end_user_id) logger.info(f"遗忘后节点总数: {nodes_after}") # 步骤7：生成遗忘报告 @@ -315,7 +316,7 @@ class ForgettingScheduler: async def _count_knowledge_nodes( self, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> int: """ 统计知识层节点总数 @@ -323,7 +324,7 @@ class ForgettingScheduler: 统计 Statement、ExtractedEntity 和 MemorySummary 节点的总数。 Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） Returns: int: 知识层节点总数 @@ -333,16 +334,16 @@ class ForgettingScheduler: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ RETURN count(n) as total """ params = {} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py index ccd8d2ca..a8c62dd4 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py @@ -13,6 +13,7 @@ Classes: import logging from typing import List, Dict, Any, Optional +from uuid import UUID from datetime import datetime, timedelta from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -90,7 +91,7 @@ class ForgettingStrategy: async def find_forgettable_nodes( self, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, min_days_since_access: int = 30 ) -> List[Dict[str, Any]]: """ @@ -102,7 +103,7 @@ class ForgettingStrategy: 3. Statement 和 Entity 之间存在关系边 Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） min_days_since_access: 最小未访问天数（默认 30 天） Returns: @@ -136,8 +137,8 @@ class ForgettingStrategy: AND (e.entity_type IS NULL OR e.entity_type <> 'Person') """ - if group_id: - query += " AND s.group_id = $group_id AND e.group_id = $group_id" + if end_user_id: + query += " AND s.end_user_id = $end_user_id AND e.end_user_id = $end_user_id" query += """ RETURN s.id as statement_id, @@ -159,8 +160,8 @@ class ForgettingStrategy: 'threshold': self.forgetting_threshold, 'cutoff_time': cutoff_time_iso } - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) @@ -176,7 +177,7 @@ class ForgettingStrategy: self, statement_node: Dict[str, Any], entity_node: Dict[str, Any], - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> str: """ @@ -247,8 +248,8 @@ class ForgettingStrategy: entity_activation = entity_node['entity_activation'] entity_importance = entity_node['entity_importance'] - # 获取 group_id（从 statement 或 entity 节点） - group_id = statement_node.get('group_id') or entity_node.get('group_id') + # 获取 end_user_id（从 statement 或 entity 节点） + end_user_id = statement_node.get('end_user_id') or entity_node.get('end_user_id') # 生成摘要内容 summary_text = await self._generate_summary( @@ -325,7 +326,7 @@ class ForgettingStrategy: last_access_time: $current_time, access_count: 1, version: 1, - group_id: $group_id, + end_user_id: $end_user_id, created_at: datetime($current_time), merged_at: datetime($current_time) }) @@ -423,7 +424,7 @@ class ForgettingStrategy: 'inherited_activation': inherited_activation, 'inherited_importance': inherited_importance, 'current_time': current_time_iso, - 'group_id': group_id + 'end_user_id': end_user_id } try: @@ -462,7 +463,7 @@ class ForgettingStrategy: statement_text: str, entity_name: str, entity_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> str: """ @@ -527,7 +528,7 @@ class ForgettingStrategy: statement_text, entity_name, entity_type ) - async def _get_llm_client(self, db, config_id: int): + async def _get_llm_client(self, db, config_id: UUID): """ 从数据库获取 LLM 客户端 @@ -539,11 +540,11 @@ class ForgettingStrategy: LLM 客户端实例，如果无法获取则返回 None """ try: - from app.repositories.data_config_repository import DataConfigRepository + from app.repositories.memory_config_repository import MemoryConfigRepository from app.core.memory.utils.llm.llm_utils import MemoryClientFactory # 从数据库读取配置 - repository = DataConfigRepository() + repository = MemoryConfigRepository() db_config = repository.get_by_id(db, config_id) if db_config is None or db_config.llm_id is None: diff --git a/api/app/core/memory/storage_services/search/__init__.py b/api/app/core/memory/storage_services/search/__init__.py index 2bec5bf1..c12c39b0 100644 --- a/api/app/core/memory/storage_services/search/__init__.py +++ b/api/app/core/memory/storage_services/search/__init__.py @@ -37,7 +37,7 @@ __all__ = [ async def run_hybrid_search( query_text: str, search_type: str = "hybrid", - group_id: str | None = None, + end_user_id: str | None = None, apply_id: str | None = None, user_id: str | None = None, limit: int = 50, @@ -54,7 +54,7 @@ async def run_hybrid_search( Args: query_text: 查询文本 search_type: 搜索类型（"hybrid", "keyword", "semantic"） - group_id: 组ID过滤 + end_user_id: 组ID过滤 apply_id: 应用ID过滤 user_id: 用户ID过滤 limit: 每个类别的最大结果数 @@ -104,7 +104,7 @@ async def run_hybrid_search( # 执行搜索 result = await strategy.search( query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, alpha=alpha, diff --git a/api/app/core/memory/storage_services/search/hybrid_search.py b/api/app/core/memory/storage_services/search/hybrid_search.py index 43215df5..4111b09c 100644 --- a/api/app/core/memory/storage_services/search/hybrid_search.py +++ b/api/app/core/memory/storage_services/search/hybrid_search.py @@ -77,7 +77,7 @@ # async def search( # self, # query_text: str, -# group_id: Optional[str] = None, +# end_user_id: Optional[str] = None, # limit: int = 50, # include: Optional[List[str]] = None, # **kwargs @@ -86,7 +86,7 @@ # Args: # query_text: 查询文本 -# group_id: 可选的组ID过滤 +# end_user_id: 可选的组ID过滤 # limit: 每个类别的最大结果数 # include: 要包含的搜索类别列表 # **kwargs: 其他搜索参数（如alpha, use_forgetting_curve） @@ -94,7 +94,7 @@ # Returns: # SearchResult: 搜索结果对象 # """ -# logger.info(f"执行混合搜索: query='{query_text}', group_id={group_id}, limit={limit}") +# logger.info(f"执行混合搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # # 从kwargs中获取参数 # alpha = kwargs.get("alpha", self.alpha) @@ -107,14 +107,14 @@ # # 并行执行关键词搜索和语义搜索 # keyword_result = await self.keyword_strategy.search( # query_text=query_text, -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list # ) # semantic_result = await self.semantic_strategy.search( # query_text=query_text, -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list # ) @@ -139,7 +139,7 @@ # metadata = self._create_metadata( # query_text=query_text, # search_type="hybrid", -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list, # alpha=alpha, @@ -165,7 +165,7 @@ # metadata=self._create_metadata( # query_text=query_text, # search_type="hybrid", -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # error=str(e) # ) diff --git a/api/app/core/memory/storage_services/search/keyword_search.py b/api/app/core/memory/storage_services/search/keyword_search.py index 95dd0581..d2591945 100644 --- a/api/app/core/memory/storage_services/search/keyword_search.py +++ b/api/app/core/memory/storage_services/search/keyword_search.py @@ -44,7 +44,7 @@ class KeywordSearchStrategy(SearchStrategy): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -53,7 +53,7 @@ class KeywordSearchStrategy(SearchStrategy): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表 **kwargs: 其他搜索参数 @@ -61,7 +61,7 @@ class KeywordSearchStrategy(SearchStrategy): Returns: SearchResult: 搜索结果对象 """ - logger.info(f"执行关键词搜索: query='{query_text}', group_id={group_id}, limit={limit}") + logger.info(f"执行关键词搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # 获取有效的搜索类别 include_list = self._get_include_list(include) @@ -75,7 +75,7 @@ class KeywordSearchStrategy(SearchStrategy): results_dict = await search_graph( connector=self.connector, q=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -84,7 +84,7 @@ class KeywordSearchStrategy(SearchStrategy): metadata = self._create_metadata( query_text=query_text, search_type="keyword", - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -115,7 +115,7 @@ class KeywordSearchStrategy(SearchStrategy): metadata=self._create_metadata( query_text=query_text, search_type="keyword", - group_id=group_id, + end_user_id=end_user_id, limit=limit, error=str(e) ) diff --git a/api/app/core/memory/storage_services/search/search_strategy.py b/api/app/core/memory/storage_services/search/search_strategy.py index 27c02c89..3a670dd6 100644 --- a/api/app/core/memory/storage_services/search/search_strategy.py +++ b/api/app/core/memory/storage_services/search/search_strategy.py @@ -58,7 +58,7 @@ class SearchStrategy(ABC): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -67,7 +67,7 @@ class SearchStrategy(ABC): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表（statements, chunks, entities, summaries） **kwargs: 其他搜索参数 @@ -81,7 +81,7 @@ class SearchStrategy(ABC): self, query_text: str, search_type: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, **kwargs ) -> Dict[str, Any]: @@ -90,7 +90,7 @@ class SearchStrategy(ABC): Args: query_text: 查询文本 search_type: 搜索类型 - group_id: 组ID + end_user_id: 组ID limit: 结果限制 **kwargs: 其他元数据 @@ -100,7 +100,7 @@ class SearchStrategy(ABC): metadata = { "query": query_text, "search_type": search_type, - "group_id": group_id, + "end_user_id": end_user_id, "limit": limit, "timestamp": datetime.now().isoformat() } diff --git a/api/app/core/memory/storage_services/search/semantic_search.py b/api/app/core/memory/storage_services/search/semantic_search.py index b20f90a5..8d4eb05f 100644 --- a/api/app/core/memory/storage_services/search/semantic_search.py +++ b/api/app/core/memory/storage_services/search/semantic_search.py @@ -85,7 +85,7 @@ class SemanticSearchStrategy(SearchStrategy): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -94,7 +94,7 @@ class SemanticSearchStrategy(SearchStrategy): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表 **kwargs: 其他搜索参数 @@ -102,7 +102,7 @@ class SemanticSearchStrategy(SearchStrategy): Returns: SearchResult: 搜索结果对象 """ - logger.info(f"执行语义搜索: query='{query_text}', group_id={group_id}, limit={limit}") + logger.info(f"执行语义搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # 获取有效的搜索类别 include_list = self._get_include_list(include) @@ -119,7 +119,7 @@ class SemanticSearchStrategy(SearchStrategy): connector=self.connector, embedder_client=self.embedder_client, query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -128,7 +128,7 @@ class SemanticSearchStrategy(SearchStrategy): metadata = self._create_metadata( query_text=query_text, search_type="semantic", - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -159,7 +159,7 @@ class SemanticSearchStrategy(SearchStrategy): metadata=self._create_metadata( query_text=query_text, search_type="semantic", - group_id=group_id, + end_user_id=end_user_id, limit=limit, error=str(e) ) diff --git a/api/app/core/memory/utils/config/get_data.py b/api/app/core/memory/utils/config/get_data.py index 1de6f6aa..e37ad723 100644 --- a/api/app/core/memory/utils/config/get_data.py +++ b/api/app/core/memory/utils/config/get_data.py @@ -23,7 +23,7 @@ async def _load_(data: List[Any]) -> List[Dict]: target_keys = [ "id", "statement", - "group_id", + "end_user_id", "chunk_id", "created_at", "expired_at", @@ -75,7 +75,7 @@ async def get_data(result): """ EXCLUDE_FIELDS = { "user_id", - "group_id", + "end_user_id", "entity_type", "connect_strength", "relationship_type", diff --git a/api/app/core/memory/utils/log/audit_logger.py b/api/app/core/memory/utils/log/audit_logger.py index 9010aad5..f80ad4d5 100644 --- a/api/app/core/memory/utils/log/audit_logger.py +++ b/api/app/core/memory/utils/log/audit_logger.py @@ -62,7 +62,7 @@ class ConfigAuditLogger: self, config_id: str, user_id: Optional[str] = None, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, success: bool = True, details: Optional[Dict[str, Any]] = None ): @@ -72,14 +72,14 @@ class ConfigAuditLogger: Args: config_id: 配置 ID user_id: 用户 ID（可选） - group_id: 组 ID（可选） + end_user_id: 组 ID（可选） success: 是否成功 details: 详细信息（可选） """ result = "SUCCESS" if success else "FAILED" msg = ( f"CONFIG_LOAD config_id={config_id} " - f"user={user_id or 'N/A'} group={group_id or 'N/A'} " + f"user={user_id or 'N/A'} group={end_user_id or 'N/A'} " f"result={result}" ) if details: @@ -121,7 +121,7 @@ class ConfigAuditLogger: self, operation: str, config_id: str, - group_id: str, + end_user_id: str, success: bool = True, duration: Optional[float] = None, error: Optional[str] = None, @@ -133,7 +133,7 @@ class ConfigAuditLogger: Args: operation: 操作类型（WRITE, READ 等） config_id: 配置 ID - group_id: 组 ID + end_user_id: 组 ID success: 是否成功 duration: 操作耗时（秒） error: 错误信息（可选） @@ -142,7 +142,7 @@ class ConfigAuditLogger: result = "SUCCESS" if success else "FAILED" msg = ( f"{operation.upper()} config_id={config_id} " - f"group={group_id} result={result}" + f"group={end_user_id} result={result}" ) if duration is not None: msg += f" duration={duration:.2f}s" diff --git a/api/app/core/memory/utils/prompt/prompt_utils.py b/api/app/core/memory/utils/prompt/prompt_utils.py index 50593e49..a4d2af95 100644 --- a/api/app/core/memory/utils/prompt/prompt_utils.py +++ b/api/app/core/memory/utils/prompt/prompt_utils.py @@ -177,7 +177,7 @@ def render_entity_dedup_prompt( # Args: # entity_a: Dict of entity A attributes -async def render_triplet_extraction_prompt(statement: str, chunk_content: str, json_schema: dict, predicate_instructions: dict = None) -> str: +async def render_triplet_extraction_prompt(statement: str, chunk_content: str, json_schema: dict, predicate_instructions: dict = None, language: str = "zh") -> str: """ Renders the triplet extraction prompt using the extract_triplet.jinja2 template. @@ -186,6 +186,7 @@ async def render_triplet_extraction_prompt(statement: str, chunk_content: str, j chunk_content: The content of the chunk to process json_schema: JSON schema for the expected output format predicate_instructions: Optional predicate instructions + language: The language to use for entity descriptions ("zh" for Chinese, "en" for English) Returns: Rendered prompt content as string @@ -195,7 +196,8 @@ async def render_triplet_extraction_prompt(statement: str, chunk_content: str, j statement=statement, chunk_content=chunk_content, json_schema=json_schema, - predicate_instructions=predicate_instructions + predicate_instructions=predicate_instructions, + language=language ) # 记录渲染结果到提示日志（与示例日志结构一致） log_prompt_rendering('triplet extraction', rendered_prompt) @@ -204,7 +206,8 @@ async def render_triplet_extraction_prompt(statement: str, chunk_content: str, j 'statement': 'str', 'chunk_content': 'str', 'json_schema': 'TripletExtractionResponse.schema', - 'predicate_instructions': 'PREDICATE_DEFINITIONS' + 'predicate_instructions': 'PREDICATE_DEFINITIONS', + 'language': language }) return rendered_prompt @@ -213,6 +216,7 @@ async def render_memory_summary_prompt( chunk_texts: str, json_schema: dict, max_words: int = 200, + language: str = "zh", ) -> str: """ Renders the memory summary prompt using the memory_summary.jinja2 template. @@ -221,6 +225,7 @@ async def render_memory_summary_prompt( chunk_texts: Concatenated text of conversation chunks json_schema: JSON schema for the expected output format max_words: Maximum words for the summary + language: The language to use for summary generation ("zh" for Chinese, "en" for English) Returns: Rendered prompt content as string. @@ -230,12 +235,14 @@ async def render_memory_summary_prompt( chunk_texts=chunk_texts, json_schema=json_schema, max_words=max_words, + language=language, ) log_prompt_rendering('memory summary', rendered_prompt) log_template_rendering('memory_summary.jinja2', { 'chunk_texts_len': len(chunk_texts or ""), 'max_words': max_words, - 'json_schema': 'MemorySummaryResponse.schema' + 'json_schema': 'MemorySummaryResponse.schema', + 'language': language }) return rendered_prompt @@ -388,24 +395,65 @@ async def render_memory_insight_prompt( return rendered_prompt -async def render_episodic_title_and_type_prompt(content: str) -> str: +async def render_episodic_title_and_type_prompt(content: str, language: str = "zh") -> str: """ Renders the episodic title and type classification prompt using the episodic_type_classification.jinja2 template. Args: content: The content of the episodic memory summary to analyze + language: The language to use for title generation ("zh" for Chinese, "en" for English) Returns: Rendered prompt content as string """ template = prompt_env.get_template("episodic_type_classification.jinja2") - rendered_prompt = template.render(content=content) + rendered_prompt = template.render(content=content, language=language) # 记录渲染结果到提示日志 log_prompt_rendering('episodic title and type classification', rendered_prompt) # 可选：记录模板渲染信息 log_template_rendering('episodic_type_classification.jinja2', { - 'content_len': len(content) if content else 0 + 'content_len': len(content) if content else 0, + 'language': language + }) + + return rendered_prompt + + +async def render_ontology_extraction_prompt( + scenario: str, + domain: str | None = None, + max_classes: int = 15, + json_schema: dict | None = None +) -> str: + """ + Renders the ontology extraction prompt using the extract_ontology.jinja2 template. + + Args: + scenario: The scenario description text to extract ontology classes from + domain: Optional domain hint for the scenario (e.g., "Healthcare", "Education") + max_classes: Maximum number of classes to extract (default: 15) + json_schema: JSON schema for the expected output format + + Returns: + Rendered prompt content as string + """ + template = prompt_env.get_template("extract_ontology.jinja2") + rendered_prompt = template.render( + scenario=scenario, + domain=domain, + max_classes=max_classes, + json_schema=json_schema + ) + + # 记录渲染结果到提示日志 + log_prompt_rendering('ontology extraction', rendered_prompt) + # 可选：记录模板渲染信息 + log_template_rendering('extract_ontology.jinja2', { + 'scenario_len': len(scenario) if scenario else 0, + 'domain': domain, + 'max_classes': max_classes, + 'json_schema': 'OntologyExtractionResponse.schema' }) return rendered_prompt diff --git a/api/app/core/memory/utils/prompt/prompts/episodic_type_classification.jinja2 b/api/app/core/memory/utils/prompt/prompts/episodic_type_classification.jinja2 index fa382ec7..d778890b 100644 --- a/api/app/core/memory/utils/prompt/prompts/episodic_type_classification.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/episodic_type_classification.jinja2 @@ -1,8 +1,19 @@ === Task === Generate a concise title and classify the episodic memory into the most appropriate category. +{% if language == "zh" %} +**重要：请使用中文生成标题和分类。** +{% else %} +**Important: Please generate the title and classification in English.** +{% endif %} + === Requirements === - Extract a clear, concise title (10-20 characters) that captures the core content +{% if language == "zh" %} +- 标题必须使用中文 +{% else %} +- Title must be in English +{% endif %} - Classify into exactly one category based on the primary theme - Be specific and avoid ambiguity - Output must be valid JSON conforming to the schema below diff --git a/api/app/core/memory/utils/prompt/prompts/extract_ontology.jinja2 b/api/app/core/memory/utils/prompt/prompts/extract_ontology.jinja2 new file mode 100644 index 00000000..80594ad9 --- /dev/null +++ b/api/app/core/memory/utils/prompt/prompts/extract_ontology.jinja2 @@ -0,0 +1,210 @@ +===Task=== +Extract ontology classes from the given scenario description following ontology engineering standards. + +===Role=== +You are a professional ontology engineer with expertise in knowledge representation and OWL (Web Ontology Language) standards. Your task is to identify abstract classes and concepts from scenario descriptions, not concrete instances. + +===Scenario Description=== +{{ scenario }} + +{% if domain -%} +===Domain Hint=== +This scenario belongs to the **{{ domain }}** domain. Consider domain-specific concepts and terminology when extracting classes. +{%- endif %} + +===Extraction Rules=== + +**1. Abstract Classes, Not Instances:** +- Extract abstract categories and concepts (e.g., "MedicalProcedure", "Patient", "Diagnosis") +- Do NOT extract concrete instances (e.g., "John Smith", "Room 301", "2024-01-15") +- Think in terms of "types of things" rather than "specific things" + +**2. Naming Convention (PascalCase):** +- Use PascalCase format for the "name" field: start with uppercase letter, capitalize each word, no spaces +- Examples: "MedicalProcedure", "HealthcareProvider", "DiagnosticTest" +- Avoid: "medical procedure", "healthcare_provider", "diagnostic-test" +- Use clear, descriptive names in English +- Avoid abbreviations unless they are standard in the domain (e.g., "API", "DNA") +- Provide Chinese translation in the "name_chinese" field (e.g., "医疗程序", "医疗服务提供者", "诊断测试") + +**3. Domain Relevance:** +- Focus on classes that are central to the scenario's domain +- Prioritize classes that represent key concepts, entities, or relationships +- Avoid overly generic classes (e.g., "Thing", "Object") unless they have specific domain meaning + +**4. Class Quantity:** +- Extract between 5 and {{ max_classes }} classes +- Aim for a balanced set covering the main concepts in the scenario +- Quality over quantity: prefer well-defined classes over exhaustive lists + +**5. Clear Descriptions:** +- Provide concise, informative descriptions in Chinese (max 500 characters) +- Describe what the class represents, not specific instances +- Use clear, natural Chinese language that explains the class's role in the domain + +**6. Concrete Examples:** +- Provide 2-5 concrete instance examples in Chinese for each class +- Examples should be specific, realistic instances of the class +- Examples help clarify the class's scope and meaning +- Use natural Chinese language for examples +- Example format: ["示例1", "示例2", "示例3"] + +**7. Class Hierarchy:** +- Identify parent-child relationships where applicable +- Use the parent_class field to specify inheritance +- Parent class must be one of the extracted classes or a standard OWL class +- Leave parent_class as null for top-level classes + +**8. Entity Types:** +- Classify each class with an appropriate entity_type +- Common types: "Person", "Organization", "Location", "Event", "Concept", "Process", "Object", "Role" +- Choose the most specific type that applies + +**9. OWL Reserved Words:** +- Do NOT use OWL reserved words as class names +- Reserved words include: "Thing", "Nothing", "Class", "Property", "ObjectProperty", "DatatypeProperty", "AnnotationProperty", "Ontology", "Individual", "Literal" +- If a reserved word is needed, add a domain-specific prefix (e.g., "MedicalClass" instead of "Class") + +**10. Language Consistency:** +- Extract all class names in English (PascalCase format) for the "name" field +- Provide Chinese translation for class names in the "name_chinese" field +- Descriptions MUST be in Chinese (中文) +- Examples MUST be in Chinese (中文) +- Use clear, natural Chinese language for descriptions and examples + +===Examples=== + +**Example 1 (Healthcare Domain):** +Scenario: "A hospital manages patient records, schedules appointments, and coordinates medical procedures. Doctors diagnose conditions and prescribe treatments." + +Output: +{ + "classes": [ + { + "name": "Patient", + "name_chinese": "患者", + "description": "在医疗机构接受医疗护理或治疗的人", + "examples": ["张三", "李四", "患有糖尿病的老年患者"], + "parent_class": null, + "entity_type": "Person", + "domain": "Healthcare" + }, + { + "name": "MedicalProcedure", + "name_chinese": "医疗程序", + "description": "为医疗诊断或治疗而执行的系统性操作流程", + "examples": ["手术", "血液检查", "X光检查", "疫苗接种"], + "parent_class": null, + "entity_type": "Process", + "domain": "Healthcare" + }, + { + "name": "Diagnosis", + "name_chinese": "诊断", + "description": "基于症状和检查结果对疾病或状况的识别", + "examples": ["糖尿病诊断", "癌症诊断", "流感诊断"], + "parent_class": null, + "entity_type": "Concept", + "domain": "Healthcare" + }, + { + "name": "Doctor", + "name_chinese": "医生", + "description": "诊断和治疗患者的持证医疗专业人员", + "examples": ["全科医生", "外科医生", "心脏病专家"], + "parent_class": null, + "entity_type": "Role", + "domain": "Healthcare" + }, + { + "name": "Treatment", + "name_chinese": "治疗", + "description": "为治愈或管理疾病状况而提供的医疗护理或疗法", + "examples": ["药物治疗", "物理治疗", "化疗", "手术治疗"], + "parent_class": null, + "entity_type": "Process", + "domain": "Healthcare" + } + ], + "domain": "Healthcare", + "namespace": "http://example.org/healthcare#" +} + +**Example 2 (Education Domain):** +Scenario: "A university offers courses taught by professors. Students enroll in programs, attend lectures, and complete assignments to earn degrees." + +Output: +{ + "classes": [ + { + "name": "Student", + "name_chinese": "学生", + "description": "在教育机构注册学习的人", + "examples": ["本科生", "研究生", "在职学生"], + "parent_class": null, + "entity_type": "Role", + "domain": "Education" + }, + { + "name": "Course", + "name_chinese": "课程", + "description": "涵盖特定学科或主题的结构化教育课程", + "examples": ["计算机科学导论", "微积分I", "世界历史"], + "parent_class": null, + "entity_type": "Concept", + "domain": "Education" + }, + { + "name": "Professor", + "name_chinese": "教授", + "description": "教授课程并进行研究的学术教师", + "examples": ["助理教授", "副教授", "正教授"], + "parent_class": null, + "entity_type": "Role", + "domain": "Education" + }, + { + "name": "AcademicProgram", + "name_chinese": "学术项目", + "description": "通向学位或证书的结构化课程体系", + "examples": ["理学学士", "文学硕士", "博士项目"], + "parent_class": null, + "entity_type": "Concept", + "domain": "Education" + }, + { + "name": "Assignment", + "name_chinese": "作业", + "description": "分配给学生以评估学习成果的任务或项目", + "examples": ["论文", "习题集", "研究报告", "实验报告"], + "parent_class": null, + "entity_type": "Object", + "domain": "Education" + }, + { + "name": "Lecture", + "name_chinese": "讲座", + "description": "由教师进行的教育性演讲或讲座", + "examples": ["入门讲座", "客座讲座", "在线讲座"], + "parent_class": null, + "entity_type": "Event", + "domain": "Education" + } + ], + "domain": "Education", + "namespace": "http://example.org/education#" +} + +===Output Format=== + +**JSON Requirements:** +- Use only ASCII double quotes (") for JSON structure +- Never use Chinese quotation marks ("") or Unicode quotes +- Escape quotation marks in text with backslashes (\") +- Ensure proper string closure and comma separation +- No line breaks within JSON string values +- All class names must be in PascalCase format +- All class names must be unique (case-insensitive) +- Extract between 5 and {{ max_classes }} classes + +{{ json_schema }} diff --git a/api/app/core/memory/utils/prompt/prompts/extract_triplet.jinja2 b/api/app/core/memory/utils/prompt/prompts/extract_triplet.jinja2 index 03691a04..67df162a 100644 --- a/api/app/core/memory/utils/prompt/prompts/extract_triplet.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/extract_triplet.jinja2 @@ -5,6 +5,12 @@ ===Task=== Extract entities and knowledge triplets from the given statement. +{% if language == "zh" %} +**重要：请使用中文生成实体描述（description）和示例（example）。** +{% else %} +**Important: Please generate entity descriptions and examples in English.** +{% endif %} + ===Inputs=== **Chunk Content:** "{{ chunk_content }}" **Statement:** "{{ statement }}" @@ -13,6 +19,13 @@ Extract entities and knowledge triplets from the given statement. **Entity Extraction:** - Extract entities with their types, context-independent descriptions, **concise examples**, aliases, and semantic memory classification +{% if language == "zh" %} +- **实体描述（description）必须使用中文** +- **示例（example）必须使用中文** +{% else %} +- **Entity descriptions must be in English** +- **Examples must be in English** +{% endif %} - **Semantic Memory Classification (is_explicit_memory):** * Set to `true` if the entity represents **explicit/semantic memory**: - **Concepts:** "Machine Learning", "Photosynthesis", "Democracy", "人工智能", "光合作用", "民主" @@ -334,9 +347,11 @@ Output: - Escape quotation marks in text with backslashes (\") - Ensure proper string closure and comma separation - No line breaks within JSON string values -- The output language should ALWAYS match the input language -- If input is in English, extract statements in English -- If input is in Chinese, extract statements in Chinese +{% if language == "zh" %} +- **语言要求：实体描述（description）和示例（example）必须使用中文** +{% else %} +- **Language Requirement: Entity descriptions and examples must be in English** +{% endif %} - Preserve the original language and do not translate {{ json_schema }} \ No newline at end of file diff --git a/api/app/core/memory/utils/prompt/prompts/memory_summary.jinja2 b/api/app/core/memory/utils/prompt/prompts/memory_summary.jinja2 index 1dd86ca3..82f91cc4 100644 --- a/api/app/core/memory/utils/prompt/prompts/memory_summary.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/memory_summary.jinja2 @@ -5,10 +5,21 @@ === Task === Summarize the provided conversation chunks into a concise Memory summary. +{% if language == "zh" %} +**重要：请使用中文生成摘要内容。** +{% else %} +**Important: Please generate the summary content in English.** +{% endif %} + === Requirements === - Focus on factual statements, user preferences, relationships, and salient temporal context. - Avoid repetition and filler; be specific. - Keep it under {{ max_words or 200 }} words. +{% if language == "zh" %} +- 摘要内容必须使用中文 +{% else %} +- Summary content must be in English +{% endif %} - Output must be valid JSON conforming to the schema below. === Input === @@ -24,6 +35,11 @@ Summarize the provided conversation chunks into a concise Memory summary. 4. Do not include line breaks within JSON string values 5. Example of proper escaping: "statement": "张曼婷说：\"我很喜欢这本书。\"" -The output language should always be the same as the input language. +{% if language == "zh" %} +**语言要求：输出内容必须使用中文。** +{% else %} +**Language Requirement: The output content must be in English.** +{% endif %} + Return only a list of extracted labelled statements in the JSON ARRAY of objects that match the schema below: {{ json_schema }} \ No newline at end of file diff --git a/api/app/core/memory/utils/validation/__init__.py b/api/app/core/memory/utils/validation/__init__.py new file mode 100644 index 00000000..d5dd41e7 --- /dev/null +++ b/api/app/core/memory/utils/validation/__init__.py @@ -0,0 +1,10 @@ +"""Validation utilities for ontology extraction. + +This module provides validation classes for ontology class names, +descriptions, and OWL compliance checking. +""" + +from .ontology_validator import OntologyValidator +from .owl_validator import OWLValidator + +__all__ = ['OntologyValidator', 'OWLValidator'] diff --git a/api/app/core/memory/utils/validation/ontology_validator.py b/api/app/core/memory/utils/validation/ontology_validator.py new file mode 100644 index 00000000..eb7492ad --- /dev/null +++ b/api/app/core/memory/utils/validation/ontology_validator.py @@ -0,0 +1,268 @@ +"""String validation for ontology class names and descriptions. + +This module provides the OntologyValidator class for validating and sanitizing +ontology class names according to OWL standards and naming conventions. + +Classes: + OntologyValidator: Validates class names, removes duplicates, and truncates descriptions +""" + +import logging +import re +from typing import List, Tuple + +from app.core.memory.models.ontology_models import OntologyClass + + +logger = logging.getLogger(__name__) + + +class OntologyValidator: + """Validator for ontology class names and descriptions. + + This validator performs string-level validation including: + - PascalCase naming convention validation + - OWL reserved word checking + - Duplicate class name removal + - Description length truncation + + Attributes: + OWL_RESERVED_WORDS: Set of OWL reserved words that cannot be used as class names + """ + + # OWL reserved words that cannot be used as class names + OWL_RESERVED_WORDS = { + 'Thing', 'Nothing', 'Class', 'Property', + 'ObjectProperty', 'DatatypeProperty', 'FunctionalProperty', + 'InverseFunctionalProperty', 'TransitiveProperty', 'SymmetricProperty', + 'AsymmetricProperty', 'ReflexiveProperty', 'IrreflexiveProperty', + 'Restriction', 'Ontology', 'Individual', 'NamedIndividual', + 'Annotation', 'AnnotationProperty', 'Axiom', + 'AllDifferent', 'AllDisjointClasses', 'AllDisjointProperties', + 'Datatype', 'DataRange', 'Literal', + 'DeprecatedClass', 'DeprecatedProperty', + 'Imports', 'IncompatibleWith', 'PriorVersion', 'VersionInfo', + 'BackwardCompatibleWith', 'OntologyProperty', + } + + def validate_class_name(self, name: str) -> Tuple[bool, str]: + """Validate that a class name follows OWL naming conventions. + + Validation rules: + 1. Must not be empty + 2. Must start with an uppercase letter (PascalCase) + 3. Cannot contain spaces + 4. Can only contain alphanumeric characters and underscores + 5. Cannot be an OWL reserved word + + Args: + name: The class name to validate + + Returns: + Tuple of (is_valid, error_message) + - is_valid: True if the name is valid, False otherwise + - error_message: Empty string if valid, error description if invalid + + Examples: + >>> validator = OntologyValidator() + >>> validator.validate_class_name("MedicalProcedure") + (True, "") + >>> validator.validate_class_name("medical procedure") + (False, "Class name 'medical procedure' cannot contain spaces") + >>> validator.validate_class_name("Thing") + (False, "Class name 'Thing' is an OWL reserved word") + """ + logger.debug(f"Validating class name: '{name}'") + + # Check if empty + if not name or not name.strip(): + error_msg = "Class name cannot be empty" + logger.warning(f"Validation failed: {error_msg}") + return False, error_msg + + name = name.strip() + + # Check if it's an OWL reserved word + if name in self.OWL_RESERVED_WORDS: + error_msg = f"Class name '{name}' is an OWL reserved word" + logger.warning(f"Validation failed: {error_msg}") + return False, error_msg + + # Check if starts with uppercase letter + if not name[0].isupper(): + error_msg = f"Class name '{name}' must start with an uppercase letter (PascalCase)" + logger.warning(f"Validation failed: {error_msg}") + return False, error_msg + + # Check for spaces + if ' ' in name: + error_msg = f"Class name '{name}' cannot contain spaces" + logger.warning(f"Validation failed: {error_msg}") + return False, error_msg + + # Check for invalid characters (only alphanumeric and underscore allowed) + if not re.match(r'^[A-Za-z0-9_]+$', name): + error_msg = f"Class name '{name}' contains invalid characters. Only alphanumeric characters and underscores are allowed" + logger.warning(f"Validation failed: {error_msg}") + return False, error_msg + + logger.debug(f"Class name '{name}' is valid") + return True, "" + + def sanitize_class_name(self, name: str) -> str: + """Attempt to sanitize an invalid class name into a valid format. + + Sanitization steps: + 1. Strip whitespace + 2. Remove invalid characters + 3. Replace spaces with empty string (PascalCase) + 4. Capitalize first letter of each word + 5. If result is empty or starts with number, prefix with 'Class' + + Args: + name: The class name to sanitize + + Returns: + Sanitized class name that should pass validation + + Examples: + >>> validator = OntologyValidator() + >>> validator.sanitize_class_name("medical procedure") + 'MedicalProcedure' + >>> validator.sanitize_class_name("patient-record") + 'PatientRecord' + >>> validator.sanitize_class_name("123invalid") + 'Class123Invalid' + """ + logger.debug(f"Sanitizing class name: '{name}'") + + if not name or not name.strip(): + logger.warning("Empty class name provided for sanitization, returning 'UnnamedClass'") + return "UnnamedClass" + + # Strip whitespace + name = name.strip() + original_name = name + + # Split on spaces, hyphens, and underscores, then capitalize each word + words = re.split(r'[\s\-_]+', name) + + # Capitalize first letter of each word and keep rest as is + sanitized_words = [] + for word in words: + if word: + # Remove non-alphanumeric characters except underscore + clean_word = re.sub(r'[^A-Za-z0-9_]', '', word) + if clean_word: + # Capitalize first letter + sanitized_words.append(clean_word[0].upper() + clean_word[1:]) + + # Join words + sanitized = ''.join(sanitized_words) + + # If empty or starts with number, prefix with 'Class' + if not sanitized or sanitized[0].isdigit(): + sanitized = 'Class' + sanitized + logger.info(f"Prefixed class name with 'Class': '{original_name}' -> '{sanitized}'") + + # If it's a reserved word, append 'Class' suffix + if sanitized in self.OWL_RESERVED_WORDS: + sanitized = sanitized + 'Class' + logger.info(f"Appended 'Class' suffix to reserved word: '{original_name}' -> '{sanitized}'") + + logger.info(f"Sanitized class name: '{original_name}' -> '{sanitized}'") + return sanitized + + def remove_duplicates(self, classes: List[OntologyClass]) -> List[OntologyClass]: + """Remove duplicate ontology classes based on case-insensitive name comparison. + + When duplicates are found, keeps the first occurrence and discards subsequent ones. + Comparison is case-insensitive to catch variations like 'Patient' and 'patient'. + + Args: + classes: List of OntologyClass objects + + Returns: + List of OntologyClass objects with duplicates removed + + Examples: + >>> validator = OntologyValidator() + >>> classes = [ + ... OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare"), + ... OntologyClass(name="patient", description="Another patient", entity_type="Person", domain="Healthcare"), + ... OntologyClass(name="Doctor", description="A doctor", entity_type="Person", domain="Healthcare"), + ... ] + >>> unique = validator.remove_duplicates(classes) + >>> len(unique) + 2 + >>> [c.name for c in unique] + ['Patient', 'Doctor'] + """ + if not classes: + logger.debug("No classes to check for duplicates") + return classes + + logger.debug(f"Checking {len(classes)} classes for duplicates") + + seen_names = set() + unique_classes = [] + duplicates_found = [] + + for ontology_class in classes: + # Use lowercase for comparison + name_lower = ontology_class.name.lower() + + if name_lower not in seen_names: + seen_names.add(name_lower) + unique_classes.append(ontology_class) + else: + duplicates_found.append(ontology_class.name) + logger.debug(f"Duplicate class found and removed: '{ontology_class.name}'") + + if duplicates_found: + logger.info( + f"Removed {len(duplicates_found)} duplicate classes: {duplicates_found}" + ) + else: + logger.debug("No duplicate classes found") + + return unique_classes + + def truncate_description(self, description: str, max_length: int = 500) -> str: + """Truncate a description to a maximum length. + + If the description exceeds max_length, it will be truncated and + an ellipsis (...) will be appended to indicate truncation. + + Args: + description: The description text to truncate + max_length: Maximum allowed length (default: 500) + + Returns: + Truncated description string + + Examples: + >>> validator = OntologyValidator() + >>> long_desc = "A" * 600 + >>> truncated = validator.truncate_description(long_desc, max_length=500) + >>> len(truncated) + 500 + >>> truncated.endswith("...") + True + """ + if not description: + return "" + + if len(description) <= max_length: + return description + + # Truncate and add ellipsis + # Reserve 3 characters for "..." + truncate_at = max_length - 3 + truncated = description[:truncate_at] + "..." + + logger.debug( + f"Truncated description from {len(description)} to {len(truncated)} characters" + ) + + return truncated diff --git a/api/app/core/memory/utils/validation/owl_validator.py b/api/app/core/memory/utils/validation/owl_validator.py new file mode 100644 index 00000000..2398d528 --- /dev/null +++ b/api/app/core/memory/utils/validation/owl_validator.py @@ -0,0 +1,585 @@ +"""OWL semantic validation for ontology classes using Owlready2. + +This module provides the OWLValidator class for validating ontology classes +against OWL standards using the Owlready2 library. It performs semantic +validation including consistency checking, circular inheritance detection, +and OWL file export. + +Classes: + OWLValidator: Validates ontology classes using OWL reasoning and exports to OWL formats +""" + +import logging +from typing import List, Optional, Tuple + +from owlready2 import ( + World, + Thing, + get_ontology, + sync_reasoner_pellet, + OwlReadyInconsistentOntologyError, +) + +from app.core.memory.models.ontology_models import OntologyClass +logger = logging.getLogger(__name__) + + +class OWLValidator: + """Validator for OWL semantic validation of ontology classes. + + This validator performs semantic-level validation using Owlready2 including: + - Creating OWL classes from ontology class definitions + - Running consistency checking with Pellet reasoner + - Detecting circular inheritance + - Validating Protégé compatibility + - Exporting ontologies to various OWL formats (RDF/XML, Turtle, N-Triples) + + Attributes: + base_namespace: Base URI for the ontology namespace + """ + + def __init__(self, base_namespace: str = "http://example.org/ontology#"): + """Initialize the OWL validator. + + Args: + base_namespace: Base URI for the ontology namespace (default: http://example.org/ontology#) + """ + self.base_namespace = base_namespace + + def validate_ontology_classes( + self, + classes: List[OntologyClass], + ) -> Tuple[bool, List[str], Optional[World]]: + """Validate extracted ontology classes against OWL standards. + + This method creates an OWL ontology from the provided classes using Owlready2, + runs consistency checking with the Pellet reasoner, and detects common issues + like circular inheritance. + + Args: + classes: List of OntologyClass objects to validate + + Returns: + Tuple of (is_valid, error_messages, world): + - is_valid: True if ontology is valid and consistent, False otherwise + - error_messages: List of error/warning messages + - world: Owlready2 World object containing the ontology (None if validation failed) + + Examples: + >>> validator = OWLValidator() + >>> classes = [ + ... OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare"), + ... OntologyClass(name="Doctor", description="A doctor", entity_type="Person", domain="Healthcare"), + ... ] + >>> is_valid, errors, world = validator.validate_ontology_classes(classes) + >>> is_valid + True + >>> len(errors) + 0 + """ + if not classes: + return False, ["No classes provided for validation"], None + + errors = [] + + try: + # Create a new world (isolated ontology environment) + world = World() + + # Use a proper ontology IRI + # Owlready2 expects the IRI to end with .owl or similar + onto_iri = self.base_namespace.rstrip('#/') + if not onto_iri.endswith('.owl'): + onto_iri = onto_iri + '.owl' + + # Create ontology + onto = world.get_ontology(onto_iri) + + with onto: + # Dictionary to store created OWL classes for parent reference + owl_classes = {} + + # First pass: Create all classes without parent relationships + for ontology_class in classes: + try: + # Create OWL class dynamically using type() with Thing as base + # The key is to NOT set namespace in the dict, let Owlready2 handle it + owl_class = type( + ontology_class.name, # Class name + (Thing,), # Base classes + {} # Class dict (empty, let Owlready2 manage) + ) + + # Add label (rdfs:label) - include both English and Chinese names + labels = [ontology_class.name] + if ontology_class.name_chinese: + labels.append(ontology_class.name_chinese) + owl_class.label = labels + + # Add comment (rdfs:comment) with description + if ontology_class.description: + owl_class.comment = [ontology_class.description] + + # Store for parent relationship setup + owl_classes[ontology_class.name] = owl_class + + logger.debug( + f"Created OWL class: {ontology_class.name} " + f"(Chinese: {ontology_class.name_chinese}) " + f"IRI: {owl_class.iri if hasattr(owl_class, 'iri') else 'N/A'}" + ) + + except Exception as e: + error_msg = f"Failed to create OWL class '{ontology_class.name}': {str(e)}" + errors.append(error_msg) + logger.error(error_msg, exc_info=True) + + # Second pass: Set up parent relationships + for ontology_class in classes: + if ontology_class.parent_class and ontology_class.name in owl_classes: + parent_name = ontology_class.parent_class + + # Check if parent exists + if parent_name in owl_classes: + try: + child_class = owl_classes[ontology_class.name] + parent_class = owl_classes[parent_name] + + # Set parent by modifying is_a + child_class.is_a = [parent_class] + + logger.debug( + f"Set parent relationship: {ontology_class.name} -> {parent_name}" + ) + + except Exception as e: + error_msg = ( + f"Failed to set parent relationship " + f"'{ontology_class.name}' -> '{parent_name}': {str(e)}" + ) + errors.append(error_msg) + logger.warning(error_msg) + else: + warning_msg = ( + f"Parent class '{parent_name}' not found for '{ontology_class.name}'" + ) + errors.append(warning_msg) + logger.warning(warning_msg) + + # Check for circular inheritance + for class_name, owl_class in owl_classes.items(): + if self._has_circular_inheritance(owl_class): + error_msg = f"Circular inheritance detected for class '{class_name}'" + errors.append(error_msg) + logger.error(error_msg) + + # Run consistency checking with Pellet reasoner + try: + logger.info("Running Pellet reasoner for consistency checking...") + sync_reasoner_pellet(world, infer_property_values=True, infer_data_property_values=True) + logger.info("Consistency check passed") + + except OwlReadyInconsistentOntologyError as e: + error_msg = f"Ontology is inconsistent: {str(e)}" + errors.append(error_msg) + logger.error(error_msg) + return False, errors, world + + except Exception as e: + # Reasoner errors are often due to Java not being installed or configured + # Log as warning but don't fail validation - ontology structure is still valid + warning_msg = f"Reasoner check skipped: {str(e)}" + if str(e).strip(): # Only log if there's an actual error message + logger.warning(warning_msg) + else: + logger.warning("Reasoner check skipped: Java may not be installed or configured") + # Continue - ontology structure is valid even without reasoner check + + # If we have errors (excluding warnings), validation failed + is_valid = len(errors) == 0 + + return is_valid, errors, world + + except Exception as e: + error_msg = f"OWL validation failed: {str(e)}" + errors.append(error_msg) + logger.error(error_msg, exc_info=True) + return False, errors, None + + def _has_circular_inheritance(self, owl_class) -> bool: + """Check if an OWL class has circular inheritance. + + Circular inheritance occurs when a class inherits from itself through + a chain of parent relationships (e.g., A -> B -> C -> A). + + Args: + owl_class: Owlready2 class object to check + + Returns: + True if circular inheritance is detected, False otherwise + """ + visited = set() + current = owl_class + + while current: + # Get class IRI or name as identifier + class_id = str(current.iri) if hasattr(current, 'iri') else str(current) + + if class_id in visited: + # Found a cycle + return True + + visited.add(class_id) + + # Get parent classes (is_a relationship) + parents = getattr(current, 'is_a', []) + + # Filter out Thing and other base classes + parent_classes = [p for p in parents if p != Thing and hasattr(p, 'is_a')] + + if not parent_classes: + # No more parents, no cycle + break + + # Check first parent (in single inheritance) + current = parent_classes[0] if parent_classes else None + + return False + + def export_to_owl( + self, + world: World, + output_path: Optional[str] = None, + format: str = "rdfxml", + classes: Optional[List] = None + ) -> str: + """Export ontology to OWL file in specified format. + + Supported formats: + - rdfxml: RDF/XML format (default, most compatible) + - turtle: Turtle format (more readable) + - ntriples: N-Triples format (simplest) + - json: JSON format (simplified, human-readable) + + Args: + world: Owlready2 World object containing the ontology + output_path: Optional file path to save the ontology (if None, returns string) + format: Export format - "rdfxml", "turtle", "ntriples", or "json" (default: "rdfxml") + classes: Optional list of OntologyClass objects (required for json format) + + Returns: + String representation of the exported ontology + + Raises: + ValueError: If format is not supported + RuntimeError: If export fails + + Examples: + >>> validator = OWLValidator() + >>> is_valid, errors, world = validator.validate_ontology_classes(classes) + >>> owl_content = validator.export_to_owl(world, "ontology.owl", format="rdfxml") + """ + # Validate format + valid_formats = ["rdfxml", "turtle", "ntriples", "json"] + if format not in valid_formats: + raise ValueError( + f"Unsupported format '{format}'. Must be one of: {', '.join(valid_formats)}" + ) + + # JSON format doesn't need OWL processing + if format == "json": + if not classes: + raise ValueError("Classes list is required for JSON format export") + return self._export_to_json(classes) + + # For OWL formats, world is required + if not world: + raise ValueError("World object is None. Cannot export ontology.") + + # Note: Owlready2 has issues with turtle format export + # We'll handle it specially by converting from rdfxml + use_conversion = (format == "turtle") + + try: + # Get all ontologies in the world + ontologies = list(world.ontologies.values()) + + if not ontologies: + raise RuntimeError("No ontologies found in world") + + # Find the ontology with classes (skip anonymous/empty ontologies) + onto = None + for ont in ontologies: + classes_count = len(list(ont.classes())) + logger.debug(f"Checking ontology {ont.base_iri}: {classes_count} classes") + if classes_count > 0: + onto = ont + break + + # If no ontology with classes found, use the last non-anonymous one + if onto is None: + for ont in reversed(ontologies): + if ont.base_iri != "http://anonymous/": + onto = ont + break + + # If still no ontology, use the first one + if onto is None: + onto = ontologies[0] + + # Log ontology contents for debugging + logger.info(f"Ontology IRI: {onto.base_iri}") + logger.info(f"Ontology contains {len(list(onto.classes()))} classes") + + # List all classes in the ontology + all_classes = list(onto.classes()) + for cls in all_classes: + logger.info(f"Class in ontology: {cls.name} (IRI: {cls.iri})") + if hasattr(cls, 'label'): + logger.debug(f" Labels: {cls.label}") + if hasattr(cls, 'comment'): + logger.debug(f" Comments: {cls.comment}") + + if len(all_classes) == 0: + logger.warning("No classes found in ontology! This may indicate a problem with class creation.") + + if output_path: + # Save to file + export_format = "rdfxml" if use_conversion else format + logger.info(f"Exporting ontology to {output_path} in {export_format} format") + onto.save(file=output_path, format=export_format) + + # Read back the file content to return + with open(output_path, 'r', encoding='utf-8') as f: + content = f.read() + + # Convert to turtle if needed + if use_conversion: + content = self._convert_to_turtle(content) + + logger.info(f"Successfully exported ontology to {output_path}") + + # Format the content for better readability + content = self._format_owl_content(content, format) + + return content + else: + # Export to string (save to temporary location and read) + import tempfile + import os + + with tempfile.NamedTemporaryFile(mode='w', suffix='.owl', delete=False) as tmp: + tmp_path = tmp.name + + try: + export_format = "rdfxml" if use_conversion else format + onto.save(file=tmp_path, format=export_format) + + with open(tmp_path, 'r', encoding='utf-8') as f: + content = f.read() + + # Convert to turtle if needed + if use_conversion: + content = self._convert_to_turtle(content) + + # Format the content for better readability + content = self._format_owl_content(content, format) + + return content + + finally: + # Clean up temporary file + if os.path.exists(tmp_path): + os.remove(tmp_path) + + except Exception as e: + error_msg = f"Failed to export ontology: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def _export_to_json(self, classes: List) -> str: + """Export ontology classes to simplified JSON format. + + This format is more compact and easier to parse than OWL XML. + + Args: + classes: List of OntologyClass objects + + Returns: + JSON string representation (compact format) + """ + import json + + result = { + "ontology": { + "namespace": self.base_namespace, + "classes": [] + } + } + + for cls in classes: + class_data = { + "name": cls.name, + "name_chinese": cls.name_chinese, + "description": cls.description, + "entity_type": cls.entity_type, + "domain": cls.domain, + "parent_class": cls.parent_class, + "examples": cls.examples if hasattr(cls, 'examples') else [] + } + result["ontology"]["classes"].append(class_data) + + # 使用紧凑格式：无缩进，使用分隔符减少空格 + return json.dumps(result, ensure_ascii=False, separators=(',', ':')) + + def _convert_to_turtle(self, rdfxml_content: str) -> str: + """Convert RDF/XML content to Turtle format using rdflib. + + Args: + rdfxml_content: RDF/XML format content + + Returns: + Turtle format content + """ + try: + from rdflib import Graph + + # Parse RDF/XML + g = Graph() + g.parse(data=rdfxml_content, format="xml") + + # Serialize to Turtle + turtle_content = g.serialize(format="turtle") + + # Handle bytes vs string + if isinstance(turtle_content, bytes): + turtle_content = turtle_content.decode('utf-8') + + return turtle_content + + except ImportError: + logger.warning( + "rdflib is not installed. Cannot convert to Turtle format. " + "Install with: pip install rdflib" + ) + return rdfxml_content + except Exception as e: + logger.error(f"Failed to convert to Turtle format: {e}") + return rdfxml_content + + def _format_owl_content(self, content: str, format: str) -> str: + """Format OWL content for better readability. + + Args: + content: Raw OWL content string + format: Format type (rdfxml, turtle, ntriples) + + Returns: + Formatted OWL content string + """ + if format == "rdfxml": + # Format XML with proper indentation + try: + import xml.dom.minidom as minidom + dom = minidom.parseString(content) + # Pretty print with 2-space indentation + formatted = dom.toprettyxml(indent=" ", encoding="utf-8").decode("utf-8") + + # Remove extra blank lines + lines = [] + prev_blank = False + for line in formatted.split('\n'): + is_blank = not line.strip() + if not (is_blank and prev_blank): # Skip consecutive blank lines + lines.append(line) + prev_blank = is_blank + + formatted = '\n'.join(lines) + + return formatted + except Exception as e: + logger.warning(f"Failed to format XML content: {e}") + return content + + elif format == "turtle": + # Turtle format is already relatively readable + # Just ensure consistent line endings and not empty + if not content or content.strip() == "": + logger.warning("Turtle content is empty, this may indicate an export issue") + return content.strip() + '\n' if content.strip() else content + + elif format == "ntriples": + # N-Triples format is line-based, ensure proper line endings + return content.strip() + '\n' if content.strip() else content + + return content + + def validate_with_protege_compatibility( + self, + classes: List[OntologyClass] + ) -> Tuple[bool, List[str]]: + """Validate that ontology classes are compatible with Protégé editor. + + Protégé compatibility checks: + - Class names are valid OWL identifiers + - No special characters that Protégé cannot handle + - Namespace is properly formatted + - Labels and comments are properly encoded + + Args: + classes: List of OntologyClass objects to validate + + Returns: + Tuple of (is_compatible, warnings): + - is_compatible: True if compatible with Protégé, False otherwise + - warnings: List of compatibility warning messages + + Examples: + >>> validator = OWLValidator() + >>> classes = [OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare")] + >>> is_compatible, warnings = validator.validate_with_protege_compatibility(classes) + >>> is_compatible + True + """ + warnings = [] + + # Check namespace format + if not self.base_namespace.startswith(('http://', 'https://')): + warnings.append( + f"Namespace '{self.base_namespace}' should start with http:// or https:// " + "for Protégé compatibility" + ) + + if not self.base_namespace.endswith(('#', '/')): + warnings.append( + f"Namespace '{self.base_namespace}' should end with # or / " + "for Protégé compatibility" + ) + + # Check each class + for ontology_class in classes: + # Check for special characters that might cause issues + if any(char in ontology_class.name for char in ['<', '>', '"', '{', '}', '|', '^', '`']): + warnings.append( + f"Class name '{ontology_class.name}' contains special characters " + "that may cause issues in Protégé" + ) + + # Check description length (Protégé can handle long descriptions but may display poorly) + if ontology_class.description and len(ontology_class.description) > 1000: + warnings.append( + f"Class '{ontology_class.name}' has a very long description ({len(ontology_class.description)} chars) " + "which may display poorly in Protégé" + ) + + # Check for non-ASCII characters (Protégé supports them but encoding issues may occur) + if not ontology_class.name.isascii(): + warnings.append( + f"Class name '{ontology_class.name}' contains non-ASCII characters " + "which may cause encoding issues in some Protégé versions" + ) + + # If no warnings, it's compatible + is_compatible = len(warnings) == 0 + + return is_compatible, warnings diff --git a/api/app/core/models/base.py b/api/app/core/models/base.py index f92a0cb3..f5f49af0 100644 --- a/api/app/core/models/base.py +++ b/api/app/core/models/base.py @@ -81,6 +81,8 @@ class RedBearModelFactory: # api_key 格式: "access_key_id:secret_access_key" 或只是 access_key_id # region 从 base_url 或 extra_params 获取 from botocore.config import Config as BotoConfig + from app.core.models.bedrock_model_mapper import normalize_bedrock_model_id + max_pool_connections = int(os.getenv("BEDROCK_MAX_POOL_CONNECTIONS", "50")) max_retries = int(os.getenv("BEDROCK_MAX_RETRIES", "2")) # Configure with increased connection pool @@ -89,8 +91,11 @@ class RedBearModelFactory: retries={'max_attempts': max_retries, 'mode': 'adaptive'} ) + # 标准化模型 ID（自动转换简化名称为完整 Bedrock Model ID） + model_id = normalize_bedrock_model_id(config.model_name) + params = { - "model_id": config.model_name, + "model_id": model_id, "config": boto_config, **config.extra_params } diff --git a/api/app/core/models/bedrock_model_mapper.py b/api/app/core/models/bedrock_model_mapper.py new file mode 100644 index 00000000..565a02c8 --- /dev/null +++ b/api/app/core/models/bedrock_model_mapper.py @@ -0,0 +1,188 @@ +""" +AWS Bedrock 模型名称映射器 + +将简化的模型名称自动转换为正确的 Bedrock Model ID +""" +from typing import Optional +from app.core.logging_config import get_business_logger + +logger = get_business_logger() + +# Bedrock 模型名称映射表 +BEDROCK_MODEL_MAPPING = { + # Claude 3.5 系列 + "claude-3.5-sonnet": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "claude-3-5-sonnet": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "claude-sonnet-3.5": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "claude-sonnet-3-5": "anthropic.claude-3-5-sonnet-20240620-v1:0", + + # Claude 3 系列 + "claude-3-sonnet": "anthropic.claude-3-sonnet-20240229-v1:0", + "claude-3-haiku": "anthropic.claude-3-haiku-20240307-v1:0", + "claude-3-opus": "anthropic.claude-3-opus-20240229-v1:0", + "claude-sonnet": "anthropic.claude-3-sonnet-20240229-v1:0", + "claude-haiku": "anthropic.claude-3-haiku-20240307-v1:0", + "claude-opus": "anthropic.claude-3-opus-20240229-v1:0", + + # Claude 2 系列 + "claude-2": "anthropic.claude-v2", + "claude-2.1": "anthropic.claude-v2:1", + "claude-instant": "anthropic.claude-instant-v1", + + # Amazon Titan 系列 + "titan-text-express": "amazon.titan-text-express-v1", + "titan-text-lite": "amazon.titan-text-lite-v1", + "titan-embed-text": "amazon.titan-embed-text-v1", + "titan-embed-image": "amazon.titan-embed-image-v1", + + # Meta Llama 系列 + "llama3-70b": "meta.llama3-70b-instruct-v1:0", + "llama3-8b": "meta.llama3-8b-instruct-v1:0", + "llama2-70b": "meta.llama2-70b-chat-v1", + "llama2-13b": "meta.llama2-13b-chat-v1", + + # Mistral 系列 + "mistral-7b": "mistral.mistral-7b-instruct-v0:2", + "mixtral-8x7b": "mistral.mixtral-8x7b-instruct-v0:1", + "mistral-large": "mistral.mistral-large-2402-v1:0", + + # 常见错误格式的映射 + "claude-sonnet-4-5": "anthropic.claude-3-5-sonnet-20240620-v1:0", # 常见错误 + "claude-4-5-sonnet": "anthropic.claude-3-5-sonnet-20240620-v1:0", # 常见错误 + "claude-sonnet-4.5": "anthropic.claude-3-5-sonnet-20240620-v1:0", # 常见错误 +} + + +def normalize_bedrock_model_id(model_name: str, region: Optional[str] = None) -> str: + """ + 标准化 Bedrock 模型 ID + + 将简化的模型名称转换为正确的 Bedrock Model ID 格式 + + Args: + model_name: 模型名称（可能是简化格式或完整格式） + region: AWS 区域（可选，如 "us", "eu", "apac"） + + Returns: + str: 标准化的 Bedrock Model ID + + Examples: + >>> normalize_bedrock_model_id("claude-sonnet-4-5") + 'anthropic.claude-3-5-sonnet-20240620-v1:0' + + >>> normalize_bedrock_model_id("claude-3.5-sonnet", region="eu") + 'eu.anthropic.claude-3-5-sonnet-20240620-v1:0' + + >>> normalize_bedrock_model_id("anthropic.claude-3-5-sonnet-20240620-v1:0") + 'anthropic.claude-3-5-sonnet-20240620-v1:0' + """ + # 如果已经是正确的格式（包含 provider），直接返回 + if "." in model_name and not model_name.startswith(("us.", "eu.", "apac.", "sa.", "amer.", "global.", "us-gov.")): + # 检查是否是有效的 provider + provider = model_name.split(".", 1)[0] + valid_providers = ["anthropic", "amazon", "meta", "mistral", "deepseek", "openai", "ai21", "cohere", "stability"] + if provider in valid_providers: + logger.debug(f"Model ID 已经是正确格式: {model_name}") + return model_name + + # 移除区域前缀（如果存在） + original_model_name = model_name + region_prefix = None + if model_name.startswith(("us.", "eu.", "apac.", "sa.", "amer.", "global.", "us-gov.")): + parts = model_name.split(".", 1) + region_prefix = parts[0] + model_name = parts[1] if len(parts) > 1 else model_name + + # 转换为小写进行匹配 + model_name_lower = model_name.lower() + + # 尝试从映射表中查找 + if model_name_lower in BEDROCK_MODEL_MAPPING: + mapped_id = BEDROCK_MODEL_MAPPING[model_name_lower] + logger.info(f"映射模型名称: {original_model_name} -> {mapped_id}") + + # 如果指定了区域或原始名称包含区域前缀，添加区域前缀 + if region: + mapped_id = f"{region}.{mapped_id}" + elif region_prefix: + mapped_id = f"{region_prefix}.{mapped_id}" + + return mapped_id + + # 如果没有找到映射，返回原始名称并记录警告 + logger.warning( + f"未找到模型名称映射: {original_model_name}。" + f"请确保使用正确的 Bedrock Model ID 格式，如 'anthropic.claude-3-5-sonnet-20240620-v1:0'" + ) + return original_model_name + + +def is_bedrock_model_id(model_name: str) -> bool: + """ + 检查是否是 Bedrock Model ID 格式 + + Args: + model_name: 模型名称 + + Returns: + bool: 是否是 Bedrock Model ID 格式 + """ + # 移除区域前缀 + if model_name.startswith(("us.", "eu.", "apac.", "sa.", "amer.", "global.", "us-gov.")): + model_name = model_name.split(".", 1)[1] + + # 检查是否包含 provider + if "." not in model_name: + return False + + provider = model_name.split(".", 1)[0] + valid_providers = ["anthropic", "amazon", "meta", "mistral", "deepseek", "openai", "ai21", "cohere", "stability"] + return provider in valid_providers + + +def get_provider_from_model_id(model_id: str) -> str: + """ + 从 Bedrock Model ID 中提取 provider + + Args: + model_id: Bedrock Model ID + + Returns: + str: Provider 名称 + + Examples: + >>> get_provider_from_model_id("anthropic.claude-3-5-sonnet-20240620-v1:0") + 'anthropic' + + >>> get_provider_from_model_id("eu.anthropic.claude-3-5-sonnet-20240620-v1:0") + 'anthropic' + """ + # 移除区域前缀 + if model_id.startswith(("us.", "eu.", "apac.", "sa.", "amer.", "global.", "us-gov.")): + parts = model_id.split(".", 2) + return parts[1] if len(parts) > 1 else model_id.split(".", 1)[0] + + return model_id.split(".", 1)[0] + + +# 添加更多映射的辅助函数 +def add_model_mapping(short_name: str, full_model_id: str) -> None: + """ + 添加自定义模型名称映射 + + Args: + short_name: 简化的模型名称 + full_model_id: 完整的 Bedrock Model ID + """ + BEDROCK_MODEL_MAPPING[short_name.lower()] = full_model_id + logger.info(f"添加模型映射: {short_name} -> {full_model_id}") + + +def get_all_mappings() -> dict: + """ + 获取所有模型名称映射 + + Returns: + dict: 模型名称映射字典 + """ + return BEDROCK_MODEL_MAPPING.copy() diff --git a/api/app/core/models/scripts/__init__.py b/api/app/core/models/scripts/__init__.py new file mode 100644 index 00000000..657b12fd --- /dev/null +++ b/api/app/core/models/scripts/__init__.py @@ -0,0 +1 @@ +"""模型配置脚本模块""" diff --git a/api/app/core/models/scripts/bedrock_models.yaml b/api/app/core/models/scripts/bedrock_models.yaml new file mode 100644 index 00000000..453aaa13 --- /dev/null +++ b/api/app/core/models/scripts/bedrock_models.yaml @@ -0,0 +1,174 @@ +provider: bedrock +enabled: false +models: +- name: ai21 + type: llm + provider: bedrock + description: AI21 Labs大语言模型，completion生成模式，256000上下文窗口 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + logo: bedrock +- name: amazon nova + type: llm + provider: bedrock + description: Amazon Nova大语言模型，支持智能体思考、工具调用、流式工具调用、视觉能力，300000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - stream-tool-call + - vision + logo: bedrock +- name: anthropic claude + type: llm + provider: bedrock + description: Anthropic Claude大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用、文档处理，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - vision + - tool-call + - stream-tool-call + - document + logo: bedrock +- name: cohere + type: llm + provider: bedrock + description: Cohere大语言模型，支持智能体思考、工具调用、流式工具调用，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - stream-tool-call + logo: bedrock +- name: deepseek + type: llm + provider: bedrock + description: DeepSeek大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用，32768上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - vision + - tool-call + - stream-tool-call + logo: bedrock +- name: meta + type: llm + provider: bedrock + description: Meta Llama大语言模型，支持智能体思考、工具调用，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + logo: bedrock +- name: mistral + type: llm + provider: bedrock + description: Mistral AI大语言模型，支持智能体思考、工具调用，32000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + logo: bedrock +- name: openai + type: llm + provider: bedrock + description: OpenAI大语言模型，支持智能体思考、工具调用、流式工具调用，32768上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - stream-tool-call + logo: bedrock +- name: qwen + type: llm + provider: bedrock + description: Qwen大语言模型，支持智能体思考、工具调用、流式工具调用，32768上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - stream-tool-call + logo: bedrock +- name: amazon.rerank-v1:0 + type: rerank + provider: bedrock + description: amazon.rerank-v1:0重排序模型，5120上下文窗口 + is_deprecated: false + is_official: true + tags: + - 重排序模型 + logo: bedrock +- name: cohere.rerank-v3-5:0 + type: rerank + provider: bedrock + description: cohere.rerank-v3-5:0重排序模型，5120上下文窗口 + is_deprecated: false + is_official: true + tags: + - 重排序模型 + logo: bedrock +- name: amazon.nova-2-multimodal-embeddings-v1:0 + type: embedding + provider: bedrock + description: amazon.nova-2-multimodal-embeddings-v1:0文本嵌入模型，支持视觉能力，8192上下文窗口 + is_deprecated: false + is_official: true + tags: + - 文本嵌入模型 + - vision + logo: bedrock +- name: amazon.titan-embed-text-v1 + type: embedding + provider: bedrock + description: amazon.titan-embed-text-v1文本嵌入模型，8192上下文窗口 + is_deprecated: false + is_official: true + tags: + - 文本嵌入模型 + logo: bedrock +- name: amazon.titan-embed-text-v2:0 + type: embedding + provider: bedrock + description: amazon.titan-embed-text-v2:0文本嵌入模型，8192上下文窗口 + is_deprecated: false + is_official: true + tags: + - 文本嵌入模型 + logo: bedrock +- name: cohere.embed-english-v3 + type: embedding + provider: bedrock + description: Cohere Embed 3 English文本嵌入模型，512上下文窗口 + is_deprecated: false + is_official: true + tags: + - 文本嵌入模型 + logo: bedrock +- name: cohere.embed-multilingual-v3 + type: embedding + provider: bedrock + description: Cohere Embed 3 Multilingual文本嵌入模型，512上下文窗口 + is_deprecated: false + is_official: true + tags: + - 文本嵌入模型 + logo: bedrock diff --git a/api/app/core/models/scripts/dashscope_models.yaml b/api/app/core/models/scripts/dashscope_models.yaml new file mode 100644 index 00000000..bcdb467e --- /dev/null +++ b/api/app/core/models/scripts/dashscope_models.yaml @@ -0,0 +1,820 @@ +provider: dashscope +enabled: false +models: +- name: deepseek-r1-distill-qwen-14b + type: llm + provider: dashscope + description: DeepSeek-R1-Distill-Qwen-14B大语言模型，支持智能体思考，32000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: deepseek-r1-distill-qwen-32b + type: llm + provider: dashscope + description: DeepSeek-R1-Distill-Qwen-32B大语言模型，支持智能体思考，32000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: deepseek-r1 + type: llm + provider: dashscope + description: DeepSeek-R1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: deepseek-v3.1 + type: llm + provider: dashscope + description: DeepSeek-V3.1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: deepseek-v3.2-exp + type: llm + provider: dashscope + description: DeepSeek-V3.2-exp实验版大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: deepseek-v3.2 + type: llm + provider: dashscope + description: DeepSeek-V3.2大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: deepseek-v3 + type: llm + provider: dashscope + description: DeepSeek-V3大语言模型，支持智能体思考，64000上下文窗口，对话模式，支持文本与JSON格式输出 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: dashscope +- name: farui-plus + type: llm + provider: dashscope + description: farui-plus大语言模型，支持多工具调用、智能体思考、流式工具调用，12288上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: glm-4.7 + type: llm + provider: dashscope + description: GLM-4.7大语言模型，支持多工具调用、智能体思考、流式工具调用，202752超大上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qvq-max-latest + type: llm + provider: dashscope + description: qvq-max-latest大语言模型，支持视觉、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - vision + - agent-thought + - stream-tool-call + logo: dashscope +- name: qvq-max + type: llm + provider: dashscope + description: qvq-max大语言模型，支持视觉、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - vision + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-coder-turbo-0919 + type: llm + provider: dashscope + description: qwen-coder-turbo-0919代码专用大语言模型，支持智能体思考，131072上下文窗口，对话模式，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - 代码模型 + - agent-thought + logo: dashscope +- name: qwen-max-latest + type: llm + provider: dashscope + description: qwen-max-latest大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-max-longcontext + type: llm + provider: dashscope + description: qwen-max-longcontext长上下文大语言模型，支持多工具调用、智能体思考、流式工具调用，32000上下文窗口，对话模式，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-max + type: llm + provider: dashscope + description: qwen-max大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，支持联网搜索 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-mt-plus + type: llm + provider: dashscope + description: qwen-mt-plus多语言翻译大语言模型，支持智能体思考，16384上下文窗口，对话模式，支持多语种互译与领域翻译适配 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 翻译模型 + - agent-thought + logo: dashscope +- name: qwen-mt-turbo + type: llm + provider: dashscope + description: qwen-mt-turbo轻量化多语言翻译大语言模型，支持智能体思考，16384上下文窗口，对话模式，支持多语种互译与领域翻译适配 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 翻译模型 + - agent-thought + logo: dashscope +- name: qwen-plus-0112 + type: llm + provider: dashscope + description: qwen-plus-0112大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-0125 + type: llm + provider: dashscope + description: qwen-plus-0125大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-0723 + type: llm + provider: dashscope + description: qwen-plus-0723大语言模型，支持多工具调用、智能体思考、流式工具调用，32000上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-0806 + type: llm + provider: dashscope + description: qwen-plus-0806大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-0919 + type: llm + provider: dashscope + description: qwen-plus-0919大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-1125 + type: llm + provider: dashscope + description: qwen-plus-1125大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-1127 + type: llm + provider: dashscope + description: qwen-plus-1127大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-plus-1220 + type: llm + provider: dashscope + description: qwen-plus-1220大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen-vl-max + type: llm + provider: dashscope + description: qwen-vl-max多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen-vl-plus-0809 + type: llm + provider: dashscope + description: qwen-vl-plus-0809多模态大模型，支持视觉理解、智能体思考、视频理解，32768上下文窗口，对话模式，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen-vl-plus-2025-01-02 + type: llm + provider: dashscope + description: qwen-vl-plus-2025-01-02多模态大模型，支持视觉理解、智能体思考、视频理解，32768上下文窗口，对话模式，未废弃 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen-vl-plus-2025-01-25 + type: llm + provider: dashscope + description: qwen-vl-plus-2025-01-25多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen-vl-plus-latest + type: llm + provider: dashscope + description: qwen-vl-plus-latest多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen-vl-plus + type: llm + provider: dashscope + description: qwen-vl-plus多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen2.5-0.5b-instruct + type: llm + provider: dashscope + description: qwen2.5-0.5b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，未废弃 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-14b + type: llm + provider: dashscope + description: qwen3-14b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-235b-a22b-instruct-2507 + type: llm + provider: dashscope + description: qwen3-235b-a22b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-235b-a22b-thinking-2507 + type: llm + provider: dashscope + description: qwen3-235b-a22b-thinking-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-235b-a22b + type: llm + provider: dashscope + description: qwen3-235b-a22b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-30b-a3b-instruct-2507 + type: llm + provider: dashscope + description: qwen3-30b-a3b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-30b-a3b + type: llm + provider: dashscope + description: qwen3-30b-a3b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-32b + type: llm + provider: dashscope + description: qwen3-32b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-4b + type: llm + provider: dashscope + description: qwen3-4b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-8b + type: llm + provider: dashscope + description: qwen3-8b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-coder-30b-a3b-instruct + type: llm + provider: dashscope + description: qwen3-coder-30b-a3b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 代码模型 + - agent-thought + logo: dashscope +- name: qwen3-coder-480b-a35b-instruct + type: llm + provider: dashscope + description: qwen3-coder-480b-a35b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 代码模型 + - agent-thought + logo: dashscope +- name: qwen3-coder-plus-2025-09-23 + type: llm + provider: dashscope + description: qwen3-coder-plus-2025-09-23大语言模型，支持智能体思考，1000000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 代码模型 + - agent-thought + logo: dashscope +- name: qwen3-coder-plus + type: llm + provider: dashscope + description: qwen3-coder-plus大语言模型，支持智能体思考，1000000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 代码模型 + - agent-thought + logo: dashscope +- name: qwen3-max-2025-09-23 + type: llm + provider: dashscope + description: qwen3-max-2025-09-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - 联网搜索 + logo: dashscope +- name: qwen3-max-2026-01-23 + type: llm + provider: dashscope + description: qwen3-max-2026-01-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - 联网搜索 + logo: dashscope +- name: qwen3-max-preview + type: llm + provider: dashscope + description: qwen3-max-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-max + type: llm + provider: dashscope + description: qwen3-max大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - 联网搜索 + logo: dashscope +- name: qwen3-next-80b-a3b-instruct + type: llm + provider: dashscope + description: qwen3-next-80b-a3b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-next-80b-a3b-thinking + type: llm + provider: dashscope + description: qwen3-next-80b-a3b-thinking大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwen3-omni-flash-2025-12-01 + type: llm + provider: dashscope + description: qwen3-omni-flash-2025-12-01多模态大语言模型，支持视觉、智能体思考、视频、音频能力，65536上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + - audio + logo: dashscope +- name: qwen3-vl-235b-a22b-instruct + type: llm + provider: dashscope + description: qwen3-vl-235b-a22b-instruct多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + - video + logo: dashscope +- name: qwen3-vl-235b-a22b-thinking + type: llm + provider: dashscope + description: qwen3-vl-235b-a22b-thinking多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + - video + logo: dashscope +- name: qwen3-vl-30b-a3b-instruct + type: llm + provider: dashscope + description: qwen3-vl-30b-a3b-instruct多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + - video + logo: dashscope +- name: qwen3-vl-30b-a3b-thinking + type: llm + provider: dashscope + description: qwen3-vl-30b-a3b-thinking多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + - video + logo: dashscope +- name: qwen3-vl-flash + type: llm + provider: dashscope + description: qwen3-vl-flash多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + - video + logo: dashscope +- name: qwen3-vl-plus-2025-09-23 + type: llm + provider: dashscope + description: qwen3-vl-plus-2025-09-23多模态大语言模型，支持视觉、智能体思考、视频能力，262144上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwen3-vl-plus + type: llm + provider: dashscope + description: qwen3-vl-plus多模态大语言模型，支持视觉、智能体思考、视频能力，262144上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - 多模态模型 + - vision + - agent-thought + - video + logo: dashscope +- name: qwq-32b + type: llm + provider: dashscope + description: qwq-32b大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwq-plus-0305 + type: llm + provider: dashscope + description: qwq-plus-0305大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - stream-tool-call + logo: dashscope +- name: qwq-plus + type: llm + provider: dashscope + description: qwq-plus大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - stream-tool-call + logo: dashscope +- name: gte-rerank-v2 + type: rerank + provider: dashscope + description: gte-rerank-v2重排序模型，4000上下文窗口 + is_deprecated: false + is_official: true + tags: + - 重排序模型 + logo: dashscope +- name: gte-rerank + type: rerank + provider: dashscope + description: gte-rerank重排序模型，4000上下文窗口 + is_deprecated: false + is_official: true + tags: + - 重排序模型 + logo: dashscope +- name: multimodal-embedding-v1 + type: embedding + provider: dashscope + description: multimodal-embedding-v1多模态嵌入模型，支持视觉能力，8192上下文窗口，最大分块数10 + is_deprecated: false + is_official: true + tags: + - 嵌入模型 + - 多模态模型 + - vision + logo: dashscope +- name: text-embedding-v1 + type: embedding + provider: dashscope + description: text-embedding-v1文本嵌入模型，2048上下文窗口，最大分块数25 + is_deprecated: false + is_official: true + tags: + - 嵌入模型 + - 文本嵌入 + logo: dashscope +- name: text-embedding-v2 + type: embedding + provider: dashscope + description: text-embedding-v2文本嵌入模型，2048上下文窗口，最大分块数25 + is_deprecated: false + is_official: true + tags: + - 嵌入模型 + - 文本嵌入 + logo: dashscope +- name: text-embedding-v3 + type: embedding + provider: dashscope + description: text-embedding-v3文本嵌入模型，8192上下文窗口，最大分块数10 + is_deprecated: false + is_official: true + tags: + - 嵌入模型 + - 文本嵌入 + logo: dashscope +- name: text-embedding-v4 + type: embedding + provider: dashscope + description: text-embedding-v4文本嵌入模型，8192上下文窗口，最大分块数10 + is_deprecated: false + is_official: true + tags: + - 嵌入模型 + - 文本嵌入 + logo: dashscope diff --git a/api/app/core/models/scripts/loader.py b/api/app/core/models/scripts/loader.py new file mode 100644 index 00000000..6469656c --- /dev/null +++ b/api/app/core/models/scripts/loader.py @@ -0,0 +1,143 @@ +"""模型配置加载器 - 用于将预定义模型批量导入到数据库""" + +import os +from pathlib import Path +from typing import Callable + +import yaml +from sqlalchemy.orm import Session +from app.models.models_model import ModelBase, ModelProvider + + +def _load_yaml_config(provider: ModelProvider) -> list[dict]: + """从YAML文件加载指定供应商的模型配置""" + config_dir = Path(__file__).parent + config_file = config_dir / f"{provider.value}_models.yaml" + + if not config_file.exists(): + return [] + + with open(config_file, 'r', encoding='utf-8') as f: + data = yaml.safe_load(f) + + # 检查是否需要加载（默认为 true） + if not data.get('enabled', True): + return [] + + return data.get('models', []) + + +def _disable_yaml_config(provider: ModelProvider) -> None: + """将YAML文件的enabled标志设置为false""" + config_dir = Path(__file__).parent + config_file = config_dir / f"{provider.value}_models.yaml" + + if not config_file.exists(): + return + + with open(config_file, 'r', encoding='utf-8') as f: + data = yaml.safe_load(f) + + data['enabled'] = False + + with open(config_file, 'w', encoding='utf-8') as f: + yaml.dump(data, f, allow_unicode=True, sort_keys=False) + + +def load_models(db: Session, providers: list[str] = None, silent: bool = False) -> dict: + """ + 加载模型配置到数据库 + + Args: + db: 数据库会话 + providers: 要加载的供应商列表，None表示加载所有 + silent: 是否静默模式（不输出详细日志） + + Returns: + dict: 加载结果统计 {"success": int, "skipped": int, "failed": int} + """ + result = {"success": 0, "skipped": 0, "failed": 0} + + # 确定要加载的供应商 + if providers: + target_providers = [ModelProvider(p) if isinstance(p, str) else p for p in providers] + else: + target_providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE] + + for provider in target_providers: + # 从YAML文件加载模型配置 + models = _load_yaml_config(provider) + + if not models: + if not silent: + print(f"警告: 供应商 '{provider.value}' 暂无预定义模型") + continue + + if not silent: + print(f"\n正在加载 {provider.value} 的 {len(models)} 个模型...") + + # provider_success = 0 + for model_data in models: + try: + # 检查模型是否已存在 + existing = db.query(ModelBase).filter( + ModelBase.name == model_data["name"], + ModelBase.provider == model_data["provider"] + ).first() + + if existing: + # 更新现有模型配置 + for key, value in model_data.items(): + setattr(existing, key, value) + db.commit() + if not silent: + print(f"更新成功: {model_data['name']}") + result["success"] += 1 + # provider_success += 1 + else: + # 创建新模型 + model = ModelBase(**model_data) + db.add(model) + db.commit() + if not silent: + print(f"添加成功: {model_data['name']}") + result["success"] += 1 + # provider_success += 1 + + except Exception as e: + db.rollback() + if not silent: + print(f"添加失败: {model_data['name']} - {str(e)}") + result["failed"] += 1 + + # 如果该供应商的模型全部加载成功，将enabled设置为false + # if provider_success == len(models): + _disable_yaml_config(provider) + + return result + + +def load_models_by_provider(db: Session, provider: str) -> dict: + """ + 加载指定供应商的模型配置 + + Args: + db: 数据库会话 + provider: 供应商名称（字符串或ModelProvider枚举） + + Returns: + dict: 加载结果统计 + """ + provider_enum = ModelProvider(provider) if isinstance(provider, str) else provider + return load_models(db, providers=[provider_enum]) + + +def get_available_providers() -> list[Callable[[], str]]: + """获取所有可用的供应商列表（从ModelProvider枚举获取，排除COMPOSITE）""" + return [p.value for p in ModelProvider if p != ModelProvider.COMPOSITE] + + +def get_models_by_provider(provider: str) -> list[dict]: + """获取指定供应商的模型配置列表""" + provider_enum = ModelProvider(provider) if isinstance(provider, str) else provider + return _load_yaml_config(provider_enum) diff --git a/api/app/core/models/scripts/openai_models.yaml b/api/app/core/models/scripts/openai_models.yaml new file mode 100644 index 00000000..5a416264 --- /dev/null +++ b/api/app/core/models/scripts/openai_models.yaml @@ -0,0 +1,294 @@ +provider: openai +enabled: false +models: +- name: chatgpt-4o-latest + type: llm + provider: openai + description: chatgpt-4o-latest大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + logo: openai +- name: gpt-3.5-turbo-0125 + type: llm + provider: openai + description: gpt-3.5-turbo-0125大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-3.5-turbo-1106 + type: llm + provider: openai + description: gpt-3.5-turbo-1106大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-3.5-turbo-16k + type: llm + provider: openai + description: gpt-3.5-turbo-16k大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-3.5-turbo-instruct + type: llm + provider: openai + description: gpt-3.5-turbo-instruct大语言模型，4096上下文窗口，文本补全模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + logo: openai +- name: gpt-3.5-turbo + type: llm + provider: openai + description: gpt-3.5-turbo大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-4-0125-preview + type: llm + provider: openai + description: gpt-4-0125-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-4-1106-preview + type: llm + provider: openai + description: gpt-4-1106-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-4-turbo-2024-04-09 + type: llm + provider: openai + description: gpt-4-turbo-2024-04-09大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + logo: openai +- name: gpt-4-turbo-preview + type: llm + provider: openai + description: gpt-4-turbo-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + logo: openai +- name: gpt-4-turbo + type: llm + provider: openai + description: gpt-4-turbo大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + logo: openai +- name: o1-preview + type: llm + provider: openai + description: o1-preview大语言模型，支持智能体思考，128000上下文窗口，对话模式，已废弃 + is_deprecated: true + is_official: true + tags: + - 大语言模型 + - agent-thought + logo: openai +- name: o1 + type: llm + provider: openai + description: o1大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - multi-tool-call + - agent-thought + - stream-tool-call + - vision + - structured-output + logo: openai +- name: o3-2025-04-16 + type: llm + provider: openai + description: o3-2025-04-16大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - vision + - stream-tool-call + - structured-output + logo: openai +- name: o3-mini-2025-01-31 + type: llm + provider: openai + description: o3-mini-2025-01-31大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - stream-tool-call + - structured-output + logo: openai +- name: o3-mini + type: llm + provider: openai + description: o3-mini大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - stream-tool-call + - structured-output + logo: openai +- name: o3-pro-2025-06-10 + type: llm + provider: openai + description: o3-pro-2025-06-10大语言模型，支持智能体思考、工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - vision + - structured-output + logo: openai +- name: o3-pro + type: llm + provider: openai + description: o3-pro大语言模型，支持智能体思考、工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - vision + - structured-output + logo: openai +- name: o3 + type: llm + provider: openai + description: o3大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - vision + - tool-call + - stream-tool-call + - structured-output + logo: openai +- name: o4-mini-2025-04-16 + type: llm + provider: openai + description: o4-mini-2025-04-16大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - vision + - stream-tool-call + - structured-output + logo: openai +- name: o4-mini + type: llm + provider: openai + description: o4-mini大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式 + is_deprecated: false + is_official: true + tags: + - 大语言模型 + - agent-thought + - tool-call + - vision + - stream-tool-call + - structured-output + logo: openai +- name: text-embedding-3-large + type: embedding + provider: openai + description: text-embedding-3-large文本向量模型，8191上下文窗口，最大分块数32 + is_deprecated: false + is_official: true + tags: + - 文本向量模型 + logo: openai +- name: text-embedding-3-small + type: embedding + provider: openai + description: text-embedding-3-small文本向量模型，8191上下文窗口，最大分块数32 + is_deprecated: false + is_official: true + tags: + - 文本向量模型 + logo: openai +- name: text-embedding-ada-002 + type: embedding + provider: openai + description: text-embedding-ada-002文本向量模型，8097上下文窗口，最大分块数32 + is_deprecated: false + is_official: true + tags: + - 文本向量模型 + logo: openai diff --git a/api/app/core/rag/app/presentation.py b/api/app/core/rag/app/presentation.py deleted file mode 100644 index d62e0096..00000000 --- a/api/app/core/rag/app/presentation.py +++ /dev/null @@ -1,165 +0,0 @@ -import copy -import re -from io import BytesIO -from PIL import Image - -from app.core.rag.nlp import tokenize, is_english -from app.core.rag.nlp import rag_tokenizer -from app.core.rag.deepdoc.parser import PdfParser, PlainParser -from app.core.rag.deepdoc.parser.ppt_parser import RAGPptParser as PptParser -from PyPDF2 import PdfReader as pdf2_read -from app.core.rag.app.naive import by_plaintext, PARSERS - -class Ppt(PptParser): - def __call__(self, fnm, from_page, to_page, callback=None): - txts = super().