diff --git a/api/app/cache/__init__.py b/api/app/cache/__init__.py index a79d4cb2..46d1c959 100644 --- a/api/app/cache/__init__.py +++ b/api/app/cache/__init__.py @@ -3,9 +3,10 @@ Cache 缓存模块 提供各种缓存功能的统一入口 """ -from .memory import EmotionMemoryCache, ImplicitMemoryCache +from .memory import EmotionMemoryCache, ImplicitMemoryCache, InterestMemoryCache __all__ = [ "EmotionMemoryCache", "ImplicitMemoryCache", + "InterestMemoryCache", ] diff --git a/api/app/cache/memory/__init__.py b/api/app/cache/memory/__init__.py index 4ada3153..0e21df0f 100644 --- a/api/app/cache/memory/__init__.py +++ b/api/app/cache/memory/__init__.py @@ -5,8 +5,10 @@ Memory 缓存模块 """ from .emotion_memory import EmotionMemoryCache from .implicit_memory import ImplicitMemoryCache +from .interest_memory import InterestMemoryCache __all__ = [ "EmotionMemoryCache", "ImplicitMemoryCache", + "InterestMemoryCache", ] diff --git a/api/app/cache/memory/interest_memory.py b/api/app/cache/memory/interest_memory.py new file mode 100644 index 00000000..108e2a37 --- /dev/null +++ b/api/app/cache/memory/interest_memory.py @@ -0,0 +1,122 @@ +""" +Interest Distribution Cache + +兴趣分布缓存模块 +用于缓存用户的兴趣分布标签数据，避免重复调用模型生成 +""" +import json +import logging +from typing import Optional, List, Dict, Any +from datetime import datetime + +from app.aioRedis import aio_redis + +logger = logging.getLogger(__name__) + +# 缓存过期时间：24小时 +INTEREST_CACHE_EXPIRE = 86400 + + +class InterestMemoryCache: + """兴趣分布缓存类""" + + PREFIX = "cache:memory:interest_distribution" + + @classmethod + def _get_key(cls, end_user_id: str, language: str) -> str: + """生成 Redis key + + Args: + end_user_id: 用户ID + language: 语言类型 + + Returns: + 完整的 Redis key + """ + return f"{cls.PREFIX}:by_user:{end_user_id}:{language}" + + @classmethod + async def set_interest_distribution( + cls, + end_user_id: str, + language: str, + data: List[Dict[str, Any]], + expire: int = INTEREST_CACHE_EXPIRE, + ) -> bool: + """设置用户兴趣分布缓存 + + Args: + end_user_id: 用户ID + language: 语言类型 + data: 兴趣分布列表，格式 [{"name": "...", "frequency": ...}, ...] + expire: 过期时间（秒），默认24小时 + + Returns: + 是否设置成功 + """ + try: + key = cls._get_key(end_user_id, language) + payload = { + "data": data, + "generated_at": datetime.now().isoformat(), + "cached": True, + } + value = json.dumps(payload, ensure_ascii=False) + await aio_redis.set(key, value, ex=expire) + logger.info(f"设置兴趣分布缓存成功: {key}, 过期时间: {expire}秒") + return True + except Exception as e: + logger.error(f"设置兴趣分布缓存失败: {e}", exc_info=True) + return False + + @classmethod + async def get_interest_distribution( + cls, + end_user_id: str, + language: str, + ) -> Optional[List[Dict[str, Any]]]: + """获取用户兴趣分布缓存 + + Args: + end_user_id: 用户ID + language: 语言类型 + + Returns: + 兴趣分布列表，缓存不存在或已过期返回 None + """ + try: + key = cls._get_key(end_user_id, language) + value = await aio_redis.get(key) + if value: + payload = json.loads(value) + logger.info(f"命中兴趣分布缓存: {key}") + return payload.get("data") + logger.info(f"兴趣分布缓存不存在或已过期: {key}") + return None + except Exception as e: + logger.error(f"获取兴趣分布缓存失败: {e}", exc_info=True) + return None + + @classmethod + async def delete_interest_distribution( + cls, + end_user_id: str, + language: str, + ) -> bool: + """删除用户兴趣分布缓存 + + Args: + end_user_id: 用户ID + language: 语言类型 + + Returns: + 是否删除成功 + """ + try: + key = cls._get_key(end_user_id, language) + result = await aio_redis.delete(key) + logger.info(f"删除兴趣分布缓存: {key}, 结果: {result}") + return result > 0 + except Exception as e: + logger.error(f"删除兴趣分布缓存失败: {e}", exc_info=True) + return False diff --git a/api/app/celery_app.py b/api/app/celery_app.py index f422f4a0..ee32715b 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -1,6 +1,7 @@ import os import platform from datetime import timedelta +from celery.schedules import crontab from urllib.parse import quote from celery import Celery @@ -43,8 +44,8 @@ celery_app.conf.update( task_ignore_result=False, # 超时设置 - task_time_limit=1800, # 30分钟硬超时 - task_soft_time_limit=1500, # 25分钟软超时 + task_time_limit=3600, # 60分钟硬超时 + task_soft_time_limit=3000, # 50分钟软超时 # Worker 设置 (per-worker settings are in docker-compose command line) worker_prefetch_multiplier=1, # Don't hoard tasks, fairer distribution @@ -90,11 +91,10 @@ 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_increment_schedule = crontab(hour=settings.MEMORY_INCREMENT_HOUR, minute=settings.MEMORY_INCREMENT_MINUTE) memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS) -# 这个30秒的设计不合理 -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 +workspace_reflection_schedule = timedelta(seconds=settings.WORKSPACE_REFLECTION_INTERVAL_SECONDS) +forgetting_cycle_schedule = timedelta(hours=settings.FORGETTING_CYCLE_INTERVAL_HOURS) #构建定时任务配置 beat_schedule_config = { diff --git a/api/app/controllers/chunk_controller.py b/api/app/controllers/chunk_controller.py index 620d8a1a..988aa706 100644 --- a/api/app/controllers/chunk_controller.py +++ b/api/app/controllers/chunk_controller.py @@ -441,14 +441,14 @@ async def retrieve_chunks( # 1 participle search, 2 semantic search, 3 hybrid search match retrieve_data.retrieve_type: case chunk_schema.RetrieveType.PARTICIPLE: - rs = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold) + rs = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold, file_names_filter=retrieve_data.file_names_filter) return success(data=rs, msg="retrieval successful") case chunk_schema.RetrieveType.SEMANTIC: - rs = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight) + rs = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight, file_names_filter=retrieve_data.file_names_filter) return success(data=rs, msg="retrieval successful") case _: - rs1 = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight) - rs2 = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold) + rs1 = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight, file_names_filter=retrieve_data.file_names_filter) + rs2 = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold, file_names_filter=retrieve_data.file_names_filter) # Efficient deduplication seen_ids = set() unique_rs = [] diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index b88e65ff..ccf93d68 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -1,5 +1,6 @@ from typing import List, Optional +from app.cache.memory.interest_memory import InterestMemoryCache from app.celery_app import celery_app from app.core.error_codes import BizCode from app.core.language_utils import get_language_from_header @@ -661,34 +662,56 @@ async def get_knowledge_type_stats_api( return fail(BizCode.INTERNAL_ERROR, "获取知识库类型统计失败", str(e)) -@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse) -async def get_hot_memory_tags_by_user_api( - end_user_id: Optional[str] = Query(None, description="用户ID（可选）"), - limit: int = Query(20, description="返回标签数量限制"), +@router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse) +async def get_interest_distribution_by_user_api( + end_user_id: str = Query(..., description="用户ID（必填）"), + limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"), + language_type: str = Header(default=None, alias="X-Language-Type"), current_user: User = Depends(get_current_user), - db: Session=Depends(get_db), + db: Session = Depends(get_db), ): """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签 - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习、创作等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] """ - api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}") + language = get_language_from_header(language_type) + api_logger.info(f"Interest distribution by user requested: end_user_id={end_user_id}, language={language}") try: - result = await memory_agent_service.get_hot_memory_tags_by_user( + # 优先读取缓存 + cached = await InterestMemoryCache.get_interest_distribution( end_user_id=end_user_id, - limit=limit + language=language, ) - return success(data=result, msg="获取热门记忆标签成功") + if cached is not None: + api_logger.info(f"Interest distribution cache hit: end_user_id={end_user_id}") + return success(data=cached, msg="获取兴趣分布标签成功") + + # 缓存未命中，调用模型生成 + result = await memory_agent_service.get_interest_distribution_by_user( + end_user_id=end_user_id, + limit=limit, + language=language + ) + + # 写入缓存，24小时过期 + await InterestMemoryCache.set_interest_distribution( + end_user_id=end_user_id, + language=language, + data=result, + ) + + return success(data=result, msg="获取兴趣分布标签成功") except Exception as e: - api_logger.error(f"Hot memory tags by user failed: {str(e)}") - return fail(BizCode.INTERNAL_ERROR, "获取热门记忆标签失败", str(e)) + api_logger.error(f"Interest distribution by user failed: {str(e)}") + return fail(BizCode.INTERNAL_ERROR, "获取兴趣分布标签失败", str(e)) @router.get("/analytics/user_profile", response_model=ApiResponse) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 475d184e..1b5b45fb 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -606,8 +606,8 @@ async def dashboard_data( # 获取RAG相关数据 try: - # total_memory: 使用 total_chunk（总chunk数） - total_chunk = memory_dashboard_service.get_rag_total_chunk(db, current_user) + # total_memory: 只统计用户知识库（permission_id='Memory'）的chunk数 + total_chunk = memory_dashboard_service.get_rag_user_kb_total_chunk(db, current_user) rag_data["total_memory"] = total_chunk # total_app: 统计当前空间下的所有app数量 diff --git a/api/app/controllers/service/app_api_controller.py b/api/app/controllers/service/app_api_controller.py index 61a919b1..64143f57 100644 --- a/api/app/controllers/service/app_api_controller.py +++ b/api/app/controllers/service/app_api_controller.py @@ -249,6 +249,7 @@ async def chat( app_id=app.id, workspace_id=workspace_id, release_id=app.current_release.id, + public=True ): event_type = event.get("event", "message") event_data = event.get("data", {}) diff --git a/api/app/controllers/service/memory_api_controller.py b/api/app/controllers/service/memory_api_controller.py index accd749e..34489e8a 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}, tenant_id: {api_key_auth.tenant_id}") + logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, workspace_id: {api_key_auth.workspace_id}") memory_api_service = MemoryAPIService(db) diff --git a/api/app/core/config.py b/api/app/core/config.py index 6a2cf206..62ff5c37 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -1,9 +1,10 @@ import json import os from pathlib import Path -from typing import Any, Dict, Optional +from typing import Annotated, Any, Dict, Optional from dotenv import load_dotenv +from pydantic import Field, TypeAdapter load_dotenv() @@ -200,12 +201,25 @@ class Settings: REFLECTION_INTERVAL_SECONDS: float = float(os.getenv("REFLECTION_INTERVAL_SECONDS", "300")) HEALTH_CHECK_SECONDS: float = float(os.getenv("HEALTH_CHECK_SECONDS", "600")) - MEMORY_INCREMENT_INTERVAL_HOURS: float = float(os.getenv("MEMORY_INCREMENT_INTERVAL_HOURS", "24")) REFLECTION_INTERVAL_TIME: Optional[str] = int(os.getenv("REFLECTION_INTERVAL_TIME", 30)) # Memory Cache Regeneration Configuration MEMORY_CACHE_REGENERATION_HOURS: int = int(os.getenv("MEMORY_CACHE_REGENERATION_HOURS", "24")) + # Celery Beat Schedule Configuration (定时任务执行频率) + MEMORY_INCREMENT_HOUR: int = TypeAdapter( + Annotated[int, Field(ge=0, le=23, description="cron hour [0, 23]")] + ).validate_python(int(os.getenv("MEMORY_INCREMENT_HOUR", "2"))) + MEMORY_INCREMENT_MINUTE: int = TypeAdapter( + Annotated[int, Field(ge=0, le=59, description="cron minute [0, 59]")] + ).validate_python(int(os.getenv("MEMORY_INCREMENT_MINUTE", "0"))) + WORKSPACE_REFLECTION_INTERVAL_SECONDS: int = TypeAdapter( + Annotated[int, Field(ge=1, description="reflection interval in seconds, must be >= 1")] + ).validate_python(int(os.getenv("WORKSPACE_REFLECTION_INTERVAL_SECONDS", "30"))) + FORGETTING_CYCLE_INTERVAL_HOURS: int = TypeAdapter( + Annotated[int, Field(ge=1, description="forgetting cycle interval in hours, must be >= 1")] + ).validate_python(int(os.getenv("FORGETTING_CYCLE_INTERVAL_HOURS", "24"))) + # Memory Module Configuration (internal) MEMORY_OUTPUT_DIR: str = os.getenv("MEMORY_OUTPUT_DIR", "logs/memory-output") MEMORY_CONFIG_DIR: str = os.getenv("MEMORY_CONFIG_DIR", "app/core/memory") @@ -230,7 +244,7 @@ class Settings: # General Ontology Type Configuration # ======================================================================== # 通用本体文件路径列表（逗号分隔） - GENERAL_ONTOLOGY_FILES: str = os.getenv("GENERAL_ONTOLOGY_FILES", "app/core/memory/ontology_services/General_purpose_entity.ttl") + GENERAL_ONTOLOGY_FILES: str = os.getenv("GENERAL_ONTOLOGY_FILES", "api/app/core/memory/ontology_services/General_purpose_entity.ttl") # 是否启用通用本体类型功能 ENABLE_GENERAL_ONTOLOGY_TYPES: bool = os.getenv("ENABLE_GENERAL_ONTOLOGY_TYPES", "true").lower() == "true" diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index abb0f138..6afcec6d 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -1,9 +1,12 @@ import asyncio import json +import logging import os from typing import List, Tuple from app.core.config import settings + +logger = logging.getLogger(__name__) 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 @@ -16,6 +19,10 @@ 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。") +class InterestTags(BaseModel): + """用于接收LLM筛选后的兴趣活动标签列表的模型。""" + interest_tags: List[str] = Field(..., description="从原始列表中筛选出的代表用户兴趣活动的标签列表。") + 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。 @@ -85,10 +92,74 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: return structured_response.meaningful_tags except Exception as e: - print(f"LLM筛选过程中发生错误: {e}") + logger.error(f"LLM筛选过程中发生错误: {e}", exc_info=True) # 在LLM失败时返回原始标签，确保流程继续 return tags +async def filter_interests_with_llm(tags: List[str], end_user_id: str, language: str = "zh") -> List[str]: + """ + 使用LLM从标签列表中筛选出代表用户兴趣活动的标签。 + + 与 filter_tags_with_llm 不同，此函数专注于识别"活动/行为"类兴趣， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 + + Args: + tags: 原始标签列表 + end_user_id: 用户ID，用于获取LLM配置 + + Returns: + 筛选后的兴趣活动标签列表 + """ + try: + with get_db_context() as db: + 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") + workspace_id = connected_config.get("workspace_id") + + if not config_id and not workspace_id: + raise ValueError( + f"No memory_config_id found for end_user_id: {end_user_id}." + ) + + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + + if not memory_config.llm_model_id: + raise ValueError( + f"No llm_model_id found in memory config {config_id}." + ) + + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(memory_config.llm_model_id) + + tag_list_str = ", ".join(tags) + from app.core.memory.utils.prompt.prompt_utils import render_interest_filter_prompt + rendered_prompt = render_interest_filter_prompt(tag_list_str, language=language) + messages = [ + { + "role": "user", + "content": rendered_prompt + } + ] + + structured_response = await llm_client.response_structured( + messages=messages, + response_model=InterestTags + ) + + return structured_response.interest_tags + + except Exception as e: + logger.error(f"兴趣标签LLM筛选过程中发生错误: {e}", exc_info=True) + return tags + + async def get_raw_tags_from_db( connector: Neo4jConnector, end_user_id: str, @@ -183,3 +254,56 @@ async def get_hot_memory_tags(end_user_id: str, limit: int = 10, by_user: bool = finally: # 确保关闭连接 await connector.close() + +async def get_interest_distribution(end_user_id: str, limit: int = 10, by_user: bool = False, language: str = "zh") -> List[Tuple[str, int]]: + """ + 获取用户的兴趣分布标签。 + + 与 get_hot_memory_tags 不同，此函数使用专门针对"活动/行为"的LLM prompt， + 过滤掉纯物品、工具、地点等，只保留能代表用户兴趣爱好的活动类标签。 + + Args: + 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: 最终返回的标签数量限制（默认10） + by_user: 是否按user_id查询（默认False，按end_user_id查询） + + Raises: + ValueError: 如果end_user_id未提供或为空 + """ + if not end_user_id or not end_user_id.strip(): + raise ValueError( + "end_user_id is required. Please provide a valid end_user_id or user_id." + ) + + connector = Neo4jConnector() + try: + # 查询更多原始标签，给LLM提供充足上下文 + query_limit = 40 + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, query_limit, by_user=by_user) + if not raw_tags_with_freq: + return [] + + raw_tag_names = [tag for tag, freq in raw_tags_with_freq] + raw_freq_map = {tag: freq for tag, freq in raw_tags_with_freq} + + # 使用兴趣活动专用prompt进行筛选（支持语义推断出新标签） + interest_tag_names = await filter_interests_with_llm(raw_tag_names, end_user_id, language=language) + + # 构建最终标签列表： + # - 原始标签中存在的，保留原始频率 + # - LLM推断出的新标签（不在原始列表中），赋予默认频率1 + final_tags = [] + seen = set() + for tag in interest_tag_names: + if tag in seen: + continue + seen.add(tag) + freq = raw_freq_map.get(tag, 1) + final_tags.append((tag, freq)) + + # 按频率降序排列 + final_tags.sort(key=lambda x: x[1], reverse=True) + + return final_tags[:limit] + finally: + await connector.close() diff --git a/api/app/core/memory/utils/prompt/prompt_utils.py b/api/app/core/memory/utils/prompt/prompt_utils.py index d88f50cf..0cea98f2 100644 --- a/api/app/core/memory/utils/prompt/prompt_utils.py +++ b/api/app/core/memory/utils/prompt/prompt_utils.py @@ -548,3 +548,20 @@ async def render_ontology_extraction_prompt( }) return rendered_prompt + + +def render_interest_filter_prompt(tag_list: str, language: str = "zh") -> str: + """ + Renders the interest filter prompt using the interest_filter.jinja2 template. + + Args: + tag_list: Comma-separated string of raw tags to filter + language: Output language ("zh" for Chinese, "en" for English) + + Returns: + Rendered prompt content as string + """ + template = prompt_env.get_template("interest_filter.jinja2") + rendered_prompt = template.render(tag_list=tag_list, language=language) + log_prompt_rendering('interest filter', rendered_prompt) + return rendered_prompt diff --git a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 new file mode 100644 index 00000000..7957bf1c --- /dev/null +++ b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 @@ -0,0 +1,67 @@ +{% if language == "zh" %} +You are a user interest analysis expert. Your task is to infer and extract the user's core hobby/interest activities from a tag list. The tags may be specific project names, tool names, or compound nouns — your job is to identify the underlying interest they represent. + +**Step 1 - Infer the underlying interest from each tag**: +Look at each tag and ask: "What hobby or interest does this tag suggest the user has?" + +Examples of inference: +- '攀岩', '室内攀岩馆', '攀岩者数据仪表盘', '路线解锁地图', '指力', '路线等级', '当日攀岩流畅度' → '攀岩' +- '风光摄影元数据增强器', 'EXIF数据', '.CR2文件', '.NEF文件', '日出拍摄点', '曝光补偿', '光圈', '太阳高度角', '云量预测图层' → '摄影' +- '晨间冥想坚持天数', '身心协同峰值' → '冥想' +- '川味可视化', '川菜' → '烹饪' +- '开源项目命名建议', 'climbviz', '可视化', '力量增长雷达图' → '编程' 或 '数据可视化' +- '吉他', '指弹', '琴谱' → '吉他' +- '跑步', '5公里', '跑鞋' → '跑步' +- '瑜伽垫', '瑜伽课' → '瑜伽' + +**Step 2 - Consolidate and deduplicate**: +- Merge tags that point to the same interest into one representative label +- Use concise, standard hobby names (e.g., '攀岩', '摄影', '编程', '烹饪', '冥想', '吉他', '跑步') +- If multiple tags all point to '攀岩', output '攀岩' only once + +**Step 3 - Filter out non-interest tags**: +Remove tags that do NOT suggest any hobby or interest: +- Generic system/assistant terms (e.g., '助手', '用户', 'AI') +- Pure abstract metrics with no clear hobby link (e.g., '完成时间', '日期', '自我评分') +- Location names with no clear hobby link (e.g., '青城山后山' alone — but if combined with photography context, infer '摄影') + +**Output format**: Return a list of concise interest activity names in Chinese. + +**Example**: +Input: ['攀岩', '攀岩者数据仪表盘', '路线解锁地图', '指力', '风光摄影元数据增强器', 'EXIF数据', '晨间冥想坚持天数', '川味可视化', '可视化', '助手', '完成时间'] +Output: ['攀岩', '摄影', '冥想', '烹饪', '编程'] + +Now process the following tag list and return the inferred interest activities in Chinese: {{ tag_list }} +{% else %} +You are a user interest analysis expert. Your task is to infer and extract the user's core hobby/interest activities from a tag list. The tags may be specific project names, tool names, or compound nouns — your job is to identify the underlying interest they represent. + +**Step 1 - Infer the underlying interest from each tag**: +Look at each tag and ask: "What hobby or interest does this tag suggest the user has?" + +Examples of inference: +- 'rock climbing', 'indoor climbing gym', 'climber dashboard', 'route map', 'finger strength' → 'rock climbing' +- 'landscape photography metadata enhancer', 'EXIF data', 'sunrise shooting spot', 'exposure compensation' → 'photography' +- 'morning meditation streak', 'mind-body peak' → 'meditation' +- 'Sichuan cuisine visualization', 'Sichuan food' → 'cooking' +- 'open source project', 'data visualization tool', 'Python' → 'programming' +- 'guitar', 'fingerpicking', 'sheet music' → 'guitar' +- 'running', '5km', 'running shoes' → 'running' + +**Step 2 - Consolidate and deduplicate**: +- Merge tags that point to the same interest into one representative label +- Use concise, standard hobby names (e.g., 