__call__(fnm, from_page, to_page) - - callback(0.5, "Text extraction finished.") - import aspose.slides as slides - import aspose.pydrawing as drawing - imgs = [] - with slides.Presentation(BytesIO(fnm)) as presentation: - for i, slide in enumerate(presentation.slides[from_page: to_page]): - try: - with BytesIO() as buffered: - slide.get_thumbnail( - 0.1, 0.1).save( - buffered, drawing.imaging.ImageFormat.jpeg) - buffered.seek(0) - imgs.append(Image.open(buffered).copy()) - except RuntimeError as e: - raise RuntimeError(f'ppt parse error at page {i+1}, original error: {str(e)}') from e - assert len(imgs) == len( - txts), "Slides text and image do not match: {} vs. {}".format(len(imgs), len(txts)) - callback(0.9, "Image extraction finished") - self.is_english = is_english(txts) - return [(txts[i], imgs[i]) for i in range(len(txts))] - -class Pdf(PdfParser): - def __init__(self): - super().__init__() - - def __garbage(self, txt): - txt = txt.lower().strip() - if re.match(r"[0-9\.,%/-]+$", txt): - return True - if len(txt) < 3: - return True - return False - - def __call__(self, filename, binary=None, from_page=0, - to_page=100000, zoomin=3, callback=None): - from timeit import default_timer as timer - start = timer() - callback(msg="OCR started") - self.__images__(filename if not binary else binary, - zoomin, from_page, to_page, callback) - callback(msg="Page {}~{}: OCR finished ({:.2f}s)".format(from_page, min(to_page, self.total_page), timer() - start)) - assert len(self.boxes) == len(self.page_images), "{} vs. {}".format( - len(self.boxes), len(self.page_images)) - res = [] - for i in range(len(self.boxes)): - lines = "\n".join([b["text"] for b in self.boxes[i] - if not self.__garbage(b["text"])]) - res.append((lines, self.page_images[i])) - callback(0.9, "Page {}~{}: Parsing finished".format( - from_page, min(to_page, self.total_page))) - return res, [] - - -class PlainPdf(PlainParser): - def __call__(self, filename, binary=None, from_page=0, - to_page=100000, callback=None, **kwargs): - self.pdf = pdf2_read(filename if not binary else BytesIO(binary)) - page_txt = [] - for page in self.pdf.pages[from_page: to_page]: - page_txt.append(page.extract_text()) - callback(0.9, "Parsing finished") - return [(txt, None) for txt in page_txt], [] - - -def chunk(filename, binary=None, from_page=0, to_page=100000, - lang="Chinese", callback=None, vision_model=None, parser_config=None, **kwargs): - """ - The supported file formats are pdf, pptx. - Every page will be treated as a chunk. And the thumbnail of every page will be stored. - PPT file will be parsed by using this method automatically, setting-up for every PPT file is not necessary. - """ - if parser_config is None: - parser_config = {} - eng = lang.lower() == "english" - doc = { - "docnm_kwd": filename, - "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) - } - doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) - res = [] - if re.search(r"\.pptx?$", filename, re.IGNORECASE): - if not binary: - with open(filename, "rb") as f: - binary = f.read() - ppt_parser = Ppt() - for pn, (txt, img) in enumerate(ppt_parser( - filename if not binary else binary, from_page, 1000000, callback)): - d = copy.deepcopy(doc) - pn += from_page - d["image"] = img - d["doc_type_kwd"] = "image" - d["page_num_int"] = [pn + 1] - d["top_int"] = [0] - d["position_int"] = [(pn + 1, 0, img.size[0], 0, img.size[1])] - tokenize(d, txt, eng) - res.append(d) - return res - elif re.search(r"\.pdf$", filename, re.IGNORECASE): - layout_recognizer = parser_config.get("layout_recognize", "DeepDOC") - - if isinstance(layout_recognizer, bool): - layout_recognizer = "DeepDOC" if layout_recognizer else "Plain Text" - - name = layout_recognizer.strip().lower() - parser = PARSERS.get(name, by_plaintext) - callback(0.1, "Start to parse.") - - sections, _, _ = parser( - filename=filename, - binary=binary, - from_page=from_page, - to_page=to_page, - lang=lang, - callback=callback, - vision_model=vision_model, - pdf_cls=Pdf, - **kwargs - ) - - if not sections: - return [] - - if name in ["tcadp", "docling", "mineru"]: - parser_config["chunk_token_num"] = 0 - - callback(0.8, "Finish parsing.") - - for pn, (txt, img) in enumerate(sections): - d = copy.deepcopy(doc) - pn += from_page - if img: - d["image"] = img - d["page_num_int"] = [pn + 1] - d["top_int"] = [0] - d["position_int"] = [(pn + 1, 0, img.size[0] if img else 0, 0, img.size[1] if img else 0)] - tokenize(d, txt, eng) - res.append(d) - return res - - raise NotImplementedError( - "file type not supported yet(pptx, pdf supported)") - - -if __name__ == "__main__": - import sys - - def dummy(a, b): - pass - chunk(sys.argv[1], callback=dummy) diff --git a/api/app/core/rag/vdb/field.py b/api/app/core/rag/vdb/field.py index 86d39060..99d872c2 100644 --- a/api/app/core/rag/vdb/field.py +++ b/api/app/core/rag/vdb/field.py @@ -4,7 +4,7 @@ from enum import StrEnum, auto class Field(StrEnum): CONTENT_KEY = "page_content" METADATA_KEY = "metadata" - GROUP_KEY = "group_id" + GROUP_KEY = "end_user_id" VECTOR = auto() # Sparse Vector aims to support full text search SPARSE_VECTOR = auto() diff --git a/api/app/core/storage/url_signer.py b/api/app/core/storage/url_signer.py index 480c8ef4..712b298e 100644 --- a/api/app/core/storage/url_signer.py +++ b/api/app/core/storage/url_signer.py @@ -36,7 +36,7 @@ def generate_signed_url( """ if base_url is None: # Use SERVER_IP or default to localhost - server_url = f"http://{settings.SERVER_IP}:8000/api" + server_url = settings.FILE_LOCAL_SERVER_URL base_url = server_url # Calculate expiration timestamp diff --git a/api/app/core/tools/builtin/baidu_search_tool.py b/api/app/core/tools/builtin/baidu_search_tool.py index 02431aed..45d4c359 100644 --- a/api/app/core/tools/builtin/baidu_search_tool.py +++ b/api/app/core/tools/builtin/baidu_search_tool.py @@ -16,7 +16,7 @@ class BaiduSearchTool(BuiltinTool): @property def description(self) -> str: - return "百度搜索 - 搜索引擎服务：网页搜索、新闻搜索、图片搜索、实时结果" + return "百度搜索 - 搜索引擎服务：网页搜索、新闻搜索、图片搜索、视频搜索" def get_required_config_parameters(self) -> List[str]: return ["api_key"] @@ -33,7 +33,7 @@ class BaiduSearchTool(BuiltinTool): ToolParameter( name="search_type", type=ParameterType.STRING, - description="搜索类型", + description="搜索类型, web: 网页搜索；news：新闻搜索；image：图片搜索；video视频搜索", required=False, default="web", enum=["web", "news", "image", "video"] diff --git a/api/app/core/validators/memory_config_validators.py b/api/app/core/validators/memory_config_validators.py index 333572e6..ba26c5f2 100644 --- a/api/app/core/validators/memory_config_validators.py +++ b/api/app/core/validators/memory_config_validators.py @@ -26,7 +26,7 @@ logger = get_config_logger() def _parse_model_id(model_id: Union[str, UUID, None], model_type: str, - config_id: Optional[int] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]: + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]: """Parse model ID from string or UUID.""" if model_id is None: return None @@ -59,7 +59,7 @@ def validate_model_exists_and_active( model_type: str, db: Session, tenant_id: Optional[UUID] = None, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None ) -> tuple[str, bool]: """Validate that a model exists and is active. @@ -166,7 +166,7 @@ def validate_and_resolve_model_id( db: Session, tenant_id: Optional[UUID] = None, required: bool = False, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None ) -> tuple[Optional[UUID], Optional[str]]: """Validate and resolve a model ID, checking existence and active status. @@ -204,7 +204,7 @@ def validate_and_resolve_model_id( def validate_embedding_model( - config_id: int, + config_id: UUID, embedding_id: Union[str, UUID, None], db: Session, tenant_id: Optional[UUID] = None, @@ -256,7 +256,7 @@ def validate_embedding_model( def validate_llm_model( - config_id: int, + config_id: UUID, llm_id: Union[str, UUID, None], db: Session, tenant_id: Optional[UUID] = None, diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index 6721d7b0..b7abf659 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -11,17 +11,12 @@ from typing import Any from langchain_core.runnables import RunnableConfig from langgraph.graph.state import CompiledStateGraph -from app.core.workflow.graph_builder import GraphBuilder +from app.core.workflow.expression_evaluator import evaluate_expression +from app.core.workflow.graph_builder import GraphBuilder, StreamOutputConfig from app.core.workflow.nodes import WorkflowState from app.core.workflow.nodes.base_config import VariableType from app.core.workflow.nodes.enums import NodeType -# from app.core.tools.registry import ToolRegistry -# from app.core.tools.executor import ToolExecutor -# from app.core.tools.langchain_adapter import LangchainAdapter -# TOOL_MANAGEMENT_AVAILABLE = True -# from app.db import get_db - logger = logging.getLogger(__name__) @@ -55,6 +50,8 @@ class WorkflowExecutor: self.execution_config = workflow_config.get("execution_config", {}) self.start_node_id = None + self.end_outputs: dict[str, StreamOutputConfig] = {} + self.activate_end: str | None = None self.checkpoint_config = RunnableConfig( configurable={ @@ -127,21 +124,19 @@ class WorkflowExecutor: "user_id": self.user_id, "error": None, "error_node": None, - "streaming_buffer": {}, # 流式缓冲区 "cycle_nodes": [ node.get("id") for node in self.workflow_config.get("nodes") if node.get("type") in [NodeType.LOOP, NodeType.ITERATION] ], # loop, iteration node id - "looping": False, # loop runing flag, only use in loop node,not use in main loop + "looping": 0, # loop runing flag, only use in loop node,not use in main loop "activate": { self.start_node_id: True } } - def _build_final_output(self, result, elapsed_time): + def _build_final_output(self, result, elapsed_time, final_output): node_outputs = result.get("node_outputs", {}) - final_output = self._extract_final_output(node_outputs) token_usage = self._aggregate_token_usage(node_outputs) conversation_id = None for node_id, node_output in node_outputs.items(): @@ -161,6 +156,146 @@ class WorkflowExecutor: "error": result.get("error"), } + def _update_scope_activate(self, scope, status=None): + """ + Update the activation state of all End nodes based on a completed scope (node or variable). + + Iterates over all End nodes in `self.end_outputs` and calls + `update_activate` on each, which may: + - Activate variable segments that depend on the completed node/scope. + - Activate the entire End node output if all control conditions are met. + + If any End node becomes active and `self.activate_end` is not yet set, + this node will be marked as the currently active End node. + + Args: + scope (str): The node ID or scope that has completed execution. + status (str | None): Optional status of the node (used for branch/control nodes). + """ + for node in self.end_outputs.keys(): + self.end_outputs[node].update_activate(scope, status) + if self.end_outputs[node].activate and self.activate_end is None: + self.activate_end = node + + def _update_stream_output_status(self, activate, data): + """ + Update the stream output state of End nodes based on workflow state updates. + + This method checks which nodes/scopes are activated and propagates + activation to End nodes accordingly. + + Args: + activate (dict): Mapping of node_id -> bool indicating which nodes/scopes are activated. + data (dict): Mapping of node_id -> node runtime data, including outputs. + + Behavior: + For each node in `data`: + 1. If the node is activated (`activate[node_id]` is True), + retrieve its output status from `runtime_vars`. + 2. Call `_update_scope_activate` to propagate the activation + to all relevant End nodes and update `self.activate_end`. + """ + for node_id in data.keys(): + if activate.get(node_id): + node_output_status = ( + data[node_id] + .get('runtime_vars', {}) + .get(node_id) + .get("output") + ) + self._update_scope_activate(node_id, status=node_output_status) + + async def _emit_active_chunks( + self, + node_outputs: dict, + variables: dict, + force=False + ): + """ + Process and yield all currently active output segments for the currently active End node. + + This method handles stream-mode output for an End node by iterating through its output segments + (`OutputContent`). Only segments marked as active (`activate=True`) are processed, unless + `force=True`, which allows all segments to be processed regardless of their activation state. + + Behavior: + 1. Iterates from the current `cursor` position to the end of the outputs list. + 2. For each segment: + - If the segment is literal text (`is_variable=False`), append it directly. + - If the segment is a variable (`is_variable=True`), evaluate it using + `evaluate_expression` with the given `node_outputs` and `variables`, + then transform the result with `_trans_output_string`. + 3. Yield a stream event of type "message" containing the processed chunk. + 4. Move the `cursor` forward after processing each segment. + 5. When all segments have been processed, remove this End node from `end_outputs` + and reset `activate_end` to None. + + Args: + node_outputs (dict): Current runtime node outputs, used for variable evaluation. + variables (dict): Current runtime variables, used for variable evaluation. + force (bool, default=False): If True, process segments even if `activate=False`. + + Yields: + dict: A stream event of type "message" containing the processed chunk. + + Notes: + - Segments that fail evaluation (ValueError) are skipped with a warning logged. + - This method only processes the currently active End node (`self.activate_end`). + - Use `force=True` for final emission regardless of activation state. + """ + + end_info = self.end_outputs[self.activate_end] + + while end_info.cursor < len(end_info.outputs): + final_chunk = '' + current_segment = end_info.outputs[end_info.cursor] + + if not current_segment.activate and not force: + # Stop processing until this segment becomes active + break + + # Literal segment + if not current_segment.is_variable: + final_chunk += current_segment.literal + else: + # Variable segment: evaluate and transform + try: + chunk = evaluate_expression( + current_segment.literal, + variables=variables, + node_outputs=node_outputs + ) + chunk = self._trans_output_string(chunk) + final_chunk += chunk + except ValueError: + # Log failed evaluation but continue streaming + logger.warning(f"[STREAM] Failed to evaluate segment: {current_segment.literal}") + + if final_chunk: + yield { + "event": "message", + "data": { + "chunk": final_chunk + } + } + + # Advance cursor after processing + end_info.cursor += 1 + + # Remove End node from active tracking if all segments have been processed + if end_info.cursor >= len(end_info.outputs): + self.end_outputs.pop(self.activate_end) + self.activate_end = None + + @staticmethod + def _trans_output_string(content): + if isinstance(content, str): + return content + elif isinstance(content, list): + return "\n".join(content) + else: + return str(content) + def build_graph(self, stream=False) -> CompiledStateGraph: """构建 LangGraph @@ -173,6 +308,7 @@ class WorkflowExecutor: stream=stream, ) self.start_node_id = builder.start_node_id + self.end_outputs = builder.end_node_map graph = builder.build() logger.info(f"工作流图构建完成: execution_id={self.execution_id}") @@ -205,14 +341,28 @@ class WorkflowExecutor: try: result = await graph.ainvoke(initial_state, config=self.checkpoint_config) - + full_content = '' + for end_id in self.end_outputs.keys(): + full_content += result.get('runtime_vars', {}).get(end_id, {}).get('output', '') + result["messages"].extend( + [ + { + "role": "user", + "content": input_data.get("message", '') + }, + { + "role": "assistant", + "content": full_content + } + ] + ) # 计算耗时 end_time = datetime.datetime.now() elapsed_time = (end_time - start_time).total_seconds() logger.info(f"工作流执行完成: execution_id={self.execution_id}, elapsed_time={elapsed_time:.2f}s") - return self._build_final_output(result, elapsed_time) + return self._build_final_output(result, elapsed_time, full_content) except Exception as e: # 计算耗时（即使失败也记录） @@ -261,7 +411,7 @@ class WorkflowExecutor: "data": { "execution_id": self.execution_id, "workspace_id": self.workspace_id, - "timestamp": start_time.isoformat() + "timestamp": int(start_time.timestamp() * 1000) } } @@ -273,7 +423,8 @@ class WorkflowExecutor: # 3. Execute workflow try: chunk_count = 0 - + full_content = '' + self._update_scope_activate("sys") async for event in graph.astream( initial_state, stream_mode=["updates", "debug", "custom"], # Use updates + debug + custom mode @@ -293,20 +444,42 @@ class WorkflowExecutor: # Handle custom streaming events (chunks from nodes via stream writer) chunk_count += 1 event_type = data.get("type", "node_chunk") # "message" or "node_chunk" - logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}" - f"- execution_id: {self.execution_id}") - yield { - "event": event_type, # "message" or "node_chunk" - "data": { - "node_id": data.get("node_id"), - "chunk": data.get("chunk"), - "full_content": data.get("full_content"), - "chunk_index": data.get("chunk_index"), - "is_prefix": data.get("is_prefix"), - "is_suffix": data.get("is_suffix"), - "conversation_id": input_data.get("conversation_id"), + if event_type == "node_chunk": + node_id = data.get("node_id") + if self.activate_end: + end_info = self.end_outputs.get(self.activate_end) + if not end_info or end_info.cursor >= len(end_info.outputs): + continue + current_output = end_info.outputs[end_info.cursor] + if current_output.is_variable and current_output.depends_on_scope(node_id): + if data.get("done"): + end_info.cursor += 1 + if end_info.cursor >= len(end_info.outputs): + self.end_outputs.pop(self.activate_end) + self.activate_end = None + else: + full_content += data.get("chunk") + yield { + "event": "message", + "data": { + "chunk": data.get("chunk") + } + } + logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}" + f"- execution_id: {self.execution_id}") + + elif event_type == "node_error": + yield { + "event": event_type, # "message" or "node_chunk" + "data": { + "node_id": data.get("node_id"), + "status": "failed", + "input": data.get("input_data"), + "elapsed_time": data.get("elapsed_time"), + "output": None, + "error": data.get("error") + } } - } elif mode == "debug": # Handle debug information (node execution status) @@ -325,14 +498,15 @@ class WorkflowExecutor: conversation_id = input_data.get("conversation_id") logger.info(f"[NODE-START] Node starts execution: {node_name} " f"- execution_id: {self.execution_id}") - yield { "event": "node_start", "data": { "node_id": node_name, "conversation_id": conversation_id, "execution_id": self.execution_id, - "timestamp": data.get("timestamp"), + "timestamp": int(datetime.datetime.fromisoformat( + data.get("timestamp") + ).timestamp() * 1000), } } elif event_type == "task_result": @@ -351,20 +525,82 @@ class WorkflowExecutor: "node_id": node_name, "conversation_id": conversation_id, "execution_id": self.execution_id, - "timestamp": data.get("timestamp"), - "state": result.get("node_outputs", {}).get(node_name), + "timestamp": int(datetime.datetime.fromisoformat( + data.get("timestamp") + ).timestamp() * 1000), + "input": result.get("node_outputs", {}).get(node_name, {}).get("input"), + "output": result.get("node_outputs", {}).get(node_name, {}).get("output"), + "elapsed_time": result.get("node_outputs", {}).get(node_name, {}).get("elapsed_time"), } } elif mode == "updates": # Handle state updates - store final state + state = graph.get_state(config=self.checkpoint_config).values + node_outputs = state.get("runtime_vars", {}) + variables = state.get("variables", {}) + activate = state.get("activate", {}) + for _, node_data in data.items(): + node_outputs |= node_data.get("runtime_vars", {}) + variables |= node_data.get("variables", {}) + + self._update_stream_output_status(activate, data) + wait = False + while self.activate_end and not wait: + async for msg_event in self._emit_active_chunks( + node_outputs=node_outputs, + variables=variables + ): + full_content += msg_event["data"]['chunk'] + yield msg_event + + if self.activate_end: + wait = True + else: + self._update_stream_output_status(activate, data) + logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} " f"- execution_id: {self.execution_id}") + result = graph.get_state(self.checkpoint_config).values + node_outputs = result.get("runtime_vars", {}) + variables = result.get("variables", {}) + self.end_outputs = { + node_id: node_info + for node_id, node_info in self.end_outputs.items() + if node_info.activate + } + + if self.end_outputs or self.activate_end: + while self.activate_end: + async for msg_event in self._emit_active_chunks( + node_outputs=node_outputs, + variables=variables, + force=True + ): + full_content += msg_event["data"]['chunk'] + yield msg_event + + if not self.activate_end and self.end_outputs: + self.activate_end = list(self.end_outputs.keys())[0] + # 计算耗时 end_time = datetime.datetime.now() elapsed_time = (end_time - start_time).total_seconds() result = graph.get_state(self.checkpoint_config).values + logger.info(result) + result["messages"].extend( + [ + { + "role": "user", + "content": input_data.get("message", '') + }, + { + "role": "assistant", + "content": full_content + } + ] + ) logger.info( f"Workflow execution completed (streaming), " f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s, execution_id: {self.execution_id}" @@ -373,7 +609,7 @@ class WorkflowExecutor: # 发送 workflow_end 事件 yield { "event": "workflow_end", - "data": self._build_final_output(result, elapsed_time) + "data": self._build_final_output(result, elapsed_time, full_content) } except Exception as e: @@ -395,31 +631,6 @@ class WorkflowExecutor: } } - @staticmethod - def _extract_final_output(node_outputs: dict[str, Any]) -> str | None: - """从节点输出中提取最终输出 - - 优先级： - 1. 最后一个执行的非 start/end 节点的 output - 2. 如果没有节点输出，返回 None - - Args: - node_outputs: 所有节点的输出 - - Returns: - 最终输出字符串或 None - """ - if not node_outputs: - return None - - # 获取最后一个节点的输出 - last_node_output = list(node_outputs.values())[-1] if node_outputs else None - - if last_node_output and isinstance(last_node_output, dict): - return last_node_output.get("output") - - return None - @staticmethod def _aggregate_token_usage(node_outputs: dict[str, Any]) -> dict[str, int] | None: """聚合所有节点的 token 使用情况 @@ -510,178 +721,3 @@ async def execute_workflow_stream( ) async for event in executor.execute_stream(input_data): yield event - -# ==================== 工具管理系统集成 ==================== - -# def get_workflow_tools(workspace_id: str, user_id: str) -> list: -# """获取工作流可用的工具列表 -# -# Args: -# workspace_id: 工作空间ID -# user_id: 用户ID -# -# Returns: -# 可用工具列表 -# """ -# if not TOOL_MANAGEMENT_AVAILABLE: -# logger.warning("工具管理系统不可用") -# return [] -# -# try: -# db = next(get_db()) -# -# # 创建工具注册表 -# registry = ToolRegistry(db) -# -# # 注册内置工具类 -# from app.core.tools.builtin import ( -# DateTimeTool, JsonTool, BaiduSearchTool, MinerUTool, TextInTool -# ) -# registry.register_tool_class(DateTimeTool) -# registry.register_tool_class(JsonTool) -# registry.register_tool_class(BaiduSearchTool) -# registry.register_tool_class(MinerUTool) -# registry.register_tool_class(TextInTool) -# -# # 获取活跃的工具 -# import uuid -# tools = registry.list_tools(workspace_id=uuid.UUID(workspace_id)) -# active_tools = [tool for tool in tools if tool.status.value == "active"] -# -# # 转换为Langchain工具 -# langchain_tools = [] -# for tool_info in active_tools: -# try: -# tool_instance = registry.get_tool(tool_info.id) -# if tool_instance: -# langchain_tool = LangchainAdapter.convert_tool(tool_instance) -# langchain_tools.append(langchain_tool) -# except Exception as e: -# logger.error(f"转换工具失败: {tool_info.name}, 错误: {e}") -# -# logger.info(f"为工作流获取了 {len(langchain_tools)} 个工具") -# return langchain_tools -# -# except Exception as e: -# logger.error(f"获取工作流工具失败: {e}") -# return [] -# -# -# class ToolWorkflowNode: -# """工具工作流节点 - 在工作流中执行工具""" -# -# def __init__(self, node_config: dict, workflow_config: dict): -# """初始化工具节点 -# -# Args: -# node_config: 节点配置 -# workflow_config: 工作流配置 -# """ -# self.node_config = node_config -# self.workflow_config = workflow_config -# self.tool_id = node_config.get("tool_id") -# self.tool_parameters = node_config.get("parameters", {}) -# -# async def run(self, state: WorkflowState) -> WorkflowState: -# """执行工具节点""" -# if not TOOL_MANAGEMENT_AVAILABLE: -# logger.error("工具管理系统不可用") -# state["error"] = "工具管理系统不可用" -# return state -# -# try: -# from sqlalchemy.orm import Session -# db = next(get_db()) -# -# # 创建工具执行器 -# registry = ToolRegistry(db) -# executor = ToolExecutor(db, registry) -# -# # 准备参数（支持变量替换） -# parameters = self._prepare_parameters(state) -# -# # 执行工具 -# result = await executor.execute_tool( -# tool_id=self.tool_id, -# parameters=parameters, -# user_id=uuid.UUID(state["user_id"]), -# workspace_id=uuid.UUID(state["workspace_id"]) -# ) -# -# # 更新状态 -# node_id = self.node_config.get("id") -# if result.success: -# state["node_outputs"][node_id] = { -# "type": "tool", -# "tool_id": self.tool_id, -# "output": result.data, -# "execution_time": result.execution_time, -# "token_usage": result.token_usage -# } -# -# # 更新运行时变量 -# if isinstance(result.data, dict): -# for key, value in result.data.items(): -# state["runtime_vars"][f"{node_id}.{key}"] = value -# else: -# state["runtime_vars"][f"{node_id}.result"] = result.data -# else: -# state["error"] = result.error -# state["error_node"] = node_id -# state["node_outputs"][node_id] = { -# "type": "tool", -# "tool_id": self.tool_id, -# "error": result.error, -# "execution_time": result.execution_time -# } -# -# return state -# -# except Exception as e: -# logger.error(f"工具节点执行失败: {e}") -# state["error"] = str(e) -# state["error_node"] = self.node_config.get("id") -# return state -# -# def _prepare_parameters(self, state: WorkflowState) -> dict: -# """准备工具参数（支持变量替换）""" -# parameters = {} -# -# for key, value in self.tool_parameters.items(): -# if isinstance(value, str) and value.startswith("${") and value.endswith("}"): -# # 变量替换 -# var_path = value[2:-1] -# -# # 支持多层级变量访问，如 ${sys.message} 或 ${node1.result} -# if "." in var_path: -# parts = var_path.split(".") -# current = state.get("variables", {}) -# -# for part in parts: -# if isinstance(current, dict) and part in current: -# current = current[part] -# else: -# # 尝试从运行时变量获取 -# runtime_key = ".".join(parts) -# current = state.get("runtime_vars", {}).get(runtime_key, value) -# break -# -# parameters[key] = current -# else: -# # 简单变量 -# variables = state.get("variables", {}) -# parameters[key] = variables.get(var_path, value) -# else: -# parameters[key] = value -# -# return parameters -# -# -# # 注册工具节点到NodeFactory（如果存在） -# try: -# from app.core.workflow.nodes import NodeFactory -# if hasattr(NodeFactory, 'register_node_type'): -# NodeFactory.register_node_type("tool", ToolWorkflowNode) -# logger.info("工具节点已注册到工作流系统") -# except Exception as e: -# logger.warning(f"注册工具节点失败: {e}") diff --git a/api/app/core/workflow/graph_builder.py b/api/app/core/workflow/graph_builder.py index 5b9388fc..b1d43e08 100644 --- a/api/app/core/workflow/graph_builder.py +++ b/api/app/core/workflow/graph_builder.py @@ -1,12 +1,15 @@ import logging +import re import uuid from collections import defaultdict +from functools import lru_cache from typing import Any from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import START, END from langgraph.graph.state import CompiledStateGraph, StateGraph from langgraph.types import Send +from pydantic import BaseModel, Field from app.core.workflow.expression_evaluator import evaluate_condition from app.core.workflow.nodes import WorkflowState, NodeFactory @@ -15,6 +18,149 @@ from app.core.workflow.nodes.enums import NodeType, BRANCH_NODES logger = logging.getLogger(__name__) +class OutputContent(BaseModel): + """ + Represents a single output segment of an End node. + + An output segment can be either: + - literal text (static string) + - a variable placeholder (e.g. {{ node.field }}) + + Each segment has its own activation state, which is especially + important in stream mode. + """ + + literal: str = Field( + ..., + description="Raw output content. Can be literal text or a variable placeholder." + ) + + activate: bool = Field( + ..., + description=( + "Whether this output segment is currently active.\n" + "- True: allowed to be emitted/output\n" + "- False: blocked until activated by branch control" + ) + ) + + is_variable: bool = Field( + ..., + description=( + "Whether this segment represents a variable placeholder.\n" + "True -> variable (e.g. {{ node.field }})\n" + "False -> literal text" + ) + ) + + def depends_on_scope(self, scope: str) -> bool: + """ + Check if this segment depends on a given scope. + + Args: + scope (str): Node ID or special variable prefix (e.g., "sys"). + + Returns: + bool: True if this segment references the given scope. + """ + pattern = rf"\{{\{{\s*{re.escape(scope)}\.[a-zA-Z0-9_]+\s*\}}\}}" + return bool(re.search(pattern, self.literal)) + + +class StreamOutputConfig(BaseModel): + """ + Streaming output configuration for an End node. + + This configuration describes how the End node output behaves in streaming mode, + including: + - whether output emission is globally activated + - which upstream branch/control nodes gate the activation + - how each parsed output segment is streamed and activated + """ + + activate: bool = Field( + ..., + description=( + "Global activation flag for the End node output.\n" + "When False, output segments should not be emitted even if available.\n" + "This flag typically becomes True once required control branch conditions " + "are satisfied." + ) + ) + + control_nodes: dict[str, str] = Field( + ..., + description=( + "Control branch conditions for this End node output.\n" + "Mapping of `branch_node_id -> expected_branch_label`.\n" + "The End node output becomes globally active when a controlling branch node " + "reports a matching completion status." + ) + ) + + outputs: list[OutputContent] = Field( + ..., + description=( + "Ordered list of output segments parsed from the output template.\n" + "Each segment represents either a literal text block or a variable placeholder " + "that may be activated independently." + ) + ) + + cursor: int = Field( + ..., + description=( + "Streaming cursor index.\n" + "Indicates the next output segment index to be emitted.\n" + "Segments with index < cursor are considered already streamed." + ) + ) + + def update_activate(self, scope: str, status=None): + """ + Update streaming activation state based on an upstream node or special variable. + + Args: + scope (str): + Identifier of the completed upstream entity. + - If a control branch node, it should match a key in `control_nodes`. + - If a variable placeholder (e.g., "sys.xxx"), it may appear in output segments. + status (optional): + Completion status of the control branch node. + Required when `scope` refers to a control node. + + Behavior: + 1. Control branch nodes: + - If `scope` matches a key in `control_nodes` and `status` matches the expected + branch label, the End node output becomes globally active (`activate = True`). + + 2. Variable output segments: + - For each segment that is a variable (`is_variable=True`): + - If the segment literal references `scope`, mark the segment as active. + - This applies both to regular node variables (e.g., "node_id.field") + and special system variables (e.g., "sys.xxx"). + + Notes: + - This method does not emit output or advance the streaming cursor. + - It only updates activation flags based on upstream events or special variables. + """ + + # Case 1: resolve control branch dependency + if scope in self.control_nodes.keys(): + if status is None: + raise RuntimeError("[Stream Output] Control node activation status not provided") + if status == self.control_nodes[scope]: + self.activate = True + + # Case 2: activate variable segments related to this node + for i in range(len(self.outputs)): + if ( + self.outputs[i].is_variable + and self.outputs[i].depends_on_scope(scope) + ): + self.outputs[i].activate = True + + class GraphBuilder: def __init__( self, @@ -29,10 +175,16 @@ class GraphBuilder: self.start_node_id = None self.end_node_ids = [] + self.node_map = {node["id"]: node for node in self.nodes} + self.end_node_map: dict[str, StreamOutputConfig] = {} + self._find_upstream_branch_node = lru_cache( + maxsize=len(self.nodes) * 2 + )(self._find_upstream_branch_node) self.graph = StateGraph(WorkflowState) self.add_nodes() self.add_edges() + self._analyze_end_node_output() # EDGES MUST BE ADDED AFTER NODES ARE ADDED. @property @@ -43,79 +195,207 @@ class GraphBuilder: def edges(self) -> list[dict[str, Any]]: return self.workflow_config.get("edges", []) - def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]: - """ - Analyze the prefix configuration for End nodes. + def get_node_type(self, node_id: str) -> str: + """Retrieve the type of node given its ID. - This function scans each End node's output template, identifies - references to its direct upstream nodes, and extracts the prefix - string appearing before the first reference. + Args: + node_id (str): The unique identifier of the node. Returns: - tuple: - - dict[str, str]: Mapping from upstream node ID to its End node prefix - - set[str]: Set of node IDs that are directly adjacent to End nodes and referenced + str: The type of the node. + + Raises: + RuntimeError: If no node with the given `node_id` exists. """ - import re + try: + return self.node_map[node_id]["type"] + except KeyError: + raise RuntimeError(f"Node not found: Id={node_id}") - prefixes = {} - adjacent_and_referenced = set() # Record nodes directly adjacent to End and referenced + def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[tuple[str, str]]]: + """ + Recursively find all upstream branch (control) nodes that influence the execution + of the given target node. - # 找到所有 End 节点 + This method walks upstream along the workflow graph starting from `target_node`. + It distinguishes between: + - branch nodes (node types listed in `BRANCH_NODES`) + - non-branch nodes (ordinary processing nodes) + + Traversal rules: + 1. For each immediate upstream node: + - If it is a branch node, it is recorded as an affecting control node. + - If it is a non-branch node, the traversal continues recursively upstream. + 2. If ANY upstream path reaches a START / CYCLE_START node without encountering + a branch node, the traversal is considered invalid: + - `has_branch` will be False + - no branch nodes are returned. + 3. Only when ALL upstream non-branch paths eventually lead to at least one + branch node will `has_branch` be True. + + Special case: + - If `target_node` has no upstream nodes AND its type is START or CYCLE_START, + it is considered directly reachable from the workflow entry, and therefore + has no controlling branch nodes. + + Args: + target_node (str): + The identifier of the node whose upstream control branches + are to be resolved. + + Returns: + tuple[bool, tuple[tuple[str, str]]]: + - has_branch (bool): + True if every upstream path from `target_node` encounters + at least one branch node. + False if any path reaches a start node without a branch. + - branch_nodes (tuple[tuple[str, str]]): + A deduplicated tuple of `(branch_node_id, branch_label)` pairs + representing all branch nodes that can influence `target_node`. + Returns an empty tuple if `has_branch` is False. + """ + source_nodes = [ + { + "id": edge.get("source"), + "branch": edge.get("label") + } + for edge in self.edges + if edge.get("target") == target_node + ] + if not source_nodes and self.get_node_type(target_node) in [NodeType.START, NodeType.CYCLE_START]: + return False, tuple() + + branch_nodes = [] + non_branch_nodes = [] + + for node_info in source_nodes: + if self.get_node_type(node_info["id"]) in BRANCH_NODES: + branch_nodes.append( + (node_info["id"], node_info["branch"]) + ) + else: + non_branch_nodes.append(node_info["id"]) + + has_branch = True + for node_id in non_branch_nodes: + node_has_branch, nodes = self._find_upstream_branch_node(node_id) + has_branch = has_branch and node_has_branch + if not has_branch: + break + branch_nodes.extend(nodes) + if not has_branch: + branch_nodes = [] + + return has_branch, tuple(set(branch_nodes)) + + def _analyze_end_node_output(self): + """ + Analyze output templates of all End nodes and generate StreamOutputConfig. + + This method is responsible for parsing the `output` field of End nodes, + splitting literal text and variable placeholders (e.g. {{ node.field }}), + and determining whether each output segment should be activated immediately + or controlled by upstream branch nodes. + + In stream mode: + - If the End node is controlled by any upstream branch node, the output + will be initially inactive and controlled by those branch nodes. + - Otherwise, the output is activated immediately. + + In non-stream mode: + - All outputs are activated by default. + """ + + # Collect all End nodes in the workflow end_nodes = [node for node in self.nodes if node.get("type") == "end"] logger.info(f"[Prefix Analysis] Found {len(end_nodes)} End nodes") + # Iterate through each End node to analyze its output for end_node in end_nodes: end_node_id = end_node.get("id") - output_template = end_node.get("config", {}).get("output") + config = end_node.get("config", {}) + output = config.get("output") - logger.info(f"[Prefix Analysis] End node {end_node_id} template: {output_template}") - - if not output_template: + # Skip End nodes without output configuration + if not output: continue - # Find all node references in the template - # Matches {{node_id.xxx}} or {{ node_id.xxx }} format (allowing spaces) - pattern = r'\{\{\s*([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\s*\}\}' - matches = list(re.finditer(pattern, output_template)) + # Regex to split output into: + # - variable placeholders: {{ ... }} + # - normal literal text + # + # Example: + # "Hello {{user.name}}!" -> + # ["Hello ", "{{user.name}}", "!"] + pattern = r'\{\{.*?\}\}|[^{}]+' - logger.info(f"[Prefix Analysis] 模板中找到 {len(matches)} 个节点引用") + # Strict variable format: {{ node_id.field_name }} + variable_pattern_string = r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+\s*\}\}' + variable_pattern = re.compile(variable_pattern_string) - # Identify all direct upstream nodes connected to the End node - direct_upstream_nodes = [] - for edge in self.edges: - if edge.get("target") == end_node_id: - source_node_id = edge.get("source") - direct_upstream_nodes.append(source_node_id) + # Split output into ordered segments + output_template = list(re.findall(pattern, output)) - logger.info(f"[Prefix Analysis] Direct upstream nodes of End node: {direct_upstream_nodes}") + # Determine whether each segment is literal text + # True -> literal (can be directly output) + # False -> variable placeholder (needs runtime value) + output_flag = [ + not bool(variable_pattern.match(item)) + for item in output_template + ] - # 找到第一个直接上游节点的引用 - for match in matches: - referenced_node_id = match.group(1) - logger.info(f"[Prefix Analysis] Checking reference: {referenced_node_id}") + # Stream mode: output activation depends on upstream branch nodes + if self.stream: + # Find upstream branch nodes that can control this End node + has_branch, control_nodes = self._find_upstream_branch_node(end_node_id) - if referenced_node_id in direct_upstream_nodes: - # 这是直接上游节点的引用，提取前缀 - prefix = output_template[:match.start()] + # Build StreamOutputConfig for this End node + self.end_node_map[end_node_id] = StreamOutputConfig( + # If there is no upstream branch, output is active immediately + activate=not has_branch, - logger.info(f"[Prefix Analysis] " - f"✅ Found reference to direct upstream node {referenced_node_id}, prefix: '{prefix}'") + # Branch nodes that control activation of this End node + control_nodes=dict(control_nodes), - # 标记这个节点为"相邻且被引用" - adjacent_and_referenced.add(referenced_node_id) + # Convert output segments into OutputContent objects + outputs=list( + [ + OutputContent( + literal=output_string, + # Literal text can be activated immediately unless blocked by branch + activate=activate, + # Variable segments are marked explicitly + is_variable=not activate + ) + for output_string, activate in zip(output_template, output_flag) + ] + ), + # Cursor for streaming output (initially 0) + cursor=0 + ) + logger.info(f"[Stream Analysis] end_id: {end_node_id}, " + f"activate: {not has_branch}, " + f"control_nodes: {control_nodes}," + f"output: {output_template}," + f"output_activate: {output_flag}") - if prefix: - prefixes[referenced_node_id] = prefix - logger.info(f"[Prefix Analysis] " - f"✅ Assign prefix for node {referenced_node_id}: '{prefix[:50]}...'") - - # 只处理第一个直接上游节点的引用 - break - - logger.info(f"[Prefix Analysis] Final prefixes: {prefixes}") - logger.info(f"[Prefix Analysis] Nodes adjacent to End and referenced: {adjacent_and_referenced}") - return prefixes, adjacent_and_referenced + # Non-stream mode: all outputs are activated by default + else: + self.end_node_map[end_node_id] = StreamOutputConfig( + activate=True, + control_nodes={}, + outputs=list( + [ + OutputContent( + literal=output_string, + activate=True, + is_variable=not activate + ) + for output_string, activate in zip(output_template, output_flag) + ] + ), + cursor=0 + ) def add_nodes(self): """Add all nodes from the workflow configuration to the state graph. @@ -135,9 +415,6 @@ class GraphBuilder: Returns: None """ - # Analyze End node prefixes if in stream mode - end_prefixes, adjacent_and_referenced = self._analyze_end_node_prefixes() if self.stream else ({}, set()) - for node in self.nodes: node_type = node.get("type") node_id = node.get("id") @@ -171,17 +448,6 @@ class GraphBuilder: related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'" if node_instance: - # Inject End node prefix configuration if in stream mode - if self.stream and node_id in end_prefixes: - node_instance._end_node_prefix = end_prefixes[node_id] - logger.info(f"Injected End prefix for node {node_id}") - - # Mark nodes as adjacent and referenced to End node in stream mode - if self.stream: - node_instance._is_adjacent_to_end = node_id in adjacent_and_referenced - if node_id in adjacent_and_referenced: - logger.info(f"Node {node_id} marked as adjacent and referenced to End node") - # Wrap node's run method to avoid closure issues if self.stream: # Stream mode: create an async generator function @@ -261,6 +527,7 @@ class GraphBuilder: for source_node, branches in conditional_edges.items(): def make_router(src, branch_list): """reate a router function for each source node that routes to a NOP node for later merging.""" + def make_branch_node(node_name, targets): def node(s): # NOTE: NOP NODE MUST NOT MODIFY STATE diff --git a/api/app/core/workflow/nodes/base_node.py b/api/app/core/workflow/nodes/base_node.py index d8311a16..4dcdf2bb 100644 --- a/api/app/core/workflow/nodes/base_node.py +++ b/api/app/core/workflow/nodes/base_node.py @@ -19,13 +19,17 @@ from app.core.workflow.variable_pool import VariablePool logger = logging.getLogger(__name__) -def merget_activate_state(x, y): +def merge_activate_state(x, y): return { k: x.get(k, False) or y.get(k, False) for k in set(x) | set(y) } +def merge_looping_state(x, y): + return y if y > x else x + + class WorkflowState(TypedDict): """Workflow state @@ -36,7 +40,7 @@ class WorkflowState(TypedDict): # Set of loop node IDs, used for assigning values in loop nodes cycle_nodes: list - looping: Annotated[bool, lambda x, y: x and y] + looping: Annotated[int, merge_looping_state] # Input variables (passed from configured variables) # Uses a deep merge function, supporting nested dict updates (e.g., conv.xxx) @@ -63,12 +67,8 @@ class WorkflowState(TypedDict): error: str | None error_node: str | None - # Streaming buffer (stores real-time streaming output of nodes) - # Format: {node_id: {"chunks": [...], "full_content": "..."}} - streaming_buffer: Annotated[dict[str, Any], lambda x, y: {**x, **y}] - # node activate status - activate: Annotated[dict[str, bool], merget_activate_state] + activate: Annotated[dict[str, bool], merge_activate_state] class BaseNode(ABC): @@ -296,7 +296,7 @@ class BaseNode(ABC): """ if not self.check_activate(state): yield self.trans_activate(state) - logger.info(f"跳过节点{self.node_id}") + logger.info(f"jump node: {self.node_id}") return import time @@ -309,19 +309,6 @@ class BaseNode(ABC): # Get LangGraph's stream writer for sending custom data writer = get_stream_writer() - # Check if this is an End node - # End nodes CAN send chunks (for suffix), but only after LLM content - is_end_node = self.node_type == "end" - - # Check if this node is adjacent to End node (for message type) - is_adjacent_to_end = getattr(self, '_is_adjacent_to_end', False) - - # Determine chunk type: "message" for End and adjacent nodes, "node_chunk" for others - chunk_type = "message" if (is_end_node or is_adjacent_to_end) else "node_chunk" - - logger.debug( - f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})") - # Accumulate complete result (for final wrapping) chunks = [] final_result = None @@ -336,66 +323,25 @@ class BaseNode(ABC): raise TimeoutError() # Check if it's a completion marker - if isinstance(item, dict) and item.get("__final__"): + if item.get("__final__"): final_result = item["result"] - elif isinstance(item, str): - # String is a chunk + else: chunk_count += 1 - chunks.append(item) - full_content = "".join(chunks) + content = str(item.get("chunk")) + done = item.get("done", False) + chunks.append(content) # Send chunks for all nodes (including End nodes for suffix) - logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {item[:50]}...") + logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {content[:50]}...") # 1. Send via stream writer (for real-time client updates) writer({ - "type": chunk_type, # "message" or "node_chunk" + "type": "node_chunk", "node_id": self.node_id, - "chunk": item, - "full_content": full_content, - "chunk_index": chunk_count + "chunk": content, + "done": done }) - # 2. Update streaming buffer in state (for downstream nodes) - # Only non-End nodes need streaming buffer - if not is_end_node: - yield { - "streaming_buffer": { - self.node_id: { - "full_content": full_content, - "chunk_count": chunk_count, - "is_complete": False - } - } - } - else: - # Other types are also treated as chunks - chunk_count += 1 - chunk_str = str(item) - chunks.append(chunk_str) - full_content = "".join(chunks) - - # Send chunks for all nodes - writer({ - "type": chunk_type, # "message" or "node_chunk" - "node_id": self.node_id, - "chunk": chunk_str, - "full_content": full_content, - "chunk_index": chunk_count - }) - - # Only non-End nodes need streaming buffer - if not is_end_node: - yield { - "streaming_buffer": { - self.node_id: { - "full_content": full_content, - "chunk_count": chunk_count, - "is_complete": False - } - } - } - elapsed_time = time.time() - start_time logger.info(f"节点 {self.node_id} 流式执行完成，耗时: {elapsed_time:.2f}s, chunks: {chunk_count}") @@ -422,16 +368,6 @@ class BaseNode(ABC): "looping": state["looping"] } - # Add streaming buffer for non-End nodes - if not is_end_node: - state_update["streaming_buffer"] = { - self.node_id: { - "full_content": "".join(chunks), - "chunk_count": chunk_count, - "is_complete": True # Mark as complete - } - } - # Finally yield state update # LangGraph will merge this into state yield state_update | self.trans_activate(state) @@ -540,6 +476,11 @@ class BaseNode(ABC): "error_node": self.node_id } else: + writer = get_stream_writer() + writer({ + "type": "node_error", + **node_output + }) # 无错误边：抛出异常停止工作流 logger.error(f"节点 {self.node_id} 执行失败，停止工作流: {error_message}") raise Exception(f"节点 {self.node_id} 执行失败: {error_message}") diff --git a/api/app/core/workflow/nodes/breaker/node.py b/api/app/core/workflow/nodes/breaker/node.py index 882ffda0..f00015d1 100644 --- a/api/app/core/workflow/nodes/breaker/node.py +++ b/api/app/core/workflow/nodes/breaker/node.py @@ -28,6 +28,6 @@ class BreakNode(BaseNode): Returns: Optional dictionary indicating the loop has been stopped. """ - state["looping"] = False + state["looping"] = 2 logger.info(f"Setting cycle node exit flag, cycle={self.cycle}, looping={state['looping']}") diff --git a/api/app/core/workflow/nodes/code/__init__.py b/api/app/core/workflow/nodes/code/__init__.py index e69de29b..758ab3a5 100644 --- a/api/app/core/workflow/nodes/code/__init__.py +++ b/api/app/core/workflow/nodes/code/__init__.py @@ -0,0 +1,3 @@ +from app.core.workflow.nodes.code.node import CodeNode + +__all__ = ["CodeNode"] diff --git a/api/app/core/workflow/nodes/code/config.py b/api/app/core/workflow/nodes/code/config.py new file mode 100644 index 00000000..8af13f12 --- /dev/null +++ b/api/app/core/workflow/nodes/code/config.py @@ -0,0 +1,50 @@ +from typing import Literal +from pydantic import Field, BaseModel + +from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableType + + +class InputVariable(BaseModel): + name: str = Field( + ..., + description="variable name" + ) + + variable: str = Field( + ..., + description="variable selector" + ) + + +class OutputVariable(BaseModel): + name: str = Field( + ..., + description="variable name" + ) + + type: VariableType = Field( + ..., + description="variable selector" + ) + + +class CodeNodeConfig(BaseNodeConfig): + input_variables: list[InputVariable] = Field( + default_factory=list, + description="input variables" + ) + + output_variables: list[OutputVariable] = Field( + default_factory=list, + description="output variables" + ) + + code: str = Field( + default="", + description="code content" + ) + + language: Literal['python3', 'nodejs'] = Field( + ..., + description="language" + ) diff --git a/api/app/core/workflow/nodes/code/node.py b/api/app/core/workflow/nodes/code/node.py new file mode 100644 index 00000000..892708f2 --- /dev/null +++ b/api/app/core/workflow/nodes/code/node.py @@ -0,0 +1,143 @@ +import base64 +import json +import logging +import re +from string import Template +from textwrap import dedent +from typing import Any + +import httpx + +from app.core.workflow.nodes import BaseNode, WorkflowState +from app.core.workflow.nodes.base_config import VariableType +from app.core.workflow.nodes.code.config import CodeNodeConfig + +logger = logging.getLogger(__name__) + +PYTHON_SCRIPT_TEMPLATE = Template(dedent(""" +$code + +import json +from base64 import b64decode + +# decode and prepare input dict +inputs_obj = json.loads(b64decode('$inputs_variable').decode('utf-8')) + +# execute main function +output_obj = main(**inputs_obj) + +# convert output to json and print +output_json = json.dumps(output_obj, indent=4) +result = "<>" + output_json + "<>" +print(result) +""")) + +NODEJS_SCRIPT_TEMPLATE = Template(dedent(""" +$code +// decode and prepare input object +var inputs_obj = JSON.parse(Buffer.from('$inputs_variable', 'base64').toString('utf-8')) + +// execute main function +var output_obj = main(inputs_obj) + +// convert output to json and print +var output_json = JSON.stringify(output_obj) +var result = `<>$${output_json}<>` +console.log(result) +""")) + + +class CodeNode(BaseNode): + def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): + super().__init__(node_config, workflow_config) + self.typed_config: CodeNodeConfig | None = None + + def extract_result(self, content: str): + match = re.search(r'<>(.*?)<>', content, re.DOTALL) + if match: + extracted = match.group(1) + exec_result = json.loads(extracted) + result = {} + for output in self.typed_config.output_variables: + value = exec_result.get(output.name) + if value is None: + raise RuntimeError(f"Return value {output.name} does not exist") + match output.type: + case VariableType.STRING: + if not isinstance(value, str): + raise RuntimeError(f"Return value {output.name} should be a string") + case VariableType.BOOLEAN: + if not isinstance(value, bool): + raise RuntimeError(f"Return value {output.name} should be a boolean") + case VariableType.NUMBER: + if not isinstance(value, (int, float)): + raise RuntimeError(f"Return value {output.name} should be a number") + case VariableType.OBJECT: + if not isinstance(value, dict): + raise RuntimeError(f"Return value {output.name} should be a dictionary") + case VariableType.ARRAY_STRING: + if not isinstance(value, list) or not all(isinstance(v, str) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of strings") + case VariableType.ARRAY_NUMBER: + if not isinstance(value, list) or not all(isinstance(v, (int, float)) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of numbers") + case VariableType.ARRAY_OBJECT: + if not isinstance(value, list) or not all(isinstance(v, dict) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of dictionaries") + case VariableType.ARRAY_BOOLEAN: + if not isinstance(value, list) or not all(isinstance(v, bool) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of booleans") + result[output.name] = value + return result + else: + raise RuntimeError("The output of main must be a dictionary") + + async def execute(self, state: WorkflowState) -> Any: + self.typed_config = CodeNodeConfig(**self.config) + input_variable_dict = {} + for input_variable in self.typed_config.input_variables: + input_variable_dict[input_variable.name] = self.get_variable(input_variable.variable, state) + + code = base64.b64decode( + self.typed_config.code + ).decode("utf-8") + + input_variable_dict = base64.b64encode( + json.dumps(input_variable_dict).encode("utf-8") + ).decode("utf-8") + if self.typed_config.language == "python3": + final_script = PYTHON_SCRIPT_TEMPLATE.substitute( + code=code, + inputs_variable=input_variable_dict, + ) + elif self.typed_config.language == 'nodejs': + final_script = NODEJS_SCRIPT_TEMPLATE.substitute( + code=code, + inputs_variable=input_variable_dict, + ) + else: + raise ValueError(f"Unsupported language: {self.typed_config.language}") + + async with httpx.AsyncClient() as client: + response = await client.post( + "http://sandbox:8194/v1/sandbox/run", + headers={ + "x-api-key": 'redbear-sandbox' + }, + json={ + "language": self.typed_config.language, + "code": base64.b64encode(final_script.encode("utf-8")).decode("utf-8"), + "options": { + "enable_network": True + } + } + ) + resp = response.json() + + match resp['code']: + case 31: + raise RuntimeError("Operation not permitted") + case 0: + return self.extract_result(resp["data"]["stdout"]) + case _: + raise Exception(resp["message"]) diff --git a/api/app/core/workflow/nodes/configs.py b/api/app/core/workflow/nodes/configs.py index 4d31efaa..d73754f6 100644 --- a/api/app/core/workflow/nodes/configs.py +++ b/api/app/core/workflow/nodes/configs.py @@ -10,21 +10,22 @@ from app.core.workflow.nodes.base_config import ( VariableDefinition, VariableType, ) +from app.core.workflow.nodes.code.config import CodeNodeConfig +from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig from app.core.workflow.nodes.end.config import EndNodeConfig from app.core.workflow.nodes.http_request.config import HttpRequestNodeConfig from app.core.workflow.nodes.if_else.config import IfElseNodeConfig from app.core.workflow.nodes.jinja_render.config import JinjaRenderNodeConfig from app.core.workflow.nodes.knowledge.config import KnowledgeRetrievalNodeConfig from app.core.workflow.nodes.llm.config import LLMNodeConfig, MessageConfig -from app.core.workflow.nodes.start.config import StartNodeConfig -from app.core.workflow.nodes.transform.config import TransformNodeConfig -from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig +from app.core.workflow.nodes.memory.config import MemoryReadNodeConfig, MemoryWriteNodeConfig from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig from app.core.workflow.nodes.question_classifier.config import QuestionClassifierNodeConfig +from app.core.workflow.nodes.start.config import StartNodeConfig from app.core.workflow.nodes.tool.config import ToolNodeConfig -from app.core.workflow.nodes.memory.config import MemoryReadNodeConfig, MemoryWriteNodeConfig +from app.core.workflow.nodes.transform.config import TransformNodeConfig +from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig -from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig __all__ = [ # 基础类 "BaseNodeConfig", @@ -49,5 +50,6 @@ __all__ = [ "QuestionClassifierNodeConfig", "ToolNodeConfig", "MemoryReadNodeConfig", - "MemoryWriteNodeConfig" + "MemoryWriteNodeConfig", + "CodeNodeConfig" ] diff --git a/api/app/core/workflow/nodes/cycle_graph/iteration.py b/api/app/core/workflow/nodes/cycle_graph/iteration.py index da093864..cd63d233 100644 --- a/api/app/core/workflow/nodes/cycle_graph/iteration.py +++ b/api/app/core/workflow/nodes/cycle_graph/iteration.py @@ -1,5 +1,4 @@ import asyncio -import copy import logging import re from typing import Any @@ -58,10 +57,10 @@ class IterationRuntime: idx: Index of the element in the input array. Returns: - A deep copy of the workflow state with iteration-specific variables set. + A copy of the workflow state with iteration-specific variables set. """ loopstate = WorkflowState( - **copy.deepcopy(self.state) + **self.state ) loopstate["runtime_vars"][self.node_id] = { "item": item, @@ -71,7 +70,7 @@ class IterationRuntime: "item": item, "index": idx, } - loopstate["looping"] = True + loopstate["looping"] = 1 loopstate["activate"][self.start_id] = True return loopstate @@ -89,7 +88,7 @@ class IterationRuntime: self.result.extend(output) else: self.result.append(output) - if not result["looping"]: + if result["looping"] == 2: self.looping = False return result @@ -150,10 +149,9 @@ class IterationRuntime: self.result.extend(output) else: self.result.append(output) - if not result["looping"]: + if result["looping"] == 2: self.looping = False idx += 1 - logger.info(f"Iteration node {self.node_id}: execution completed") return { "output": self.result, diff --git a/api/app/core/workflow/nodes/cycle_graph/loop.py b/api/app/core/workflow/nodes/cycle_graph/loop.py index c5dc5457..6a15891f 100644 --- a/api/app/core/workflow/nodes/cycle_graph/loop.py +++ b/api/app/core/workflow/nodes/cycle_graph/loop.py @@ -46,6 +46,7 @@ class LoopRuntime: self.state = state self.node_id = node_id self.typed_config = LoopNodeConfig(**config) + self.looping = True def _init_loop_state(self): """ @@ -88,7 +89,7 @@ class LoopRuntime: loopstate = WorkflowState( **self.state ) - loopstate["looping"] = True + loopstate["looping"] = 1 loopstate["activate"][self.start_id] = True return loopstate @@ -179,9 +180,12 @@ class LoopRuntime: loopstate = self._init_loop_state() loop_time = self.typed_config.max_loop child_state = [] - while self.evaluate_conditional(loopstate) and loopstate["looping"] and loop_time > 0: + while self.evaluate_conditional(loopstate) and self.looping and loop_time > 0: logger.info(f"loop node {self.node_id}: running") - child_state.append(await self.graph.ainvoke(loopstate)) + result = await self.graph.ainvoke(loopstate) + child_state.append(result) + if result["looping"] == 2: + self.looping = False loop_time -= 1 logger.info(f"loop node {self.node_id}: execution completed") diff --git a/api/app/core/workflow/nodes/cycle_graph/node.py b/api/app/core/workflow/nodes/cycle_graph/node.py index 1f550b0b..82782658 100644 --- a/api/app/core/workflow/nodes/cycle_graph/node.py +++ b/api/app/core/workflow/nodes/cycle_graph/node.py @@ -6,7 +6,6 @@ from langgraph.graph.state import CompiledStateGraph from app.core.workflow.nodes import WorkflowState from app.core.workflow.nodes.base_node import BaseNode -from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig from app.core.workflow.nodes.cycle_graph.iteration import IterationRuntime from app.core.workflow.nodes.cycle_graph.loop import LoopRuntime from app.core.workflow.nodes.enums import NodeType diff --git a/api/app/core/workflow/nodes/end/node.py b/api/app/core/workflow/nodes/end/node.py index 0cbd9e8e..3a5153a9 100644 --- a/api/app/core/workflow/nodes/end/node.py +++ b/api/app/core/workflow/nodes/end/node.py @@ -5,10 +5,8 @@ End 节点实现 """ import logging -import re from app.core.workflow.nodes.base_node import BaseNode, WorkflowState -from app.core.workflow.nodes.enums import NodeType logger = logging.getLogger(__name__) @@ -37,24 +35,8 @@ class EndNode(BaseNode): # 如果配置了输出模板，使用模板渲染；否则使用默认输出 if output_template: output = self._render_template(output_template, state, strict=False) - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - { - "role": "assistant", - "content": output - } - ]) else: - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - ]) - output = "工作流已完成" + output = "" # 统计信息（用于日志） node_outputs = state.get("node_outputs", {}) @@ -63,274 +45,3 @@ class EndNode(BaseNode): logger.info(f"节点 {self.node_id} (End) 执行完成，共执行 {total_nodes} 个节点") return output - - def _extract_referenced_nodes(self, template: str) -> list[str]: - """从模板中提取引用的节点 ID - - 例如：'结果：{{llm_qa.output}}' -> ['llm_qa'] - - Args: - template: 模板字符串 - - Returns: - 引用的节点 ID 列表 - """ - # 匹配 {{node_id.xxx}} 格式 - pattern = r'\{\{([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\}\}' - matches = re.findall(pattern, template) - return list(set(matches)) # 去重 - - def _parse_template_parts(self, template: str, state: WorkflowState) -> list[dict]: - """解析模板，分离静态文本和动态引用 - - 例如：'你好 {{llm.output}}, 这是后缀' - 返回：[ - {"type": "static", "content": "你好 "}, - {"type": "dynamic", "node_id": "llm", "field": "output"}, - {"type": "static", "content": ", 这是后缀"} - ] - - Args: - template: 模板字符串 - state: 工作流状态 - - Returns: - 模板部分列表 - """ - import re - - parts = [] - last_end = 0 - - # 匹配 {{xxx}} 或 {{ xxx }} 格式（支持空格） - pattern = r'\{\{\s*([^}]+?)\s*\}\}' - - for match in re.finditer(pattern, template): - start, end = match.span() - - # 添加前面的静态文本 - if start > last_end: - static_text = template[last_end:start] - if static_text: - parts.append({"type": "static", "content": static_text}) - - # 解析动态引用 - ref = match.group(1).strip() - - # 检查是否是节点引用（如 llm.output 或 llm_qa.output） - if '.' in ref: - node_id, field = ref.split('.', 1) - parts.append({ - "type": "dynamic", - "node_id": node_id, - "field": field, - "raw": ref - }) - else: - # 其他引用（如 {{var.xxx}}），当作静态处理 - # 直接渲染这部分 - rendered = self._render_template(f"{{{{{ref}}}}}", state) - parts.append({"type": "static", "content": rendered}) - - last_end = end - - # 添加最后的静态文本 - if last_end < len(template): - static_text = template[last_end:] - if static_text: - parts.append({"type": "static", "content": static_text}) - - return parts - - async def execute_stream(self, state: WorkflowState): - """Execute End node business logic (streaming) - - Smart output strategy: - 1. Check if template references a direct upstream LLM node - 2. If yes, only output the part AFTER that reference (suffix) - 3. Prefix and LLM content have already been sent during LLM node streaming - - Note: Only LLM nodes get this special treatment. Other node types output normally. - - Example: '{{start.test}}hahaha {{ llm_qa.output }} lalalalala a' - - Direct upstream LLM node is llm_qa - - Prefix '{{start.test}}hahaha ' was sent before LLM node streaming - - LLM content was streamed during LLM node execution - - End node only outputs ' lalalalala a' (suffix, sent as one chunk) - - Args: - state: Workflow state - - Yields: - Completion marker - """ - logger.info(f"节点 {self.node_id} (End) 开始执行（流式）") - - # 获取配置的输出模板 - output_template = self.config.get("output") - - if not output_template: - output = "工作流已完成" - from langgraph.config import get_stream_writer - writer = get_stream_writer() - writer({ - "type": "message", # End node output uses message type - "node_id": self.node_id, - "chunk": "", - "full_content": output, - "chunk_index": 1, - "is_suffix": False - }) - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - } - ]) - yield {"__final__": True, "result": output} - return - - # Find direct upstream LLM nodes - direct_upstream_llm_nodes = [] - for edge in self.workflow_config.get("edges", []): - if edge.get("target") == self.node_id: - source_node_id = edge.get("source") - # Check if the source node is an LLM node - for node in self.workflow_config.get("nodes", []): - logger.info(f"节点 {self.node_id} 的类型 {node.get("type")}") - if node.get("id") == source_node_id and node.get("type") == NodeType.LLM: - direct_upstream_llm_nodes.append(source_node_id) - break - - logger.info(f"节点 {self.node_id} 的直接上游 LLM 节点: {direct_upstream_llm_nodes}") - - # Parse template parts - parts = self._parse_template_parts(output_template, state) - logger.info(f"节点 {self.node_id} 解析模板，共 {len(parts)} 个部分") - for i, part in enumerate(parts): - logger.info(f"[模板解析] part[{i}]: {part}") - - # Find the first reference to a direct upstream LLM node - upstream_llm_ref_index = None - for i, part in enumerate(parts): - if part["type"] == "dynamic" and part["node_id"] in direct_upstream_llm_nodes: - upstream_llm_ref_index = i - logger.info(f"节点 {self.node_id} 找到直接上游 LLM 节点 {part['node_id']} 的引用，索引: {i}") - break - - if upstream_llm_ref_index is None: - # No reference to direct upstream LLM node, output complete template content - output = self._render_template(output_template, state, strict=False) - logger.info(f"节点 {self.node_id} 没有引用直接上游 LLM 节点，输出完整内容: '{output[:50]}...'") - - # Send complete content via writer (as a single message chunk) - from langgraph.config import get_stream_writer - writer = get_stream_writer() - writer({ - "type": "message", # End node output uses message type - "node_id": self.node_id, - "chunk": output, - "full_content": output, - "chunk_index": 1, - "is_suffix": False - }) - logger.info(f"节点 {self.node_id} 已通过 writer 发送完整内容") - - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - { - "role": "assistant", - "content": output - } - ]) - - # yield completion marker - yield {"__final__": True, "result": output} - return - - # Has reference to direct upstream LLM node, only output the part after that reference (suffix) - logger.info( - f"节点 {self.node_id} 检测到直接上游 LLM 节点引用，只输出后缀部分（从索引 {upstream_llm_ref_index + 1} 开始）") - - # Collect suffix parts - suffix_parts = [] - logger.info(f"[后缀调试] 开始收集后缀，从索引 {upstream_llm_ref_index + 1} 到 {len(parts) - 1}") - for i in range(upstream_llm_ref_index + 1, len(parts)): - part = parts[i] - logger.info(f"[后缀调试] 处理 part[{i}]: {part}") - if part["type"] == "static": - # 静态文本 - logger.info(f"[后缀调试] 添加静态文本: '{part['content']}'") - suffix_parts.append(part["content"]) - - elif part["type"] == "dynamic": - # Other dynamic references (if there are multiple references) - node_id = part["node_id"] - field = part["field"] - - # Use VariablePool to get variable value - pool = self.get_variable_pool(state) - try: - # Try to get variable value with default empty string - content = pool.get([node_id, field], default="") - logger.info(f"[后缀调试] 获取变量 {node_id}.{field} 成功: '{content}'") - except Exception as e: - logger.warning(f"[后缀调试] 获取变量 {node_id}.{field} 失败: {e}") - content = "" - - # Convert to string if not None - suffix_parts.append(str(content) if content is not None else "") - - # 拼接后缀 - suffix = "".join(suffix_parts) - - # 构建完整输出（用于返回，包含前缀 + 动态内容 + 后缀） - full_output = self._render_template(output_template, state, strict=False) - - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - { - "role": "assistant", - "content": full_output - } - ]) - - logger.info(f"[后缀调试] 节点 {self.node_id} 后缀部分数量: {len(suffix_parts)}") - logger.info(f"[后缀调试] 后缀内容: '{suffix}'") - logger.info(f"[后缀调试] 后缀长度: {len(suffix)}") - logger.info(f"[后缀调试] 后缀是否为空: {not suffix}") - - if suffix: - logger.info(f"节点 {self.node_id} 输出后缀: '{suffix}...' (长度: {len(suffix)})") - # 一次性输出后缀（作为单个 chunk） - # 注意：不要直接 yield 字符串，因为 base_node 会逐字符处理 - # 而是通过 writer 直接发送 - from langgraph.config import get_stream_writer - writer = get_stream_writer() - writer({ - "type": "message", # End 节点的输出使用 message 类型 - "node_id": self.node_id, - "chunk": suffix, - "full_content": full_output, # full_content 是完整的渲染结果（前缀+LLM+后缀） - "chunk_index": 1, - "is_suffix": True - }) - logger.info(f"节点 {self.node_id} 已通过 writer 发送后缀，full_content 长度: {len(full_output)}") - else: - logger.warning(f"[后缀调试] 节点 {self.node_id} 后缀为空，不发送！" - f"upstream_llm_ref_index={upstream_llm_ref_index}, parts数量={len(parts)}") - - # 统计信息 - node_outputs = state.get("node_outputs", {}) - total_nodes = len(node_outputs) - - logger.info(f"节点 {self.node_id} (End) 执行完成（流式），共执行了 {total_nodes} 个节点") - - # yield 完成标记（包含完整输出） - yield {"__final__": True, "result": full_output} diff --git a/api/app/core/workflow/nodes/if_else/node.py b/api/app/core/workflow/nodes/if_else/node.py index 41f1138b..cf5a1499 100644 --- a/api/app/core/workflow/nodes/if_else/node.py +++ b/api/app/core/workflow/nodes/if_else/node.py @@ -13,7 +13,7 @@ logger = logging.getLogger(__name__) class IfElseNode(BaseNode): def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) - self.typed_config: IfElseNodeConfig | None= None + self.typed_config: IfElseNodeConfig | None = None @staticmethod def _evaluate(operator, instance: CompareOperatorInstance) -> Any: diff --git a/api/app/core/workflow/nodes/llm/node.py b/api/app/core/workflow/nodes/llm/node.py index a74e0b60..f315b238 100644 --- a/api/app/core/workflow/nodes/llm/node.py +++ b/api/app/core/workflow/nodes/llm/node.py @@ -7,18 +7,18 @@ LLM 节点实现 import logging import re from typing import Any -from langchain_core.messages import AIMessage, SystemMessage, HumanMessage -from app.core.workflow.nodes.base_node import BaseNode, WorkflowState +from langchain_core.messages import AIMessage + +from app.core.error_codes import BizCode +from app.core.exceptions import BusinessException from app.core.models import RedBearLLM, RedBearModelConfig +from app.core.workflow.nodes.base_node import BaseNode, WorkflowState from app.core.workflow.nodes.llm.config import LLMNodeConfig from app.db import get_db_context from app.models import ModelType from app.services.model_service import ModelConfigService -from app.core.exceptions import BusinessException -from app.core.error_codes import BizCode - logger = logging.getLogger(__name__) @@ -231,42 +231,14 @@ class LLMNode(BaseNode): 文本片段（chunk）或完成标记 """ self.typed_config = LLMNodeConfig(**self.config) - from langgraph.config import get_stream_writer llm, prompt_or_messages = self._prepare_llm(state, True) logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（流式）") logger.debug(f"LLM 配置: streaming={getattr(llm._model, 'streaming', 'unknown')}") - # 检查是否有注入的 End 节点前缀配置 - writer = get_stream_writer() - end_prefix = getattr(self, '_end_node_prefix', None) - - logger.info(f"[LLM前缀] 节点 {self.node_id} 检查前缀配置: {end_prefix is not None}") - if end_prefix: - logger.info(f"[LLM前缀] 前缀内容: '{end_prefix}'") - - if end_prefix: - # 渲染前缀（可能包含其他变量） - try: - rendered_prefix = self._render_template(end_prefix, state) - logger.info(f"节点 {self.node_id} 提前发送 End 节点前缀: '{rendered_prefix[:50]}...'") - - # 提前发送 End 节点的前缀（使用 "message" 类型） - writer({ - "type": "message", # End 相关的内容都是 message 类型 - "node_id": "end", # 标记为 end 节点的输出 - "chunk": rendered_prefix, - "full_content": rendered_prefix, - "chunk_index": 0, - "is_prefix": True # 标记这是前缀 - }) - except Exception as e: - logger.warning(f"渲染/发送 End 节点前缀失败: {e}") - # 累积完整响应 full_response = "" - last_chunk = None chunk_count = 0 # 调用 LLM（流式，支持字符串或消息列表） @@ -284,12 +256,19 @@ class LLMNode(BaseNode): # 只有当内容不为空时才处理 if content: full_response += content - last_chunk = chunk chunk_count += 1 # 流式返回每个文本片段 - yield content + yield { + "__final__": False, + "chunk": content + } + yield { + "__final__": False, + "chunk": "", + "done": True + } logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(full_response)}, 总 chunks: {chunk_count}") # 构建完整的 AIMessage（包含元数据） diff --git a/api/app/core/workflow/nodes/memory/config.py b/api/app/core/workflow/nodes/memory/config.py index 987230c1..31881e24 100644 --- a/api/app/core/workflow/nodes/memory/config.py +++ b/api/app/core/workflow/nodes/memory/config.py @@ -1,7 +1,6 @@ -import uuid +from uuid import UUID from pydantic import Field -from typing import Literal from app.core.workflow.nodes.base_config import BaseNodeConfig @@ -11,7 +10,7 @@ class MemoryReadNodeConfig(BaseNodeConfig): ... ) - config_id: int = Field( + config_id: UUID | int = Field( ... ) @@ -26,6 +25,6 @@ class MemoryWriteNodeConfig(BaseNodeConfig): ... ) - config_id: int = Field( + config_id: UUID | int = Field( ... ) diff --git a/api/app/core/workflow/nodes/memory/node.py b/api/app/core/workflow/nodes/memory/node.py index 08a2b280..13860bec 100644 --- a/api/app/core/workflow/nodes/memory/node.py +++ b/api/app/core/workflow/nodes/memory/node.py @@ -22,9 +22,9 @@ class MemoryReadNode(BaseNode): raise RuntimeError("End user id is required") return await MemoryAgentService().read_memory( - group_id=end_user_id, + end_user_id=end_user_id, message=self._render_template(self.typed_config.message, state), - config_id=str(self.typed_config.config_id), + config_id=self.typed_config.config_id, search_switch=self.typed_config.search_switch, history=[], db=db, @@ -36,9 +36,10 @@ class MemoryReadNode(BaseNode): class MemoryWriteNode(BaseNode): def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) - self.typed_config = MemoryWriteNodeConfig(**self.config) + self.typed_config: MemoryWriteNodeConfig | None = None async def execute(self, state: WorkflowState) -> Any: + self.typed_config = MemoryWriteNodeConfig(**self.config) end_user_id = self.get_variable("sys.user_id", state) if not end_user_id: diff --git a/api/app/core/workflow/nodes/node_factory.py b/api/app/core/workflow/nodes/node_factory.py index 9fca8d7a..fb2fe00f 100644 --- a/api/app/core/workflow/nodes/node_factory.py +++ b/api/app/core/workflow/nodes/node_factory.py @@ -10,6 +10,7 @@ from typing import Any, Union from app.core.workflow.nodes.agent import AgentNode from app.core.workflow.nodes.assigner import AssignerNode from app.core.workflow.nodes.base_node import BaseNode +from app.core.workflow.nodes.code import CodeNode from app.core.workflow.nodes.cycle_graph.node import CycleGraphNode from app.core.workflow.nodes.end import EndNode from app.core.workflow.nodes.enums import NodeType @@ -49,7 +50,8 @@ WorkflowNode = Union[ QuestionClassifierNode, ToolNode, MemoryReadNode, - MemoryWriteNode + MemoryWriteNode, + CodeNode ] @@ -81,6 +83,7 @@ class NodeFactory: NodeType.TOOL: ToolNode, NodeType.MEMORY_READ: MemoryReadNode, NodeType.MEMORY_WRITE: MemoryWriteNode, + NodeType.CODE: CodeNode, } @classmethod diff --git a/api/app/core/workflow/nodes/question_classifier/config.py b/api/app/core/workflow/nodes/question_classifier/config.py index 998e2fb4..2dd8d28a 100644 --- a/api/app/core/workflow/nodes/question_classifier/config.py +++ b/api/app/core/workflow/nodes/question_classifier/config.py @@ -5,6 +5,7 @@ from pydantic import Field, BaseModel from app.core.workflow.nodes.base_config import BaseNodeConfig + class ClassifierConfig(BaseModel): """分类器节点配置""" @@ -13,7 +14,7 @@ class ClassifierConfig(BaseModel): class QuestionClassifierNodeConfig(BaseNodeConfig): """问题分类器节点配置""" - + model_id: uuid.UUID = Field(..., description="LLM模型ID") input_variable: str = Field(default="{{sys.message}}", description="输入变量选择器(用户问题)") user_supplement_prompt: Optional[str] = Field(default=None, description="用户补充提示词，额外分类指令") diff --git a/api/app/core/workflow/nodes/question_classifier/node.py b/api/app/core/workflow/nodes/question_classifier/node.py index aee72eda..6df410cb 100644 --- a/api/app/core/workflow/nodes/question_classifier/node.py +++ b/api/app/core/workflow/nodes/question_classifier/node.py @@ -18,30 +18,30 @@ DEFAULT_EMPTY_QUESTION_CASE = f"{DEFAULT_CASE_PREFIX}1" class QuestionClassifierNode(BaseNode): """问题分类器节点""" - + def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) self.typed_config: QuestionClassifierNodeConfig | None = None self.category_to_case_map = {} - + def _get_llm_instance(self) -> RedBearLLM: """获取LLM实例""" with get_db_read() as db: config = ModelConfigService.get_model_by_id(db=db, model_id=self.typed_config.model_id) - + if not config: raise BusinessException("配置的模型不存在", BizCode.NOT_FOUND) - + if not config.api_keys or len(config.api_keys) == 0: raise BusinessException("模型配置缺少 API Key", BizCode.INVALID_PARAMETER) - + api_config = config.api_keys[0] model_name = api_config.model_name provider = api_config.provider api_key = api_config.api_key base_url = api_config.api_base model_type = config.type - + return RedBearLLM( RedBearModelConfig( model_name=model_name, @@ -64,7 +64,7 @@ class QuestionClassifierNode(BaseNode): case_tag = f"{DEFAULT_CASE_PREFIX}{idx}" category_map[category_name] = case_tag return category_map - + async def execute(self, state: WorkflowState) -> dict: """执行问题分类""" self.typed_config = QuestionClassifierNodeConfig(**self.config) @@ -74,11 +74,12 @@ class QuestionClassifierNode(BaseNode): categories = self.typed_config.categories or [] category_names = [class_item.class_name.strip() for class_item in categories] category_count = len(category_names) - + if not question: logger.warning( f"节点 {self.node_id} 未获取到输入问题，使用默认分支" - f"（默认分支：{DEFAULT_EMPTY_QUESTION_CASE}，分类总数：{category_count}）" + f"(默认分支:{DEFAULT_EMPTY_QUESTION_CASE}" + f"分类总数: {category_count})" ) # 若分类列表为空，返回默认unknown分支，否则返回CASE1 if category_count > 0: diff --git a/api/app/core/workflow/nodes/tool/__init__.py b/api/app/core/workflow/nodes/tool/__init__.py index 8392f05c..a311139e 100644 --- a/api/app/core/workflow/nodes/tool/__init__.py +++ b/api/app/core/workflow/nodes/tool/__init__.py @@ -1,4 +1,4 @@ from app.core.workflow.nodes.tool.config import ToolNodeConfig from app.core.workflow.nodes.tool.node import ToolNode -__all__ = ["ToolNode", "ToolNodeConfig"] \ No newline at end of file +__all__ = ["ToolNode", "ToolNodeConfig"] diff --git a/api/app/core/workflow/nodes/tool/node.py b/api/app/core/workflow/nodes/tool/node.py index 3e79b075..aba96303 100644 --- a/api/app/core/workflow/nodes/tool/node.py +++ b/api/app/core/workflow/nodes/tool/node.py @@ -16,11 +16,11 @@ TEMPLATE_PATTERN = re.compile(r"\{\{.*?\}\}") class ToolNode(BaseNode): """工具节点""" - + def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) self.typed_config: ToolNodeConfig | None = None - + async def execute(self, state: WorkflowState) -> dict[str, Any]: """执行工具""" self.typed_config = ToolNodeConfig(**self.config) @@ -28,21 +28,21 @@ class ToolNode(BaseNode): tenant_id = self.get_variable("sys.tenant_id", state) user_id = self.get_variable("sys.user_id", state) workspace_id = self.get_variable("sys.workspace_id", state) - + # 如果没有租户ID，尝试从工作流ID获取 if not tenant_id: if workspace_id: from app.repositories.tool_repository import ToolRepository with get_db_read() as db: tenant_id = ToolRepository.get_tenant_id_by_workspace_id(db, workspace_id) - + if not tenant_id: logger.error(f"节点 {self.node_id} 缺少租户ID") return { "success": False, "data": "缺少租户ID" } - + # 渲染工具参数 rendered_parameters = {} for param_name, param_template in self.typed_config.tool_parameters.items(): @@ -55,9 +55,9 @@ class ToolNode(BaseNode): # 非模板参数（数字/布尔/普通字符串）直接保留原值 rendered_value = param_template rendered_parameters[param_name] = rendered_value - + logger.info(f"节点 {self.node_id} 执行工具 {self.typed_config.tool_id}，参数: {rendered_parameters}") - + # 执行工具 with get_db_read() as db: tool_service = ToolService(db) @@ -79,7 +79,7 @@ class ToolNode(BaseNode): else: logger.error(f"节点 {self.node_id} 工具执行失败: {result.error}") return { - "data": result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False), + "data": result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False), "error_code": result.error_code, "execution_time": result.execution_time - } \ No newline at end of file + } diff --git a/api/app/main.py b/api/app/main.py index 87bfecf8..7e16d2c0 100644 --- a/api/app/main.py +++ b/api/app/main.py @@ -16,6 +16,8 @@ from app.core.error_codes import BizCode, HTTP_MAPPING from app.core.exceptions import BusinessException from app.core.logging_config import LoggingConfig, get_logger from app.core.response_utils import fail +from app.core.models.scripts.loader import load_models +from app.db import get_db_context # Initialize logging system LoggingConfig.setup_logging() @@ -47,6 +49,15 @@ async def lifespan(app: FastAPI): else: logger.info("自动数据库升级已禁用 (DB_AUTO_UPGRADE=false)") + # 加载预定义模型 + logger.info("开始加载预定义模型...") + try: + with get_db_context() as db: + result = load_models(db, silent=True) + logger.info(f"预定义模型加载完成: 成功{result['success']}个, 跳过{result['skipped']}个, 失败{result['failed']}个") + except Exception as e: + logger.warning(f"加载预定义模型时出错: {str(e)}") + logger.info("应用程序启动完成") yield # 应用关闭事件 diff --git a/api/app/models/__init__.py b/api/app/models/__init__.py index bf3a1b3d..984212de 100644 --- a/api/app/models/__init__.py +++ b/api/app/models/__init__.py @@ -6,7 +6,7 @@ from .document_model import Document from .file_model import File from .file_metadata_model import FileMetadata from .generic_file_model import GenericFile -from .models_model import ModelConfig, ModelProvider, ModelType, ModelApiKey +from .models_model import ModelConfig, ModelProvider, ModelType, ModelApiKey, ModelBase, LoadBalanceStrategy from .memory_short_model import ShortTermMemory, LongTermMemory from .knowledgeshare_model import KnowledgeShare from .app_model import App @@ -18,7 +18,7 @@ from .appshare_model import AppShare from .release_share_model import ReleaseShare from .conversation_model import Conversation, Message from .api_key_model import ApiKey, ApiKeyLog, ApiKeyType -from .data_config_model import DataConfig +from .memory_config_model import MemoryConfig from .multi_agent_model import MultiAgentConfig, AgentInvocation from .workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution from .retrieval_info import RetrievalInfo @@ -28,6 +28,10 @@ from .tool_model import ( ToolExecution, ToolType, ToolStatus, AuthType, ExecutionStatus ) from .memory_perceptual_model import MemoryPerceptualModel +from .ontology_scene import OntologyScene +from .ontology_class import OntologyClass +from .ontology_scene import OntologyScene +from .ontology_class import OntologyClass __all__ = [ "Tenants", @@ -57,7 +61,7 @@ __all__ = [ "ApiKey", "ApiKeyLog", "ApiKeyType", - "DataConfig", + "MemoryConfig", "MultiAgentConfig", "AgentInvocation", "WorkflowConfig", @@ -79,4 +83,6 @@ __all__ = [ "AuthType", "ExecutionStatus", "MemoryPerceptualModel", + "ModelBase", + "LoadBalanceStrategy" ] diff --git a/api/app/models/agent_app_config_model.py b/api/app/models/agent_app_config_model.py index 0a7a5935..96752c8e 100644 --- a/api/app/models/agent_app_config_model.py +++ b/api/app/models/agent_app_config_model.py @@ -6,7 +6,7 @@ from sqlalchemy.orm import relationship from app.base.type import PydanticType from app.db import Base -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters class AgentConfig(Base): diff --git a/api/app/models/data_config_model.py b/api/app/models/data_config_model.py deleted file mode 100644 index 06f87cb2..00000000 --- a/api/app/models/data_config_model.py +++ /dev/null @@ -1,88 +0,0 @@ -import datetime -from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float -from sqlalchemy.dialects.postgresql import UUID -from app.db import Base - - -class DataConfig(Base): - """数据配置表 - 用于存储记忆系统的配置参数""" - __tablename__ = "data_config" - - # 主键 - config_id = Column(Integer, primary_key=True, autoincrement=True, comment="配置ID") - - # 基本信息 - config_name = Column(String, nullable=False, comment="配置名称") - config_desc = Column(String, nullable=True, comment="配置描述") - - # 组织信息 - workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") - group_id = Column(String, nullable=True, comment="组ID") - user_id = Column(String, nullable=True, comment="用户ID") - apply_id = Column(String, nullable=True, comment="应用ID") - - # 模型选择（从workspace继承） - llm_id = Column(String, nullable=True, comment="LLM模型配置ID") - embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID") - rerank_id = Column(String, nullable=True, comment="重排序模型配置ID") - - # 记忆萃取引擎配置 - enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重") - enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧") - deep_retrieval = Column(Boolean, default=True, comment="深度检索开关") - - # 阈值配置 (0-1 之间的浮点数) - t_type_strict = Column(Float, default=0.8, comment="类型严格阈值") - t_name_strict = Column(Float, default=0.8, comment="名称严格阈值") - t_overall = Column(Float, default=0.8, comment="综合阈值") - - # 状态配置 - state = Column(Boolean, default=False, comment="配置使用状态") - - # 分块策略 - chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略") - - # 剪枝配置 - pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝") - pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound") - pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）") - - # 自我反思配置 - enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思") - iteration_period = Column(String, default="3", comment="反思迭代周期") - reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部") - baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实") - reflection_model_id = Column(String, nullable=True, comment="反思模型ID") - memory_verify = Column(Boolean, default=True, comment="记忆验证") - quality_assessment = Column(Boolean, default=True, comment="质量评估") - - # 遗忘引擎配置 - statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3") - include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文") - max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量") - lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数") - lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数") - offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数") - - # ACT-R 遗忘引擎配置 - decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5") - forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3") - forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24") - enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True") - max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100") - max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100") - min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30") - - # 情绪引擎配置 - emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取") - emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID") - emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词") - emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值") - emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类") - - # 时间戳 - created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") - updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") - - def __repr__(self): - return f"" diff --git a/api/app/models/memory_config_model.py b/api/app/models/memory_config_model.py index d47c3b52..8a451f2d 100644 --- a/api/app/models/memory_config_model.py +++ b/api/app/models/memory_config_model.py @@ -1,39 +1,91 @@ -# -*- coding: utf-8 -*- -"""Memory Configuration Model - Backward Compatibility +import datetime +from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float +from sqlalchemy.dialects.postgresql import UUID +from app.db import Base -This module provides backward compatibility for imports. -All classes have been moved to app.schemas.memory_config_schema. -DEPRECATED: Import from app.schemas.memory_config_schema instead. -""" +class MemoryConfig(Base): + """记忆配置表 - 用于存储记忆系统的配置参数""" + __tablename__ = "memory_config" -# Re-export for backward compatibility -from app.schemas.memory_config_schema import ( - ConfigurationError, - InvalidConfigError, - MemoryConfig, - MemoryConfigValidation, - ModelInactiveError, - ModelNotFoundError, - ModelValidation, - WorkspaceNotFoundError, - WorkspaceValidation, - validate_memory_config_data, - validate_model_data, - validate_workspace_data, -) + # 主键 + config_id = Column(UUID(as_uuid=True), primary_key=True, comment="配置ID") + config_id_old = Column(Integer, nullable=True, comment="备份的配置ID") + # 基本信息 + config_name = Column(String, nullable=False, comment="配置名称") + config_desc = Column(String, nullable=True, comment="配置描述") -__all__ = [ - "ConfigurationError", - "InvalidConfigError", - "MemoryConfig", - "MemoryConfigValidation", - "ModelInactiveError", - "ModelNotFoundError", - "ModelValidation", - "WorkspaceNotFoundError", - "WorkspaceValidation", - "validate_memory_config_data", - "validate_model_data", - "validate_workspace_data", -] + # 组织信息 + workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") + end_user_id = Column(String, nullable=True, comment="组ID") + user_id = Column(String, nullable=True, comment="用户ID") + apply_id = Column(String, nullable=True, comment="应用ID") + + # 本体场景关联 + scene_id = Column(UUID(as_uuid=True), nullable=True, comment="本体场景ID，关联ontology_scene表") + + # 模型选择（从workspace继承） + llm_id = Column(String, nullable=True, comment="LLM模型配置ID") + embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID") + rerank_id = Column(String, nullable=True, comment="重排序模型配置ID") + + # 记忆萃取引擎配置 + enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重") + enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧") + deep_retrieval = Column(Boolean, default=True, comment="深度检索开关") + + # 阈值配置 (0-1 之间的浮点数) + t_type_strict = Column(Float, default=0.8, comment="类型严格阈值") + t_name_strict = Column(Float, default=0.8, comment="名称严格阈值") + t_overall = Column(Float, default=0.8, comment="综合阈值") + + # 状态配置 + state = Column(Boolean, default=False, comment="配置使用状态") + + # 分块策略 + chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略") + + # 剪枝配置 + pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝") + pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound") + pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）") + + # 自我反思配置 + enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思") + iteration_period = Column(String, default="3", comment="反思迭代周期") + reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部") + baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实") + reflection_model_id = Column(String, nullable=True, comment="反思模型ID") + memory_verify = Column(Boolean, default=True, comment="记忆验证") + quality_assessment = Column(Boolean, default=True, comment="质量评估") + + # 遗忘引擎配置 + statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3") + include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文") + max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量") + lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数") + lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数") + offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数") + + # ACT-R 遗忘引擎配置 + decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5") + forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3") + forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24") + enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True") + max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100") + max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100") + min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30") + + # 情绪引擎配置 + emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取") + emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID") + emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词") + emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值") + emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类") + + # 时间戳 + created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") + updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") + + def __repr__(self): + return f"" diff --git a/api/app/models/memory_perceptual_model.py b/api/app/models/memory_perceptual_model.py index 59eb0222..cafb18d4 100644 --- a/api/app/models/memory_perceptual_model.py +++ b/api/app/models/memory_perceptual_model.py @@ -16,7 +16,7 @@ class PerceptualType(IntEnum): CONVERSATION = 4 -class FileStorageType(IntEnum): +class FileStorageService(IntEnum): LOCAL = 1 REMOTE = 2 diff --git a/api/app/models/models_model.py b/api/app/models/models_model.py index 2e60ef1c..3e378f17 100644 --- a/api/app/models/models_model.py +++ b/api/app/models/models_model.py @@ -1,19 +1,34 @@ import datetime import uuid from enum import StrEnum -from typing import Optional, List -from sqlalchemy import Column, String, Boolean, DateTime, Text, ForeignKey, Enum as SQLEnum + +from sqlalchemy import Column, String, Boolean, DateTime, Text, ForeignKey, Enum as SQLEnum, UniqueConstraint, Integer, ARRAY, Table from sqlalchemy.dialects.postgresql import UUID, JSON from sqlalchemy.orm import relationship +from sqlalchemy.sql import func from app.db import Base +class BaseModel(Base): + """基础模型（抽象类，提取公共字段）""" + __abstract__ = True # 标记为抽象类，不生成表 + id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True) + created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") + updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") + is_active = Column(Boolean, default=True, nullable=False, comment="是否激活") + + class ModelType(StrEnum): """模型类型枚举""" LLM = "llm" CHAT = "chat" EMBEDDING = "embedding" RERANK = "rerank" + # TTS = "tts" + # SPEECH2TEXT = "speech2text" + # IMAGE = "image" + # AUDIO = "audio" + # VISION = "vision" class ModelProvider(StrEnum): @@ -30,16 +45,36 @@ class ModelProvider(StrEnum): XINFERENCE = "xinference" GPUSTACK = "gpustack" BEDROCK = "bedrock" + COMPOSITE = "composite" -class ModelConfig(Base): +class LoadBalanceStrategy(StrEnum): + """API Key负载均衡策略枚举""" + ROUND_ROBIN = "round_robin" # 轮询 + NONE = "none" # 无 + + +# 多对多关联表 +model_config_api_key_association = Table( + 'model_config_api_key_association', + Base.metadata, + Column('model_config_id', UUID(as_uuid=True), ForeignKey('model_configs.id'), primary_key=True), + Column('api_key_id', UUID(as_uuid=True), ForeignKey('model_api_keys.id'), primary_key=True), + Column('created_at', DateTime, default=datetime.datetime.now) +) + + +class ModelConfig(BaseModel): """模型配置表""" __tablename__ = "model_configs" - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True) + model_id = Column(UUID(as_uuid=True), ForeignKey("model_bases.id"), nullable=True, index=True, comment="基础模型ID") tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, index=True, comment="租户ID") + logo = Column(String(255), nullable=True, comment="模型logo图片URL") name = Column(String, nullable=False, comment="模型显示名称") + provider = Column(String, nullable=False, comment="供应商", server_default=ModelProvider.COMPOSITE) type = Column(String, nullable=False, index=True, comment="模型类型") + is_composite = Column(Boolean, default=False, server_default="true", nullable=False, comment="是否为组合模型") description = Column(String, comment="模型描述") # 模型配置参数 @@ -56,29 +91,29 @@ class ModelConfig(Base): # context_length = Column(String, comment="上下文长度") # 状态管理 - is_active = Column(Boolean, default=True, nullable=False, comment="是否激活") is_public = Column(Boolean, default=False, nullable=False, comment="是否公开") - - # 时间戳 - created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") - updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") + load_balance_strategy = Column(String, nullable=True, comment="负载均衡策略", default=LoadBalanceStrategy.NONE, + server_default=LoadBalanceStrategy.NONE) # 关联关系 - api_keys = relationship("ModelApiKey", back_populates="model_config", cascade="all, delete-orphan") + model_base = relationship("ModelBase", back_populates="configs") + api_keys = relationship( + "ModelApiKey", + secondary=model_config_api_key_association, + back_populates="model_configs" + ) def __repr__(self): return f"" -class ModelApiKey(Base): +class ModelApiKey(BaseModel): """模型API密钥表""" __tablename__ = "model_api_keys" - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True) - model_config_id = Column(UUID(as_uuid=True), ForeignKey("model_configs.id"), nullable=False, comment="模型配置ID") # API Key 信息 model_name = Column(String, nullable=False, comment="模型实际名称") + description = Column(String, comment="备注") provider = Column(String, nullable=False, comment="API Key提供商") api_key = Column(String, nullable=False, comment="API密钥") api_base = Column(String, comment="API基础URL") @@ -91,15 +126,42 @@ class ModelApiKey(Base): last_used_at = Column(DateTime, comment="最后使用时间") # 状态管理 - is_active = Column(Boolean, default=True, nullable=False, comment="是否激活") priority = Column(String, default="1", comment="优先级") - - # 时间戳 - created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") - updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") - + # 关联关系 - model_config = relationship("ModelConfig", back_populates="api_keys") + model_configs = relationship( + "ModelConfig", + secondary=model_config_api_key_association, + back_populates="api_keys" + ) + def __repr__(self): - return f"" + return f"" + + +class ModelBase(Base): + """基础模型信息表（模型广场）""" + __tablename__ = "model_bases" + + id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True) + logo = Column(String(255), nullable=True, comment="模型logo图片URL") + name = Column(String, nullable=False, comment="模型唯一标识（如gpt-3.5-turbo）") + type = Column(String, nullable=False, index=True, comment="模型类型") + provider = Column(String, nullable=False, index=True) + description = Column(Text, comment="模型描述") + is_deprecated = Column(Boolean, default=False, nullable=False, comment="是否弃用") + is_official = Column(Boolean, default=True, comment="是否供应商官方模型（区分自定义）") + tags = Column(ARRAY(String), default=list, nullable=False, comment="模型标签（如['聊天', '创作']）") + add_count = Column(Integer, default=0, nullable=False, comment="模型被用户添加的次数") + created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间", server_default=func.now()) + + # 关联关系 + configs = relationship("ModelConfig", back_populates="model_base", cascade="all, delete-orphan") + + __table_args__ = ( + UniqueConstraint("name", "provider", name="uk_model_name_provider"), + ) + + def __repr__(self): + return f"" \ No newline at end of file diff --git a/api/app/models/multi_agent_model.py b/api/app/models/multi_agent_model.py index 544ddb27..400c05ad 100644 --- a/api/app/models/multi_agent_model.py +++ b/api/app/models/multi_agent_model.py @@ -10,7 +10,7 @@ from sqlalchemy.orm import relationship from app.base.type import PydanticType from app.db import Base -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters class OrchestrationMode(StrEnum): diff --git a/api/app/models/ontology_class.py b/api/app/models/ontology_class.py new file mode 100644 index 00000000..528d934e --- /dev/null +++ b/api/app/models/ontology_class.py @@ -0,0 +1,40 @@ +# -*- coding: utf-8 -*- +"""本体类型模型 + +本模块定义本体类型的数据模型。 + +Classes: + OntologyClass: 本体类型表模型 +""" + +import datetime +import uuid +from sqlalchemy import Column, String, DateTime, Text, ForeignKey +from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy.orm import relationship +from app.db import Base + + +class OntologyClass(Base): + """本体类型表 - 用于存储某个场景提取出来的本体类型信息""" + __tablename__ = "ontology_class" + + # 主键 + class_id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="类型ID") + + # 类型信息 + class_name = Column(String(200), nullable=False, comment="类型名称") + class_description = Column(Text, nullable=True, comment="类型描述") + + # 外键：关联到本体场景 + scene_id = Column(UUID(as_uuid=True), ForeignKey("ontology_scene.scene_id", ondelete="CASCADE"), nullable=False, index=True, comment="所属场景ID") + + # 时间戳 + created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, comment="创建时间") + updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, nullable=False, comment="更新时间") + + # 关系：类型属于某个场景 + scene = relationship("OntologyScene", back_populates="classes") + + def __repr__(self): + return f"" diff --git a/api/app/models/ontology_scene.py b/api/app/models/ontology_scene.py new file mode 100644 index 00000000..350bfdd6 --- /dev/null +++ b/api/app/models/ontology_scene.py @@ -0,0 +1,43 @@ +# -*- coding: utf-8 -*- +"""本体场景模型 + +本模块定义本体场景的数据模型。 + +Classes: + OntologyScene: 本体场景表模型 +""" + +import datetime +import uuid +from sqlalchemy import Column, String, DateTime, Integer, Text, ForeignKey, UniqueConstraint +from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy.orm import relationship +from app.db import Base + + +class OntologyScene(Base): + """本体场景表 - 用于存储本体场景下不同的类型信息""" + __tablename__ = "ontology_scene" + __table_args__ = ( + UniqueConstraint('workspace_id', 'scene_name', name='uq_workspace_scene_name'), + ) + + # 主键 + scene_id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="场景ID") + + # 场景信息 + scene_name = Column(String(200), nullable=False, comment="场景名称") + scene_description = Column(Text, nullable=True, comment="场景描述") + + # 外键：关联到工作空间 + workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False, index=True, comment="所属工作空间ID") + + # 时间戳 + created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, comment="创建时间") + updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, nullable=False, comment="更新时间") + + # 关系：一个场景可以有多个类型 + classes = relationship("OntologyClass", back_populates="scene", cascade="all, delete-orphan") + + def __repr__(self): + return f"" diff --git a/api/app/models/prompt_optimizer_model.py b/api/app/models/prompt_optimizer_model.py index 39845ee7..f96b0a66 100644 --- a/api/app/models/prompt_optimizer_model.py +++ b/api/app/models/prompt_optimizer_model.py @@ -2,7 +2,7 @@ import datetime import uuid from enum import StrEnum -from sqlalchemy import Column, ForeignKey, Text, DateTime, String, Index +from sqlalchemy import Column, ForeignKey, Text, DateTime, String, Index, Boolean from sqlalchemy.dialects.postgresql import UUID from app.db import Base @@ -121,10 +121,33 @@ class PromptOptimizerSessionHistory(Base): id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True) tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, comment="Tenant ID") # app_id = Column(UUID(as_uuid=True), ForeignKey("apps.id"), nullable=False, comment="Application ID") - session_id = Column(UUID(as_uuid=True), ForeignKey("prompt_opt_session_list.id"),nullable=False, comment="Session ID") + session_id = Column( + UUID(as_uuid=True), + ForeignKey("prompt_opt_session_list.id"), + nullable=False, + comment="Session ID" + ) user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False, comment="User ID") role = Column(String, nullable=False, comment="Message Role") content = Column(Text, nullable=False, comment="Message Content") # prompt = Column(Text, nullable=False, comment="Prompt") created_at = Column(DateTime, default=datetime.datetime.now, comment="Creation Time", index=True) + + +class PromptHistory(Base): + __tablename__ = "prompt_history" + + id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True) + tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, comment="Tenant ID") + + session_id = Column( + UUID(as_uuid=True), + ForeignKey("prompt_opt_session_list.id"), + nullable=False, + comment="Session ID" + ) + title = Column(String, nullable=False, comment="Title") + prompt = Column(Text, nullable=False, comment="Prompt") + created_at = Column(DateTime, default=datetime.datetime.now, comment="Creation Time", index=True) + is_delete = Column(Boolean, default=False, comment="Delete") diff --git a/api/app/models/tenant_model.py b/api/app/models/tenant_model.py index 552e87b5..54a3e347 100644 --- a/api/app/models/tenant_model.py +++ b/api/app/models/tenant_model.py @@ -16,6 +16,10 @@ class Tenants(Base): updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now) is_active = Column(Boolean, default=True) + # SSO 外部关联字段 + external_id = Column(String(100), nullable=True, index=True) # 外部企业ID + external_source = Column(String(50), nullable=True) # 来源系统 + # Relationship to users - one tenant has many users users = relationship("User", back_populates="tenant") diff --git a/api/app/models/user_model.py b/api/app/models/user_model.py index 89971a3a..663bfc71 100644 --- a/api/app/models/user_model.py +++ b/api/app/models/user_model.py @@ -18,6 +18,10 @@ class User(Base): updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now) last_login_at = Column(DateTime, nullable=True) # 最后登录时间，可为空 + # SSO 外部关联字段 + external_id = Column(String(100), nullable=True) # 外部用户ID + external_source = Column(String(50), nullable=True) # 来源系统 + current_workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id"), nullable=True) # 当前工作空间ID，可为空 # Foreign key to tenant - each user belongs to exactly one tenant diff --git a/api/app/plugins/__init__.py b/api/app/plugins/__init__.py new file mode 100644 index 00000000..e9ef92fd --- /dev/null +++ b/api/app/plugins/__init__.py @@ -0,0 +1,74 @@ +# app/plugins/__init__.py +""" +插件系统 - 支持开源核心 + 闭源增值模块 + +使用方式： +1. 开源版（community）：基础功能 +2. 商业版（enterprise）：加载 premium 包中的高级实现 +""" +import os +from typing import Dict, Any, Optional +from app.core.logging_config import get_logger + +logger = get_logger(__name__) + +# 版本标识 +EDITION = os.environ.get("EDITION", "community") +IS_ENTERPRISE = EDITION == "enterprise" + +# 插件注册表 +_plugins: Dict[str, Any] = {} + +# 路由注册表（用于动态注册闭源模块的路由） +_routers: list = [] + + +def is_enterprise() -> bool: + """是否为商业版""" + return IS_ENTERPRISE + + +def list_plugins() -> list: + """列出所有已注册插件""" + return list(_plugins.keys()) + + +def register_plugin(name: str, instance: Any): + """注册插件""" + _plugins[name] = instance + logger.info(f"插件已注册: {name}") + + +def get_plugin(name: str) -> Optional[Any]: + """获取插件实例""" + return _plugins.get(name) + + +def register_router(router, prefix: str = "", tags: list = None): + """注册路由（供闭源模块使用）""" + _routers.append({ + "router": router, + "prefix": prefix, + "tags": tags or [] + }) + logger.info(f"路由已注册: {prefix}") + + +def get_registered_routers() -> list: + """获取所有注册的路由""" + return _routers + + +def register_premium_routers(app): + """ + 注册 premium 模块的路由到 FastAPI app + + 在商业版 main.py 中调用 + """ + for router_info in _routers: + app.include_router( + router_info["router"], + prefix=f"/api{router_info['prefix']}", + tags=router_info["tags"] + ) + logger.info(f"Premium 路由已挂载: /api{router_info['prefix']}") diff --git a/api/app/repositories/app_repository.py b/api/app/repositories/app_repository.py index 11a2ea3e..0c7ba6a4 100644 --- a/api/app/repositories/app_repository.py +++ b/api/app/repositories/app_repository.py @@ -15,9 +15,13 @@ class AppRepository: self.db = db def get_apps_by_workspace_id(self, workspace_id: uuid.UUID) -> list[App]: - """根据工作空间ID查询应用""" + """根据工作空间ID查询应用（仅返回未删除的应用）""" try: - apps = self.db.query(App).filter(App.workspace_id == workspace_id).all() + apps = ( + self.db.query(App) + .filter(App.workspace_id == workspace_id, App.is_active.is_(True)) + .all() + ) db_logger.info(f"成功查询工作空间 {workspace_id} 下的 {len(apps)} 个应用") return apps except Exception as e: @@ -26,7 +30,7 @@ class AppRepository: def get_apps_by_id(self, app_id: uuid.UUID) -> App: try: - app = self.db.query(App).filter(App.id == app_id, App.is_active == True).first() + app = self.db.query(App).filter(App.id == app_id, App.is_active.is_(True)).first() return app except Exception as e: raise diff --git a/api/app/repositories/home_page_repository.py b/api/app/repositories/home_page_repository.py index 888071ac..bcb3b622 100644 --- a/api/app/repositories/home_page_repository.py +++ b/api/app/repositories/home_page_repository.py @@ -17,24 +17,24 @@ class HomePageRepository: """获取模型统计数据""" total_models = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True + ModelConfig.is_active.is_(True) ).count() total_llm = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True, + ModelConfig.is_active.is_(True), ModelConfig.type == "llm" ).count() total_embedding = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True, + ModelConfig.is_active.is_(True), ModelConfig.type == "embedding" ).count() new_models_this_week = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True, + ModelConfig.is_active.is_(True), ModelConfig.created_at >= week_start ).count() @@ -56,12 +56,12 @@ class HomePageRepository: """获取工作空间统计数据""" active_workspaces = db.query(Workspace).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).count() new_workspaces_this_week = db.query(Workspace).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True, + Workspace.is_active.is_(True), Workspace.created_at >= week_start ).count() @@ -83,7 +83,7 @@ class HomePageRepository: """获取用户统计数据""" workspace_ids = db.query(Workspace.id).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).subquery() total_users = db.query(EndUser).join( @@ -91,7 +91,7 @@ class HomePageRepository: EndUser.app_id == App.id ).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active" ).count() @@ -100,7 +100,7 @@ class HomePageRepository: EndUser.app_id == App.id ).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active", EndUser.created_at >= week_start ).count() @@ -123,18 +123,18 @@ class HomePageRepository: """获取应用统计数据""" workspace_ids = db.query(Workspace.id).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).subquery() running_apps = db.query(App).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active" ).count() new_apps_this_week = db.query(App).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active", App.created_at >= week_start ).count() @@ -158,7 +158,7 @@ class HomePageRepository: # 获取工作空间列表 workspaces = db.query(Workspace).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).all() workspace_ids = [ws.id for ws in workspaces] @@ -169,7 +169,7 @@ class HomePageRepository: func.count(App.id).label('count') ).filter( App.workspace_id.in_(workspace_ids), - App.is_active, + App.is_active.is_(True), App.status == "active" ).group_by(App.workspace_id).all() @@ -184,7 +184,7 @@ class HomePageRepository: EndUser.app_id == App.id ).filter( App.workspace_id.in_(workspace_ids), - App.is_active, + App.is_active.is_(True), App.status == "active" ).group_by(App.workspace_id).all() diff --git a/api/app/repositories/data_config_repository.py b/api/app/repositories/memory_config_repository.py similarity index 71% rename from api/app/repositories/data_config_repository.py rename to api/app/repositories/memory_config_repository.py index 3df7f800..568c262f 100644 --- a/api/app/repositories/data_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -1,18 +1,19 @@ # -*- coding: utf-8 -*- -"""数据配置Repository模块 +"""记忆配置Repository模块 -本模块提供data_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。 +本模块提供memory_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。 包括CRUD操作和Neo4j Cypher查询常量。 Classes: - DataConfigRepository: 数据配置仓储类，提供CRUD操作 + MemoryConfigRepository: 记忆配置仓储类，提供CRUD操作 """ import uuid +from uuid import UUID from typing import Dict, List, Optional, Tuple from app.core.exceptions import BusinessException from app.core.logging_config import get_config_logger, get_db_logger -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig from app.schemas.memory_storage_schema import ( ConfigKey, ConfigParamsCreate, @@ -23,16 +24,20 @@ from app.schemas.memory_storage_schema import ( from sqlalchemy import desc, select from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + # 获取数据库专用日志器 db_logger = get_db_logger() # 获取配置专用日志器 config_logger = get_config_logger() -TABLE_NAME = "data_config" -class DataConfigRepository: - """数据配置Repository +TABLE_NAME = "memory_config" - 提供data_config表的数据访问方法，包括： + +class MemoryConfigRepository: + """记忆配置Repository + + 提供memory_config表的数据访问方法，包括： - SQLAlchemy ORM 数据库操作 - Neo4j Cypher查询常量 """ @@ -41,48 +46,48 @@ class DataConfigRepository: # Dialogue count by group SEARCH_FOR_DIALOGUE = """ - MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # Chunk count by group SEARCH_FOR_CHUNK = """ - MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # Statement count by group SEARCH_FOR_STATEMENT = """ - MATCH (n:Statement) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # ExtractedEntity count by group SEARCH_FOR_ENTITY = """ - MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # All counts by label and total SEARCH_FOR_ALL = """ - OPTIONAL MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN 'Chunk' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN 'Chunk' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:Statement) WHERE n.group_id = $group_id RETURN 'Statement' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN 'Statement' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n) WHERE n.group_id = $group_id RETURN 'ALL' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n) WHERE n.end_user_id = $end_user_id RETURN 'ALL' AS Label, COUNT(n) AS Count """ # Extracted entity details within group/app/user SEARCH_FOR_DETIALS = """ MATCH (n:ExtractedEntity) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN n.entity_idx AS entity_idx, n.connect_strength AS connect_strength, n.description AS description, n.entity_type AS entity_type, n.name AS name, COALESCE(n.fact_summary, '') AS fact_summary, - n.group_id AS group_id, + n.end_user_id AS end_user_id, n.apply_id AS apply_id, n.user_id AS user_id, n.id AS id @@ -91,9 +96,9 @@ class DataConfigRepository: # Edges between extracted entities within group/app/user SEARCH_FOR_EDGES = """ MATCH (n:ExtractedEntity)-[r]->(m:ExtractedEntity) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN - r.group_id AS group_id, + r.end_user_id AS end_user_id, r.apply_id AS apply_id, r.user_id AS user_id, elementId(r) AS rel_id, @@ -107,7 +112,7 @@ class DataConfigRepository: @staticmethod def update_reflection_config( db: Session, - config_id: int, + config_id: uuid.UUID, enable_self_reflexion: bool, iteration_period: str, reflexion_range: str, @@ -115,7 +120,7 @@ class DataConfigRepository: reflection_model_id: str, memory_verify: bool, quality_assessment: bool - ) -> DataConfig: + ) -> MemoryConfig: """构建反思配置更新语句（SQLAlchemy text() 命名参数） Args: @@ -130,28 +135,28 @@ class DataConfigRepository: config_id: 配置ID Returns: - Data + MemoryConfig Raises: ValueError: 没有字段需要更新时抛出 """ db_logger.debug(f"构建反思配置更新语句: config_id={config_id}") - stmt = select(DataConfig).where(DataConfig.config_id == config_id) - data_config_obj = db.scalars(stmt).first() - if not data_config_obj: + stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id) + memory_config_obj = db.scalars(stmt).first() + if not memory_config_obj: raise BusinessException - data_config_obj.enable_self_reflexion = enable_self_reflexion - data_config_obj.iteration_period = iteration_period - data_config_obj.reflexion_range = reflexion_range - data_config_obj.baseline = baseline - data_config_obj.reflection_model_id = reflection_model_id - data_config_obj.memory_verify = memory_verify - data_config_obj.quality_assessment = quality_assessment + memory_config_obj.enable_self_reflexion = enable_self_reflexion + memory_config_obj.iteration_period = iteration_period + memory_config_obj.reflexion_range = reflexion_range + memory_config_obj.baseline = baseline + memory_config_obj.reflection_model_id = reflection_model_id + memory_config_obj.memory_verify = memory_verify + memory_config_obj.quality_assessment = quality_assessment - return data_config_obj + return memory_config_obj @staticmethod - def query_reflection_config_by_id(db: Session, config_id: int) -> DataConfig: + def query_reflection_config_by_id(db: Session, config_id: uuid.UUID|int|str) -> MemoryConfig: """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数） Args: @@ -162,13 +167,14 @@ class DataConfigRepository: Tuple[str, Dict]: (SQL查询字符串, 参数字典) """ db_logger.debug(f"构建反思配置查询语句: config_id={config_id}") - stmt = select(DataConfig).where(DataConfig.config_id == config_id) - data_config = db.scalars(stmt).first() - if not data_config: + stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id) + memory_config = db.scalars(stmt).first() + if not memory_config: raise RuntimeError("reflection config not found") - return data_config + return memory_config + @staticmethod - def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> DataConfig: + def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> MemoryConfig: """构建查询所有配置的语句（SQLAlchemy text() 命名参数） Args: @@ -180,12 +186,11 @@ class DataConfigRepository: """ db_logger.debug(f"构建查询所有配置语句: workspace_id={workspace_id}") - stmt = select(DataConfig).where(DataConfig.workspace_id == workspace_id) - data_config = db.scalars(stmt).first() - if not data_config: + stmt = select(MemoryConfig).where(MemoryConfig.workspace_id == workspace_id) + memory_config = db.scalars(stmt).first() + if not memory_config: raise RuntimeError("reflection config not found") - return data_config - + return memory_config @staticmethod def build_select_all(workspace_id: uuid.UUID) -> Tuple[str, Dict]: @@ -208,23 +213,25 @@ class DataConfigRepository: return query, params @staticmethod - def create(db: Session, params: ConfigParamsCreate) -> DataConfig: - """创建数据配置 + def create(db: Session, params: ConfigParamsCreate) -> MemoryConfig: + """创建记忆配置 Args: db: 数据库会话 params: 配置参数创建模型 Returns: - DataConfig: 创建的配置对象 + MemoryConfig: 创建的配置对象 """ - db_logger.debug(f"创建数据配置: config_name={params.config_name}, workspace_id={params.workspace_id}") + db_logger.debug(f"创建记忆配置: config_name={params.config_name}, workspace_id={params.workspace_id}") try: - db_config = DataConfig( + db_config = MemoryConfig( + config_id=uuid.uuid4(), config_name=params.config_name, config_desc=params.config_desc, workspace_id=params.workspace_id, + scene_id=params.scene_id, llm_id=params.llm_id, embedding_id=params.embedding_id, rerank_id=params.rerank_id, @@ -232,16 +239,16 @@ class DataConfigRepository: db.add(db_config) db.flush() # 获取自增ID但不提交事务 - db_logger.info(f"数据配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})") + db_logger.info(f"记忆配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})") return db_config except Exception as e: db.rollback() - db_logger.error(f"创建数据配置失败: {params.config_name} - {str(e)}") + db_logger.error(f"创建记忆配置失败: {params.config_name} - {str(e)}") raise @staticmethod - def update(db: Session, update: ConfigUpdate) -> Optional[DataConfig]: + def update(db: Session, update: ConfigUpdate) -> Optional[MemoryConfig]: """更新基础配置 Args: @@ -249,17 +256,17 @@ class DataConfigRepository: update: 配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 """ - db_logger.debug(f"更新数据配置: config_id={update.config_id}") + db_logger.debug(f"更新记忆配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段 @@ -277,17 +284,16 @@ class DataConfigRepository: db.commit() db.refresh(db_config) - db_logger.info(f"数据配置更新成功: {db_config.config_name} (ID: {update.config_id})") + db_logger.info(f"记忆配置更新成功: {db_config.config_name} (ID: {update.config_id})") return db_config except Exception as e: db.rollback() - db_logger.error(f"更新数据配置失败: config_id={update.config_id} - {str(e)}") + db_logger.error(f"更新记忆配置失败: config_id={update.config_id} - {str(e)}") raise - @staticmethod - def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[DataConfig]: + def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[MemoryConfig]: """更新记忆萃取引擎配置 Args: @@ -295,7 +301,7 @@ class DataConfigRepository: update: 萃取配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 @@ -303,9 +309,9 @@ class DataConfigRepository: db_logger.debug(f"更新萃取配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段映射 @@ -360,7 +366,7 @@ class DataConfigRepository: raise @staticmethod - def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[DataConfig]: + def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[MemoryConfig]: """更新遗忘引擎配置 Args: @@ -368,7 +374,7 @@ class DataConfigRepository: update: 遗忘配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 @@ -376,9 +382,9 @@ class DataConfigRepository: db_logger.debug(f"更新遗忘配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段 @@ -408,7 +414,7 @@ class DataConfigRepository: raise @staticmethod - def get_extracted_config(db: Session, config_id: int) -> Optional[Dict]: + def get_extracted_config(db: Session, config_id: UUID | int) -> Optional[Dict]: """获取萃取配置，通过主键查询某条配置 Args: @@ -418,10 +424,10 @@ class DataConfigRepository: Returns: Optional[Dict]: 萃取配置字典，不存在则返回None """ + config_id = resolve_config_id(config_id, db) db_logger.debug(f"查询萃取配置: config_id={config_id}") - try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: db_logger.debug(f"萃取配置不存在: config_id={config_id}") return None @@ -457,7 +463,7 @@ class DataConfigRepository: raise @staticmethod - def get_forget_config(db: Session, config_id: int) -> Optional[Dict]: + def get_forget_config(db: Session, config_id: UUID) -> Optional[Dict]: """获取遗忘配置，通过主键查询某条配置 Args: @@ -470,7 +476,7 @@ class DataConfigRepository: db_logger.debug(f"查询遗忘配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: db_logger.debug(f"遗忘配置不存在: config_id={config_id}") return None @@ -489,49 +495,51 @@ class DataConfigRepository: raise @staticmethod - def get_by_id(db: Session, config_id: int) -> Optional[DataConfig]: - """根据ID获取数据配置 + def get_by_id(db: Session, config_id: uuid.UUID) -> Optional[MemoryConfig]: + """根据ID获取记忆配置 Args: db: 数据库会话 config_id: 配置ID Returns: - Optional[DataConfig]: 配置对象，不存在则返回None + Optional[MemoryConfig]: 配置对象，不存在则返回None """ - db_logger.debug(f"根据ID查询数据配置: config_id={config_id}") + db_logger.debug(f"根据ID查询记忆配置: config_id={config_id}") try: - config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if config: - db_logger.debug(f"数据配置查询成功: {config.config_name} (ID: {config_id})") + db_logger.debug(f"记忆配置查询成功: {config.config_name} (ID: {config_id})") else: - db_logger.debug(f"数据配置不存在: config_id={config_id}") + db_logger.debug(f"记忆配置不存在: config_id={config_id}") return config except Exception as e: - db_logger.error(f"根据ID查询数据配置失败: config_id={config_id} - {str(e)}") + db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}") raise + @staticmethod - def get_config_with_workspace(db: Session, config_id: int) -> Optional[tuple]: - """Get data config and its associated workspace information - + def get_config_with_workspace(db: Session, config_id: uuid.UUID | int | str) -> Optional[tuple]: + """Get memory config and its associated workspace information + Args: db: Database session config_id: Configuration ID - + Returns: - Optional[tuple]: (DataConfig, Workspace) tuple, None if not found - + Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found + Raises: ValueError: Raised when config exists but workspace doesn't """ import time from app.models.workspace_model import Workspace - + start_time = time.time() - + config_id = resolve_config_id(config_id, db) + # Log configuration loading start config_logger.info( "Loading configuration with workspace", @@ -540,20 +548,20 @@ class DataConfigRepository: "config_id": config_id } ) - - db_logger.debug(f"Querying data config and workspace: config_id={config_id}") - + + db_logger.debug(f"Querying memory config and workspace: config_id={config_id}") + try: # Use join query to get both config and workspace - result = db.query(DataConfig, Workspace).join( - Workspace, DataConfig.workspace_id == Workspace.id - ).filter(DataConfig.config_id == config_id).first() - + result = db.query(MemoryConfig, Workspace).join( + Workspace, MemoryConfig.workspace_id == Workspace.id + ).filter(MemoryConfig.config_id == config_id).first() + elapsed_ms = (time.time() - start_time) * 1000 - + if not result: # Check if config exists but workspace is missing - config_only = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + config_only = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if config_only: if config_only.workspace_id is None: config_logger.error( @@ -566,7 +574,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Data config {config_id} has no associated workspace ID") + db_logger.error(f"Memory config {config_id} has no associated workspace ID") raise ValueError(f"Configuration {config_id} has no associated workspace") else: config_logger.error( @@ -579,9 +587,11 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Data config {config_id} references non-existent workspace {config_only.workspace_id}") - raise ValueError(f"Workspace {config_only.workspace_id} not found for configuration {config_id}") - + db_logger.error( + f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}") + raise ValueError( + f"Workspace {config_only.workspace_id} not found for configuration {config_id}") + config_logger.debug( "Configuration not found", extra={ @@ -591,11 +601,11 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.debug(f"Data config not found: config_id={config_id}") + db_logger.debug(f"Memory config not found: config_id={config_id}") return None - + config, workspace = result - + # Log successful configuration loading config_logger.info( "Configuration with workspace loaded successfully", @@ -610,16 +620,17 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - - db_logger.debug(f"Data config and workspace query successful: config={config.config_name}, workspace={workspace.name}") + + db_logger.debug( + f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}") return (config, workspace) - + except ValueError: # Re-raise known business exceptions raise except Exception as e: elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.error( "Failed to load configuration with workspace", extra={ @@ -632,11 +643,12 @@ class DataConfigRepository: }, exc_info=True ) - - db_logger.error(f"Failed to query data config and workspace: config_id={config_id} - {str(e)}") + + db_logger.error(f"Failed to query memory config and workspace: config_id={config_id} - {str(e)}") raise + @staticmethod - def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[DataConfig]: + def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[MemoryConfig]: """获取所有配置参数 Args: @@ -644,17 +656,17 @@ class DataConfigRepository: workspace_id: 工作空间ID，用于过滤查询结果 Returns: - List[DataConfig]: 配置列表 + List[MemoryConfig]: 配置列表 """ db_logger.debug(f"查询所有配置: workspace_id={workspace_id}") try: - query = db.query(DataConfig) + query = db.query(MemoryConfig) if workspace_id: - query = query.filter(DataConfig.workspace_id == workspace_id) + query = query.filter(MemoryConfig.workspace_id == workspace_id) - configs = query.order_by(desc(DataConfig.updated_at)).all() + configs = query.order_by(desc(MemoryConfig.updated_at)).all() db_logger.debug(f"配置列表查询成功: 数量={len(configs)}") return configs @@ -664,8 +676,8 @@ class DataConfigRepository: raise @staticmethod - def delete(db: Session, config_id: int) -> bool: - """删除数据配置 + def delete(db: Session, config_id: uuid.UUID) -> bool: + """删除记忆配置 Args: db: 数据库会话 @@ -674,22 +686,22 @@ class DataConfigRepository: Returns: bool: 删除成功返回True，配置不存在返回False """ - db_logger.debug(f"删除数据配置: config_id={config_id}") + db_logger.debug(f"删除记忆配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={config_id}") + db_logger.warning(f"记忆配置不存在: config_id={config_id}") return False db.delete(db_config) db.commit() - db_logger.info(f"数据配置删除成功: config_id={config_id}") + db_logger.info(f"记忆配置删除成功: config_id={config_id}") return True except Exception as e: db.rollback() - db_logger.error(f"删除数据配置失败: config_id={config_id} - {str(e)}") + db_logger.error(f"删除记忆配置失败: config_id={config_id} - {str(e)}") raise diff --git a/api/app/repositories/memory_perceptual_repository.py b/api/app/repositories/memory_perceptual_repository.py index 8415c2d0..9fa9536e 100644 --- a/api/app/repositories/memory_perceptual_repository.py +++ b/api/app/repositories/memory_perceptual_repository.py @@ -6,7 +6,7 @@ from sqlalchemy import and_, desc from sqlalchemy.orm import Session from app.core.logging_config import get_db_logger -from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageType +from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageService from app.schemas.memory_perceptual_schema import PerceptualQuerySchema db_logger = get_db_logger() @@ -28,7 +28,7 @@ class MemoryPerceptualRepository: file_ext: str, summary: Optional[str] = None, meta_data: Optional[dict] = None, - storage_service: FileStorageType = FileStorageType.LOCAL + storage_service: FileStorageService = FileStorageService.LOCAL ) -> MemoryPerceptualModel: diff --git a/api/app/repositories/model_repository.py b/api/app/repositories/model_repository.py index 1fe29d66..3d66964a 100644 --- a/api/app/repositories/model_repository.py +++ b/api/app/repositories/model_repository.py @@ -1,12 +1,12 @@ -from sqlalchemy.orm import Session, joinedload -from sqlalchemy import and_, or_, func, desc +from sqlalchemy.orm import Session, joinedload, selectinload +from sqlalchemy import and_, or_, func, desc, select from typing import List, Optional, Dict, Any, Tuple import uuid -from app.models.models_model import ModelConfig, ModelApiKey, ModelType +from app.models.models_model import ModelConfig, ModelApiKey, ModelType, ModelBase, model_config_api_key_association from app.schemas.model_schema import ( ModelConfigUpdate, ModelApiKeyCreate, ModelApiKeyUpdate, - ModelConfigQuery + ModelConfigQuery, ModelConfigQueryNew ) from app.core.logging_config import get_db_logger @@ -107,6 +107,80 @@ class ModelConfigRepository: def get_list(db: Session, query: ModelConfigQuery, tenant_id: uuid.UUID | None = None) -> Tuple[List[ModelConfig], int]: """获取模型配置列表""" db_logger.debug(f"查询模型配置列表: {query.dict()}, tenant_id={tenant_id}") + + try: + # 构建查询条件 + filters = [] + + # 添加租户过滤（查询本租户的模型或公开模型） + if tenant_id: + filters.append( + or_( + ModelConfig.tenant_id == tenant_id, + ModelConfig.is_public + ) + ) + + # 支持多个 type 值（使用 IN 查询） + # 兼容 chat 和 llm 类型：如果查询包含其中一个，则同时匹配两者 + if query.type: + type_values = list(query.type) + # 如果包含 chat 或 llm，则同时包含两者 + if ModelType.CHAT in type_values or ModelType.LLM in type_values: + if ModelType.CHAT not in type_values: + type_values.append(ModelType.CHAT) + if ModelType.LLM not in type_values: + type_values.append(ModelType.LLM) + filters.append(ModelConfig.type.in_(type_values)) + + if query.is_active is not None: + filters.append(ModelConfig.is_active == query.is_active) + + if query.is_public is not None: + filters.append(ModelConfig.is_public == query.is_public) + + if query.search: + # 搜索逻辑需要join ModelApiKey表来搜索model_name + search_filter = or_( + ModelConfig.name.ilike(f"%{query.search}%"), + # ModelConfig.description.ilike(f"%{query.search}%") + ) + filters.append(search_filter) + + # 构建基础查询 + base_query = db.query(ModelConfig).options( + joinedload(ModelConfig.api_keys) + ) + + # 如果需要按provider筛选，需要join ModelApiKey表 + if query.provider: + base_query = base_query.join(ModelApiKey).filter( + ModelApiKey.provider == query.provider + ).distinct() + + if filters: + base_query = base_query.filter(and_(*filters)) + + # 获取总数 + total = base_query.count() + + # 分页查询 + models = base_query.order_by(desc(ModelConfig.created_at)).offset( + (query.page - 1) * query.pagesize + ).limit(query.pagesize).all() + + db_logger.debug(f"模型配置列表查询成功: 总数={total}, 当前页={len(models)}, type筛选={query.type}") + return models, total + + except Exception as e: + db_logger.error(f"查询模型配置列表失败: {str(e)}") + raise + + @staticmethod + def get_list_new(db: Session, query: ModelConfigQueryNew, tenant_id: uuid.UUID | None = None) -> tuple[ + dict[str, list[ModelConfig]], Any]: + """获取模型配置列表""" + db_logger.debug(f"查询模型配置列表: {query.model_dump()}, tenant_id={tenant_id}") try: # 构建查询条件 @@ -138,13 +212,15 @@ class ModelConfigRepository: if query.is_public is not None: filters.append(ModelConfig.is_public == query.is_public) + + if query.is_composite is not None: + filters.append(ModelConfig.is_composite == query.is_composite) + + if query.provider: + filters.append(ModelConfig.provider == query.provider) if query.search: - # 搜索逻辑需要join ModelApiKey表来搜索model_name - search_filter = or_( - ModelConfig.name.ilike(f"%{query.search}%"), - # ModelConfig.description.ilike(f"%{query.search}%") - ) + search_filter = ModelConfig.name.ilike(f"%{query.search}%") filters.append(search_filter) # 构建基础查询 @@ -152,28 +228,30 @@ class ModelConfigRepository: joinedload(ModelConfig.api_keys) ) - # 如果需要按provider筛选，需要join ModelApiKey表 - if query.provider: - base_query = base_query.join(ModelApiKey).filter( - ModelApiKey.provider == query.provider - ).distinct() - if filters: base_query = base_query.filter(and_(*filters)) # 获取总数 total = base_query.count() + + query_results = base_query.order_by(desc(ModelConfig.created_at)).all() + + provider_groups: Dict[str, List[ModelConfig]] = {} + for model_config in query_results: + provider = model_config.provider + if provider not in provider_groups: + provider_groups[provider] = [] + provider_groups[provider].append(model_config) - # 分页查询 - models = base_query.order_by(desc(ModelConfig.updated_at)).offset( - (query.page - 1) * query.pagesize - ).limit(query.pagesize).all() - - db_logger.debug(f"模型配置列表查询成功: 总数={total}, 当前页={len(models)}, type筛选={query.type}") - return models, total + db_logger.debug( + f"模型配置列表查询成功: 总数={total}, " + f"分组数={len(provider_groups)}, " + f"各分组模型数={[len(v) for v in provider_groups.values()]}, " + f"type筛选={query.type}") + return provider_groups, total except Exception as e: - db_logger.error(f"查询模型配置列表失败: {str(e)}") + db_logger.error(f"查询模型配置列表失败(按provider分组/无分页): {str(e)}") raise @staticmethod @@ -241,7 +319,7 @@ class ModelConfigRepository: return None # 更新字段 - update_data = model_data.dict(exclude_unset=True) + update_data = model_data.model_dump(exclude_unset=True) for field, value in update_data.items(): setattr(db_model, field, value) @@ -303,8 +381,18 @@ class ModelConfigRepository: # 按提供商统计 - 现在从ModelApiKey表获取 provider_stats = {} provider_results = db.query( - ModelApiKey.provider, func.count(func.distinct(ModelApiKey.model_config_id)) - ).group_by(ModelApiKey.provider).all() + # 保留 provider 字段 + ModelApiKey.provider, + # 统计中间表中 唯一的 model_config_id 数量（替换原 ModelApiKey.model_config_id） + func.count(func.distinct(model_config_api_key_association.c.model_config_id)) + ).join( + # 联表：ModelApiKey <-> 中间表（多对多关联） + model_config_api_key_association, + ModelApiKey.id == model_config_api_key_association.c.api_key_id + ).group_by( + # 按 provider 分组（保留原有逻辑） + ModelApiKey.provider + ).all() for provider, count in provider_results: provider_stats[provider.value] = count @@ -325,6 +413,38 @@ class ModelConfigRepository: db_logger.error(f"获取模型统计信息失败: {str(e)}") raise + @staticmethod + def get_model_config_ids_by_provider( + db: Session, + tenant_id: uuid.UUID, + provider: Any + ) -> List[uuid.UUID]: + """根据tenant_id和provider获取model_config_id列表""" + db_logger.debug(f"查询model_config_id列表: tenant_id={tenant_id}, provider={provider}") + + try: + # 查询ModelConfig关联的ModelApiKey，筛选出匹配的model_config_id + model_config_ids = db.query(ModelConfig.id).join( + ModelBase, ModelConfig.model_id == ModelBase.id + ).filter( + and_( + or_( + ModelConfig.tenant_id == tenant_id, + ModelConfig.is_public + ), + ModelBase.provider == provider, + ModelConfig.is_active, + ~ModelConfig.is_composite + ) + ).distinct().all() + + db_logger.debug(f"查询成功: 数量={len(model_config_ids)}") + return [row[0] for row in model_config_ids] + + except Exception as e: + db_logger.error(f"查询model_config_id列表失败: {str(e)}") + raise + class ModelApiKeyRepository: """模型API Key Repository""" @@ -349,7 +469,14 @@ class ModelApiKeyRepository: db_logger.debug(f"根据模型配置ID查询API Key: model_config_id={model_config_id}") try: - query = db.query(ModelApiKey).filter(ModelApiKey.model_config_id == model_config_id) + from app.models.models_model import ModelConfig, model_config_api_key_association + + query = db.query(ModelApiKey).join( + model_config_api_key_association, + ModelApiKey.id == model_config_api_key_association.c.api_key_id + ).filter( + model_config_api_key_association.c.model_config_id == model_config_id + ) if is_active: query = query.filter(ModelApiKey.is_active) @@ -368,8 +495,20 @@ class ModelApiKeyRepository: db_logger.debug(f"创建API Key: {api_key_data.provider}") try: - db_api_key = ModelApiKey(**api_key_data.dict()) + from app.models.models_model import ModelConfig + + # 创建API Key，不包含model_config_ids + api_key_dict = api_key_data.model_dump(exclude={"model_config_ids"}) + db_api_key = ModelApiKey(**api_key_dict) db.add(db_api_key) + db.flush() # 获取生成的ID + + # 关联ModelConfig + if api_key_data.model_config_ids: + for model_config_id in api_key_data.model_config_ids: + model_config = db.query(ModelConfig).filter(ModelConfig.id == model_config_id).first() + if model_config: + db_api_key.model_configs.append(model_config) db_logger.info(f"API Key已添加到会话: {db_api_key.provider}") return db_api_key @@ -391,7 +530,7 @@ class ModelApiKeyRepository: return None # 更新字段 - update_data = api_key_data.dict(exclude_unset=True) + update_data = api_key_data.model_dump(exclude_unset=True) for field, value in update_data.items(): setattr(db_api_key, field, value) @@ -451,4 +590,92 @@ class ModelApiKeyRepository: except Exception as e: db.rollback() db_logger.error(f"更新API Key使用统计失败: api_key_id={api_key_id} - {str(e)}") - raise \ No newline at end of file + raise + + +class ModelBaseRepository: + """基础模型Repository""" + + @staticmethod + def get_by_id(db: Session, model_base_id: uuid.UUID) -> Optional['ModelBase']: + return db.query(ModelBase).filter(ModelBase.id == model_base_id).first() + + @staticmethod + def get_list(db: Session, query: 'ModelBaseQuery') -> List['ModelBase']: + + filters = [] + if query.type: + filters.append(ModelBase.type == query.type) + if query.provider: + filters.append(ModelBase.provider == query.provider) + if query.is_official is not None: + filters.append(ModelBase.is_official == query.is_official) + if query.is_deprecated is not None: + filters.append(ModelBase.is_deprecated == query.is_deprecated) + if query.search: + filters.append(or_( + ModelBase.name.ilike(f"%{query.search}%"), + # ModelBase.description.ilike(f"%{query.search}%") + )) + + q = db.query(ModelBase) + if filters: + q = q.filter(and_(*filters)) + + return q.order_by(ModelBase.add_count.desc(), ModelBase.created_at.desc()).all() + + @staticmethod + def create(db: Session, data: dict) -> 'ModelBase': + model_base = ModelBase(**data) + db.add(model_base) + return model_base + + @staticmethod + def get_by_name_and_provider(db: Session, name: str, provider: str) -> Optional['ModelBase']: + return db.query(ModelBase).filter( + ModelBase.name == name, + ModelBase.provider == provider + ).first() + + @staticmethod + def update(db: Session, model_base_id: uuid.UUID, data: dict) -> Optional['ModelBase']: + model_base = db.query(ModelBase).filter(ModelBase.id == model_base_id).first() + if not model_base: + return None + for key, value in data.items(): + setattr(model_base, key, value) + + # 同步更新绑定的非组合模型配置 + if any(k in data for k in ['name', 'description', 'logo']): + db.query(ModelConfig).filter( + ModelConfig.model_id == model_base_id, + ModelConfig.is_composite == False + ).update({ + k: v for k, v in data.items() + if k in ['name', 'description', 'logo'] + }, synchronize_session=False) + + return model_base + + @staticmethod + def delete(db: Session, model_base_id: uuid.UUID) -> bool: + model_base = db.query(ModelBase).filter(ModelBase.id == model_base_id).first() + if not model_base: + return False + db.delete(model_base) + return True + + @staticmethod + def increment_add_count(db: Session, model_base_id: uuid.UUID) -> bool: + model_base = db.query(ModelBase).filter(ModelBase.id == model_base_id).first() + if not model_base: + return False + model_base.add_count += 1 + return True + + @staticmethod + def check_added_by_tenant(db: Session, model_base_id: uuid.UUID, tenant_id: uuid.UUID) -> bool: + return db.query(ModelConfig).filter( + ModelConfig.model_id == model_base_id, + ModelConfig.tenant_id == tenant_id + ).first() is not None diff --git a/api/app/repositories/neo4j/add_edges.py b/api/app/repositories/neo4j/add_edges.py index 3b45867e..162bf411 100644 --- a/api/app/repositories/neo4j/add_edges.py +++ b/api/app/repositories/neo4j/add_edges.py @@ -32,7 +32,7 @@ async def add_chunk_statement_edges(chunks: List[Chunk], connector: Neo4jConnect "id": stable_edge_id, "source": chunk.id, "target": stmt.id, - "group_id": getattr(stmt, 'group_id', None), + "end_user_id": getattr(stmt, 'end_user_id', None), "user_id":getattr(stmt, 'user_id', None), "apply_id": getattr(stmt, 'apply_id', None), "run_id": getattr(stmt, 'run_id', None) or getattr(chunk, 'run_id', None), @@ -83,7 +83,7 @@ async def add_memory_summary_statement_edges(summaries: List[MemorySummaryNode], edges.append({ "summary_id": s.id, "chunk_id": chunk_id, - "group_id": s.group_id, + "end_user_id": s.end_user_id, "run_id": s.run_id, "created_at": s.created_at.isoformat() if s.created_at else None, "expired_at": s.expired_at.isoformat() if s.expired_at else None, diff --git a/api/app/repositories/neo4j/add_nodes.py b/api/app/repositories/neo4j/add_nodes.py index cf60a773..fcf700b5 100644 --- a/api/app/repositories/neo4j/add_nodes.py +++ b/api/app/repositories/neo4j/add_nodes.py @@ -6,10 +6,10 @@ from app.core.memory.models.graph_models import DialogueNode, StatementNode, Chu from app.repositories.neo4j.neo4j_connector import Neo4jConnector -async def delete_all_nodes(group_id: str, connector: Neo4jConnector): +async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector): """Delete all nodes in the database.""" - result = await connector.execute_query(f"MATCH (n {{group_id: '{group_id}'}}) DETACH DELETE n") - print(f"All group_id: {group_id} node and edge deleted successfully") + result = await connector.execute_query(f"MATCH (n {{end_user_id: '{end_user_id}'}}) DETACH DELETE n") + print(f"All end_user_id: {end_user_id} node and edge deleted successfully") return result async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConnector) -> Optional[List[str]]: @@ -32,9 +32,7 @@ async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConn for dialogue in dialogues: flattened_dialogues.append({ "id": dialogue.id, - "group_id": dialogue.group_id, - "user_id": dialogue.user_id, - "apply_id": dialogue.apply_id, + "end_user_id": dialogue.end_user_id, "run_id": dialogue.run_id, "ref_id": dialogue.ref_id, "name": dialogue.name, @@ -79,9 +77,7 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC flattened_statement = { "id": statement.id, "name": statement.name, - "group_id": statement.group_id, - "user_id": statement.user_id, - "apply_id": statement.apply_id, + "end_user_id": statement.end_user_id, "run_id": statement.run_id, "chunk_id": statement.chunk_id, # "created_at": statement.created_at.isoformat(), @@ -154,9 +150,7 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) -> flattened_chunk = { "id": chunk.id, "name": chunk.name, - "group_id": chunk.group_id, - "user_id": chunk.user_id, - "apply_id": chunk.apply_id, + "end_user_id": chunk.end_user_id, "run_id": chunk.run_id, "created_at": chunk.created_at.isoformat() if chunk.created_at else None, "expired_at": chunk.expired_at.isoformat() if chunk.expired_at else None, @@ -206,9 +200,7 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector flattened.append({ "id": s.id, "name": s.name, - "group_id": s.group_id, - "user_id": s.user_id, - "apply_id": s.apply_id, + "end_user_id": s.end_user_id, "run_id": s.run_id, "created_at": s.created_at.isoformat() if s.created_at else None, "expired_at": s.expired_at.isoformat() if s.expired_at else None, diff --git a/api/app/repositories/neo4j/base_neo4j_repository.py b/api/app/repositories/neo4j/base_neo4j_repository.py index 959a1e68..df953eb9 100644 --- a/api/app/repositories/neo4j/base_neo4j_repository.py +++ b/api/app/repositories/neo4j/base_neo4j_repository.py @@ -152,7 +152,7 @@ class BaseNeo4jRepository(BaseRepository[T]): Example: >>> results = await repository.find( - ... {"group_id": "group_123", "user_id": "user_456"}, + ... {"end_user_id": "group_123", "user_id": "user_456"}, ... limit=50 ... ) """ diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index cd3cbed7..cf1732fd 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -3,9 +3,7 @@ DIALOGUE_NODE_SAVE = """ UNWIND $dialogues AS dialogue MERGE (n:Dialogue {id: dialogue.id}) SET n.uuid = coalesce(n.uuid, dialogue.id), - n.group_id = dialogue.group_id, - n.user_id = dialogue.user_id, - n.apply_id = dialogue.apply_id, + n.end_user_id = dialogue.end_user_id, n.run_id = dialogue.run_id, n.ref_id = dialogue.ref_id, n.created_at = dialogue.created_at, @@ -22,9 +20,7 @@ SET s += { id: statement.id, run_id: statement.run_id, chunk_id: statement.chunk_id, - group_id: statement.group_id, - user_id: statement.user_id, - apply_id: statement.apply_id, + end_user_id: statement.end_user_id, stmt_type: statement.stmt_type, statement: statement.statement, emotion_intensity: statement.emotion_intensity, @@ -54,9 +50,7 @@ MERGE (c:Chunk {id: chunk.id}) SET c += { id: chunk.id, name: chunk.name, - group_id: chunk.group_id, - user_id: chunk.user_id, - apply_id: chunk.apply_id, + end_user_id: chunk.end_user_id, run_id: chunk.run_id, created_at: chunk.created_at, expired_at: chunk.expired_at, @@ -76,9 +70,7 @@ EXTRACTED_ENTITY_NODE_SAVE = """ UNWIND $entities AS entity MERGE (e:ExtractedEntity {id: entity.id}) SET e.name = CASE WHEN entity.name IS NOT NULL AND entity.name <> '' THEN entity.name ELSE e.name END, - e.group_id = CASE WHEN entity.group_id IS NOT NULL AND entity.group_id <> '' THEN entity.group_id ELSE e.group_id END, - e.user_id = CASE WHEN entity.user_id IS NOT NULL AND entity.user_id <> '' THEN entity.user_id ELSE e.user_id END, - e.apply_id = CASE WHEN entity.apply_id IS NOT NULL AND entity.apply_id <> '' THEN entity.apply_id ELSE e.apply_id END, + e.end_user_id = CASE WHEN entity.end_user_id IS NOT NULL AND entity.end_user_id <> '' THEN entity.end_user_id ELSE e.end_user_id END, e.run_id = CASE WHEN entity.run_id IS NOT NULL AND entity.run_id <> '' THEN entity.run_id ELSE e.run_id END, e.created_at = CASE WHEN entity.created_at IS NOT NULL AND (e.created_at IS NULL OR entity.created_at < e.created_at) @@ -134,9 +126,9 @@ RETURN e.id AS uuid # Add back ENTITY_RELATIONSHIP_SAVE to be used by graph_saver.save_entities_and_relationships ENTITY_RELATIONSHIP_SAVE = """ UNWIND $relationships AS rel -// Match entities by stable id within group, do not constrain by run_id -MATCH (subject:ExtractedEntity {id: rel.source_id, group_id: rel.group_id}) -MATCH (object:ExtractedEntity {id: rel.target_id, group_id: rel.group_id}) +// Match entities by stable id within end_user_id, do not constrain by run_id +MATCH (subject:ExtractedEntity {id: rel.source_id, end_user_id: rel.end_user_id}) +MATCH (object:ExtractedEntity {id: rel.target_id, end_user_id: rel.end_user_id}) // Avoid duplicate edges across runs for the same endpoints MERGE (subject)-[r:EXTRACTED_RELATIONSHIP]->(object) SET r.predicate = rel.predicate, @@ -148,7 +140,7 @@ SET r.predicate = rel.predicate, r.created_at = rel.created_at, r.expired_at = rel.expired_at, r.run_id = rel.run_id, - r.group_id = rel.group_id + r.end_user_id = rel.end_user_id RETURN elementId(r) AS uuid """ @@ -160,7 +152,7 @@ UNWIND $weak_entities AS entity MERGE (e:ExtractedEntity {id: entity.id, run_id: entity.run_id}) SET e += { name: entity.name, - group_id: entity.group_id, + end_user_id: entity.end_user_id, run_id: entity.run_id, description: entity.description, chunk_id: entity.chunk_id, @@ -175,11 +167,11 @@ RETURN e.id AS id SAVE_STRONG_TRIPLE_ENTITIES = """ UNWIND $items AS item MERGE (s:ExtractedEntity {id: item.source_id, run_id: item.run_id}) -SET s += {name: item.subject, group_id: item.group_id, run_id: item.run_id} +SET s += {name: item.subject, end_user_id: item.end_user_id, run_id: item.run_id} // Independent strong flag SET s.is_strong = true MERGE (o:ExtractedEntity {id: item.target_id, run_id: item.run_id}) -SET o += {name: item.object, group_id: item.group_id, run_id: item.run_id} +SET o += {name: item.object, end_user_id: item.end_user_id, run_id: item.run_id} // Independent strong flag SET o.is_strong = true """ @@ -194,7 +186,7 @@ DIALOGUE_STATEMENT_EDGE_SAVE = """ // 仅按端点去重，关系属性可更新 MERGE (dialogue)-[e:MENTIONS]->(statement) SET e.uuid = edge.id, - e.group_id = edge.group_id, + e.end_user_id = edge.end_user_id, e.created_at = edge.created_at, e.expired_at = edge.expired_at RETURN e.uuid AS uuid @@ -208,7 +200,7 @@ CHUNK_STATEMENT_EDGE_SAVE = """ MATCH (statement:Statement {id: edge.source, run_id: edge.run_id}) MATCH (chunk:Chunk {id: edge.target, run_id: edge.run_id}) MERGE (chunk)-[e:CONTAINS {id: edge.id}]->(statement) - SET e.group_id = edge.group_id, + SET e.end_user_id = edge.end_user_id, e.run_id = edge.run_id, e.created_at = edge.created_at, e.expired_at = edge.expired_at @@ -218,13 +210,12 @@ CHUNK_STATEMENT_EDGE_SAVE = """ STATEMENT_ENTITY_EDGE_SAVE = """ UNWIND $relationships AS rel // Statement nodes are per-run; keep run_id constraint on statements -// Statement nodes are per-run; keep run_id constraint on statements MATCH (statement:Statement {id: rel.source, run_id: rel.run_id}) -// Entities are shared across runs within a group; do not constrain by run_id -MATCH (entity:ExtractedEntity {id: rel.target, group_id: rel.group_id}) +// Entities are shared across runs within end_user_id; do not constrain by run_id +MATCH (entity:ExtractedEntity {id: rel.target, end_user_id: rel.end_user_id}) // Avoid duplicate edges across runs for same endpoints MERGE (statement)-[r:REFERENCES_ENTITY]->(entity) -SET r.group_id = rel.group_id, +SET r.end_user_id = rel.end_user_id, r.run_id = rel.run_id, r.created_at = rel.created_at, r.expired_at = rel.expired_at, @@ -236,10 +227,10 @@ ENTITY_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('entity_embedding_index', $limit * 100, $embedding) YIELD node AS e, score WHERE e.name_embedding IS NOT NULL - AND ($group_id IS NULL OR e.group_id = $group_id) + AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) RETURN e.id AS id, e.name AS name, - e.group_id AS group_id, + e.end_user_id AS end_user_id, e.entity_type AS entity_type, COALESCE(e.activation_value, e.importance_score, 0.5) AS activation_value, COALESCE(e.importance_score, 0.5) AS importance_score, @@ -254,10 +245,10 @@ STATEMENT_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('statement_embedding_index', $limit * 100, $embedding) YIELD node AS s, score WHERE s.statement_embedding IS NOT NULL - AND ($group_id IS NULL OR s.group_id = $group_id) + AND ($end_user_id IS NULL OR s.end_user_id = $end_user_id) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.expired_at AS expired_at, @@ -277,9 +268,9 @@ CHUNK_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('chunk_embedding_index', $limit * 100, $embedding) YIELD node AS c, score WHERE c.chunk_embedding IS NOT NULL - AND ($group_id IS NULL OR c.group_id = $group_id) + AND ($end_user_id IS NULL OR c.end_user_id = $end_user_id) RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, COALESCE(c.activation_value, 0.5) AS activation_value, @@ -292,12 +283,12 @@ LIMIT $limit SEARCH_STATEMENTS_BY_KEYWORD = """ CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score -WHERE ($group_id IS NULL OR s.group_id = $group_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.expired_at AS expired_at, @@ -316,15 +307,13 @@ LIMIT $limit # 查询实体名称包含指定字符串的实体 SEARCH_ENTITIES_BY_NAME = """ CALL db.index.fulltext.queryNodes("entitiesFulltext", $q) YIELD node AS e, score -WHERE ($group_id IS NULL OR e.group_id = $group_id) +WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e) OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) RETURN e.id AS id, e.name AS name, - e.group_id AS group_id, + e.end_user_id AS end_user_id, e.entity_type AS entity_type, - e.apply_id AS apply_id, - e.user_id AS user_id, e.created_at AS created_at, e.expired_at AS expired_at, e.entity_idx AS entity_idx, @@ -347,11 +336,11 @@ LIMIT $limit SEARCH_CHUNKS_BY_CONTENT = """ CALL db.index.fulltext.queryNodes("chunksFulltext", $q) YIELD node AS c, score -WHERE ($group_id IS NULL OR c.group_id = $group_id) +WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id) OPTIONAL MATCH (c)-[:CONTAINS]->(s:Statement) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, c.sequence_number AS sequence_number, @@ -413,10 +402,10 @@ LIMIT $limit SEARCH_DIALOGUE_BY_DIALOG_ID = """ MATCH (d:Dialogue) -WHERE ($group_id IS NULL OR d.group_id = $group_id) +WHERE ($end_user_id IS NULL OR d.end_user_id = $end_user_id) AND d.id = $dialog_id RETURN d.id AS dialog_id, - d.group_id AS group_id, + d.end_user_id AS end_user_id, d.content AS content, d.created_at AS created_at, d.expired_at AS expired_at @@ -426,10 +415,10 @@ LIMIT $limit SEARCH_CHUNK_BY_CHUNK_ID = """ MATCH (c:Chunk) -WHERE ($group_id IS NULL OR c.group_id = $group_id) +WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id) AND c.id = $chunk_id RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, c.created_at AS created_at, @@ -441,18 +430,14 @@ LIMIT $limit SEARCH_STATEMENTS_BY_TEMPORAL = """ MATCH (s:Statement) -WHERE ($group_id IS NULL OR s.group_id = $group_id) - AND ($apply_id IS NULL OR s.apply_id = $apply_id) - AND ($user_id IS NULL OR s.user_id = $user_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND ((($start_date IS NULL OR datetime(s.created_at) >= datetime($start_date)) AND ($end_date IS NULL OR datetime(s.created_at) <= datetime($end_date))) OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date))) AND ($invalid_date IS NULL OR (s.invalid_at IS NOT NULL AND datetime(s.invalid_at) <= datetime($invalid_date))))) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, - s.apply_id AS apply_id, - s.user_id AS user_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.valid_at AS valid_at, @@ -468,9 +453,7 @@ LIMIT $limit SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL = """ CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score -WHERE ($group_id IS NULL OR s.group_id = $group_id) - AND ($apply_id IS NULL OR s.apply_id = $apply_id) - AND ($user_id IS NULL OR s.user_id = $user_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND ((($start_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) >= datetime($start_date))) AND ($end_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) <= datetime($end_date)))) OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date))) @@ -479,9 +462,7 @@ OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, - s.apply_id AS apply_id, - s.user_id AS user_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.valid_at AS valid_at, @@ -499,15 +480,11 @@ LIMIT $limit SEARCH_STATEMENTS_BY_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 10)) = date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -519,15 +496,11 @@ LIMIT $limit SEARCH_STATEMENTS_BY_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) = date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -539,15 +512,11 @@ LIMIT $limit SEARCH_STATEMENTS_G_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) = date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -559,15 +528,11 @@ LIMIT $limit SEARCH_STATEMENTS_L_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) < date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -579,15 +544,11 @@ LIMIT $limit SEARCH_STATEMENTS_G_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) > date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -599,15 +560,11 @@ LIMIT $limit SEARCH_STATEMENTS_L_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) < date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -665,18 +622,18 @@ LIMIT $limit # 根据id修改句子的invalid_at的值 UPDATE_STATEMENT_INVALID_AT = """ -MATCH (n:Statement {group_id: $group_id, id: $id}) +MATCH (n:Statement {end_user_id: $end_user_id, id: $id}) SET n.invalid_at = $new_invalid_at """ # MemorySummary keyword search using fulltext index SEARCH_MEMORY_SUMMARIES_BY_KEYWORD = """ CALL db.index.fulltext.queryNodes("summariesFulltext", $q) YIELD node AS m, score -WHERE ($group_id IS NULL OR m.group_id = $group_id) +WHERE ($end_user_id IS NULL OR m.end_user_id = $end_user_id) OPTIONAL MATCH (m)-[:DERIVED_FROM_STATEMENT]->(s:Statement) RETURN m.id AS id, m.name AS name, - m.group_id AS group_id, + m.end_user_id AS end_user_id, m.dialog_id AS dialog_id, m.chunk_ids AS chunk_ids, m.content AS content, @@ -695,10 +652,10 @@ MEMORY_SUMMARY_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('summary_embedding_index', $limit * 100, $embedding) YIELD node AS m, score WHERE m.summary_embedding IS NOT NULL - AND ($group_id IS NULL OR m.group_id = $group_id) + AND ($end_user_id IS NULL OR m.end_user_id = $end_user_id) RETURN m.id AS id, m.name AS name, - m.group_id AS group_id, + m.end_user_id AS end_user_id, m.dialog_id AS dialog_id, m.chunk_ids AS chunk_ids, m.content AS content, @@ -718,9 +675,7 @@ MERGE (m:MemorySummary {id: summary.id}) SET m += { id: summary.id, name: summary.name, - group_id: summary.group_id, - user_id: summary.user_id, - apply_id: summary.apply_id, + end_user_id: summary.end_user_id, run_id: summary.run_id, created_at: summary.created_at, expired_at: summary.expired_at, @@ -745,7 +700,7 @@ MATCH (ms:MemorySummary {id: e.summary_id, run_id: e.run_id}) MATCH (c:Chunk {id: e.chunk_id, run_id: e.run_id}) MATCH (c)-[:CONTAINS]->(s:Statement {run_id: e.run_id}) MERGE (ms)-[r:DERIVED_FROM_STATEMENT]->(s) -SET r.group_id = e.group_id, +SET r.end_user_id = e.end_user_id, r.run_id = e.run_id, r.created_at = e.created_at, r.expired_at = e.expired_at @@ -774,7 +729,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END | source_statement_id: rel.source_statement_id, valid_at: rel.valid_at, invalid_at: rel.invalid_at, - group_id: rel.group_id, + end_user_id: rel.end_user_id, user_id: rel.user_id, apply_id: rel.apply_id, run_id: rel.run_id, @@ -796,7 +751,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END | source_statement_id: rel.source_statement_id, valid_at: rel.valid_at, invalid_at: rel.invalid_at, - group_id: rel.group_id, + end_user_id: rel.end_user_id, user_id: rel.user_id, apply_id: rel.apply_id, run_id: rel.run_id, @@ -814,7 +769,7 @@ RETURN count(losing) as deleted neo4j_statement_part = ''' MATCH (n:Statement) -WHERE n.group_id = "{}" +WHERE n.end_user_id = "{}" AND datetime(n.created_at) >= datetime() - duration('P3D') RETURN n.statement as statement_name, @@ -824,7 +779,7 @@ RETURN ''' neo4j_statement_all = ''' MATCH (n:Statement) -WHERE n.group_id = "{}" +WHERE n.end_user_id = "{}" RETURN n.statement as statement_name, n.id as statement_id @@ -832,7 +787,7 @@ RETURN ''' neo4j_query_part = """ MATCH (n)-[r]-(m:ExtractedEntity) - WHERE n.group_id = "{}" + WHERE n.end_user_id = "{}" AND datetime(n.created_at) >= datetime() - duration('P3D') WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) @@ -853,7 +808,7 @@ neo4j_query_part = """ """ neo4j_query_all = """ MATCH (n)-[r]-(m:ExtractedEntity) - WHERE n.group_id = "{}" + WHERE n.end_user_id = "{}" WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) RETURN @@ -922,7 +877,8 @@ RETURN CASE WHEN ms:ExtractedEntity THEN { text: ms.name, - created_at: ms.created_at + created_at: ms.created_at, + type: "情景记忆" } END ) AS ExtractedEntity, @@ -932,7 +888,8 @@ RETURN CASE WHEN n:MemorySummary THEN { text: n.content, - created_at: n.created_at + created_at: n.created_at, + type: "长期沉淀" } END ) AS MemorySummary, @@ -940,7 +897,8 @@ RETURN collect( DISTINCT { text: e.statement, - created_at: e.created_at + created_at: e.created_at, + type: "情绪记忆" } ) AS statement; """ @@ -1027,14 +985,14 @@ RETURN DISTINCT Memory_Space_User=""" MATCH (n)-[r]->(m) -WHERE n.group_id = $group_id AND m.name="用户" +WHERE n.end_user_id = $end_user_id AND m.name="用户" return DISTINCT elementId(m) as id """ Memory_Space_Entity=""" MATCH (n)-[]-(m) WHERE elementId(m) = $id AND m.entity_type = "Person" RETURN -DISTINCT m.name as name,m.group_id as group_id +DISTINCT m.name as name,m.end_user_id as end_user_id """ Memory_Space_Associative=""" MATCH (u)-[]-(x)-[]-(h) diff --git a/api/app/repositories/neo4j/dialog_repository.py b/api/app/repositories/neo4j/dialog_repository.py index ccb3d94c..020e7346 100644 --- a/api/app/repositories/neo4j/dialog_repository.py +++ b/api/app/repositories/neo4j/dialog_repository.py @@ -19,7 +19,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """对话仓储 管理对话节点的创建、查询、更新和删除操作。 - 提供按group_id、user_id、ref_id等条件查询对话的方法。 + 提供按end_user_id、user_id、ref_id等条件查询对话的方法。 Attributes: connector: Neo4j连接器实例 @@ -54,17 +54,17 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): return DialogueNode(**n) - async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[DialogueNode]: - """根据group_id查询对话 + async def find_by_end_user_id(self, end_user_id: str, limit: int = 100) -> List[DialogueNode]: + """根据end_user_id查询对话 Args: - group_id: 组ID + end_user_id: 组ID limit: 返回结果的最大数量 Returns: List[DialogueNode]: 对话列表 """ - return await self.find({"group_id": group_id}, limit=limit) + return await self.find({"end_user_id": end_user_id}, limit=limit) async def find_by_user_id(self, user_id: str, limit: int = 100) -> List[DialogueNode]: """根据user_id查询对话 @@ -94,14 +94,14 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): async def find_by_group_and_user( self, - group_id: str, + end_user_id: str, user_id: str, limit: int = 100 ) -> List[DialogueNode]: - """根据group_id和user_id查询对话 + """根据end_user_id和user_id查询对话 Args: - group_id: 组ID + end_user_id: 组ID user_id: 用户ID limit: 返回结果的最大数量 @@ -109,20 +109,20 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): List[DialogueNode]: 对话列表 """ return await self.find( - {"group_id": group_id, "user_id": user_id}, + {"end_user_id": end_user_id, "user_id": user_id}, limit=limit ) async def find_recent_dialogs( self, - group_id: str, + end_user_id: str, days: int = 7, limit: int = 100 ) -> List[DialogueNode]: """查询最近的对话 Args: - group_id: 组ID + end_user_id: 组ID days: 查询最近多少天的对话 limit: 返回结果的最大数量 @@ -131,7 +131,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND n.created_at >= datetime() - duration({{days: $days}}) RETURN n ORDER BY n.created_at DESC @@ -139,7 +139,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """ results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, days=days, limit=limit ) @@ -164,22 +164,22 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): async def find_by_config_and_group( self, config_id: str, - group_id: str, + end_user_id: str, limit: int = 100 ) -> List[DialogueNode]: - """根据config_id和group_id查询对话 + """根据config_id和end_user_id查询对话 支持按配置ID和组ID同时过滤,确保只返回使用特定配置处理的对话。 Args: config_id: 配置ID - group_id: 组ID + end_user_id: 组ID limit: 返回结果的最大数量 Returns: List[DialogueNode]: 对话列表 """ return await self.find( - {"config_id": config_id, "group_id": group_id}, + {"config_id": config_id, "end_user_id": end_user_id}, limit=limit ) diff --git a/api/app/repositories/neo4j/emotion_repository.py b/api/app/repositories/neo4j/emotion_repository.py index d445c8d4..e39968ac 100644 --- a/api/app/repositories/neo4j/emotion_repository.py +++ b/api/app/repositories/neo4j/emotion_repository.py @@ -40,7 +40,7 @@ class EmotionRepository: async def get_emotion_tags( self, - group_id: str, + end_user_id: str, emotion_type: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, @@ -51,7 +51,7 @@ class EmotionRepository: 查询指定用户的情绪类型分布，包括计数、百分比和平均强度。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） emotion_type: 可选的情绪类型过滤（joy/sadness/anger/fear/surprise/neutral） start_date: 可选的开始日期（ISO格式字符串） end_date: 可选的结束日期（ISO格式字符串） @@ -65,8 +65,8 @@ class EmotionRepository: - avg_intensity: 平均强度 """ # 构建查询条件 - where_clauses = ["s.group_id = $group_id", "s.emotion_type IS NOT NULL"] - params = {"group_id": group_id, "limit": limit} + where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_type IS NOT NULL"] + params = {"end_user_id": end_user_id, "limit": limit} if emotion_type: where_clauses.append("s.emotion_type = $emotion_type") @@ -119,7 +119,7 @@ class EmotionRepository: async def get_emotion_wordcloud( self, - group_id: str, + end_user_id: str, emotion_type: Optional[str] = None, limit: int = 50 ) -> List[Dict[str, Any]]: @@ -128,7 +128,7 @@ class EmotionRepository: 查询情绪关键词及其频率，用于生成词云可视化。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） emotion_type: 可选的情绪类型过滤 limit: 返回关键词的最大数量 @@ -140,8 +140,8 @@ class EmotionRepository: - avg_intensity: 平均强度 """ # 构建查询条件 - where_clauses = ["s.group_id = $group_id", "s.emotion_keywords IS NOT NULL"] - params = {"group_id": group_id, "limit": limit} + where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_keywords IS NOT NULL"] + params = {"end_user_id": end_user_id, "limit": limit} if emotion_type: where_clauses.append("s.emotion_type = $emotion_type") @@ -186,7 +186,7 @@ class EmotionRepository: async def get_emotions_in_range( self, - group_id: str, + end_user_id: str, time_range: str = "30d" ) -> List[Dict[str, Any]]: """获取时间范围内的情绪数据 @@ -194,7 +194,7 @@ class EmotionRepository: 查询指定时间范围内的所有情绪数据，用于健康指数计算。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） time_range: 时间范围（7d/30d/90d） Returns: @@ -214,7 +214,7 @@ class EmotionRepository: # 优化的 Cypher 查询：使用字符串比较避免时区问题 query = """ MATCH (s:Statement) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id AND s.emotion_type IS NOT NULL AND s.created_at >= $start_date RETURN s.id as statement_id, @@ -227,7 +227,7 @@ class EmotionRepository: try: results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, start_date=start_date ) formatted_results = [ diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index 13215e0f..1575315f 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -44,9 +44,7 @@ async def save_entities_and_relationships( 'created_at': edge.created_at.isoformat(), 'expired_at': edge.expired_at.isoformat(), 'run_id': edge.run_id, - 'group_id': edge.group_id, - 'user_id': edge.user_id, - 'apply_id': edge.apply_id, + 'end_user_id': edge.end_user_id, } all_relationships.append(relationship) @@ -101,9 +99,7 @@ async def save_statement_chunk_edges( "id": edge.id, "source": edge.source, "target": edge.target, - "group_id": edge.group_id, - "user_id": edge.user_id, - "apply_id": edge.apply_id, + "end_user_id": edge.end_user_id, "run_id": edge.run_id, "created_at": edge.created_at.isoformat() if edge.created_at else None, "expired_at": edge.expired_at.isoformat() if edge.expired_at else None, @@ -132,9 +128,7 @@ async def save_statement_entity_edges( edge_data = { "source": edge.source, "target": edge.target, - "group_id": edge.group_id, - "user_id": edge.user_id, - "apply_id": edge.apply_id, + "end_user_id": edge.end_user_id, "run_id": edge.run_id, "connect_strength": edge.connect_strength, "created_at": edge.created_at.isoformat() if edge.created_at else None, diff --git a/api/app/repositories/neo4j/graph_search.py b/api/app/repositories/neo4j/graph_search.py index 6f5764b4..e8f52535 100644 --- a/api/app/repositories/neo4j/graph_search.py +++ b/api/app/repositories/neo4j/graph_search.py @@ -33,7 +33,7 @@ async def _update_activation_values_batch( connector: Neo4jConnector, nodes: List[Dict[str, Any]], node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, max_retries: int = 3 ) -> List[Dict[str, Any]]: """ @@ -46,7 +46,7 @@ async def _update_activation_values_batch( connector: Neo4j连接器 nodes: 节点列表，每个节点必须包含 'id' 字段 node_label: 节点标签（Statement, ExtractedEntity, MemorySummary） - group_id: 组ID（可选） + end_user_id: 组ID（可选） max_retries: 最大重试次数 Returns: @@ -97,7 +97,7 @@ async def _update_activation_values_batch( updated_nodes = await access_manager.record_batch_access( node_ids=unique_node_ids, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) logger.info( @@ -118,7 +118,7 @@ async def _update_activation_values_batch( async def _update_search_results_activation( connector: Neo4jConnector, results: Dict[str, List[Dict[str, Any]]], - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Dict[str, List[Dict[str, Any]]]: """ 更新搜索结果中所有知识节点的激活值 @@ -129,7 +129,7 @@ async def _update_search_results_activation( Args: connector: Neo4j连接器 results: 搜索结果字典，包含不同类型节点的列表 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Dict[str, List[Dict[str, Any]]]: 更新后的搜索结果 @@ -152,7 +152,7 @@ async def _update_search_results_activation( connector=connector, nodes=results[key], node_label=label, - group_id=group_id + end_user_id=end_user_id ) ) update_keys.append(key) @@ -218,7 +218,7 @@ async def _update_search_results_activation( async def search_graph( connector: Neo4jConnector, q: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: List[str] = None, ) -> Dict[str, List[Dict[str, Any]]]: @@ -236,7 +236,7 @@ async def search_graph( Args: connector: Neo4j connector q: Query text - group_id: Optional group filter + end_user_id: Optional group filter limit: Max results per category include: List of categories to search (default: all) @@ -254,7 +254,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_STATEMENTS_BY_KEYWORD, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("statements") @@ -263,7 +263,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("entities") @@ -272,7 +272,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_CHUNKS_BY_CONTENT, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("chunks") @@ -281,7 +281,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_MEMORY_SUMMARIES_BY_KEYWORD, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("summaries") @@ -310,12 +310,12 @@ async def search_graph( key in include and key in results and results[key] for key in ['statements', 'entities', 'chunks'] ) - + if needs_activation_update: results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -325,7 +325,7 @@ async def search_graph_by_embedding( connector: Neo4jConnector, embedder_client, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: List[str] = ["statements", "chunks", "entities","summaries"], ) -> Dict[str, List[Dict[str, Any]]]: @@ -337,7 +337,7 @@ async def search_graph_by_embedding( - Computes query embedding with the provided embedder_client - Ranks by cosine similarity in Cypher - - Filters by group_id if provided + - Filters by end_user_id if provided - Returns up to 'limit' per included type """ import time @@ -346,7 +346,7 @@ async def search_graph_by_embedding( embed_start = time.time() embeddings = await embedder_client.response([query_text]) embed_time = time.time() - embed_start - logger.info(f"[PERF] Embedding generation took: {embed_time:.4f}s") + print(f"[PERF] Embedding generation took: {embed_time:.4f}s") if not embeddings or not embeddings[0]: return {"statements": [], "chunks": [], "entities": [], "summaries": []} @@ -361,7 +361,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( STATEMENT_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("statements") @@ -371,7 +371,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( CHUNK_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("chunks") @@ -381,7 +381,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( ENTITY_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("entities") @@ -391,7 +391,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( MEMORY_SUMMARY_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("summaries") @@ -400,7 +400,7 @@ async def search_graph_by_embedding( query_start = time.time() task_results = await asyncio.gather(*tasks, return_exceptions=True) query_time = time.time() - query_start - logger.info(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s") + print(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s") # Build results dictionary results: Dict[str, List[Dict[str, Any]]] = { @@ -429,13 +429,13 @@ async def search_graph_by_embedding( key in include and key in results and results[key] for key in ['statements', 'entities', 'chunks'] ) - + if needs_activation_update: update_start = time.time() results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) update_time = time.time() - update_start logger.info(f"[PERF] Activation value updates took: {update_time:.4f}s") @@ -445,7 +445,7 @@ async def search_graph_by_embedding( return results async def get_dedup_candidates_for_entities( # 适配新版查询：使用全文索引按名称检索候选实体 connector: Neo4jConnector, - group_id: str, + end_user_id: str, entities: List[Dict[str, Any]], use_contains_fallback: bool = True, batch_size: int = 500, @@ -453,7 +453,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 ) -> Dict[str, List[Dict[str, Any]]]: """ 为第二层去重消歧批量检索候选实体（适配新版 cypher_queries）： - - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (group_id, name) 检索候选； + - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (end_user_id, name) 检索候选； - 保留并发控制与返回结构（incoming_id -> [db_entity_props...]）