'rock climbing', 'photography', 'programming', 'cooking', 'meditation') +- If multiple tags all point to 'rock climbing', output 'rock climbing' only once + +**Step 3 - Filter out non-interest tags**: +Remove tags that do NOT suggest any hobby or interest: +- Generic system/assistant terms (e.g., 'assistant', 'user', 'AI') +- Pure abstract metrics with no clear hobby link (e.g., 'completion time', 'date', 'self-rating') + +**Output format**: Return a list of concise interest activity names in English. + +**Example**: +Input: ['rock climbing', 'climber dashboard', 'route map', 'finger strength', 'landscape photography metadata enhancer', 'EXIF data', 'morning meditation streak', 'Sichuan cuisine visualization', 'visualization', 'assistant', 'completion time'] +Output: ['rock climbing', 'photography', 'meditation', 'cooking', 'programming'] + +Now process the following tag list and return the inferred interest activities in English: {{ tag_list }} +{% endif %} diff --git a/api/app/core/workflow/engine/event_stream_handler.py b/api/app/core/workflow/engine/event_stream_handler.py index 5b7d8de2..dc3cd04d 100644 --- a/api/app/core/workflow/engine/event_stream_handler.py +++ b/api/app/core/workflow/engine/event_stream_handler.py @@ -127,7 +127,7 @@ class EventStreamHandler: yield { "event": "message", "data": { - "chunk": data.get("chunk") + "content": data.get("chunk") } } diff --git a/api/app/core/workflow/engine/stream_output_coordinator.py b/api/app/core/workflow/engine/stream_output_coordinator.py index ba6af156..c2885ab0 100644 --- a/api/app/core/workflow/engine/stream_output_coordinator.py +++ b/api/app/core/workflow/engine/stream_output_coordinator.py @@ -274,7 +274,7 @@ class StreamOutputCoordinator: yield { "event": "message", "data": { - "chunk": final_chunk + "content": final_chunk } } diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index e781b6c4..78149e4c 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -272,7 +272,7 @@ class WorkflowExecutor: event_type = data.get("type", "node_chunk") # "message" or "node_chunk" if event_type == "node_chunk": async for msg_event in self.event_handler.handle_node_chunk_event(data): - full_content += msg_event["data"]["chunk"] + full_content += msg_event["data"]["content"] yield msg_event elif event_type == "node_error": @@ -295,12 +295,12 @@ class WorkflowExecutor: self.graph, self.execution_context.checkpoint_config ): - full_content += msg_event["data"]['chunk'] + full_content += msg_event["data"]['content'] yield msg_event # Flush any remaining chunks async for msg_event in self.stream_coordinator.flush_remaining_chunk(self.variable_pool): - full_content += msg_event["data"]['chunk'] + full_content += msg_event["data"]['content'] yield msg_event result = graph.get_state(self.execution_context.checkpoint_config).values diff --git a/api/app/repositories/knowledge_repository.py b/api/app/repositories/knowledge_repository.py index 681d1c10..e3832214 100644 --- a/api/app/repositories/knowledge_repository.py +++ b/api/app/repositories/knowledge_repository.py @@ -211,3 +211,46 @@ def get_total_kb_count_by_workspace(db: Session, workspace_id: uuid.UUID) -> int except Exception as e: db_logger.error(f"Failed to query total knowledge base count: workspace_id={workspace_id} - {str(e)}") raise + + +def get_user_kb_chunk_num_by_workspace(db: Session, workspace_id: uuid.UUID) -> int: + """ + 根据workspace_id查询knowledges表中permission_id='Memory'（用户知识库）的chunk_num总和 + """ + db_logger.debug(f"Query user KB chunk_num by workspace_id: workspace_id={workspace_id}") + + try: + from sqlalchemy import func + result = db.query(func.sum(Knowledge.chunk_num)).filter( + Knowledge.workspace_id == workspace_id, + Knowledge.status == 1, + Knowledge.permission_id == "Memory" + ).scalar() + + total = result if result is not None else 0 + db_logger.info(f"User KB chunk_num query successful: workspace_id={workspace_id}, total={total}") + return total + except Exception as e: + db_logger.error(f"Failed to query user KB chunk_num: workspace_id={workspace_id} - {str(e)}") + raise + + +def get_non_user_kb_count_by_workspace(db: Session, workspace_id: uuid.UUID) -> int: + """ + 根据workspace_id查询knowledges表中排除用户知识库（permission_id!='Memory'）的数量 + """ + db_logger.debug(f"Query non-user KB count by workspace_id: workspace_id={workspace_id}") + + try: + count = db.query(Knowledge).filter( + Knowledge.workspace_id == workspace_id, + Knowledge.status == 1, + Knowledge.permission_id != "Memory" + ).count() + + db_logger.info(f"Non-user KB count query successful: workspace_id={workspace_id}, count={count}") + return count + except Exception as e: + db_logger.error(f"Failed to query non-user KB count: workspace_id={workspace_id} - {str(e)}") + raise + diff --git a/api/app/schemas/chunk_schema.py b/api/app/schemas/chunk_schema.py index cef9b9cb..ce8f70f2 100644 --- a/api/app/schemas/chunk_schema.py +++ b/api/app/schemas/chunk_schema.py @@ -46,6 +46,7 @@ class ChunkUpdate(BaseModel): class ChunkRetrieve(BaseModel): query: str kb_ids: list[uuid.UUID] + file_names_filter: list[str] | None = Field(None) similarity_threshold: float | None = Field(None) vector_similarity_weight: float | None = Field(None) top_k: int | None = Field(None) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 1f3667a6..16aee283 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -36,7 +36,7 @@ from app.core.memory.agent.utils.messages_tools import ( ) from app.core.memory.agent.utils.type_classifier import status_typle from app.core.memory.agent.utils.write_tools import write # 新增：直接导入 write 函数 -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_interest_distribution 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 @@ -890,36 +890,36 @@ class MemoryAgentService: return result - async def get_hot_memory_tags_by_user( + + async def get_interest_distribution_by_user( self, end_user_id: Optional[str] = None, - limit: int = 20 + limit: int = 5, + language: str = "zh" ) -> List[Dict[str, Any]]: """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签。 + + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 + - end_user_id: 用户ID（必填） - limit: 返回标签数量限制 + - language: 输出语言（"zh" 中文, "en" 英文） 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] - - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 """ try: - # 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: - payload.append({"name": tag, "frequency": freq}) - return payload + tags = await get_interest_distribution(end_user_id, limit=limit, by_user=False, language=language) + return [{"name": tag, "frequency": freq} for tag, freq in tags] except Exception as e: - logger.error(f"热门记忆标签查询失败: {e}") - raise Exception(f"热门记忆标签查询失败: {e}") + logger.error(f"兴趣分布标签查询失败: {e}") + raise Exception(f"兴趣分布标签查询失败: {e}") async def get_user_profile( diff --git a/api/app/services/memory_api_service.py b/api/app/services/memory_api_service.py index a8c39a5a..f86fbed8 100644 --- a/api/app/services/memory_api_service.py +++ b/api/app/services/memory_api_service.py @@ -140,9 +140,11 @@ class MemoryAPIService: try: # Delegate to MemoryAgentService + # Convert string message to list[dict] format expected by MemoryAgentService + messages = message if isinstance(message, list) else [{"role": "user", "content": message}] result = await MemoryAgentService().write_memory( end_user_id=end_user_id, - messages=message, + messages=messages, config_id=config_id, db=self.db, storage_type=storage_type, @@ -151,9 +153,18 @@ class MemoryAPIService: logger.info(f"Memory write successful for end_user: {end_user_id}") + # result may be a string "success" or a dict with a "status" key + # Preserve the full dict so callers don't silently lose extra fields + # (e.g. error codes, metadata) returned by MemoryAgentService. + if isinstance(result, dict): + return { + **result, + "status": result.get("status", "unknown"), + "end_user_id": end_user_id, + } return { - "status": "success" if result == "success" else result, - "end_user_id": end_user_id + "status": result if isinstance(result, str) else "success", + "end_user_id": end_user_id, } except ConfigurationError as e: diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index 8d6071cc..05aed57e 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -390,19 +390,59 @@ def get_rag_total_kb( current_user: User ) -> int: """ - 根据当前用户所在的workspace_id查询konwledges表所有不同id的数量 + 根据当前用户所在的workspace_id查询konwledges表中排除用户知识库（permission_id!='Memory'）的数量 """ workspace_id = current_user.current_workspace_id - business_logger.info(f"获取RAG总知识库数: workspace_id={workspace_id}, 操作者: {current_user.username}") + business_logger.info(f"获取RAG总知识库数(排除用户知识库): workspace_id={workspace_id}, 操作者: {current_user.username}") try: - total_kb = knowledge_repository.get_total_kb_count_by_workspace(db, workspace_id) + total_kb = knowledge_repository.get_non_user_kb_count_by_workspace(db, workspace_id) business_logger.info(f"成功获取RAG总知识库数: {total_kb}") return total_kb except Exception as e: business_logger.error(f"获取RAG总知识库数失败: workspace_id={workspace_id} - {str(e)}") raise + +def get_rag_user_kb_total_chunk( + db: Session, + current_user: User +) -> int: + """ + 根据当前用户所在的workspace_id，从documents表统计所有用户知识库的chunk总数。 + 与 /end_users 接口保持同源：查询 file_name 匹配 end_user_id.txt 的文档 chunk_num 之和。 + """ + workspace_id = current_user.current_workspace_id + business_logger.info(f"获取用户知识库总chunk数(documents表): workspace_id={workspace_id}, 操作者: {current_user.username}") + + try: + from app.models.document_model import Document + from app.models.end_user_model import EndUser + from app.models.app_model import App + from sqlalchemy import func + + # 通过 App 关联取该 workspace 下所有 end_user_id + end_user_ids = [ + str(eid) for (eid,) in db.query(EndUser.id) + .join(App, EndUser.app_id == App.id) + .filter(App.workspace_id == workspace_id) + .all() + ] + if not end_user_ids: + return 0 + + file_names = [f"{uid}.txt" for uid in end_user_ids] + result = db.query(func.sum(Document.chunk_num)).filter( + Document.file_name.in_(file_names) + ).scalar() + + total_chunk = int(result or 0) + business_logger.info(f"成功获取用户知识库总chunk数: {total_chunk}") + return total_chunk + except Exception as e: + business_logger.error(f"获取用户知识库总chunk数失败: workspace_id={workspace_id} - {str(e)}") + raise + def get_current_user_total_chunk( end_user_id: str, db: Session, diff --git a/api/app/services/workflow_service.py b/api/app/services/workflow_service.py index a388ca75..02819efb 100644 --- a/api/app/services/workflow_service.py +++ b/api/app/services/workflow_service.py @@ -13,6 +13,7 @@ from sqlalchemy.orm import Session from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.workflow.adapters.registry import PlatformAdapterRegistry +from app.core.workflow.executor import execute_workflow, execute_workflow_stream from app.core.workflow.nodes.enums import NodeType from app.core.workflow.validator import validate_workflow_config from app.db import get_db @@ -23,7 +24,7 @@ from app.repositories.workflow_repository import ( WorkflowExecutionRepository, WorkflowNodeExecutionRepository ) -from app.schemas import DraftRunRequest +from app.schemas import DraftRunRequest, FileInput from app.services.conversation_service import ConversationService from app.services.multi_agent_service import convert_uuids_to_str from app.services.multimodal_service import MultimodalService @@ -445,6 +446,91 @@ class WorkflowService: "success_rate": completed / total if total > 0 else 0 } + async def _handle_file_input(self, files: list[FileInput]): + if not files: + return [] + + files_struct = [] + for file in files: + files_struct.append( + { + "type": file.type, + "url": await self.multimodal_service.get_file_url(file), + "__file": True + } + ) + return files_struct + + @staticmethod + def _map_public_event(event: dict) -> dict | None: + """ + Map internal workflow events to public-facing event formats. + + Purpose: + - Hide internal execution details + - Expose a stable and simplified public event schema + - Filter out non-public events + - Maintain backward compatibility when possible + + Args: + event (dict): Internal event object, e.g.: + { + "event": "workflow_start", + "data": {...} + } + + Returns: + dict | None: + - Returns the mapped public event + - Returns None if the event should not be exposed + """ + event_type = event.get("event") + payload = event.get("data") + match event_type: + case "workflow_start": + return { + "event": "start", + "data": { + "conversation_id": payload.get("conversation_id"), + } + } + case "workflow_end": + return { + "event": "end", + "data": { + "elapsed_time": payload.get("elapsed_time"), + "message_length": len(payload.get("output", "")), + "error": payload.get("error", "") + } + } + case "node_start" | "node_end" | "node_error" | "cycle_item": + return None + case _: + return event + + def _emit(self, public: bool, internal_event: dict): + """ + Unified event emission entry. + + Args: + public (bool): + - True -> Emit mapped public event + - False -> Emit raw internal event + + internal_event (dict): + The original internal event object + + Returns: + dict | None: + - The mapped event + - Or None if the event is filtered out + """ + if public: + mapped = self._map_public_event(internal_event) + else: + mapped = internal_event + return mapped + # ==================== 工作流执行 ==================== async def run( @@ -479,10 +565,11 @@ class WorkflowService: message=f"工作流配置不存在: app_id={app_id}" ) - input_data = {"message": payload.message, "variables": payload.variables, - "conversation_id": payload.conversation_id, - "files": [file.model_dump(mode='json') for file in payload.files] - } + input_data = { + "message": payload.message, "variables": payload.variables, + "conversation_id": payload.conversation_id, + "files": [file.model_dump(mode='json') for file in payload.files] + } # 转换 conversation_id 为 UUID conversation_id_uuid = uuid.UUID(payload.conversation_id) if payload.conversation_id else None @@ -506,22 +593,8 @@ class WorkflowService: "execution_config": config.execution_config } - # 4. 