； - 若提供 `entity_type`，在本地对返回结果做类型过滤； - `use_contains_fallback` 保留形参以兼容，必要时可扩展二次查询策略。 @@ -477,7 +477,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 rows = await connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=name, - group_id=group_id, + end_user_id=end_user_id, limit=100, ) except Exception: @@ -501,7 +501,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 rows = await connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=name.lower(), - group_id=group_id, + end_user_id=end_user_id, limit=100, ) for r in rows: @@ -532,9 +532,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 async def search_graph_by_keyword_temporal( connector: Neo4jConnector, query_text: str, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -547,32 +545,30 @@ async def search_graph_by_keyword_temporal( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements containing query_text created between start_date and end_date - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ if not query_text: - logger.warning(f"query_text cannot be empty") + print(f"query_text不能为空") return {"statements": []} statements = await connector.execute_query( SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL, q=query_text, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, start_date=start_date, end_date=end_date, valid_date=valid_date, invalid_date=invalid_date, limit=limit, ) - logger.debug(f"Temporal keyword search results: {len(statements)} statements found") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -580,9 +576,7 @@ async def search_graph_by_keyword_temporal( async def search_graph_by_temporal( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -595,14 +589,12 @@ async def search_graph_by_temporal( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created between start_date and end_date - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_TEMPORAL, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, start_date=start_date, end_date=end_date, valid_date=valid_date, @@ -610,16 +602,16 @@ async def search_graph_by_temporal( limit=limit, ) - logger.debug(f"Temporal search query: {SEARCH_STATEMENTS_BY_TEMPORAL}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, start_date={start_date}, end_date={end_date}, valid_date={valid_date}, invalid_date={invalid_date}, limit={limit}") - logger.debug(f"Temporal search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_TEMPORAL}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -628,23 +620,23 @@ async def search_graph_by_temporal( async def search_graph_by_dialog_id( connector: Neo4jConnector, dialog_id: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: """ Temporal search across Dialogues. - Matches dialogues with dialog_id - - Optionally filters by group_id + - Optionally filters by end_user_id - Returns up to 'limit' dialogues """ if not dialog_id: - logger.warning(f"dialog_id cannot be empty") + print(f"dialog_id不能为空") return {"dialogues": []} dialogues = await connector.execute_query( SEARCH_DIALOGUE_BY_DIALOG_ID, - group_id=group_id, + end_user_id=end_user_id, dialog_id=dialog_id, limit=limit, ) @@ -654,15 +646,15 @@ async def search_graph_by_dialog_id( async def search_graph_by_chunk_id( connector: Neo4jConnector, chunk_id : str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: if not chunk_id: - logger.warning(f"chunk_id cannot be empty") + print(f"chunk_id不能为空") return {"chunks": []} chunks = await connector.execute_query( SEARCH_CHUNK_BY_CHUNK_ID, - group_id=group_id, + end_user_id=end_user_id, chunk_id=chunk_id, limit=limit, ) @@ -671,9 +663,9 @@ async def search_graph_by_chunk_id( async def search_graph_by_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -683,37 +675,37 @@ async def search_graph_by_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search by created_at query: {SEARCH_STATEMENTS_BY_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id} created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_by_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -723,37 +715,37 @@ async def search_graph_by_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search by valid_at query: {SEARCH_STATEMENTS_BY_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}， valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_g_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -763,37 +755,37 @@ async def search_graph_g_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_G_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search greater than created_at query: {SEARCH_STATEMENTS_G_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_G_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_g_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -803,37 +795,37 @@ async def search_graph_g_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_G_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search greater than valid_at query: {SEARCH_STATEMENTS_G_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_G_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_l_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -843,37 +835,37 @@ async def search_graph_l_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_L_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search less than created_at query: {SEARCH_STATEMENTS_L_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_L_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_l_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -883,28 +875,28 @@ async def search_graph_l_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_L_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search less than valid_at query: {SEARCH_STATEMENTS_L_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_L_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results diff --git a/api/app/repositories/neo4j/memory_summary_repository.py b/api/app/repositories/neo4j/memory_summary_repository.py index fc743f33..d7cd4fd4 100644 --- a/api/app/repositories/neo4j/memory_summary_repository.py +++ b/api/app/repositories/neo4j/memory_summary_repository.py @@ -18,7 +18,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """Memory Summary Repository Manages CRUD operations for MemorySummary nodes. - Provides methods to query summaries by group_id, user_id, and time ranges. + Provides methods to query summaries by end_user_id, user_id, and time ranges. Attributes: connector: Neo4j connector instance @@ -51,17 +51,17 @@ class MemorySummaryRepository(BaseNeo4jRepository): return dict(n) - async def find_by_group_id( + async def find_by_end_user_id( self, - group_id: str, + end_user_id: str, limit: int = 1000, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None ) -> List[Dict[str, Any]]: - """Query memory summaries by group_id + """Query memory summaries by end_user_id Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by limit: Maximum number of results to return start_date: Optional start date filter end_date: Optional end date filter @@ -71,10 +71,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id """ - params = {"group_id": group_id, "limit": limit} + params = {"end_user_id": end_user_id, "limit": limit} # Add date range filters if provided if start_date: @@ -139,16 +139,16 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_by_group_and_user( self, - group_id: str, + end_user_id: str, user_id: str, limit: int = 1000, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None ) -> List[Dict[str, Any]]: - """Query memory summaries by both group_id and user_id + """Query memory summaries by both end_user_id and user_id Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by user_id: User ID to filter by limit: Maximum number of results to return start_date: Optional start date filter @@ -159,10 +159,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id AND n.user_id = $user_id + WHERE n.end_user_id = $end_user_id AND n.user_id = $user_id """ - params = {"group_id": group_id, "user_id": user_id, "limit": limit} + params = {"end_user_id": end_user_id, "user_id": user_id, "limit": limit} # Add date range filters if provided if start_date: @@ -184,14 +184,14 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_recent_summaries( self, - group_id: str, + end_user_id: str, days: int = 7, limit: int = 1000 ) -> List[Dict[str, Any]]: """Query recent memory summaries Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by days: Number of recent days to query limit: Maximum number of results to return @@ -200,7 +200,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND n.created_at >= datetime() - duration({{days: $days}}) RETURN n ORDER BY n.created_at DESC @@ -209,7 +209,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, days=days, limit=limit ) @@ -217,14 +217,14 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_by_content_keywords( self, - group_id: str, + end_user_id: str, keywords: List[str], limit: int = 100 ) -> List[Dict[str, Any]]: """Query memory summaries by content keywords Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by keywords: List of keywords to search for in content limit: Maximum number of results to return @@ -233,7 +233,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ # Build keyword search conditions keyword_conditions = [] - params = {"group_id": group_id, "limit": limit} + params = {"end_user_id": end_user_id, "limit": limit} for i, keyword in enumerate(keywords): keyword_conditions.append(f"toLower(n.content) CONTAINS toLower($keyword_{i})") @@ -243,7 +243,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND ({keyword_filter}) RETURN n ORDER BY n.created_at DESC @@ -253,21 +253,21 @@ class MemorySummaryRepository(BaseNeo4jRepository): results = await self.connector.execute_query(query, **params) return [self._map_to_dict(r) for r in results] - async def get_summary_count_by_group(self, group_id: str) -> int: + async def get_summary_count_by_group(self, end_user_id: str) -> int: """Get count of memory summaries for a group Args: - group_id: Group ID to count summaries for + end_user_id: Group ID to count summaries for Returns: int: Number of memory summaries """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - results = await self.connector.execute_query(query, group_id=group_id) + results = await self.connector.execute_query(query, end_user_id=end_user_id) return results[0]['count'] if results else 0 \ No newline at end of file diff --git a/api/app/repositories/neo4j/neo4j_connector.py b/api/app/repositories/neo4j/neo4j_connector.py index 7c4b43b5..d96e4431 100644 --- a/api/app/repositories/neo4j/neo4j_connector.py +++ b/api/app/repositories/neo4j/neo4j_connector.py @@ -70,11 +70,7 @@ class Neo4jConnector: List[Dict[str, Any]]: 查询结果列表，每个元素是一个字典 Example: - >>> connector = Neo4jConnector() - >>> results = await connector.execute_query( - ... "MATCH (n:Person {name: $name}) RETURN n", - ... name="Alice" - ... ) + """ result = await self.driver.execute_query( query, @@ -98,17 +94,7 @@ class Neo4jConnector: Any: 事务函数的返回值 Example: - >>> async def create_node(tx, name): - ... result = await tx.run( - ... "CREATE (n:Person {name: $name}) RETURN n", - ... name=name - ... ) - ... return await result.single() - >>> - >>> connector = Neo4jConnector() - >>> result = await connector.execute_write_transaction( - ... create_node, name="Alice" - ... ) + """ async with self.driver.session(database="neo4j") as session: return await session.execute_write(transaction_func, **kwargs) @@ -126,45 +112,33 @@ class Neo4jConnector: Any: 事务函数的返回值 Example: - >>> async def get_node(tx, name): - ... result = await tx.run( - ... "MATCH (n:Person {name: $name}) RETURN n", - ... name=name - ... ) - ... return await result.single() - >>> - >>> connector = Neo4jConnector() - >>> result = await connector.execute_read_transaction( - ... get_node, name="Alice" - ... ) + """ async with self.driver.session(database="neo4j") as session: return await session.execute_read(transaction_func, **kwargs) - async def delete_group(self, group_id: str): + async def delete_group(self, end_user_id: str): """删除指定组的所有数据 - 删除所有属于指定group_id的节点和边。 + 删除所有属于指定end_user_id的节点和边。 这是一个危险操作，会永久删除数据。 Args: - group_id: 要删除的组ID + end_user_id: 要删除的组ID Example: - >>> connector = Neo4jConnector() - >>> await connector.delete_group("group_123") Group group_123 deleted. """ # 删除节点（DETACH DELETE会同时删除相关的边） await self.driver.execute_query( - "MATCH (n) WHERE n.group_id = $group_id DETACH DELETE n", + "MATCH (n) WHERE n.end_user_id = $end_user_id DETACH DELETE n", database="neo4j", - group_id=group_id + end_user_id=end_user_id ) # 删除独立的边（如果有的话） await self.driver.execute_query( - "MATCH ()-[r]->() WHERE r.group_id = $group_id DELETE r", + "MATCH ()-[r]->() WHERE r.end_user_id = $end_user_id DELETE r", database="neo4j", - group_id=group_id + end_user_id=end_user_id ) - print(f"Group {group_id} deleted.") + print(f"Group {end_user_id} deleted.") diff --git a/api/app/repositories/neo4j/statement_repository.py b/api/app/repositories/neo4j/statement_repository.py index cd9f2fac..4f12af83 100644 --- a/api/app/repositories/neo4j/statement_repository.py +++ b/api/app/repositories/neo4j/statement_repository.py @@ -20,7 +20,7 @@ class StatementRepository(BaseNeo4jRepository[StatementNode]): """陈述句仓储 管理陈述句节点的创建、查询、更新和删除操作。 - 提供按chunk_id、group_id、向量相似度等条件查询陈述句的方法。 + 提供按chunk_id、end_user_id、向量相似度等条件查询陈述句的方法。 Attributes: connector: Neo4j连接器实例 diff --git a/api/app/repositories/ontology_class_repository.py b/api/app/repositories/ontology_class_repository.py new file mode 100644 index 00000000..68f261ff --- /dev/null +++ b/api/app/repositories/ontology_class_repository.py @@ -0,0 +1,404 @@ +# -*- coding: utf-8 -*- +"""本体类型Repository层 + +本模块提供本体类型的数据访问层实现。 + +Classes: + OntologyClassRepository: 本体类型数据访问类 +""" + +import logging +from typing import List, Optional +from uuid import UUID + +from sqlalchemy.orm import Session, joinedload + +from app.core.logging_config import get_db_logger +from app.models.ontology_class import OntologyClass +from app.models.ontology_scene import OntologyScene + + +logger = get_db_logger() + + +class OntologyClassRepository: + """本体类型Repository + + 提供本体类型的CRUD操作和权限检查。 + + Attributes: + db: SQLAlchemy数据库会话 + """ + + def __init__(self, db: Session): + """初始化Repository + + Args: + db: SQLAlchemy数据库会话 + """ + self.db = db + + def create(self, class_data: dict, scene_id: UUID) -> OntologyClass: + """创建本体类型 + + Args: + class_data: 类型数据字典，包含class_name和class_description + scene_id: 所属场景ID + + Returns: + OntologyClass: 创建的类型对象 + + Raises: + Exception: 数据库操作失败 + + Examples: + >>> repo = OntologyClassRepository(db) + >>> ontology_class = repo.create( + ... {"class_name": "患者", "class_description": "描述"}, + ... scene_id + ... ) + """ + try: + logger.info( + f"Creating ontology class - " + f"name={class_data.get('class_name')}, " + f"scene_id={scene_id}" + ) + + ontology_class = OntologyClass( + class_name=class_data.get("class_name"), + class_description=class_data.get("class_description"), + scene_id=scene_id + ) + + self.db.add(ontology_class) + self.db.flush() # 获取ID但不提交 + + logger.info( + f"Ontology class created successfully - " + f"class_id={ontology_class.class_id}" + ) + + return ontology_class + + except Exception as e: + logger.error( + f"Failed to create ontology class: {str(e)}", + exc_info=True + ) + raise + + def get_by_id(self, class_id: UUID) -> Optional[OntologyClass]: + """根据ID获取类型 + + Args: + class_id: 类型ID + + Returns: + Optional[OntologyClass]: 类型对象，不存在则返回None + + Examples: + >>> repo = OntologyClassRepository(db) + >>> ontology_class = repo.get_by_id(class_id) + """ + try: + logger.debug(f"Getting ontology class by ID: {class_id}") + + ontology_class = self.db.query(OntologyClass).filter( + OntologyClass.class_id == class_id + ).first() + + if ontology_class: + logger.debug(f"Ontology class found: {class_id}") + else: + logger.debug(f"Ontology class not found: {class_id}") + + return ontology_class + + except Exception as e: + logger.error( + f"Failed to get ontology class by ID: {str(e)}", + exc_info=True + ) + raise + + def get_by_name(self, class_name: str, scene_id: UUID) -> Optional[OntologyClass]: + """根据类型名称和场景ID获取类型（精确匹配） + + Args: + class_name: 类型名称 + scene_id: 场景ID + + Returns: + Optional[OntologyClass]: 类型对象，不存在则返回None + + Examples: + >>> repo = OntologyClassRepository(db) + >>> ontology_class = repo.get_by_name("患者", scene_id) + """ + try: + logger.debug(f"Getting ontology class by name: {class_name}, scene_id: {scene_id}") + + ontology_class = self.db.query(OntologyClass).filter( + OntologyClass.class_name == class_name, + OntologyClass.scene_id == scene_id + ).first() + + if ontology_class: + logger.debug(f"Ontology class found: {class_name}") + else: + logger.debug(f"Ontology class not found: {class_name}") + + return ontology_class + + except Exception as e: + logger.error( + f"Failed to get ontology class by name: {str(e)}", + exc_info=True + ) + raise + + def search_by_name(self, keyword: str, scene_id: UUID) -> List[OntologyClass]: + """根据关键词模糊搜索类型 + + 使用 LIKE 进行模糊匹配，支持中文和英文。 + + Args: + keyword: 搜索关键词 + scene_id: 场景ID + + Returns: + List[OntologyClass]: 匹配的类型列表 + + Examples: + >>> repo = OntologyClassRepository(db) + >>> classes = repo.search_by_name("患者", scene_id) + """ + try: + logger.debug( + f"Searching ontology classes by keyword - " + f"keyword={keyword}, scene_id={scene_id}" + ) + + # 使用 ilike 进行不区分大小写的模糊匹配 + classes = self.db.query(OntologyClass).filter( + OntologyClass.class_name.ilike(f"%{keyword}%"), + OntologyClass.scene_id == scene_id + ).order_by( + OntologyClass.created_at.desc() + ).all() + + logger.info( + f"Found {len(classes)} ontology classes matching keyword '{keyword}' " + f"in scene {scene_id}" + ) + + return classes + + except Exception as e: + logger.error( + f"Failed to search ontology classes by keyword: {str(e)}", + exc_info=True + ) + raise + + def get_by_scene(self, scene_id: UUID) -> List[OntologyClass]: + """获取场景下的所有类型 + + 按创建时间倒序排列。 + + Args: + scene_id: 场景ID + + Returns: + List[OntologyClass]: 类型列表 + + Examples: + >>> repo = OntologyClassRepository(db) + >>> classes = repo.get_by_scene(scene_id) + """ + try: + logger.debug(f"Getting ontology classes by scene: {scene_id}") + + classes = self.db.query(OntologyClass).filter( + OntologyClass.scene_id == scene_id + ).order_by( + OntologyClass.created_at.desc() + ).all() + + logger.info( + f"Found {len(classes)} ontology classes in scene {scene_id}" + ) + + return classes + + except Exception as e: + logger.error( + f"Failed to get ontology classes by scene: {str(e)}", + exc_info=True + ) + raise + + def update(self, class_id: UUID, update_data: dict) -> Optional[OntologyClass]: + """更新类型信息 + + Args: + class_id: 类型ID + update_data: 更新数据字典 + + Returns: + Optional[OntologyClass]: 更新后的类型对象，不存在则返回None + + Raises: + Exception: 数据库操作失败 + + Examples: + >>> repo = OntologyClassRepository(db) + >>> ontology_class = repo.update( + ... class_id, + ... {"class_name": "新名称"} + ... ) + """ + try: + logger.info(f"Updating ontology class: {class_id}") + + ontology_class = self.get_by_id(class_id) + if not ontology_class: + logger.warning(f"Ontology class not found for update: {class_id}") + return None + + # 更新字段 + if "class_name" in update_data and update_data["class_name"] is not None: + ontology_class.class_name = update_data["class_name"] + + if "class_description" in update_data: + ontology_class.class_description = update_data["class_description"] + + self.db.flush() + + logger.info(f"Ontology class updated successfully: {class_id}") + + return ontology_class + + except Exception as e: + logger.error( + f"Failed to update ontology class: {str(e)}", + exc_info=True + ) + raise + + def delete(self, class_id: UUID) -> bool: + """删除类型 + + Args: + class_id: 类型ID + + Returns: + bool: 删除成功返回True，类型不存在返回False + + Raises: + Exception: 数据库操作失败 + + Examples: + >>> repo = OntologyClassRepository(db) + >>> success = repo.delete(class_id) + """ + try: + logger.info(f"Deleting ontology class: {class_id}") + + ontology_class = self.get_by_id(class_id) + if not ontology_class: + logger.warning(f"Ontology class not found for delete: {class_id}") + return False + + self.db.delete(ontology_class) + self.db.flush() + + logger.info(f"Ontology class deleted successfully: {class_id}") + + return True + + except Exception as e: + logger.error( + f"Failed to delete ontology class: {str(e)}", + exc_info=True + ) + raise + + def check_ownership(self, class_id: UUID, workspace_id: UUID) -> bool: + """检查类型是否属于指定工作空间（通过场景关联） + + Args: + class_id: 类型ID + workspace_id: 工作空间ID + + Returns: + bool: 属于返回True，否则返回False + + Examples: + >>> repo = OntologyClassRepository(db) + >>> is_owner = repo.check_ownership(class_id, workspace_id) + """ + try: + logger.debug( + f"Checking class ownership - " + f"class_id={class_id}, workspace_id={workspace_id}" + ) + + count = self.db.query(OntologyClass).join( + OntologyScene, + OntologyClass.scene_id == OntologyScene.scene_id + ).filter( + OntologyClass.class_id == class_id, + OntologyScene.workspace_id == workspace_id + ).count() + + is_owner = count > 0 + + logger.debug( + f"Class ownership check result: {is_owner} - " + f"class_id={class_id}" + ) + + return is_owner + + except Exception as e: + logger.error( + f"Failed to check class ownership: {str(e)}", + exc_info=True + ) + raise + + def get_scene_id_by_class(self, class_id: UUID) -> Optional[UUID]: + """根据类型ID获取所属场景ID + + Args: + class_id: 类型ID + + Returns: + Optional[UUID]: 场景ID，类型不存在则返回None + + Examples: + >>> repo = OntologyClassRepository(db) + >>> scene_id = repo.get_scene_id_by_class(class_id) + """ + try: + logger.debug(f"Getting scene ID by class: {class_id}") + + ontology_class = self.get_by_id(class_id) + if not ontology_class: + logger.debug(f"Class not found: {class_id}") + return None + + logger.debug( + f"Found scene ID: {ontology_class.scene_id} for class: {class_id}" + ) + + return ontology_class.scene_id + + except Exception as e: + logger.error( + f"Failed to get scene ID by class: {str(e)}", + exc_info=True + ) + raise diff --git a/api/app/repositories/ontology_scene_repository.py b/api/app/repositories/ontology_scene_repository.py new file mode 100644 index 00000000..322e111c --- /dev/null +++ b/api/app/repositories/ontology_scene_repository.py @@ -0,0 +1,394 @@ +# -*- coding: utf-8 -*- +"""本体场景Repository层 + +本模块提供本体场景的数据访问层实现。 + +Classes: + OntologySceneRepository: 本体场景数据访问类 +""" + +import logging +from typing import List, Optional +from uuid import UUID + +from sqlalchemy.orm import Session, joinedload + +from app.core.logging_config import get_db_logger +from app.models.ontology_scene import OntologyScene + + +logger = get_db_logger() + + +class OntologySceneRepository: + """本体场景Repository + + 提供本体场景的CRUD操作和权限检查。 + + Attributes: + db: SQLAlchemy数据库会话 + """ + + def __init__(self, db: Session): + """初始化Repository + + Args: + db: SQLAlchemy数据库会话 + """ + self.db = db + + def create(self, scene_data: dict, workspace_id: UUID) -> OntologyScene: + """创建本体场景 + + Args: + scene_data: 场景数据字典，包含scene_name和scene_description + workspace_id: 所属工作空间ID + + Returns: + OntologyScene: 创建的场景对象 + + Raises: + Exception: 数据库操作失败 + + Examples: + >>> repo = OntologySceneRepository(db) + >>> scene = repo.create( + ... {"scene_name": "医疗场景", "scene_description": "描述"}, + ... workspace_id + ... ) + """ + try: + logger.info( + f"Creating ontology scene - " + f"name={scene_data.get('scene_name')}, " + f"workspace_id={workspace_id}" + ) + + scene = OntologyScene( + scene_name=scene_data.get("scene_name"), + scene_description=scene_data.get("scene_description"), + workspace_id=workspace_id + ) + + self.db.add(scene) + self.db.flush() # 获取ID但不提交 + + logger.info( + f"Ontology scene created successfully - " + f"scene_id={scene.scene_id}" + ) + + return scene + + except Exception as e: + logger.error( + f"Failed to create ontology scene: {str(e)}", + exc_info=True + ) + raise + + def get_by_id(self, scene_id: UUID) -> Optional[OntologyScene]: + """根据ID获取场景 + + Args: + scene_id: 场景ID + + Returns: + Optional[OntologyScene]: 场景对象，不存在则返回None + + Examples: + >>> repo = OntologySceneRepository(db) + >>> scene = repo.get_by_id(scene_id) + """ + try: + logger.debug(f"Getting ontology scene by ID: {scene_id}") + + scene = self.db.query(OntologyScene).filter( + OntologyScene.scene_id == scene_id + ).first() + + if scene: + logger.debug(f"Ontology scene found: {scene_id}") + else: + logger.debug(f"Ontology scene not found: {scene_id}") + + return scene + + except Exception as e: + logger.error( + f"Failed to get ontology scene by ID: {str(e)}", + exc_info=True + ) + raise + + def get_by_name(self, scene_name: str, workspace_id: UUID) -> Optional[OntologyScene]: + """根据场景名称和工作空间ID获取场景（精确匹配） + + Args: + scene_name: 场景名称 + workspace_id: 工作空间ID + + Returns: + Optional[OntologyScene]: 场景对象，不存在则返回None + + Examples: + >>> repo = OntologySceneRepository(db) + >>> scene = repo.get_by_name("医疗场景", workspace_id) + """ + try: + logger.debug( + f"Getting ontology scene by name - " + f"scene_name={scene_name}, workspace_id={workspace_id}" + ) + + scene = self.db.query(OntologyScene).options( + joinedload(OntologyScene.classes) + ).filter( + OntologyScene.scene_name == scene_name, + OntologyScene.workspace_id == workspace_id + ).first() + + if scene: + logger.debug(f"Ontology scene found: {scene_name}") + else: + logger.debug(f"Ontology scene not found: {scene_name}") + + return scene + + except Exception as e: + logger.error( + f"Failed to get ontology scene by name: {str(e)}", + exc_info=True + ) + raise + + def search_by_name(self, keyword: str, workspace_id: UUID) -> List[OntologyScene]: + """根据关键词模糊搜索场景 + + 使用 LIKE 进行模糊匹配，支持中文和英文。 + + Args: + keyword: 搜索关键词 + workspace_id: 工作空间ID + + Returns: + List[OntologyScene]: 匹配的场景列表 + + Examples: + >>> repo = OntologySceneRepository(db) + >>> scenes = repo.search_by_name("医疗", workspace_id) + """ + try: + logger.debug( + f"Searching ontology scenes by keyword - " + f"keyword={keyword}, workspace_id={workspace_id}" + ) + + # 使用 ilike 进行不区分大小写的模糊匹配 + scenes = self.db.query(OntologyScene).options( + joinedload(OntologyScene.classes) + ).filter( + OntologyScene.scene_name.ilike(f"%{keyword}%"), + OntologyScene.workspace_id == workspace_id + ).order_by( + OntologyScene.updated_at.desc() + ).all() + + logger.info( + f"Found {len(scenes)} ontology scenes matching keyword '{keyword}' " + f"in workspace {workspace_id}" + ) + + return scenes + + except Exception as e: + logger.error( + f"Failed to search ontology scenes by keyword: {str(e)}", + exc_info=True + ) + raise + + def get_by_workspace(self, workspace_id: UUID, page: Optional[int] = None, page_size: Optional[int] = None) -> tuple: + """获取工作空间下的所有场景（支持分页） + + 使用joinedload预加载classes关系以统计数量。 + + Args: + workspace_id: 工作空间ID + page: 页码（可选，从1开始） + page_size: 每页数量（可选） + + Returns: + tuple: (场景列表, 总数量) + + Examples: + >>> repo = OntologySceneRepository(db) + >>> scenes, total = repo.get_by_workspace(workspace_id) + >>> scenes, total = repo.get_by_workspace(workspace_id, page=1, page_size=10) + """ + try: + logger.debug(f"Getting ontology scenes by workspace: {workspace_id}, page={page}, page_size={page_size}") + + # 构建基础查询 + query = self.db.query(OntologyScene).options( + joinedload(OntologyScene.classes) + ).filter( + OntologyScene.workspace_id == workspace_id + ).order_by( + OntologyScene.updated_at.desc() + ) + + # 获取总数 + total = query.count() + + # 如果提供了分页参数，应用分页 + if page is not None and page_size is not None: + offset = (page - 1) * page_size + query = query.offset(offset).limit(page_size) + logger.debug(f"Applying pagination: offset={offset}, limit={page_size}") + + scenes = query.all() + + logger.info( + f"Found {len(scenes)} ontology scenes (total: {total}) in workspace {workspace_id}" + ) + + return scenes, total + + except Exception as e: + logger.error( + f"Failed to get ontology scenes by workspace: {str(e)}", + exc_info=True + ) + raise + + def update(self, scene_id: UUID, update_data: dict) -> Optional[OntologyScene]: + """更新场景信息 + + Args: + scene_id: 场景ID + update_data: 更新数据字典 + + Returns: + Optional[OntologyScene]: 更新后的场景对象，不存在则返回None + + Raises: + Exception: 数据库操作失败 + + Examples: + >>> repo = OntologySceneRepository(db) + >>> scene = repo.update( + ... scene_id, + ... {"scene_name": "新名称"} + ... ) + """ + try: + logger.info(f"Updating ontology scene: {scene_id}") + + scene = self.get_by_id(scene_id) + if not scene: + logger.warning(f"Ontology scene not found for update: {scene_id}") + return None + + # 更新字段 + if "scene_name" in update_data and update_data["scene_name"] is not None: + scene.scene_name = update_data["scene_name"] + + if "scene_description" in update_data: + scene.scene_description = update_data["scene_description"] + + self.db.flush() + + logger.info(f"Ontology scene updated successfully: {scene_id}") + + return scene + + except Exception as e: + logger.error( + f"Failed to update ontology scene: {str(e)}", + exc_info=True + ) + raise + + def delete(self, scene_id: UUID) -> bool: + """删除场景（级联删除类型） + + 依赖数据库级联删除配置（ondelete="CASCADE"）。 + + Args: + scene_id: 场景ID + + Returns: + bool: 删除成功返回True，场景不存在返回False + + Raises: + Exception: 数据库操作失败 + + Examples: + >>> repo = OntologySceneRepository(db) + >>> success = repo.delete(scene_id) + """ + try: + logger.info(f"Deleting ontology scene: {scene_id}") + + scene = self.get_by_id(scene_id) + if not scene: + logger.warning(f"Ontology scene not found for delete: {scene_id}") + return False + + self.db.delete(scene) + self.db.flush() + + logger.info( + f"Ontology scene deleted successfully (cascade): {scene_id}" + ) + + return True + + except Exception as e: + logger.error( + f"Failed to delete ontology scene: {str(e)}", + exc_info=True + ) + raise + + def check_ownership(self, scene_id: UUID, workspace_id: UUID) -> bool: + """检查场景是否属于指定工作空间 + + Args: + scene_id: 场景ID + workspace_id: 工作空间ID + + Returns: + bool: 属于返回True，否则返回False + + Examples: + >>> repo = OntologySceneRepository(db) + >>> is_owner = repo.check_ownership(scene_id, workspace_id) + """ + try: + logger.debug( + f"Checking scene ownership - " + f"scene_id={scene_id}, workspace_id={workspace_id}" + ) + + count = self.db.query(OntologyScene).filter( + OntologyScene.scene_id == scene_id, + OntologyScene.workspace_id == workspace_id + ).count() + + is_owner = count > 0 + + logger.debug( + f"Scene ownership check result: {is_owner} - " + f"scene_id={scene_id}" + ) + + return is_owner + + except Exception as e: + logger.error( + f"Failed to check scene ownership: {str(e)}", + exc_info=True + ) + raise diff --git a/api/app/repositories/prompt_optimizer_repository.py b/api/app/repositories/prompt_optimizer_repository.py index ba65257a..e73ab513 100644 --- a/api/app/repositories/prompt_optimizer_repository.py +++ b/api/app/repositories/prompt_optimizer_repository.py @@ -4,7 +4,10 @@ from sqlalchemy.orm import Session from app.core.logging_config import get_db_logger from app.models.prompt_optimizer_model import ( - PromptOptimizerSession, PromptOptimizerSessionHistory, RoleType + PromptOptimizerSession, + PromptOptimizerSessionHistory, + RoleType, + PromptHistory ) db_logger = get_db_logger() @@ -16,6 +19,12 @@ class PromptOptimizerSessionRepository: def __init__(self, db: Session): self.db = db + def get_session_by_id(self, session_id: uuid.UUID) -> PromptOptimizerSession | None: + session = self.db.query(PromptOptimizerSession).filter( + PromptOptimizerSession.id == session_id, + ).first() + return session + def create_session( self, tenant_id: uuid.UUID, @@ -38,12 +47,9 @@ class PromptOptimizerSessionRepository: user_id=user_id, ) self.db.add(session) - self.db.commit() - self.db.refresh(session) - db_logger.debug(f"Prompt optimization session created: ID:{session.id}") return session except Exception as e: - db_logger.error(f"Error creating prompt optimization session: user_id={user_id} - {str(e)}") + db_logger.error(f"Error creating prompt optimization session: - {str(e)}") raise def get_session_history( @@ -71,10 +77,10 @@ class PromptOptimizerSessionRepository: PromptOptimizerSession.id == session_id, PromptOptimizerSession.user_id == user_id ).first() - + if not session: return [] - + history = self.db.query(PromptOptimizerSessionHistory).filter( PromptOptimizerSessionHistory.session_id == session.id, PromptOptimizerSessionHistory.user_id == user_id @@ -104,11 +110,11 @@ class PromptOptimizerSessionRepository: PromptOptimizerSession.user_id == user_id, PromptOptimizerSession.tenant_id == tenant_id ).first() - + if not session: db_logger.error(f"Session {session_id} not found for user {user_id}") raise ValueError(f"Session {session_id} not found for user {user_id}") - + message = PromptOptimizerSessionHistory( tenant_id=tenant_id, session_id=session.id, @@ -117,8 +123,199 @@ class PromptOptimizerSessionRepository: content=content, ) self.db.add(message) - self.db.commit() + return message except Exception as e: db_logger.error(f"Error creating prompt optimization session history: session_id={session_id} - {str(e)}") raise + + def get_first_user_message(self, session_id: uuid.UUID) -> str | None: + """ + Get the first user message from a session. + + Args: + session_id (uuid.UUID): The session ID. + + Returns: + str | None: The content of the first user message, or None if not found. + """ + try: + message = self.db.query(PromptOptimizerSessionHistory).filter( + PromptOptimizerSessionHistory.session_id == session_id, + PromptOptimizerSessionHistory.role == RoleType.USER.value + ).order_by( + PromptOptimizerSessionHistory.created_at.asc() + ).first() + + return message.content if message else None + except Exception as e: + db_logger.error(f"Error getting first user message: session_id={session_id} - {str(e)}") + raise + + +class PromptReleaseRepository: + def __init__(self, db: Session): + self.db = db + + def get_prompt_by_session_id(self, session_id: uuid.UUID) -> PromptHistory | None: + prompt_obj = self.db.query(PromptHistory).filter( + PromptHistory.session_id == session_id, + PromptHistory.is_delete.is_(False) + ).first() + return prompt_obj + + def create_prompt_release( + self, + tenant_id: uuid.UUID, + title: str, + session_id: uuid.UUID, + prompt: str, + ) -> PromptHistory: + try: + prompt_obj = PromptHistory( + tenant_id=tenant_id, + title=title, + session_id=session_id, + prompt=prompt, + ) + self.db.add(prompt_obj) + return prompt_obj + except Exception as e: + db_logger.error(f"Error creating prompt release: session_id={session_id} - {str(e)}") + raise + + def soft_delete_prompt(self, prompt_obj: PromptHistory) -> None: + """ + Soft delete a prompt release by setting is_delete flag to True. + + Args: + prompt_obj (PromptHistory): The prompt release object to delete. + """ + try: + prompt_obj.is_delete = True + db_logger.debug(f"Soft deleted prompt release: id={prompt_obj.id}, session_id={prompt_obj.session_id}") + except Exception as e: + db_logger.error(f"Error soft deleting prompt release: id={prompt_obj.id} - {str(e)}") + raise + + def get_prompt_by_id(self, prompt_id: uuid.UUID) -> PromptHistory | None: + """ + Get a prompt release by its ID. + + Args: + prompt_id (uuid.UUID): The prompt release ID. + + Returns: + PromptHistory | None: The prompt release object or None if not found. + """ + try: + prompt_obj = self.db.query(PromptHistory).filter( + PromptHistory.id == prompt_id + ).first() + return prompt_obj + except Exception as e: + db_logger.error(f"Error getting prompt release by id: id={prompt_id} - {str(e)}") + raise + + def count_prompts(self, tenant_id: uuid.UUID) -> int: + """ + Count total number of non-deleted prompts for a tenant. + + Args: + tenant_id (uuid.UUID): The tenant ID. + + Returns: + int: Total count of prompts. + """ + try: + count = self.db.query(PromptHistory).filter( + PromptHistory.tenant_id == tenant_id, + PromptHistory.is_delete.is_(False) + ).count() + return count + except Exception as e: + db_logger.error(f"Error counting prompts: tenant_id={tenant_id} - {str(e)}") + raise + + def get_prompts_paginated( + self, + tenant_id: uuid.UUID, + offset: int, + limit: int + ) -> list[PromptHistory]: + """ + Get paginated list of prompt releases for a tenant. + + Args: + tenant_id (uuid.UUID): The tenant ID. + offset (int): Number of records to skip. + limit (int): Maximum number of records to return. + + Returns: + list[PromptHistory]: List of prompt releases. + """ + try: + prompts = self.db.query(PromptHistory).filter( + PromptHistory.tenant_id == tenant_id, + PromptHistory.is_delete.is_(False) + ).order_by( + PromptHistory.created_at.desc() + ).offset(offset).limit(limit).all() + return prompts + except Exception as e: + db_logger.error(f"Error getting paginated prompts: tenant_id={tenant_id} - {str(e)}") + raise + + def count_prompts_by_keyword(self, tenant_id: uuid.UUID, keyword: str) -> int: + """ + Count total number of non-deleted prompts matching keyword for a tenant. + + Args: + tenant_id (uuid.UUID): The tenant ID. + keyword (str): Search keyword for title. + + Returns: + int: Total count of matching prompts. + """ + try: + count = self.db.query(PromptHistory).filter( + PromptHistory.tenant_id == tenant_id, + PromptHistory.is_delete.is_(False), + PromptHistory.title.ilike(f"%{keyword}%") + ).count() + return count + except Exception as e: + db_logger.error(f"Error counting prompts by keyword: tenant_id={tenant_id}, keyword={keyword} - {str(e)}") + raise + + def search_prompts_paginated( + self, + tenant_id: uuid.UUID, + keyword: str, + offset: int, + limit: int + ) -> list[PromptHistory]: + """ + Search prompt releases by keyword in title with pagination. + + Args: + tenant_id (uuid.UUID): The tenant ID. + keyword (str): Search keyword for title. + offset (int): Number of records to skip. + limit (int): Maximum number of records to return. + + Returns: + list[PromptHistory]: List of matching prompt releases. + """ + try: + prompts = self.db.query(PromptHistory).filter( + PromptHistory.tenant_id == tenant_id, + PromptHistory.is_delete.is_(False), + PromptHistory.title.ilike(f"%{keyword}%") + ).order_by( + PromptHistory.created_at.desc() + ).offset(offset).limit(limit).all() + return prompts + except Exception as e: + db_logger.error(f"Error searching prompts: tenant_id={tenant_id}, keyword={keyword} - {str(e)}") + raise diff --git a/api/app/repositories/user_repository.py b/api/app/repositories/user_repository.py index a43c5869..b4c11aa4 100644 --- a/api/app/repositories/user_repository.py +++ b/api/app/repositories/user_repository.py @@ -68,7 +68,7 @@ class UserRepository: db_logger.debug("查询超级用户") try: - user = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active == True).filter(User.is_superuser == True).first() + user = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active.is_(True)).filter(User.is_superuser.is_(True)).first() if user: db_logger.debug(f"超级用户查询成功: {user.username}") else: @@ -82,7 +82,7 @@ class UserRepository: db_logger.debug("检查是否只有一个超级用户") try: - count = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active == True).filter(User.is_superuser == True).count() + count = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active.is_(True)).filter(User.is_superuser.is_(True)).count() return count == 1 except Exception as e: db_logger.error(f"检查超级用户数量失败: {str(e)}") diff --git a/api/app/repositories/workflow_repository.py b/api/app/repositories/workflow_repository.py index 04734640..b22673e6 100644 --- a/api/app/repositories/workflow_repository.py +++ b/api/app/repositories/workflow_repository.py @@ -33,7 +33,7 @@ class WorkflowConfigRepository: """ return self.db.query(WorkflowConfig).filter( WorkflowConfig.app_id == app_id, - WorkflowConfig.is_active == True + WorkflowConfig.is_active.is_(True) ).first() def create_or_update( diff --git a/api/app/repositories/workspace_repository.py b/api/app/repositories/workspace_repository.py index 106830be..70ed7521 100644 --- a/api/app/repositories/workspace_repository.py +++ b/api/app/repositories/workspace_repository.py @@ -103,7 +103,7 @@ class WorkspaceRepository: workspaces = ( self.db.query(Workspace) .filter(Workspace.tenant_id == user.tenant_id) - .filter(Workspace.is_active == True) + .filter(Workspace.is_active.is_(True)) .order_by(Workspace.updated_at.desc()) .all() ) @@ -115,7 +115,7 @@ class WorkspaceRepository: self.db.query(Workspace) .join(WorkspaceMember, Workspace.id == WorkspaceMember.workspace_id) .filter(WorkspaceMember.user_id == user_id) - .filter(Workspace.is_active == True) + .filter(Workspace.is_active.is_(True)) .order_by(Workspace.updated_at.desc()) .all() ) @@ -134,7 +134,7 @@ class WorkspaceRepository: workspaces = ( self.db.query(Workspace) .filter(Workspace.tenant_id == tenant_id) - .filter(Workspace.is_active == True) + .filter(Workspace.is_active.is_(True)) .all() ) db_logger.debug(f"租户工作空间查询成功: tenant_id={tenant_id}, 数量={len(workspaces)}") @@ -169,7 +169,7 @@ class WorkspaceRepository: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.user_id == user_id, WorkspaceMember.workspace_id == workspace_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if member: db_logger.debug(f"工作空间成员查询成功: user_id={user_id}, workspace_id={workspace_id}, role={member.role}") @@ -189,8 +189,8 @@ class WorkspaceRepository: .join(User, WorkspaceMember.user_id == User.id) .options(joinedload(WorkspaceMember.user), joinedload(WorkspaceMember.workspace)) .filter(WorkspaceMember.workspace_id == workspace_id) - .filter(WorkspaceMember.is_active == True) - .filter(User.is_active == True) + .filter(WorkspaceMember.is_active.is_(True)) + .filter(User.is_active.is_(True)) .all() ) db_logger.debug(f"成员列表查询成功: workspace_id={workspace_id}, 数量={len(members)}") @@ -208,8 +208,8 @@ class WorkspaceRepository: .join(User, WorkspaceMember.user_id == User.id) .options(joinedload(WorkspaceMember.user), joinedload(WorkspaceMember.workspace)) .filter(WorkspaceMember.id == member_id) - .filter(WorkspaceMember.is_active == True) - .filter(User.is_active == True) + .filter(WorkspaceMember.is_active.is_(True)) + .filter(User.is_active.is_(True)) .first() ) if member: @@ -226,7 +226,7 @@ class WorkspaceRepository: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.workspace_id == workspace_id, WorkspaceMember.user_id == user_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None @@ -243,7 +243,7 @@ class WorkspaceRepository: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.workspace_id == workspace_id, WorkspaceMember.user_id == user_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None @@ -259,7 +259,7 @@ class WorkspaceRepository: try: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.id == member_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None @@ -275,7 +275,7 @@ class WorkspaceRepository: try: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.id == id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None diff --git a/api/app/schemas/__init__.py b/api/app/schemas/__init__.py index 5eb36dd6..299251f4 100644 --- a/api/app/schemas/__init__.py +++ b/api/app/schemas/__init__.py @@ -26,6 +26,9 @@ from .app_schema import ( MemoryConfig, ToolConfig, VariableDefinition, + FileInput, + FileType, + TransferMethod, ) from .conversation_schema import ( Conversation, @@ -94,6 +97,9 @@ __all__ = [ "MemoryConfig", "ToolConfig", "VariableDefinition", + "FileInput", + "FileType", + "TransferMethod", "Conversation", "ConversationCreate", "ConversationWithMessages", diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index 35d2e424..26d9b246 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -1,10 +1,51 @@ import datetime import uuid from typing import Optional, Any, List, Dict, Union +from enum import Enum from pydantic import BaseModel, Field, ConfigDict, field_serializer, field_validator +# ---------- Multimodal File Support ---------- + +class FileType(str, Enum): + """文件类型枚举""" + IMAGE = "image" + DOCUMENT = "document" + AUDIO = "audio" + VIDEO = "video" + + +class TransferMethod(str, Enum): + """文件传输方式枚举""" + LOCAL_FILE = "local_file" # 已上传到系统的文件 + REMOTE_URL = "remote_url" # 外部URL + + +class FileInput(BaseModel): + """文件输入 Schema""" + type: FileType = Field(..., description="文件类型: image/document/audio/video") + transfer_method: TransferMethod = Field(..., description="传输方式: local_file/remote_url") + upload_file_id: Optional[uuid.UUID] = Field(None, description="已上传文件ID（local_file时必填）") + url: Optional[str] = Field(None, description="远程URL（remote_url时必填）") + + @field_validator("upload_file_id") + @classmethod + def validate_local_file(cls, v, info): + """验证 local_file 时必须提供 upload_file_id""" + if info.data.get("transfer_method") == TransferMethod.LOCAL_FILE and not v: + raise ValueError("transfer_method 为 local_file 时，upload_file_id 不能为空") + return v + + @field_validator("url") + @classmethod + def validate_remote_url(cls, v, info): + """验证 remote_url 时必须提供 url""" + if info.data.get("transfer_method") == TransferMethod.REMOTE_URL and not v: + raise ValueError("transfer_method 为 remote_url 时，url 不能为空") + return v + + # ---------- Input Schemas ---------- class KnowledgeBaseConfig(BaseModel): @@ -12,8 +53,8 @@ class KnowledgeBaseConfig(BaseModel): kb_id: str = Field(..., description="知识库ID") top_k: int = Field(default=3, ge=1, le=20, description="检索返回的文档数量") similarity_threshold: float = Field(default=0.7, ge=0.0, le=1.0, description="相似度阈值") - strategy: str = Field(default="hybrid", description="检索策略: hybrid | bm25 | dense") - weight: float = Field(default=1.0, ge=0.0, le=1.0, description="知识库权重（用于多知识库融合）") + # strategy: str = Field(default="hybrid", description="检索策略: hybrid | bm25 | dense") + # weight: float = Field(default=1.0, ge=0.0, le=1.0, description="知识库权重（用于多知识库融合）") vector_similarity_weight: float = Field(default=0.5, ge=0.0, le=1.0, description="向量相似度权重") retrieve_type: str = Field(default="hybrid", description="检索方式participle｜ semantic｜hybrid") @@ -299,6 +340,18 @@ class AppRelease(BaseModel): created_at: datetime.datetime updated_at: datetime.datetime + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v if v is not None else {} + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None @@ -348,6 +401,7 @@ class AppChatRequest(BaseModel): user_id: Optional[str] = Field(default=None, description="用户ID（用于会话管理）") variables: Optional[Dict[str, Any]] = Field(default=None, description="自定义变量参数值") stream: bool = Field(default=False, description="是否流式返回") + files: Optional[List[FileInput]] = Field(default=None, description="附件列表（支持多文件）") class DraftRunRequest(BaseModel): @@ -357,6 +411,7 @@ class DraftRunRequest(BaseModel): user_id: Optional[str] = Field(default=None, description="用户ID（用于会话管理）") variables: Optional[Dict[str, Any]] = Field(default=None, description="自定义变量参数值") stream: bool = Field(default=False, description="是否流式返回") + files: Optional[List[FileInput]] = Field(default=None, description="附件列表（支持多文件）") class DraftRunResponse(BaseModel): diff --git a/api/app/schemas/conversation_schema.py b/api/app/schemas/conversation_schema.py index 6ec9b9b6..0fcbc718 100644 --- a/api/app/schemas/conversation_schema.py +++ b/api/app/schemas/conversation_schema.py @@ -4,6 +4,9 @@ import datetime from typing import Optional, Dict, Any, List from pydantic import BaseModel, Field, ConfigDict, field_serializer +# 导入 FileInput（用于体验运行） +from app.schemas.app_schema import FileInput + # ---------- Input Schemas ---------- @@ -28,6 +31,7 @@ class ChatRequest(BaseModel): stream: bool = Field(default=False, description="是否流式返回") web_search: bool = Field(default=False, description="是否启用网络搜索") memory: bool = Field(default=True, description="是否启用记忆功能") + files: Optional[List[FileInput]] = Field(default=None, description="附件列表（支持多文件）") # ---------- Output Schemas ---------- diff --git a/api/app/schemas/emotion_schema.py b/api/app/schemas/emotion_schema.py index c48fbd41..13c802b5 100644 --- a/api/app/schemas/emotion_schema.py +++ b/api/app/schemas/emotion_schema.py @@ -1,11 +1,12 @@ """情绪分析相关的请求和响应模型""" from typing import Optional +from uuid import UUID from pydantic import BaseModel, Field class EmotionTagsRequest(BaseModel): """获取情绪标签统计请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）") end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）") @@ -14,14 +15,14 @@ class EmotionTagsRequest(BaseModel): class EmotionWordcloudRequest(BaseModel): """获取情绪词云数据请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") limit: int = Field(50, ge=1, le=200, description="返回词语数量") class EmotionHealthRequest(BaseModel): """获取情绪健康指数请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") time_range: str = Field("30d", description="时间范围（7d/30d/90d）") @@ -29,8 +30,8 @@ class EmotionHealthRequest(BaseModel): class EmotionSuggestionsRequest(BaseModel): """获取个性化情绪建议请求""" - group_id: str = Field(..., description="组ID") - config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）") + end_user_id: str = Field(..., description="组ID") + config_id: Optional[UUID] = Field(None, description="配置ID（用于指定LLM模型）") class EmotionGenerateSuggestionsRequest(BaseModel): diff --git a/api/app/schemas/memory_agent_schema.py b/api/app/schemas/memory_agent_schema.py index d4354c40..b6f50dd7 100644 --- a/api/app/schemas/memory_agent_schema.py +++ b/api/app/schemas/memory_agent_schema.py @@ -7,11 +7,11 @@ class UserInput(BaseModel): message: str history: list[dict] search_switch: str - group_id: str + end_user_id: str config_id: Optional[str] = None class Write_UserInput(BaseModel): messages: list[dict] - group_id: str - config_id: Optional[str] = None + end_user_id: str + config_id: Optional[str] = None \ No newline at end of file diff --git a/api/app/schemas/memory_config_schema.py b/api/app/schemas/memory_config_schema.py index 0443dcc4..76acee5c 100644 --- a/api/app/schemas/memory_config_schema.py +++ b/api/app/schemas/memory_config_schema.py @@ -35,7 +35,7 @@ class ConfigurationError(Exception): def __init__( self, message: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, context: Optional[Dict[str, Any]] = None, ): @@ -72,7 +72,7 @@ class WorkspaceNotFoundError(ConfigurationError): def __init__( self, workspace_id: UUID, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, message: Optional[str] = None, ): if message is None: @@ -89,7 +89,7 @@ class ModelNotFoundError(ConfigurationError): self, model_id: Union[str, UUID], model_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, message: Optional[str] = None, ): @@ -112,7 +112,7 @@ class ModelInactiveError(ConfigurationError): model_id: Union[str, UUID], model_name: str, model_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, message: Optional[str] = None, ): @@ -136,7 +136,7 @@ class InvalidConfigError(ConfigurationError): message: str, field_name: Optional[str] = None, invalid_value: Optional[Any] = None, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, ): context = {} @@ -155,7 +155,7 @@ class InvalidConfigError(ConfigurationError): class MemoryConfigValidation(BaseModel): """Pydantic model for validating memory configuration data from database.""" - config_id: int = Field(..., gt=0, description="Configuration ID must be positive") + config_id: UUID = Field(..., description="Configuration ID (UUID)") config_name: str = Field(..., min_length=1, max_length=255) workspace_id: UUID = Field(..., description="Workspace UUID") workspace_name: str = Field(..., min_length=1, max_length=255) @@ -275,7 +275,7 @@ class ModelValidation(BaseModel): def validate_memory_config_data( - config_data: Dict[str, Any], config_id: Optional[int] = None + config_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> MemoryConfigValidation: """Validate memory configuration data using Pydantic model.""" try: @@ -302,7 +302,7 @@ def validate_memory_config_data( def validate_workspace_data( - workspace_data: Dict[str, Any], config_id: Optional[int] = None + workspace_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> WorkspaceValidation: """Validate workspace data using Pydantic model.""" try: @@ -331,7 +331,7 @@ def validate_workspace_data( def validate_model_data( - model_data: Dict[str, Any], config_id: Optional[int] = None + model_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> ModelValidation: """Validate model data using Pydantic model.""" try: @@ -364,7 +364,7 @@ def validate_model_data( class MemoryConfig: """Immutable memory configuration loaded from database.""" - config_id: int + config_id: UUID config_name: str workspace_id: UUID workspace_name: str diff --git a/api/app/schemas/memory_perceptual_schema.py b/api/app/schemas/memory_perceptual_schema.py index 05e01d2a..7dfefe01 100644 --- a/api/app/schemas/memory_perceptual_schema.py +++ b/api/app/schemas/memory_perceptual_schema.py @@ -4,7 +4,7 @@ from typing import Optional from pydantic import BaseModel, Field -from app.models.memory_perceptual_model import PerceptualType, FileStorageType +from app.models.memory_perceptual_model import PerceptualType, FileStorageService class PerceptualFilter(BaseModel): @@ -38,12 +38,14 @@ class PerceptualMemoryItem(BaseModel): """感知记忆项""" id: uuid.UUID = Field(..., description="Unique memory ID") perceptual_type: PerceptualType = Field(..., description="Type of perception, e.g., text, audio, or video") + storage_service: FileStorageService = Field(..., description="Storage service for file") file_path: str = Field(..., description="File path in the storage service") - file_ext: str = Field(..., description="File extension") file_name: str = Field(..., description="File name") + file_ext: str = Field(..., description="File extension") summary: Optional[str] = Field(None, description="summary") - storage_type: FileStorageType = Field(..., description="Storage type for file") + meta_data: Optional[dict] = Field(None, description="Metadata information") created_time: int = Field(..., description="create time") + topic: str = Field(..., description="topic") domain: str = Field(..., description="domain") keywords: list[str] = Field(..., description="keywords") diff --git a/api/app/schemas/memory_reflection_schemas.py b/api/app/schemas/memory_reflection_schemas.py index 860f1ef1..88454364 100644 --- a/api/app/schemas/memory_reflection_schemas.py +++ b/api/app/schemas/memory_reflection_schemas.py @@ -1,5 +1,8 @@ +import uuid + from pydantic import BaseModel, Field -from typing import Optional +from typing import Optional, Union +from uuid import UUID from enum import Enum @@ -9,7 +12,7 @@ class OptimizationStrategy(str, Enum): ACCURACY_FIRST = "accuracy_first" BALANCED = "balanced" class Memory_Reflection(BaseModel): - config_id: Optional[int] = None + config_id: Union[uuid.UUID, int, str] = None reflection_enabled: bool reflection_period_in_hours: str reflexion_range: Optional[str] = "partial" diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index d17a9f2c..5e22d70f 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -1,5 +1,5 @@ """ -所有的内容是放错误地方了，应该放在models + """ from typing import Any, Optional, List, Dict, Literal, Union @@ -8,20 +8,8 @@ import uuid from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator -# ============================================================================ -# 原 UserInput 相关 Schema (保留原有功能) -# ============================================================================ -class UserInput(BaseModel): - message: str - history: list[dict] - search_switch: str - group_id: str -class Write_UserInput(BaseModel): - message: str - group_id: str - # ============================================================================ # 从 json_schema.py 迁移的 Schema @@ -159,7 +147,7 @@ class ReflexionResultSchema(BaseModel): # Composite key identifying a config row class ConfigKey(BaseModel): # 配置参数键模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field("config_id", description="配置唯一标识（字符串）") + config_id:Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)") user_id: str = Field("user_id", description="用户标识（字符串）") apply_id: str = Field("apply_id", description="应用或场景标识（字符串）") @@ -241,6 +229,9 @@ class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body， config_desc: str = Field("配置描述", description="配置描述（字符串）") workspace_id: Optional[uuid.UUID] = Field(None, description="工作空间ID（UUID）") + # 本体场景关联（可选） + scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID（UUID），关联ontology_scene表") + # 模型配置字段（可选，用于手动指定或自动填充） llm_id: Optional[str] = Field(None, description="LLM模型配置ID") embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID") @@ -250,17 +241,17 @@ class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body， class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体） model_config = ConfigDict(populate_by_name=True, extra="forbid") # config_name: str = Field("配置名称", description="配置名称（字符串）") - config_id: int = Field("配置ID", description="配置ID（字符串）") + config_id:Union[uuid.UUID, int, str] = Field(..., description="配置ID（支持UUID、整数或字符串）") class ConfigUpdate(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型 - config_id: Optional[int] = None + config_id: Union[uuid.UUID, int, str] = None config_name: str = Field("配置名称", description="配置名称（字符串）") config_desc: str = Field("配置描述", description="配置描述（字符串）") class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型 - config_id: Optional[int] = None + config_id:Union[uuid.UUID, int, str] = None llm_id: Optional[str] = Field(None, description="LLM模型配置ID") embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID") rerank_id: Optional[str] = Field(None, description="重排序模型配置ID") @@ -327,14 +318,14 @@ class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数 class ConfigUpdateForget(BaseModel): # 更新遗忘引擎配置参数时使用的模型 # 遗忘引擎配置参数更新模型 - config_id: Optional[int] = None + config_id:Union[uuid.UUID, int, str] = None lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5") lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5") offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0") class ConfigPilotRun(BaseModel): # 试运行触发请求模型 - config_id: int = Field(..., description="配置ID（唯一）") + config_id:Union[uuid.UUID, int, str] = Field(..., description="配置ID（唯一，支持UUID、整数或字符串）") dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填") model_config = ConfigDict(populate_by_name=True, extra="forbid") @@ -342,7 +333,7 @@ class ConfigPilotRun(BaseModel): # 试运行触发请求模型 class ConfigFilter(BaseModel): # 查询配置参数时使用的模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: Optional[int] = None + config_id: Union[uuid.UUID, int, str] = None user_id: Optional[str] = None apply_id: Optional[str] = None @@ -418,7 +409,7 @@ class ForgettingConfigResponse(BaseModel): """遗忘引擎配置响应模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field(..., description="配置ID") + config_id: Union[uuid.UUID, int, str] = Field(..., description="配置ID（支持UUID、整数或字符串）") decay_constant: float = Field(..., description="衰减常数 d") lambda_time: float = Field(..., description="时间衰减参数") lambda_mem: float = Field(..., description="记忆衰减参数") @@ -435,8 +426,8 @@ class ForgettingConfigResponse(BaseModel): class ForgettingConfigUpdateRequest(BaseModel): """遗忘引擎配置更新请求模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - - config_id: int = Field(..., description="配置ID") + + config_id: Union[uuid.UUID, int,str] = Field(..., description="配置唯一标识（UUID或int)") decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d") lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数") lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数") @@ -511,7 +502,7 @@ class ForgettingCurveRequest(BaseModel): importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）") days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）") - config_id: Optional[int] = Field(None, description="配置ID（可选，如果为None则使用默认配置）") + config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)") class ForgettingCurveResponse(BaseModel): diff --git a/api/app/schemas/model_schema.py b/api/app/schemas/model_schema.py index 5b1fe6d9..a2d3650a 100644 --- a/api/app/schemas/model_schema.py +++ b/api/app/schemas/model_schema.py @@ -1,10 +1,12 @@ -from pydantic import BaseModel, Field, field_serializer, ConfigDict +from pydantic import BaseModel, Field, field_serializer, field_validator, ConfigDict from typing import Optional, List, Dict, Any import datetime import uuid -from app.models.models_model import ModelProvider, ModelType +from app.models.models_model import ModelProvider, ModelType, LoadBalanceStrategy +from app.core.logging_config import get_business_logger +schema_logger = get_business_logger() # ModelConfig Schemas @@ -12,15 +14,19 @@ class ModelConfigBase(BaseModel): """模型配置基础Schema""" name: str = Field(..., description="模型显示名称", max_length=255) type: ModelType = Field(..., description="模型类型") + logo: Optional[str] = Field(None, description="模型logo图片URL", max_length=255) description: Optional[str] = Field(None, description="模型描述") + provider: str = Field(..., description="供应商") config: Optional[Dict[str, Any]] = Field({}, description="模型配置参数") is_active: bool = Field(True, description="是否激活") is_public: bool = Field(False, description="是否公开") + load_balance_strategy: Optional[str] = Field(LoadBalanceStrategy.NONE.value, description="负载均衡策略") class ApiKeyCreateNested(BaseModel): """用于在创建模型时内嵌创建API Key的Schema""" model_name: str = Field(..., description="模型实际名称", max_length=255) + description: Optional[str] = Field(None, description="备注") provider: ModelProvider = Field(..., description="API Key提供商") api_key: str = Field(..., description="API密钥", max_length=500) api_base: Optional[str] = Field(None, description="API基础URL", max_length=500) @@ -30,10 +36,23 @@ class ApiKeyCreateNested(BaseModel): class ModelConfigCreate(ModelConfigBase): """创建模型配置Schema""" - api_keys: Optional[ApiKeyCreateNested] = Field(None, description="同时创建的API Key配置") + api_keys: Optional[List[ApiKeyCreateNested]] = Field(None, description="同时创建的API Key配置") skip_validation: Optional[bool] = Field(False, description="是否跳过配置验证") +class CompositeModelCreate(BaseModel): + """创建组合模型Schema""" + name: str = Field(..., description="组合模型名称", max_length=255) + type: Optional[ModelType] = Field(None, description="模型类型") + logo: Optional[str] = Field(None, description="模型logo图片URL", max_length=255) + description: Optional[str] = Field(None, description="模型描述") + config: Optional[Dict[str, Any]] = Field({}, description="模型配置参数") + is_active: bool = Field(True, description="是否激活") + is_public: bool = Field(False, description="是否公开") + api_key_ids: List[uuid.UUID] = Field(..., description="绑定的API Key ID列表") + load_balance_strategy: Optional[str] = Field(default=LoadBalanceStrategy.NONE.value, description="负载均衡策略") + + class ModelConfigUpdate(BaseModel): """更新模型配置Schema""" name: Optional[str] = Field(None, description="模型显示名称", max_length=255) @@ -53,22 +72,48 @@ class ModelConfig(ModelConfigBase): updated_at: datetime.datetime api_keys: List["ModelApiKey"] = [] + @field_validator("api_keys", mode="after") + @classmethod + def filter_active_api_keys(cls, api_keys: List["ModelApiKey"]) -> List["ModelApiKey"]: + return [key for key in api_keys if key.is_active] + + @field_serializer("created_at", when_used="json") + def _serialize_created_at(self, dt: datetime.datetime | None): + return int(dt.timestamp() * 1000) if dt else None + + @field_serializer("updated_at", when_used="json") + def _serialize_updated_at(self, dt: datetime.datetime): + return int(dt.timestamp() * 1000) if dt else None + # ModelApiKey Schemas -class ModelApiKeyBase(BaseModel): - """API Key基础Schema""" - model_name: str = Field(..., description="模型实际名称", max_length=255) +class ModelApiKeyCreateByProvider(BaseModel): + """基于供应商创建API Key Schema""" provider: ModelProvider = Field(..., description="API Key提供商") api_key: str = Field(..., description="API密钥", max_length=500) api_base: Optional[str] = Field(None, description="API基础URL", max_length=500) - config: Optional[Dict[str, Any]] = Field(None, description="API Key特定配置") + description: Optional[str] = Field(None, description="备注") + config: Optional[Dict[str, Any]] = Field({}, description="API Key特定配置") + is_active: bool = Field(True, description="是否激活") + priority: str = Field("1", description="优先级", max_length=10) + model_config_ids: Optional[List[uuid.UUID]] = Field(None, description="关联的模型配置ID列表") + + +class ModelApiKeyBase(BaseModel): + """API Key基础Schema""" + model_name: str = Field(..., description="模型实际名称", max_length=255) + description: Optional[str] = Field(None, description="备注") + provider: ModelProvider = Field(..., description="API Key提供商") + api_key: str = Field(..., description="API密钥", max_length=500) + api_base: Optional[str] = Field(None, description="API基础URL", max_length=500) + config: Optional[Dict[str, Any]] = Field({}, description="API Key特定配置") is_active: bool = Field(True, description="是否激活") priority: str = Field("1", description="优先级", max_length=10) class ModelApiKeyCreate(ModelApiKeyBase): """创建API Key Schema""" - model_config_id: uuid.UUID = Field(..., description="模型配置ID") + model_config_ids: Optional[List[uuid.UUID]] = Field(None, description="关联的模型配置ID列表") class ModelApiKeyUpdate(BaseModel): @@ -85,11 +130,54 @@ class ModelApiKeyUpdate(BaseModel): class ModelApiKey(ModelApiKeyBase): """API Key Schema""" id: uuid.UUID - model_config_id: uuid.UUID usage_count: str last_used_at: Optional[datetime.datetime] created_at: datetime.datetime updated_at: datetime.datetime + model_configs: Any = Field(default=None, exclude=True) + model_config_ids: List[uuid.UUID] = Field(default_factory=list, description="关联的模型配置ID列表") + + def model_post_init(self, __context: Any) -> None: + """实例化后强制提取 model_configs 的ID到 model_config_ids""" + # 如果手动传入了 model_config_ids，不覆盖 + if self.model_config_ids and len(self.model_config_ids) > 0: + return + + # 从 model_configs 提取ID（只提取与 model_name 相同的非组合模型） + if self.model_configs is not None: + try: + # 情况1：ORM 对象列表（SQLAlchemy 关联） + if hasattr(self.model_configs, '__iter__') and not isinstance(self.model_configs, dict): + self.model_config_ids = [ + mc.id for mc in self.model_configs + if hasattr(mc, 'id') + and not getattr(mc, 'is_composite', False) + and getattr(mc, 'name', None) == self.model_name + ] + # 情况2：字典列表 + elif isinstance(self.model_configs, list): + self.model_config_ids = [ + mc['id'] if isinstance(mc, dict) else mc.id + for mc in self.model_configs + if ((isinstance(mc, dict) + and 'id' in mc + and not mc.get('is_composite', False) + and mc.get('name') == self.model_name) or + (hasattr(mc, 'id') + and not getattr(mc, 'is_composite', False) + and getattr(mc, 'name', None) == self.model_name)) + ] + except Exception as e: + schema_logger.warning(f"提取 model_config_ids 失败：{e}") + self.model_config_ids = [] + + model_config = ConfigDict( + from_attributes=True, # 支持从 ORM 解析 + arbitrary_types_allowed=True, # 允许任意类型（ORM 对象） + populate_by_name=True, # 按属性名匹配字段 + validate_assignment=True # 确保赋值触发校验 + ) + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): @@ -98,15 +186,12 @@ class ModelApiKey(ModelApiKeyBase): @field_serializer("updated_at", when_used="json") def _serialize_updated_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None - - model_config = ConfigDict(from_attributes=True) @field_serializer("last_used_at", when_used="json") def _serialize_last_used_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None -# 查询和响应Schemas class ModelConfigQuery(BaseModel): """模型配置查询Schema""" type: Optional[List[ModelType]] = Field(None, description="模型类型筛选（支持多个）") @@ -117,6 +202,17 @@ class ModelConfigQuery(BaseModel): page: int = Field(1, description="页码", ge=1) pagesize: int = Field(10, description="每页数量", ge=1, le=100) + +# 查询和响应Schemas +class ModelConfigQueryNew(BaseModel): + """模型配置查询Schema""" + type: Optional[List[ModelType]] = Field(None, description="模型类型筛选（支持多个）") + provider: Optional[ModelProvider] = Field(None, description="提供商筛选(通过API Key)") + is_active: Optional[bool] = Field(None, description="激活状态筛选") + is_public: Optional[bool] = Field(None, description="公开状态筛选") + is_composite: Optional[bool] = Field(None, description="组合模型筛选") + search: Optional[str] = Field(None, description="搜索关键词", max_length=255) + class ModelMarketplace(BaseModel): """模型广场响应Schema""" llm_models: List[ModelConfig] = [] @@ -159,4 +255,53 @@ class ModelValidateResponse(BaseModel): # 更新前向引用 -ModelConfig.model_rebuild() \ No newline at end of file +ModelConfig.model_rebuild() + + +# ModelBase Schemas +class ModelBaseCreate(BaseModel): + """创建基础模型Schema""" + name: str = Field(..., description="模型唯一标识", max_length=255) + type: ModelType = Field(..., description="模型类型") + provider: ModelProvider = Field(..., description="提供商") + logo: Optional[str] = Field(None, description="模型logo图片URL", max_length=255) + description: Optional[str] = Field(None, description="模型描述") + is_official: bool = Field(True, description="是否供应商官方模型") + tags: List[str] = Field(default_factory=list, description="模型标签") + + +class ModelBaseUpdate(BaseModel): + """更新基础模型Schema""" + name: Optional[str] = Field(None, description="模型唯一标识", max_length=255) + type: Optional[ModelType] = Field(None, description="模型类型") + provider: Optional[ModelProvider] = Field(None, description="提供商") + logo: Optional[str] = Field(None, description="模型logo图片URL", max_length=255) + description: Optional[str] = Field(None, description="模型描述") + is_deprecated: Optional[bool] = Field(None, description="是否弃用") + is_official: Optional[bool] = Field(None, description="是否供应商官方模型") + tags: Optional[List[str]] = Field(None, description="模型标签") + + +class ModelBase(BaseModel): + """基础模型Schema""" + model_config = ConfigDict(from_attributes=True) + + id: uuid.UUID + name: str + type: str + provider: str + logo: Optional[str] + description: Optional[str] + is_deprecated: bool + is_official: bool + tags: List[str] + add_count: int + + +class ModelBaseQuery(BaseModel): + """基础模型查询Schema""" + type: Optional[ModelType] = Field(None, description="模型类型") + provider: Optional[ModelProvider] = Field(None, description="提供商") + is_official: Optional[bool] = Field(None, description="是否官方模型") + is_deprecated: Optional[bool] = Field(None, description="是否弃用") + search: Optional[str] = Field(None, description="搜索关键词", max_length=255) diff --git a/api/app/schemas/multi_agent_schema.py b/api/app/schemas/multi_agent_schema.py index c0d72cdd..8fba2929 100644 --- a/api/app/schemas/multi_agent_schema.py +++ b/api/app/schemas/multi_agent_schema.py @@ -4,7 +4,7 @@ import datetime from typing import Optional, List, Dict, Any, Union from pydantic import BaseModel, Field, ConfigDict, field_serializer -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters # ==================== 子 Agent 配置 ==================== diff --git a/api/app/schemas/ontology_schemas.py b/api/app/schemas/ontology_schemas.py new file mode 100644 index 00000000..5a88f84d --- /dev/null +++ b/api/app/schemas/ontology_schemas.py @@ -0,0 +1,461 @@ +"""本体提取API的请求和响应模型 + +本模块定义了本体提取系统的所有API请求和响应的Pydantic模型。 + +Classes: + ExtractionRequest: 本体提取请求模型 + ExtractionResponse: 本体提取响应模型 + ExportRequest: OWL文件导出请求模型 + ExportResponse: OWL文件导出响应模型 + OntologyResultResponse: 本体提取结果响应模型(带毫秒时间戳) + SceneCreateRequest: 场景创建请求模型 + SceneUpdateRequest: 场景更新请求模型 + SceneResponse: 场景响应模型 + SceneListResponse: 场景列表响应模型 + ClassCreateRequest: 类型创建请求模型 + ClassUpdateRequest: 类型更新请求模型 + ClassResponse: 类型响应模型 + ClassListResponse: 类型列表响应模型 +""" + +from typing import List, Optional +import datetime +from uuid import UUID + +from pydantic import BaseModel, Field, field_serializer, ConfigDict + +from app.core.memory.models.ontology_models import OntologyClass + + +class ExtractionRequest(BaseModel): + """本体提取请求模型 + + 用于POST /api/ontology/extract端点的请求体。 + + Attributes: + scenario: 场景描述文本,不能为空 + domain: 可选的领域提示(如Healthcare, Education等) + llm_id: LLM模型ID,必须提供 + scene_id: 场景ID,必须提供,用于将提取的类保存到指定场景 + + Examples: + >>> request = ExtractionRequest( + ... scenario="医院管理患者记录...", + ... domain="Healthcare", + ... llm_id="550e8400-e29b-41d4-a716-446655440000", + ... scene_id="660e8400-e29b-41d4-a716-446655440000" + ... ) + """ + scenario: str = Field(..., description="场景描述文本", min_length=1) + domain: Optional[str] = Field(None, description="可选的领域提示") + llm_id: str = Field(..., description="LLM模型ID") + scene_id: UUID = Field(..., description="场景ID,用于将提取的类保存到指定场景") + + +class ExtractionResponse(BaseModel): + """本体提取响应模型 + + 用于POST /api/ontology/extract端点的响应体。 + + Attributes: + classes: 提取的本体类列表 + domain: 识别的领域 + extracted_count: 提取的类数量 + + Examples: + >>> response = ExtractionResponse( + ... classes=[...], + ... domain="Healthcare", + ... extracted_count=7 + ... ) + """ + classes: List[OntologyClass] = Field(default_factory=list, description="提取的本体类列表") + domain: str = Field(..., description="识别的领域") + extracted_count: int = Field(..., description="提取的类数量") + + +class ExportRequest(BaseModel): + """OWL文件导出请求模型 + + 用于POST /api/ontology/export端点的请求体。 + + Attributes: + classes: 要导出的本体类列表 + format: 导出格式,可选值: rdfxml, turtle, ntriples, json + include_metadata: 是否包含完整的OWL元数据(命名空间等),默认True + + Examples: + >>> request = ExportRequest( + ... classes=[...], + ... format="rdfxml", + ... include_metadata=True + ... ) + """ + classes: List[OntologyClass] = Field(..., description="要导出的本体类列表", min_length=1) + format: str = Field("rdfxml", description="导出格式: rdfxml, turtle, ntriples, json") + include_metadata: bool = Field(True, description="是否包含完整的OWL元数据") + + +class ExportResponse(BaseModel): + """OWL文件导出响应模型 + + 用于POST /api/ontology/export端点的响应体。 + + Attributes: + owl_content: OWL文件内容 + format: 导出格式 + classes_count: 导出的类数量 + + Examples: + >>> response = ExportResponse( + ... owl_content="...", + ... format="rdfxml", + ... classes_count=7 + ... ) + """ + owl_content: str = Field(..., description="OWL文件内容") + format: str = Field(..., description="导出格式") + classes_count: int = Field(..., description="导出的类数量") + + +class OntologyResultResponse(BaseModel): + """本体提取结果响应模型 + + 用于返回数据库中存储的提取结果,时间戳为毫秒级。 + + Attributes: + id: 结果ID (UUID) + scenario: 场景描述文本 + domain: 领域 + classes_json: 提取的本体类数据(JSON格式) + extracted_count: 提取的类数量 + user_id: 用户ID + created_at: 创建时间(毫秒时间戳) + + Examples: + >>> response = OntologyResultResponse( + ... id=uuid.uuid4(), + ... scenario="医院管理患者记录...", + ... domain="Healthcare", + ... classes_json={"classes": [...]}, + ... extracted_count=7, + ... user_id=123, + ... created_at=datetime.now() + ... ) + """ + id: UUID = Field(..., description="结果ID") + scenario: str = Field(..., description="场景描述文本") + domain: Optional[str] = Field(None, description="领域") + classes_json: dict = Field(..., description="提取的本体类数据(JSON格式)") + extracted_count: int = Field(..., description="提取的类数量") + user_id: Optional[int] = Field(None, description="用户ID") + created_at: datetime.datetime = Field(..., description="创建时间") + + @field_serializer("created_at", when_used="json") + def _serialize_created_at(self, dt: datetime.datetime): + """将创建时间序列化为毫秒时间戳""" + return int(dt.timestamp() * 1000) if dt else None + + class Config: + from_attributes = True + + + +# ==================== 本体场景相关 Schema ==================== + +class SceneCreateRequest(BaseModel): + """场景创建请求模型 + + 用于创建新的本体场景。 + + Attributes: + scene_name: 场景名称，必填，1-200字符 + scene_description: 场景描述，可选 + + Examples: + >>> request = SceneCreateRequest( + ... scene_name="医疗场景", + ... scene_description="用于医疗领域的本体建模" + ... ) + """ + scene_name: str = Field(..., min_length=1, max_length=200, description="场景名称") + scene_description: Optional[str] = Field(None, description="场景描述") + + +class SceneUpdateRequest(BaseModel): + """场景更新请求模型 + + 用于更新已有本体场景信息。 + + Attributes: + scene_name: 场景名称，可选，1-200字符 + scene_description: 场景描述，可选 + + Examples: + >>> request = SceneUpdateRequest( + ... scene_name="更新后的场景名称", + ... scene_description="更新后的描述" + ... ) + """ + scene_name: Optional[str] = Field(None, min_length=1, max_length=200, description="场景名称") + scene_description: Optional[str] = Field(None, description="场景描述") + + +class SceneResponse(BaseModel): + """场景响应模型 + + 用于返回本体场景信息。 + + Attributes: + scene_id: 场景ID + scene_name: 场景名称 + scene_description: 场景描述 + type_num: 类型数量 + workspace_id: 所属工作空间ID + created_at: 创建时间（毫秒时间戳） + updated_at: 更新时间（毫秒时间戳） + classes_count: 类型数量 + + Examples: + >>> response = SceneResponse( + ... scene_id=uuid.uuid4(), + ... scene_name="医疗场景", + ... scene_description="用于医疗领域的本体建模", + ... type_num=0, + ... workspace_id=uuid.uuid4(), + ... created_at=datetime.now(), + ... updated_at=datetime.now(), + ... classes_count=5 + ... ) + """ + scene_id: UUID = Field(..., description="场景ID") + scene_name: str = Field(..., description="场景名称") + scene_description: Optional[str] = Field(None, description="场景描述") + type_num: int = Field(..., description="类型数量") + entity_type: Optional[List[str]] = Field(None, description="实体类型列表（最多3个class_name）") + workspace_id: UUID = Field(..., description="所属工作空间ID") + created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）") + updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）") + classes_count: int = Field(0, description="类型数量") + + @field_serializer("created_at", when_used="json") + def _serialize_created_at(self, dt: datetime.datetime): + """将创建时间序列化为毫秒时间戳""" + return int(dt.timestamp() * 1000) if dt else None + + @field_serializer("updated_at", when_used="json") + def _serialize_updated_at(self, dt: datetime.datetime): + """将更新时间序列化为毫秒时间戳""" + return int(dt.timestamp() * 1000) if dt else None + + model_config = ConfigDict(from_attributes=True) + + +class PaginationInfo(BaseModel): + """分页信息模型 + + Attributes: + page: 当前页码 + pagesize: 每页数量 + total: 总数量 + hasnext: 是否有下一页 + """ + page: int = Field(..., description="当前页码") + pagesize: int = Field(..., description="每页数量") + total: int = Field(..., description="总数量") + hasnext: bool = Field(..., description="是否有下一页") + + +class SceneListResponse(BaseModel): + """场景列表响应模型（支持分页） + + 用于返回本体场景列表。 + + Attributes: + items: 场景列表 + page: 分页信息（可选，分页时返回） + + Examples: + >>> # 不分页 + >>> response = SceneListResponse( + ... items=[scene1, scene2] + ... ) + >>> # 分页 + >>> response = SceneListResponse( + ... items=[scene1, scene2, ...], + ... page=PaginationInfo(page=1, pagesize=100, total=150, hasnext=True) + ... ) + """ + items: List[SceneResponse] = Field(..., description="场景列表") + page: Optional[PaginationInfo] = Field(None, description="分页信息") + + +# ==================== 本体类型相关 Schema ==================== + +class ClassItem(BaseModel): + """单个类型信息模型 + + Attributes: + class_name: 类型名称，必填，1-200字符 + class_description: 类型描述，可选 + + Examples: + >>> item = ClassItem( + ... class_name="患者", + ... class_description="医院患者信息" + ... ) + """ + class_name: str = Field(..., min_length=1, max_length=200, description="类型名称") + class_description: Optional[str] = Field(None, description="类型描述") + + +class ClassCreateRequest(BaseModel): + """类型创建请求模型（统一使用列表形式） + + 通过列表中元素数量决定创建模式： + - 列表包含 1 个元素：单个创建 + - 列表包含多个元素：批量创建 + + Attributes: + scene_id: 所属场景ID，必填 + classes: 类型列表，必填，至少包含 1 个元素 + + Examples: + # 单个创建（列表中 1 个元素） + >>> request = ClassCreateRequest( + ... scene_id=uuid.uuid4(), + ... classes=[ + ... ClassItem(class_name="患者", class_description="医院患者信息") + ... ] + ... ) + + # 批量创建（列表中多个元素） + >>> request = ClassCreateRequest( + ... scene_id=uuid.uuid4(), + ... classes=[ + ... ClassItem(class_name="患者", class_description="医院患者信息"), + ... ClassItem(class_name="医生", class_description="医院医生信息"), + ... ClassItem(class_name="药品", class_description="医院药品信息") + ... ] + ... ) + """ + scene_id: UUID = Field(..., description="所属场景ID") + classes: List[ClassItem] = Field(..., min_length=1, description="类型列表，至少包含 1 个元素") + + +class ClassUpdateRequest(BaseModel): + """类型更新请求模型 + + 用于更新已有本体类型信息。 + + Attributes: + class_name: 类型名称，可选，1-200字符 + class_description: 类型描述，可选 + + Examples: + >>> request = ClassUpdateRequest( + ... class_name="更新后的类型名称", + ... class_description="更新后的描述" + ... ) + """ + class_name: Optional[str] = Field(None, min_length=1, max_length=200, description="类型名称") + class_description: Optional[str] = Field(None, description="类型描述") + + +class ClassResponse(BaseModel): + """类型响应模型 + + 用于返回本体类型信息。 + + Attributes: + class_id: 类型ID + class_name: 类型名称 + class_description: 类型描述 + scene_id: 所属场景ID + created_at: 创建时间（毫秒时间戳） + updated_at: 更新时间（毫秒时间戳） + + Examples: + >>> response = ClassResponse( + ... class_id=uuid.uuid4(), + ... class_name="患者", + ... class_description="医院患者信息", + ... scene_id=uuid.uuid4(), + ... created_at=datetime.now(), + ... updated_at=datetime.now() + ... ) + """ + class_id: UUID = Field(..., description="类型ID") + class_name: str = Field(..., description="类型名称") + class_description: Optional[str] = Field(None, description="类型描述") + scene_id: UUID = Field(..., description="所属场景ID") + created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）") + updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）") + + @field_serializer("created_at", when_used="json") + def _serialize_created_at(self, dt: datetime.datetime): + """将创建时间序列化为毫秒时间戳""" + return int(dt.timestamp() * 1000) if dt else None + + @field_serializer("updated_at", when_used="json") + def _serialize_updated_at(self, dt: datetime.datetime): + """将更新时间序列化为毫秒时间戳""" + return int(dt.timestamp() * 1000) if dt else None + + model_config = ConfigDict(from_attributes=True) + + +class ClassBatchCreateResponse(BaseModel): + """批量创建类型响应模型 + + 用于返回批量创建的结果统计和详情。 + + Attributes: + total: 总共尝试创建的数量 + success_count: 成功创建的数量 + failed_count: 失败的数量 + items: 成功创建的类型列表 + errors: 失败的错误信息列表（可选） + + Examples: + >>> response = ClassBatchCreateResponse( + ... total=3, + ... success_count=2, + ... failed_count=1, + ... items=[class1, class2], + ... errors=["创建类型 '药品' 失败: 类型名称已存在"] + ... ) + """ + total: int = Field(..., description="总共尝试创建的数量") + success_count: int = Field(..., description="成功创建的数量") + failed_count: int = Field(0, description="失败的数量") + items: List[ClassResponse] = Field(..., description="成功创建的类型列表") + errors: Optional[List[str]] = Field(None, description="失败的错误信息列表") + + +class ClassListResponse(BaseModel): + """类型列表响应模型 + + 用于返回本体类型列表。 + + Attributes: + total: 总数量 + scene_id: 所属场景ID + scene_name: 场景名称 + scene_description: 场景描述 + items: 类型列表 + + Examples: + >>> response = ClassListResponse( + ... total=3, + ... scene_id=uuid.uuid4(), + ... scene_name="医疗场景", + ... scene_description="用于医疗领域的本体建模", + ... items=[class1, class2, class3] + ... ) + """ + total: int = Field(..., description="总数量") + scene_id: UUID = Field(..., description="所属场景ID") + scene_name: str = Field(..., description="场景名称") + scene_description: Optional[str] = Field(None, description="场景描述") + items: List[ClassResponse] = Field(..., description="类型列表") diff --git a/api/app/schemas/prompt_optimizer_schema.py b/api/app/schemas/prompt_optimizer_schema.py index e1f27be0..08a11317 100644 --- a/api/app/schemas/prompt_optimizer_schema.py +++ b/api/app/schemas/prompt_optimizer_schema.py @@ -22,6 +22,23 @@ class PromptOptMessage(BaseModel): ) +class PromptSaveRequest(BaseModel): + session_id: UUID = Field( + ..., + description="Session ID" + ) + + title: str = Field( + ..., + description="Prompt Title" + ) + + prompt: str = Field( + ..., + description="Optimized prompt content" + ) + + class PromptOptModelSet(BaseModel): id: UUID | None = Field( default=None, diff --git a/api/app/schemas/release_share_schema.py b/api/app/schemas/release_share_schema.py index 069b78a9..47897847 100644 --- a/api/app/schemas/release_share_schema.py +++ b/api/app/schemas/release_share_schema.py @@ -1,7 +1,7 @@ import uuid import datetime from typing import Optional, List, Dict, Any -from pydantic import BaseModel, Field, ConfigDict, field_serializer +from pydantic import BaseModel, Field, ConfigDict, field_serializer, field_validator # ---------- Input Schemas ---------- @@ -88,6 +88,18 @@ class SharedReleaseInfo(BaseModel): # 嵌入配置 allow_embed: bool + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v if v is not None else {} + class EmbedCode(BaseModel): """嵌入代码""" diff --git a/api/app/services/agent_registry.py b/api/app/services/agent_registry.py index 2b6d92e3..d221bbf5 100644 --- a/api/app/services/agent_registry.py +++ b/api/app/services/agent_registry.py @@ -55,8 +55,8 @@ class AgentRegistry: """ # 构建查询 stmt = select(AgentConfig).join(App).where( - AgentConfig.is_active == True, - App.is_active == True + AgentConfig.is_active.is_(True), + App.is_active.is_(True) ) # 工作空间过滤（同工作空间或公开） diff --git a/api/app/services/app_chat_service.py b/api/app/services/app_chat_service.py index c0a66e03..1d9ab4a8 100644 --- a/api/app/services/app_chat_service.py +++ b/api/app/services/app_chat_service.py @@ -3,7 +3,7 @@ import asyncio import json import time import uuid -from typing import Optional, Dict, Any, AsyncGenerator, Annotated +from typing import Optional, Dict, Any, AsyncGenerator, Annotated, List from fastapi import Depends from sqlalchemy.orm import Session @@ -15,6 +15,7 @@ from app.core.logging_config import get_business_logger from app.db import get_db, get_db_context from app.models import MultiAgentConfig, AgentConfig, WorkflowConfig from app.schemas import DraftRunRequest +from app.schemas.app_schema import FileInput from app.services.tool_service import ToolService from app.repositories.tool_repository import ToolRepository from app.db import get_db @@ -26,6 +27,7 @@ from app.services.draft_run_service import create_web_search_tool from app.services.model_service import ModelApiKeyService from app.services.multi_agent_orchestrator import MultiAgentOrchestrator from app.services.workflow_service import WorkflowService +from app.services.multimodal_service import MultimodalService logger = get_business_logger() @@ -48,7 +50,8 @@ class AppChatService: memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, - workspace_id: Optional[str] = None + workspace_id: Optional[str] = None, + files: Optional[List[FileInput]] = None # 新增：多模态文件 ) -> Dict[str, Any]: """聊天（非流式）""" @@ -155,7 +158,14 @@ class AppChatService: for msg in messages ] - # 调用 Agent + # 处理多模态文件 + processed_files = None + if files: + multimodal_service = MultimodalService(self.db) + processed_files = await multimodal_service.process_files(files) + logger.info(f"处理了 {len(processed_files)} 个文件") + + # 调用 Agent（支持多模态） result = await agent.chat( message=message, history=history, @@ -164,14 +174,22 @@ class AppChatService: storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, config_id=config_id, - memory_flag=memory_flag + memory_flag=memory_flag, + files=processed_files # 传递处理后的文件 ) # 保存消息 self.conversation_service.save_conversation_messages( conversation_id=conversation_id, user_message=message, - assistant_message=result["content"] + assistant_message=result["content"], + meta_data={ + "usage": result.get("usage", { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0 + }) + } ) elapsed_time = time.time() - start_time @@ -199,6 +217,7 @@ class AppChatService: storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, workspace_id: Optional[str] = None, + files: Optional[List[FileInput]] = None # 新增：多模态文件 ) -> AsyncGenerator[str, None]: """聊天（流式）""" @@ -305,11 +324,19 @@ class AppChatService: for msg in messages ] + # 处理多模态文件 + processed_files = None + if files: + multimodal_service = MultimodalService(self.db) + processed_files = await multimodal_service.process_files(files) + logger.info(f"处理了 {len(processed_files)} 个文件") + # 发送开始事件 yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n" - # 流式调用 Agent + # 流式调用 Agent（支持多模态） full_content = "" + total_tokens = 0 async for chunk in agent.chat_stream( message=message, history=history, @@ -318,11 +345,15 @@ class AppChatService: storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, config_id=config_id, - memory_flag=memory_flag + memory_flag=memory_flag, + files=processed_files # 传递处理后的文件 ): - full_content += chunk - # 发送消息块事件 - yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n" + if isinstance(chunk, int): + total_tokens = chunk + else: + full_content += chunk + # 发送消息块事件 + yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n" elapsed_time = time.time() - start_time @@ -339,7 +370,7 @@ class AppChatService: content=full_content, meta_data={ "model": api_key_obj.model_name, - "usage": {} + "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens} } ) @@ -416,7 +447,11 @@ class AppChatService: meta_data={ "mode": result.get("mode"), "elapsed_time": result.get("elapsed_time"), - "sub_results": result.get("sub_results") + "usage": result.get("usage", { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0 + }) } ) @@ -458,6 +493,7 @@ class AppChatService: yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n" full_content = "" + total_tokens = 0 # 2. 创建编排器 orchestrator = MultiAgentOrchestrator(self.db, config) @@ -474,16 +510,26 @@ class AppChatService: storage_type=storage_type, user_rag_memory_id=user_rag_memory_id ): - yield event - # 尝试提取内容（用于保存） - if "data:" in event: - try: - data_line = event.split("data: ", 1)[1].strip() - data = json.loads(data_line) - if "content" in data: - full_content += data["content"] - except: - pass + if "sub_usage" in event: + if "data:" in event: + try: + data_line = event.split("data: ", 1)[1].strip() + data = json.loads(data_line) + if "total_tokens" in data: + total_tokens += data["total_tokens"] + except: + pass + else: + yield event + # 尝试提取内容（用于保存） + if "data:" in event: + try: + data_line = event.split("data: ", 1)[1].strip() + data = json.loads(data_line) + if "content" in data: + full_content += data["content"] + except: + pass elapsed_time = time.time() - start_time @@ -499,7 +545,12 @@ class AppChatService: role="assistant", content=full_content, meta_data={ - "elapsed_time": elapsed_time + "elapsed_time": elapsed_time, + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": total_tokens + } } ) diff --git a/api/app/services/app_service.py b/api/app/services/app_service.py index 68acab1d..7ec4bc0e 100644 --- a/api/app/services/app_service.py +++ b/api/app/services/app_service.py @@ -758,7 +758,7 @@ class AppService: ) # 构建查询条件 - filters = [App.is_active == True] + filters = [App.is_active.is_(True)] if type: filters.append(App.type == type) if visibility: @@ -873,7 +873,7 @@ class AppService: self._validate_workspace_access(app, workspace_id) - stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active == True).order_by( + stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active.is_(True)).order_by( AgentConfig.updated_at.desc()) agent_cfg: Optional[AgentConfig] = self.db.scalars(stmt).first() now = datetime.datetime.now() @@ -1204,7 +1204,7 @@ class AppService: default_model_config_id = None if app.type == AppType.AGENT: - stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active == True).order_by( + stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active.is_(True)).order_by( AgentConfig.updated_at.desc()) agent_cfg = self.db.scalars(stmt).first() if not agent_cfg: @@ -1226,7 +1226,7 @@ class AppService: select(MultiAgentConfig) .where( MultiAgentConfig.app_id == app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ) .order_by(MultiAgentConfig.updated_at.desc()) ) @@ -1380,7 +1380,7 @@ class AppService: stmt = ( select(AppRelease) - .where(AppRelease.app_id == app_id, AppRelease.is_active == True) + .where(AppRelease.app_id == app_id, AppRelease.is_active.is_(True)) .order_by(AppRelease.version.desc()) ) return list(self.db.scalars(stmt).all()) diff --git a/api/app/services/app_statistics_service.py b/api/app/services/app_statistics_service.py new file mode 100644 index 00000000..c164924a --- /dev/null +++ b/api/app/services/app_statistics_service.py @@ -0,0 +1,193 @@ +"""应用统计服务""" +from datetime import datetime, timedelta +from typing import Dict, Any, List +import uuid +from sqlalchemy import func, and_, cast, Date +from sqlalchemy.orm import Session + +from app.models.conversation_model import Conversation, Message +from app.models.end_user_model import EndUser +from app.models.api_key_model import ApiKey, ApiKeyLog +from app.core.exceptions import BusinessException +from app.core.error_codes import BizCode + + +class AppStatisticsService: + """应用统计服务""" + + def __init__(self, db: Session): + self.db = db + + def get_app_statistics( + self, + app_id: uuid.UUID, + workspace_id: uuid.UUID, + start_date: int, + end_date: int + ) -> Dict[str, Any]: + """获取应用统计数据 + + Args: + app_id: 应用ID + workspace_id: 工作空间ID + start_date: 开始时间戳（毫秒） + end_date: 结束时间戳（毫秒） + + Returns: + 统计数据字典 + """ + # 将毫秒时间戳转换为 datetime + start_dt = datetime.fromtimestamp(start_date / 1000) + end_dt = datetime.fromtimestamp(end_date / 1000) + timedelta(days=1) + + # 1. 会话统计 + conversations_stats = self._get_conversations_statistics(app_id, workspace_id, start_dt, end_dt) + + # 2. 新增用户统计 + users_stats = self._get_new_users_statistics(app_id, start_dt, end_dt) + + # 3. API调用统计 + api_stats = self._get_api_calls_statistics(app_id, start_dt, end_dt) + + # 4. Token消耗统计 + token_stats = self._get_token_statistics(app_id, start_dt, end_dt) + + return { + "daily_conversations": conversations_stats["daily"], + "total_conversations": conversations_stats["total"], + "daily_new_users": users_stats["daily"], + "total_new_users": users_stats["total"], + "daily_api_calls": api_stats["daily"], + "total_api_calls": api_stats["total"], + "daily_tokens": token_stats["daily"], + "total_tokens": token_stats["total"] + } + + def _get_conversations_statistics( + self, + app_id: uuid.UUID, + workspace_id: uuid.UUID, + start_dt: datetime, + end_dt: datetime + ) -> Dict[str, Any]: + """获取会话统计""" + # 每日会话数 + daily_query = self.db.query( + cast(Conversation.created_at, Date).label('date'), + func.count(Conversation.id).label('count') + ).filter( + and_( + Conversation.app_id == app_id, + Conversation.workspace_id == workspace_id, + Conversation.created_at >= start_dt, + Conversation.created_at < end_dt + ) + ).group_by(cast(Conversation.created_at, Date)).all() + + daily_data = [{"date": str(row.date), "count": row.count} for row in daily_query] + total = sum(row["count"] for row in daily_data) + + return {"daily": daily_data, "total": total} + + def _get_new_users_statistics( + self, + app_id: uuid.UUID, + start_dt: datetime, + end_dt: datetime + ) -> Dict[str, Any]: + """获取新增用户统计""" + # 每日新增用户数 + daily_query = self.db.query( + cast(EndUser.created_at, Date).label('date'), + func.count(EndUser.id).label('count') + ).filter( + and_( + EndUser.app_id == app_id, + EndUser.created_at >= start_dt, + EndUser.created_at < end_dt + ) + ).group_by(cast(EndUser.created_at, Date)).all() + + daily_data = [{"date": str(row.date), "count": row.count} for row in daily_query] + total = sum(row["count"] for row in daily_data) + + return {"daily": daily_data, "total": total} + + def _get_api_calls_statistics( + self, + app_id: uuid.UUID, + start_dt: datetime, + end_dt: datetime + ) -> Dict[str, Any]: + """获取API调用统计""" + # 每日API调用次数 + daily_query = self.db.query( + cast(ApiKeyLog.created_at, Date).label('date'), + func.count(ApiKeyLog.id).label('count') + ).join( + ApiKey, ApiKeyLog.api_key_id == ApiKey.id + ).filter( + and_( + ApiKey.resource_id == app_id, + ApiKeyLog.created_at >= start_dt, + ApiKeyLog.created_at < end_dt + ) + ).group_by(cast(ApiKeyLog.created_at, Date)).all() + + daily_data = [{"date": str(row.date), "count": row.count} for row in daily_query] + total = sum(row["count"] for row in daily_data) + + return {"daily": daily_data, "total": total} + + def _get_token_statistics( + self, + app_id: uuid.UUID, + start_dt: datetime, + end_dt: datetime + ) -> Dict[str, Any]: + """获取Token消耗统计（从Message的meta_data中提取）""" + from sqlalchemy import text + + # 查询所有相关消息的token使用情况 + # meta_data中可能包含: {"usage": {"total_tokens": 100}} 或 {"tokens": 100} + daily_query = self.db.query( + cast(Message.created_at, Date).label('date'), + Message.meta_data + ).join( + Conversation, Message.conversation_id == Conversation.id + ).filter( + and_( + Conversation.app_id == app_id, + Message.created_at >= start_dt, + Message.created_at < end_dt, + Message.meta_data.isnot(None) + ) + ).all() + + # 按日期聚合token + daily_tokens = {} + for row in daily_query: + date_str = str(row.date) + meta = row.meta_data or {} + + # 提取token数量（支持多种格式） + tokens = 0 + if isinstance(meta, dict): + # 格式1: {"usage": {"total_tokens": 100}} + if "usage" in meta and isinstance(meta["usage"], dict): + tokens = meta["usage"].get("total_tokens", 0) + # 格式2: {"tokens": 100} + elif "tokens" in meta: + tokens = meta.get("tokens", 0) + # 格式3: {"total_tokens": 100} + elif "total_tokens" in meta: + tokens = meta.get("total_tokens", 0) + + if date_str not in daily_tokens: + daily_tokens[date_str] = 0 + daily_tokens[date_str] += int(tokens) + + daily_data = [{"date": date, "tokens": tokens} for date, tokens in sorted(daily_tokens.items()) if tokens != 0] + total = sum(row["tokens"] for row in daily_data) + + return {"daily": daily_data, "total": total} diff --git a/api/app/services/conversation_service.py b/api/app/services/conversation_service.py index 275d6413..553aefc4 100644 --- a/api/app/services/conversation_service.py +++ b/api/app/services/conversation_service.py @@ -1,4 +1,5 @@ """会话服务""" +import os import uuid from datetime import datetime, timedelta from typing import Annotated @@ -298,7 +299,8 @@ class ConversationService: self, conversation_id: uuid.UUID, user_message: str, - assistant_message: str + assistant_message: str, + meta_data: Optional[dict] = None ): """ Save a pair of user and assistant messages to the conversation. @@ -307,6 +309,7 @@ class ConversationService: conversation_id (uuid.UUID): Conversation UUID. user_message (str): User's message content. assistant_message (str): Assistant's response content. + meta_data (Optional[dict]): Optional metadata for the messages. """ self.add_message( conversation_id=conversation_id, @@ -317,7 +320,8 @@ class ConversationService: self.add_message( conversation_id=conversation_id, role="assistant", - content=assistant_message + content=assistant_message, + meta_data=meta_data ) logger.debug( @@ -526,12 +530,12 @@ class ConversationService: takeaways=[], info_score=0, ) - - with open('app/services/prompt/conversation_summary_system.jinja2', 'r', encoding='utf-8') as f: + prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt') + with open(os.path.join(prompt_path, 'conversation_summary_system.jinja2'), 'r', encoding='utf-8') as f: system_prompt = f.read() rendered_system_message = Template(system_prompt).render() - with open('app/services/prompt/conversation_summary_user.jinja2', 'r', encoding='utf-8') as f: + with open(os.path.join(prompt_path, 'conversation_summary_user.jinja2'), 'r', encoding='utf-8') as f: user_prompt = f.read() rendered_user_message = Template(user_prompt).render( language=language, diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 46bda5f6..edad0123 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -16,13 +16,16 @@ from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger from app.core.rag.nlp.search import knowledge_retrieval from app.models import AgentConfig, ModelApiKey, ModelConfig +from app.repositories.model_repository import ModelApiKeyRepository from app.repositories.tool_repository import ToolRepository from app.schemas.prompt_schema import PromptMessageRole, render_prompt_message +from app.schemas.app_schema import FileInput from app.services import task_service from app.services.langchain_tool_server import Search from app.services.memory_agent_service import MemoryAgentService from app.services.model_parameter_merger import ModelParameterMerger from app.services.tool_service import ToolService +from app.services.multimodal_service import MultimodalService from langchain.tools import tool from pydantic import BaseModel, Field from sqlalchemy import select @@ -56,31 +59,28 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str 长期记忆工具 """ # search_switch = memory_config.get("search_switch", "2") - config_id= memory_config.get("memory_content",None) + config_id= memory_config.get("memory_content") or memory_config.get("memory_config",None) logger.info(f"创建长期记忆工具，配置: end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") @tool(args_schema=LongTermMemoryInput) def long_term_memory(question: str) -> str: """ - 从用户的历史记忆中检索相关信息。这是一个强大的工具，可以帮助你了解用户的背景、偏好和历史对话内容。 + 从用户的历史记忆中检索相关信息。用于了解用户的背景、偏好和历史对话内容。 - 以下场景不需要使用此工具： - 1. 情绪/社交问候场景（如"你好"、"谢谢"、"再见"等简单寒暄） - 2. 纯任务性场景（如"帮我写代码"、"翻译这段文字"等不需要历史上下文的任务） - 3. 处理外部内容时（如用户提供的文本、代码、RAG数据等，这些内容本身已经包含所需信息） + **何时使用此工具：** + - 用户明确询问历史信息（如"我之前说过什么"、"上次我们聊了什么"） + - 用户询问个人信息或偏好（如"我喜欢什么"、"我的习惯是什么"） + - 需要基于历史上下文提供个性化建议 - 除上述场景外的所有其他情况都应该使用此工具，特别是： - - 用户询问个人信息或历史对话内容 - - 需要了解用户偏好、习惯或背景 - - 用户提到"之前"、"上次"、"记得"等涉及历史的词汇 - - 需要个性化回复或基于历史上下文的建议 - - 用户询问关于自己的任何信息 + **何时不使用此工具：** + - 简单问候（如"你好"、"谢谢"、"再见"） + - 纯任务性请求（如"写代码"、"翻译文字"、"分析图片"） + - 用户已提供完整信息（如提供了文本、图片、文档等内容） + - 创作性任务（如"写诗"、"编故事"、"创作谜语"） + + **重要：如果用户的问题可以直接回答，不要调用此工具。只在确实需要历史信息时才使用。** - 需要对question改写/优化： - 需要重点关注一以下几点 - - 相关的关键词，保持原问题的核心语义不变， 根据上下文，使问题更具体、更清晰，将模糊的表达转换为明确的搜索词 - - 使用同义词或相关术语扩展查询 Args: - question: question改写之后的内容 + question: 需要检索的问题（保持原问题的核心语义，使用清晰的关键词） Returns: 检索到的历史记忆内容 @@ -92,7 +92,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str try: memory_content = asyncio.run( MemoryAgentService().read_memory( - group_id=end_user_id, + end_user_id=end_user_id, message=question, history=[], search_switch="2", @@ -106,9 +106,9 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str "app.core.memory.agent.read_message", args=[end_user_id, question, [], "1", config_id, storage_type, user_rag_memory_id] ) - # result = task_service.get_task_memory_read_result(task.id) - # status = result.get("status") - # logger.info(f"读取任务状态：{status}") + result = task_service.get_task_memory_read_result(task.id) + status = result.get("status") + logger.info(f"读取任务状态：{status}") finally: db.close() @@ -123,6 +123,10 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str } ) + # 检查是否有有效内容 + if not memory_content or str(memory_content).strip() == "" or "answer" in str(memory_content) and str(memory_content).count("''") > 0: + return "未找到相关的历史记忆。请直接回答用户的问题，不要再次调用此工具。" + return f"检索到以下历史记忆：\n\n{memory_content}" except Exception as e: logger.error("长期记忆检索失败", extra={"error": str(e), "error_type": type(e).__name__}) @@ -245,7 +249,8 @@ class DraftRunService: user_rag_memory_id: Optional[str] = None, web_search: bool = True, memory: bool = True, - sub_agent: bool = False + sub_agent: bool = False, + files: Optional[List[FileInput]] = None # 新增：多模态文件 ) -> Dict[str, Any]: """执行试运行（使用 LangChain Agent） @@ -405,7 +410,16 @@ class DraftRunService: max_history=agent_config.memory.get("max_history", 10) ) - # 6. 知识库检索 + # 6. 处理多模态文件 + processed_files = None + if files: + # 获取 provider 信息 + provider = api_key_config.get("provider", "openai") + multimodal_service = MultimodalService(self.db, provider=provider) + processed_files = await multimodal_service.process_files(files) + logger.info(f"处理了 {len(processed_files)} 个文件，provider={provider}") + + # 7. 知识库检索 context = None logger.debug( @@ -413,14 +427,15 @@ class DraftRunService: extra={ "model": api_key_config["model_name"], "has_history": bool(history), - "has_context": bool(context) + "has_context": bool(context), + "has_files": bool(processed_files) } ) memory_config_= agent_config.memory - config_id = memory_config_.get("memory_content") + config_id = memory_config_.get("memory_content") or memory_config_.get("memory_config",None) - # 7. 调用 Agent + # 8. 调用 Agent（支持多模态） result = await agent.chat( message=message, history=history, @@ -429,19 +444,27 @@ class DraftRunService: config_id=config_id, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, - memory_flag=memory_flag + memory_flag=memory_flag, + files=processed_files # 传递处理后的文件 ) elapsed_time = time.time() - start_time - # 8. 保存会话消息 + # 9. 保存会话消息 if not sub_agent and agent_config.memory and agent_config.memory.get("enabled"): await self._save_conversation_message( conversation_id=conversation_id, user_message=message, assistant_message=result["content"], app_id=agent_config.app_id, - user_id=user_id + user_id=user_id, + meta_data={ + "usage": result.get("usage", { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0 + }) + } ) response = { @@ -485,7 +508,8 @@ class DraftRunService: user_rag_memory_id: Optional[str] = None, web_search: bool = True, # 布尔类型默认值 memory: bool = True, # 布尔类型默认值 - sub_agent: bool = False # 是否是作为子Agent运行 + sub_agent: bool = False, # 是否是作为子Agent运行 + files: Optional[List[FileInput]] = None # 新增：多模态文件 ) -> AsyncGenerator[str, None]: """执行试运行（流式返回，使用 LangChain Agent） @@ -634,6 +658,15 @@ class DraftRunService: max_history=agent_config.memory.get("max_history", 10) ) + # 6. 处理多模态文件 + processed_files = None + if files: + # 获取 provider 信息 + provider = api_key_config.get("provider", "openai") + multimodal_service = MultimodalService(self.db, provider=provider) + processed_files = await multimodal_service.process_files(files) + logger.info(f"处理了 {len(processed_files)} 个文件，provider={provider}") + # 7. 知识库检索 context = None @@ -644,10 +677,11 @@ class DraftRunService: }) memory_config_ = agent_config.memory - config_id = memory_config_.get("memory_content") + config_id = memory_config_.get("memory_content") or memory_config_.get("memory_config",None) - # 9. 流式调用 Agent + # 9. 流式调用 Agent（支持多模态） full_content = "" + total_tokens = 0 async for chunk in agent.chat_stream( message=message, history=history, @@ -656,16 +690,25 @@ class DraftRunService: config_id=config_id, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, - memory_flag=memory_flag + memory_flag=memory_flag, + files=processed_files # 传递处理后的文件 ): - full_content += chunk - # 发送消息块事件 - yield self._format_sse_event("message", { - "content": chunk - }) + if isinstance(chunk, int): + total_tokens = chunk + else: + full_content += chunk + # 发送消息块事件 + yield self._format_sse_event("message", { + "content": chunk + }) elapsed_time = time.time() - start_time + if sub_agent: + yield self._format_sse_event("sub_usage", { + "total_tokens": total_tokens + }) + # 10. 保存会话消息 if not sub_agent and agent_config.memory and agent_config.memory.get("enabled"): await self._save_conversation_message( @@ -673,7 +716,10 @@ class DraftRunService: user_message=message, assistant_message=full_content, app_id=agent_config.app_id, - user_id=user_id + user_id=user_id, + meta_data={ + "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens} + } ) # 11. 发送结束事件 @@ -724,17 +770,21 @@ class DraftRunService: Raises: BusinessException: 当没有可用的 API Key 时 """ - stmt = ( - select(ModelApiKey) - .where( - ModelApiKey.model_config_id == model_config_id, - ModelApiKey.is_active == True - ) - .order_by(ModelApiKey.priority.desc()) - .limit(1) - ) - - api_key = self.db.scalars(stmt).first() + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config_id) + # stmt = ( + # select(ModelApiKey).join( + # ModelConfig, ModelApiKey.model_configs + # ) + # .where( + # ModelConfig.id == model_config_id, + # ModelApiKey.is_active.is_(True) + # ) + # .order_by(ModelApiKey.priority.desc()) + # .limit(1) + # ) + # + # api_key = self.db.scalars(stmt).first() + api_key = api_keys[0] if api_keys else None if not api_key: raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING) @@ -893,6 +943,7 @@ class DraftRunService: conversation_id: str, user_message: str, assistant_message: str, + meta_data: dict, app_id: Optional[uuid.UUID] = None, user_id: Optional[str] = None ) -> None: @@ -904,6 +955,7 @@ class DraftRunService: assistant_message: AI 回复消息 app_id: 应用ID（未使用，保留用于兼容性） user_id: 用户ID（未使用，保留用于兼容性） + meta_data: token消耗 """ try: from app.services.conversation_service import ConversationService @@ -922,7 +974,8 @@ class DraftRunService: conversation_service.add_message( conversation_id=conv_uuid, role="assistant", - content=assistant_message + content=assistant_message, + meta_data=meta_data ) logger.debug( diff --git a/api/app/services/emotion_analytics_service.py b/api/app/services/emotion_analytics_service.py index 601d2921..7bc776ed 100644 --- a/api/app/services/emotion_analytics_service.py +++ b/api/app/services/emotion_analytics_service.py @@ -17,12 +17,15 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector from pydantic import BaseModel, Field from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + logger = get_business_logger() class EmotionSuggestion(BaseModel): """情绪建议模型""" - type: str = Field(..., description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management") + type: str = Field(..., + description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management") title: str = Field(..., description="建议标题") content: str = Field(..., description="建议内容") priority: str = Field(..., description="优先级：high/medium/low") @@ -37,33 +40,33 @@ class EmotionSuggestionsResponse(BaseModel): class EmotionAnalyticsService: """情绪分析服务 - + 提供情绪数据的分析和统计功能，包括： - 情绪标签统计 - 情绪词云数据 - 情绪健康指数计算 - 个性化情绪建议生成 - + Attributes: emotion_repo: 情绪数据仓储实例 """ - + def __init__(self): """初始化情绪分析服务""" connector = Neo4jConnector() self.emotion_repo = EmotionRepository(connector) logger.info("情绪分析服务初始化完成") - + async def get_emotion_tags( - self, - end_user_id: str, - emotion_type: Optional[str] = None, - start_date: Optional[str] = None, - end_date: Optional[str] = None, - limit: int = 10 + self, + end_user_id: str, + emotion_type: Optional[str] = None, + start_date: Optional[str] = None, + end_date: Optional[str] = None, + limit: int = 10 ) -> Dict[str, Any]: """获取情绪标签统计 - + 查询指定用户的情绪类型分布，包括计数、百分比和平均强度。 确保返回所有6个情绪维度（joy、sadness、anger、fear、surprise、neutral）， 即使某些维度没有数据也会返回count=0的记录。 @@ -71,23 +74,23 @@ class EmotionAnalyticsService: """ try: logger.info(f"获取情绪标签统计: user={end_user_id}, type={emotion_type}, " - f"start={start_date}, end={end_date}, limit={limit}") - + f"start={start_date}, end={end_date}, limit={limit}") + # 调用仓储层查询 tags = await self.emotion_repo.get_emotion_tags( - group_id=end_user_id, + end_user_id=end_user_id, emotion_type=emotion_type, start_date=start_date, end_date=end_date, limit=limit ) - + # 定义所有6个情绪维度 all_emotion_types = ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral'] - + # 将查询结果转换为字典，方便查找 tags_dict = {tag["emotion_type"]: tag for tag in tags} - + # 补全缺失的情绪维度 complete_tags = [] for emotion in all_emotion_types: @@ -101,52 +104,52 @@ class EmotionAnalyticsService: "percentage": 0.0, "avg_intensity": 0.0 }) - + # 计算总数 total_count = sum(tag["count"] for tag in complete_tags) - + # 如果有数据，重新计算百分比（因为补全了0值项） if total_count > 0: for tag in complete_tags: if tag["count"] > 0: tag["percentage"] = round((tag["count"] / total_count) * 100, 2) - + # 构建时间范围信息 time_range = {} if start_date: time_range["start_date"] = start_date if end_date: time_range["end_date"] = end_date - + # 格式化响应 response = { "tags": complete_tags, "total_count": total_count, "time_range": time_range if time_range else None } - + logger.info(f"情绪标签统计完成: total_count={total_count}, tags_count={len(complete_tags)}") return response - + except Exception as e: logger.error(f"获取情绪标签统计失败: {str(e)}", exc_info=True) raise - + async def get_emotion_wordcloud( - self, - end_user_id: str, - emotion_type: Optional[str] = None, - limit: int = 50 + self, + end_user_id: str, + emotion_type: Optional[str] = None, + limit: int = 50 ) -> Dict[str, Any]: """获取情绪词云数据 - + 查询情绪关键词及其频率，用于生成词云可视化。 - + Args: end_user_id: 宿主ID（用户组ID） emotion_type: 可选的情绪类型过滤 limit: 返回关键词的最大数量 - + Returns: Dict: 包含情绪词云数据的响应： - keywords: 关键词列表 @@ -154,39 +157,39 @@ class EmotionAnalyticsService: """ try: logger.info(f"获取情绪词云数据: user={end_user_id}, type={emotion_type}, limit={limit}") - + # 调用仓储层查询 keywords = await self.emotion_repo.get_emotion_wordcloud( - group_id=end_user_id, + end_user_id=end_user_id, emotion_type=emotion_type, limit=limit ) - + # 计算总关键词数量 total_keywords = len(keywords) - + # 格式化响应 response = { "keywords": keywords, "total_keywords": total_keywords } - + logger.info(f"情绪词云数据获取完成: total_keywords={total_keywords}") return response - + except Exception as e: logger.error(f"获取情绪词云数据失败: {str(e)}", exc_info=True) raise - + def _calculate_positivity_rate(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """计算积极率 - + 根据情绪类型分类正面、负面和中性情绪，计算积极率。 公式：(正面数 / (正面数 + 负面数)) * 100 - + Args: emotions: 情绪数据列表，每个包含 emotion_type 字段 - + Returns: Dict: 包含积极率计算结果： - score: 积极率分数（0-100） @@ -197,38 +200,38 @@ class EmotionAnalyticsService: # 定义情绪分类 positive_emotions = {'joy', 'surprise'} negative_emotions = {'sadness', 'anger', 'fear'} - + # 统计各类情绪数量 positive_count = sum(1 for e in emotions if e.get('emotion_type') in positive_emotions) negative_count = sum(1 for e in emotions if e.get('emotion_type') in negative_emotions) neutral_count = sum(1 for e in emotions if e.get('emotion_type') == 'neutral') - + # 计算积极率 total_non_neutral = positive_count + negative_count if total_non_neutral > 0: score = (positive_count / total_non_neutral) * 100 else: score = 50.0 # 如果没有非中性情绪，默认为50 - + logger.debug(f"积极率计算: positive={positive_count}, negative={negative_count}, " - f"neutral={neutral_count}, score={score:.2f}") - + f"neutral={neutral_count}, score={score:.2f}") + return { "score": round(score, 2), "positive_count": positive_count, "negative_count": negative_count, "neutral_count": neutral_count } - + def _calculate_stability(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """计算稳定性 - + 基于情绪强度的标准差计算情绪稳定性。 公式：(1 - min(std_deviation, 1.0)) * 100 - + Args: emotions: 情绪数据列表，每个包含 emotion_intensity 字段 - + Returns: Dict: 包含稳定性计算结果： - score: 稳定性分数（0-100） @@ -236,7 +239,7 @@ class EmotionAnalyticsService: """ # 提取所有情绪强度 intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None] - + # 计算标准差 if len(intensities) >= 2: std_deviation = statistics.stdev(intensities) @@ -244,29 +247,29 @@ class EmotionAnalyticsService: std_deviation = 0.0 # 只有一个数据点，标准差为0 else: std_deviation = 0.0 # 没有数据，标准差为0 - + # 计算稳定性分数 # 标准差越小，稳定性越高 score = (1 - min(std_deviation, 1.0)) * 100 - + logger.debug(f"稳定性计算: intensities_count={len(intensities)}, " - f"std_deviation={std_deviation:.3f}, score={score:.2f}") - + f"std_deviation={std_deviation:.3f}, score={score:.2f}") + return { "score": round(score, 2), "std_deviation": round(std_deviation, 3) } - + def _calculate_resilience(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """计算恢复力 - + 分析情绪转换模式，统计从负面情绪恢复到正面情绪的能力。 公式：(负面到正面转换次数 / 总负面情绪数) * 100 - + Args: emotions: 情绪数据列表，每个包含 emotion_type 和 created_at 字段 应该按时间顺序排列 - + Returns: Dict: 包含恢复力计算结果： - score: 恢复力分数（0-100） @@ -275,24 +278,24 @@ class EmotionAnalyticsService: # 定义情绪分类 positive_emotions = {'joy', 'surprise'} negative_emotions = {'sadness', 'anger', 'fear'} - + # 统计负面到正面的转换次数 recovery_count = 0 negative_count = 0 - + for i in range(len(emotions)): current_emotion = emotions[i].get('emotion_type') - + # 统计负面情绪总数 if current_emotion in negative_emotions: negative_count += 1 - + # 检查下一个情绪是否为正面 if i + 1 < len(emotions): next_emotion = emotions[i + 1].get('emotion_type') if next_emotion in positive_emotions: recovery_count += 1 - + # 计算恢复力分数 if negative_count > 0: recovery_rate = recovery_count / negative_count @@ -301,28 +304,28 @@ class EmotionAnalyticsService: # 如果没有负面情绪，恢复力设为100（最佳状态） recovery_rate = 1.0 score = 100.0 - + logger.debug(f"恢复力计算: negative_count={negative_count}, " - f"recovery_count={recovery_count}, score={score:.2f}") - + f"recovery_count={recovery_count}, score={score:.2f}") + return { "score": round(score, 2), "recovery_rate": round(recovery_rate, 3) } - + async def calculate_emotion_health_index( - self, - end_user_id: str, - time_range: str = "30d" + self, + end_user_id: str, + time_range: str = "30d" ) -> Dict[str, Any]: """计算情绪健康指数 - + 综合积极率、稳定性和恢复力计算情绪健康指数。 - + Args: end_user_id: 宿主ID（用户组ID） time_range: 时间范围（7d/30d/90d） - + Returns: Dict: 包含情绪健康指数的完整响应： - health_score: 综合健康分数（0-100） @@ -336,13 +339,13 @@ class EmotionAnalyticsService: """ try: logger.info(f"计算情绪健康指数: user={end_user_id}, time_range={time_range}") - + # 获取时间范围内的情绪数据 emotions = await self.emotion_repo.get_emotions_in_range( - group_id=end_user_id, + end_user_id=end_user_id, time_range=time_range ) - + # 如果没有数据，返回默认值 if not emotions: logger.warning(f"用户 {end_user_id} 在时间范围 {time_range} 内没有情绪数据") @@ -357,20 +360,20 @@ class EmotionAnalyticsService: "emotion_distribution": {}, "time_range": time_range } - + # 计算各维度指标 positivity_rate = self._calculate_positivity_rate(emotions) stability = self._calculate_stability(emotions) resilience = self._calculate_resilience(emotions) - + # 计算综合健康分数 # 公式：positivity_rate * 0.4 + stability * 0.3 + resilience * 0.3 health_score = ( - positivity_rate["score"] * 0.4 + - stability["score"] * 0.3 + - resilience["score"] * 0.3 + positivity_rate["score"] * 0.4 + + stability["score"] * 0.3 + + resilience["score"] * 0.3 ) - + # 确定健康等级 if health_score >= 80: level = "优秀" @@ -380,13 +383,13 @@ class EmotionAnalyticsService: level = "一般" else: level = "较差" - + # 统计情绪分布 emotion_distribution = {} for emotion_type in ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']: count = sum(1 for e in emotions if e.get('emotion_type') == emotion_type) emotion_distribution[emotion_type] = count - + # 格式化响应 response = { "health_score": round(health_score, 2), @@ -399,22 +402,22 @@ class EmotionAnalyticsService: "emotion_distribution": emotion_distribution, "time_range": time_range } - + logger.info(f"情绪健康指数计算完成: score={health_score:.2f}, level={level}") return response - + except Exception as e: logger.error(f"计算情绪健康指数失败: {str(e)}", exc_info=True) raise - + def _analyze_emotion_patterns(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """分析情绪模式 - + 识别主要负面情绪、情绪触发因素和波动时段。 - + Args: emotions: 情绪数据列表，每个包含 emotion_type、emotion_intensity、created_at 字段 - + Returns: Dict: 包含情绪模式分析结果： - dominant_negative_emotion: 主要负面情绪类型 @@ -422,19 +425,19 @@ class EmotionAnalyticsService: - emotion_volatility: 情绪波动性（高/中/低） """ negative_emotions = {'sadness', 'anger', 'fear'} - + # 统计负面情绪分布 negative_emotion_counts = {} for emotion in emotions: emotion_type = emotion.get('emotion_type') if emotion_type in negative_emotions: negative_emotion_counts[emotion_type] = negative_emotion_counts.get(emotion_type, 0) + 1 - + # 识别主要负面情绪 dominant_negative_emotion = None if negative_emotion_counts: dominant_negative_emotion = max(negative_emotion_counts, key=negative_emotion_counts.get) - + # 识别高强度情绪（强度 >= 0.7） high_intensity_emotions = [ { @@ -445,7 +448,7 @@ class EmotionAnalyticsService: for e in emotions if e.get('emotion_intensity', 0) >= 0.7 ] - + # 评估情绪波动性 intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None] if len(intensities) >= 2: @@ -458,29 +461,29 @@ class EmotionAnalyticsService: volatility = "低" else: volatility = "未知" - + logger.debug(f"情绪模式分析: dominant_negative={dominant_negative_emotion}, " - f"high_intensity_count={len(high_intensity_emotions)}, volatility={volatility}") - + f"high_intensity_count={len(high_intensity_emotions)}, volatility={volatility}") + return { "dominant_negative_emotion": dominant_negative_emotion, "high_intensity_emotions": high_intensity_emotions[:5], # 最多返回5个 "emotion_volatility": volatility } - + async def generate_emotion_suggestions( - self, - end_user_id: str, - db: Session, + self, + end_user_id: str, + db: Session, ) -> Dict[str, Any]: """生成个性化情绪建议 - + 基于情绪健康数据和用户画像生成个性化建议。 - + Args: end_user_id: 宿主ID（用户组ID） db: 数据库会话 - + Returns: Dict: 包含个性化建议的响应： - health_summary: 健康状态摘要 @@ -488,24 +491,24 @@ class EmotionAnalyticsService: """ try: logger.info(f"生成个性化情绪建议: user={end_user_id}") - + # 1. 从 end_user_id 获取关联的 memory_config_id llm_client = None try: from app.services.memory_agent_service import ( get_end_user_connected_config, ) - + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") - + config_id = resolve_config_id(config_id, db) if config_id is not None: from app.services.memory_config_service import ( MemoryConfigService, ) config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config( - config_id=int(config_id), + config_id=(config_id), service_name="EmotionAnalyticsService.generate_emotion_suggestions" ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -513,35 +516,35 @@ class EmotionAnalyticsService: llm_client = factory.get_llm_client(str(memory_config.llm_model_id)) except Exception as e: logger.warning(f"无法获取 end_user {end_user_id} 的配置，将使用默认配置: {e}") - + # 2. 获取情绪健康数据 health_data = await self.calculate_emotion_health_index(end_user_id, time_range="30d") - + # 3. 获取情绪数据用于模式分析 emotions = await self.emotion_repo.get_emotions_in_range( - group_id=end_user_id, + end_user_id=end_user_id, time_range="30d" ) - + # 4. 分析情绪模式 patterns = self._analyze_emotion_patterns(emotions) - + # 5. 获取用户画像数据（简化版，直接从Neo4j获取） user_profile = await self._get_simple_user_profile(end_user_id) - + # 6. 构建LLM prompt prompt = await self._build_suggestion_prompt(health_data, patterns, user_profile) - + # 7. 调用LLM生成建议（使用配置中的LLM） if llm_client is None: # 无法获取配置时，抛出错误而不是使用默认配置 raise ValueError("无法获取LLM配置，请确保end_user关联了有效的memory_config") - + # 将 prompt 转换为 messages 格式 messages = [ {"role": "user", "content": prompt} ] - + # 8. 使用结构化输出直接获取 Pydantic 模型 try: suggestions_response = await llm_client.response_structured( @@ -552,7 +555,7 @@ class EmotionAnalyticsService: logger.error(f"LLM 结构化输出失败: {str(e)}") # 返回默认建议 suggestions_response = self._get_default_suggestions(health_data) - + # 8. 验证建议数量（3-5条） if len(suggestions_response.suggestions) < 3: logger.warning(f"建议数量不足: {len(suggestions_response.suggestions)}") @@ -560,7 +563,7 @@ class EmotionAnalyticsService: elif len(suggestions_response.suggestions) > 5: logger.warning(f"建议数量过多: {len(suggestions_response.suggestions)}") suggestions_response.suggestions = suggestions_response.suggestions[:5] - + # 9. 格式化响应 response = { "health_summary": suggestions_response.health_summary, @@ -575,87 +578,87 @@ class EmotionAnalyticsService: for s in suggestions_response.suggestions ] } - + logger.info(f"个性化建议生成完成: suggestions_count={len(response['suggestions'])}") return response - + except Exception as e: logger.error(f"生成个性化建议失败: {str(e)}", exc_info=True) raise - + async def _get_simple_user_profile(self, end_user_id: str) -> Dict[str, Any]: """获取简化的用户画像数据 - + Args: end_user_id: 用户ID - + Returns: Dict: 用户画像数据 """ try: connector = Neo4jConnector() - + # 查询用户的实体和标签 query = """ MATCH (e:Entity) - WHERE e.group_id = $group_id + WHERE e.end_user_id = $end_user_id RETURN e.name as name, e.type as type ORDER BY e.created_at DESC LIMIT 20 """ - - entities = await connector.execute_query(query, group_id=end_user_id) - + + entities = await connector.execute_query(query, end_user_id=end_user_id) + # 提取兴趣标签 interests = [e["name"] for e in entities if e.get("type") in ["INTEREST", "HOBBY"]][:5] # 后期会引入用户的习惯。。 return { "interests": interests if interests else ["未知"] } - + except Exception as e: logger.error(f"获取用户画像失败: {str(e)}") return {"interests": ["未知"]} - + async def _build_suggestion_prompt( - self, - health_data: Dict[str, Any], - patterns: Dict[str, Any], - user_profile: Dict[str, Any] + self, + health_data: Dict[str, Any], + patterns: Dict[str, Any], + user_profile: Dict[str, Any] ) -> str: """构建情绪建议生成的prompt - + Args: health_data: 情绪健康数据 patterns: 情绪模式分析结果 user_profile: 用户画像数据 - + Returns: str: LLM prompt """ from app.core.memory.utils.prompt.prompt_utils import ( render_emotion_suggestions_prompt, ) - + prompt = await render_emotion_suggestions_prompt( health_data=health_data, patterns=patterns, user_profile=user_profile ) - + return prompt - + def _get_default_suggestions(self, health_data: Dict[str, Any]) -> EmotionSuggestionsResponse: """获取默认建议（当LLM调用失败时使用） - + Args: health_data: 情绪健康数据 - + Returns: EmotionSuggestionsResponse: 默认建议 """ health_score = health_data.get('health_score', 0) - + if health_score >= 80: summary = "您的情绪健康状况优秀，请继续保持积极的生活态度。" elif health_score >= 60: @@ -664,7 +667,7 @@ class EmotionAnalyticsService: summary = "您的情绪健康需要关注，建议采取一些改善措施。" else: summary = "您的情绪健康需要重点关注，建议寻求专业帮助。" - + suggestions = [ EmotionSuggestion( type="emotion_balance", @@ -700,54 +703,54 @@ class EmotionAnalyticsService: ] ) ] - + return EmotionSuggestionsResponse( health_summary=summary, suggestions=suggestions ) - + async def get_cached_suggestions( - self, - end_user_id: str, - db: Session, + self, + end_user_id: str, + db: Session, ) -> Optional[Dict[str, Any]]: """从 Redis 缓存获取个性化情绪建议 - + Args: end_user_id: 宿主ID（用户组ID） db: 数据库会话（保留参数以保持接口兼容性） - + Returns: Dict: 缓存的建议数据，如果不存在或已过期返回 None """ try: from app.cache.memory.emotion_memory import EmotionMemoryCache - + logger.info(f"尝试从 Redis 缓存获取情绪建议: user={end_user_id}") - + # 从 Redis 获取缓存 cached_data = await EmotionMemoryCache.get_emotion_suggestions(end_user_id) - + if cached_data is None: logger.info(f"用户 {end_user_id} 的建议缓存不存在或已过期") return None - + logger.info(f"成功从 Redis 缓存获取建议: user={end_user_id}") return cached_data - + except Exception as e: logger.error(f"从 Redis 缓存获取建议失败: {str(e)}", exc_info=True) return None - + async def save_suggestions_cache( - self, - end_user_id: str, - suggestions_data: Dict[str, Any], - db: Session, - expires_hours: int = 24 + self, + end_user_id: str, + suggestions_data: Dict[str, Any], + db: Session, + expires_hours: int = 24 ) -> None: """保存建议到 Redis 缓存 - + Args: end_user_id: 宿主ID（用户组ID） suggestions_data: 建议数据 @@ -756,24 +759,24 @@ class EmotionAnalyticsService: """ try: from app.cache.memory.emotion_memory import EmotionMemoryCache - + logger.info(f"保存建议到 Redis 缓存: user={end_user_id}, expires={expires_hours}小时") - + # 计算过期时间（秒） expire_seconds = expires_hours * 3600 - + # 保存到 Redis success = await EmotionMemoryCache.set_emotion_suggestions( user_id=end_user_id, suggestions_data=suggestions_data, expire=expire_seconds ) - + if success: logger.info(f"建议缓存保存成功: user={end_user_id}") else: logger.warning(f"建议缓存保存失败: user={end_user_id}") - + except Exception as e: logger.error(f"保存建议缓存失败: {str(e)}", exc_info=True) # 不抛出异常，缓存失败不应影响主流程 \ No newline at end of file diff --git a/api/app/services/emotion_config_service.py b/api/app/services/emotion_config_service.py index 37171640..9880d4e1 100644 --- a/api/app/services/emotion_config_service.py +++ b/api/app/services/emotion_config_service.py @@ -8,9 +8,11 @@ Classes: """ from typing import Dict, Any +from uuid import UUID + from sqlalchemy.orm import Session -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig from app.core.logging_config import get_business_logger logger = get_business_logger() @@ -37,7 +39,7 @@ class EmotionConfigService: self.db = db logger.info("情绪配置服务初始化完成") - def get_emotion_config(self, config_id: int) -> Dict[str, Any]: + def get_emotion_config(self, config_id: UUID) -> Dict[str, Any]: """获取情绪引擎配置 查询指定配置ID的情绪相关配置字段。 @@ -61,8 +63,8 @@ class EmotionConfigService: logger.info(f"获取情绪配置: config_id={config_id}") # 查询配置 - config = self.db.query(DataConfig).filter( - DataConfig.config_id == config_id + config = self.db.query(MemoryConfig).filter( + MemoryConfig.config_id == config_id ).first() if not config: @@ -144,7 +146,7 @@ class EmotionConfigService: def update_emotion_config( self, - config_id: int, + config_id: UUID, config_data: Dict[str, Any] ) -> Dict[str, Any]: """更新情绪引擎配置 @@ -173,8 +175,8 @@ class EmotionConfigService: self.validate_emotion_config(config_data) # 查询配置 - config = self.db.query(DataConfig).filter( - DataConfig.config_id == config_id + config = self.db.query(MemoryConfig).filter( + MemoryConfig.config_id == config_id ).first() if not config: diff --git a/api/app/services/emotion_extraction_service.py b/api/app/services/emotion_extraction_service.py index d134251d..6b596a80 100644 --- a/api/app/services/emotion_extraction_service.py +++ b/api/app/services/emotion_extraction_service.py @@ -14,7 +14,7 @@ from app.core.memory.llm_tools.llm_client import LLMClientException from app.core.memory.models.emotion_models import EmotionExtraction from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig logger = logging.getLogger(__name__) @@ -60,7 +60,7 @@ class EmotionExtractionService: async def extract_emotion( self, statement: str, - config: DataConfig + config: MemoryConfig ) -> Optional[EmotionExtraction]: """Extract emotion information from a statement. diff --git a/api/app/services/handoffs_service.py b/api/app/services/handoffs_service.py index 114e9945..10e4d646 100644 --- a/api/app/services/handoffs_service.py +++ b/api/app/services/handoffs_service.py @@ -4,7 +4,7 @@ import uuid from typing import List, Dict, Any, Optional, AsyncGenerator, Annotated from typing_extensions import TypedDict -from langchain_core.messages import HumanMessage, AIMessage, BaseMessage +from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, AIMessageChunk from langgraph.graph import StateGraph, START, END from langgraph.types import Command from langgraph.checkpoint.memory import MemorySaver @@ -727,9 +727,12 @@ class HandoffsService: # 提取响应 response_content = "" + total_tokens = 0 for msg in result.get("messages", []): if isinstance(msg, AIMessage): response_content = msg.content + response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None + total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0 break return { @@ -737,7 +740,12 @@ class HandoffsService: "active_agent": result.get("active_agent"), "response": response_content, "message_count": len(result.get("messages", [])), - "handoff_count": result.get("handoff_count", 0) + "handoff_count": result.get("handoff_count", 0), + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": total_tokens + } } async def chat_stream( @@ -830,6 +838,12 @@ class HandoffsService: # 捕获 LLM 结束事件，输出收集到的工具调用 elif kind == "on_chat_model_end": + output_message = event.get("data", {}).get("output", {}) + if isinstance(output_message, AIMessageChunk): + response_meta = output_message.response_metadata if hasattr(output_message, 'response_metadata') else None + total_tokens = response_meta.get("token_usage", {}).get("total_tokens", + 0) if response_meta else 0 + yield f"event: sub_usage\ndata: {json.dumps({"total_tokens": total_tokens}, ensure_ascii=False)}\n\n" if collected_tool_calls: # 找到参数最完整的 transfer 工具调用 best_tc = None diff --git a/api/app/services/llm_router.py b/api/app/services/llm_router.py index 9ef9dbb1..9e102ac3 100644 --- a/api/app/services/llm_router.py +++ b/api/app/services/llm_router.py @@ -5,6 +5,7 @@ import uuid from typing import Dict, Any, List, Optional, Tuple from sqlalchemy.orm import Session +from app.repositories.model_repository import ModelApiKeyRepository from app.services.conversation_state_manager import ConversationStateManager from app.models import ModelConfig, AgentConfig from app.core.logging_config import get_business_logger @@ -382,11 +383,14 @@ class LLMRouter: from app.core.models.base import RedBearModelConfig from app.models import ModelApiKey, ModelType - # 获取 API Key 配置 - api_key_config = self.db.query(ModelApiKey).filter( - ModelApiKey.model_config_id == self.routing_model_config.id, - ModelApiKey.is_active - ).first() + # 获取 API Key 配置（通过关联关系） + # api_key_config = self.db.query(ModelApiKey).join( + # ModelConfig, ModelApiKey.model_configs + # ).filter(ModelConfig.id == self.routing_model_config.id, + # ModelApiKey.is_active == True + # ).first() + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, self.routing_model_config.id) + api_key_config = api_keys[0] if api_keys else None if not api_key_config: raise Exception("路由模型没有可用的 API Key") @@ -419,6 +423,9 @@ class LLMRouter: # 调用模型 response = await llm.ainvoke(prompt) + + from app.services.model_service import ModelApiKeyService + ModelApiKeyService.record_api_key_usage(self.db, api_key_config.id) # 提取响应内容 if hasattr(response, 'content'): diff --git a/api/app/services/master_agent_router.py b/api/app/services/master_agent_router.py index 3971aab7..87fdb22c 100644 --- a/api/app/services/master_agent_router.py +++ b/api/app/services/master_agent_router.py @@ -5,7 +5,7 @@ import uuid from typing import Dict, Any, List, Optional, Tuple from sqlalchemy.orm import Session -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters from app.services.conversation_state_manager import ConversationStateManager from app.models import ModelConfig, AgentConfig from app.core.logging_config import get_business_logger diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 8170bdd8..823d5d43 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -9,6 +9,7 @@ import os import re import time import uuid +from uuid import UUID from typing import Any, AsyncGenerator, Dict, List, Optional import redis @@ -27,6 +28,7 @@ from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType +from app.repositories.memory_short_repository import ShortTermMemoryRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_agent_schema import Write_UserInput from app.schemas.memory_config_schema import ConfigurationError @@ -35,6 +37,7 @@ from app.services.memory_config_service import MemoryConfigService from app.services.memory_konwledges_server import ( write_rag, ) +from langchain_core.messages import AIMessage from langchain_core.messages import HumanMessage from pydantic import BaseModel, Field from sqlalchemy import func @@ -54,25 +57,24 @@ _neo4j_connector = Neo4jConnector() class MemoryAgentService: """Service for memory agent operations""" - def writer_messages_deal(self, messages, start_time, group_id, config_id, message, context): + def writer_messages_deal(self, messages, start_time, end_user_id, config_id, message, context): duration = time.time() - start_time - if str(messages) == 'success': - logger.info(f"Write operation successful for group {group_id} with config_id {config_id}") + logger.info(f"Write operation successful for group {end_user_id} with config_id {config_id}") # 记录成功的操作 if audit_logger: - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=True, + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=True, duration=duration, details={"message_length": len(message)}) return context else: - logger.warning(f"Write operation failed for group {group_id}") + logger.warning(f"Write operation failed for group {end_user_id}") # 记录失败的操作 if audit_logger: audit_logger.log_operation( operation="WRITE", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=f"写入失败: {messages[:100]}" @@ -173,10 +175,9 @@ class MemoryAgentService: """ logger.info("Reading log file") - - current_file = os.path.abspath(__file__) # app/services/memory_agent_service.py - app_dir = os.path.dirname(os.path.dirname(current_file)) # app directory - project_root = os.path.dirname(app_dir) # redbear-mem directory + # Get log file path - use project root directory + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) # api directory log_path = os.path.join(project_root, "logs", "agent_service.log") summer = '' @@ -215,9 +216,8 @@ class MemoryAgentService: logger.info("Starting log content streaming") # Get log file path - use project root directory - current_file = os.path.abspath(__file__) # app/services/memory_agent_service.py - app_dir = os.path.dirname(os.path.dirname(current_file)) # app directory - project_root = os.path.dirname(app_dir) # redbear-mem directory + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) # api directory log_path = os.path.join(project_root, "logs", "agent_service.log") # Check if file exists before starting stream @@ -265,13 +265,13 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, group_id: str, messages: list[dict], config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id Args: - group_id: Group identifier (also used as end_user_id) - messages: Structured message list [{"role": "user", "content": "..."}, ...] + end_user_id: Group identifier (also used as end_user_id) + message: Message to write config_id: Configuration ID from database db: SQLAlchemy database session storage_type: Storage type (neo4j or rag) @@ -286,15 +286,15 @@ class MemoryAgentService: # Resolve config_id if None using end_user's connected config if config_id is None: try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): - raise - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + raise # Re-raise our specific error + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") import time start_time = time.time() @@ -314,7 +314,7 @@ class MemoryAgentService: # Log failed operation if audit_logger: duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg) + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) raise ValueError(error_msg) @@ -322,24 +322,25 @@ class MemoryAgentService: if storage_type == "rag": # For RAG storage, convert messages to single string message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - result = await write_rag(group_id, message_text, user_rag_memory_id) + result = await write_rag(end_user_id, message_text, user_rag_memory_id) return result else: async with make_write_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # Convert structured messages to LangChain messages langchain_messages = [] for msg in messages: if msg['role'] == 'user': langchain_messages.append(HumanMessage(content=msg['content'])) elif msg['role'] == 'assistant': - from langchain_core.messages import AIMessage langchain_messages.append(AIMessage(content=msg['content'])) - + print(100*'-') + print(langchain_messages) + print(100*'-') # 初始状态 - 包含所有必要字段 initial_state = { "messages": langchain_messages, - "group_id": group_id, + "end_user_id": end_user_id, "memory_config": memory_config } @@ -356,14 +357,14 @@ class MemoryAgentService: contents = massages.get('write_result') # Convert messages back to string for logging message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - return self.writer_messages_deal(massagesstatus, start_time, group_id, config_id, message_text, contents) + return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) except Exception as e: # Ensure proper error handling and logging error_msg = f"Write operation failed: {str(e)}" logger.error(error_msg) if audit_logger: duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg) + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) raise ValueError(error_msg) @@ -371,15 +372,14 @@ class MemoryAgentService: async def read_memory( self, - group_id: str, + end_user_id: str, message: str, history: List[Dict], search_switch: str, - config_id: Optional[str], + config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, - user_rag_memory_id: str - ) -> Dict: + user_rag_memory_id: str) -> Dict: """ Process read operation with config_id @@ -389,7 +389,7 @@ class MemoryAgentService: - "2": Direct answer based on context Args: - group_id: Group identifier (also used as end_user_id) + end_user_id: Group identifier (also used as end_user_id) message: User message history: Conversation history search_switch: Search mode switch @@ -407,22 +407,22 @@ class MemoryAgentService: import time start_time = time.time() - logger.info(f"[PERF] read_memory started for group_id={group_id}, search_switch={search_switch}") + ori_message= message # Resolve config_id if None using end_user's connected config if config_id is None: try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): raise # Re-raise our specific error - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") - logger.info(f"Read operation for group {group_id} with config_id {config_id}") + logger.info(f"Read operation for group {end_user_id} with config_id {config_id}") # 导入审计日志记录器 try: @@ -450,7 +450,7 @@ class MemoryAgentService: audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=error_msg @@ -460,16 +460,16 @@ class MemoryAgentService: # Step 2: Prepare history history.append({"role": "user", "content": message}) - logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}") + logger.debug(f"Group ID:{end_user_id}, Message:{message}, History:{history}, Config ID:{config_id}") # Step 3: Initialize MCP client and execute read workflow graph_exec_start = time.time() try: async with make_read_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch, - "group_id": group_id + "end_user_id": end_user_id , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id, "memory_config": memory_config} # 获取节点更新信息 @@ -544,9 +544,8 @@ class MemoryAgentService: if intermediate_type == "search_result": query = intermediate.get('query', '') raw_results = intermediate.get('raw_results', {}) - reranked_results = raw_results.get('reranked_results', []) - try: + reranked_results = raw_results.get('reranked_results', []) statements = [statement['statement'] for statement in reranked_results.get('statements', [])] except Exception: statements = [] @@ -565,13 +564,13 @@ class MemoryAgentService: if '信息不足，无法回答。' != str(summary) and str(search_switch).strip() != "2": # 使用 upsert 方法 repo.upsert( - end_user_id=group_id, - messages=message, + end_user_id=end_user_id, + messages=ori_message, aimessages=summary, retrieved_content=retrieved_content, search_switch=str(search_switch) ) - logger.info(f"成功保存短期记忆: group_id={group_id}, search_switch={search_switch}") + logger.info(f"成功保存短期记忆: end_user_id={end_user_id}, search_switch={search_switch}") else: logger.debug(f"跳过保存短期记忆: summary={summary[:50] if summary else 'None'}, search_switch={search_switch}") @@ -587,7 +586,7 @@ class MemoryAgentService: audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=True, duration=duration ) @@ -599,20 +598,20 @@ class MemoryAgentService: except Exception as e: # Ensure proper error handling and logging error_msg = f"Read operation failed: {str(e)}" - total_time = time.time() - start_time - logger.error(f"[PERF] read_memory failed after {total_time:.4f}s: {error_msg}") + logger.error(error_msg) if audit_logger: duration = time.time() - start_time audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=error_msg ) raise ValueError(error_msg) + def get_messages_list(self, user_input: Write_UserInput) -> list[dict]: """ Get standardized message list from user input. @@ -657,7 +656,7 @@ class MemoryAgentService: logger.info(f"Validation successful: Structured message list, count: {len(user_input.messages)}") return user_input.messages - async def classify_message_type(self, message: str, config_id: int, db: Session) -> Dict: + async def classify_message_type(self, message: str, config_id: UUID, db: Session) -> Dict: """ Determine the type of user message (read or write) Updated to eliminate global variables in favor of explicit parameters. @@ -672,6 +671,8 @@ class MemoryAgentService: """ logger.info("Classifying message type") + + # Load configuration to get LLM model ID config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config( @@ -682,9 +683,9 @@ class MemoryAgentService: status = await status_typle(message, memory_config.llm_model_id) logger.debug(f"Message type: {status}") return status - async def generate_summary_from_retrieve( self, + end_user_id: str, retrieve_info: str, history: List[Dict], query: str, @@ -706,6 +707,18 @@ class MemoryAgentService: Returns: 生成的答案文本 """ + if config_id is None: + try: + config_id = get_end_user_connected_config(end_user_id, db) + config_id = config_id.get('memory_config_id') + if config_id is None: + raise ValueError( + f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") + except Exception as e: + if "No memory configuration found" in str(e): + raise # Re-raise our specific error + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") logger.info(f"Generating summary from retrieve info for query: {query[:50]}...") try: @@ -731,7 +744,7 @@ class MemoryAgentService: state=state, history=history, retrieve_info=retrieve_info, - template_name='Retrieve_Summary_prompt.jinja2', + template_name='direct_summary_prompt.jinja2', operation_name='retrieve_summary', response_model=RetrieveSummaryResponse, search_mode="1" @@ -755,7 +768,7 @@ class MemoryAgentService: """ 统计知识库类型分布，包含： 1. PostgreSQL 中的知识库类型：General, Web, Third-party, Folder（根据 workspace_id 过滤） - 2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/group_id 过滤） + 2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/end_user_id 过滤） 3. total: 所有类型的总和 参数： @@ -841,11 +854,11 @@ class MemoryAgentService: for end_user in end_users: end_user_id_str = str(end_user.id) memory_query = """ - MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN count(n) AS Count + MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN count(n) AS Count """ neo4j_result = await _neo4j_connector.execute_query( memory_query, - group_id=end_user_id_str, + end_user_id=end_user_id_str, ) chunk_count = neo4j_result[0]["Count"] if neo4j_result else 0 total_chunks += chunk_count @@ -885,7 +898,7 @@ class MemoryAgentService: 获取指定用户的热门记忆标签 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的group_id字段 + - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 - limit: 返回标签数量限制 返回格式： @@ -895,7 +908,7 @@ class MemoryAgentService: ] """ try: - # by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度） + # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度） tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) payload=[] for tag, freq in tags: @@ -970,21 +983,21 @@ class MemoryAgentService: # 查询该用户的语句 query = ( "MATCH (s:Statement) " - "WHERE ($group_id IS NULL OR s.group_id = $group_id) AND s.statement IS NOT NULL " + "WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND s.statement IS NOT NULL " "RETURN s.statement AS statement " "ORDER BY s.created_at DESC LIMIT 100" ) - rows = await connector.execute_query(query, group_id=end_user_id) + rows = await connector.execute_query(query, end_user_id=end_user_id) statements = [r.get("statement", "") for r in rows if r.get("statement")] # 查询该用户的热门实体 entity_query = ( "MATCH (e:ExtractedEntity) " - "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL " + "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC LIMIT 20" ) - entity_rows = await connector.execute_query(entity_query, group_id=end_user_id) + entity_rows = await connector.execute_query(entity_query, end_user_id=end_user_id) entities = [f"{r['name']} ({r['frequency']})" for r in entity_rows] await connector.close() @@ -1037,14 +1050,14 @@ class MemoryAgentService: names_to_exclude = ['AI', 'Caroline', 'Melanie', 'Jon', 'Gina', '用户', 'AI助手', 'John', 'Maria'] hot_tag_query = ( "MATCH (e:ExtractedEntity) " - "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' " + "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' " "AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC LIMIT 4" ) hot_tag_rows = await connector.execute_query( hot_tag_query, - group_id=end_user_id, + end_user_id=end_user_id, names_to_exclude=names_to_exclude ) await connector.close() @@ -1079,9 +1092,8 @@ class MemoryAgentService: logger.info("Starting log content streaming") # Get log file path - use project root directory - current_file = os.path.abspath(__file__) # app/services/memory_agent_service.py - app_dir = os.path.dirname(os.path.dirname(current_file)) # app directory - project_root = os.path.dirname(app_dir) # redbear-mem directory + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) # api directory log_path = os.path.join(project_root, "logs", "agent_service.log") # Check if file exists before starting stream @@ -1179,6 +1191,16 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An # 3. 从 config 中提取 memory_config_id config = latest_release.config or {} + + # 如果 config 是字符串，解析为字典 + if isinstance(config, str): + import json + try: + config = json.loads(config) + except json.JSONDecodeError: + logger.warning(f"Failed to parse config JSON for release {latest_release.id}") + config = {} + memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None @@ -1217,7 +1239,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) """ from app.models.app_release_model import AppRelease from app.models.end_user_model import EndUser - from app.models.data_config_model import DataConfig + from app.models.memory_config_model import MemoryConfig from sqlalchemy import select logger.info(f"Batch getting connected configs for {len(end_user_ids)} end_users") @@ -1230,10 +1252,10 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 1. 批量查询所有 end_user 及其 app_id end_users = db.query(EndUser).filter(EndUser.id.in_(end_user_ids)).all() - + # 创建 end_user_id -> app_id 的映射 user_to_app = {str(eu.id): eu.app_id for eu in end_users} - + # 记录未找到的用户 found_user_ids = set(user_to_app.keys()) missing_user_ids = set(end_user_ids) - found_user_ids @@ -1243,7 +1265,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) result[user_id] = {"memory_config_id": None, "memory_config_name": None} # 2. 批量获取所有相关应用的最新发布版本 - app_ids = list(user_to_app.values()) + app_ids = list(set(user_to_app.values())) if not app_ids: return result @@ -1263,6 +1285,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 3. 收集所有 memory_config_id 并批量查询配置名称 memory_config_ids = [] + old_config_ids = [] # 存储旧的整数ID + for end_user_id, app_id in user_to_app.items(): release = app_to_release.get(app_id) if release: @@ -1270,18 +1294,42 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None if memory_config_id: - memory_config_ids.append(memory_config_id) - + # 判断是否为UUID格式 + if len(str(memory_config_id))>=5: + uuid.UUID(str(memory_config_id)) + memory_config_ids.append(memory_config_id) + else: + old_config_ids.append(str(memory_config_id)) + # 批量查询 memory_config_name config_id_to_name = {} + + # 记录分类结果 + if memory_config_ids or old_config_ids: + logger.info(f"Collected {len(memory_config_ids)} UUID config_ids and {len(old_config_ids)} old integer config_ids") + if old_config_ids: + logger.debug(f"Old config IDs: {old_config_ids}") + + # 查询新的UUID格式的config_id if memory_config_ids: - memory_configs = db.query(DataConfig).filter(DataConfig.config_id.in_(memory_config_ids)).all() - config_id_to_name = {str(mc.config_id): mc.config_name for mc in memory_configs} + memory_configs = db.query(MemoryConfig).filter(MemoryConfig.config_id.in_(memory_config_ids)).all() + config_id_to_name.update({str(mc.config_id): mc.config_name for mc in memory_configs}) + + # 查询旧的整数ID（通过config_id_old字段） + if old_config_ids: + old_memory_configs = db.query(MemoryConfig).filter(MemoryConfig.config_id_old.in_(old_config_ids)).all() + # 使用config_id_old作为key，这样后面查找时能匹配上 + config_id_to_name.update({str(mc.config_id_old): mc.config_name for mc in old_memory_configs}) + # 同时也添加config_id作为key，方便后续使用 + for mc in old_memory_configs: + if mc.config_id_old: + config_id_to_name[str(mc.config_id)] = mc.config_name + logger.info(f"Found {len(old_memory_configs)} configs for old IDs") # 4. 构建最终结果 for end_user_id, app_id in user_to_app.items(): release = app_to_release.get(app_id) - + if not release: logger.warning(f"No active release found for app: {app_id} (end_user: {end_user_id})") result[end_user_id] = {"memory_config_id": None, "memory_config_name": None} @@ -1292,7 +1340,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None - # 获取配置名称 + # 获取配置名称（使用字符串形式的ID进行查找，兼容新旧格式） memory_config_name = config_id_to_name.get(str(memory_config_id)) if memory_config_id else None result[end_user_id] = { diff --git a/api/app/services/memory_api_service.py b/api/app/services/memory_api_service.py index 0ae2b965..a8c39a5a 100644 --- a/api/app/services/memory_api_service.py +++ b/api/app/services/memory_api_service.py @@ -25,7 +25,7 @@ class MemoryAPIService: This service provides a thin layer that: 1. Validates end_user exists and belongs to the authorized workspace - 2. Maps end_user_id to group_id for memory operations + 2. Maps end_user_id to end_user_id for memory operations 3. Delegates to MemoryAgentService for actual memory read/write operations """ @@ -68,7 +68,7 @@ class MemoryAPIService: ) end_user = self.db.query(EndUser).filter(EndUser.id == end_user_uuid).first() - + if not end_user: logger.warning(f"End user not found: {end_user_id}") raise ResourceNotFoundException( @@ -77,7 +77,10 @@ class MemoryAPIService: ) # Verify end_user belongs to the workspace via App relationship - app = self.db.query(App).filter(App.id == end_user.app_id).first() + app = self.db.query(App).filter( + App.id == end_user.app_id, + App.is_active.is_(True) + ).first() if not app: logger.warning(f"App not found for end_user: {end_user_id}") @@ -115,7 +118,7 @@ class MemoryAPIService: Args: workspace_id: Workspace ID for resource validation - end_user_id: End user identifier (used as group_id) + end_user_id: End user identifier (used as end_user_id) message: Message content to store config_id: Optional memory configuration ID storage_type: Storage backend (neo4j or rag) @@ -133,14 +136,13 @@ class MemoryAPIService: # Validate end_user exists and belongs to workspace self.validate_end_user(end_user_id, workspace_id) - # Use end_user_id as group_id for memory operations - group_id = end_user_id + # Use end_user_id as end_user_id for memory operations try: # Delegate to MemoryAgentService result = await MemoryAgentService().write_memory( - group_id=group_id, - message=message, + end_user_id=end_user_id, + messages=message, config_id=config_id, db=self.db, storage_type=storage_type, @@ -186,7 +188,7 @@ class MemoryAPIService: Args: workspace_id: Workspace ID for resource validation - end_user_id: End user identifier (used as group_id) + end_user_id: End user identifier (used as end_user_id) message: Query message search_switch: Search mode (0=deep search with verification, 1=deep search, 2=fast search) config_id: Optional memory configuration ID @@ -205,13 +207,13 @@ class MemoryAPIService: # Validate end_user exists and belongs to workspace self.validate_end_user(end_user_id, workspace_id) - # Use end_user_id as group_id for memory operations - group_id = end_user_id + # Use end_user_id as end_user_id for memory operations + try: # Delegate to MemoryAgentService result = await MemoryAgentService().read_memory( - group_id=group_id, + end_user_id=end_user_id, message=message, history=[], search_switch=search_switch, diff --git a/api/app/services/memory_base_service.py b/api/app/services/memory_base_service.py index 25a8281d..bc647752 100644 --- a/api/app/services/memory_base_service.py +++ b/api/app/services/memory_base_service.py @@ -326,7 +326,7 @@ class MemoryBaseService: Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 最大emotion_intensity对应的emotion_type，如果没有则返回None @@ -334,7 +334,7 @@ class MemoryBaseService: try: query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) WHERE stmt.emotion_type IS NOT NULL AND stmt.emotion_intensity IS NOT NULL @@ -347,7 +347,7 @@ class MemoryBaseService: result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) if result and len(result) > 0: @@ -381,10 +381,10 @@ class MemoryBaseService: if end_user_id: query = """ MATCH (n:MemorySummary) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await self.neo4j_connector.execute_query(query, group_id=end_user_id) + result = await self.neo4j_connector.execute_query(query, end_user_id=end_user_id) else: query = """ MATCH (n:MemorySummary) @@ -423,12 +423,12 @@ class MemoryBaseService: if end_user_id: semantic_query = """ MATCH (e:ExtractedEntity) - WHERE e.group_id = $group_id AND e.is_explicit_memory = true + WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN count(e) as count """ semantic_result = await self.neo4j_connector.execute_query( semantic_query, - group_id=end_user_id + end_user_id=end_user_id ) else: semantic_query = """ @@ -519,7 +519,7 @@ class MemoryBaseService: """ if end_user_id: - query += " AND n.group_id = $group_id" + query += " AND n.end_user_id = $end_user_id" query += """ RETURN sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes @@ -528,7 +528,7 @@ class MemoryBaseService: # 设置查询参数 params = {'threshold': forgetting_threshold} if end_user_id: - params['group_id'] = end_user_id + params['end_user_id'] = end_user_id # 执行查询 result = await self.neo4j_connector.execute_query(query, **params) diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index 0099eb18..e09cf67f 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -7,14 +7,15 @@ This service eliminates code duplication between MemoryAgentService and MemorySt import time from datetime import datetime - +from app.models.memory_config_model import MemoryConfig as MemoryConfigModel +from sqlalchemy import select from app.core.logging_config import get_config_logger, get_logger from app.core.validators.memory_config_validators import ( validate_and_resolve_model_id, validate_embedding_model, validate_model_exists_and_active, ) -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.schemas.memory_config_schema import ( ConfigurationError, InvalidConfigError, @@ -23,20 +24,24 @@ from app.schemas.memory_config_schema import ( ModelNotFoundError, ) from sqlalchemy.orm import Session +from uuid import UUID logger = get_logger(__name__) config_logger = get_config_logger() +import uuid - -def _validate_config_id(config_id): - """Validate configuration ID format.""" +def _validate_config_id(config_id, db: Session = None): + """Validate configuration ID format (supports both UUID and integer).""" + if isinstance(config_id, uuid.UUID): + return config_id + if config_id is None: raise InvalidConfigError( "Configuration ID cannot be None", field_name="config_id", invalid_value=config_id, ) - + if isinstance(config_id, int): if config_id <= 0: raise InvalidConfigError( @@ -44,27 +49,56 @@ def _validate_config_id(config_id): field_name="config_id", invalid_value=config_id, ) + # 如果提供了数据库会话，尝试通过 user_id 查询 config_id + if db is not None: + # 查询 user_id 匹配的记录 + stmt = select(MemoryConfigModel).where(MemoryConfigModel.config_id_old == str(config_id)) + result = db.execute(stmt).scalars().first() + if result: + logger.info(f"Found config_id {result.config_id} for user_id {config_id}") + return result.config_id + return config_id - + if isinstance(config_id, str): + config_id_stripped = config_id.strip() + + # Try parsing as UUID first try: - parsed_id = int(config_id.strip()) + return uuid.UUID(config_id_stripped) + except ValueError: + pass + + # Fall back to integer parsing + try: + parsed_id = int(config_id_stripped) if parsed_id <= 0: raise InvalidConfigError( f"Configuration ID must be positive: {parsed_id}", field_name="config_id", invalid_value=config_id, ) + + # 如果提供了数据库会话，尝试通过 user_id 查询 config_id + if db is not None: + # 查询 user_id 匹配的记录 + stmt = select(MemoryConfigModel).where(MemoryConfigModel.user_id == str(parsed_id)) + result = db.execute(stmt).scalars().first() + + if result: + logger.info(f"Found config_id {result.config_id} for user_id {parsed_id}") + return result.config_id + return parsed_id except ValueError: raise InvalidConfigError( - f"Invalid configuration ID format: '{config_id}'", + f"Invalid configuration ID format: '{config_id}' (must be UUID or positive integer)", field_name="config_id", invalid_value=config_id, ) - + raise InvalidConfigError( - f"Invalid type for configuration ID: expected int or str, got {type(config_id).__name__}", + f"Invalid type for configuration ID: expected UUID, int or str, got {type(config_id).__name__}", field_name="config_id", invalid_value=config_id, ) @@ -73,61 +107,61 @@ def _validate_config_id(config_id): class MemoryConfigService: """ Centralized service for memory configuration loading and validation. - + This class provides a single implementation of configuration loading logic that can be shared across multiple services, eliminating code duplication. - + Usage: config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config(config_id) model_config = config_service.get_model_config(model_id) """ - + def __init__(self, db: Session): """Initialize the service with a database session. - + Args: db: SQLAlchemy database session """ self.db = db - + def load_memory_config( self, - config_id: int, + config_id: UUID, service_name: str = "MemoryConfigService", ) -> MemoryConfig: """ Load memory configuration from database by config_id. - + Args: - config_id: Configuration ID from database + config_id: Configuration ID (UUID) from database service_name: Name of the calling service (for logging purposes) - + Returns: MemoryConfig: Immutable configuration object - + Raises: ConfigurationError: If validation fails """ start_time = time.time() - + config_logger.info( "Starting memory configuration loading", extra={ "operation": "load_memory_config", "service": service_name, - "config_id": config_id, + "config_id": str(config_id), }, ) - + logger.info(f"Loading memory configuration from database: config_id={config_id}") - + try: - validated_config_id = _validate_config_id(config_id) - + validated_config_id = _validate_config_id(config_id, self.db) + # Step 1: Get config and workspace db_query_start = time.time() - result = DataConfigRepository.get_config_with_workspace(self.db, validated_config_id) + result = MemoryConfigRepository.get_config_with_workspace(self.db, validated_config_id) db_query_time = time.time() - db_query_start logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s") if not result: @@ -136,18 +170,18 @@ class MemoryConfigService: "Configuration not found in database", extra={ "operation": "load_memory_config", - "config_id": validated_config_id, + "config_id": str(config_id), "load_result": "not_found", "elapsed_ms": elapsed_ms, "service": service_name, }, ) raise ConfigurationError( - f"Configuration {validated_config_id} not found in database" + f"Configuration {config_id} not found in database" ) - + memory_config, workspace = result - + # Step 2: Validate embedding model (returns both UUID and name) embed_start = time.time() embedding_uuid, embedding_name = validate_embedding_model( @@ -159,7 +193,7 @@ class MemoryConfigService: ) embed_time = time.time() - embed_start logger.info(f"[PERF] Embedding validation: {embed_time:.4f}s") - + # Step 3: Resolve LLM model llm_start = time.time() llm_uuid, llm_name = validate_and_resolve_model_id( @@ -173,7 +207,7 @@ class MemoryConfigService: ) llm_time = time.time() - llm_start logger.info(f"[PERF] LLM validation: {llm_time:.4f}s") - + # Step 4: Resolve optional rerank model rerank_start = time.time() rerank_uuid = None @@ -191,10 +225,10 @@ class MemoryConfigService: rerank_time = time.time() - rerank_start if memory_config.rerank_id: logger.info(f"[PERF] Rerank validation: {rerank_time:.4f}s") - + # Note: embedding_name is now returned from validate_embedding_model above # No need for redundant query! - + # Create immutable MemoryConfig object config = MemoryConfig( config_id=memory_config.config_id, @@ -235,9 +269,9 @@ class MemoryConfigService: pruning_scene=memory_config.pruning_scene or "education", pruning_threshold=float(memory_config.pruning_threshold) if memory_config.pruning_threshold is not None else 0.5, ) - + elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.info( "Memory configuration loaded successfully", extra={ @@ -250,13 +284,13 @@ class MemoryConfigService: "elapsed_ms": elapsed_ms, }, ) - + logger.info(f"Memory configuration loaded successfully: {config.config_name}") return config - + except Exception as e: elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.error( "Failed to load memory configuration", extra={ @@ -270,7 +304,7 @@ class MemoryConfigService: }, exc_info=True, ) - + logger.error(f"Failed to load memory configuration {config_id}: {e}") if isinstance(e, (ConfigurationError, ValueError)): raise @@ -304,7 +338,7 @@ class MemoryConfigService: "provider": api_config.provider, "api_key": api_config.api_key, "base_url": api_config.api_base, - "model_config_id": api_config.model_config_id, + "model_config_id": str(config.id), "type": config.type, "timeout": settings.LLM_TIMEOUT, "max_retries": settings.LLM_MAX_RETRIES, @@ -336,7 +370,7 @@ class MemoryConfigService: "provider": api_config.provider, "api_key": api_config.api_key, "base_url": api_config.api_base, - "model_config_id": api_config.model_config_id, + "model_config_id": str(config.id), "type": config.type, "timeout": 120.0, "max_retries": 5, diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index a774647e..06a94060 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -53,18 +53,28 @@ def get_workspace_end_users( workspace_id: uuid.UUID, current_user: User ) -> List[EndUser]: - """获取工作空间的所有宿主""" + """获取工作空间的所有宿主（优化版本：减少数据库查询次数）""" business_logger.info(f"获取工作空间宿主列表: workspace_id={workspace_id}, 操作者: {current_user.username}") try: - # 查询应用（ORM）并转换为 Pydantic 模型 + # 查询应用（ORM） apps_orm = app_repository.get_apps_by_workspace_id(db, workspace_id) - apps = [AppSchema.model_validate(h) for h in apps_orm] - app_ids = [app.id for app in apps] - end_users = [] - for app_id in app_ids: - end_user_orm_list = end_user_repository.get_end_users_by_app_id(db, app_id) - end_users.extend([EndUserSchema.model_validate(h) for h in end_user_orm_list]) + + if not apps_orm: + business_logger.info("工作空间下没有应用") + return [] + + # 提取所有 app_id + app_ids = [app.id for app in apps_orm] + + # 批量查询所有 end_users（一次查询而非循环查询） + from app.models.end_user_model import EndUser as EndUserModel + end_users_orm = db.query(EndUserModel).filter( + EndUserModel.app_id.in_(app_ids) + ).all() + + # 转换为 Pydantic 模型（只在需要时转换） + end_users = [EndUserSchema.model_validate(eu) for eu in end_users_orm] business_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return end_users @@ -414,6 +424,67 @@ def get_current_user_total_chunk( business_logger.error(f"获取用户总chunk数失败: end_user_id={end_user_id} - {str(e)}") raise + +def get_users_total_chunk_batch( + end_user_ids: List[str], + db: Session, + current_user: User +) -> dict: + """ + 批量获取多个用户的总chunk数（性能优化版本） + + Args: + end_user_ids: 用户ID列表 + db: 数据库会话 + current_user: 当前用户 + + Returns: + 字典，key为end_user_id，value为chunk总数 + 格式: {"user_id_1": 100, "user_id_2": 50, ...} + """ + business_logger.info(f"批量获取 {len(end_user_ids)} 个用户的总chunk数, 操作者: {current_user.username}") + + try: + from app.models.document_model import Document + from sqlalchemy import func, case + + if not end_user_ids: + return {} + + # 构造所有文件名 + file_names = [f"{user_id}.txt" for user_id in end_user_ids] + + # 一次查询获取所有用户的chunk总数 + # 使用 GROUP BY file_name 来分组统计 + results = db.query( + Document.file_name, + func.sum(Document.chunk_num).label('total_chunk') + ).filter( + Document.file_name.in_(file_names) + ).group_by( + Document.file_name + ).all() + + # 构建结果字典 + chunk_map = {} + for file_name, total_chunk in results: + # 从文件名中提取 end_user_id (去掉 .txt 后缀) + user_id = file_name.replace('.txt', '') + chunk_map[user_id] = int(total_chunk or 0) + + # 对于没有记录的用户，设置为0 + for user_id in end_user_ids: + if user_id not in chunk_map: + chunk_map[user_id] = 0 + + business_logger.info(f"成功批量获取 {len(chunk_map)} 个用户的总chunk数") + return chunk_map + + except Exception as e: + business_logger.error(f"批量获取用户总chunk数失败: {str(e)}") + raise + + def get_rag_content( end_user_id: str, limit: int, diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index 9b5f3c99..7081d28b 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -717,8 +717,8 @@ class MemoryInteraction: ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id) if ori_data!=[]: # name = ori_data[0]['name'] - group_id = [i['group_id'] for i in ori_data][0] - Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) + end_user_id = [i['end_user_id'] for i in ori_data][0] + Space_User = await self.connector.execute_query(Memory_Space_User, end_user_id=end_user_id) if not Space_User: return [] user_id=Space_User[0]['id'] diff --git a/api/app/services/memory_episodic_service.py b/api/app/services/memory_episodic_service.py index 12eeff6e..08751fd1 100644 --- a/api/app/services/memory_episodic_service.py +++ b/api/app/services/memory_episodic_service.py @@ -34,7 +34,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: (标题, 类型)元组，如果不存在则返回默认值 @@ -43,14 +43,14 @@ class MemoryEpisodicService(MemoryBaseService): # 查询Summary节点的name(作为title)和memory_type(作为type) query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id RETURN s.name AS title, s.memory_type AS type """ result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) if not result or len(result) == 0: @@ -77,7 +77,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 前3个实体的name属性列表 @@ -87,7 +87,7 @@ class MemoryEpisodicService(MemoryBaseService): # 按activation_value降序排序,返回前3个 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity) WHERE entity.activation_value IS NOT NULL @@ -99,7 +99,7 @@ class MemoryEpisodicService(MemoryBaseService): result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 提取实体名称 @@ -123,7 +123,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 所有Statement节点的statement属性内容列表 @@ -132,7 +132,7 @@ class MemoryEpisodicService(MemoryBaseService): # 查询Summary节点指向的所有Statement节点 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) WHERE stmt.statement IS NOT NULL AND stmt.statement <> '' RETURN stmt.statement AS statement @@ -141,7 +141,7 @@ class MemoryEpisodicService(MemoryBaseService): result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 提取statement内容 @@ -214,12 +214,12 @@ class MemoryEpisodicService(MemoryBaseService): # 1. 先查询所有情景记忆的总数（不受筛选条件限制） total_all_query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id RETURN count(s) AS total_all """ total_all_result = await self.neo4j_connector.execute_query( total_all_query, - group_id=end_user_id + end_user_id=end_user_id ) total_all = total_all_result[0]["total_all"] if total_all_result else 0 @@ -229,7 +229,7 @@ class MemoryEpisodicService(MemoryBaseService): # 3. 构建Cypher查询 query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id """ # 添加时间范围过滤 @@ -248,7 +248,7 @@ class MemoryEpisodicService(MemoryBaseService): ORDER BY s.created_at DESC """ - params = {"group_id": end_user_id} + params = {"end_user_id": end_user_id} if time_filter: params["time_filter"] = time_filter if title_keyword: @@ -333,14 +333,14 @@ class MemoryEpisodicService(MemoryBaseService): # 1. 查询指定的MemorySummary节点 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id RETURN elementId(s) AS id, s.created_at AS created_at """ result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 2. 如果节点不存在，返回错误 diff --git a/api/app/services/memory_explicit_service.py b/api/app/services/memory_explicit_service.py index 713215c3..f8d39ae8 100644 --- a/api/app/services/memory_explicit_service.py +++ b/api/app/services/memory_explicit_service.py @@ -60,7 +60,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 1. 查询情景记忆（MemorySummary节点） ========== episodic_query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id RETURN elementId(s) AS id, s.name AS title, s.content AS content, @@ -70,7 +70,7 @@ class MemoryExplicitService(MemoryBaseService): episodic_result = await self.neo4j_connector.execute_query( episodic_query, - group_id=end_user_id + end_user_id=end_user_id ) # 处理情景记忆数据 @@ -96,7 +96,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 2. 查询语义记忆（ExtractedEntity节点） ========== semantic_query = """ MATCH (e:ExtractedEntity) - WHERE e.group_id = $group_id + WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN elementId(e) AS id, e.name AS name, @@ -107,7 +107,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_result = await self.neo4j_connector.execute_query( semantic_query, - group_id=end_user_id + end_user_id=end_user_id ) # 处理语义记忆数据 @@ -189,7 +189,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 1. 