获取工作空间 ID（从 app 获取） - - # 5. 执行工作流 - from app.core.workflow.executor import execute_workflow - try: - files = [] - if payload.files: - for file in payload.files: - files.append( - { - "type": file.type, - "url": await self.multimodal_service.get_file_url(file), - "__file": True - } - ) + files = await self._handle_file_input(payload.files) input_data["files"] = files # 更新状态为运行中 self.update_execution_status(execution.execution_id, "running") @@ -601,42 +674,6 @@ class WorkflowService: message=f"工作流执行失败: {str(e)}" ) - @staticmethod - def _map_public_event(event: dict) -> dict | None: - event_type = event.get("event") - payload = event.get("data") - match event_type: - case "workflow_start": - return { - "event": "start", - "data": { - "conversation_id": payload.get("conversation_id"), - } - } - case "workflow_end": - return { - "event": "end", - "data": { - "elapsed_time": payload.get("elapsed_time"), - "message_length": len(payload.get("output", "")), - "error": payload.get("error", "") - } - } - case "node_start" | "node_end" | "node_error" | "cycle_item": - return None - case _: - return event - - def _emit(self, public: bool, internal_event: dict): - """ - decide - """ - if public: - mapped = self._map_public_event(internal_event) - else: - mapped = internal_event - return mapped - async def run_stream( self, app_id: uuid.UUID, @@ -671,10 +708,11 @@ class WorkflowService: message=f"工作流配置不存在: app_id={app_id}" ) - input_data = {"message": payload.message, "variables": payload.variables, - "conversation_id": payload.conversation_id, - "files": [file.model_dump(mode='json') for file in payload.files] - } + input_data = { + "message": payload.message, "variables": payload.variables, + "conversation_id": payload.conversation_id, + "files": [file.model_dump(mode='json') for file in payload.files] + } # 转换 conversation_id 为 UUID conversation_id_uuid = uuid.UUID(payload.conversation_id) if payload.conversation_id else None @@ -699,16 +737,7 @@ class WorkflowService: } try: - files = [] - if payload.files: - for file in payload.files: - files.append( - { - "type": file.type, - "url": await self.multimodal_service.get_file_url(file), - "__file": True - } - ) + files = await self._handle_file_input(payload.files) input_data["files"] = files self.update_execution_status(execution.execution_id, "running") executions = self.execution_repo.get_by_conversation_id(conversation_id=conversation_id_uuid) @@ -723,7 +752,6 @@ class WorkflowService: input_data["conv_messages"] = last_state.get("messages") or [] break init_message_length = len(input_data.get("conv_messages", [])) - from app.core.workflow.executor import execute_workflow_stream async for event in execute_workflow_stream( workflow_config=workflow_config_dict, @@ -789,37 +817,6 @@ class WorkflowService: return node.get("config", {}).get("variables", []) raise BusinessException("workflow config error - start node not found") - def _clean_event_for_json(self, event: dict[str, Any]) -> dict[str, Any]: - """清理事件数据，移除不可序列化的对象 - - Args: - event: 原始事件数据 - - Returns: - 可序列化的事件数据 - """ - from langchain_core.messages import BaseMessage - - def clean_value(value): - """递归清理值""" - if isinstance(value, BaseMessage): - # 将 Message 对象转换为字典 - return { - "type": value.__class__.__name__, - "content": value.content, - } - elif isinstance(value, dict): - return {k: clean_value(v) for k, v in value.items()} - elif isinstance(value, list): - return [clean_value(item) for item in value] - elif isinstance(value, (str, int, float, bool, type(None))): - return value - else: - # 其他不可序列化的对象转换为字符串 - return str(value) - - return clean_value(event) - # ==================== 依赖注入函数 ==================== diff --git a/api/app/tasks.py b/api/app/tasks.py index 299d188b..093f081f 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -257,7 +257,7 @@ def parse_document(file_path: str, document_id: uuid.UUID): progress_msg += f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task result for task {task}:\n{result}\n" return result - try: + def sync_task(): trio.run( lambda: _run( row=task, @@ -272,6 +272,10 @@ def parse_document(file_path: str, document_id: uuid.UUID): with_community=with_community, ) ) + try: + with ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(sync_task) + future.result() # Blocks until the task completes except Exception as e: progress_msg += f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task failed for task {task}:\n{str(e)}\n" progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Knowledge Graph done ({time.time() - start_time}s)" diff --git a/api/env.example b/api/env.example index d67bbf7c..1dc4536c 100644 --- a/api/env.example +++ b/api/env.example @@ -139,7 +139,7 @@ SMTP_USER= SMTP_PASSWORD= # 本体类型融合配置 (记得写入env_example) -GENERAL_ONTOLOGY_FILES=app/core/memory/ontology_services/General_purpose_entity.ttl # 指定要加载的本体文件路径，多个文件用逗号分隔 +GENERAL_ONTOLOGY_FILES=api/app/core/memory/ontology_services/General_purpose_entity.ttl # 指定要加载的本体文件路径，多个文件用逗号分隔 ENABLE_GENERAL_ONTOLOGY_TYPES=true # 总开关，控制是否启用通用本体类型融合功能(false = 不使用任何本体类型指导) MAX_ONTOLOGY_TYPES_IN_PROMPT=100 # 限制传给 LLM 的类型数量，防止 Prompt 过长 CORE_GENERAL_TYPES=Person,Organization,Place,Event,Work,Concept # 定义核心类型列表，这些类型会优先包含在合并结果中 diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index fcd016c2..7782c2ce 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -456,6 +456,7 @@ export const en = { logoTip: `Supported image formats: JPG, PNG \n Suggested size: square ratio \n Maximum size: ≤ 2MB`, imageSquareRequired: 'Please upload a square image', nameInvalid: 'Name cannot start or end with a space', + notAllSpaces: 'Cannot be all spaces', }, model: { searchPlaceholder: 'search model…', @@ -1782,6 +1783,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re mcp: 'MCP Services', inner: 'Built-in Tools', custom: 'Custom Tools', + market: 'Tool Market', mcpSearchPlaceholder: 'Search MCP Services...', innerSearchPlaceholder: 'Search Tools...', customSearchPlaceholder: 'Search Custom Tools...', @@ -1955,7 +1957,9 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re path: 'Path', viewDetail: 'View Details', textLink: 'Test Connection', - noResult: 'Processing results will be displayed here' + noResult: 'Processing results will be displayed here', + serverUrlInvalid: 'Must start with http:// or https://, and cannot have leading or trailing spaces', + requestHeaderKeyInvalid: 'Only English letters, numbers, hyphens (-), and underscores (_) are allowed, and cannot start or end with a hyphen or underscore', }, workflow: { coreNode: 'Core Nodes', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 5130a0c6..ebf9ce31 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1036,6 +1036,7 @@ export const zh = { logoTip: `支持图片格式（JPG、PNG）\n 尺寸：正方形比例 \n 文件大小限制：≤ 2MB`, imageSquareRequired: '请上传正方形比例图片', nameInvalid: '不能是空格开头或结尾', + notAllSpaces: '不能是纯空格', }, model: { searchPlaceholder: '搜索模型…', @@ -1779,6 +1780,7 @@ export const zh = { mcp: 'MCP 服务', inner: '内置工具', custom: '自定义工具', + market: '工具市场', mcpSearchPlaceholder: '搜索MCP服务...', innerSearchPlaceholder: '搜索工具...', customSearchPlaceholder: '搜索自定义工具...', @@ -1952,7 +1954,9 @@ export const zh = { path: '路径', viewDetail: '查看详情', textLink: '测试连接', - noResult: '处理结果将显示在这里' + noResult: '处理结果将显示在这里', + serverUrlInvalid: '必须以 http:// 或 https:// 开头，且不能有前后空格', + requestHeaderKeyInvalid: '只支持英文、数字、连字符(-)、下划线(_)，不能以连字符或下划线开头结尾', }, workflow: { coreNode: '核心节点', diff --git a/web/src/views/Conversation/index.tsx b/web/src/views/Conversation/index.tsx index 5509ad0a..8a67b3ae 100644 --- a/web/src/views/Conversation/index.tsx +++ b/web/src/views/Conversation/index.tsx @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-03 16:58:03 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-03-03 13:46:22 + * @Last Modified time: 2026-03-04 12:10:44 */ /** * Conversation Page @@ -267,8 +267,8 @@ const Conversation: FC = () => { currentConversationId = newId break case 'message': - const { content, chunk, conversation_id: curId } = item.data as { content: string; chunk: string; conversation_id: string; } - updateAssistantMessage(content ?? chunk) + const { content, conversation_id: curId } = item.data as { content: string; conversation_id: string; } + updateAssistantMessage(content) if (curId) { currentConversationId = curId; diff --git a/web/src/views/Ontology/components/OntologyClassExtractModal.tsx b/web/src/views/Ontology/components/OntologyClassExtractModal.tsx index 2fd305c6..8d3e1a91 100644 --- a/web/src/views/Ontology/components/OntologyClassExtractModal.tsx +++ b/web/src/views/Ontology/components/OntologyClassExtractModal.tsx @@ -185,6 +185,7 @@ const OntologyClassExtractModal = forwardRef diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx new file mode 100644 index 00000000..59fbddcc --- /dev/null +++ b/web/src/views/ToolManagement/Market.tsx @@ -0,0 +1,315 @@ +import React, { useState, useRef, type ReactNode } from 'react'; +import { Input, Button, Spin, App } from 'antd'; +import { SearchOutlined, SettingOutlined, GlobalOutlined, SyncOutlined } from '@ant-design/icons'; +import { useTranslation } from 'react-i18next'; +import MarketConfigModal, { type MarketConfigModalRef } from './components/MarketConfigModal'; + +interface MarketSource { + id: string; + name: string; + category: string; + icon: string; + url: string; + desc: string; + apiKey: string; + connected: boolean; + mcpCount: number; +} + +interface MarketMcp { + id: string; + name: string; + provider: string; + type: string; + desc: string; + downloads?: string; + stars?: string; + icon: string; + configTemplate: any; +} + +interface MarketCategory { + id: string; + name: string; + icon: string; +} + +const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => { + const { t } = useTranslation(); + const { message } = App.useApp(); + const [loading, setLoading] = useState(false); + const [selectedSource, setSelectedSource] = useState(null); + const marketConfigModalRef = useRef(null); + const [marketSources, setMarketSources] = useState([ + { id: 'smithery', name: 'Smithery', category: 'official', icon: '🔧', url: 'https://mcp.smithery.ai', desc: '官方 MCP 服务市场，提供丰富的 MCP 服务', apiKey: '', connected: false, mcpCount: 2847 }, + { id: 'mcpmarket', name: 'MCP Market', category: 'official', icon: '🏪', url: 'https://mcpmarket.com', desc: '综合性 MCP 市场平台', apiKey: '', connected: false, mcpCount: 1523 }, + { id: 'glama', name: 'Glama.ai MCP', category: 'official', icon: '✨', url: 'https://glama.ai/mcp', desc: 'Glama AI 提供的 MCP 服务集合', apiKey: '', connected: false, mcpCount: 892 }, + { id: 'github-mcp', name: 'modelcontextprotocol/servers', category: 'official', icon: '🐙', url: 'https://github.com/modelcontextprotocol/servers', desc: 'GitHub 官方 MCP 服务器仓库', apiKey: '', connected: true, mcpCount: 156 }, + { id: 'aliyun-bailian', name: '阿里云百炼 MCP', category: 'china-cloud', icon: '☁️', url: 'https://bailian.console.aliyun.com/mcp', desc: '阿里云百炼平台 MCP 市场', apiKey: '', connected: false, mcpCount: 423 }, + { id: 'modelscope', name: '魔搭社区 MCP', category: 'china-cloud', icon: '🎭', url: 'https://modelscope.cn/mcp', desc: '阿里达摩院魔搭社区 MCP 市场', apiKey: '', connected: false, mcpCount: 312 }, + ]); + + const [categories] = useState([ + { id: 'official', name: '官方/综合', icon: '🌐' }, + { id: 'china-cloud', name: '国内云', icon: '☁️' }, + { id: 'community', name: '社区/垂直', icon: '👥' } + ]); + + const [mcpCache, setMcpCache] = useState>({ + 'github-mcp': [ + { id: 'gh-1', name: 'Fetch', provider: 'modelcontextprotocol', type: 'Hosted', desc: '使用浏览器模拟大型语言模型检索和处理网页内容', downloads: '203.7m', stars: '308.2k', icon: '🌐', configTemplate: {} }, + { id: 'gh-2', name: 'Filesystem', provider: 'modelcontextprotocol', type: 'Local', desc: '安全的文件系统操作，支持读写文件和目录管理', downloads: '156.2m', stars: '245.1k', icon: '📁', configTemplate: {} }, + { id: 'gh-3', name: 'GitHub', provider: 'modelcontextprotocol', type: 'Hosted', desc: 'GitHub API 集成，支持仓库、Issue、PR 等操作', downloads: '89.4m', stars: '178.3k', icon: '🐙', configTemplate: {} }, + ] + }); + + const [searchKeyword, setSearchKeyword] = useState(''); + + const handleSelectSource = (sourceId: string) => { + setSelectedSource(sourceId); + }; + + const handleRefresh = (sourceId: string) => { + setLoading(true); + setTimeout(() => { + // 模拟刷新数据 + const source = marketSources.find(s => s.id === sourceId); + if (source) { + message.success(`${source.name} 列表已刷新`); + } + setLoading(false); + }, 600); + }; + + const handleOpenConfig = (sourceId: string) => { + const source = marketSources.find(s => s.id === sourceId); + if (source) { + marketConfigModalRef.current?.handleOpen(source); + } + }; + + const handleConnect = (sourceId: string, apiKey: string) => { + // 更新市场源状态 + setMarketSources(prev => prev.map(source => { + if (source.id === sourceId) { + return { + ...source, + apiKey, + connected: true + }; + } + return source; + })); + + // 模拟获取MCP列表 + setTimeout(() => { + const source = marketSources.find(s => s.id === sourceId); + if (source && !mcpCache[sourceId]) { + // 生成模拟数据 + const mockData: MarketMcp[] = [ + { id: `${sourceId}-1`, name: `${source.name} 服务 1`, provider: source.name, type: 'Hosted', desc: `来自 ${source.name} 的 MCP 服务`, downloads: '10.2m', stars: '23.4k', icon: '🔧', configTemplate: {} }, + { id: `${sourceId}-2`, name: `${source.name} 服务 2`, provider: source.name, type: 'Local', desc: `来自 ${source.name} 的本地 MCP 服务`, downloads: '8.5m', stars: '18.7k', icon: '⚙️', configTemplate: {} } + ]; + setMcpCache(prev => ({ + ...prev, + [sourceId]: mockData + })); + } + message.success(`已连接 ${source?.name}`); + }, 800); + }; + + const renderSourceDetail = () => { + if (!selectedSource) { + return ( +
+
🏪
+