先尝试查询情景记忆 ========== episodic_query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $memory_id AND s.group_id = $group_id + WHERE elementId(s) = $memory_id AND s.end_user_id = $end_user_id RETURN s.name AS title, s.content AS content, s.created_at AS created_at @@ -198,7 +198,7 @@ class MemoryExplicitService(MemoryBaseService): episodic_result = await self.neo4j_connector.execute_query( episodic_query, memory_id=memory_id, - group_id=end_user_id + end_user_id=end_user_id ) if episodic_result and len(episodic_result) > 0: @@ -229,7 +229,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_query = """ MATCH (e:ExtractedEntity) WHERE elementId(e) = $memory_id - AND e.group_id = $group_id + AND e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN e.name AS name, e.description AS core_definition, @@ -240,7 +240,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_result = await self.neo4j_connector.execute_query( semantic_query, memory_id=memory_id, - group_id=end_user_id + end_user_id=end_user_id ) if semantic_result and len(semantic_result) > 0: diff --git a/api/app/services/memory_forget_service.py b/api/app/services/memory_forget_service.py index 2db4cdc7..e1030b24 100644 --- a/api/app/services/memory_forget_service.py +++ b/api/app/services/memory_forget_service.py @@ -12,6 +12,7 @@ from typing import Optional, Dict, Any, Tuple from datetime import datetime, timezone +from uuid import UUID from sqlalchemy.orm import Session @@ -23,7 +24,7 @@ from app.core.memory.storage_services.forgetting_engine.config_utils import ( load_actr_config_from_db, ) from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.forgetting_cycle_history_repository import ForgettingCycleHistoryRepository @@ -70,7 +71,7 @@ class MemoryForgetService: def __init__(self): """初始化服务""" - self.config_repository = DataConfigRepository() + self.config_repository = MemoryConfigRepository() self.history_repository = ForgettingCycleHistoryRepository() def _get_neo4j_connector(self) -> Neo4jConnector: @@ -87,7 +88,7 @@ class MemoryForgetService: async def _get_forgetting_components( self, db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Tuple[ACTRCalculator, ForgettingStrategy, ForgettingScheduler, Dict[str, Any]]: """ 获取遗忘引擎组件（计算器、策略、调度器） @@ -132,7 +133,7 @@ class MemoryForgetService: async def _get_knowledge_stats( self, connector: Neo4jConnector, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, forgetting_threshold: float = 0.3 ) -> Dict[str, Any]: """ @@ -140,7 +141,7 @@ class MemoryForgetService: Args: connector: Neo4j 连接器 - group_id: 组ID（可选） + end_user_id: 组ID（可选） forgetting_threshold: 遗忘阈值 Returns: @@ -152,8 +153,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ WITH n, @@ -172,8 +173,8 @@ class MemoryForgetService: """ params = {'threshold': forgetting_threshold} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await connector.execute_query(query, **params) @@ -200,7 +201,7 @@ class MemoryForgetService: async def _get_pending_forgetting_nodes( self, connector: Neo4jConnector, - group_id: str, + end_user_id: str, forgetting_threshold: float, min_days_since_access: int, limit: int = 20 @@ -212,7 +213,7 @@ class MemoryForgetService: Args: connector: Neo4j 连接器 - group_id: 组ID + end_user_id: 组ID forgetting_threshold: 遗忘阈值 min_days_since_access: 最小未访问天数 limit: 返回节点数量限制 @@ -229,7 +230,7 @@ class MemoryForgetService: query = """ MATCH (n) WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) - AND n.group_id = $group_id + AND n.end_user_id = $end_user_id AND n.activation_value IS NOT NULL AND n.activation_value < $threshold AND n.last_access_time IS NOT NULL @@ -250,7 +251,7 @@ class MemoryForgetService: """ params = { - 'group_id': group_id, + 'end_user_id': end_user_id, 'threshold': forgetting_threshold, 'min_access_time_str': min_access_time_str, 'limit': limit @@ -291,10 +292,10 @@ class MemoryForgetService: async def trigger_forgetting_cycle( self, db: Session, - group_id: str, + end_user_id: str, max_merge_batch_size: Optional[int] = None, min_days_since_access: Optional[int] = None, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 手动触发遗忘周期 @@ -303,10 +304,10 @@ class MemoryForgetService: Args: db: 数据库会话 - group_id: 组ID（即终端用户ID，必填） + end_user_id: 组ID（即终端用户ID，必填） max_merge_batch_size: 最大融合批次大小（可选） min_days_since_access: 最小未访问天数（可选） - config_id: 配置ID（必填，由控制器层通过 group_id 获取） + config_id: 配置ID（必填，由控制器层通过 end_user_id 获取） Returns: dict: 遗忘报告 @@ -319,7 +320,7 @@ class MemoryForgetService: # 运行遗忘周期（LLM 客户端将在需要时由 forgetting_strategy 内部获取） report = await forgetting_scheduler.run_forgetting_cycle( - group_id=group_id, + end_user_id=end_user_id, max_merge_batch_size=max_merge_batch_size, min_days_since_access=min_days_since_access, config_id=config_id, @@ -338,7 +339,7 @@ class MemoryForgetService: stats_query = """ MATCH (n) WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) - AND n.group_id = $group_id + AND n.end_user_id = $end_user_id RETURN count(n) as total_nodes, avg(n.activation_value) as average_activation, @@ -347,7 +348,7 @@ class MemoryForgetService: stats_results = await connector.execute_query( stats_query, - group_id=group_id, + end_user_id=end_user_id, threshold=config['forgetting_threshold'] ) @@ -364,7 +365,7 @@ class MemoryForgetService: # 保存历史记录到数据库 self.history_repository.create( db=db, - end_user_id=group_id, + end_user_id=end_user_id, execution_time=execution_time, merged_count=report['merged_count'], failed_count=report['failed_count'], @@ -376,7 +377,7 @@ class MemoryForgetService: ) api_logger.info( - f"已保存遗忘周期历史记录: end_user_id={group_id}, " + f"已保存遗忘周期历史记录: end_user_id={end_user_id}, " f"merged_count={report['merged_count']}" ) @@ -389,7 +390,7 @@ class MemoryForgetService: def read_forgetting_config( self, db: Session, - config_id: int + config_id: UUID ) -> Dict[str, Any]: """ 获取遗忘引擎配置 @@ -416,7 +417,7 @@ class MemoryForgetService: def update_forgetting_config( self, db: Session, - config_id: int, + config_id: UUID, update_fields: Dict[str, Any] ) -> Dict[str, Any]: """ @@ -465,8 +466,8 @@ class MemoryForgetService: async def get_forgetting_stats( self, db: Session, - group_id: Optional[str] = None, - config_id: Optional[int] = None + end_user_id: Optional[str] = None, + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 获取遗忘引擎统计信息 @@ -475,7 +476,7 @@ class MemoryForgetService: Args: db: 数据库会话 - group_id: 组ID（可选） + end_user_id: 组ID（可选） config_id: 配置ID（可选，用于获取遗忘阈值） Returns: @@ -493,8 +494,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) """ - if group_id: - activation_query += " AND n.group_id = $group_id" + if end_user_id: + activation_query += " AND n.end_user_id = $end_user_id" activation_query += """ RETURN @@ -506,8 +507,8 @@ class MemoryForgetService: """ params = {'threshold': forgetting_threshold} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id activation_results = await connector.execute_query(activation_query, **params) @@ -539,8 +540,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) """ - if group_id: - distribution_query += " AND n.group_id = $group_id" + if end_user_id: + distribution_query += " AND n.end_user_id = $end_user_id" distribution_query += """ WITH n, @@ -558,8 +559,8 @@ class MemoryForgetService: """ dist_params = {} - if group_id: - dist_params['group_id'] = group_id + if end_user_id: + dist_params['end_user_id'] = end_user_id distribution_results = await connector.execute_query(distribution_query, **dist_params) @@ -582,11 +583,11 @@ class MemoryForgetService: # 获取最近7个日期的历史趋势数据（每天取最后一次执行） recent_trends = [] try: - if group_id: + if end_user_id: # 查询所有历史记录 history_records = self.history_repository.get_recent_by_end_user( db=db, - end_user_id=group_id + end_user_id=end_user_id ) # 按日期分组（一天可能有多次执行，取最后一次） @@ -632,7 +633,7 @@ class MemoryForgetService: # 获取待遗忘节点列表（前20个满足遗忘条件的节点） pending_nodes = [] try: - if group_id: + if end_user_id: # 验证 min_days_since_access 配置值 min_days = config.get('min_days_since_access') if min_days is None or not isinstance(min_days, (int, float)) or min_days < 0: @@ -643,7 +644,7 @@ class MemoryForgetService: pending_nodes = await self._get_pending_forgetting_nodes( connector=connector, - group_id=group_id, + end_user_id=end_user_id, forgetting_threshold=forgetting_threshold, min_days_since_access=int(min_days), limit=20 @@ -677,7 +678,7 @@ class MemoryForgetService: db: Session, importance_score: float, days: int, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 获取遗忘曲线数据 diff --git a/api/app/services/memory_konwledges_server.py b/api/app/services/memory_konwledges_server.py index c6297e12..420f7ca1 100644 --- a/api/app/services/memory_konwledges_server.py +++ b/api/app/services/memory_konwledges_server.py @@ -450,12 +450,12 @@ async def create_document_chunk( return success(data=chunk, msg="文档块创建成功") -async def write_rag(group_id, message, user_rag_memory_id): +async def write_rag(end_user_id, message, user_rag_memory_id): """ 将消息写入 RAG 知识库 Args: - group_id: 组ID，用作文件标题 + end_user_id: 组ID，用作文件标题 message: 消息内容 user_rag_memory_id: 知识库ID（必须是有效的UUID） @@ -487,10 +487,10 @@ async def write_rag(group_id, message, user_rag_memory_id): db = next(db_gen) try: - create_data = CustomTextFileCreate(title=group_id, content=message) + create_data = CustomTextFileCreate(title=end_user_id, content=message) current_user = SimpleUser(user_rag_memory_id) # 检查文档是否已存在 - document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{group_id}.txt") + document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{end_user_id}.txt") print('======',document) api_logger.info(f"查找文档结果: document_id={document}") if document is not None: @@ -508,7 +508,7 @@ async def write_rag(group_id, message, user_rag_memory_id): return result else: # 文档不存在，创建新文档 - api_logger.info(f"文档不存在，创建新文档: group_id={group_id}") + api_logger.info(f"文档不存在，创建新文档: end_user_id={end_user_id}") result = await memory_konwledges_up( kb_id=user_rag_memory_id, parent_id=user_rag_memory_id, @@ -520,13 +520,13 @@ async def write_rag(group_id, message, user_rag_memory_id): new_document_id = find_document_id_by_kb_and_filename( db=db, kb_id=user_rag_memory_id, - file_name=f"{group_id}.txt" + file_name=f"{end_user_id}.txt" ) if new_document_id: await parse_document_by_id(new_document_id, db=db, current_user=current_user) else: - api_logger.error(f"创建文档后无法找到文档ID: group_id={group_id}") + api_logger.error(f"创建文档后无法找到文档ID: end_user_id={end_user_id}") return result finally: # 确保数据库会话被关闭 diff --git a/api/app/services/memory_perceptual_service.py b/api/app/services/memory_perceptual_service.py index d257e80f..b9d96a0b 100644 --- a/api/app/services/memory_perceptual_service.py +++ b/api/app/services/memory_perceptual_service.py @@ -6,7 +6,7 @@ from sqlalchemy.orm import Session from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger -from app.models.memory_perceptual_model import PerceptualType, FileStorageType +from app.models.memory_perceptual_model import PerceptualType, FileStorageService from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository from app.schemas.memory_perceptual_schema import ( PerceptualQuerySchema, @@ -137,8 +137,19 @@ class MemoryPerceptualService: memory_items = [] for memory in memories: meta_data = memory.meta_data or {} - content = meta_data.get("content") - content = Content(**content) + content = meta_data.get("content", {}) + + # 安全地提取 content 字段，提供默认值 + if content: + content_obj = Content(**content) + topic = content_obj.topic + domain = content_obj.domain + keywords = content_obj.keywords + else: + topic = "Unknown" + domain = "Unknown" + keywords = [] + memory_item = PerceptualMemoryItem( id=memory.id, perceptual_type=PerceptualType(memory.perceptual_type), @@ -146,11 +157,12 @@ class MemoryPerceptualService: file_name=memory.file_name, file_ext=memory.file_ext, summary=memory.summary, - topic=content.topic, - domain=content.domain, - keywords=content.keywords, + meta_data=meta_data, + topic=topic, + domain=domain, + keywords=keywords, created_time=int(memory.created_time.timestamp()*1000), - storage_type=FileStorageType(memory.storage_service), + storage_service=FileStorageService(memory.storage_service), ) memory_items.append(memory_item) diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index 46e42b46..e025c1b3 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -13,11 +13,12 @@ from app.db import get_db from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine import ReflectionConfig, ReflectionEngine from app.core.memory.storage_services.reflection_engine.self_reflexion import ReflectionRange, ReflectionBaseline -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.models.app_model import App from app.models.app_release_model import AppRelease from app.models.end_user_model import EndUser +from app.utils.config_utils import resolve_config_id api_logger = get_api_logger() @@ -38,7 +39,10 @@ class WorkspaceAppService: Returns: Dictionary containing detailed application information """ - apps = self.db.query(App).filter(App.workspace_id == workspace_id).all() + apps = self.db.query(App).filter( + App.workspace_id == workspace_id, + App.is_active.is_(True) + ).all() app_ids = [str(app.id) for app in apps] apps_detailed_info = [] @@ -70,7 +74,7 @@ class WorkspaceAppService: "created_at": app.created_at.isoformat() if app.created_at else None, "updated_at": app.updated_at.isoformat() if app.updated_at else None, "releases": [], - "data_configs": [], + "memory_configs": [], "end_users": [] } @@ -85,76 +89,71 @@ class WorkspaceAppService: for release in app_releases: memory_content = self._extract_memory_content(release.config) - - if memory_content and memory_content in processed_configs: continue - + release_info = { "app_id": str(release.app_id), "config": memory_content } - + if memory_content: processed_configs.add(memory_content) - data_config_info = self._get_data_config(memory_content) - - if data_config_info: - if not any(dc["config_id"] == data_config_info["config_id"] for dc in app_info["data_configs"]): - app_info["data_configs"].append(data_config_info) - + memory_config_info = self._get_memory_config(memory_content) + if memory_config_info: + if not any(dc["config_id"] == memory_config_info["config_id"] for dc in app_info["memory_configs"]): + app_info["memory_configs"].append(memory_config_info) + app_info["releases"].append(release_info) - + def _extract_memory_content(self, config: Any) -> str: """Extract memory_comtent from config""" if not config or not isinstance(config, dict): return None - + memory_obj = config.get('memory') if memory_obj and isinstance(memory_obj, dict): return memory_obj.get('memory_content') - - return None - - def _get_data_config(self, memory_content: str) -> Dict[str, Any]: - """Retrieve data_comfig information based on memory_comtent""" - try: - data_config_result = DataConfigRepository.query_reflection_config_by_id(self.db, int(memory_content)) - # data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content) - # data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone() - # if data_config_result is None: - # return None - - if data_config_result: + return None + + def _get_memory_config(self, memory_content: str) -> Dict[str, Any]: + """Retrieve memory_config information based on memory_content""" + try: + memory_content = resolve_config_id(memory_content, self.db) + memory_config_result = MemoryConfigRepository.query_reflection_config_by_id(self.db, (memory_content)) + + if memory_config_result: return { - "config_id": data_config_result.config_id, - "enable_self_reflexion": data_config_result.enable_self_reflexion, - "iteration_period": data_config_result.iteration_period, - "reflexion_range": data_config_result.reflexion_range, - "baseline": data_config_result.baseline, - "reflection_model_id": data_config_result.reflection_model_id, - "memory_verify": data_config_result.memory_verify, - "quality_assessment": data_config_result.quality_assessment, - "user_id": data_config_result.user_id + "config_id": memory_content, + "enable_self_reflexion": memory_config_result.enable_self_reflexion, + "iteration_period": memory_config_result.iteration_period, + "reflexion_range": memory_config_result.reflexion_range, + "baseline": memory_config_result.baseline, + "reflection_model_id": memory_config_result.reflection_model_id, + "memory_verify": memory_config_result.memory_verify, + "quality_assessment": memory_config_result.quality_assessment, + "user_id": memory_config_result.user_id } except Exception as e: - api_logger.warning(f"查询data_config失败，memory_content: {memory_content}, 错误: {str(e)}") - + api_logger.warning(f"查询memory_config失败，memory_content: {memory_content}, 错误: {str(e)}") + return None - + def _process_end_users(self, app: App, app_info: Dict[str, Any]) -> None: """Processing end-user information for applications""" end_users = self.db.query(EndUser).filter(EndUser.app_id == app.id).all() - + for end_user in end_users: end_user_info = { "id": str(end_user.id), "app_id": str(end_user.app_id) } app_info["end_users"].append(end_user_info) - + print(100*'-') + print(app_info) + def get_end_user_reflection_time(self, end_user_id: str) -> Optional[Any]: """ Read the reflection time of end users @@ -173,7 +172,7 @@ class WorkspaceAppService: except Exception as e: api_logger.error(f"读取用户反思时间失败，end_user_id: {end_user_id}, 错误: {str(e)}") return None - + def update_end_user_reflection_time(self, end_user_id: str) -> bool: """ Update the reflection time of end users to the current time @@ -186,7 +185,7 @@ class WorkspaceAppService: """ try: from datetime import datetime - + end_user = self.db.query(EndUser).filter(EndUser.id == end_user_id).first() if end_user: end_user.reflection_time = datetime.now() @@ -204,7 +203,7 @@ class WorkspaceAppService: class MemoryReflectionService: """Memory reflection service category""" - + def __init__(self,db: Session = Depends(get_db)): self.db=db @@ -223,7 +222,7 @@ class MemoryReflectionService: } config_data_id = config_data['config_id'] - reflection_config = WorkspaceAppService(self.db)._get_data_config(config_data_id) + reflection_config = WorkspaceAppService(self.db)._get_memory_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config = self._create_reflection_config_from_data(reflection_config) # 3. 执行反思引擎 @@ -249,22 +248,22 @@ class MemoryReflectionService: "end_user_id": end_user_id, "config_data": config_data } - + async def start_reflection_from_data(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]: """ Starting Reflection from Configuration Data - + Args: config_data: Configure data dictionary, including reflective configuration information end_user_id: end_user_id - + Returns: Reflect on the execution results """ try: config_id = config_data.get("config_id") api_logger.info(f"从配置数据启动反思，config_id: {config_id}, end_user_id: {end_user_id}") - + if not config_data.get("enable_self_reflexion", False): return { @@ -274,10 +273,10 @@ class MemoryReflectionService: "end_user_id": end_user_id, "config_data": config_data } - + config_data_id=config_data['config_id'] - reflection_config=WorkspaceAppService(self.db)._get_data_config(config_data_id) + reflection_config=WorkspaceAppService(self.db)._get_memory_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config= self._create_reflection_config_from_data(reflection_config) iteration_period = int(reflection_config.iteration_period) @@ -287,7 +286,7 @@ class MemoryReflectionService: # 检查是否需要执行反思 should_execute = False hours_diff = 0 - + if current_reflection_time is None: # 首次执行反思 should_execute = True @@ -299,11 +298,11 @@ class MemoryReflectionService: reflection_time = datetime.fromisoformat(current_reflection_time) else: reflection_time = current_reflection_time - + current_time = datetime.now() time_diff = current_time - reflection_time hours_diff = int(time_diff.total_seconds() / 3600) - + # 检查是否达到反思周期 if hours_diff >= iteration_period: should_execute = True @@ -313,7 +312,7 @@ class MemoryReflectionService: except (ValueError, TypeError) as e: api_logger.warning(f"解析反思时间失败: {e}，将执行反思") should_execute = True - + if should_execute: api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时") # 3. 执行反思引擎 @@ -346,7 +345,7 @@ class MemoryReflectionService: "next_reflection_in_hours": iteration_period - hours_diff } - + except Exception as e: config_id = config_data.get("config_id", "unknown") api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}") @@ -357,7 +356,7 @@ class MemoryReflectionService: "end_user_id": end_user_id, "config_data": config_data } - + def _create_reflection_config_from_data(self, config_data: Dict[str, Any]) -> ReflectionConfig: """Create reflective configuration objects from configuration data""" @@ -365,12 +364,12 @@ class MemoryReflectionService: if reflexion_range_value is None or reflexion_range_value == "": reflexion_range_value = "partial" reflexion_range = ReflectionRange(reflexion_range_value) - + baseline_value = config_data.get("baseline") if baseline_value is None or baseline_value == "": baseline_value = "TIME" baseline = ReflectionBaseline(baseline_value) - + # iteration_period = iteration_period = config_data.get("iteration_period", 24) if isinstance(iteration_period, str): @@ -378,7 +377,6 @@ class MemoryReflectionService: iteration_period = int(iteration_period) except (ValueError, TypeError): iteration_period = 24 # 默认24小时 - return ReflectionConfig( enabled=config_data.get("enable_self_reflexion", False), iteration_period=str(iteration_period), # ReflectionConfig期望字符串 diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 83d5923d..eec1007b 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -12,10 +12,14 @@ from datetime import datetime from typing import Any, AsyncGenerator, Dict, List, Optional from app.core.logging_config import get_config_logger, get_logger -from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags +from app.core.memory.analytics.hot_memory_tags import ( + get_hot_memory_tags, + get_raw_tags_from_db, + filter_tags_with_llm, +) from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats from app.models.user_model import User -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_config_schema import ConfigurationError from app.schemas.memory_storage_schema import ( @@ -125,7 +129,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） if not params.rerank_id: params.rerank_id = configs.get('rerank') - config = DataConfigRepository.create(self.db, params) + config = MemoryConfigRepository.create(self.db, params) self.db.commit() return {"affected": 1, "config_id": config.config_id} @@ -142,20 +146,20 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Delete --- def delete(self, key: ConfigParamsDelete) -> Dict[str, Any]: # 删除配置参数（按配置ID） - success = DataConfigRepository.delete(self.db, key.config_id) + success = MemoryConfigRepository.delete(self.db, key.config_id) if not success: raise ValueError("未找到配置") return {"affected": 1} # --- Update --- def update(self, update: ConfigUpdate) -> Dict[str, Any]: # 部分更新配置参数 - config = DataConfigRepository.update(self.db, update) + config = MemoryConfigRepository.update(self.db, update) if not config: raise ValueError("未找到配置") return {"affected": 1} def update_extracted(self, update: ConfigUpdateExtracted) -> Dict[str, Any]: # 更新记忆萃取引擎配置参数 - config = DataConfigRepository.update_extracted(self.db, update) + config = MemoryConfigRepository.update_extracted(self.db, update) if not config: raise ValueError("未找到配置") return {"affected": 1} @@ -166,25 +170,38 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Read --- def get_extracted(self, key: ConfigKey) -> Dict[str, Any]: # 获取萃取配置参数 - result = DataConfigRepository.get_extracted_config(self.db, key.config_id) + result = MemoryConfigRepository.get_extracted_config(self.db, key.config_id) if not result: raise ValueError("未找到配置") return result # --- Read All --- def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数 - configs = DataConfigRepository.get_all(self.db, workspace_id) + configs = MemoryConfigRepository.get_all(self.db, workspace_id) # 将 ORM 对象转换为字典列表 data_list = [] for config in configs: + # 安全地转换 user_id 为 int + config_id_old = None + if config.config_id_old: + try: + config_id_old = int(config.config_id_old) + except (ValueError, TypeError): + config_id_old = None + + + if config_id_old: + memory_config=config_id_old + else: + memory_config=config.config_id config_dict = { - "config_id": config.config_id, + "config_id": memory_config, "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, - "group_id": config.group_id, - "user_id": config.user_id, + "end_user_id": config.end_user_id, + "config_id_old": config_id_old, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, @@ -237,7 +254,8 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） ValueError: 当配置无效或参数缺失时 RuntimeError: 当管线执行失败时 """ - project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) try: # 发出初始进度事件 @@ -263,7 +281,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） try: config_service = MemoryConfigService(self.db) memory_config = config_service.load_memory_config( - config_id=int(cid), + config_id=str(cid), service_name="MemoryStorageService.pilot_run_stream" ) logger.info(f"Configuration loaded successfully: {memory_config.config_name}") @@ -390,8 +408,8 @@ _neo4j_connector = Neo4jConnector() async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_DIALOGUE, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_DIALOGUE, + end_user_id=end_user_id, ) data = {"search_for": "dialogue", "num": result[0]["num"]} return data @@ -399,8 +417,8 @@ async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_CHUNK, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_CHUNK, + end_user_id=end_user_id, ) data = {"search_for": "chunk", "num": result[0]["num"]} return data @@ -408,8 +426,8 @@ async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_STATEMENT, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_STATEMENT, + end_user_id=end_user_id, ) data = {"search_for": "statement", "num": result[0]["num"]} return data @@ -417,8 +435,8 @@ async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ENTITY, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_ENTITY, + end_user_id=end_user_id, ) data = {"search_for": "entity", "num": result[0]["num"]} return data @@ -426,8 +444,8 @@ async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ALL, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_ALL, + end_user_id=end_user_id, ) # 检查结果是否为空或长度不足 @@ -461,8 +479,8 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A 聚合 dialogue/chunk/statement/entity 四类计数，返回统一的分布结构，便于前端一次性消费。 """ result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ALL, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_ALL, + end_user_id=end_user_id, ) # 检查结果是否为空或长度不足 @@ -492,21 +510,19 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_DETIALS, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_DETIALS, + end_user_id=end_user_id, ) return result async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_EDGES, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_EDGES, + end_user_id=end_user_id, ) return result - - async def analytics_hot_memory_tags( db: Session, current_user: User, @@ -514,27 +530,79 @@ async def analytics_hot_memory_tags( ) -> List[Dict[str, Any]]: """ 获取热门记忆标签，按数量排序并返回前N个 + + 优化策略： + 1. 先从所有用户收集原始标签（不调用LLM） + 2. 聚合并合并相同标签的频率 + 3. 排序后取前N个 + 4. 只调用一次LLM进行筛选 """ workspace_id = current_user.current_workspace_id # 获取更多标签供LLM筛选（获取limit*4个标签） raw_limit = limit * 4 from app.services.memory_dashboard_service import get_workspace_end_users - end_users = get_workspace_end_users(db, workspace_id, current_user) + # 使用 asyncio.to_thread 避免阻塞事件循环 + end_users = await asyncio.to_thread(get_workspace_end_users, db, workspace_id, current_user) - tags = [] - for end_user in end_users: - tag = await get_hot_memory_tags(str(end_user.id), limit=raw_limit) - if tag: - # 将每个用户的标签列表展平到总列表中 - tags.extend(tag) - - # 按频率降序排序（虽然数据库已经排序，但为了确保正确性再次排序） - sorted_tags = sorted(tags, key=lambda x: x[1], reverse=True) + if not end_users: + return [] - # 只返回前limit个 - top_tags = sorted_tags[:limit] - - return [{"name": t, "frequency": f} for t, f in top_tags] + # 步骤1: 收集所有用户的原始标签（不调用LLM） + connector = Neo4jConnector() + try: + all_raw_tags = [] + for end_user in end_users: + raw_tags = await get_raw_tags_from_db( + connector, + str(end_user.id), + limit=raw_limit, + by_user=False + ) + if raw_tags: + all_raw_tags.extend(raw_tags) + + if not all_raw_tags: + return [] + + # 步骤2: 聚合相同标签的频率 + tag_frequency_map = {} + for tag_name, frequency in all_raw_tags: + if tag_name in tag_frequency_map: + tag_frequency_map[tag_name] += frequency + else: + tag_frequency_map[tag_name] = frequency + + # 步骤3: 按频率降序排序，取前raw_limit个 + sorted_tags = sorted( + tag_frequency_map.items(), + key=lambda x: x[1], + reverse=True + )[:raw_limit] + + if not sorted_tags: + return [] + + # 步骤4: 只调用一次LLM进行筛选 + tag_names = [tag for tag, _ in sorted_tags] + + # 使用第一个用户的end_user_id来获取LLM配置 + # 因为同一工作空间下的用户应该使用相同的配置 + first_end_user_id = str(end_users[0].id) + filtered_tag_names = await filter_tags_with_llm(tag_names, first_end_user_id) + + # 步骤5: 根据LLM筛选结果构建最终列表（保留频率） + final_tags = [] + for tag, freq in sorted_tags: + if tag in filtered_tag_names: + final_tags.append((tag, freq)) + + # 步骤6: 只返回前limit个 + top_tags = final_tags[:limit] + + return [{"name": t, "frequency": f} for t, f in top_tags] + + finally: + await connector.close() async def analytics_recent_activity_stats() -> Dict[str, Any]: diff --git a/api/app/services/model_service.py b/api/app/services/model_service.py index e94a889b..dee6cd1d 100644 --- a/api/app/services/model_service.py +++ b/api/app/services/model_service.py @@ -1,3 +1,4 @@ +from datetime import datetime from sqlalchemy.orm import Session from typing import List, Optional, Dict, Any import uuid @@ -6,11 +7,11 @@ import time import asyncio from app.models.models_model import ModelConfig, ModelApiKey, ModelType -from app.repositories.model_repository import ModelConfigRepository, ModelApiKeyRepository +from app.repositories.model_repository import ModelConfigRepository, ModelApiKeyRepository, ModelBaseRepository from app.schemas import model_schema from app.schemas.model_schema import ( ModelConfigCreate, ModelConfigUpdate, ModelApiKeyCreate, ModelApiKeyUpdate, - ModelConfigQuery, ModelStats + ModelConfigQuery, ModelStats, ModelConfigQueryNew ) from app.core.logging_config import get_business_logger from app.schemas.response_schema import PageData, PageMeta @@ -47,6 +48,26 @@ class ModelConfigService: items=[model_schema.ModelConfig.model_validate(model) for model in models] ) + @staticmethod + def get_model_list_new(db: Session, query: ModelConfigQueryNew, tenant_id: uuid.UUID | None = None) -> List[dict]: + """获取模型配置列表""" + provider_groups, total = ModelConfigRepository.get_list_new(db, query, tenant_id=tenant_id) + + items = [] + for provider, models in provider_groups.items(): + # 验证每个模型并封装分组信息 + validated_models = [model_schema.ModelConfig.model_validate(model) for model in models] + tags = list({model.type for model in validated_models}) + group_item = { + "provider": provider, # 服务商名称 + "logo": validated_models[0].logo, + "tags": tags, + "models": validated_models # 该服务商下的所有模型 + } + items.append(group_item) + + return items + @staticmethod def get_model_by_name(db: Session, name: str, tenant_id: uuid.UUID | None = None) -> ModelConfig: """根据名称获取模型配置""" @@ -228,37 +249,39 @@ class ModelConfigService: # 验证配置 if not model_data.skip_validation and model_data.api_keys: - api_key_data = model_data.api_keys - validation_result = await ModelConfigService.validate_model_config( - db=db, - model_name=api_key_data.model_name, - provider=api_key_data.provider, - api_key=api_key_data.api_key, - api_base=api_key_data.api_base, - model_type=model_data.type, # 传递模型类型 - test_message="Hello" - ) - if not validation_result["valid"]: - raise BusinessException( - f"模型配置验证失败: {validation_result['error']}", - BizCode.INVALID_PARAMETER + api_key_data_list = model_data.api_keys + for api_key_data in api_key_data_list: + validation_result = await ModelConfigService.validate_model_config( + db=db, + model_name=api_key_data.model_name, + provider=api_key_data.provider, + api_key=api_key_data.api_key, + api_base=api_key_data.api_base, + model_type=model_data.type, # 传递模型类型 + test_message="Hello" ) + if not validation_result["valid"]: + raise BusinessException( + f"模型配置验证失败: {validation_result['error']}", + BizCode.INVALID_PARAMETER + ) # 事务处理 - api_key_data = model_data.api_keys - model_config_data = model_data.dict(exclude={"api_keys", "skip_validation"}) + api_key_datas = model_data.api_keys + model_config_data = model_data.model_dump(exclude={"api_keys", "skip_validation"}) # 添加租户ID model_config_data["tenant_id"] = tenant_id model = ModelConfigRepository.create(db, model_config_data) db.flush() # 获取生成的 ID - if api_key_data: - api_key_create_schema = ModelApiKeyCreate( - model_config_id=model.id, - **api_key_data.dict() - ) - ModelApiKeyRepository.create(db, api_key_create_schema) + if api_key_datas: + for api_key_data in api_key_datas: + api_key_create_schema = ModelApiKeyCreate( + model_config_ids=[model.id], + **api_key_data.model_dump() + ) + ModelApiKeyRepository.create(db, api_key_create_schema) db.commit() db.refresh(model) @@ -280,6 +303,116 @@ class ModelConfigService: db.refresh(model) return model + @staticmethod + async def create_composite_model(db: Session, model_data: model_schema.CompositeModelCreate, tenant_id: uuid.UUID) -> ModelConfig: + """创建组合模型""" + if ModelConfigRepository.get_by_name(db, model_data.name, tenant_id=tenant_id): + raise BusinessException("模型名称已存在", BizCode.DUPLICATE_NAME) + + # 验证所有 API Key 存在且类型匹配 + for api_key_id in model_data.api_key_ids: + api_key = ModelApiKeyRepository.get_by_id(db, api_key_id) + if not api_key: + raise BusinessException(f"API Key {api_key_id} 不存在", BizCode.NOT_FOUND) + + # 检查 API Key 关联的模型配置类型 + for model_config in api_key.model_configs: + # chat 和 llm 类型可以兼容 + compatible_types = {ModelType.LLM, ModelType.CHAT} + config_type = model_config.type + request_type = model_data.type + + if not (config_type == request_type or + (config_type in compatible_types and request_type in compatible_types)): + raise BusinessException( + f"API Key {api_key_id} 关联的模型类型 ({model_config.type}) 与组合模型类型 ({model_data.type}) 不匹配", + BizCode.INVALID_PARAMETER + ) + # if model_config.is_composite: + # raise BusinessException( + # f"API Key {api_key_id} 关联的模型是组合模型，不能用于创建新的组合模型", + # BizCode.INVALID_PARAMETER + # ) + + # 创建组合模型 + model_config_data = { + "tenant_id": tenant_id, + "name": model_data.name, + "type": model_data.type, + "logo": model_data.logo, + "description": model_data.description, + "provider": "composite", + "config": model_data.config, + "is_active": model_data.is_active, + "is_public": model_data.is_public, + "is_composite": True + } + if "load_balance_strategy" in model_data.model_fields_set: + model_config_data["load_balance_strategy"] = model_data.load_balance_strategy + + model = ModelConfigRepository.create(db, model_config_data) + db.flush() + + # 关联 API Keys + for api_key_id in model_data.api_key_ids: + api_key = ModelApiKeyRepository.get_by_id(db, api_key_id) + if api_key: + model.api_keys.append(api_key) + + db.commit() + db.refresh(model) + return model + + @staticmethod + async def update_composite_model(db: Session, model_id: uuid.UUID, model_data: model_schema.CompositeModelCreate, tenant_id: uuid.UUID) -> ModelConfig: + """更新组合模型""" + existing_model = ModelConfigRepository.get_by_id(db, model_id, tenant_id=tenant_id) + if not existing_model: + raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND) + + if not existing_model.is_composite: + raise BusinessException("该模型不是组合模型", BizCode.INVALID_PARAMETER) + + # 验证所有 API Key 存在且类型匹配 + for api_key_id in model_data.api_key_ids: + api_key = ModelApiKeyRepository.get_by_id(db, api_key_id) + if not api_key: + raise BusinessException(f"API Key {api_key_id} 不存在", BizCode.NOT_FOUND) + + for model_config in api_key.model_configs: + compatible_types = {ModelType.LLM, ModelType.CHAT} + config_type = model_config.type + request_type = existing_model.type + + if not (config_type == request_type or + (config_type in compatible_types and request_type in compatible_types)): + raise BusinessException( + f"API Key {api_key_id} 关联的模型类型 ({model_config.type}) 与组合模型类型 ({model_data.type}) 不匹配", + BizCode.INVALID_PARAMETER + ) + + # 更新基本信息 + existing_model.name = model_data.name + # existing_model.type = model_data.type + existing_model.logo = model_data.logo + existing_model.description = model_data.description + existing_model.config = model_data.config + existing_model.is_active = model_data.is_active + existing_model.is_public = model_data.is_public + if "load_balance_strategy" in model_data.model_fields_set: + existing_model.load_balance_strategy = model_data.load_balance_strategy + + # 更新 API Keys 关联 + existing_model.api_keys.clear() + for api_key_id in model_data.api_key_ids: + api_key = ModelApiKeyRepository.get_by_id(db, api_key_id) + if api_key: + existing_model.api_keys.append(api_key) + + db.commit() + db.refresh(existing_model) + return existing_model + @staticmethod def delete_model(db: Session, model_id: uuid.UUID, tenant_id: uuid.UUID | None = None) -> bool: """删除模型配置""" @@ -324,27 +457,133 @@ class ModelApiKeyService: return ModelApiKeyRepository.get_by_model_config(db, model_config_id, is_active) @staticmethod - async def create_api_key(db: Session, api_key_data: ModelApiKeyCreate) -> ModelApiKey: - """创建API Key""" - model_config = ModelConfigRepository.get_by_id(db, api_key_data.model_config_id) - if not model_config: - raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND) - - validation_result = await ModelConfigService.validate_model_config( + async def create_api_key_by_provider(db: Session, data: model_schema.ModelApiKeyCreateByProvider) -> tuple[ + list[Any], list[Any]]: + """根据provider为多个ModelConfig创建API Key""" + created_keys = [] + failed_models = [] # 记录验证失败的模型 + + for model_config_id in data.model_config_ids: + model_config = ModelConfigRepository.get_by_id(db, model_config_id) + if not model_config: + continue + + # 从ModelBase获取model_name + model_name = model_config.model_base.name if model_config.model_base else model_config.name + + # 检查是否存在API Key（包括软删除） + existing_key = db.query(ModelApiKey).filter( + ModelApiKey.api_key == data.api_key, + ModelApiKey.provider == data.provider, + ModelApiKey.model_name == model_name + ).first() + + if existing_key: + # 如果已存在，重新激活并更新 + if existing_key.is_active: + continue + existing_key.is_active = True + existing_key.api_base = data.api_base + existing_key.description = data.description + existing_key.config = data.config + existing_key.priority = data.priority + existing_key.model_name = model_name + + # 检查是否已关联该模型配置 + if model_config not in existing_key.model_configs: + existing_key.model_configs.append(model_config) + + created_keys.append(existing_key) + continue + + # 验证配置 + validation_result = await ModelConfigService.validate_model_config( db=db, - model_name=api_key_data.model_name, - provider=api_key_data.provider, - api_key=api_key_data.api_key, - api_base=api_key_data.api_base, - model_type=model_config.type, # 传递模型类型 + model_name=model_name, + provider=data.provider, + api_key=data.api_key, + api_base=data.api_base, + model_type=model_config.type, test_message="Hello" ) - print(validation_result) - if not validation_result["valid"]: - raise BusinessException( - f"模型配置验证失败: {validation_result['error']}", - BizCode.INVALID_PARAMETER + if not validation_result["valid"]: + # 记录验证失败的模型，但不抛出异常 + failed_models.append(model_name) + continue + + # 创建API Key + api_key_data = ModelApiKeyCreate( + model_config_ids=[model_config_id], + model_name=model_name, + description=data.description, + provider=data.provider, + api_key=data.api_key, + api_base=data.api_base, + config=data.config, + is_active=data.is_active, + priority=data.priority + ) + api_key_obj = ModelApiKeyRepository.create(db, api_key_data) + created_keys.append(api_key_obj) + + if created_keys: + db.commit() + for key in created_keys: + db.refresh(key) + + return created_keys, failed_models + + @staticmethod + async def create_api_key(db: Session, api_key_data: ModelApiKeyCreate) -> ModelApiKey: + # 验证所有关联的模型配置是否存在 + if api_key_data.model_config_ids: + for model_config_id in api_key_data.model_config_ids: + model_config = ModelConfigRepository.get_by_id(db, model_config_id) + if not model_config: + raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND) + + # 检查API Key是否已存在(包括软删除) + existing_key = db.query(ModelApiKey).filter( + ModelApiKey.api_key == api_key_data.api_key, + ModelApiKey.provider == api_key_data.provider, + ModelApiKey.model_name == api_key_data.model_name + ).first() + + if existing_key: + if existing_key.is_active: + # 如果已激活，跳过 + raise BusinessException("该API Key已存在", BizCode.DUPLICATE_NAME) + # 如果已存在，重新激活并更新 + existing_key.is_active = True + existing_key.api_base = api_key_data.api_base + existing_key.description = api_key_data.description + existing_key.config = api_key_data.config + existing_key.priority = api_key_data.priority + existing_key.model_name = api_key_data.model_name + + # 检查是否已关联该模型配置 + if model_config not in existing_key.model_configs: + existing_key.model_configs.append(model_config) + + db.commit() + db.refresh(existing_key) + return existing_key + + # 验证配置 + validation_result = await ModelConfigService.validate_model_config( + db=db, + model_name=api_key_data.model_name, + provider=api_key_data.provider, + api_key=api_key_data.api_key, + api_base=api_key_data.api_base, + model_type=model_config.type, + test_message="Hello" ) + if not validation_result["valid"]: + raise BusinessException( + f"模型配置验证失败: {validation_result['error']}", + BizCode.INVALID_PARAMETER + ) api_key = ModelApiKeyRepository.create(db, api_key_data) db.commit() @@ -359,21 +598,19 @@ class ModelApiKeyService: raise BusinessException("API Key不存在", BizCode.NOT_FOUND) # 获取关联的模型配置以获取模型类型 - model_config = ModelConfigRepository.get_by_id(db, existing_api_key.model_config_id) - if not model_config: - raise BusinessException("关联的模型配置不存在", BizCode.MODEL_NOT_FOUND) - - validation_result = await ModelConfigService.validate_model_config( + if existing_api_key.model_configs: + model_config = existing_api_key.model_configs[0] + + validation_result = await ModelConfigService.validate_model_config( db=db, - model_name=api_key_data.model_name, - provider=api_key_data.provider, - api_key=api_key_data.api_key, - api_base=api_key_data.api_base, - model_type=model_config.type, # 传递模型类型 + model_name=api_key_data.model_name or existing_api_key.model_name, + provider=api_key_data.provider or existing_api_key.provider, + api_key=api_key_data.api_key or existing_api_key.api_key, + api_base=api_key_data.api_base or existing_api_key.api_base, + model_type=model_config.type, test_message="Hello" ) - print(validation_result) - if not validation_result["valid"]: + if not validation_result["valid"]: raise BusinessException( f"模型配置验证失败: {validation_result['error']}", BizCode.INVALID_PARAMETER @@ -417,3 +654,87 @@ class ModelApiKeyService: if api_kes and len(api_kes) > 0: return api_kes[0] raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING) + + + +class ModelBaseService: + """基础模型服务""" + + @staticmethod + def get_model_base_list(db: Session, query: model_schema.ModelBaseQuery, tenant_id: uuid.UUID = None) -> List: + models = ModelBaseRepository.get_list(db, query) + + provider_groups = {} + for m in models: + model_dict = model_schema.ModelBase.model_validate(m).model_dump() + if tenant_id: + model_dict['is_added'] = ModelBaseRepository.check_added_by_tenant(db, m.id, tenant_id) + + provider = m.provider + if provider not in provider_groups: + provider_groups[provider] = { + "provider": provider, + "models": [] + } + provider_groups[provider]["models"].append(model_dict) + + return list(provider_groups.values()) + + @staticmethod + def get_model_base_by_id(db: Session, model_base_id: uuid.UUID): + model = ModelBaseRepository.get_by_id(db, model_base_id) + if not model: + raise BusinessException("基础模型不存在", BizCode.MODEL_NOT_FOUND) + return model + + @staticmethod + def create_model_base(db: Session, data: model_schema.ModelBaseCreate): + existing = ModelBaseRepository.get_by_name_and_provider(db, data.name, data.provider) + if existing: + raise BusinessException("模型已存在", BizCode.DUPLICATE_NAME) + model_base = ModelBaseRepository.create(db, data.model_dump()) + db.commit() + db.refresh(model_base) + return model_base + + @staticmethod + def update_model_base(db: Session, model_base_id: uuid.UUID, data: model_schema.ModelBaseUpdate): + model_base = ModelBaseRepository.update(db, model_base_id, data.model_dump(exclude_unset=True)) + if not model_base: + raise BusinessException("基础模型不存在", BizCode.MODEL_NOT_FOUND) + db.commit() + db.refresh(model_base) + return model_base + + @staticmethod + def delete_model_base(db: Session, model_base_id: uuid.UUID) -> bool: + success = ModelBaseRepository.delete(db, model_base_id) + if not success: + raise BusinessException("基础模型不存在", BizCode.MODEL_NOT_FOUND) + db.commit() + return success + + @staticmethod + def add_model_from_plaza(db: Session, model_base_id: uuid.UUID, tenant_id: uuid.UUID) -> ModelConfig: + model_base = ModelBaseRepository.get_by_id(db, model_base_id) + if not model_base: + raise BusinessException("基础模型不存在", BizCode.MODEL_NOT_FOUND) + + if ModelBaseRepository.check_added_by_tenant(db, model_base_id, tenant_id): + raise BusinessException("模型已添加", BizCode.DUPLICATE_NAME) + + model_config_data = { + "model_id": model_base_id, + "tenant_id": tenant_id, + "name": model_base.name, + "provider": model_base.provider, + "type": model_base.type, + "logo": model_base.logo, + "description": model_base.description, + "is_composite": False + } + model_config = ModelConfigRepository.create(db, model_config_data) + ModelBaseRepository.increment_add_count(db, model_base_id) + db.commit() + db.refresh(model_config) + return model_config diff --git a/api/app/services/multi_agent_orchestrator.py b/api/app/services/multi_agent_orchestrator.py index 1972f344..b28bafbf 100644 --- a/api/app/services/multi_agent_orchestrator.py +++ b/api/app/services/multi_agent_orchestrator.py @@ -7,6 +7,7 @@ from sqlalchemy.orm import Session from app.models import MultiAgentConfig, AgentConfig, ModelConfig from app.models.multi_agent_model import AggregationStrategy, OrchestrationMode +from app.repositories.model_repository import ModelApiKeyRepository from app.services.agent_registry import AgentRegistry from app.services.master_agent_router import MasterAgentRouter from app.services.conversation_state_manager import ConversationStateManager @@ -279,14 +280,22 @@ class MultiAgentOrchestrator: # 4. 提取子 Agent 的 conversation_id（用于多轮对话） sub_conversation_id = None + total_tokens = 0 + if isinstance(results, dict): sub_conversation_id = results.get("conversation_id") or results.get("result", {}).get("conversation_id") + # 提取 token 信息 + usage = results.get("usage", {}) or results.get("result", {}).get("usage", {}) + total_tokens += usage.get("total_tokens", 0) elif isinstance(results, list) and results: for item in results: if "result" in item: sub_conversation_id = item["result"].get("conversation_id") if sub_conversation_id: break + # 累加每个子 Agent 的 token + usage = item.get("usage", {}) or item.get("result", {}).get("usage", {}) + total_tokens += usage.get("total_tokens", 0) logger.info( "多 Agent 任务完成", @@ -300,9 +309,15 @@ class MultiAgentOrchestrator: return { "message": final_result, "conversation_id": sub_conversation_id, + "mode": OrchestrationMode.SUPERVISOR, "elapsed_time": elapsed_time, "strategy": routing_decision.get("collaboration_strategy", "single"), - "sub_results": results + "sub_results": results, + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": total_tokens + } } except Exception as e: @@ -1551,10 +1566,12 @@ class MultiAgentOrchestrator: return { "message": result.get("response", ""), "conversation_id": result.get("conversation_id"), + "mode": OrchestrationMode.COLLABORATION, "elapsed_time": elapsed_time, "strategy": "collaboration", "active_agent": result.get("active_agent"), - "sub_results": result + "sub_results": result, + "usage": result.get("usage") } except Exception as e: @@ -2546,10 +2563,14 @@ class MultiAgentOrchestrator: return self._smart_merge_results(results, strategy) # 获取 API Key 配置 - api_key_config = self.db.query(ModelApiKey).filter( - ModelApiKey.model_config_id == default_model_config_id, - ModelApiKey.is_active == True - ).first() + # api_key_config = self.db.query(ModelApiKey).join( + # ModelConfig, ModelApiKey.model_configs + # ).filter( + # ModelConfig.id == default_model_config_id, + # ModelApiKey.is_active.is_(True) + # ).first() + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, default_model_config_id) + api_key_config = api_keys[0] if api_keys else None if not api_key_config: logger.warning("Master Agent 没有可用的 API Key，使用简单整合") @@ -2703,10 +2724,14 @@ class MultiAgentOrchestrator: return # 获取 API Key 配置 - api_key_config = self.db.query(ModelApiKey).filter( - ModelApiKey.model_config_id == default_model_config_id, - ModelApiKey.is_active == True - ).first() + # api_key_config = self.db.query(ModelApiKey).join( + # ModelConfig, ModelApiKey.model_configs + # ).filter( + # ModelConfig.id == default_model_config_id, + # ModelApiKey.is_active.is_(True) + # ).first() + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, default_model_config_id) + api_key_config = api_keys[0] if api_keys else None if not api_key_config: logger.warning("Master Agent 没有可用的 API Key，使用简单整合") diff --git a/api/app/services/multi_agent_service.py b/api/app/services/multi_agent_service.py index 1a08a5af..c52814ed 100644 --- a/api/app/services/multi_agent_service.py +++ b/api/app/services/multi_agent_service.py @@ -1,5 +1,6 @@ """多 Agent 配置管理服务""" import uuid +import json from typing import Optional, List, Tuple, Any, Annotated from fastapi import Depends @@ -74,7 +75,7 @@ class MultiAgentService: select(MultiAgentConfig) .where( MultiAgentConfig.app_id == app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ) .order_by(MultiAgentConfig.updated_at.desc()) ).first() @@ -144,7 +145,7 @@ class MultiAgentService: select(MultiAgentConfig) .where( MultiAgentConfig.app_id == app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ) .order_by(MultiAgentConfig.updated_at.desc()) ).first() @@ -427,6 +428,23 @@ class MultiAgentService: memory=getattr(request, 'memory', True) # 记忆功能参数 ) + await self._save_conversation_message( + conversation_id=request.conversation_id, + user_message=request.message, + assistant_message=result.get("message", ""), + app_id=app_id, + user_id=request.user_id, + meta_data={ + "mode": result.get("mode"), + "elapsed_time": result.get("elapsed_time"), + "usage": result.get("usage", { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0 + }) + } + ) + return result async def run_stream( @@ -451,11 +469,14 @@ class MultiAgentService: raise ResourceNotFoundException("多 Agent 配置", str(app_id)) if not config.is_active: - raise BusinessException("多 Agent 配置已禁用", BizCode.RESOURCE_DISABLED) + raise BusinessException("多 Agent 配置已禁用", BizCode.NOT_FOUND) # 2. 创建编排器 orchestrator = MultiAgentOrchestrator(self.db, config) + full_content = "" + total_tokens = 0 + # 3. 流式执行任务 async for event in orchestrator.execute_stream( message=request.message, @@ -468,7 +489,88 @@ class MultiAgentService: storage_type=storage_type, user_rag_memory_id=user_rag_memory_id ): - yield event + if "sub_usage" in event: + if "data:" in event: + try: + data_line = event.split("data: ", 1)[1].strip() + data = json.loads(data_line) + if "total_tokens" in data: + total_tokens += data["total_tokens"] + except: + pass + else: + yield event + if "data:" in event: + try: + data_line = event.split("data: ", 1)[1].strip() + data = json.loads(data_line) + if "content" in data: + full_content += data["content"] + except: + pass + + await self._save_conversation_message( + conversation_id=request.conversation_id, + user_message=request.message, + assistant_message=full_content, + app_id=app_id, + user_id=request.user_id, + meta_data={ + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": total_tokens + } + } + ) + + async def _save_conversation_message( + self, + conversation_id: uuid.UUID, + user_message: str, + assistant_message: str, + meta_data: dict, + app_id: Optional[uuid.UUID] = None, + user_id: Optional[str] = None + ) -> None: + """保存会话消息 + + Args: + conversation_id: 会话ID + user_message: 用户消息 + assistant_message: AI 回复消息 + meta_data: 元数据（包括 token 消耗） + app_id: 应用ID + user_id: 用户ID + """ + try: + from app.services.conversation_service import ConversationService + + conversation_service = ConversationService(self.db) + + conversation_service.add_message( + conversation_id=conversation_id, + role="user", + content=user_message + ) + conversation_service.add_message( + conversation_id=conversation_id, + role="assistant", + content=assistant_message, + meta_data=meta_data + ) + + logger.debug( + "保存多 Agent 会话消息", + extra={ + "conversation_id": conversation_id, + "user_message_length": len(user_message), + "assistant_message_length": len(assistant_message) + } + ) + + except Exception as e: + logger.warning("保存会话消息失败", extra={"error": str(e)}) # def add_sub_agent( # self, diff --git a/api/app/services/multimodal_service.py b/api/app/services/multimodal_service.py new file mode 100644 index 00000000..a460a7ba --- /dev/null +++ b/api/app/services/multimodal_service.py @@ -0,0 +1,429 @@ +""" +多模态文件处理服务 + +处理图片、文档等多模态文件，转换为 LLM 可用的格式 + +支持的 Provider: +- DashScope (通义千问): 支持 URL 格式 +- Bedrock/Anthropic: 仅支持 base64 格式 +- OpenAI: 支持 URL 和 base64 格式 +""" +import uuid +from typing import List, Dict, Any, Optional, Protocol +from sqlalchemy.orm import Session + +from app.core.logging_config import get_business_logger +from app.core.exceptions import BusinessException +from app.core.error_codes import BizCode +from app.schemas.app_schema import FileInput, FileType, TransferMethod +from app.models.generic_file_model import GenericFile + +logger = get_business_logger() + + +class ImageFormatStrategy(Protocol): + """图片格式策略接口""" + + async def format_image(self, url: str) -> Dict[str, Any]: + """将图片 URL 转换为特定 provider 的格式""" + ... + + +class DashScopeImageStrategy: + """通义千问图片格式策略""" + + async def format_image(self, url: str) -> Dict[str, Any]: + """通义千问格式: {"type": "image", "image": "url"}""" + return { + "type": "image", + "image": url + } + + +class BedrockImageStrategy: + """Bedrock/Anthropic 图片格式策略""" + + async def format_image(self, url: str) -> Dict[str, Any]: + """ + Bedrock/Anthropic 格式: base64 编码 + {"type": "image", "source": {"type": "base64", "media_type": "...", "data": "..."}} + """ + import httpx + import base64 + from mimetypes import guess_type + + logger.info(f"下载并编码图片: {url}") + + # 下载图片 + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.get(url) + response.raise_for_status() + + # 获取图片数据 + image_data = response.content + + # 确定 media type + content_type = response.headers.get("content-type") + if content_type and content_type.startswith("image/"): + media_type = content_type + else: + guessed_type, _ = guess_type(url) + media_type = guessed_type if guessed_type and guessed_type.startswith("image/") else "image/jpeg" + + # 转换为 base64 + base64_data = base64.b64encode(image_data).decode("utf-8") + + logger.info(f"图片编码完成: media_type={media_type}, size={len(base64_data)}") + + return { + "type": "image", + "source": { + "type": "base64", + "media_type": media_type, + "data": base64_data + } + } + + +class OpenAIImageStrategy: + """OpenAI 图片格式策略""" + + async def format_image(self, url: str) -> Dict[str, Any]: + """OpenAI 格式: {"type": "image_url", "image_url": {"url": "..."}}""" + return { + "type": "image_url", + "image_url": { + "url": url + } + } + + +# Provider 到策略的映射 +PROVIDER_STRATEGIES = { + "dashscope": DashScopeImageStrategy, + "bedrock": BedrockImageStrategy, + "anthropic": BedrockImageStrategy, + "openai": OpenAIImageStrategy, +} + + +class MultimodalService: + """多模态文件处理服务""" + + def __init__(self, db: Session, provider: str = "dashscope"): + """ + 初始化多模态服务 + + Args: + db: 数据库会话 + provider: 模型提供商（dashscope, bedrock, anthropic 等） + """ + self.db = db + self.provider = provider.lower() + + async def process_files( + self, + files: Optional[List[FileInput]] + ) -> List[Dict[str, Any]]: + """ + 处理文件列表，返回 LLM 可用的格式 + + Args: + files: 文件输入列表 + + Returns: + List[Dict]: LLM 可用的内容格式列表（根据 provider 返回不同格式） + """ + if not files: + return [] + + result = [] + for idx, file in enumerate(files): + try: + if file.type == FileType.IMAGE: + content = await self._process_image(file) + result.append(content) + elif file.type == FileType.DOCUMENT: + content = await self._process_document(file) + result.append(content) + elif file.type == FileType.AUDIO: + content = await self._process_audio(file) + result.append(content) + elif file.type == FileType.VIDEO: + content = await self._process_video(file) + result.append(content) + else: + logger.warning(f"不支持的文件类型: {file.type}") + except Exception as e: + logger.error( + f"处理文件失败", + extra={ + "file_index": idx, + "file_type": file.type, + "error": str(e) + } + ) + # 继续处理其他文件，不中断整个流程 + result.append({ + "type": "text", + "text": f"[文件处理失败: {str(e)}]" + }) + + logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}") + return result + + async def _process_image(self, file: FileInput) -> Dict[str, Any]: + """ + 处理图片文件 + + Args: + file: 图片文件输入 + + Returns: + Dict: 根据 provider 返回不同格式 + - Anthropic/Bedrock: {"type": "image", "source": {"type": "base64", "media_type": "...", "data": "..."}} + - 通义千问: {"type": "image", "image": "url"} + """ + if file.transfer_method == TransferMethod.REMOTE_URL: + url = file.url + else: + # 本地文件，获取访问 URL + url = await self._get_file_url(file.upload_file_id) + + logger.debug(f"处理图片: {url}, provider={self.provider}") + + # 根据 provider 返回不同格式 + if self.provider in ["bedrock", "anthropic"]: + # Anthropic/Bedrock 只支持 base64 格式，需要下载并转换 + try: + logger.info(f"开始下载并编码图片: {url}") + base64_data, media_type = await self._download_and_encode_image(url) + result = { + "type": "image", + "source": { + "type": "base64", + "media_type": media_type, + "data": base64_data[:100] + "..." # 只记录前100个字符 + } + } + logger.info(f"图片编码完成: media_type={media_type}, data_length={len(base64_data)}") + # 返回完整数据 + result["source"]["data"] = base64_data + return result + except Exception as e: + logger.error(f"下载并编码图片失败: {e}", exc_info=True) + # 返回错误提示 + return { + "type": "text", + "text": f"[图片加载失败: {str(e)}]" + } + else: + # 通义千问等其他格式支持 URL + return { + "type": "image", + "image": url + } + + async def _download_and_encode_image(self, url: str) -> tuple[str, str]: + """ + 下载图片并转换为 base64 + + Args: + url: 图片 URL + + Returns: + tuple: (base64_data, media_type) + """ + import httpx + import base64 + from mimetypes import guess_type + + # 下载图片 + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.get(url) + response.raise_for_status() + + # 获取图片数据 + image_data = response.content + + # 确定 media type + content_type = response.headers.get("content-type") + if content_type and content_type.startswith("image/"): + media_type = content_type + else: + # 从 URL 推断 + guessed_type, _ = guess_type(url) + media_type = guessed_type if guessed_type and guessed_type.startswith("image/") else "image/jpeg" + + # 转换为 base64 + base64_data = base64.b64encode(image_data).decode("utf-8") + + logger.debug(f"图片编码完成: media_type={media_type}, size={len(base64_data)}") + + return base64_data, media_type + + async def _process_document(self, file: FileInput) -> Dict[str, Any]: + """ + 处理文档文件（PDF、Word 等） + + Args: + file: 文档文件输入 + + Returns: + Dict: text 格式的内容（包含提取的文本） + """ + if file.transfer_method == TransferMethod.REMOTE_URL: + # 远程文档暂不支持提取 + return { + "type": "text", + "text": f"\n[远程文档，暂不支持内容提取]\n" + } + else: + # 本地文件，提取文本内容 + text = await self._extract_document_text(file.upload_file_id) + generic_file = self.db.query(GenericFile).filter( + GenericFile.id == file.upload_file_id + ).first() + + file_name = generic_file.file_name if generic_file else "unknown" + + return { + "type": "text", + "text": f"\n{text}\n" + } + + async def _process_audio(self, file: FileInput) -> Dict[str, Any]: + """ + 处理音频文件 + + Args: + file: 音频文件输入 + + Returns: + Dict: 音频内容（暂时返回占位符） + """ + # TODO: 实现音频转文字功能 + return { + "type": "text", + "text": "[音频文件，暂不支持处理]" + } + + async def _process_video(self, file: FileInput) -> Dict[str, Any]: + """ + 处理视频文件 + + Args: + file: 视频文件输入 + + Returns: + Dict: 视频内容（暂时返回占位符） + """ + # TODO: 实现视频处理功能 + return { + "type": "text", + "text": "[视频文件，暂不支持处理]" + } + + async def _get_file_url(self, file_id: uuid.UUID) -> str: + """ + 获取文件的访问 URL + + Args: + file_id: 文件ID + + Returns: + str: 文件访问 URL + + Raises: + BusinessException: 文件不存在 + """ + generic_file = self.db.query(GenericFile).filter( + GenericFile.id == file_id, + GenericFile.status == "active" + ).first() + + if not generic_file: + raise BusinessException( + f"文件不存在或已删除: {file_id}", + BizCode.NOT_FOUND + ) + + # 如果有 access_url，直接返回 + if generic_file.access_url: + return generic_file.access_url + + # 否则，根据 storage_path 生成 URL + # TODO: 根据实际存储方式生成 URL（本地存储、OSS 等） + # 这里暂时返回一个占位 URL + return f"/api/files/{file_id}/download" + + async def _extract_document_text(self, file_id: uuid.UUID) -> str: + """ + 提取文档文本内容 + + Args: + file_id: 文件ID + + Returns: + str: 提取的文本内容 + """ + generic_file = self.db.query(GenericFile).filter( + GenericFile.id == file_id, + GenericFile.status == "active" + ).first() + + if not generic_file: + raise BusinessException( + f"文件不存在或已删除: {file_id}", + BizCode.NOT_FOUND + ) + + # TODO: 根据文件类型提取文本 + # - PDF: 使用 PyPDF2 或 pdfplumber + # - Word: 使用 python-docx + # - TXT/MD: 直接读取 + + file_ext = generic_file.file_ext.lower() + + if file_ext in ['.txt', '.md', '.markdown']: + return await self._read_text_file(generic_file.storage_path) + elif file_ext == '.pdf': + return await self._extract_pdf_text(generic_file.storage_path) + elif file_ext in ['.doc', '.docx']: + return await self._extract_word_text(generic_file.storage_path) + else: + return f"[不支持的文档格式: {file_ext}]" + + async def _read_text_file(self, storage_path: str) -> str: + """读取纯文本文件""" + try: + with open(storage_path, 'r', encoding='utf-8') as f: + return f.read() + except Exception as e: + logger.error(f"读取文本文件失败: {e}") + return f"[文件读取失败: {str(e)}]" + + async def _extract_pdf_text(self, storage_path: str) -> str: + """提取 PDF 文本""" + try: + # TODO: 实现 PDF 文本提取 + # import PyPDF2 或 pdfplumber + return "[PDF 文本提取功能待实现]" + except Exception as e: + logger.error(f"提取 PDF 文本失败: {e}") + return f"[PDF 提取失败: {str(e)}]" + + async def _extract_word_text(self, storage_path: str) -> str: + """提取 Word 文档文本""" + try: + # TODO: 实现 Word 文本提取 + # import docx + return "[Word 文本提取功能待实现]" + except Exception as e: + logger.error(f"提取 Word 文本失败: {e}") + return f"[Word 提取失败: {str(e)}]" + + +def get_multimodal_service(db: Session) -> MultimodalService: + """获取多模态服务实例（依赖注入）""" + return MultimodalService(db) diff --git a/api/app/services/ontology_service.py b/api/app/services/ontology_service.py new file mode 100644 index 00000000..c832b0cc --- /dev/null +++ b/api/app/services/ontology_service.py @@ -0,0 +1,1162 @@ +"""本体提取服务层 + +本模块提供本体提取的业务逻辑封装,协调OntologyExtractor和OWLValidator。 +包括本体提取、OWL文件导出等功能。 + +Classes: + OntologyService: 本体提取服务类,封装业务逻辑 +""" + +import logging +import time +from typing import Any, Dict, List, Optional + +from sqlalchemy.orm import Session + +from app.core.memory.llm_tools.openai_client import OpenAIClient +from app.core.memory.models.ontology_models import ( + OntologyClass, + OntologyExtractionResponse, +) +from app.core.memory.storage_services.extraction_engine.knowledge_extraction.ontology_extraction import ( + OntologyExtractor, +) +from app.core.memory.utils.validation.owl_validator import OWLValidator + + +logger = logging.getLogger(__name__) + + +class OntologyService: + """本体提取服务层 + + 封装本体提取的业务逻辑,协调各个组件: + - OntologyExtractor: 执行LLM驱动的本体提取 + - OWLValidator: OWL语义验证 + + Attributes: + extractor: 本体提取器实例 + owl_validator: OWL验证器实例 + db: 数据库会话 + """ + + # 默认配置参数 + DEFAULT_MAX_CLASSES = 15 + DEFAULT_MIN_CLASSES = 5 + DEFAULT_MAX_DESCRIPTION_LENGTH = 500 + DEFAULT_LLM_TEMPERATURE = 0.3 + DEFAULT_LLM_MAX_TOKENS = 2000 + DEFAULT_LLM_TIMEOUT = 30.0 + DEFAULT_ENABLE_OWL_VALIDATION = True + + def __init__( + self, + llm_client: OpenAIClient, + db: Session + ): + """初始化本体提取服务 + + Args: + llm_client: OpenAI客户端实例 + db: SQLAlchemy数据库会话 + """ + self.extractor = OntologyExtractor(llm_client) + self.owl_validator = OWLValidator() + self.db = db + + # 初始化Repository + from app.repositories.ontology_scene_repository import OntologySceneRepository + from app.repositories.ontology_class_repository import OntologyClassRepository + + self.scene_repo = OntologySceneRepository(db) + self.class_repo = OntologyClassRepository(db) + + logger.info("OntologyService initialized") + + async def extract_ontology( + self, + scenario: str, + domain: Optional[str] = None, + scene_id: Optional[Any] = None, + workspace_id: Optional[Any] = None + ) -> OntologyExtractionResponse: + """执行本体提取 + + 使用默认配置参数调用OntologyExtractor执行提取。 + 提取结果仅返回给前端，不会自动保存到数据库。 + 前端需要调用 /class 接口来保存选中的类型。 + + Args: + scenario: 场景描述文本 + domain: 可选的领域提示 + scene_id: 可选的场景ID,用于权限验证（不再用于自动保存） + workspace_id: 可选的工作空间ID,用于权限验证 + + Returns: + OntologyExtractionResponse: 提取结果 + + Raises: + ValueError: 场景描述为空、场景不存在或无权限 + RuntimeError: 提取过程失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> response = await service.extract_ontology( + ... scenario="医院管理患者记录...", + ... domain="Healthcare", + ... scene_id=scene_uuid, + ... workspace_id=workspace_uuid + ... ) + >>> len(response.classes) + 7 + """ + # 开始计时 + start_time = time.time() + + # 验证输入 + if not scenario or not scenario.strip(): + logger.error("Scenario description is empty") + raise ValueError("Scenario description cannot be empty") + + # 如果提供了scene_id,验证场景是否存在且有权限 + if scene_id and workspace_id: + logger.info(f"Validating scene access - scene_id={scene_id}, workspace_id={workspace_id}") + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限在该场景下创建类型") + + logger.info( + f"Starting ontology extraction service - " + f"scenario_length={len(scenario)}, " + f"domain={domain}, " + f"scene_id={scene_id}" + ) + + try: + # 调用提取器执行提取(使用默认配置) + logger.info("Calling OntologyExtractor with default config") + extraction_start_time = time.time() + + response = await self.extractor.extract_ontology_classes( + scenario=scenario, + domain=domain, + max_classes=self.DEFAULT_MAX_CLASSES, + min_classes=self.DEFAULT_MIN_CLASSES, + enable_owl_validation=self.DEFAULT_ENABLE_OWL_VALIDATION, + llm_temperature=self.DEFAULT_LLM_TEMPERATURE, + llm_max_tokens=self.DEFAULT_LLM_MAX_TOKENS, + max_description_length=self.DEFAULT_MAX_DESCRIPTION_LENGTH, + timeout=self.DEFAULT_LLM_TIMEOUT, + ) + + extraction_duration = time.time() - extraction_start_time + + # 检查是否成功提取到类 + if not response.classes: + logger.error("Ontology extraction failed: No classes extracted (structured output may have failed)") + raise RuntimeError("本体提取失败：结构化输出失败，未能提取到任何本体类") + + # 注释：提取结果仅返回给前端，不保存到数据库 + # 前端将从返回结果中选择需要的类型，然后调用 /class 接口创建 + logger.info( + f"Extraction completed. Classes will be saved to ontology_class " + f"via /class endpoint based on user selection" + ) + + total_duration = time.time() - start_time + + # 记录提取统计 + logger.info( + f"Ontology extraction service completed - " + f"extracted_classes={len(response.classes)}, " + f"domain={response.domain}, " + f"extraction_duration={extraction_duration:.2f}s, " + f"total_duration={total_duration:.2f}s" + ) + + return response + + except ValueError: + # 重新抛出验证错误 + total_duration = time.time() - start_time + logger.error( + f"Validation error after {total_duration:.2f}s", + exc_info=True + ) + raise + except Exception as e: + total_duration = time.time() - start_time + error_msg = f"Ontology extraction failed after {total_duration:.2f}s: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + async def export_owl_file( + self, + classes: List[OntologyClass], + output_path: str, + format: str = "rdfxml", + ) -> str: + """导出OWL文件 + + 将提取的本体类导出为OWL文件,支持多种格式。 + + Args: + classes: 本体类列表 + output_path: 输出文件路径 + format: 导出格式,可选值: "rdfxml", "turtle", "ntriples" (默认: "rdfxml") + + Returns: + str: 导出的OWL文件内容 + + Raises: + ValueError: 类列表为空或格式不支持 + RuntimeError: 导出失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> owl_content = await service.export_owl_file( + ... classes=response.classes, + ... output_path="ontology.owl", + ... format="rdfxml" + ... ) + """ + # 验证输入 + if not classes: + logger.error("Classes list is empty") + raise ValueError("Classes list cannot be empty") + + valid_formats = ["rdfxml", "turtle", "ntriples"] + if format not in valid_formats: + error_msg = f"Unsupported format '{format}'. Must be one of: {', '.join(valid_formats)}" + logger.error(error_msg) + raise ValueError(error_msg) + + logger.info( + f"Starting OWL export - " + f"classes_count={len(classes)}, " + f"output_path={output_path}, " + f"format={format}" + ) + + try: + # 步骤1: 验证本体类 + logger.debug("Validating ontology classes") + is_valid, errors, world = self.owl_validator.validate_ontology_classes( + classes=classes, + ) + + if not is_valid: + logger.warning( + f"OWL validation found {len(errors)} issues during export: {errors}" + ) + # 继续导出,但记录警告 + + if not world: + error_msg = "Failed to create OWL world for export" + logger.error(error_msg) + raise RuntimeError(error_msg) + + # 步骤2: 导出OWL文件 + logger.info(f"Exporting to {format} format") + owl_content = self.owl_validator.export_to_owl( + world=world, + output_path=output_path, + format=format + ) + + logger.info( + f"OWL export completed - " + f"output_path={output_path}, " + f"content_length={len(owl_content)}" + ) + + return owl_content + + except Exception as e: + error_msg = f"OWL export failed: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + + # ==================== 本体场景管理方法 ==================== + + def create_scene( + self, + scene_name: str, + scene_description: Optional[str], + workspace_id: Any + ): + """创建本体场景 + + Args: + scene_name: 场景名称 + scene_description: 场景描述 + workspace_id: 所属工作空间ID + + Returns: + OntologyScene: 创建的场景对象 + + Raises: + ValueError: 场景名称为空 + RuntimeError: 创建失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> scene = service.create_scene( + ... "医疗场景", + ... "用于医疗领域的本体建模", + ... workspace_id + ... ) + """ + # 验证输入 + if not scene_name or not scene_name.strip(): + logger.error("Scene name is empty") + raise ValueError("场景名称不能为空") + + logger.info( + f"Creating scene - " + f"name={scene_name}, workspace_id={workspace_id}" + ) + + try: + scene_data = { + "scene_name": scene_name.strip(), + "scene_description": scene_description + } + + scene = self.scene_repo.create(scene_data, workspace_id) + self.db.commit() + + logger.info(f"Scene created successfully: {scene.scene_id}") + + return scene + + except ValueError: + raise + except Exception as e: + self.db.rollback() + error_msg = f"Failed to create scene: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def update_scene( + self, + scene_id: Any, + scene_name: Optional[str], + scene_description: Optional[str], + workspace_id: Any + ): + """更新本体场景 + + Args: + scene_id: 场景ID + scene_name: 场景名称（可选） + scene_description: 场景描述（可选） + workspace_id: 工作空间ID（用于权限验证） + + Returns: + OntologyScene: 更新后的场景对象 + + Raises: + ValueError: 场景不存在或无权限 + RuntimeError: 更新失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> scene = service.update_scene( + ... scene_id, + ... "新名称", + ... "新描述", + ... workspace_id + ... ) + """ + logger.info(f"Updating scene: {scene_id}") + + try: + # 检查场景是否存在 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + # 检查权限 + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限操作该场景") + + # 准备更新数据 + update_data = {} + if scene_name is not None: + if not scene_name.strip(): + raise ValueError("场景名称不能为空") + update_data["scene_name"] = scene_name.strip() + + if scene_description is not None: + update_data["scene_description"] = scene_description + + # 如果没有更新数据，直接返回 + if not update_data: + logger.info("No update data provided, returning existing scene") + return scene + + # 执行更新 + updated_scene = self.scene_repo.update(scene_id, update_data) + self.db.commit() + + logger.info(f"Scene updated successfully: {scene_id}") + + return updated_scene + + except ValueError: + raise + except Exception as e: + self.db.rollback() + error_msg = f"Failed to update scene: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def delete_scene( + self, + scene_id: Any, + workspace_id: Any + ) -> bool: + """删除本体场景 + + Args: + scene_id: 场景ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + bool: 删除成功返回True + + Raises: + ValueError: 场景不存在或无权限 + RuntimeError: 删除失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> success = service.delete_scene(scene_id, workspace_id) + """ + logger.info(f"Deleting scene: {scene_id}") + + try: + # 检查场景是否存在 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + # 检查权限 + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限操作该场景") + + # 执行删除 + success = self.scene_repo.delete(scene_id) + self.db.commit() + + logger.info(f"Scene deleted successfully: {scene_id}") + + return success + + except ValueError: + raise + except Exception as e: + self.db.rollback() + error_msg = f"Failed to delete scene: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def get_scene_by_id( + self, + scene_id: Any, + workspace_id: Any + ): + """获取单个场景 + + Args: + scene_id: 场景ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + Optional[OntologyScene]: 场景对象 + + Raises: + ValueError: 场景不存在或无权限 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> scene = service.get_scene_by_id(scene_id, workspace_id) + """ + logger.debug(f"Getting scene by ID: {scene_id}") + + try: + # 获取场景 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + # 检查权限 + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限访问该场景") + + return scene + + except ValueError: + raise + except Exception as e: + error_msg = f"Failed to get scene: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def get_scene_by_name( + self, + scene_name: str, + workspace_id: Any + ): + """根据场景名称获取场景（精确匹配） + + Args: + scene_name: 场景名称 + workspace_id: 工作空间ID + + Returns: + Optional[OntologyScene]: 场景对象 + + Raises: + ValueError: 场景不存在 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> scene = service.get_scene_by_name("医疗场景", workspace_id) + """ + logger.debug(f"Getting scene by name: {scene_name}, workspace_id: {workspace_id}") + + try: + # 获取场景 + scene = self.scene_repo.get_by_name(scene_name, workspace_id) + if not scene: + logger.warning(f"Scene not found: {scene_name} in workspace {workspace_id}") + raise ValueError("场景不存在") + + return scene + + except ValueError: + raise + except Exception as e: + error_msg = f"Failed to get scene by name: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def search_scenes_by_name( + self, + keyword: str, + workspace_id: Any + ) -> List: + """根据关键词模糊搜索场景 + + Args: + keyword: 搜索关键词 + workspace_id: 工作空间ID + + Returns: + List[OntologyScene]: 匹配的场景列表 + + Raises: + RuntimeError: 搜索失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> scenes = service.search_scenes_by_name("医疗", workspace_id) + """ + logger.debug(f"Searching scenes by keyword: {keyword}, workspace_id: {workspace_id}") + + try: + scenes = self.scene_repo.search_by_name(keyword, workspace_id) + + logger.info( + f"Found {len(scenes)} scenes matching keyword '{keyword}' " + f"in workspace {workspace_id}" + ) + + return scenes + + except Exception as e: + error_msg = f"Failed to search scenes by keyword: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def list_scenes( + self, + workspace_id: Any, + page: Optional[int] = None, + page_size: Optional[int] = None + ) -> tuple: + """获取工作空间下的所有场景（支持分页） + + Args: + workspace_id: 工作空间ID + page: 页码（可选，从1开始） + page_size: 每页数量（可选） + + Returns: + tuple: (场景列表, 总数量) + + Raises: + RuntimeError: 查询失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> scenes, total = service.list_scenes(workspace_id) + >>> scenes, total = service.list_scenes(workspace_id, page=1, page_size=10) + """ + logger.debug(f"Listing scenes for workspace: {workspace_id}, page={page}, page_size={page_size}") + + try: + scenes, total = self.scene_repo.get_by_workspace(workspace_id, page, page_size) + + logger.info(f"Found {len(scenes)} scenes (total: {total}) in workspace {workspace_id}") + + return scenes, total + + except Exception as e: + error_msg = f"Failed to list scenes: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + # ==================== 本体类型管理方法 ==================== + + def create_class( + self, + scene_id: Any, + class_name: str, + class_description: Optional[str], + workspace_id: Any + ): + """创建本体类型 + + Args: + scene_id: 所属场景ID + class_name: 类型名称 + class_description: 类型描述 + workspace_id: 工作空间ID（用于权限验证） + + Returns: + OntologyClass: 创建的类型对象 + + Raises: + ValueError: 类型名称为空、场景不存在或无权限 + RuntimeError: 创建失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> ontology_class = service.create_class( + ... scene_id, + ... "患者", + ... "医院患者信息", + ... workspace_id + ... ) + """ + # 验证输入 + if not class_name or not class_name.strip(): + logger.error("Class name is empty") + raise ValueError("类型名称不能为空") + + logger.info( + f"Creating class - " + f"name={class_name}, scene_id={scene_id}" + ) + + try: + # 检查场景是否存在且属于当前工作空间 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("所属场景不存在") + + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限在该场景下创建类型") + + # 创建类型 + class_data = { + "class_name": class_name.strip(), + "class_description": class_description + } + + ontology_class = self.class_repo.create(class_data, scene_id) + self.db.commit() + + logger.info(f"Class created successfully: {ontology_class.class_id}") + + return ontology_class + + except ValueError: + raise + except Exception as e: + self.db.rollback() + error_msg = f"Failed to create class: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def create_classes_batch( + self, + scene_id: Any, + classes: List[Dict[str, Optional[str]]], + workspace_id: Any + ): + """批量创建本体类型 + + Args: + scene_id: 所属场景ID + classes: 类型列表，每个元素包含 class_name 和 class_description + workspace_id: 工作空间ID（用于权限验证） + + Returns: + Tuple[List, List[str]]: (成功创建的类型列表, 错误信息列表) + + Raises: + ValueError: 场景不存在或无权限 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> classes_data = [ + ... {"class_name": "患者", "class_description": "医院患者信息"}, + ... {"class_name": "医生", "class_description": "医院医生信息"} + ... ] + >>> created_classes, errors = service.create_classes_batch( + ... scene_id, + ... classes_data, + ... workspace_id + ... ) + """ + logger.info( + f"Batch creating classes - " + f"count={len(classes)}, scene_id={scene_id}" + ) + + # 检查场景是否存在且属于当前工作空间（只检查一次） + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("所属场景不存在") + + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限在该场景下创建类型") + + created_classes = [] + errors = [] + + for idx, class_data in enumerate(classes): + class_name = class_data.get("class_name", "").strip() + class_description = class_data.get("class_description") + + if not class_name: + error_msg = f"第 {idx + 1} 个类型名称为空，已跳过" + logger.warning(error_msg) + errors.append(error_msg) + continue + + try: + # 创建类型（不需要再次检查权限） + create_data = { + "class_name": class_name, + "class_description": class_description + } + + ontology_class = self.class_repo.create(create_data, scene_id) + created_classes.append(ontology_class) + logger.info(f"Class created successfully: {class_name}") + + except Exception as e: + error_msg = f"创建类型 '{class_name}' 失败: {str(e)}" + logger.error(error_msg) + errors.append(error_msg) + + # 统一提交所有成功的创建 + try: + self.db.commit() + logger.info( + f"Batch creation completed - " + f"success={len(created_classes)}, failed={len(errors)}" + ) + except Exception as e: + self.db.rollback() + error_msg = f"批量创建提交失败: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + return created_classes, errors + + def update_class( + self, + class_id: Any, + class_name: Optional[str], + class_description: Optional[str], + workspace_id: Any + ): + """更新本体类型 + + Args: + class_id: 类型ID + class_name: 类型名称（可选） + class_description: 类型描述（可选） + workspace_id: 工作空间ID（用于权限验证） + + Returns: + OntologyClass: 更新后的类型对象 + + Raises: + ValueError: 类型不存在或无权限 + RuntimeError: 更新失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> ontology_class = service.update_class( + ... class_id, + ... "新名称", + ... "新描述", + ... workspace_id + ... ) + """ + logger.info(f"Updating class: {class_id}") + + try: + # 检查类型是否存在 + ontology_class = self.class_repo.get_by_id(class_id) + if not ontology_class: + logger.warning(f"Class not found: {class_id}") + raise ValueError("类型不存在") + + # 检查权限（通过场景关联） + if not self.class_repo.check_ownership(class_id, workspace_id): + logger.warning( + f"Permission denied - class_id={class_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限操作该类型") + + # 准备更新数据 + update_data = {} + if class_name is not None: + if not class_name.strip(): + raise ValueError("类型名称不能为空") + update_data["class_name"] = class_name.strip() + + if class_description is not None: + update_data["class_description"] = class_description + + # 如果没有更新数据，直接返回 + if not update_data: + logger.info("No update data provided, returning existing class") + return ontology_class + + # 执行更新 + updated_class = self.class_repo.update(class_id, update_data) + self.db.commit() + + logger.info(f"Class updated successfully: {class_id}") + + return updated_class + + except ValueError: + raise + except Exception as e: + self.db.rollback() + error_msg = f"Failed to update class: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def delete_class( + self, + class_id: Any, + workspace_id: Any + ) -> bool: + """删除本体类型 + + Args: + class_id: 类型ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + bool: 删除成功返回True + + Raises: + ValueError: 类型不存在或无权限 + RuntimeError: 删除失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> success = service.delete_class(class_id, workspace_id) + """ + logger.info(f"Deleting class: {class_id}") + + try: + # 检查类型是否存在 + ontology_class = self.class_repo.get_by_id(class_id) + if not ontology_class: + logger.warning(f"Class not found: {class_id}") + raise ValueError("类型不存在") + + # 检查权限（通过场景关联） + if not self.class_repo.check_ownership(class_id, workspace_id): + logger.warning( + f"Permission denied - class_id={class_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限操作该类型") + + # 执行删除 + success = self.class_repo.delete(class_id) + self.db.commit() + + logger.info(f"Class deleted successfully: {class_id}") + + return success + + except ValueError: + raise + except Exception as e: + self.db.rollback() + error_msg = f"Failed to delete class: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def get_class_by_id( + self, + class_id: Any, + workspace_id: Any + ): + """获取单个类型 + + Args: + class_id: 类型ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + Optional[OntologyClass]: 类型对象 + + Raises: + ValueError: 类型不存在或无权限 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> ontology_class = service.get_class_by_id(class_id, workspace_id) + """ + logger.debug(f"Getting class by ID: {class_id}") + + try: + # 获取类型 + ontology_class = self.class_repo.get_by_id(class_id) + if not ontology_class: + logger.warning(f"Class not found: {class_id}") + raise ValueError("类型不存在") + + # 检查权限（通过场景关联） + if not self.class_repo.check_ownership(class_id, workspace_id): + logger.warning( + f"Permission denied - class_id={class_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限访问该类型") + + return ontology_class + + except ValueError: + raise + except Exception as e: + error_msg = f"Failed to get class: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def get_class_by_name( + self, + class_name: str, + scene_id: Any, + workspace_id: Any + ): + """根据类型名称获取类型（精确匹配） + + Args: + class_name: 类型名称 + scene_id: 场景ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + Optional[OntologyClass]: 类型对象 + + Raises: + ValueError: 类型不存在或无权限 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> ontology_class = service.get_class_by_name("患者", scene_id, workspace_id) + """ + logger.debug(f"Getting class by name: {class_name}, scene_id: {scene_id}") + + try: + # 检查场景是否存在且属于当前工作空间 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限访问该场景") + + # 获取类型 + ontology_class = self.class_repo.get_by_name(class_name, scene_id) + if not ontology_class: + logger.warning(f"Class not found: {class_name} in scene {scene_id}") + raise ValueError("类型不存在") + + return ontology_class + + except ValueError: + raise + except Exception as e: + error_msg = f"Failed to get class by name: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def search_classes_by_name( + self, + keyword: str, + scene_id: Any, + workspace_id: Any + ) -> List: + """根据关键词模糊搜索类型 + + Args: + keyword: 搜索关键词 + scene_id: 场景ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + List[OntologyClass]: 匹配的类型列表 + + Raises: + ValueError: 场景不存在或无权限 + RuntimeError: 搜索失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> classes = service.search_classes_by_name("患者", scene_id, workspace_id) + """ + logger.debug( + f"Searching classes by keyword: {keyword}, " + f"scene_id: {scene_id}, workspace_id: {workspace_id}" + ) + + try: + # 检查场景是否存在且属于当前工作空间 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限访问该场景") + + # 搜索类型 + classes = self.class_repo.search_by_name(keyword, scene_id) + + logger.info( + f"Found {len(classes)} classes matching keyword '{keyword}' " + f"in scene {scene_id}" + ) + + return classes + + except ValueError: + raise + except Exception as e: + error_msg = f"Failed to search classes by keyword: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e + + def list_classes_by_scene( + self, + scene_id: Any, + workspace_id: Any + ) -> List: + """获取场景下的所有类型 + + Args: + scene_id: 场景ID + workspace_id: 工作空间ID（用于权限验证） + + Returns: + List[OntologyClass]: 类型列表 + + Raises: + ValueError: 场景不存在或无权限 + RuntimeError: 查询失败 + + Examples: + >>> service = OntologyService(llm_client, db) + >>> classes = service.list_classes_by_scene(scene_id, workspace_id) + """ + logger.debug(f"Listing classes for scene: {scene_id}") + + try: + # 检查场景是否存在且属于当前工作空间 + scene = self.scene_repo.get_by_id(scene_id) + if not scene: + logger.warning(f"Scene not found: {scene_id}") + raise ValueError("场景不存在") + + if not self.scene_repo.check_ownership(scene_id, workspace_id): + logger.warning( + f"Permission denied - scene_id={scene_id}, " + f"workspace_id={workspace_id}" + ) + raise ValueError("无权限访问该场景的类型") + + # 获取类型列表 + classes = self.class_repo.get_by_scene(scene_id) + + logger.info(f"Found {len(classes)} classes in scene {scene_id}") + + return classes + + except ValueError: + raise + except Exception as e: + error_msg = f"Failed to list classes: {str(e)}" + logger.error(error_msg, exc_info=True) + raise RuntimeError(error_msg) from e diff --git a/api/app/services/pilot_run_service.py b/api/app/services/pilot_run_service.py index 17dfd7eb..755dda14 100644 --- a/api/app/services/pilot_run_service.py +++ b/api/app/services/pilot_run_service.py @@ -91,7 +91,7 @@ async def run_pilot_extraction( dialog = DialogData( context=context, ref_id="pilot_dialog_1", - group_id=str(memory_config.workspace_id), + end_user_id=str(memory_config.workspace_id), user_id=str(memory_config.tenant_id), apply_id=str(memory_config.config_id), metadata={"source": "pilot_run", "input_type": "frontend_text"}, diff --git a/api/app/services/prompt_optimizer_service.py b/api/app/services/prompt_optimizer_service.py index c6142c01..2c0b57ac 100644 --- a/api/app/services/prompt_optimizer_service.py +++ b/api/app/services/prompt_optimizer_service.py @@ -1,3 +1,4 @@ +import os import re import uuid from typing import Any, AsyncGenerator @@ -16,9 +17,10 @@ from app.models.prompt_optimizer_model import ( PromptOptimizerSession, RoleType ) -from app.repositories.model_repository import ModelConfigRepository +from app.repositories.model_repository import ModelConfigRepository, ModelApiKeyRepository from app.repositories.prompt_optimizer_repository import ( - PromptOptimizerSessionRepository + PromptOptimizerSessionRepository, + PromptReleaseRepository ) from app.schemas.prompt_optimizer_schema import OptimizePromptResult @@ -28,6 +30,8 @@ logger = get_business_logger() class PromptOptimizerService: def __init__(self, db: Session): self.db = db + self.optim_repo = PromptOptimizerSessionRepository(self.db) + self.release_repo = PromptReleaseRepository(self.db) def get_model_config( self, @@ -78,10 +82,12 @@ class PromptOptimizerService: Returns: PromptOptimzerSession: The newly created prompt optimization session. """ - session = PromptOptimizerSessionRepository(self.db).create_session( + session = self.optim_repo.create_session( tenant_id=tenant_id, user_id=user_id ) + self.db.commit() + self.db.refresh(session) return session def get_session_message_history( @@ -106,7 +112,7 @@ class PromptOptimizerService: - role (str): The role of the message sender, e.g., 'system', 'user', or 'assistant'. - content (str): The content of the message. """ - history = PromptOptimizerSessionRepository(self.db).get_session_history( + history = self.optim_repo.get_session_history( session_id=session_id, user_id=user_id ) @@ -168,7 +174,8 @@ class PromptOptimizerService: logger.info(f"Prompt optimization started, user_id={user_id}, session_id={session_id}") # Create LLM instance - api_config: ModelApiKey = model_config.api_keys[0] + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config.id) + api_config: ModelApiKey = api_keys[0] if api_keys else None llm = RedBearLLM(RedBearModelConfig( model_name=api_config.model_name, provider=api_config.provider, @@ -176,11 +183,12 @@ class PromptOptimizerService: base_url=api_config.api_base ), type=ModelType(model_config.type)) try: - with open('app/services/prompt/prompt_optimizer_system.jinja2', 'r', encoding='utf-8') as f: + prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt') + with open(os.path.join(prompt_path, 'prompt_optimizer_system.jinja2'), 'r', encoding='utf-8') as f: opt_system_prompt = f.read() rendered_system_message = Template(opt_system_prompt).render() - with open('app/services/prompt/prompt_optimizer_user.jinja2', 'r', encoding='utf-8') as f: + with open(os.path.join(prompt_path, 'prompt_optimizer_user.jinja2'), 'r', encoding='utf-8') as f: opt_user_prompt = f.read() except FileNotFoundError: raise BusinessException(message="System prompt template not found", code=BizCode.NOT_FOUND) @@ -295,4 +303,165 @@ class PromptOptimizerService: role=role, content=content ) + self.db.commit() + self.db.refresh(message) return message + + def save_prompt( + self, + tenant_id: uuid.UUID, + session_id: uuid.UUID, + title: str, + prompt: str + ) -> dict: + """ + Create and save a new prompt release for a given session. + + Args: + tenant_id (uuid.UUID): The ID of the tenant owning the prompt. + session_id (uuid.UUID): The ID of the session to associate with this prompt. + title (str): The title of the prompt release. + prompt (str): The content of the prompt. + + Returns: + dict: A dictionary containing: + - id (UUID): The unique ID of the created prompt release. + - session_id (UUID): The session ID linked to the release. + - title (str): The title of the prompt. + - prompt (str): The prompt content. + - created_at (int): Timestamp (in milliseconds) of when the prompt was created. + + Raises: + BusinessException: If a prompt release already exists for the given session. + """ + session = self.optim_repo.get_session_by_id(session_id) + if session is None or session.tenant_id != tenant_id: + raise BusinessException( + "Session does not exist or the current user has no access", + BizCode.BAD_REQUEST + ) + + if self.release_repo.get_prompt_by_session_id(session_id): + raise BusinessException( + "A release already exists for the current session", + BizCode.BAD_REQUEST + ) + + prompt_obj = self.release_repo.create_prompt_release( + tenant_id=tenant_id, + title=title, + session_id=session_id, + prompt=prompt + ) + self.db.commit() + self.db.refresh(prompt_obj) + return { + "id": prompt_obj.id, + "session_id": prompt_obj.session_id, + "title": prompt_obj.title, + "prompt": prompt_obj.prompt, + "created_at": int(prompt_obj.created_at.timestamp() * 1000) + } + + def delete_prompt( + self, + tenant_id: uuid.UUID, + prompt_id: uuid.UUID + ) -> None: + """ + Soft delete a prompt release by prompt_id. + + Args: + tenant_id (uuid.UUID): Tenant identifier. + prompt_id (uuid.UUID): Prompt identifier. + + Raises: + BusinessException: If the prompt does not exist or already deleted. + """ + prompt_obj = self.release_repo.get_prompt_by_id(prompt_id) + if not prompt_obj or prompt_obj.is_delete: + raise BusinessException( + "Prompt does not exist or has already been deleted", + BizCode.NOT_FOUND + ) + + if prompt_obj.tenant_id != tenant_id: + raise BusinessException( + "No permission to delete this prompt", + BizCode.FORBIDDEN + ) + + self.release_repo.soft_delete_prompt(prompt_obj) + self.db.commit() + logger.info(f"Prompt soft deleted, prompt_id={prompt_id}, tenant_id={tenant_id}") + + def get_release_list( + self, + tenant_id: uuid.UUID, + page: int, + page_size: int, + filter_keyword: str | None = None + ) -> dict[str, int | list[Any]]: + """ + Get paginated list of prompt releases with optional filter. + + Args: + tenant_id (uuid.UUID): Tenant identifier. + page (int): Page number (starting from 1). + page_size (int): Number of items per page. + filter_keyword (str | None): Optional keyword to filter by title. + + Returns: + dict: Contains total count, pagination info, and list of releases. + """ + offset = (page - 1) * page_size + + # Get total count and releases based on filter + if filter_keyword: + total = self.release_repo.count_prompts_by_keyword(tenant_id, filter_keyword) + releases = self.release_repo.search_prompts_paginated( + tenant_id=tenant_id, + keyword=filter_keyword, + offset=offset, + limit=page_size + ) + else: + total = self.release_repo.count_prompts(tenant_id) + releases = self.release_repo.get_prompts_paginated( + tenant_id=tenant_id, + offset=offset, + limit=page_size + ) + + items = [] + for release in releases: + # Get first user message from session + first_message = self.optim_repo.get_first_user_message( + session_id=release.session_id + ) + + items.append({ + "id": release.id, + "title": release.title, + "prompt": release.prompt, + "created_at": int(release.created_at.timestamp() * 1000), + "first_message": first_message + }) + + log_msg = f"Retrieved {len(items)} prompt releases, page={page}, tenant_id={tenant_id}" + if filter_keyword: + log_msg += f", filter='{filter_keyword}'" + logger.info(log_msg) + + result = { + "page": { + "total": total, + "page": page, + "page_size": page_size, + "hasnext": page * page_size < total + }, + "keyword": filter_keyword, + "items": items + } + + return result diff --git a/api/app/services/shared_chat_service.py b/api/app/services/shared_chat_service.py index e5247e5e..a92c2649 100644 --- a/api/app/services/shared_chat_service.py +++ b/api/app/services/shared_chat_service.py @@ -4,6 +4,8 @@ import time import asyncio from typing import Optional, Dict, Any, AsyncGenerator from sqlalchemy.orm import Session + +from app.repositories.model_repository import ModelApiKeyRepository from app.services.memory_konwledges_server import write_rag from app.models import ReleaseShare, AppRelease, Conversation from app.services.conversation_service import ConversationService @@ -164,16 +166,20 @@ class SharedChatService: raise ResourceNotFoundException("模型配置", str(model_config_id)) # 获取 API Key - stmt = ( - select(ModelApiKey) - .where( - ModelApiKey.model_config_id == model_config_id, - ModelApiKey.is_active == True - ) - .order_by(ModelApiKey.priority.desc()) - .limit(1) - ) - api_key_obj = self.db.scalars(stmt).first() + # stmt = ( + # select(ModelApiKey).join( + # ModelConfig, ModelApiKey.model_configs + # ) + # .where( + # ModelConfig.id == model_config_id, + # ModelApiKey.is_active.is_(True) + # ) + # .order_by(ModelApiKey.priority.desc()) + # .limit(1) + # ) + # api_key_obj = self.db.scalars(stmt).first() + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config_id) + api_key_obj = api_keys[0] if api_keys else None if not api_key_obj: raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING) @@ -276,7 +282,14 @@ class SharedChatService: self.conversation_service.save_conversation_messages( conversation_id=conversation.id, user_message=message, - assistant_message=result["content"] + assistant_message=result["content"], + meta_data={ + "usage": result.get("usage", { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0 + }) + } ) # self.conversation_service.add_message( # conversation_id=conversation.id, @@ -358,16 +371,20 @@ class SharedChatService: raise ResourceNotFoundException("模型配置", str(model_config_id)) # 获取 API Key - stmt = ( - select(ModelApiKey) - .where( - ModelApiKey.model_config_id == model_config_id, - ModelApiKey.is_active == True - ) - .order_by(ModelApiKey.priority.desc()) - .limit(1) - ) - api_key_obj = self.db.scalars(stmt).first() + # stmt = ( + # select(ModelApiKey).join( + # ModelConfig, ModelApiKey.model_configs + # ) + # .where( + # ModelConfig.id == model_config_id, + # ModelApiKey.is_active.is_(True) + # ) + # .order_by(ModelApiKey.priority.desc()) + # .limit(1) + # ) + # api_key_obj = self.db.scalars(stmt).first() + api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config_id) + api_key_obj = api_keys[0] if api_keys else None if not api_key_obj: raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING) @@ -459,6 +476,7 @@ class SharedChatService: # 流式调用 Agent full_content = "" + total_tokens = 0 async for chunk in agent.chat_stream( message=message, history=history, @@ -469,9 +487,12 @@ class SharedChatService: config_id=config_id, memory_flag=memory_flag ): - full_content += chunk - # 发送消息块事件 - yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n" + if isinstance(chunk, int): + total_tokens = chunk + else: + full_content += chunk + # 发送消息块事件 + yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n" elapsed_time = time.time() - start_time @@ -488,7 +509,7 @@ class SharedChatService: content=full_content, meta_data={ "model": api_key_obj.model_name, - "usage": {} + "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens} } ) @@ -598,7 +619,7 @@ class SharedChatService: # 获取多 Agent 配置 multi_agent_config = self.db.query(MultiAgentConfig).filter( MultiAgentConfig.app_id == release.app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ).first() if not multi_agent_config: @@ -695,7 +716,7 @@ class SharedChatService: # 获取多 Agent 配置 multi_agent_config = self.db.query(MultiAgentConfig).filter( MultiAgentConfig.app_id == release.app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ).first() if not multi_agent_config: diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 863bccb0..3a90a821 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -155,10 +155,10 @@ class MemoryInsightHelper: """ query = """ MATCH (d:Dialogue) - WHERE d.group_id = $group_id AND d.created_at IS NOT NULL AND d.created_at <> '' + WHERE d.end_user_id = $end_user_id AND d.created_at IS NOT NULL AND d.created_at <> '' RETURN d.created_at AS creation_time """ - records = await self.neo4j_connector.execute_query(query, group_id=self.user_id) + records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id) if not records: return [] @@ -211,17 +211,17 @@ class MemoryInsightHelper: async def get_social_connections(self) -> dict | None: """Find the user with whom the most memories are shared.""" query = """ - MATCH (c1:Chunk {group_id: $group_id}) + MATCH (c1:Chunk {end_user_id: $end_user_id}) OPTIONAL MATCH (c1)-[:CONTAINS]->(s:Statement) OPTIONAL MATCH (s)<-[:CONTAINS]-(c2:Chunk) - WHERE c1.group_id <> c2.group_id AND s IS NOT NULL AND c2 IS NOT NULL - WITH c2.group_id AS other_user_id, COUNT(DISTINCT s) AS common_statements + WHERE c1.end_user_id <> c2.end_user_id AND s IS NOT NULL AND c2 IS NOT NULL + WITH c2.end_user_id AS other_user_id, COUNT(DISTINCT s) AS common_statements WHERE common_statements > 0 RETURN other_user_id, common_statements ORDER BY common_statements DESC LIMIT 1 """ - records = await self.neo4j_connector.execute_query(query, group_id=self.user_id) + records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id) if not records or not records[0].get("other_user_id"): return None @@ -230,7 +230,7 @@ class MemoryInsightHelper: time_range_query = """ MATCH (c:Chunk) - WHERE c.group_id IN [$user_id, $other_user_id] + WHERE c.end_user_id IN [$user_id, $other_user_id] RETURN min(c.created_at) AS start_time, max(c.created_at) AS end_time """ time_records = await self.neo4j_connector.execute_query( @@ -294,11 +294,11 @@ class UserSummaryHelper: """Fetch recent statements authored by the user/group for context.""" query = ( "MATCH (s:Statement) " - "WHERE s.group_id = $group_id AND s.statement IS NOT NULL " + "WHERE s.end_user_id = $end_user_id AND s.statement IS NOT NULL " "RETURN s.statement AS statement, s.created_at AS created_at " "ORDER BY created_at DESC LIMIT $limit" ) - rows = await self.connector.execute_query(query, group_id=self.user_id, limit=limit) + rows = await self.connector.execute_query(query, end_user_id=self.user_id, limit=limit) records = [] for r in rows: try: @@ -1152,7 +1152,7 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, import re # 创建 UserSummaryHelper 实例 - user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_GROUP_ID", "group_123")) + user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_end_user_id", "group_123")) try: # 1) 收集上下文数据 @@ -1273,10 +1273,10 @@ async def analytics_node_statistics( if end_user_id: query = f""" MATCH (n:{node_type}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await _neo4j_connector.execute_query(query, group_id=end_user_id) + result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id) else: query = f""" MATCH (n:{node_type}) @@ -1387,10 +1387,10 @@ async def analytics_memory_types( # 查询 Statement 节点数量 query = """ MATCH (n:Statement) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await _neo4j_connector.execute_query(query, group_id=end_user_id) + result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id) statement_count = result[0]["count"] if result and len(result) > 0 else 0 # 取三分之一作为隐性记忆数量 implicit_count = round(statement_count / 3) @@ -1504,7 +1504,7 @@ async def analytics_graph_data( 包含节点、边和统计信息的字典 """ try: - # 1. 获取 group_id + # 1. 获取 end_user_id user_uuid = uuid.UUID(end_user_id) repo = EndUserRepository(db) end_user = repo.get_by_id(user_uuid) @@ -1528,7 +1528,7 @@ async def analytics_graph_data( # 基于中心节点的扩展查询 node_query = f""" MATCH path = (center)-[*1..{depth}]-(connected) - WHERE center.group_id = $group_id + WHERE center.end_user_id = $end_user_id AND elementId(center) = $center_node_id WITH collect(DISTINCT center) + collect(DISTINCT connected) as all_nodes UNWIND all_nodes as n @@ -1539,7 +1539,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "center_node_id": center_node_id, "limit": limit } @@ -1547,7 +1547,7 @@ async def analytics_graph_data( # 按节点类型过滤查询 node_query = """ MATCH (n) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND labels(n)[0] IN $node_types RETURN elementId(n) as id, @@ -1556,7 +1556,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "node_types": node_types, "limit": limit } @@ -1564,7 +1564,7 @@ async def analytics_graph_data( # 查询所有节点 node_query = """ MATCH (n) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN elementId(n) as id, labels(n)[0] as label, @@ -1572,7 +1572,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "limit": limit } diff --git a/api/app/services/workflow_service.py b/api/app/services/workflow_service.py index b7d5df02..2958f4f9 100644 --- a/api/app/services/workflow_service.py +++ b/api/app/services/workflow_service.py @@ -528,7 +528,8 @@ class WorkflowService: self.conversation_service.add_message( conversation_id=conversation_id_uuid, role=message["role"], - content=message["content"] + content=message["content"], + meta_data=None if message["role"] == "user" else {"usage": token_usage} ) logger.info(f"Workflow Run Success, " f"execution_id: {execution.execution_id}, message count: {len(final_messages)}") @@ -678,7 +679,8 @@ class WorkflowService: self.conversation_service.add_message( conversation_id=conversation_id_uuid, role=message["role"], - content=message["content"] + content=message["content"], + meta_data=None if message["role"] == "user" else {"usage": token_usage} ) logger.info(f"Workflow Run Success, " f"execution_id: {execution.execution_id}, message count: {len(final_messages)}") @@ -761,7 +763,10 @@ class WorkflowService: # 4. 获取工作空间 ID（从 app 获取） from app.models import App - app = self.db.query(App).filter(App.id == app_id).first() + app = self.db.query(App).filter( + App.id == app_id, + App.is_active.is_(True) + ).first() if not app: raise BusinessException( code=BizCode.NOT_FOUND, diff --git a/api/app/tasks.py b/api/app/tasks.py index fa9d1fdf..48b41e4f 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -4,6 +4,7 @@ import os import re import time import uuid +from uuid import UUID from datetime import datetime, timezone from math import ceil from typing import Any, Dict, List, Optional @@ -382,16 +383,16 @@ def build_graphrag_for_kb(kb_id: uuid.UUID): @celery_app.task(name="app.core.memory.agent.read_message", bind=True) -def read_message_task(self, group_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: +def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a read message via MemoryAgentService. Args: - group_id: Group ID for the memory agent (also used as end_user_id) + end_user_id: Group ID for the memory agent (also used as end_user_id) message: User message to process history: Conversation history search_switch: Search switch parameter - config_id: Optional configuration ID + config_id: Configuration ID as string (will be converted to UUID) Returns: Dict containing the result and metadata @@ -401,14 +402,22 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, """ start_time = time.time() + # Convert config_id string to UUID + actual_config_id = None + if config_id: + try: + actual_config_id = uuid.UUID(config_id) if isinstance(config_id, str) else config_id + except (ValueError, AttributeError): + # If conversion fails, leave as None and try to resolve + pass + # Resolve config_id if None - actual_config_id = config_id if actual_config_id is None: try: from app.services.memory_agent_service import get_end_user_connected_config db = next(get_db()) try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) actual_config_id = connected_config.get("memory_config_id") finally: db.close() @@ -420,24 +429,42 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, db = next(get_db()) try: service = MemoryAgentService() - return await service.read_memory(group_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id) + return await service.read_memory(end_user_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id) finally: db.close() try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time return { "status": "SUCCESS", "result": result, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id } except BaseException as e: elapsed_time = time.time() - start_time + # Handle ExceptionGroup from TaskGroup if hasattr(e, 'exceptions'): error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions] detailed_error = "; ".join(error_messages) @@ -446,7 +473,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, return { "status": "FAILURE", "error": detailed_error, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id @@ -454,19 +481,13 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, @celery_app.task(name="app.core.memory.agent.write_message", bind=True) -def write_message_task(self, group_id: str, message, config_id: str, storage_type: str, user_rag_memory_id: str) -> Dict[str, Any]: +def write_message_task(self, end_user_id: str, message: str, config_id: str, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a write message via MemoryAgentService. - 支持两种消息格式： - 1. 字符串格式（向后兼容）：message="user: xxx\nassistant: yyy" - 2. 结构化消息列表（推荐）：message=[{"role": "user", "content": "xxx"}, {"role": "assistant", "content": "yyy"}] - Args: - group_id: Group ID for the memory agent (also used as end_user_id) - message: Message to write (str or list[dict]) - config_id: Optional configuration ID - storage_type: Storage type (neo4j/rag) - user_rag_memory_id: RAG memory ID + end_user_id: Group ID for the memory agent (also used as end_user_id) + message: Message to write + config_id: Configuration ID as string (will be converted to UUID) Returns: Dict containing the result and metadata @@ -477,30 +498,46 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ from app.core.logging_config import get_logger logger = get_logger(__name__) - logger.info(f"[CELERY WRITE] Starting write task - group_id={group_id}, config_id={config_id}, storage_type={storage_type}") + logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") start_time = time.time() + # Convert config_id string to UUID + actual_config_id = None + if config_id: + try: + actual_config_id = uuid.UUID(config_id) if isinstance(config_id, str) else config_id + logger.info(f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} (type: {type(actual_config_id).__name__})") + except (ValueError, AttributeError) as e: + logger.error(f"[CELERY WRITE] Invalid config_id format: {config_id}, error: {e}") + return { + "status": "FAILURE", + "error": f"Invalid config_id format: {config_id}", + "end_user_id": end_user_id, + "config_id": config_id, + "elapsed_time": 0.0, + "task_id": self.request.id + } + # Resolve config_id if None - actual_config_id = config_id if actual_config_id is None: try: from app.services.memory_agent_service import get_end_user_connected_config db = next(get_db()) try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) actual_config_id = connected_config.get("memory_config_id") finally: db.close() except Exception: # Log but continue - will fail later with proper error pass - + async def _run() -> str: db = next(get_db()) try: - logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory") + logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory with config_id={actual_config_id} (type: {type(actual_config_id).__name__})") service = MemoryAgentService() - result = await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id) + result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type, user_rag_memory_id) logger.info(f"[CELERY WRITE] Write completed successfully: {result}") return result except Exception as e: @@ -510,7 +547,24 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ db.close() try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time logger.info(f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}") @@ -518,13 +572,14 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ return { "status": "SUCCESS", "result": result, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id } except BaseException as e: elapsed_time = time.time() - start_time + # Handle ExceptionGroup from TaskGroup if hasattr(e, 'exceptions'): error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions] detailed_error = "; ".join(error_messages) @@ -536,7 +591,7 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ return { "status": "FAILURE", "error": detailed_error, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id @@ -635,8 +690,11 @@ def write_total_memory_task(workspace_id: str) -> Dict[str, Any]: try: workspace_uuid = uuid.UUID(workspace_id) - # 1. 查询当前workspace下的所有app - apps = db.query(App).filter(App.workspace_id == workspace_uuid).all() + # 1. 查询当前workspace下的所有app（仅未删除的） + apps = db.query(App).filter( + App.workspace_id == workspace_uuid, + App.is_active.is_(True) + ).all() if not apps: # 如果没有app，总量为0 @@ -716,7 +774,15 @@ def write_total_memory_task(workspace_id: str) -> Dict[str, Any]: } -@celery_app.task(name="app.tasks.regenerate_memory_cache", bind=True) +@celery_app.task( + name="app.tasks.regenerate_memory_cache", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=3600, + soft_time_limit=3300, +) def regenerate_memory_cache(self) -> Dict[str, Any]: """定时任务：为所有用户重新生成记忆洞察和用户摘要缓存 @@ -875,7 +941,24 @@ def regenerate_memory_cache(self) -> Dict[str, Any]: } try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time result["elapsed_time"] = elapsed_time result["task_id"] = self.request.id @@ -891,7 +974,16 @@ def regenerate_memory_cache(self) -> Dict[str, Any]: } -@celery_app.task(name="app.tasks.workspace_reflection_task", bind=True) + +@celery_app.task( + name="app.tasks.workspace_reflection_task", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=300, + soft_time_limit=240, +) def workspace_reflection_task(self) -> Dict[str, Any]: """定时任务：每30秒运行工作空间反思功能 @@ -948,7 +1040,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: end_users = data['end_users'] for base, config, user in zip(releases, data_configs, end_users): - if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']: + if str(base['config']) == str(config['config_id']) and str(base['app_id']) == str(user['app_id']): # 调用反思服务 api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") @@ -1002,7 +1094,24 @@ def workspace_reflection_task(self) -> Dict[str, Any]: } try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time result["elapsed_time"] = elapsed_time result["task_id"] = self.request.id @@ -1019,8 +1128,17 @@ def workspace_reflection_task(self) -> Dict[str, Any]: -@celery_app.task(name="app.tasks.run_forgetting_cycle_task", bind=True) -def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str, Any]: + +@celery_app.task( + name="app.tasks.run_forgetting_cycle_task", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=7200, + soft_time_limit=7000, +) +def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Dict[str, Any]: """定时任务：运行遗忘周期 定期执行遗忘周期，识别并融合低激活值的知识节点。 @@ -1048,7 +1166,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str # 运行遗忘周期 report = await forget_service.trigger_forgetting( db=db, - group_id=None, # 处理所有组 + end_user_id=None, # 处理所有组 config_id=config_id ) @@ -1078,4 +1196,11 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str "duration_seconds": duration } - return asyncio.run(_run()) + # 运行异步函数 + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + result = loop.run_until_complete(_run()) + return result + finally: + loop.close() diff --git a/api/app/utils/app_config_utils.py b/api/app/utils/app_config_utils.py index 514e4565..06549989 100644 --- a/api/app/utils/app_config_utils.py +++ b/api/app/utils/app_config_utils.py @@ -57,7 +57,7 @@ def dict_to_model_parameters(data: Optional[Dict[str, Any]]) -> Optional[Any]: if data is None: return None - from app.schemas import ModelParameters + from app.schemas.app_schema import ModelParameters if isinstance(data, ModelParameters): return data @@ -83,6 +83,13 @@ class AgentConfigProxy: def agent_config_4_app_release(release: AppRelease) -> AgentConfig: config_dict = release.config + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} agent_config = AgentConfig( app_id=release.app_id, @@ -100,6 +107,14 @@ def agent_config_4_app_release(release: AppRelease) -> AgentConfig: def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig: config_dict = release.config + + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} agent_config = MultiAgentConfig( app_id=release.app_id, @@ -120,6 +135,14 @@ def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig: def workflow_config_4_app_release(release: AppRelease) -> WorkflowConfig: config_dict = release.config + + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} config = WorkflowConfig( id=config_dict.get("id"), diff --git a/api/app/utils/config_utils.py b/api/app/utils/config_utils.py new file mode 100644 index 00000000..cc67afd2 --- /dev/null +++ b/api/app/utils/config_utils.py @@ -0,0 +1,46 @@ +""" +Configuration utility functions + +Shared utilities for configuration handling to avoid circular imports. +""" +from uuid import UUID +from sqlalchemy.orm import Session + + +def resolve_config_id(config_id: UUID | int|str, db: Session) -> UUID: + """ + 解析 config_id，如果是整数则通过 config_id_old 查找对应的 UUID + + Args: + config_id: 配置ID（UUID 或整数） + db: 数据库会话 + + Returns: + UUID: 解析后的配置ID + + Raises: + ValueError: 当找不到对应的配置时 + """ + + from app.models.memory_config_model import MemoryConfig + if isinstance(config_id, UUID): + return config_id + if isinstance(config_id, str) and len(config_id)<=6: + memory_config = db.query(MemoryConfig).filter( + MemoryConfig.config_id_old == int(config_id) + ).first() + print(memory_config) + if not memory_config: + raise ValueError(f"STR 未找到 config_id_old={config_id} 对应的配置") + return memory_config.config_id + if isinstance(config_id, int): + memory_config = db.query(MemoryConfig).filter( + MemoryConfig.config_id_old == config_id + ).first() + + if not memory_config: + raise ValueError(f"INT 未找到 config_id_old={config_id} 对应的配置") + + return memory_config.config_id + + return config_id diff --git a/api/app/version_info.json b/api/app/version_info.json index 20896845..e82243a4 100644 --- a/api/app/version_info.json +++ b/api/app/version_info.json @@ -1,14 +1,74 @@ { + "v0.2.2": { + "introduction": { + "codeName": "淬锋（Temper）", + "releaseDate": "2026-1-31", + "upgradePosition": "本次发布聚焦平台稳定性和性能优化。正如\"淬锋\"之名——千锤百炼，淬火成锋，我们通过严格测试和修复打磨系统品质。引入 Agent 工作流的代码执行能力、改进模型并发管理，并修复了记忆系统的多个关键问题。", + "coreUpgrades": [ + "1. Agent平台增强
* 模型并发管理：优化模型广场的并发请求处理和资源分配能力。", + "2. 记忆系统优化
* Celery 队列修复：解决任务队列问题，提升异步记忆处理的可靠性
* 记忆 Agent 优化：提升记忆 Agent 的性能和效率
* 接口响应速度优化：优化记忆接口响应时间，加快操作速度。", + "3. 情绪记忆与识别升级
* 情绪记忆角色识别修复：解决情绪记忆上下文中的角色/人物识别问题
* 角色识别增强：提升对话记忆中的角色/人物识别准确性。", + "
", + "MemoryBear 持续致力于为 AI 应用提供类人记忆能力。本次以稳定性为核心的发布，进一步夯实了「感知→精炼→关联→遗忘」范式的基础。", + "未来版本将在此坚实基础上，扩展 Agent 能力并深化记忆智能特性。" + ] + }, + "introduction_en": { + "codeName": "Temper (淬锋)", + "releaseDate": "2026-1-31", + "upgradePosition": "This release focuses on platform stability and performance optimization — true to its codename \"淬锋\" (tempered blade), we've refined the system through rigorous testing and fixes. Introducing Python code execution for Agent workflows, improved model concurrency management, and critical fixes across the memory system.", + "coreUpgrades": [ + "1. Agent Platform Enhancements
* Model Concurrency Management: Enhanced Model Plaza with improved concurrent model request handling and resource allocation.", + "2. Memory System Improvements
* Celery Queue Fix: Resolved task queue issues for more reliable asynchronous memory processing
* Memory Agent Optimization: Improved memory Agent performance and efficiency
* API Response Speed: Optimized memory interface response times for faster operations.", + "3. Emotional Memory & Recognition Upgrades
* Emotion Memory Role Recognition Fix: Resolved issues with role/character identification in emotional memory contexts
* Role Recognition Enhancement: Improved character/role identification accuracy in conversation memory.", + "
", + "MemoryBear continues advancing toward human-like memory capabilities for AI applications. This stability-focused release strengthens the foundation for our Perception → Refinement → Association → Forgetting paradigm.", + "Future releases will build on this solid base with expanded Agent capabilities and deeper memory intelligence features." + ] + } + }, + "v0.2.1": { + "introduction": { + "codeName": "启知", + "releaseDate": "2026-1-23", + "upgradePosition": "\uD83D\uDC3B 本次更新主要优化使用体验和修复已知问题，让系统更稳定、更好用。", + "coreUpgrades": [ + "1. 工作流更好用了
* 界面更清晰，一眼看懂怎么配置
* 新增节点输出变量展示，方便其他节点引用
* 修复了几个影响体验的bug", + "2. 智能体配置更简单
* 提示词和变量联动更顺畅
* 配置界面重新整理，找功能更方便", + "3. 记忆系统更稳定
* 优化了情绪记忆和隐性记忆的缓存更新
* 修复了记忆配置页面的报错问题
* 现在能自动识别用户和AI的身份了", + "4. 知识库体验提升
* 修复了文档解析异常的问题
* 上传文档时能看到处理进度了
* 取消了操作也不会报错了", + "5. 系统整体更可靠
* 修复了新用户访问跳转问题
* 流式接口更稳定，长对话不断线
* 调整了菜单顺序，操作更顺手", + "
", + "这次更新虽然不大，但让记忆熊的基础更扎实、体验更流畅。我们继续努力，让AI记忆更好用！", + "记忆熊，记得更牢，用得更好。\uD83D\uDC3B✨" + ] + }, + "introduction_en": { + "codeName": "Qizhi", + "releaseDate": "2026-1-23", + "upgradePosition": "\uD83D\uDC3B This update focuses on improving usability and fixing known issues, making the system more stable and easier to use overall.", + "coreUpgrades": [ + "1. Improved Workflow Experience
* Cleaner, more intuitive UI for easier configuration at a glance
* Added visibility of node output variables, making them easier to reference in downstream nodes
* Fixed several usability-related bugs that affected the workflow experience", + "2. Simpler Agent Configuration
* Smoother linkage between prompts and variables
* Reorganized configuration layout for easier navigation and better clarity", + "3. More Stable Memory System
* Optimized cache refresh for emotional memory and implicit memory
* Fixed error issues on the memory configuration page
* The system can now automatically distinguish between user and AI roles", + "4. Enhanced Knowledge Base Experience
* Fixed issues with document parsing failures
* Upload progress is now displayed during document processing
* Canceling an upload no longer triggers errors", + "5. Overall System Reliability Improvements
* Fixed redirect issues affecting new users
* Improved stability of streaming APIs to prevent interruptions during long conversations
* Adjusted menu ordering for a smoother and more intuitive workflow", + "
", + "Although this is a relatively small update, it strengthens MemoryBear’s foundation and delivers a noticeably smoother experience. We’ll keep refining the system to make AI memory more powerful and easier to use.", + "MemoryBear — remember better, work smarter. \uD83D\uDC3B✨" + ] + } + }, "v0.2.0": { "introduction": { "codeName": "启知", "releaseDate": "2026-1-16", "upgradePosition": "本次为架构升级，核心目标是把\"被动存储\"升级为\"主动认知\"，让系统具备情绪感知、情景理解与类人记忆机制，为后续多智能体协作与专业场景落地奠定底座。", "coreUpgrades": [ - "记忆详情：拟人记忆——情绪引擎、情景记忆、短期记忆、工作记忆、感知记忆、显性记忆、隐性记忆，并配套类脑遗忘机制，实现从感知→情绪→情景→长期沉淀的完整人类记忆闭环", - "可视化工作流：拖拽式节点编排（LLM、知识库、逻辑、工具），业务落地周期由天缩至小时。", - "多模态知识处理：PDF、PPT、MP3、MP4 一键解析，时间感知检索准确率 94.3%，问答对数据即插即用。", - "Agent集群内置\"记忆-知识-工具-审核\"四类角色模板，用户一键生成；主控Agent把复杂任务拆为子任务并行分发，再靠情景记忆统一消解冲突、校验一致性，输出完整报告。" + "1. 记忆详情：拟人记忆——情绪引擎、情景记忆、短期记忆、工作记忆、感知记忆、显性记忆、隐性记忆，并配套类脑遗忘机制，实现从感知→情绪→情景→长期沉淀的完整人类记忆闭环", + "2. 可视化工作流：拖拽式节点编排（LLM、知识库、逻辑、工具），业务落地周期由天缩至小时。", + "3. 多模态知识处理：PDF、PPT、MP3、MP4 一键解析，时间感知检索准确率 94.3%，问答对数据即插即用。", + "4. Agent集群内置\"记忆-知识-工具-审核\"四类角色模板，用户一键生成；主控Agent把复杂任务拆为子任务并行分发，再靠情景记忆统一消解冲突、校验一致性，输出完整报告。" ] }, "introduction_en": { @@ -16,10 +76,10 @@ "releaseDate": "2026-1-16", "upgradePosition": "This release marks a foundational upgrade to the system’s cognitive architecture. The core objective is to evolve the platform from passive information storage into active cognitive intelligence—enabling emotional awareness, situational understanding, and human-like memory mechanisms. This upgrade lays the groundwork for future multi-agent collaboration and domain-specific, production-grade AI applications.", "coreUpgrades": [ - "Human-Like Memory Architecture: A comprehensive, human-inspired memory system is introduced, encompassing emotional processing, situational memory, short-term and working memory, perceptual memory, as well as explicit and implicit memory. Combined with brain-inspired forgetting mechanisms, the system now supports a complete cognitive loop—from perception → emotion → context → long-term consolidation, closely mirroring human memory formation.", - "Visual Workflow Orchestration: A fully visual, drag-and-drop workflow enables modular composition of LLMs, knowledge bases, logic, and tools. This dramatically reduces the time required to move from experimentation to production—from days to hours.", - "Multimodal Knowledge Processing: The system now supports one-click parsing and ingestion of PDF, PPT, MP3, and MP4 content. With time-aware retrieval accuracy reaching 94.3%, structured Q&A data becomes instantly usable for downstream reasoning and generation.", - "Built-in Agent Clusters: Predefined role templates across four categories—Memory, Knowledge, Tools, and Review—can be generated with a single click. A Coordinator Agent decomposes complex tasks into parallel subtasks, while situational memory is used to resolve conflicts, validate consistency, and synthesize outputs into a coherent, end-to-end report." + "1. Human-Like Memory Architecture: A comprehensive, human-inspired memory system is introduced, encompassing emotional processing, situational memory, short-term and working memory, perceptual memory, as well as explicit and implicit memory. Combined with brain-inspired forgetting mechanisms, the system now supports a complete cognitive loop—from perception → emotion → context → long-term consolidation, closely mirroring human memory formation.", + "2. Visual Workflow Orchestration: A fully visual, drag-and-drop workflow enables modular composition of LLMs, knowledge bases, logic, and tools. This dramatically reduces the time required to move from experimentation to production—from days to hours.", + "3. Multimodal Knowledge Processing: The system now supports one-click parsing and ingestion of PDF, PPT, MP3, and MP4 content. With time-aware retrieval accuracy reaching 94.3%, structured Q&A data becomes instantly usable for downstream reasoning and generation.", + "4. Built-in Agent Clusters: Predefined role templates across four categories—Memory, Knowledge, Tools, and Review—can be generated with a single click. A Coordinator Agent decomposes complex tasks into parallel subtasks, while situational memory is used to resolve conflicts, validate consistency, and synthesize outputs into a coherent, end-to-end report." ] } }, @@ -29,16 +89,17 @@ "releaseDate": "2025-12-01", "upgradePosition": "这是一款专注于管理和利用AI记忆的工具，支持RAG和知识图谱两种主流存储方式，旨在为AI应用提供持久化、结构化的\"记忆\"能力。", "coreUpgrades": [ - "记忆空间：用户可以创建独立的空间来隔离不同记忆，并灵活选择存储方式。", - "记忆配置：简化了配置流程，内置自动提取关键信息的\"记忆萃取\"和管理生命周期的\"遗忘\"引擎。", - "知识检索：提供语义、分词和混合三种检索模式，并支持多种参数微调和结果重排序，以提升召回效果。", - "全局管理：支持统一设置默认检索参数，并可一键应用到所有知识库。", - "测试与调试：内置\"召回测试\"功能，方便用户实时验证检索效果并调整参数，支持通过分享码与他人协作。", - "记忆洞察：可查看详细的对话记录、用户画像和分析报告，帮助理解AI的\"记忆\"内容。", - "集成与管理：提供API Key用于系统集成，并包含基本的用户管理功能。", - "界面与体验：采用现代化的卡片式布局和渐变色设计，注重交互的流畅性和视觉美感。", - "起步与使用：文档中提供了清晰的基础使用流程，引导用户从创建空间、配置记忆到测试检索快速上手。", - "版本说明与限制: 记忆熊 v0.1.0 版本\"初心\"囊括智能记忆管理的核心思路和基础能力，为后续开发奠定了基础。", + "1. 记忆空间：用户可以创建独立的空间来隔离不同记忆，并灵活选择存储方式。", + "2. 记忆配置：简化了配置流程，内置自动提取关键信息的\"记忆萃取\"和管理生命周期的\"遗忘\"引擎。", + "3. 知识检索：提供语义、分词和混合三种检索模式，并支持多种参数微调和结果重排序，以提升召回效果。", + "4. 全局管理：支持统一设置默认检索参数，并可一键应用到所有知识库。", + "5. 测试与调试：内置\"召回测试\"功能，方便用户实时验证检索效果并调整参数，支持通过分享码与他人协作。", + "6. 记忆洞察：可查看详细的对话记录、用户画像和分析报告，帮助理解AI的\"记忆\"内容。", + "7. 集成与管理：提供API Key用于系统集成，并包含基本的用户管理功能。", + "8. 界面与体验：采用现代化的卡片式布局和渐变色设计，注重交互的流畅性和视觉美感。", + "9. 起步与使用：文档中提供了清晰的基础使用流程，引导用户从创建空间、配置记忆到测试检索快速上手。", + "10. 版本说明与限制: 记忆熊 v0.1.0 版本\"初心\"囊括智能记忆管理的核心思路和基础能力，为后续开发奠定了基础。", + "
", "文档资源：用户手册、API文档、FAQ", "问题反馈：GitHub Issues、邮件支持", "致谢：感谢所有参与测试和提供反馈的用户！" @@ -49,16 +110,17 @@ "releaseDate": "2025-12-01", "upgradePosition": "A tool focused on managing and utilizing AI memory, supporting both RAG and knowledge graph storage methods, aiming to provide persistent and structured 'memory' capabilities for AI applications.", "coreUpgrades": [ - "Memory Space: Users can create independent spaces to isolate different memories and flexibly choose storage methods.", - "Memory Configuration: Simplified configuration process with built-in 'memory extraction' for automatic key information extraction and 'forgetting' engine for lifecycle management.", - "Knowledge Retrieval: Provides semantic, tokenization, and hybrid retrieval modes with various parameter tuning and result reranking to improve recall.", - "Global Management: Supports unified default retrieval parameter settings with one-click application to all knowledge bases.", - "Testing & Debugging: Built-in 'recall testing' for real-time verification of retrieval effects and parameter adjustment, with sharing code support for collaboration.", - "Memory Insights: View detailed conversation records, user profiles, and analysis reports to understand AI 'memory' content.", - "Integration & Management: Provides API Key for system integration with basic user management features.", - "Interface & Experience: Modern card-based layout with gradient design, focusing on interaction fluidity and visual aesthetics.", - "Getting Started: Documentation provides clear basic usage flow, guiding users from creating spaces, configuring memory to testing retrieval.", - "Version Notes: MemoryBear v0.1.0 'Original Intent' encompasses core concepts and basic capabilities of intelligent memory management, laying foundation for future development.", + "1. Memory Space: Users can create independent spaces to isolate different memories and flexibly choose storage methods.", + "2. Memory Configuration: Simplified configuration process with built-in 'memory extraction' for automatic key information extraction and 'forgetting' engine for lifecycle management.", + "3. Knowledge Retrieval: Provides semantic, tokenization, and hybrid retrieval modes with various parameter tuning and result reranking to improve recall.", + "4. Global Management: Supports unified default retrieval parameter settings with one-click application to all knowledge bases.", + "5. Testing & Debugging: Built-in 'recall testing' for real-time verification of retrieval effects and parameter adjustment, with sharing code support for collaboration.", + "6. Memory Insights: View detailed conversation records, user profiles, and analysis reports to understand AI 'memory' content.", + "7. Integration & Management: Provides API Key for system integration with basic user management features.", + "8. Interface & Experience: Modern card-based layout with gradient design, focusing on interaction fluidity and visual aesthetics.", + "9. Getting Started: Documentation provides clear basic usage flow, guiding users from creating spaces, configuring memory to testing retrieval.", + "10. Version Notes: MemoryBear v0.1.0 'Original Intent' encompasses core concepts and basic capabilities of intelligent memory management, laying foundation for future development.", + "
", "Documentation: User Manual, API Documentation, FAQ", "Feedback: GitHub Issues, Email Support", "Acknowledgments: Thanks to all users who participated in testing and provided feedback!" diff --git a/api/docker-compose.yml b/api/docker-compose.yml index a7337689..69763de2 100644 --- a/api/docker-compose.yml +++ b/api/docker-compose.yml @@ -15,9 +15,11 @@ services: networks: - default - celery + - sandbox depends_on: - worker-memory - worker-document + - worker-periodic # Memory worker - Memory read/write tasks (threads pool for asyncio) worker-memory: @@ -47,6 +49,20 @@ services: networks: - celery + # Periodic worker - Scheduled/beat tasks (prefork, low concurrency) + worker-periodic: + image: redbear-mem-open:latest + container_name: worker-periodic + env_file: + - .env + volumes: + - ./files:/files + - /etc/localtime:/etc/localtime:ro + command: celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=2 --queues=periodic_tasks --max-tasks-per-child=50 -n periodic_worker@%h + restart: unless-stopped + networks: + - celery + # Celery Beat - scheduler beat: image: redbear-mem-open:latest @@ -63,5 +79,16 @@ services: depends_on: - worker-memory + sandbox: + image: redbear_sandbox:latest + container_name: sandbox + ports: + - "8194" + command: /code/.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8194 --log-level debug + restart: unless-stopped + networks: + - sandbox + networks: celery: + sandbox: diff --git a/api/env.example b/api/env.example index 45ab6c70..98c96edc 100644 --- a/api/env.example +++ b/api/env.example @@ -1,4 +1,9 @@ +# Language Configuration +# Supported values: "zh" (Chinese), "en" (English) +# This controls the language used for memory summary titles and other generated content +DEFAULT_LANGUAGE=zh + # Neo4j Configuration (记忆系统数据库) NEO4J_URI= NEO4J_USERNAME= @@ -75,6 +80,7 @@ ENABLE_SINGLE_SESSION= MAX_FILE_SIZE=52428800 # 50MB:10 * 1024 * 1024 FILE_PATH=/files +FILE_LOCAL_SERVER_URL="http://localhost:8000/api" # Storage Backend Configuration # Supported values: local, oss, s3 # Default: local diff --git a/api/migrations/env.py b/api/migrations/env.py index 95d74019..e4cd6dfb 100644 --- a/api/migrations/env.py +++ b/api/migrations/env.py @@ -46,7 +46,8 @@ def import_all_models_from_package(package_name: str): # Add the project root to sys.path if not already there # This is crucial for relative imports like 'app.db' to work - project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) + from pathlib import Path + project_root = str(Path(__file__).resolve().parent.parent) if project_root not in sys.path: sys.path.insert(0, project_root) diff --git a/api/migrations/versions/325b759cd66b_2026011240.py b/api/migrations/versions/325b759cd66b_2026011240.py new file mode 100644 index 00000000..048b109b --- /dev/null +++ b/api/migrations/versions/325b759cd66b_2026011240.py @@ -0,0 +1,61 @@ +"""2026011240 + +Revision ID: 325b759cd66b +Revises: 9a936a9ebb20 +Create Date: 2026-01-26 12:37:35.946749 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +revision: str = '325b759cd66b' +down_revision: Union[str, None] = '9a936a9ebb20' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # 1. 重命名表 data_config -> memory_config + op.rename_table('data_config', 'memory_config') + + # 2. 重命名列 group_id -> end_user_id + op.alter_column('memory_config', 'group_id', new_column_name='end_user_id') + + # 3. config_id: INTEGER -> UUID（保留旧值以便回滚） + op.drop_constraint('data_config_pkey', 'memory_config', type_='primary') + op.alter_column('memory_config', 'config_id', new_column_name='config_id_old', nullable=True) + op.add_column('memory_config', sa.Column('config_id', sa.UUID(), nullable=True)) + # Handle rows where apply_id might be NULL or invalid - generate new UUIDs for those + op.execute(""" + UPDATE memory_config + SET config_id = CASE + WHEN apply_id IS NOT NULL AND apply_id ~ '^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$' + THEN apply_id::uuid + ELSE gen_random_uuid() + END + """) + op.alter_column('memory_config', 'config_id', nullable=False) + op.create_primary_key('memory_config_pkey', 'memory_config', ['config_id']) + op.execute("ALTER TABLE memory_config ALTER COLUMN config_id_old DROP DEFAULT") + op.execute("DROP SEQUENCE IF EXISTS data_config_config_id_seq") + + +def downgrade() -> None: + # 1. config_id: UUID -> INTEGER（恢复旧值，空值生成新ID） + op.execute("CREATE SEQUENCE IF NOT EXISTS data_config_config_id_seq") + op.execute("UPDATE memory_config SET config_id_old = nextval('data_config_config_id_seq') WHERE config_id_old IS NULL") + op.drop_constraint('memory_config_pkey', 'memory_config', type_='primary') + op.drop_column('memory_config', 'config_id') + op.alter_column('memory_config', 'config_id_old', new_column_name='config_id', nullable=False) + op.create_primary_key('data_config_pkey', 'memory_config', ['config_id']) + op.execute("ALTER SEQUENCE data_config_config_id_seq OWNED BY memory_config.config_id") + op.execute("SELECT setval('data_config_config_id_seq', COALESCE((SELECT MAX(config_id) FROM memory_config), 1))") + + # 2. 重命名列 end_user_id -> group_id + op.alter_column('memory_config', 'end_user_id', new_column_name='group_id') + + # 3. 重命名表 memory_config -> data_config + op.rename_table('memory_config', 'data_config') diff --git a/api/migrations/versions/550c10595967_202601301521.py b/api/migrations/versions/550c10595967_202601301521.py new file mode 100644 index 00000000..b2f531db --- /dev/null +++ b/api/migrations/versions/550c10595967_202601301521.py @@ -0,0 +1,78 @@ +"""202601301521 + +Revision ID: 550c10595967 +Revises: 5de9b1e28509 +Create Date: 2026-01-30 15:24:34.647440 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '550c10595967' +down_revision: Union[str, None] = '5de9b1e28509' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.create_table('ontology_scene', + sa.Column('scene_id', sa.UUID(), nullable=False, comment='场景ID'), + sa.Column('scene_name', sa.String(length=200), nullable=False, comment='场景名称'), + sa.Column('scene_description', sa.Text(), nullable=True, comment='场景描述'), + sa.Column('workspace_id', sa.UUID(), nullable=False, comment='所属工作空间ID'), + sa.Column('created_at', sa.DateTime(), nullable=False, comment='创建时间'), + sa.Column('updated_at', sa.DateTime(), nullable=False, comment='更新时间'), + sa.ForeignKeyConstraint(['workspace_id'], ['workspaces.id'], ondelete='CASCADE'), + sa.PrimaryKeyConstraint('scene_id'), + sa.UniqueConstraint('workspace_id', 'scene_name', name='uq_workspace_scene_name') + ) + op.create_index(op.f('ix_ontology_scene_scene_id'), 'ontology_scene', ['scene_id'], unique=False) + op.create_index(op.f('ix_ontology_scene_workspace_id'), 'ontology_scene', ['workspace_id'], unique=False) + op.create_table('ontology_class', + sa.Column('class_id', sa.UUID(), nullable=False, comment='类型ID'), + sa.Column('class_name', sa.String(length=200), nullable=False, comment='类型名称'), + sa.Column('class_description', sa.Text(), nullable=True, comment='类型描述'), + sa.Column('scene_id', sa.UUID(), nullable=False, comment='所属场景ID'), + sa.Column('created_at', sa.DateTime(), nullable=False, comment='创建时间'), + sa.Column('updated_at', sa.DateTime(), nullable=False, comment='更新时间'), + sa.ForeignKeyConstraint(['scene_id'], ['ontology_scene.scene_id'], ondelete='CASCADE'), + sa.PrimaryKeyConstraint('class_id') + ) + op.create_index(op.f('ix_ontology_class_class_id'), 'ontology_class', ['class_id'], unique=False) + op.create_index(op.f('ix_ontology_class_scene_id'), 'ontology_class', ['scene_id'], unique=False) + op.create_table('prompt_history', + sa.Column('id', sa.UUID(), nullable=False), + sa.Column('tenant_id', sa.UUID(), nullable=False, comment='Tenant ID'), + sa.Column('session_id', sa.UUID(), nullable=False, comment='Session ID'), + sa.Column('title', sa.String(), nullable=False, comment='Title'), + sa.Column('prompt', sa.Text(), nullable=False, comment='Prompt'), + sa.Column('created_at', sa.DateTime(), nullable=True, comment='Creation Time'), + sa.Column('is_delete', sa.Boolean(), nullable=True, comment='Delete'), + sa.ForeignKeyConstraint(['session_id'], ['prompt_opt_session_list.id'], ), + sa.ForeignKeyConstraint(['tenant_id'], ['tenants.id'], ), + sa.PrimaryKeyConstraint('id') + ) + op.create_index(op.f('ix_prompt_history_created_at'), 'prompt_history', ['created_at'], unique=False) + op.create_index(op.f('ix_prompt_history_id'), 'prompt_history', ['id'], unique=False) + + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + + op.drop_index(op.f('ix_prompt_history_id'), table_name='prompt_history') + op.drop_index(op.f('ix_prompt_history_created_at'), table_name='prompt_history') + op.drop_table('prompt_history') + op.drop_index(op.f('ix_ontology_class_scene_id'), table_name='ontology_class') + op.drop_index(op.f('ix_ontology_class_class_id'), table_name='ontology_class') + op.drop_table('ontology_class') + op.drop_index(op.f('ix_ontology_scene_workspace_id'), table_name='ontology_scene') + op.drop_index(op.f('ix_ontology_scene_scene_id'), table_name='ontology_scene') + op.drop_table('ontology_scene') + # ### end Alembic commands ### diff --git a/api/migrations/versions/5ca246ee7dd4_202601291352.py b/api/migrations/versions/5ca246ee7dd4_202601291352.py new file mode 100644 index 00000000..74931287 --- /dev/null +++ b/api/migrations/versions/5ca246ee7dd4_202601291352.py @@ -0,0 +1,30 @@ +"""202601291352 + +Revision ID: 5ca246ee7dd4 +Revises: 915bed077f8d +Create Date: 2026-01-29 13:52:47.647306 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '5ca246ee7dd4' +down_revision: Union[str, None] = '915bed077f8d' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.add_column('model_bases', sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=True, comment='创建时间')) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('model_bases', 'created_at') + # ### end Alembic commands ### diff --git a/api/migrations/versions/5de9b1e28509_20260129212722.py b/api/migrations/versions/5de9b1e28509_20260129212722.py new file mode 100644 index 00000000..cbffad68 --- /dev/null +++ b/api/migrations/versions/5de9b1e28509_20260129212722.py @@ -0,0 +1,80 @@ +"""20260129212722 + +Revision ID: 5de9b1e28509 +Revises: 5ca246ee7dd4 +Create Date: 2026-01-29 21:34:30.978031 + +""" +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '5de9b1e28509' +down_revision: Union[str, None] = '5ca246ee7dd4' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # Neo4j migration: rename group_id to end_user_id + import asyncio + + from app.repositories.neo4j.neo4j_connector import Neo4jConnector + + async def run_neo4j_upgrade(): + connector = Neo4jConnector() + try: + async def transaction_func(tx): + result = await tx.run(""" + MATCH (n) + WHERE n.group_id IS NOT NULL + SET n.end_user_id = n.group_id + REMOVE n.group_id + WITH count(n) AS node_count + MATCH ()-[r]->() + WHERE r.group_id IS NOT NULL + SET r.end_user_id = r.group_id + REMOVE r.group_id + RETURN node_count, count(r) AS rel_count + """) + return await result.data() + + await connector.execute_write_transaction(transaction_func) + finally: + await connector.close() + + asyncio.run(run_neo4j_upgrade()) + + +def downgrade() -> None: + # Neo4j migration: rename end_user_id back to group_id + import asyncio + + from app.repositories.neo4j.neo4j_connector import Neo4jConnector + + async def run_neo4j_downgrade(): + connector = Neo4jConnector() + try: + async def transaction_func(tx): + result = await tx.run(""" + MATCH (n) + WHERE n.end_user_id IS NOT NULL + SET n.group_id = n.end_user_id + REMOVE n.end_user_id + WITH count(n) AS node_count + MATCH ()-[r]->() + WHERE r.end_user_id IS NOT NULL + SET r.group_id = r.end_user_id + REMOVE r.end_user_id + RETURN node_count, count(r) AS rel_count + """) + return await result.data() + + await connector.execute_write_transaction(transaction_func) + finally: + await connector.close() + + asyncio.run(run_neo4j_downgrade()) \ No newline at end of file diff --git a/api/migrations/versions/75f0ec80e50b_202601271517.py b/api/migrations/versions/75f0ec80e50b_202601271517.py new file mode 100644 index 00000000..a70d7315 --- /dev/null +++ b/api/migrations/versions/75f0ec80e50b_202601271517.py @@ -0,0 +1,57 @@ +"""202601271517 + +Revision ID: 75f0ec80e50b +Revises: 325b759cd66b +Create Date: 2026-01-27 15:26:48.696600 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '75f0ec80e50b' +down_revision: Union[str, None] = '325b759cd66b' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.alter_column('memory_config', 'config_id', + existing_type=sa.UUID(), + comment='配置ID', + existing_nullable=False) + op.alter_column('memory_config', 'config_id_old', + existing_type=sa.INTEGER(), + comment='备份的配置ID', + existing_comment='配置ID', + existing_nullable=True) + op.add_column('tenants', sa.Column('external_id', sa.String(length=100), nullable=True)) + op.add_column('tenants', sa.Column('external_source', sa.String(length=50), nullable=True)) + op.create_index(op.f('ix_tenants_external_id'), 'tenants', ['external_id'], unique=False) + op.add_column('users', sa.Column('external_id', sa.String(length=100), nullable=True)) + op.add_column('users', sa.Column('external_source', sa.String(length=50), nullable=True)) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('users', 'external_source') + op.drop_column('users', 'external_id') + op.drop_index(op.f('ix_tenants_external_id'), table_name='tenants') + op.drop_column('tenants', 'external_source') + op.drop_column('tenants', 'external_id') + op.alter_column('memory_config', 'config_id_old', + existing_type=sa.INTEGER(), + comment='配置ID', + existing_comment='备份的配置ID', + existing_nullable=True) + op.alter_column('memory_config', 'config_id', + existing_type=sa.UUID(), + comment=None, + existing_comment='配置ID', + existing_nullable=False) + # ### end Alembic commands ### diff --git a/api/migrations/versions/915bed077f8d_202601281340.py b/api/migrations/versions/915bed077f8d_202601281340.py new file mode 100644 index 00000000..022f0d25 --- /dev/null +++ b/api/migrations/versions/915bed077f8d_202601281340.py @@ -0,0 +1,224 @@ +"""202601281340 + +Revision ID: 915bed077f8d +Revises: 75f0ec80e50b +Create Date: 2026-01-28 13:38:49.471560 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '915bed077f8d' +down_revision: Union[str, None] = '75f0ec80e50b' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + +BACKUP_TABLE_NAME = 'model_api_keys_backup_20260123' + +def get_temp_models(): + """创建临时模型，用于迁移过程中查询数据""" + metadata = sa.MetaData() + + # 临时ModelApiKey表（仅包含需要的字段） + ModelApiKey = sa.Table( + 'model_api_keys', metadata, + sa.Column('id', sa.UUID(), primary_key=True), + sa.Column('model_config_id', sa.UUID(), nullable=True), + ) + + # 临时关联表（和升级脚本创建的表结构一致） + ModelConfigApiKeyAssociation = sa.Table( + 'model_config_api_key_association', metadata, + sa.Column('model_config_id', sa.UUID(), nullable=False), + sa.Column('api_key_id', sa.UUID(), nullable=False), + sa.Column('created_at', sa.DateTime(), nullable=True), + ) + + ModelApiKeyBackup = sa.Table( + BACKUP_TABLE_NAME, metadata, + sa.Column('id', sa.UUID(), primary_key=True), + sa.Column('model_name', sa.String(), nullable=False), + sa.Column('description', sa.String(), nullable=True), + sa.Column('provider', sa.String(), nullable=False), + sa.Column('api_key', sa.String(), nullable=False), + sa.Column('api_base', sa.String(), nullable=True), + sa.Column('config', sa.JSON(), nullable=True), + sa.Column('usage_count', sa.String(), default="0"), + sa.Column('last_used_at', sa.DateTime(), nullable=True), + sa.Column('priority', sa.String(), default="1"), + sa.Column('model_config_id', sa.UUID(), nullable=True), + sa.Column('created_at', sa.DateTime(), nullable=True), + sa.Column('updated_at', sa.DateTime(), nullable=True), + sa.Column('is_active', sa.Boolean(), default=True), + ) + + return ModelApiKey, ModelConfigApiKeyAssociation, ModelApiKeyBackup + + +def backup_model_api_keys(): + """备份model_api_keys表的结构和数据""" + connection = op.get_bind() + + # 检查备份表是否已存在 + result = connection.execute(sa.text(f""" + SELECT EXISTS ( + SELECT FROM information_schema.tables + WHERE table_name = '{BACKUP_TABLE_NAME}' + ); + """)).scalar() + + if result: + # 备份表已存在，先删除再重建（确保结构一致） + op.execute(f"DROP TABLE IF EXISTS {BACKUP_TABLE_NAME};") + + # 直接复制表结构和数据（PostgreSQL专用，一步完成） + op.execute(f""" + CREATE TABLE {BACKUP_TABLE_NAME} AS + SELECT * FROM model_api_keys; + """) + + # 统计行数 + backup_count = connection.execute(sa.text(f"SELECT COUNT(*) FROM {BACKUP_TABLE_NAME}")).scalar() + original_count = connection.execute(sa.text("SELECT COUNT(*) FROM model_api_keys")).scalar() + + print( + f"已备份model_api_keys表到 {BACKUP_TABLE_NAME} \n" + f" 原表数据行数：{original_count} | 备份表数据行数：{backup_count}" + ) + +# def restore_model_api_keys_from_backup(): +# """从备份表恢复model_api_keys数据（可选，用于回滚失败时手动恢复）""" +# # 1. 清空原表（谨慎使用！） +# # op.execute("TRUNCATE TABLE model_api_keys;") +# +# # 2. 从备份表恢复数据 +# op.execute(f""" +# INSERT INTO model_api_keys +# SELECT * FROM {BACKUP_TABLE_NAME} +# ON CONFLICT (id) DO UPDATE SET +# model_name = EXCLUDED.model_name, +# description = EXCLUDED.description, +# provider = EXCLUDED.provider, +# api_key = EXCLUDED.api_key, +# api_base = EXCLUDED.api_base, +# config = EXCLUDED.config, +# usage_count = EXCLUDED.usage_count, +# last_used_at = EXCLUDED.last_used_at, +# priority = EXCLUDED.priority, +# model_config_id = EXCLUDED.model_config_id, +# created_at = EXCLUDED.created_at, +# updated_at = EXCLUDED.updated_at, +# is_active = EXCLUDED.is_active; +# """) +# print(f"✅ 已从 {BACKUP_TABLE_NAME} 恢复model_api_keys表数据") + +def upgrade() -> None: + backup_model_api_keys() + # ### commands auto generated by Alembic - please adjust! ### + op.create_table('model_bases', + sa.Column('id', sa.UUID(), nullable=False), + sa.Column('logo', sa.String(length=255), nullable=True, comment='模型logo图片URL'), + sa.Column('name', sa.String(), nullable=False, comment='模型唯一标识（如gpt-3.5-turbo）'), + sa.Column('type', sa.String(), nullable=False, comment='模型类型'), + sa.Column('provider', sa.String(), nullable=False), + sa.Column('description', sa.Text(), nullable=True, comment='模型描述'), + sa.Column('is_deprecated', sa.Boolean(), nullable=False, comment='是否弃用'), + sa.Column('is_official', sa.Boolean(), nullable=True, comment='是否供应商官方模型（区分自定义）'), + sa.Column('tags', sa.ARRAY(sa.String()), nullable=False, comment="模型标签（如['聊天', '创作']）"), + sa.Column('add_count', sa.Integer(), nullable=False, comment='模型被用户添加的次数'), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('name', 'provider', name='uk_model_name_provider') + ) + op.create_index(op.f('ix_model_bases_id'), 'model_bases', ['id'], unique=False) + op.create_index(op.f('ix_model_bases_provider'), 'model_bases', ['provider'], unique=False) + op.create_index(op.f('ix_model_bases_type'), 'model_bases', ['type'], unique=False) + op.create_table('model_config_api_key_association', + sa.Column('model_config_id', sa.UUID(), nullable=False), + sa.Column('api_key_id', sa.UUID(), nullable=False), + sa.Column('created_at', sa.DateTime(), nullable=True), + sa.ForeignKeyConstraint(['api_key_id'], ['model_api_keys.id'], ), + sa.ForeignKeyConstraint(['model_config_id'], ['model_configs.id'], ), + sa.PrimaryKeyConstraint('model_config_id', 'api_key_id') + ) + op.add_column('model_api_keys', sa.Column('description', sa.String(), nullable=True, comment='备注')) + op.add_column('model_configs', sa.Column('model_id', sa.UUID(), nullable=True, comment='基础模型ID')) + op.add_column('model_configs', sa.Column('logo', sa.String(length=255), nullable=True, comment='模型logo图片URL')) + op.add_column('model_configs', sa.Column('provider', sa.String(), server_default='composite', nullable=False, comment='供应商')) + op.add_column('model_configs', sa.Column('is_composite', sa.Boolean(), server_default='true', nullable=False, comment='是否为组合模型')) + op.add_column('model_configs', sa.Column('load_balance_strategy', sa.String(), nullable=True, comment='负载均衡策略')) + op.create_index(op.f('ix_model_configs_model_id'), 'model_configs', ['model_id'], unique=False) + op.create_foreign_key("model_configs_model_id_fkey", 'model_configs', 'model_bases', ['model_id'], ['id']) + connection = op.get_bind() + ModelApiKey, ModelConfigApiKeyAssociation, _ = get_temp_models() + + # 查询所有有model_config_id的API Key + api_keys = connection.execute( + sa.select(ModelApiKey.c.id, ModelApiKey.c.model_config_id) + .where(ModelApiKey.c.model_config_id.isnot(None)) + ).fetchall() + + # 批量插入到多对多表 + if api_keys: + association_data = [ + { + 'model_config_id': row.model_config_id, + 'api_key_id': row.id + } + for row in api_keys + ] + connection.execute(ModelConfigApiKeyAssociation.insert(), association_data) + op.drop_constraint(op.f('model_api_keys_model_config_id_fkey'), 'model_api_keys', type_='foreignkey') + op.drop_column('model_api_keys', 'model_config_id') + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_constraint("model_configs_model_id_fkey", 'model_configs', type_='foreignkey') + op.drop_index(op.f('ix_model_configs_model_id'), table_name='model_configs') + op.drop_column('model_configs', 'load_balance_strategy') + op.drop_column('model_configs', 'is_composite') + op.drop_column('model_configs', 'provider') + op.drop_column('model_configs', 'logo') + op.drop_column('model_configs', 'model_id') + op.add_column('model_api_keys', sa.Column('model_config_id', sa.UUID(), autoincrement=False, nullable=True, comment='模型配置ID')) + connection = op.get_bind() + ModelApiKey, ModelConfigApiKeyAssociation, _ = get_temp_models() + + # 查询多对多表中的关联数据（取每个API Key的第一个关联的model_config_id） + association_data = connection.execute( + sa.select( + ModelConfigApiKeyAssociation.c.api_key_id, + ModelConfigApiKeyAssociation.c.model_config_id + ).distinct(ModelConfigApiKeyAssociation.c.api_key_id) + ).fetchall() + + # 批量更新model_api_keys表 + if association_data: + for api_key_id, model_config_id in association_data: + connection.execute( + sa.update(ModelApiKey) + .where(ModelApiKey.c.id == api_key_id) + .values(model_config_id=model_config_id) + ) + + op.execute( + "UPDATE model_api_keys SET model_config_id = '00000000-0000-0000-0000-000000000000' WHERE model_config_id IS NULL") + op.alter_column('model_api_keys', 'model_config_id', nullable=False) + op.create_foreign_key(op.f('model_api_keys_model_config_id_fkey'), 'model_api_keys', 'model_configs', ['model_config_id'], ['id']) + op.drop_column('model_api_keys', 'description') + op.drop_table('model_config_api_key_association') + # ### 可选：回滚时恢复备份（如需）### + # restore_model_api_keys_from_backup() + + print( + f"回滚完成！备份表 {BACKUP_TABLE_NAME} 仍保留，如需手动恢复可执行 restore_model_api_keys_from_backup() 函数") + op.drop_index(op.f('ix_model_bases_type'), table_name='model_bases') + op.drop_index(op.f('ix_model_bases_provider'), table_name='model_bases') + op.drop_index(op.f('ix_model_bases_id'), table_name='model_bases') + op.drop_table('model_bases') + # ### end Alembic commands ### diff --git a/api/migrations/versions/9def72f79398_202601301850.py b/api/migrations/versions/9def72f79398_202601301850.py new file mode 100644 index 00000000..303a1578 --- /dev/null +++ b/api/migrations/versions/9def72f79398_202601301850.py @@ -0,0 +1,30 @@ +"""202601301850 + +Revision ID: 9def72f79398 +Revises: 550c10595967 +Create Date: 2026-01-30 18:51:18.290796 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '9def72f79398' +down_revision: Union[str, None] = '550c10595967' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.add_column('memory_config', sa.Column('scene_id', sa.UUID(), nullable=True, comment='本体场景ID，关联ontology_scene表')) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('memory_config', 'scene_id') + # ### end Alembic commands ### diff --git a/api/pyproject.toml b/api/pyproject.toml index 81ac57a1..6d23a3b9 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -88,7 +88,6 @@ dependencies = [ "cachetools==6.2.1", "ruamel.yaml==0.18.10", "strenum==0.4.15", - "aspose-slides==24.12.0", "opencv-python==4.10.0.84", "numpy>=1.26.0,<2.0.0", "huggingface-hub==0.25.2", @@ -141,6 +140,7 @@ dependencies = [ "oss2>=2.19.1", "flower>=2.0.1", "aiofiles>=23.0.0", + "owlready2>=0.46", ] [tool.pytest.ini_options] diff --git a/api/requirements.txt b/api/requirements.txt index 60e4d090..6cdae2d1 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -83,7 +83,6 @@ olefile==0.47 cachetools==6.2.1 ruamel.yaml==0.18.10 strenum==0.4.15 -aspose-slides==24.12.0 opencv-python==4.10.0.84 numpy>=1.26.0,<2.0.0 huggingface-hub==0.25.2 diff --git a/api/uv.lock b/api/uv.lock index bccaef2c..587fc5b0 100644 --- a/api/uv.lock +++ b/api/uv.lock @@ -8,30 +8,27 @@ resolution-markers = [ ] [[package]] -name = "aiofile" -version = "3.9.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "caio" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/67/e2/d7cb819de8df6b5c1968a2756c3cb4122d4fa2b8fc768b53b7c9e5edb646/aiofile-3.9.0.tar.gz", hash = "sha256:e5ad718bb148b265b6df1b3752c4d1d83024b93da9bd599df74b9d9ffcf7919b", size = 17943, upload-time = "2024-10-08T10:39:35.846Z" } +name = "aiofiles" +version = "25.1.0" +source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } +sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" } wheels = [ - { url = 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f611dc59..00000000 --- a/api_key_mcp_server.py +++ /dev/null @@ -1,38 +0,0 @@ -#!/usr/bin/env python3 -"""API Key认证MCP服务器""" - -from fastapi import FastAPI, HTTPException, Depends, Header -from typing import Optional -import uvicorn -from mcp_base import MCPRequest, handle_mcp_request, TOOLS - -app = FastAPI(title="API Key MCP Server", version="1.0.0") - -# API Key配置 -API_KEYS = {"test-api-key", "demo-key-123"} - -def verify_api_key(x_api_key: Optional[str] = Header(None)): - """验证API Key""" - if x_api_key and x_api_key in API_KEYS: - return True - raise HTTPException(status_code=401, detail="Invalid API Key") - -@app.get("/") -async def root(): - return {"name": "API Key MCP Server", "version": "1.0.0", "auth_type": "api_key"} - -@app.get("/health") -async def health(): - return {"status": "healthy", "tools": len(TOOLS), "auth_type": "api_key"} - -@app.post("/mcp") -async def mcp_handler(request: MCPRequest, _: bool = Depends(verify_api_key)): - return await handle_mcp_request(request, "API Key MCP Server") - -if __name__ == "__main__": - print("启动API Key认证MCP服务器...") - print("访问 http://localhost:8004 查看服务状态") - print("MCP端点: http://localhost:8004/mcp") - print("认证方式: API Key (Header: X-API-Key)") - print("测试API Keys: test-api-key, demo-key-123") - uvicorn.run(app, host="0.0.0.0", port=8004) \ No newline at end of file diff --git a/basic_auth_mcp_server.py b/basic_auth_mcp_server.py deleted file mode 100644 index 11bb5595..00000000 --- a/basic_auth_mcp_server.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python3 -"""Basic Auth认证MCP服务器""" - -from fastapi import FastAPI, HTTPException, Depends, Header -from typing import Optional -import uvicorn -import base64 -from mcp_base import MCPRequest, handle_mcp_request, TOOLS - -app = FastAPI(title="Basic Auth MCP Server", version="1.0.0") - -# Basic Auth配置 -BASIC_AUTH_USERS = {"admin": "password", "user": "secret"} - -def verify_basic_auth(authorization: Optional[str] = Header(None)): - """验证Basic Auth""" - if authorization and authorization.startswith("Basic "): - try: - credentials = base64.b64decode(authorization.split(" ")[1]).decode() - username, password = credentials.split(":", 1) - if username in BASIC_AUTH_USERS and BASIC_AUTH_USERS[username] == password: - return True - except: - pass - raise HTTPException(status_code=401, detail="Invalid Basic Auth") - -@app.get("/") -async def root(): - return {"name": "Basic Auth MCP Server", "version": "1.0.0", "auth_type": "basic_auth"} - -@app.get("/health") -async def health(): - return {"status": "healthy", "tools": len(TOOLS), "auth_type": "basic_auth"} - -@app.post("/mcp") -async def mcp_handler(request: MCPRequest, _: bool = Depends(verify_basic_auth)): - return await handle_mcp_request(request, "Basic Auth MCP Server") - -if __name__ == "__main__": - print("启动Basic Auth认证MCP服务器...") - print("访问 http://localhost:8006 查看服务状态") - print("MCP端点: http://localhost:8006/mcp") - print("认证方式: Basic Auth (Header: Authorization: Basic )") - print("测试用户: admin:password, user:secret") - uvicorn.run(app, host="0.0.0.0", port=8006) \ No newline at end of file diff --git a/bearer_token_mcp_server.py b/bearer_token_mcp_server.py deleted file mode 100644 index 57d27f2f..00000000 --- a/bearer_token_mcp_server.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python3 -"""Bearer Token认证MCP服务器""" - -from fastapi import FastAPI, HTTPException, Depends, Header -from typing import Optional -import uvicorn -from mcp_base import MCPRequest, handle_mcp_request, TOOLS - -app = FastAPI(title="Bearer Token MCP Server", version="1.0.0") - -# Bearer Token配置 -BEARER_TOKENS = {"bearer-token-123", "demo-bearer-token"} - -def verify_bearer_token(authorization: Optional[str] = Header(None)): - """验证Bearer Token""" - if authorization and authorization.startswith("Bearer "): - token = authorization.split(" ")[1] - if token in BEARER_TOKENS: - return True - raise HTTPException(status_code=401, detail="Invalid Bearer Token") - -@app.get("/") -async def root(): - return {"name": "Bearer Token MCP Server", "version": "1.0.0", "auth_type": "bearer_token"} - -@app.get("/health") -async def health(): - return {"status": "healthy", "tools": len(TOOLS), "auth_type": "bearer_token"} - -@app.post("/mcp") -async def mcp_handler(request: MCPRequest, _: bool = Depends(verify_bearer_token)): - return await handle_mcp_request(request, "Bearer Token MCP Server") - -if __name__ == "__main__": - print("启动Bearer Token认证MCP服务器...") - print("访问 http://localhost:8005 查看服务状态") - print("MCP端点: http://localhost:8005/mcp") - print("认证方式: Bearer Token (Header: Authorization: Bearer )") - print("测试Bearer Tokens: bearer-token-123, demo-bearer-token") - uvicorn.run(app, host="0.0.0.0", port=8005) \ No newline at end of file diff --git a/mcp_base.py b/mcp_base.py deleted file mode 100644 index f571e2fa..00000000 --- a/mcp_base.py +++ /dev/null @@ -1,111 +0,0 @@ -#!/usr/bin/env python3 -"""MCP服务器基础模块 - 共享的模型和处理逻辑""" - -from pydantic import BaseModel -from typing import Dict, Any - -class MCPRequest(BaseModel): - jsonrpc: str = "2.0" - id: str - method: str - params: Dict[str, Any] = {} - -class MCPResponse(BaseModel): - jsonrpc: str = "2.0" - id: str - result: Any = None - error: Dict[str, Any] = None - -# 工具定义 -TOOLS = [ - { - "name": "calculator", - "description": "简单计算器", - "inputSchema": { - "type": "object", - "properties": { - "expression": {"type": "string", "description": "数学表达式"} - }, - "required": ["expression"] - } - }, - { - "name": "echo", - "description": "回显工具", - "inputSchema": { - "type": "object", - "properties": { - "message": {"type": "string", "description": "要回显的消息"} - }, - "required": ["message"] - } - } -] - -async def handle_mcp_request(request: MCPRequest, server_name: str = "MCP Server"): - """处理MCP请求""" - try: - if request.method == "initialize": - return MCPResponse( - id=request.id, - result={ - "protocolVersion": "2024-11-05", - "capabilities": {"tools": {"listChanged": True}}, - "serverInfo": {"name": server_name, "version": "1.0.0"} - } - ) - - elif request.method == "tools/list": - return MCPResponse( - id=request.id, - result={"tools": TOOLS} - ) - - elif request.method == "tools/call": - tool_name = request.params.get("name") - arguments = request.params.get("arguments", {}) - - if tool_name == "calculator": - try: - expression = arguments.get("expression", "") - result = eval(expression) - return MCPResponse( - id=request.id, - result={"content": [{"type": "text", "text": f"结果: {result}"}]} - ) - except Exception as e: - return MCPResponse( - id=request.id, - error={"code": -1, "message": f"计算错误: {str(e)}"} - ) - - elif tool_name == "echo": - message = arguments.get("message", "") - return MCPResponse( - id=request.id, - result={"content": [{"type": "text", "text": f"Echo: {message}"}]} - ) - - else: - return MCPResponse( - id=request.id, - error={"code": -1, "message": f"未知工具: {tool_name}"} - ) - - elif request.method == "ping": - return MCPResponse( - id=request.id, - result={"status": "pong"} - ) - - else: - return MCPResponse( - id=request.id, - error={"code": -1, "message": f"未知方法: {request.method}"} - ) - - except Exception as e: - return MCPResponse( - id=request.id, - error={"code": -1, "message": str(e)} - ) \ No newline at end of file diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index d9a00be6..4b0257bb 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit d9a00be62d974c0ad071c27e86f878b921c675b6 +Subproject commit 4b0257bb4e7dc384b2aaf849b0bd6eae4b39835d diff --git a/sandbox/Dockerfile b/sandbox/Dockerfile new file mode 100644 index 00000000..e34b88dd --- /dev/null +++ b/sandbox/Dockerfile @@ -0,0 +1,51 @@ +FROM python:3.12-slim +USER root +WORKDIR /code + +ARG NEED_MIRROR=1 +ENV DEBIAN_FRONTEND=noninteractive + + +RUN --mount=type=cache,id=mem_apt,target=/var/cache/apt,sharing=locked \ + if [ "$NEED_MIRROR" == "1" ]; then \ + sed -i 's|https://ports.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \ + sed -i 's|https://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \ + fi; \ + rm -f /etc/apt/apt.conf.d/docker-clean && \ + echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \ + chmod 1777 /tmp && \ + apt update && \ + apt --no-install-recommends install -y ca-certificates && \ + apt update && \ + apt install -y python3-pip pipx nginx unzip curl wget git vim less && \ + apt install -y nodejs npm && \ + apt-get install -y --no-install-recommends tzdata libseccomp2 libseccomp-dev && \ + ln -snf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && \ + echo "Asia/Shanghai" > /etc/timezone && \ + apt install -y cargo + +ENV PYTHONDONTWRITEBYTECODE=1 + +COPY ./app /code/app +COPY ./dependencies /code/dependencies +COPY ./lib /code/lib +COPY ./script /code/script +COPY ./config.yaml /code/config.yaml +COPY ./main.py /code/main.py +COPY ./requirements.txt /code/requirements.txt + +RUN python -m venv .venv +RUN .venv/bin/python3 -m pip install -r requirements.txt + +RUN npm install --prefix=/code/dependencies/nodejs koffi + +RUN cargo build --release --manifest-path lib/seccomp_redbear/Cargo.toml --features python3 +RUN mv lib/seccomp_redbear/target/release/libsandbox.so lib/seccomp_redbear/target/release/libpython.so +RUN cargo build --release --manifest-path lib/seccomp_redbear/Cargo.toml --features nodejs +RUN mv lib/seccomp_redbear/target/release/libsandbox.so lib/seccomp_redbear/target/release/libnodejs.so + +HEALTHCHECK --interval=30s --timeout=5s --start-period=60s --retries=3 \ + CMD curl 127.0.0.1:8194/health + + +CMD [".venv/bin/uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8194", "--log-level", "debug"] \ No newline at end of file diff --git a/sandbox/app/__init__.py b/sandbox/app/__init__.py new file mode 100644 index 00000000..1b201ce5 --- /dev/null +++ b/sandbox/app/__init__.py @@ -0,0 +1,4 @@ +# -*- coding: UTF-8 -*- +# Author: Eternity +# @Email: 1533512157@qq.com +# @Time : 2026/1/29 14:33 diff --git a/sandbox/app/config.py b/sandbox/app/config.py new file mode 100644 index 00000000..e4930465 --- /dev/null +++ b/sandbox/app/config.py @@ -0,0 +1,130 @@ +"""Configuration management""" +import os +from typing import List, Optional +from pydantic import BaseModel, Field +import yaml + +DEFAULT_PYTHON_LIB_REQUIREMENTS_AMD = [ + "/usr/local/lib/python3.12", + "/usr/lib/python3", + "/usr/lib/x86_64-linux-gnu", + "/etc/ssl/certs/ca-certificates.crt", + "/etc/nsswitch.conf", + "/etc/hosts", + "/etc/resolv.conf", + "/etc/localtime", + "/usr/share/zoneinfo", + "/etc/timezone", +] + +DEFAULT_NODEJS_LIB_REQUIREMENTS = [ + "/etc/ssl/certs/ca-certificates.crt", + "/etc/nsswitch.conf", + "/etc/resolv.conf", + "/etc/hosts", +] + + +class AppConfig(BaseModel): + """Application configuration""" + port: int = 8194 + debug: bool = True + key: str = "redbear-sandbox" + + +class ProxyConfig(BaseModel): + """Proxy configuration""" + socks5: str = "" + http: str = "" + https: str = "" + + +class Config(BaseModel): + """Global configuration""" + app: AppConfig = Field(default_factory=AppConfig) + max_workers: int = 4 + max_requests: int = 50 + worker_timeout: int = 30 + + enable_network: bool = True + enable_preload: bool = False + + python_path: str = "" + python_lib_paths: list = Field(default=DEFAULT_PYTHON_LIB_REQUIREMENTS_AMD) + python_deps_update_interval: str = "30m" + + nodejs_path: str = "" + nodejs_lib_paths: list = Field(default=DEFAULT_NODEJS_LIB_REQUIREMENTS) + + allowed_syscalls: List[int] = Field(default_factory=list) + proxy: ProxyConfig = Field(default_factory=ProxyConfig) + + sandbox_user: str = "sandbox" + sandbox_uid: int = 65537 + sandbox_gid: int = 0 + + def set_sandbox_gid(self, gid: int): + """Update sandbox GID dynamically""" + self.sandbox_gid = gid + + def override_with_env(self): + """Override configuration with environment variables""" + env_map = { + "DEBUG": ("app.debug", lambda v: v.lower() in ("true", "1", "yes")), + "MAX_WORKERS": ("max_workers", int), + "MAX_REQUESTS": ("max_requests", int), + "SANDBOX_PORT": ("app.port", int), + "WORKER_TIMEOUT": ("worker_timeout", int), + "API_KEY": ("app.key", str), + "NODEJS_PATH": ("nodejs_path", str), + "ENABLE_NETWORK": ("enable_network", lambda v: v.lower() in ("true", "1", "yes")), + "ENABLE_PRELOAD": ("enable_preload", lambda v: v.lower() in ("true", "1", "yes")), + "ALLOWED_SYSCALLS": ("allowed_syscalls", lambda v: [int(x) for x in v.split(",")]), + "SOCKS5_PROXY": ("proxy.socks5", str), + "HTTP_PROXY": ("proxy.http", str), + "HTTPS_PROXY": ("proxy.https", str), + "PYTHON_PATH": ("python_path", str), + "PYTHON_LIB_PATH": ("python_lib_paths", lambda v: v.split(",")), + "PYTHON_DEPS_UPDATE_INTERVAL": ("python_deps_update_interval", str), + "NODEJS_LIB_PATH": ("nodejs_lib_paths", lambda v: v.split(",")), + } + + for env_var, (attr_path, cast) in env_map.items(): + value = os.getenv(env_var) + if value is not None: + # Support nested attributes like 'app.debug' + parts = attr_path.split(".") + obj = self + for part in parts[:-1]: + obj = getattr(obj, part) + setattr(obj, parts[-1], cast(value)) + + +# Global configuration instance +_config: Optional[Config] = None + + +def load_config(config_path: str = "config.yaml") -> Config: + """Load configuration from YAML file and override with env variables""" + global _config + if os.path.exists(config_path): + with open(config_path, 'r') as f: + data = yaml.safe_load(f) or {} + _config = Config(**data) + else: + _config = Config() + + # Override from environment + _config.override_with_env() + return _config + + +config_path = os.getenv("CONFIG_PATH", "config.yaml") +load_config(config_path) + + +def get_config() -> Config: + """Get global configuration""" + if _config is None: + raise RuntimeError("Configuration not loaded. Call load_config() first.") + return _config diff --git a/sandbox/app/controllers/__init__.py b/sandbox/app/controllers/__init__.py new file mode 100644 index 00000000..b1d965ae --- /dev/null +++ b/sandbox/app/controllers/__init__.py @@ -0,0 +1,8 @@ +from fastapi import APIRouter + +from . import health_controller, sandbox_controller + +manager_router = APIRouter() + +manager_router.include_router(health_controller.router) +manager_router.include_router(sandbox_controller.router) diff --git a/sandbox/app/controllers/health_controller.py b/sandbox/app/controllers/health_controller.py new file mode 100644 index 00000000..882578ec --- /dev/null +++ b/sandbox/app/controllers/health_controller.py @@ -0,0 +1,12 @@ +"""Health check endpoint""" +from fastapi import APIRouter + +from app.models import HealthResponse + +router = APIRouter() + + +@router.get("/health", response_model=HealthResponse) +async def health_check(): + """Health check endpoint""" + return HealthResponse(status="healthy", version="0.1.0") diff --git a/sandbox/app/controllers/sandbox_controller.py b/sandbox/app/controllers/sandbox_controller.py new file mode 100644 index 00000000..c5cce40c --- /dev/null +++ b/sandbox/app/controllers/sandbox_controller.py @@ -0,0 +1,57 @@ +"""Sandbox API endpoints""" +from fastapi import APIRouter, Depends + +from app.middleware.auth import verify_api_key +from app.middleware.concurrency import concurrency_guard + +from app.models import ( + RunCodeRequest, + ApiResponse, + UpdateDependencyRequest, + error_response +) +from app.services.nodejs_service import run_nodejs_code +from app.services.python_service import ( + run_python_code, + list_python_dependencies, + update_python_dependencies +) + +router = APIRouter( + prefix="/v1/sandbox", + tags=["sandbox"], + dependencies=[Depends(verify_api_key)] +) + + +@router.post( + "/run", + response_model=ApiResponse, + dependencies=[Depends(concurrency_guard)] +) +async def run_code(request: RunCodeRequest): + """Execute code in sandbox""" + if request.language == "python3": + return await run_python_code(request.code, request.preload, request.options) + elif request.language == "nodejs": + return await run_nodejs_code(request.code, request.preload, request.options) + else: + return error_response(-400, "unsupported language") + + +@router.get("/dependencies", response_model=ApiResponse) +async def get_dependencies(language: str): + """Get installed dependencies""" + if language == "python3": + return await list_python_dependencies() + else: + return error_response(-400, "unsupported language") + + +@router.post("/dependencies/update", response_model=ApiResponse) +async def update_dependencies(request: UpdateDependencyRequest): + """Update dependencies""" + if request.language == "python3": + return await update_python_dependencies() + else: + return error_response(-400, "unsupported language") diff --git a/sandbox/app/core/__init__.py b/sandbox/app/core/__init__.py new file mode 100644 index 00000000..e1abba12 --- /dev/null +++ b/sandbox/app/core/__init__.py @@ -0,0 +1 @@ +"""Core functionality package""" diff --git a/sandbox/app/core/encryption.py b/sandbox/app/core/encryption.py new file mode 100644 index 00000000..47a756c8 --- /dev/null +++ b/sandbox/app/core/encryption.py @@ -0,0 +1,33 @@ +"""Code encryption utilities""" +import base64 + + +def encrypt_code(code: bytes, key: bytes) -> str: + """Encrypt code using XOR cipher with base64 encoding + + Args: + code: Plain code string + key: Encryption key bytes + + Returns: + Base64 encoded encrypted code + """ + key_length = len(key) + encrypted_code = bytearray(len(code)) + for i in range(len(code)): + encrypted_code[i] = code[i] ^ key[i % key_length] + encoded_code = base64.b64encode(encrypted_code).decode("utf-8") + return encoded_code + + +def generate_key(length: int = 64) -> bytes: + """Generate random encryption key + + Args: + length: Key length in bytes (default 64 for 512 bits) + + Returns: + Random key bytes + """ + import secrets + return secrets.token_bytes(length) diff --git a/sandbox/app/core/executor.py b/sandbox/app/core/executor.py new file mode 100644 index 00000000..e87b510c --- /dev/null +++ b/sandbox/app/core/executor.py @@ -0,0 +1,47 @@ +"""Code execution engine""" +import os +from typing import Optional +from abc import ABC, abstractmethod + +from app.config import get_config +from app.logger import get_logger +from app.models import RunnerOptions + + +class ExecutionResult: + """Result of code execution""" + + def __init__(self, stdout: str = "", stderr: str = "", exit_code: int = 0, error: Optional[str] = None): + self.stdout = stdout + self.stderr = stderr + self.exit_code = exit_code + + +class CodeExecutor(ABC): + """Base code executor""" + + def __init__(self): + self.logger = get_logger() + self.config = get_config() + + @abstractmethod + async def run( + self, + code: str, + options: RunnerOptions, + preload: str = "", + timeout: Optional[int] = None + ) -> ExecutionResult: + pass + + def cleanup_temp_file(self, file_path: str) -> None: + """Remove temporary file + + Args: + file_path: Path to file to remove + """ + try: + if os.path.exists(file_path): + os.remove(file_path) + except Exception as e: + self.logger.warning(f"Failed to cleanup temp file {file_path}: {e}") diff --git a/sandbox/app/core/runners/__init__.py b/sandbox/app/core/runners/__init__.py new file mode 100644 index 00000000..b8021009 --- /dev/null +++ b/sandbox/app/core/runners/__init__.py @@ -0,0 +1,40 @@ +"""Code runners package""" +import pwd +import subprocess + +from app.config import get_config +from app.logger import get_logger + +logger = get_logger() + + +def init_sandbox_user(): + config = get_config() + sandbox_user = config.sandbox_user + sandbox_uid = config.sandbox_uid + try: + pwd.getpwnam(sandbox_user) + logger.info(f"User '{sandbox_user}' already exists") + except KeyError: + try: + subprocess.run( + ["useradd", "-u", str(sandbox_uid), sandbox_user], + check=True, + capture_output=True, + text=True + ) + logger.info(f"Created user '{sandbox_user}' with UID {sandbox_uid}") + except subprocess.CalledProcessError as e: + logger.error(f"Failed to create user: {e.stderr}") + raise RuntimeError(f"Failed to create user '{sandbox_user}': {e.stderr}") from e + + try: + user_info = pwd.getpwnam(sandbox_user) + config.set_sandbox_gid(user_info.pw_gid) + logger.info(f"Sandbox user GID: {config.sandbox_gid}") + except KeyError as e: + logger.error(f"Failed to get GID for user '{sandbox_user}'") + raise RuntimeError(f"Failed to get GID for user '{sandbox_user}'") from e + + + diff --git a/sandbox/app/core/runners/nodejs/__init__.py b/sandbox/app/core/runners/nodejs/__init__.py new file mode 100644 index 00000000..fa5243b7 --- /dev/null +++ b/sandbox/app/core/runners/nodejs/__init__.py @@ -0,0 +1,3 @@ +from app.core.runners.nodejs.env import release_lib_binary + +release_lib_binary(True) diff --git a/sandbox/app/core/runners/nodejs/env.py b/sandbox/app/core/runners/nodejs/env.py new file mode 100644 index 00000000..8c6a55aa --- /dev/null +++ b/sandbox/app/core/runners/nodejs/env.py @@ -0,0 +1,124 @@ +import asyncio +import ctypes +import os +import shutil +import stat +import tempfile +from pathlib import Path + +from app.logger import get_logger +from app.config import get_config + +logger = get_logger() + +RELEASE_LIB_PATH = "./lib/seccomp_redbear/target/release/libnodejs.so" +LIB_PATH = "/var/sandbox/sandbox-nodejs" +LIB_NAME = "libnodejs.so" + +lib = ctypes.CDLL(RELEASE_LIB_PATH) +lib.get_lib_version_static.restype = ctypes.c_char_p +lib.get_lib_feature_static.restype = ctypes.c_char_p +logger.info(f"Seccomp Env: nodejs, " + f"Seccomp Feature: {lib.get_lib_feature_static().decode('utf-8')}, " + f"Seccomp Version: {lib.get_lib_version_static().decode('utf-8')}") + +try: + with open(RELEASE_LIB_PATH, "rb") as f: + _NODEJS_LIB = f.read() +except: + logger.critical("failed to load nodejs lib") + raise + + +def check_lib_avaiable(): + return os.path.exists(os.path.join(LIB_PATH, LIB_NAME)) + + +def release_lib_binary(force_remove: bool): + logger.info("init runtime enviroment") + + lib_file = os.path.join(LIB_PATH, LIB_NAME) + if os.path.exists(lib_file): + if force_remove: + try: + os.remove(lib_file) + except OSError: + logger.critical(f"failed to remove {os.path.join(LIB_PATH, LIB_NAME)}") + raise + + try: + os.makedirs(LIB_PATH, mode=0o755, exist_ok=True) + except OSError: + logger.critical(f"failed to create {LIB_PATH}") + raise + + try: + with open(lib_file, "wb") as f: + f.write(_NODEJS_LIB) + os.chmod(lib_file, 0o755) + except OSError: + logger.critical(f"failed to write {lib_file}") + raise + else: + try: + os.makedirs(LIB_PATH, mode=0o755, exist_ok=True) + except OSError: + logger.critical(f"failed to create {LIB_PATH}") + raise + + try: + with open(lib_file, "wb") as f: + f.write(_NODEJS_LIB) + os.chmod(lib_file, 0o755) + except OSError: + logger.critical(f"failed to write {lib_file}") + raise + + logger.info("nodejs runner environment initialized") + + +async def prepare_nodejs_dependencies_env(): + config = get_config() + + with tempfile.TemporaryDirectory(dir="/") as root_path: + root = Path(root_path) + + env_sh = root / "env.sh" + with open("script/env.sh") as f: + env_sh.write_text(f.read()) + env_sh.chmod(env_sh.stat().st_mode | stat.S_IXUSR) + + shutil.copytree("dependencies/nodejs", os.path.join(LIB_PATH, "node_temp"), dirs_exist_ok=True) + for root, dirs, files in os.walk(os.path.join(LIB_PATH, "node_temp")): + for d in dirs: + os.chmod(os.path.join(root, d), 0o755) + for f in files: + os.chmod(os.path.join(root, f), 0o444) + + for lib_path in config.nodejs_lib_paths: + lib_path = Path(lib_path) + + if not lib_path.exists(): + logger.warning("nodejs lib path %s is not available", lib_path) + continue + + cmd = [ + "bash", + str(env_sh), + str(lib_path), + str(LIB_PATH), + ] + + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + retcode = process.returncode + + if retcode != 0: + logger.error( + f"create env error for file {lib_path}: retcode={retcode}, stderr={stderr.decode()}" + ) diff --git a/sandbox/app/core/runners/nodejs/nodejs_runner.py b/sandbox/app/core/runners/nodejs/nodejs_runner.py new file mode 100644 index 00000000..59560eee --- /dev/null +++ b/sandbox/app/core/runners/nodejs/nodejs_runner.py @@ -0,0 +1,138 @@ +"""Nodejs code runner""" +import asyncio +import os +import uuid +from typing import Optional + +from app.core.executor import CodeExecutor, ExecutionResult +from app.core.runners.nodejs.env import check_lib_avaiable, release_lib_binary, LIB_PATH +from app.logger import get_logger +from app.models import RunnerOptions + +# Nodejs sandbox prescript template +with open("app/core/runners/nodejs/prescript.js") as f: + NODEJS_PRESCRIPT = f.read() + +logger = get_logger() + + +class NodejsRunner(CodeExecutor): + """Node.js code runner with security isolation""" + + def __init__(self): + super().__init__() + + @staticmethod + def init_environment(code: str, preload: str) -> str: + if not check_lib_avaiable(): + release_lib_binary(False) + code_file_name = uuid.uuid4().hex.replace("-", "_") + + script = NODEJS_PRESCRIPT.replace("{{preload}}", preload, 1) + + eval_code = f"eval(Buffer.from('{code}', 'base64').toString('utf-8'))" + script = script.replace("{{code}}", eval_code, 1) + + code_path = f"{LIB_PATH}/node_temp/tmp/{code_file_name}.js" + try: + os.makedirs(os.path.dirname(code_path), mode=0o755, exist_ok=True) + with open(code_path, "w", encoding="utf-8") as f: + f.write(script) + os.chmod(code_path, 0o755) + + except OSError as e: + raise RuntimeError(f"Failed to write {code_path}") from e + + return code_path + + async def run( + self, + code: str, + options: RunnerOptions, + preload: str = "", + timeout: Optional[int] = None + ) -> ExecutionResult: + """Run Python code in sandbox + + Args: + options: + code: Base64 encoded encrypted code + preload: Preload code to execute before main code + timeout: Execution timeout in seconds + + Returns: + ExecutionResult with stdout, stderr, and exit code + """ + config = self.config + + if timeout is None: + timeout = config.worker_timeout + + # Check if preload is allowed + if not preload or not config.enable_preload: + preload = "" + script_path = self.init_environment(code, preload) + + try: + # Setup environment + env = { + "UV_USE_IO_URING": "0" + } + + # Add proxy settings if configured + if config.proxy.socks5: + env["HTTPS_PROXY"] = config.proxy.socks5 + env["HTTP_PROXY"] = config.proxy.socks5 + elif config.proxy.https or config.proxy.http: + if config.proxy.https: + env["HTTPS_PROXY"] = config.proxy.https + if config.proxy.http: + env["HTTP_PROXY"] = config.proxy.http + + # Add allowed syscalls if configured + if config.allowed_syscalls: + env["ALLOWED_SYSCALLS"] = ",".join(map(str, config.allowed_syscalls)) + + process = await asyncio.create_subprocess_exec( + config.nodejs_path, + script_path, + LIB_PATH, + str(config.sandbox_uid), + str(config.sandbox_gid), + options.model_dump_json(), + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + env=env, + cwd=LIB_PATH + ) + + # Wait for completion with timeout + try: + stdout, stderr = await asyncio.wait_for( + process.communicate(), + timeout=timeout + ) + + return ExecutionResult( + stdout=stdout.decode('utf-8', errors='replace'), + stderr=stderr.decode('utf-8', errors='replace'), + exit_code=process.returncode + ) + + except asyncio.TimeoutError: + # Kill process on timeout + try: + process.kill() + await process.wait() + except: + pass + + return ExecutionResult( + stdout="", + stderr="Execution timeout", + exit_code=-1, + ) + + finally: + # Cleanup temporary file + self.cleanup_temp_file(script_path) diff --git a/sandbox/app/core/runners/nodejs/prescript.js b/sandbox/app/core/runners/nodejs/prescript.js new file mode 100644 index 00000000..460aa108 --- /dev/null +++ b/sandbox/app/core/runners/nodejs/prescript.js @@ -0,0 +1,31 @@ +let argv = process.argv + +let koffi = require('koffi') + +process.chdir(argv[2]) + +let lib = koffi.load("./libnodejs.so") +/** @type {(uid: number, gid: number, enableNetwork: boolean) => number} */ +let initSeccomp = lib.func('int init_seccomp(int, int, bool)') + +let uid = parseInt(argv[3]) +let gid = parseInt(argv[4]) + +let options = JSON.parse(argv[5]) + +let seccomp_init = initSeccomp(uid, gid, options['enable_network']) +if (seccomp_init !== 0) { + throw `code executor err - ${seccomp_init}` +} + +delete process.argv +argv = undefined +koffi = undefined +lib = undefined +initSeccomp = undefined +uid = undefined +gid = undefined +options = undefined +seccomp_init = undefined + +{{code}} diff --git a/sandbox/app/core/runners/python/__init__.py b/sandbox/app/core/runners/python/__init__.py new file mode 100644 index 00000000..e1a34906 --- /dev/null +++ b/sandbox/app/core/runners/python/__init__.py @@ -0,0 +1,3 @@ +from app.core.runners.python.env import release_lib_binary + +release_lib_binary(True) diff --git a/sandbox/app/core/runners/python/env.py b/sandbox/app/core/runners/python/env.py new file mode 100644 index 00000000..541acc73 --- /dev/null +++ b/sandbox/app/core/runners/python/env.py @@ -0,0 +1,116 @@ +import asyncio +import ctypes +import os +import stat +import tempfile +from pathlib import Path + +from app.config import get_config +from app.logger import get_logger + +logger = get_logger() + +RELEASE_LIB_PATH = "./lib/seccomp_redbear/target/release/libpython.so" +LIB_PATH = "/var/sandbox/sandbox-python" +LIB_NAME = "libpython.so" + +lib = ctypes.CDLL(RELEASE_LIB_PATH) +lib.get_lib_version_static.restype = ctypes.c_char_p +lib.get_lib_feature_static.restype = ctypes.c_char_p +logger.info(f"Seccomp Env: python3, " + f"Seccomp Feature: {lib.get_lib_feature_static().decode('utf-8')}, " + f"Seccomp Version: {lib.get_lib_version_static().decode('utf-8')}") + +try: + with open(RELEASE_LIB_PATH, "rb") as f: + _PYTHON_LIB = f.read() +except: + logger.critical("failed to load python lib") + raise + + +def check_lib_avaiable(): + return os.path.exists(os.path.join(LIB_PATH, LIB_NAME)) + + +def release_lib_binary(force_remove: bool): + logger.info("init runtime enviroment") + + lib_file = os.path.join(LIB_PATH, LIB_NAME) + if os.path.exists(lib_file): + if force_remove: + try: + os.remove(lib_file) + except OSError: + logger.critical(f"failed to remove {os.path.join(LIB_PATH, LIB_NAME)}") + raise + + try: + os.makedirs(LIB_PATH, mode=0o755, exist_ok=True) + except OSError: + logger.critical(f"failed to create {LIB_PATH}") + raise + + try: + with open(lib_file, "wb") as f: + f.write(_PYTHON_LIB) + os.chmod(lib_file, 0o755) + except OSError: + logger.critical(f"failed to write {lib_file}") + raise + else: + try: + os.makedirs(LIB_PATH, mode=0o755, exist_ok=True) + except OSError: + logger.critical(f"failed to create {LIB_PATH}") + raise + + try: + with open(lib_file, "wb") as f: + f.write(_PYTHON_LIB) + os.chmod(lib_file, 0o755) + except OSError: + logger.critical(f"failed to write {lib_file}") + raise + + logger.info("python runner environment initialized") + + +async def prepare_python_dependencies_env(): + config = get_config() + + with tempfile.TemporaryDirectory(dir="/") as root_path: + root = Path(root_path) + + env_sh = root / "env.sh" + with open("script/env.sh") as f: + env_sh.write_text(f.read()) + env_sh.chmod(env_sh.stat().st_mode | stat.S_IXUSR) + + for lib_path in config.python_lib_paths: + lib_path = Path(lib_path) + + if not lib_path.exists(): + logger.warning("python lib path %s is not available", lib_path) + continue + + cmd = [ + "bash", + str(env_sh), + str(lib_path), + str(LIB_PATH), + ] + + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + retcode = process.returncode + + if retcode != 0: + logger.error( + f"create env error for file {lib_path}: retcode={retcode}, stderr={stderr.decode()}" + ) diff --git a/sandbox/app/core/runners/python/prescript.py b/sandbox/app/core/runners/python/prescript.py new file mode 100644 index 00000000..b694fe9b --- /dev/null +++ b/sandbox/app/core/runners/python/prescript.py @@ -0,0 +1,59 @@ +import ctypes +import os +import sys +import traceback +from base64 import b64decode + + +# Setup exception hook +def excepthook(etype, value, tb): + sys.stderr.write("".join(traceback.format_exception(etype, value, tb))) + sys.stderr.flush() + sys.exit(-1) + + +sys.excepthook = excepthook + +# Load security library if available +lib = ctypes.CDLL("./libpython.so") +lib.init_seccomp.argtypes = [ctypes.c_uint32, ctypes.c_uint32, ctypes.c_bool] +lib.init_seccomp.restype = ctypes.c_int + +# Get running path +running_path = sys.argv[1] +if not running_path: + exit(-1) + +# Get decrypt key +key = sys.argv[2] +if not key: + exit(-1) + +key = b64decode(key) + +os.chdir(running_path) + +# Preload code +{{preload}} + +# Apply security if library is available +init_status = lib.init_seccomp({{uid}}, {{gid}}, {{enable_network}}) +if init_status != 0: + raise Exception(f"code executor err - {str(init_status)}") +del lib + +# Decrypt and execute code +code = b64decode("{{code}}") + + +def decrypt(code, key): + key_len = len(key) + code_len = len(code) + code = bytearray(code) + for i in range(code_len): + code[i] = code[i] ^ key[i % key_len] + return bytes(code) + + +code = decrypt(code, key) +exec(code) diff --git a/sandbox/app/core/runners/python/python_runner.py b/sandbox/app/core/runners/python/python_runner.py new file mode 100644 index 00000000..eccd16e0 --- /dev/null +++ b/sandbox/app/core/runners/python/python_runner.py @@ -0,0 +1,154 @@ +"""Python code runner""" +import asyncio +import base64 +import os +import uuid +from typing import Optional + +from app.config import get_config +from app.core.encryption import generate_key, encrypt_code +from app.core.executor import CodeExecutor, ExecutionResult +from app.core.runners.python.env import check_lib_avaiable, release_lib_binary, LIB_PATH +from app.logger import get_logger +from app.models import RunnerOptions + +# Python sandbox prescript template +with open("app/core/runners/python/prescript.py") as f: + PYTHON_PRESCRIPT = f.read() + +logger = get_logger() + + +class PythonRunner(CodeExecutor): + """Python code runner with security isolation""" + + def __init__(self): + super().__init__() + + @staticmethod + def init_enviroment(code: bytes, preload, options: RunnerOptions) -> tuple[str, str]: + if not check_lib_avaiable(): + release_lib_binary(False) + config = get_config() + code_file_name = uuid.uuid4().hex.replace("-", "_") + + script = PYTHON_PRESCRIPT.replace("{{uid}}", str(config.sandbox_uid), 1) + script = script.replace("{{gid}}", str(config.sandbox_gid), 1) + script = script.replace( + "{{enable_network}}", + str(int(options.enable_network and config.enable_network) + ), + 1 + ) + script = script.replace("{{preload}}", f"{preload}\n", 1) + + key = generate_key(64) + + encoded_code = encrypt_code(code, key) + encoded_key = base64.b64encode(key).decode("utf-8") + + script = script.replace("{{code}}", encoded_code, 1) + + code_path = f"{LIB_PATH}/tmp/{code_file_name}.py" + try: + os.makedirs(os.path.dirname(code_path), mode=0o755, exist_ok=True) + with open(code_path, "w", encoding="utf-8") as f: + f.write(script) + os.chmod(code_path, 0o755) + + except OSError as e: + raise RuntimeError(f"Failed to write {code_path}") from e + + return code_path, encoded_key + + async def run( + self, + code: str, + options: RunnerOptions, + preload: str = "", + timeout: Optional[int] = None + ) -> ExecutionResult: + """Run Python code in sandbox + + Args: + options: + code: Base64 encoded encrypted code + preload: Preload code to execute before main code + timeout: Execution timeout in seconds + + Returns: + ExecutionResult with stdout, stderr, and exit code + """ + config = self.config + + if timeout is None: + timeout = config.worker_timeout + + # Check if preload is allowed + if not config.enable_preload: + preload = "" + code = base64.b64decode(code) + script_path, encoded_key = self.init_enviroment(code, preload, options=options) + + try: + # Setup environment + env = {} + + # Add proxy settings if configured + if config.proxy.socks5: + env["HTTPS_PROXY"] = config.proxy.socks5 + env["HTTP_PROXY"] = config.proxy.socks5 + elif config.proxy.https or config.proxy.http: + if config.proxy.https: + env["HTTPS_PROXY"] = config.proxy.https + if config.proxy.http: + env["HTTP_PROXY"] = config.proxy.http + + # Add allowed syscalls if configured + if config.allowed_syscalls: + env["ALLOWED_SYSCALLS"] = ",".join(map(str, config.allowed_syscalls)) + + # Execute with Python interpreter + logger.info(encoded_key) + + process = await asyncio.create_subprocess_exec( + config.python_path, + script_path, + LIB_PATH, + encoded_key, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + env=env, + cwd=LIB_PATH + ) + + # Wait for completion with timeout + try: + stdout, stderr = await asyncio.wait_for( + process.communicate(), + timeout=timeout + ) + + return ExecutionResult( + stdout=stdout.decode('utf-8', errors='replace'), + stderr=stderr.decode('utf-8', errors='replace'), + exit_code=process.returncode + ) + + except asyncio.TimeoutError: + # Kill process on timeout + try: + process.kill() + await process.wait() + except: + pass + + return ExecutionResult( + stdout="", + stderr="Execution timeout", + exit_code=-1, + ) + + finally: + # Cleanup temporary file + self.cleanup_temp_file(script_path) diff --git a/sandbox/app/dependencies.py b/sandbox/app/dependencies.py new file mode 100644 index 00000000..6fe05ee4 --- /dev/null +++ b/sandbox/app/dependencies.py @@ -0,0 +1,165 @@ +"""Dependency management""" +import asyncio +from pathlib import Path +from typing import List, Dict + +from app.config import get_config +from app.core.runners.nodejs.env import prepare_nodejs_dependencies_env +from app.core.runners.python.env import prepare_python_dependencies_env +from app.logger import get_logger + + +async def setup_dependencies(): + """Setup initial dependencies""" + logger = get_logger() + + try: + logger.info("Installing Python dependencies...") + await install_python_dependencies() + logger.info("Python dependencies installed") + + logger.info("Preparing Python dependencies environment...") + await prepare_python_dependencies_env() + logger.info("Python Environment Ready ....") + logger.info("Preparing Nodejs dependencies environment...") + await prepare_nodejs_dependencies_env() + logger.info("Nodejs Environment Ready ...") + + except Exception as e: + logger.error(f"Failed to setup dependencies: {e}") + + +async def update_dependencies(): + # TODO + return + + +async def install_python_dependencies(): + """Install Python dependencies from requirements file""" + logger = get_logger() + config = get_config() + + # Check if requirements file exists + req_file = Path("dependencies/python/python-requirements.txt") + if not req_file.exists(): + logger.warning("Python requirements file not found, skipping installation") + return + + # Read requirements + requirements = req_file.read_text().strip() + if not requirements: + logger.info("No Python requirements to install") + return + + # Install using pip + cmd = [ + config.python_path, + "-m", + "pip", + "install", + "--upgrade" + ] + + # Add packages from requirements + for line in requirements.split("\n"): + line = line.strip() + if line and not line.startswith("#"): + cmd.append(line) + + try: + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + if process.returncode != 0: + logger.error(f"Failed to install Python dependencies: {stderr.decode()}") + else: + logger.info("Python dependencies installed successfully") + + except Exception as e: + logger.error(f"Error installing Python dependencies: {e}") + + +async def list_dependencies(language: str) -> List[Dict[str, str]]: + """List installed dependencies + + Args: + language: Language (python or Node.js) + + Returns: + List of dependencies with name and version + """ + if language == "python": + return await list_python_packages() + else: + return [] + + +async def list_python_packages() -> List[Dict[str, str]]: + """List installed Python packages""" + config = get_config() + + try: + process = await asyncio.create_subprocess_exec( + config.python_path, + "-m", + "pip", + "list", + "--format=freeze", + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + if process.returncode != 0: + return [] + + # Parse output + packages = [] + for line in stdout.decode().split("\n"): + line = line.strip() + if line and "==" in line: + name, version = line.split("==", 1) + packages.append({"name": name, "version": version}) + + return packages + + except Exception as e: + get_logger().error(f"Failed to list Python packages: {e}") + return [] + + +async def update_dependencies_periodically(): + """Periodically update dependencies""" + logger = get_logger() + config = get_config() + + # Parse interval + interval_str = config.python_deps_update_interval + + # Convert to seconds + if interval_str.endswith("m"): + interval = int(interval_str[:-1]) * 60 + elif interval_str.endswith("h"): + interval = int(interval_str[:-1]) * 3600 + elif interval_str.endswith("s"): + interval = int(interval_str[:-1]) + else: + interval = 1800 # Default 30 minutes + + logger.info(f"Starting periodic dependency updates every {interval} seconds") + + while True: + await asyncio.sleep(interval) + + try: + logger.info("Updating Python dependencies...") + # TODO: await update_dependencies("python") + logger.info("Python dependencies updated successfully") + except Exception as e: + logger.error(f"Failed to update Python dependencies: {e}") diff --git a/sandbox/app/logger.py b/sandbox/app/logger.py new file mode 100644 index 00000000..9e63c8e5 --- /dev/null +++ b/sandbox/app/logger.py @@ -0,0 +1,44 @@ +"""Logging configuration""" +import logging +import sys +from typing import Optional + +from app.config import get_config + +_logger: Optional[logging.Logger] = None + + +def setup_logger() -> logging.Logger: + """Setup application logger""" + global _logger + + if _logger is not None: + return _logger + + config = get_config() + + # Create logger + _logger = logging.getLogger("sandbox") + _logger.setLevel(logging.DEBUG if config.app.debug else logging.INFO) + + # 只在 logger 没有 handler 时才添加 + if not _logger.handlers: + handler = logging.StreamHandler(sys.stdout) + handler.setLevel(logging.DEBUG if config.app.debug else logging.INFO) + + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + handler.setFormatter(formatter) + + _logger.addHandler(handler) + + return _logger + + +def get_logger() -> logging.Logger: + """Get application logger""" + if _logger is None: + return setup_logger() + return _logger diff --git a/sandbox/app/middleware/__init__.py b/sandbox/app/middleware/__init__.py new file mode 100644 index 00000000..77d6403c --- /dev/null +++ b/sandbox/app/middleware/__init__.py @@ -0,0 +1 @@ +"""Middleware package""" diff --git a/sandbox/app/middleware/auth.py b/sandbox/app/middleware/auth.py new file mode 100644 index 00000000..8a93a793 --- /dev/null +++ b/sandbox/app/middleware/auth.py @@ -0,0 +1,15 @@ +"""Authentication middleware""" +from fastapi import Header, HTTPException, status + +from app.config import get_config + + +async def verify_api_key(x_api_key: str = Header(..., alias="X-Api-Key")): + """Verify API key from request header""" + config = get_config() + if x_api_key != config.app.key: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid API key" + ) + return x_api_key diff --git a/sandbox/app/middleware/concurrency.py b/sandbox/app/middleware/concurrency.py new file mode 100644 index 00000000..e931f846 --- /dev/null +++ b/sandbox/app/middleware/concurrency.py @@ -0,0 +1,66 @@ +""" +Concurrency control middleware +""" +import asyncio +from contextlib import asynccontextmanager + +from fastapi import HTTPException, status + +from app.config import get_config +from app.logger import get_logger + +logger = get_logger() + + +class ConcurrencyController: + def __init__(self): + self._worker_semaphore: asyncio.Semaphore | None = None + self._request_counter = 0 + self._lock = asyncio.Lock() + + config = get_config() + self.max_requests = config.max_requests + + def init(self): + config = get_config() + self._worker_semaphore = asyncio.Semaphore(config.max_workers) + + async def _acquire_worker(self): + if self._worker_semaphore is None: + self.init() + async with self._worker_semaphore: + yield + + async def _limit_requests(self): + async with self._lock: + logger.info(f"Current requests: {self._request_counter}/{self.max_requests}") + if self._request_counter >= self.max_requests: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail={ + "code": 503, + "message": "Too many requests", + "data": None, + } + ) + self._request_counter += 1 + try: + yield + finally: + async with self._lock: + self._request_counter -= 1 + + def acquire_worker(self): + return asynccontextmanager(self._acquire_worker)() + + def limit_requests(self): + return asynccontextmanager(self._limit_requests)() + + +concurrency = ConcurrencyController() + + +async def concurrency_guard(): + async with concurrency.limit_requests(): + async with concurrency.acquire_worker(): + yield diff --git a/sandbox/app/models.py b/sandbox/app/models.py new file mode 100644 index 00000000..e7492b4c --- /dev/null +++ b/sandbox/app/models.py @@ -0,0 +1,80 @@ +"""Data models""" +from typing import Optional, Any + +from pydantic import BaseModel, Field + + +class RunnerOptions(BaseModel): + enable_network: bool = Field(default=False, description="Sandbox network flag") + + +class RunCodeRequest(BaseModel): + """Request model for code execution""" + language: str = Field(..., description="Programming language (python3 or nodejs)") + code: str = Field(..., description="Base64 encoded encrypted code") + preload: Optional[str] = Field(default="", description="Preload code") + options: RunnerOptions = Field(default_factory=RunnerOptions, description="Enable network access") + + +class RunCodeResponse(BaseModel): + """Response model for code execution""" + stdout: str = Field(default="", description="Standard output") + stderr: str = Field(default="", description="Standard error") + + +class DependencyRequest(BaseModel): + """Request model for dependency operations""" + language: str = Field(..., description="Programming language") + + +class UpdateDependencyRequest(BaseModel): + """Request model for updating dependencies""" + language: str = Field(..., description="Programming language") + packages: list[str] = Field(default_factory=list, description="Packages to install") + + +class Dependency(BaseModel): + """Dependency information""" + name: str + version: str + + +class ListDependenciesResponse(BaseModel): + """Response model for listing dependencies""" + dependencies: list[Dependency] = Field(default_factory=list) + + +class RefreshDependenciesResponse(BaseModel): + """Response model for refreshing dependencies""" + dependencies: list[Dependency] = Field(default_factory=list) + + +class UpdateDependenciesResponse(BaseModel): + """Response model for updating dependencies""" + success: bool = True + installed: list[str] = Field(default_factory=list) + + +class HealthResponse(BaseModel): + """Health check response""" + status: str = "healthy" + version: str = "2.0.0" + + +class ApiResponse(BaseModel): + """Standard API response wrapper""" + code: int = Field(default=0, description="Response code (0 for success, negative for error)") + message: str = Field(default="success", description="Response message") + data: Optional[Any] = Field(default=None, description="Response data") + + +def success_response(data: Any) -> ApiResponse: + """Create success response""" + return ApiResponse(code=0, message="success", data=data) + + +def error_response(code: int, message: str) -> ApiResponse: + """Create error response""" + if code >= 0: + code = -1 + return ApiResponse(code=code, message=message, data=None) diff --git a/sandbox/app/services/__init__.py b/sandbox/app/services/__init__.py new file mode 100644 index 00000000..e3726046 --- /dev/null +++ b/sandbox/app/services/__init__.py @@ -0,0 +1 @@ +"""Services package""" diff --git a/sandbox/app/services/nodejs_service.py b/sandbox/app/services/nodejs_service.py new file mode 100644 index 00000000..ffd6127b --- /dev/null +++ b/sandbox/app/services/nodejs_service.py @@ -0,0 +1,43 @@ +"""Nodejs execution service""" +import signal + +from app.core.runners.nodejs.nodejs_runner import NodejsRunner +from app.logger import get_logger +from app.models import ( + success_response, + error_response, + RunCodeResponse, + RunnerOptions +) + + +async def run_nodejs_code(code: str, preload: str, options: RunnerOptions): + """Execute Node.js code in sandbox + + Args: + options: + code: Base64 encoded encrypted code + preload: Preload code + + Returns: + API response with execution result + """ + logger = get_logger() + + try: + runner = NodejsRunner() + result = await runner.run(code, options, preload) + if result.exit_code == signal.SIGSYS + 0x80: + return error_response(31, "sandbox security policy violation") + + if result.exit_code != 0: + return error_response(500, result.stderr) + + return success_response(RunCodeResponse( + stdout=result.stdout, + stderr=result.stderr + )) + + except Exception as e: + logger.error(f"Python execution failed: {e}", exc_info=True) + return error_response(-500, str(e)) diff --git a/sandbox/app/services/python_service.py b/sandbox/app/services/python_service.py new file mode 100644 index 00000000..210b2086 --- /dev/null +++ b/sandbox/app/services/python_service.py @@ -0,0 +1,80 @@ +"""Python execution service""" +import signal + +from app.core.runners.python.python_runner import PythonRunner +from app.dependencies import ( + list_dependencies as list_deps, + update_dependencies as update_deps +) +from app.logger import get_logger +from app.models import ( + success_response, + error_response, + RunCodeResponse, + ListDependenciesResponse, + UpdateDependenciesResponse, + Dependency, + RunnerOptions +) + + +async def run_python_code(code: str, preload: str, options: RunnerOptions): + """Execute Python code in sandbox + + Args: + options: + code: Base64 encoded encrypted code + preload: Preload code + + Returns: + API response with execution result + """ + logger = get_logger() + + try: + runner = PythonRunner() + result = await runner.run(code, options, preload) + if result.exit_code == -signal.SIGSYS: + return error_response(31, "sandbox security policy violation") + + if result.stderr and result.exit_code != 0: + return error_response(500, result.stderr) + + return success_response(RunCodeResponse( + stdout=result.stdout, + stderr=result.stderr + )) + + except Exception as e: + logger.error(f"Python execution failed: {e}", exc_info=True) + return error_response(-500, str(e)) + + +async def list_python_dependencies(): + """List installed Python dependencies + + Returns: + API response with dependency list + """ + try: + deps = await list_deps("python") + dependencies = [ + Dependency(name=dep["name"], version=dep["version"]) + for dep in deps + ] + return success_response(ListDependenciesResponse(dependencies=dependencies)) + except Exception as e: + return error_response(500, str(e)) + + +async def update_python_dependencies(): + """Update Python dependencies + + Returns: + API response with update result + """ + try: + await update_deps() + return success_response(UpdateDependenciesResponse(success=True)) + except Exception as e: + return error_response(500, str(e)) diff --git a/sandbox/config.yaml b/sandbox/config.yaml new file mode 100644 index 00000000..26fb9af3 --- /dev/null +++ b/sandbox/config.yaml @@ -0,0 +1,18 @@ +app: + key: redbear-sandbox + +max_workers: 10 +max_requests: 300 +worker_timeout: 15 +python_path: /usr/local/bin/python +nodejs_path: /usr/bin/node +enable_network: true +enable_preload: false +python_deps_update_interval: 30m + +allowed_syscalls: [] + +proxy: + socks5: '' + http: '' + https: '' diff --git a/sandbox/dependencies/nodejs/node_modules/.package-lock.json b/sandbox/dependencies/nodejs/node_modules/.package-lock.json new file mode 100644 index 00000000..28b290ef --- /dev/null +++ b/sandbox/dependencies/nodejs/node_modules/.package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "nodejs", + "lockfileVersion": 3, + "requires": true, + "packages": {} +} diff --git a/sandbox/dependencies/nodejs/package-lock.json b/sandbox/dependencies/nodejs/package-lock.json new file mode 100644 index 00000000..28b290ef --- /dev/null +++ b/sandbox/dependencies/nodejs/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "nodejs", + "lockfileVersion": 3, + "requires": true, + "packages": {} +} diff --git a/sandbox/dependencies/nodejs/package.json b/sandbox/dependencies/nodejs/package.json new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/sandbox/dependencies/nodejs/package.json @@ -0,0 +1 @@ +{} diff --git a/sandbox/dependencies/python/python-requirements.txt b/sandbox/dependencies/python/python-requirements.txt new file mode 100644 index 00000000..1c3c2901 --- /dev/null +++ b/sandbox/dependencies/python/python-requirements.txt @@ -0,0 +1,4 @@ +requests==2.31.0 +# numpy==1.26.0 +# pandas==2.0.0 +jinja2==3.1.2 \ No newline at end of file diff --git a/sandbox/lib/seccomp_redbear/Cargo.lock b/sandbox/lib/seccomp_redbear/Cargo.lock new file mode 100644 index 00000000..f81d17c0 --- /dev/null +++ b/sandbox/lib/seccomp_redbear/Cargo.lock @@ -0,0 +1,23 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "libc" +version = "0.2.180" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bcc35a38544a891a5f7c865aca548a982ccb3b8650a5b06d0fd33a10283c56fc" + +[[package]] +name = "libseccomp-sys" +version = "0.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60276e2d41bbb68b323e566047a1bfbf952050b157d8b5cdc74c07c1bf4ca3b6" + +[[package]] +name = "seccomp_redbear" +version = "0.1.1" +dependencies = [ + "libc", + "libseccomp-sys", +] diff --git a/sandbox/lib/seccomp_redbear/Cargo.toml b/sandbox/lib/seccomp_redbear/Cargo.toml new file mode 100644 index 00000000..d6535987 --- /dev/null +++ b/sandbox/lib/seccomp_redbear/Cargo.toml @@ -0,0 +1,17 @@ +[package] +name = "seccomp_redbear" +version = "0.1.1" +edition = "2024" + +[lib] +name = "sandbox" +crate-type = ["cdylib"] + +[dependencies] +libc = "0.2.180" +libseccomp-sys = "0.3.0" + +[features] +default = [] +python3 = [] +nodejs = [] diff --git a/sandbox/lib/seccomp_redbear/src/lib.rs b/sandbox/lib/seccomp_redbear/src/lib.rs new file mode 100644 index 00000000..9de38a56 --- /dev/null +++ b/sandbox/lib/seccomp_redbear/src/lib.rs @@ -0,0 +1,224 @@ +#[cfg(all(feature = "python3", feature = "nodejs"))] +compile_error!("Only one feature can be enabled: either python3 or nodejs, not both!"); + +#[cfg(not(any(feature = "python3", feature = "nodejs")))] +compile_error!("You must enable one feature: either python3 or nodejs"); + +#[cfg(feature = "python3")] +mod python_syscalls; +#[cfg(feature = "python3")] +use crate::python_syscalls::*; + +#[cfg(feature = "nodejs")] +mod nodejs_syscalls; +#[cfg(feature = "nodejs")] +use crate::nodejs_syscalls::*; + +use libc::{c_char, c_int, chdir, chroot, gid_t, uid_t}; +use libseccomp_sys::*; +use std::env; +use std::ffi::CString; +use std::str::FromStr; + +/* + * get_allowed_syscalls - retrieve allowed syscalls for the sandbox + * @enable_network: enable network-related syscalls if non-zero + * + * Syscall selection order: + * 1. ALLOWED_SYSCALLS environment variable + * 2. Built-in default allowlist + * 3. Optional network syscall extension + * + * Returns: + * (allowed_syscalls, allowed_not_kill_syscalls) + * allowed_syscalls: syscalls fully allowed + * allowed_not_kill_syscalls: syscalls returning EPERM + */ +pub fn get_allowed_syscalls(enable_network: bool) -> (Vec, Vec) { + let mut allowed_syscalls = Vec::new(); + let mut allowed_not_kill_syscalls = Vec::new(); + + /* Syscalls that return error instead of killing */ + allowed_not_kill_syscalls.extend(ALLOW_ERROR_SYSCALLS); + + /* Load from environment variable ALLOWED_SYSCALLS */ + if let Ok(env_val) = env::var("ALLOWED_SYSCALLS") { + if !env_val.is_empty() { + for s in env_val.split(',') { + if let Ok(sc) = i32::from_str(s) { + allowed_syscalls.push(sc); + } + } + } + } + + /* Fallback to default syscalls if env not set */ + if allowed_syscalls.is_empty() { + allowed_syscalls.extend(ALLOW_SYSCALLS); + if enable_network { + allowed_syscalls.extend(ALLOW_NETWORK_SYSCALLS); + } + } + + (allowed_syscalls, allowed_not_kill_syscalls) +} + +/* + * setup_root - setup restricted filesystem root + * + * Perform chroot(".") and change working directory to "/". + * + * Return: + * 0 on success + * negative error code on failure + */ +fn setup_root() -> Result<(), c_int> { + let root = CString::new(".").unwrap(); + if unsafe { chroot(root.as_ptr()) } != 0 { + return Err(-1); + } + + let root_dir = CString::new("/").unwrap(); + if unsafe { chdir(root_dir.as_ptr()) } != 0 { + return Err(-2); + } + + Ok(()) +} + +/* + * set_no_new_privs - enable PR_SET_NO_NEW_PRIVS + * + * Prevent privilege escalation via execve. + * + * Return: + * 0 on success + * negative error code on failure + */ +fn set_no_new_privs() -> Result<(), c_int> { + if unsafe { libc::prctl(libc::PR_SET_NO_NEW_PRIVS, 1, 0, 0, 0) } != 0 { + return Err(-3); + } + Ok(()) +} + +/* + * drop_privileges - drop process privileges + * @uid: target user ID + * @gid: target group ID + * + * Permanently reduce process privileges. + * + * Return: + * 0 on success + * negative error code on failure + */ +fn drop_privileges(uid: uid_t, gid: gid_t) -> Result<(), c_int> { + if unsafe { libc::setgid(gid) } != 0 { + return Err(-4); + } + if unsafe { libc::setuid(uid) } != 0 { + return Err(-5); + } + Ok(()) +} + +/* + * install_seccomp - install seccomp filter + * @enable_network: enable network-related syscalls if non-zero + * + * Default action is SCMP_ACT_KILL_PROCESS. + * Allowed syscalls are explicitly whitelisted. + * + * Return: + * 0 on success + * negative error code on failure + */ +fn install_seccomp(enable_network: bool) -> Result<(), c_int> { + unsafe { + let ctx = seccomp_init(SCMP_ACT_KILL_PROCESS); + if ctx.is_null() { + return Err(-6); /* failed to init seccomp context */ + } + + let (allowed_syscalls, allowed_not_kill_syscalls) = get_allowed_syscalls(enable_network); + + /* add fully allowed syscalls */ + for &sc in &allowed_syscalls { + if seccomp_rule_add(ctx, SCMP_ACT_ALLOW, sc, 0) != 0 { + seccomp_release(ctx); + return Err(-7); + } + } + + /* add syscalls returning EPERM */ + for &sc in &allowed_not_kill_syscalls { + if seccomp_rule_add(ctx, SCMP_ACT_ERRNO(libc::EPERM as u16), sc, 0) != 0 { + seccomp_release(ctx); + return Err(-8); + } + } + + if seccomp_load(ctx) != 0 { + seccomp_release(ctx); + return Err(-9); + } + + seccomp_release(ctx); + Ok(()) + } +} + +/* + * init_seccomp - initialize seccomp sandbox + * @uid: target user ID + * @gid: target group ID + * @enable_network: enable network syscalls if non-zero + * + * Initialize the sandbox and apply privilege restrictions + * in the following order: + * 1. setup_root() + * 2. set_no_new_privs() + * 3. drop_privileges() + * 4. install_seccomp() + * + * This function must be called before executing any untrusted code. + * It is not thread-safe and must be invoked once per process. + * + * Return: + * 0 on success + * negative error code on failure + */ +#[unsafe(no_mangle)] +pub unsafe extern "C" fn init_seccomp(uid: uid_t, gid: gid_t, enable_network: i32) -> c_int { + if let Err(code) = setup_root() { + return code; + } + if let Err(code) = set_no_new_privs() { + return code; + } + if let Err(code) = drop_privileges(uid, gid) { + return code; + } + match install_seccomp(enable_network != 0) { + Ok(_) => 0, + Err(code) => code, + } +} + +#[unsafe(no_mangle)] +pub unsafe extern "C" fn get_lib_version_static() -> *const c_char { + concat!(env!("CARGO_PKG_VERSION"), "\0").as_ptr() as *const c_char +} + +#[unsafe(no_mangle)] +pub unsafe extern "C" fn get_lib_feature_static() -> *const c_char { + #[cfg(feature = "python3")] + let s = b"python3\0"; + #[cfg(feature = "nodejs")] + let s = b"nodejs\0"; + #[cfg(not(any(feature = "python3", feature = "nodejs")))] + let s = b"none\0"; + + s.as_ptr() as *const c_char +} diff --git a/sandbox/lib/seccomp_redbear/src/nodejs_syscalls.rs b/sandbox/lib/seccomp_redbear/src/nodejs_syscalls.rs new file mode 100644 index 00000000..7cf36664 --- /dev/null +++ b/sandbox/lib/seccomp_redbear/src/nodejs_syscalls.rs @@ -0,0 +1,74 @@ +// src/nodejs_syscalls.rs + +pub static ALLOW_SYSCALLS: &[i32] = &[ + // File IO + libc::SYS_open as i32, + libc::SYS_write as i32, + libc::SYS_close as i32, + libc::SYS_read as i32, + libc::SYS_openat as i32, + libc::SYS_newfstatat as i32, + libc::SYS_ioctl as i32, + libc::SYS_lseek as i32, + libc::SYS_fstat as i32, + libc::SYS_readlink as i32, + libc::SYS_dup3 as i32, + libc::SYS_fcntl as i32, + libc::SYS_fsync as i32, + // Memory + libc::SYS_mprotect as i32, + libc::SYS_mmap as i32, + libc::SYS_munmap as i32, + libc::SYS_mremap as i32, + libc::SYS_brk as i32, + libc::SYS_madvise as i32, + // Signal + libc::SYS_rt_sigaction as i32, + libc::SYS_rt_sigprocmask as i32, + libc::SYS_sigaltstack as i32, + libc::SYS_rt_sigreturn as i32, + libc::SYS_tgkill as i32, + // Thread + libc::SYS_futex as i32, + libc::SYS_sched_yield as i32, + libc::SYS_set_robust_list as i32, + libc::SYS_rseq as i32, + // User / Group + libc::SYS_getuid as i32, + // Process + libc::SYS_getpid as i32, + libc::SYS_gettid as i32, + libc::SYS_exit as i32, + libc::SYS_exit_group as i32, + libc::SYS_sched_getaffinity as i32, + // Time + libc::SYS_clock_gettime as i32, + libc::SYS_gettimeofday as i32, + libc::SYS_nanosleep as i32, + libc::SYS_time as i32, + // Epoll / Event (I/O multiplexing) + libc::SYS_epoll_ctl as i32, + libc::SYS_epoll_pwait as i32, +]; + +pub static ALLOW_ERROR_SYSCALLS: &[i32] = &[libc::SYS_clone as i32, libc::SYS_clone3 as i32]; + +pub static ALLOW_NETWORK_SYSCALLS: &[i32] = &[ + libc::SYS_socket as i32, + libc::SYS_connect as i32, + libc::SYS_bind as i32, + libc::SYS_listen as i32, + libc::SYS_accept as i32, + libc::SYS_sendto as i32, + libc::SYS_recvfrom as i32, + libc::SYS_getsockname as i32, + libc::SYS_recvmsg as i32, + libc::SYS_getpeername as i32, + libc::SYS_setsockopt as i32, + libc::SYS_ppoll as i32, + libc::SYS_uname as i32, + libc::SYS_sendmsg as i32, + libc::SYS_getsockopt as i32, + libc::SYS_fcntl as i32, + libc::SYS_fstatfs as i32, +]; diff --git a/sandbox/lib/seccomp_redbear/src/python_syscalls.rs b/sandbox/lib/seccomp_redbear/src/python_syscalls.rs new file mode 100644 index 00000000..998ae390 --- /dev/null +++ b/sandbox/lib/seccomp_redbear/src/python_syscalls.rs @@ -0,0 +1,81 @@ +// src/python_syscalls.rs + +pub static ALLOW_SYSCALLS: &[i32] = &[ + // File IO + libc::SYS_read as i32, + libc::SYS_write as i32, + libc::SYS_openat as i32, + libc::SYS_close as i32, + libc::SYS_newfstatat as i32, + libc::SYS_ioctl as i32, + libc::SYS_lseek as i32, + libc::SYS_getdents64 as i32, + libc::SYS_fstat as i32, + // Signal + libc::SYS_rt_sigreturn as i32, + libc::SYS_rt_sigaction as i32, + libc::SYS_rt_sigprocmask as i32, + libc::SYS_sigaltstack as i32, + libc::SYS_tgkill as i32, + // Thread + libc::SYS_futex as i32, + // Memory + libc::SYS_mmap as i32, + libc::SYS_brk as i32, + libc::SYS_mprotect as i32, + libc::SYS_munmap as i32, + libc::SYS_mremap as i32, + // User / Group + libc::SYS_getuid as i32, + // Process + libc::SYS_getpid as i32, + libc::SYS_getppid as i32, + libc::SYS_gettid as i32, + libc::SYS_exit as i32, + libc::SYS_exit_group as i32, + libc::SYS_sched_yield as i32, + libc::SYS_set_robust_list as i32, + libc::SYS_get_robust_list as i32, + libc::SYS_rseq as i32, + // Time + libc::SYS_clock_gettime as i32, + libc::SYS_gettimeofday as i32, + libc::SYS_time as i32, + libc::SYS_nanosleep as i32, + libc::SYS_clock_nanosleep as i32, + // Epoll / Event (I/O multiplexing) + libc::SYS_epoll_create1 as i32, + libc::SYS_epoll_ctl as i32, + libc::SYS_pselect6 as i32, + // Randomness + libc::SYS_getrandom as i32, +]; + +pub static ALLOW_ERROR_SYSCALLS: &[i32] = &[ + libc::SYS_clone as i32, + libc::SYS_mkdirat as i32, + libc::SYS_mkdir as i32, +]; + +pub static ALLOW_NETWORK_SYSCALLS: &[i32] = &[ + libc::SYS_socket as i32, + libc::SYS_connect as i32, + libc::SYS_bind as i32, + libc::SYS_listen as i32, + libc::SYS_accept as i32, + libc::SYS_sendto as i32, + libc::SYS_recvfrom as i32, + libc::SYS_getsockname as i32, + libc::SYS_recvmsg as i32, + libc::SYS_getpeername as i32, + libc::SYS_setsockopt as i32, + libc::SYS_ppoll as i32, + libc::SYS_uname as i32, + libc::SYS_sendmsg as i32, + libc::SYS_sendmmsg as i32, + libc::SYS_getsockopt as i32, + libc::SYS_fcntl as i32, + libc::SYS_fstatfs as i32, + libc::SYS_poll as i32, + libc::SYS_epoll_pwait as i32, +]; diff --git a/sandbox/main.py b/sandbox/main.py new file mode 100644 index 00000000..99b7b0a6 --- /dev/null +++ b/sandbox/main.py @@ -0,0 +1,64 @@ +""" +Redbear Sandbox - Main Entry Point +""" +import asyncio +import os +import sys +from contextlib import asynccontextmanager + +import uvicorn +from fastapi import FastAPI + +from app.config import get_config +from app.controllers import manager_router +from app.core.runners import init_sandbox_user +from app.dependencies import setup_dependencies, update_dependencies_periodically +from app.logger import setup_logger, get_logger + +setup_logger() +config = get_config() +logger = get_logger() + + +def check_root_privileges(): + """Check if running with root privileges""" + if os.geteuid() != 0: + logger.info("Error: Sandbox must be run as root for security features (chroot, setuid)") + sys.exit(1) + + +check_root_privileges() + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Application lifespan manager""" + logger = get_logger() + config = get_config() + # Startup + logger.info("Starting RedBear Sandbox...") + logger.info(f"Starting server on port {config.app.port}") + logger.info(f"Debug mode: {config.app.debug}") + logger.info(f"Max workers: {config.max_workers}") + logger.info(f"Max requests: {config.max_requests}") + logger.info(f"Network enabled: {config.enable_network}") + init_sandbox_user() + await setup_dependencies() + + if config.python_deps_update_interval: + asyncio.create_task(update_dependencies_periodically()) + + yield + + # Shutdown + logger.info("Shutting down Redbear Sandbox...") + +app = FastAPI( + title="Sandbox", + description="Secure code execution sandbox", + version="0.1.0", + lifespan=lifespan, + debug=config.app.debug +) + +app.include_router(manager_router) diff --git a/sandbox/requirements.txt b/sandbox/requirements.txt new file mode 100644 index 00000000..0c91018a --- /dev/null +++ b/sandbox/requirements.txt @@ -0,0 +1,20 @@ +# Web Framework +fastapi==0.115.0 +uvicorn[standard]==0.32.0 +pydantic==2.9.0 +pydantic-settings==2.5.0 + +# Configuration +PyYAML==6.0.2 + +# Security +pyseccomp==0.1.2 + + +# Async & Concurrency +aiofiles==24.1.0 + +# Testing +pytest==8.3.0 +pytest-asyncio==0.24.0 +httpx==0.27.0 diff --git a/sandbox/script/env.sh b/sandbox/script/env.sh new file mode 100644 index 00000000..f44f7208 --- /dev/null +++ b/sandbox/script/env.sh @@ -0,0 +1,53 @@ +#!/bin/bash + +# Check if the correct number of arguments are provided +if [ "$#" -ne 2 ]; then + echo "Usage: $0 " + exit 1 +fi + +src="$1" +dest="$2" + +# Function to copy and link files +copy_and_link() { + local src_file="$1" + local dest_file="$2" + + if [ -L "$src_file" ]; then + # If src_file is a symbolic link, copy it without changing permissions + cp -P "$src_file" "$dest_file" + elif [ -b "$src_file" ] || [ -c "$src_file" ]; then + # If src_file is a device file, copy it and change permissions + cp "$src_file" "$dest_file" + chmod 444 "$dest_file" + else + # Otherwise, create a hard link and change the permissions to read-only + ln -f "$src_file" "$dest_file" 2>/dev/null || { cp "$src_file" "$dest_file" && chmod 444 "$dest_file"; } + fi +} + +# Check if src is a file or directory +if [ -f "$src" ]; then + # src is a file, create hard link directly in dest + mkdir -p "$(dirname "$dest/$src")" + copy_and_link "$src" "$dest/$src" +elif [ -d "$src" ]; then + # src is a directory, process as before + mkdir -p "$dest/$src" + + # Find all files in the source directory + find "$src" -type f,l | while read -r file; do + # Get the relative path of the file + rel_path="${file#$src/}" + # Get the directory of the relative path + rel_dir=$(dirname "$rel_path") + # Create the same directory structure in the destination + mkdir -p "$dest/$src/$rel_dir" + # Copy and link the file + copy_and_link "$file" "$dest/$src/$rel_path" + done +else + echo "Error: $src is neither a file nor a directory" + exit 1 +fi diff --git a/simple_mcp_server.py b/simple_mcp_server.py deleted file mode 100644 index fa299e37..00000000 --- a/simple_mcp_server.py +++ /dev/null @@ -1,130 +0,0 @@ -#!/usr/bin/env python3 -"""简化的MCP服务器 - 用于测试MCP工具集成""" - -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel -from typing import Dict, Any, List -import uvicorn - -app = FastAPI(title="Simple MCP Server", version="1.0.0") - -class MCPRequest(BaseModel): - jsonrpc: str = "2.0" - id: str - method: str - params: Dict[str, Any] = {} - -class MCPResponse(BaseModel): - jsonrpc: str = "2.0" - id: str - result: Any = None - error: Dict[str, Any] = None - -# 可用工具定义 -TOOLS = [ - { - "name": "calculator", - "description": "简单计算器", - "inputSchema": { - "type": "object", - "properties": { - "expression": {"type": "string", "description": "数学表达式"} - }, - "required": ["expression"] - } - }, - { - "name": "echo", - "description": "回显工具", - "inputSchema": { - "type": "object", - "properties": { - "message": {"type": "string", "description": "要回显的消息"} - }, - "required": ["message"] - } - } -] - -@app.get("/") -async def root(): - return {"name": "Simple MCP Server", "version": "1.0.0"} - -@app.get("/health") -async def health(): - return {"status": "healthy", "tools": len(TOOLS)} - -@app.post("/mcp") -async def mcp_handler(request: MCPRequest): - """处理MCP请求""" - try: - if request.method == "initialize": - return MCPResponse( - id=request.id, - result={ - "protocolVersion": "2024-11-05", - "capabilities": {"tools": {"listChanged": True}}, - "serverInfo": {"name": "Simple MCP Server", "version": "1.0.0"} - } - ) - - elif request.method == "tools/list": - return MCPResponse( - id=request.id, - result={"tools": TOOLS} - ) - - elif request.method == "tools/call": - tool_name = request.params.get("name") - arguments = request.params.get("arguments", {}) - - if tool_name == "calculator": - try: - expression = arguments.get("expression", "") - result = eval(expression) # 注意：生产环境不要用eval - return MCPResponse( - id=request.id, - result={"content": [{"type": "text", "text": f"结果: {result}"}]} - ) - except Exception as e: - return MCPResponse( - id=request.id, - error={"code": -1, "message": f"计算错误: {str(e)}"} - ) - - elif tool_name == "echo": - message = arguments.get("message", "") - return MCPResponse( - id=request.id, - result={"content": [{"type": "text", "text": f"Echo: {message}"}]} - ) - - else: - return MCPResponse( - id=request.id, - error={"code": -1, "message": f"未知工具: {tool_name}"} - ) - - elif request.method == "ping": - return MCPResponse( - id=request.id, - result={"status": "pong"} - ) - - else: - return MCPResponse( - id=request.id, - error={"code": -1, "message": f"未知方法: {request.method}"} - ) - - except Exception as e: - return MCPResponse( - id=request.id, - error={"code": -1, "message": str(e)} - ) - -if __name__ == "__main__": - print("启动简化MCP服务器...") - print("访问 http://localhost:8002 查看服务状态") - print("MCP端点: http://localhost:8002/mcp") - uvicorn.run(app, host="0.0.0.0", port=8002) \ No newline at end of file diff --git a/web/src/api/apiKey.ts b/web/src/api/apiKey.ts index 56ad79c4..92df70c9 100644 --- a/web/src/api/apiKey.ts +++ b/web/src/api/apiKey.ts @@ -1,33 +1,39 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 13:59:41 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 13:59:41 + */ import { request } from '@/utils/request' import type { ApiKey } from '@/views/ApiKeyManagement/types' -// API Key列表 +// API Key list export const getApiKeyListUrl = '/apikeys' export const getApiKeyList = (data: Record) => { return request.get(getApiKeyListUrl, data) } -// API Key详情 +// API Key details export const getApiKey = (id: string) => { return request.get(`/apikeys/${id}`) } -// 创建API Key +// Create API Key export const createApiKey = (values: ApiKey) => { return request.post('/apikeys', values) } -// 更新API Key +// Update API Key export const updateApiKey = (id: string, values: ApiKey) => { return request.put(`/apikeys/${id}`, values) } -// 删除 API Key +// Delete API Key export const deleteApiKey = (id: string) => { return request.delete(`/apikeys/${id}`) } -// 使用统计 +// Usage statistics export const getApiKeyStats = (app_key_id: string) => { return request.get(`/apikeys/${app_key_id}/stats`) } \ No newline at end of file diff --git a/web/src/api/application.ts b/web/src/api/application.ts index 69d27d44..244f3503 100644 --- a/web/src/api/application.ts +++ b/web/src/api/application.ts @@ -1,3 +1,9 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 13:59:45 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 13:59:45 + */ import { request } from '@/utils/request' import type { ApplicationModalData } from '@/views/ApplicationManagement/types' import type { Config } from '@/views/ApplicationConfig/types' @@ -5,71 +11,72 @@ import { handleSSE, type SSEMessage } from '@/utils/stream' import type { QueryParams } from '@/views/Conversation/types' import type { WorkflowConfig } from '@/views/Workflow/types' -// 应用列表 +// Application list export const getApplicationListUrl = '/apps' export const getApplicationList = (data: Record) => { return request.get(getApplicationListUrl, data) } -// 获取应用配置 +// Get application config export const getApplicationConfig = (id: string) => { return request.get(`/apps/${id}/config`) } -// 获取集群应用配置 +// Get multi-agent config export const getMultiAgentConfig = (id: string) => { return request.get(`/apps/${id}/multi-agent`) } -// 获取 workflow应用配置 +// Get workflow config export const getWorkflowConfig = (id: string) => { return request.get(`/apps/${id}/workflow`) } -// 应用详情 +// Application details export const getApplication = (id: string) => { return request.get(`/apps/${id}`) } -// 更新应用 +// Update application export const updateApplication = (id: string, values: ApplicationModalData) => { return request.put(`/apps/${id}`, values) } -// 创建应用 +// Create application export const addApplication = (values: ApplicationModalData) => { return request.post('/apps', values) } -// 保存Agent配置 +// Save agent config export const saveAgentConfig = (app_id: string, values: Config) => { return request.put(`/apps/${app_id}/config`, values) } -// 保存集群配置 +// Save multi-agent config export const saveMultiAgentConfig = (app_id: string, values: Config) => { return request.put(`/apps/${app_id}/multi-agent`, values) } -// 保存workflow配置 +// Save workflow config export const saveWorkflowConfig = (app_id: string, values: WorkflowConfig) => { return request.put(`/apps/${app_id}/workflow`, values) } -// 模型比对试运行 +// Model comparison test run export const runCompare = (app_id: string, values: Record, onMessage?: (data: SSEMessage[]) => void) => { return handleSSE(`/apps/${app_id}/draft/run/compare`, values, onMessage) } +// Test run export const draftRun = (app_id: string, values: Record, onMessage?: (data: SSEMessage[]) => void) => { return handleSSE(`/apps/${app_id}/draft/run`, values, onMessage) } -// 删除应用 +// Delete application export const deleteApplication = (app_id: string) => { return request.delete(`/apps/${app_id}`) } -// 发布版本列表 +// Release version list export const getReleaseList = (app_id: string) => { return request.get(`/apps/${app_id}/releases`) } -// 发布版本 +// Publish release export const publishRelease = (app_id: string, values: Record) => { return request.post(`/apps/${app_id}/publish`, values) } -// 回滚版本 +// Rollback release export const rollbackRelease = (app_id: string, version: string) => { return request.post(`/apps/${app_id}/rollback/${version}`) } -// 发布版本分享 +// Share release export const shareRelease = (app_id: string, release_id: string) => { return request.post(`/apps/${app_id}/releases/${release_id}/share`, { "is_enabled": true, @@ -77,7 +84,7 @@ export const shareRelease = (app_id: string, release_id: string) => { "allow_embed": true }) } -// 获取体验对话历史 +// Get conversation history export const getConversationHistory = (share_token: string, data: { page: number; pagesize: number }) => { return request.get(`/public/share/conversations`, data, { headers: { @@ -85,7 +92,7 @@ export const getConversationHistory = (share_token: string, data: { page: number } }) } -// 发送体验对话 +// Send conversation export const sendConversation = (values: QueryParams, onMessage: (data: SSEMessage[]) => void, shareToken: string) => { return handleSSE(`/public/share/chat`, values, onMessage, { headers: { @@ -93,7 +100,7 @@ export const sendConversation = (values: QueryParams, onMessage: (data: SSEMessa } }) } -// 获取体验会话详情 +// Get conversation details export const getConversationDetail = (share_token: string, conversation_id: string) => { return request.get(`/public/share/conversations/${conversation_id}`, {}, { headers: { @@ -101,11 +108,15 @@ export const getConversationDetail = (share_token: string, conversation_id: stri } }) } -// 获取体验对话token +// Get share token export const getShareToken = (share_token: string, user_id: string) => { return request.post(`/public/share/${share_token}/token`, { user_id }) } -// 复制应用 +// Copy application export const copyApplication = (app_id: string, new_name: string) => { return request.post(`/apps/${app_id}/copy?new_name=${new_name}`) -} \ No newline at end of file +} +// Data statistics +export const getAppStatistics = (app_id: string, data: { start_date: number; end_date: number; }) => { + return request.get(`/apps/${app_id}/statistics`, data) +} diff --git a/web/src/api/common.ts b/web/src/api/common.ts index 2f6033d1..b53e4d5f 100644 --- a/web/src/api/common.ts +++ b/web/src/api/common.ts @@ -1,5 +1,5 @@ import { request } from "@/utils/request"; -// 列表查询参数 +// List query parameters export interface Query { page?: number; pagesize?: number; @@ -38,15 +38,15 @@ export interface versionResponse{ codeName: string; }; } -// 首页数据统计 +// Dashboard data statistics export const getDashboardData = `/home-page/workspaces` -// 首页数据看板统计 +// Dashboard statistics export const getDashboardStatistics = async () => { const response = await request.get(`/home-page/statistics`); return response as DataResponse; }; -// 获取版本号 +// Get version export const getVersion = async () => { const response = await request.get(`/home-page/version`); return response as versionResponse; diff --git a/web/src/api/fileStorage.ts b/web/src/api/fileStorage.ts new file mode 100644 index 00000000..86da129c --- /dev/null +++ b/web/src/api/fileStorage.ts @@ -0,0 +1,31 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 13:59:56 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 13:59:56 + */ +import { request, API_PREFIX } from '@/utils/request' + +// Upload file，file storage has expiration period +export const fileUploadUrl = `${API_PREFIX}/storage/files` +export const fileUpload = (formData?: unknown) => { + return request.uploadFile('/storage/files', formData) +} + +// Get file access URL (no token required) +export const getFileUrl = (file_id: string) => `/storage/files/${file_id}/url` +export const getFileLink = (fileId: string, data: { permanent?: boolean } = { permanent: true }) => { + return request.get(getFileUrl(fileId), data) +} + +// Get file internally +export const getInternalFileUrl = (file_id: string) => `/storage/files/${file_id}` +export const getInternalFile = (fileId: string) => { + return request.get(getInternalFileUrl(fileId)) +} + +// Delete file +export const deleteFileUrl = (file_id: string) => `/storage/files/${file_id}` +export const deleteFile = (fileId: string) => { + return request.delete(deleteFileUrl(fileId)) +} diff --git a/web/src/api/knowledgeBase.ts b/web/src/api/knowledgeBase.ts index 5f171a72..38a0d40d 100644 --- a/web/src/api/knowledgeBase.ts +++ b/web/src/api/knowledgeBase.ts @@ -65,7 +65,7 @@ export const getModelTypeList = async () => { }; // 获取模型列表 export const getModelList = async (pageInfo: PageRequest) => { - const response = await request.get(`${apiPrefix}/models`, pageInfo); + const response = await request.get(`${apiPrefix}/models`, { ...pageInfo, is_active: true }); return response as any; }; //获取模型提供者 diff --git a/web/src/api/member.ts b/web/src/api/member.ts index 8e456e24..f186fdc4 100644 --- a/web/src/api/member.ts +++ b/web/src/api/member.ts @@ -1,20 +1,26 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:01 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:01 + */ import { request } from '@/utils/request' -// 成员列表 +// Member list export const memberListUrl = '/workspaces/members' -// 邀请成员 +// Invite member export const inviteMember = (values: { email: string }) => { return request.post(`/workspaces/invites`, values) } -// 删除成员 +// Delete member export const deleteMember = (id: string) => { return request.delete(`/workspaces/members/${id}`) } -// 更新成员 +// Update member export const updateMember = (values: { id: string, role: string }) => { return request.put(`/workspaces/members`, [values]) } -// 验证邀请token +// Validate invite token export const validateInviteToken = (token: string) => { return request.get(`/workspaces/invites/validate/${token}`) } diff --git a/web/src/api/memory.ts b/web/src/api/memory.ts index bbd9f6b0..6f4e7f0e 100644 --- a/web/src/api/memory.ts +++ b/web/src/api/memory.ts @@ -1,3 +1,9 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:06 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:06 + */ import { request } from '@/utils/request' import type { MemoryFormData, @@ -116,20 +122,20 @@ export const getRagContent = (end_user_id: string) => { return request.get(`/dashboard/rag_content`, { end_user_id, limit: 20 }) } // Emotion distribution analysis -export const getWordCloud = (group_id: string) => { - return request.post(`/memory/emotion-memory/wordcloud`, { group_id, limit: 20 }) +export const getWordCloud = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/wordcloud`, { end_user_id, limit: 20 }) } // High-frequency emotion keywords -export const getEmotionTags = (group_id: string) => { - return request.post(`/memory/emotion-memory/tags`, { group_id, limit: 20 }) +export const getEmotionTags = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/tags`, { end_user_id, limit: 20 }) } // Emotion health index -export const getEmotionHealth = (group_id: string) => { - return request.post(`/memory/emotion-memory/health`, { group_id, limit: 20 }) +export const getEmotionHealth = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/health`, { end_user_id }) } // Personalized suggestions -export const getEmotionSuggestions = (group_id: string) => { - return request.post(`/memory/emotion-memory/suggestions`, { group_id, limit: 20 }) +export const getEmotionSuggestions = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/suggestions`, { end_user_id }) } export const generateSuggestions = (end_user_id: string) => { return request.post(`/memory/emotion-memory/generate_suggestions`, { end_user_id }) @@ -138,8 +144,8 @@ export const analyticsRefresh = (end_user_id: string) => { return request.post('/memory-storage/analytics/generate_cache', { end_user_id }) } // Forgetting stats -export const getForgetStats = (group_id: string) => { - return request.get(`/memory/forget-memory/stats`, { group_id }) +export const getForgetStats = (end_user_id: string) => { + return request.get(`/memory/forget-memory/stats`, { end_user_id }) } // Implicit Memory - Preferences export const getImplicitPreferences = (end_user_id: string) => { @@ -165,20 +171,20 @@ export const getShortTerm = (end_user_id: string) => { return request.get(`/memory/short/short_term`, { end_user_id }) } // Perceptual Memory - Visual memory -export const getPerceptualLastVisual = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/last_visual`) +export const getPerceptualLastVisual = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/last_visual`) } // Perceptual Memory - Audio memory -export const getPerceptualLastListen = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/last_listen`) +export const getPerceptualLastListen = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/last_listen`) } // Perceptual Memory - Text memory -export const getPerceptualLastText = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/last_text`) +export const getPerceptualLastText = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/last_text`) } // Perceptual Memory - Perceptual memory timeline -export const getPerceptualTimeline = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/timeline`) +export const getPerceptualTimeline = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/timeline`) } // Episodic Memory - Overview export const getEpisodicOverview = (data: { end_user_id: string; time_range: string; episodic_type: string; } ) => { @@ -201,14 +207,14 @@ export const getExplicitMemory = (end_user_id: string) => { export const getExplicitMemoryDetails = (data: { end_user_id: string, memory_id: string; }) => { return request.post(`/memory/explicit-memory/details`, data) } -export const getConversations = (end_user: string) => { - return request.get(`/memory/work/${end_user}/conversations`) +export const getConversations = (end_user_id: string) => { + return request.get(`/memory/work/${end_user_id}/conversations`) } -export const getConversationMessages = (end_user: string, conversation_id: string) => { - return request.get(`/memory/work/${end_user}/messages`, { conversation_id }) +export const getConversationMessages = (end_user_id: string, conversation_id: string) => { + return request.get(`/memory/work/${end_user_id}/messages`, { conversation_id }) } -export const getConversationDetail = (end_user: string, conversation_id: string) => { - return request.get(`/memory/work/${end_user}/detail`, { conversation_id }) +export const getConversationDetail = (end_user_id: string, conversation_id: string) => { + return request.get(`/memory/work/${end_user_id}/detail`, { conversation_id }) } export const forgetTrigger = (data: { max_merge_batch_size: number; min_days_since_access: number; end_user_id: string;}) => { return request.post(`/memory/forget-memory/trigger`, data) diff --git a/web/src/api/models.ts b/web/src/api/models.ts index 20fdf91a..eb18ce91 100644 --- a/web/src/api/models.ts +++ b/web/src/api/models.ts @@ -1,23 +1,74 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:09 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:09 + */ import { request } from '@/utils/request' -import type { ModelFormData } from '@/views/ModelManagement/types' +import type { MultiKeyForm, Query, KeyConfigModalForm, CompositeModelForm, CustomModelForm } from '@/views/ModelManagement/types' -// 模型列表 +// Model list export const getModelListUrl = '/models' -export const getModelList = (data: { type: string; pagesize: number; page: number; }) => { +export const getModelList = (data: Query) => { return request.get(getModelListUrl, data) } -// 创建模型 -export const addModel = (data: ModelFormData) => { - return request.post('/models', data) -} -// 更新模型 -export const updateModel = (apiKeyId: string, data: ModelFormData) => { - return request.put(`/models/apikeys/${apiKeyId}`, data) -} -// 模型类型列表 +// Model type list export const modelTypeUrl = '/models/type' -// 模型供应商列表 +// Model provider list export const modelProviderUrl = '/models/provider' export const getModelProviderList = () => { return request.get(modelProviderUrl) +} +// New model list +export const getModelNewListUrl = '/models/new' +export const getModelNewList = (data: Query) => { + return request.get(getModelNewListUrl, data) +} +// Get model information +export const getModelInfo = (model_id: string) => { + return request.get(`/models/${model_id}`) +} +// Create composite model +export const addCompositeModel = (data: CompositeModelForm) => { + return request.post('/models/composite', data) +} +// Update composite model +export const updateCompositeModel = (model_id: string, data: CompositeModelForm) => { + return request.put(`/models/composite/${model_id}`, data) +} +// Delete composite model +export const deleteCompositeModel = (model_id: string) => { + return request.delete(`/models/composite/${model_id}`) +} +// Create API keys for all matching models by provider +export const updateProviderApiKeys = (data: KeyConfigModalForm) => { + return request.post('/models/provider/apikeys', data) +} +// Create model API key +export const addModelApiKey = (model_id: string, data: MultiKeyForm) => { + return request.post(`/models/${model_id}/apikeys`, data) +} +// Delete model API key +export const deleteModelApiKey = (api_key_id: string) => { + return request.delete(`/models/apikeys/${api_key_id}`) +} +// Update model status +export const updateModelStatus = (model_id: string, data: { is_active: boolean; }) => { + return request.put(`/models/${model_id}`, data) +} +// Model plaza list +export const getModelPlaza = (data: { search?: string; provider?: string; }) => { + return request.get('/models/model_plaza', data) +} +// Add model to plaza +export const addModelPlaza = (model_base_id: string) => { + return request.post(`/models/model_plaza/${model_base_id}/add`) +} +// Create custom model +export const addCustomModel = (data: CustomModelForm) => { + return request.post('/models/model_plaza', data) +} +// Update custom model +export const updateCustomModel = (model_base_id: string, data: CustomModelForm) => { + return request.put(`/models/model_plaza/${model_base_id}`, data) } \ No newline at end of file diff --git a/web/src/api/ontology.ts b/web/src/api/ontology.ts new file mode 100644 index 00000000..becf899f --- /dev/null +++ b/web/src/api/ontology.ts @@ -0,0 +1,53 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 13:59:12 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 13:59:12 + */ +import { request } from '@/utils/request' +import type { Query, OntologyModalData, OntologyClassModalData, OntologyClassExtractModalData, OntologyExportModalData } from '@/views/Ontology/types' + +// Scene list +export const getOntologyScenesUrl = '/memory/ontology/scenes' +export const getOntologyScenesList = (data: Query) => { + return request.get(getOntologyScenesUrl, data) +} + +// Create scene +export const createOntologyScene = (data: OntologyModalData) => { + return request.post('/memory/ontology/scene', data) +} +// Update scene +export const updateOntologyScene = (scene_id: string, data: OntologyModalData) => { + return request.put(`/memory/ontology/scene/${scene_id}`, data) +} +// Delete scene +export const deleteOntologyScene = (scene_id: string) => { + return request.delete(`/memory/ontology/scene/${scene_id}`) +} + +// Get class list +export const getOntologyclassesUrl = '/memory/ontology/classes' +export const getOntologyClassList = (data: { scene_id: string; class_name?: string; }) => { + return request.get(getOntologyclassesUrl, data) +} +// Extract ontology types +export const extractOntologyTypes = (data: OntologyClassExtractModalData) => { + return request.post('/memory/ontology/extract', data) +} +// Create ontology class +export const createOntologyClass = (data: OntologyClassModalData) => { + return request.post('/memory/ontology/class', data) +} +// Delete ontology class +export const deleteOntologyClass = (class_id: string) => { + return request.delete(`/memory/ontology/class/${class_id}`) +} +// Import scenario +export const ontologyImport = (data: unknown) => { + return request.uploadFile('/memory/ontology/import', data) +} +// Export scenario +export const ontologyExport = (data: OntologyExportModalData, fileName: string, callback: () => void) => { + return request.downloadFile('/memory/ontology/export', fileName, data, callback) +} \ No newline at end of file diff --git a/web/src/api/order.ts b/web/src/api/order.ts index e5d9d916..9d83538f 100644 --- a/web/src/api/order.ts +++ b/web/src/api/order.ts @@ -1,16 +1,23 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:14 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:14 + */ import { request } from '@/utils/request' import type { VoucherForm } from '@/views/OrderPayment/types' export const getOrderListUrl = '/v1/orders/customer' -// 提交支付凭证API +// Submit payment voucher API export const submitPaymentVoucherAPI = (voucherData: VoucherForm) => { return request.post('/v1/orders/', voucherData) } -// 订单详情 +// Order details export const getOrderDetail = (order_no: string) => { return request.get(`/v1/orders/customer/${order_no}`) } +// Order status enum export const orderStatusUrl = '/v1/order-status/' export const getOrderStatus = () => { return request.get(orderStatusUrl) diff --git a/web/src/api/prompt.ts b/web/src/api/prompt.ts index 526f50ac..55398ca5 100644 --- a/web/src/api/prompt.ts +++ b/web/src/api/prompt.ts @@ -1,13 +1,32 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:17 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:17 + */ import { request } from '@/utils/request' import type { AiPromptForm } from '@/views/ApplicationConfig/types' +import type { PromptReleaseData } from '@/views/Prompt/types' import { handleSSE, type SSEMessage } from '@/utils/stream' +// Create session export const createPromptSessions = () => { return request.post(`/prompt/sessions`) } -export const getPrompt = (session_id: string) => { - return request.get(`/prompt/sessions/${session_id}`) -} +// Get prompt optimization export const updatePromptMessages = (session_id: string, data: AiPromptForm, onMessage?: (data: SSEMessage[]) => void) => { return handleSSE(`/prompt/sessions/${session_id}/messages`, data, onMessage) +} +// Prompt release list +export const getPromptReleaseListUrl = '/prompt/releases/list' +export const getPromptReleaseList = () => { + return request.get(getPromptReleaseListUrl) +} +// Save prompt +export const savePrompt = (data: PromptReleaseData) => { + return request.post('/prompt/releases', data) +} +// Delete prompt +export const deletePrompt = (prompt_id: string) => { + return request.delete(`/prompt/releases/${prompt_id}`) } \ No newline at end of file diff --git a/web/src/api/user.ts b/web/src/api/user.ts index 3ff03386..f37e685b 100644 --- a/web/src/api/user.ts +++ b/web/src/api/user.ts @@ -1,40 +1,46 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:23 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:23 + */ import { request } from '@/utils/request' import type { CreateModalData } from '@/views/UserManagement/types' import { cookieUtils } from '@/utils/request' -// 用户信息 +// User info export const getUsers = () => { return request.get('/users') } -// 用户列表 +// User list export const getUserListUrl = '/users/superusers' -// 登录 +// Login export const loginUrl = '/token' export const login = (data: { email: string; password: string; invite?: string; username?: string }) => { return request.post(loginUrl, data) } -// 刷新token +// Refresh token export const refreshTokenUrl = '/refresh' export const refreshToken = () => { return request.post(refreshTokenUrl, { refresh_token: cookieUtils.get('refreshToken') }) } -// 重置密码 +// Reset password export const changePassword = (data: { user_id: string; new_password: string }) => { return request.put('/users/admin/change-password', data) } -// 禁用用户 +// Disable user export const deleteUser = (user_id: string) => { return request.delete(`/users/${user_id}`) } -// 启用用户 +// Enable user export const enableUser = (user_id: string) => { return request.post(`/users/${user_id}/activate`) } -// 创建用户 +// Create user export const addUser = (data: CreateModalData) => { return request.post('/users/superuser', data) } -// 注销 +// Logout export const logoutUrl = '/logout' export const logout = () => { return request.post(logoutUrl) diff --git a/web/src/api/workspaces.ts b/web/src/api/workspaces.ts index 4e78194b..01f3be72 100644 --- a/web/src/api/workspaces.ts +++ b/web/src/api/workspaces.ts @@ -1,28 +1,34 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-03 14:00:26 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-02-03 14:00:26 + */ import { request } from '@/utils/request' import type { SpaceModalData } from '@/views/SpaceManagement/types' -import type { ConfigModalData } from '@/views/UserMemory/types' +import type { SpaceConfigData } from '@/views/SpaceConfig/types' -// 空间列表 +// Workspace list export const getWorkspaces = () => { return request.get('/workspaces') } -// 创建空间 +// Create workspace export const createWorkspace = (values: SpaceModalData) => { return request.post('/workspaces', values) } -// 切换空间 +// Switch workspace export const switchWorkspace = (workspaceId: string) => { return request.put(`/workspaces/${workspaceId}/switch`) } -// 获取空间存储类型 +// Get workspace storage type export const getWorkspaceStorageType = () => { return request.get(`/workspaces/storage`) } -// 获取空间模型配置 +// Get workspace model config export const getWorkspaceModels = () => { return request.get(`/workspaces/workspace_models`) } -// 更新空间模型配置 -export const updateWorkspaceModels = (data: ConfigModalData) => { +// Update workspace model config +export const updateWorkspaceModels = (data: SpaceConfigData) => { return request.put(`/workspaces/workspace_models`, data) } diff --git a/web/src/assets/images/empty/pageEmpty.png b/web/src/assets/images/empty/pageEmpty.png new file mode 100644 index 00000000..f78cc42d Binary files /dev/null and b/web/src/assets/images/empty/pageEmpty.png differ diff --git a/web/src/assets/images/menu/ontology.svg b/web/src/assets/images/menu/ontology.svg new file mode 100644 index 00000000..9bfda42b --- /dev/null +++ b/web/src/assets/images/menu/ontology.svg @@ -0,0 +1,11 @@ + + + 本体管理备份 + + + + + + + + \ No newline at end of file diff --git a/web/src/assets/images/menu/ontology_active.svg b/web/src/assets/images/menu/ontology_active.svg new file mode 100644 index 00000000..1271c2c3 --- /dev/null +++ b/web/src/assets/images/menu/ontology_active.svg @@ -0,0 +1,11 @@ + + + 本体管理 + + + + + + + + \ No newline at end of file diff --git a/web/src/assets/images/menu/prompt.svg b/web/src/assets/images/menu/prompt.svg new file mode 100644 index 00000000..ffef9a34 --- /dev/null +++ b/web/src/assets/images/menu/prompt.svg @@ -0,0 +1,15 @@ + + + 提示词备份 + + + + + + + + + + + + \ No newline at end of file diff --git a/web/src/assets/images/menu/prompt_active.svg b/web/src/assets/images/menu/prompt_active.svg new file mode 100644 index 00000000..ac45e13c --- /dev/null +++ b/web/src/assets/images/menu/prompt_active.svg @@ -0,0 +1,15 @@ + + + 提示词 + + + + + + + + + + + + \ No newline at end of file diff --git a/web/src/assets/images/model/bedrock.svg b/web/src/assets/images/model/bedrock.svg new file mode 100644 index 00000000..6a0235af --- /dev/null +++ b/web/src/assets/images/model/bedrock.svg @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/web/src/assets/images/model/dashscope.png b/web/src/assets/images/model/dashscope.png new file mode 100644 index 00000000..c1aff40e Binary files /dev/null and b/web/src/assets/images/model/dashscope.png differ diff --git a/web/src/assets/images/model/gpustack.png b/web/src/assets/images/model/gpustack.png new file mode 100644 index 00000000..b154821d Binary files /dev/null and b/web/src/assets/images/model/gpustack.png differ diff --git a/web/src/assets/images/model/ollama.svg b/web/src/assets/images/model/ollama.svg new file mode 100644 index 00000000..f8482a96 --- /dev/null +++ b/web/src/assets/images/model/ollama.svg @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/web/src/assets/images/model/openai.svg b/web/src/assets/images/model/openai.svg new file mode 100644 index 00000000..70686f9b --- /dev/null +++ b/web/src/assets/images/model/openai.svg @@ -0,0 +1,4 @@ + + + + diff --git a/web/src/assets/images/model/xinference.svg b/web/src/assets/images/model/xinference.svg new file mode 100644 index 00000000..f5c5f75e --- /dev/null +++ b/web/src/assets/images/model/xinference.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/web/src/assets/images/space/neo4j.png b/web/src/assets/images/space/neo4j.png new file mode 100644 index 00000000..74fc7a86 Binary files /dev/null and b/web/src/assets/images/space/neo4j.png differ diff --git a/web/src/assets/images/space/rag.png b/web/src/assets/images/space/rag.png new file mode 100644 index 00000000..4506efda Binary files /dev/null and b/web/src/assets/images/space/rag.png differ diff --git a/web/src/components/Chat/ChatContent.tsx b/web/src/components/Chat/ChatContent.tsx index c90f9208..a5d02b2b 100644 --- a/web/src/components/Chat/ChatContent.tsx +++ b/web/src/components/Chat/ChatContent.tsx @@ -8,6 +8,7 @@ import { type FC, useRef, useEffect } from 'react' import clsx from 'clsx' import Markdown from '@/components/Markdown' import type { ChatContentProps } from './types' +import { Spin } from 'antd' /** * 聊天内容显示组件 @@ -21,7 +22,8 @@ const ChatContent: FC = ({ empty, labelPosition = 'bottom', labelFormat, - errorDesc + errorDesc, + renderRuntime }) => { // 滚动容器引用，用于控制自动滚动到底部 const scrollContainerRef = useRef<(HTMLDivElement | null)>(null) @@ -45,8 +47,8 @@ const ChatContent: FC = ({ 'rb:left-0 rb:text-left': item.role === 'assistant', // 助手消息左对齐 })}> {/* 流式加载时且内容为空则不显示 */} - {streamLoading && item.content === '' - ? null + {streamLoading && item.content === '' && !renderRuntime + ? : <> {/* 顶部标签（如时间戳、用户名等） */} {labelPosition === 'top' && @@ -55,16 +57,17 @@ const ChatContent: FC = ({ } {/* 消息气泡框 */} -
+ {item.subContent && renderRuntime && renderRuntime(item, index)} {/* 使用Markdown组件渲染消息内容 */} - +
{/* 底部标签（如时间戳、用户名等） */} {labelPosition === 'bottom' && diff --git a/web/src/components/Chat/types.ts b/web/src/components/Chat/types.ts index 851a8ccc..264ce39c 100644 --- a/web/src/components/Chat/types.ts +++ b/web/src/components/Chat/types.ts @@ -19,7 +19,9 @@ export interface ChatItem { /** 消息内容 */ content?: string | null; /** 创建时间 */ - created_at?: number | string + created_at?: number | string; + status?: string; + subContent?: Record[] } /** @@ -81,4 +83,5 @@ export interface ChatContentProps { /** 标签格式化函数 */ labelFormat: (item: ChatItem) => any; errorDesc?: string; + renderRuntime?: (item: ChatItem, index: number) => ReactNode; } \ No newline at end of file diff --git a/web/src/components/CustomSelect/index.tsx b/web/src/components/CustomSelect/index.tsx index 1887d635..f93014c9 100644 --- a/web/src/components/CustomSelect/index.tsx +++ b/web/src/components/CustomSelect/index.tsx @@ -1,4 +1,4 @@ -import { useEffect, useState, type FC, type Key } from 'react'; +import { useEffect, useState, useMemo, type FC, type Key } from 'react'; import { Select } from 'antd'; import type { SelectProps, DefaultOptionType } from 'antd/es/select'; import { useTranslation } from 'react-i18next'; @@ -47,13 +47,14 @@ const CustomSelect: FC = ({ }) => { const { t } = useTranslation(); const [options, setOptions] = useState([]); + const memoizedParams = useMemo(() => params, [JSON.stringify(params)]); useEffect(() => { - request.get>(url, params).then((res) => { + request.get>(url, memoizedParams).then((res) => { const data = Array.isArray(res) ? res : res?.items || []; setOptions(data); }); - }, [url, params]); + }, [url, memoizedParams]); const displayOptions = format ? format(options) : options; diff --git a/web/src/components/Empty/BodyWrapper.tsx b/web/src/components/Empty/BodyWrapper.tsx index f9978184..9cdeb0e8 100644 --- a/web/src/components/Empty/BodyWrapper.tsx +++ b/web/src/components/Empty/BodyWrapper.tsx @@ -1,6 +1,6 @@ import type { FC, ReactNode } from 'react' -import { Skeleton } from 'antd' -import Empty from './index' +import PageEmpty from './PageEmpty' +import PageLoading from './PageLoading' interface BodyWrapperProps { children: ReactNode @@ -9,10 +9,10 @@ interface BodyWrapperProps { } const BodyWrapper: FC = ({ children, loading = false, empty }) => { if (loading) { - return + return } if (!loading && empty) { - return + return } return children } diff --git a/web/src/components/Empty/PageEmpty.tsx b/web/src/components/Empty/PageEmpty.tsx new file mode 100644 index 00000000..17926fde --- /dev/null +++ b/web/src/components/Empty/PageEmpty.tsx @@ -0,0 +1,16 @@ +import { useTranslation } from 'react-i18next' +import pageEmptyIcon from '@/assets/images/empty/pageEmpty.png' +import Empty from './index' +const PageEmpty = ({ size = [240, 210] }: { size?: number | number[] }) => { + const { t } = useTranslation() + return ( + + ) +} +export default PageEmpty; \ No newline at end of file diff --git a/web/src/components/Markdown/CodeBlock.tsx b/web/src/components/Markdown/CodeBlock.tsx index 23d54c34..a125a997 100644 --- a/web/src/components/Markdown/CodeBlock.tsx +++ b/web/src/components/Markdown/CodeBlock.tsx @@ -6,6 +6,9 @@ import CopyBtn from './CopyBtn'; type ICodeBlockProps = { value: string; + needCopy?: boolean; + size?: 'small' | 'default'; + showLineNumbers?: boolean; } // enum languageType { @@ -16,6 +19,9 @@ type ICodeBlockProps = { const CodeBlock: FC = ({ value, + needCopy = true, + size = 'default', + showLineNumbers = false }) => { return ( @@ -23,24 +29,26 @@ const CodeBlock: FC = ({ {value} - + />} ) } diff --git a/web/src/components/Markdown/index.tsx b/web/src/components/Markdown/index.tsx index 58650207..6737f15a 100644 --- a/web/src/components/Markdown/index.tsx +++ b/web/src/components/Markdown/index.tsx @@ -19,6 +19,7 @@ interface RbMarkdownProps { showHtmlComments?: boolean; // 是否显示 HTML 注释，默认为 false（隐藏） editable?: boolean; // 是否可编辑，默认为 false onContentChange?: (content: string) => void; // 内容变化回调 + className?: string; } const components = { @@ -98,6 +99,7 @@ const RbMarkdown: FC = ({ showHtmlComments = false, editable = false, onContentChange, + className }) => { const [editContent, setEditContent] = useState(content) const textareaRef = useRef(null) @@ -162,7 +164,7 @@ const RbMarkdown: FC = ({ // 预览模式 return ( -
+