选择一个 MCP 市场

+

从左侧选择一个市场源，配置连接后即可浏览该市场的 MCP 服务

+
+ ); + } + + const source = marketSources.find(s => s.id === selectedSource); + if (!source) return null; + + const mcpList = mcpCache[selectedSource] || []; + const filteredList = mcpList.filter(mcp => + mcp.name.toLowerCase().includes(searchKeyword.toLowerCase()) || + mcp.desc.toLowerCase().includes(searchKeyword.toLowerCase()) + ); + + return ( + <> +
+
+
+ {source.icon} +
+
+

{source.name}

+

{source.desc}

+
+
+
+ + +
+
+ +
+
+

+ 可用 MCP 服务 ({mcpList.length}) +

+
+ {source.connected && ( + + )} + {mcpList.length > 0 && ( + } + placeholder="搜索服务..." + value={searchKeyword} + onChange={(e) => setSearchKeyword(e.target.value)} + style={{ width: 200 }} + /> + )} +
+
+ + {mcpList.length > 0 ? ( + +
+ {filteredList.map(mcp => ( +
+
+
+ {mcp.icon} +
+ + {mcp.type} + +
+

{mcp.name}

+ {mcp.provider && ( +
+ @ {mcp.provider} +
+ )} +

{mcp.desc}

+
+ {mcp.downloads && ( + + {mcp.downloads} + + )} + {mcp.stars && ( + + ⭐ {mcp.stars} + + )} +
+
+ +
+
+ ))} +
+ + ) : ( +
+
{source.connected ? '📭' : '🔌'}
+

+ {source.connected ? '暂无可用的 MCP 服务' : '尚未连接此市场'} +

+

+ {source.connected ? '该市场暂时没有可用的服务' : '点击右上角"配置"按钮设置连接信息'} +

+ {!source.connected && ( + + )} +
+ )} +
+ + ); + }; + + return ( +
+ {/* 左侧市场源列表 */} +
+
+ MCP 市场 +
+ {categories.map(cat => ( +
+
+ {cat.icon} + {cat.name} +
+
+ {marketSources + .filter(s => s.category === cat.id) + .map(source => ( +
handleSelectSource(source.id)} + > + {source.icon} + + {source.name} + + + {source.mcpCount} + + {source.connected && ( + ● + )} +
+ ))} +
+
+ ))} +
+ + {/* 右侧内容区 */} +
+
+ {renderSourceDetail()} +
+
+ + {/* 配置弹窗 */} + +
+ ); +}; + +export default Market; diff --git a/web/src/views/ToolManagement/components/CustomToolModal.tsx b/web/src/views/ToolManagement/components/CustomToolModal.tsx index e3203a74..0ea8aca9 100644 --- a/web/src/views/ToolManagement/components/CustomToolModal.tsx +++ b/web/src/views/ToolManagement/components/CustomToolModal.tsx @@ -6,6 +6,7 @@ import type { CustomToolItem, CustomToolModalRef, ToolItem } from '../types' import RbModal from '@/components/RbModal'; import { parseSchema, addTool, updateTool } from '@/api/tools'; import Table from '@/components/Table'; +import { stringRegExp } from '@/utils/validator'; const FormItem = Form.Item; interface CustomToolModalProps { @@ -134,7 +135,11 @@ const CustomToolModal = forwardRef(({ diff --git a/web/src/views/ToolManagement/components/MarketConfigModal.tsx b/web/src/views/ToolManagement/components/MarketConfigModal.tsx new file mode 100644 index 00000000..d1d87563 --- /dev/null +++ b/web/src/views/ToolManagement/components/MarketConfigModal.tsx @@ -0,0 +1,173 @@ +import { forwardRef, useImperativeHandle, useState } from 'react'; +import { Form, Input, Button, App, Space } from 'antd'; +import { useTranslation } from 'react-i18next'; +import { CopyOutlined, EyeInvisibleOutlined, EyeOutlined } from '@ant-design/icons'; +import RbModal from '@/components/RbModal'; + +const FormItem = Form.Item; + +interface MarketSource { + id: string; + name: string; + icon: string; + url: string; + desc: string; + apiKey: string; + connected: boolean; +} + +interface MarketConfigModalProps { + onConnect: (sourceId: string, apiKey: string) => void; +} + +export interface MarketConfigModalRef { + handleOpen: (source: MarketSource) => void; + handleClose: () => void; +} + +const MarketConfigModal = forwardRef(({ + onConnect +}, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp(); + const [visible, setVisible] = useState(false); + const [form] = Form.useForm(); + const [loading, setLoading] = useState(false); + const [currentSource, setCurrentSource] = useState(null); + const [showApiKey, setShowApiKey] = useState(false); + + const handleClose = () => { + setVisible(false); + form.resetFields(); + setLoading(false); + setCurrentSource(null); + setShowApiKey(false); + }; + + const handleOpen = (source: MarketSource) => { + setCurrentSource(source); + form.setFieldsValue({ + url: source.url, + apiKey: source.apiKey, + }); + setVisible(true); + }; + + const handleSave = () => { + form + .validateFields() + .then((values) => { + if (!currentSource) return; + + setLoading(true); + + // 模拟连接延迟 + setTimeout(() => { + onConnect(currentSource.id, values.apiKey || ''); + message.success(`正在连接 ${currentSource.name}...`); + setLoading(false); + handleClose(); + }, 500); + }) + .catch((err) => { + console.log('表单验证失败:', err); + }); + }; + + const handleCopyUrl = () => { + if (currentSource?.url) { + navigator.clipboard.writeText(currentSource.url).then(() => { + message.success(t('common.copySuccess')); + }); + } + }; + + useImperativeHandle(ref, () => ({ + handleOpen, + handleClose + })); + + if (!currentSource) return null; + + return ( + +
+ {/* 市场源信息头部 */} +
+
+ {currentSource.icon} +
+
+

{currentSource.name}

+

{currentSource.desc}

+
+
+ +
+ {/* 市场地址 */} + + + + + + + + {/* API Key */} + + API Key (可选) + + } + extra="部分市场需要 API Key 才能获取完整的服务列表" + > + + +
+ + ); +}); + +export default MarketConfigModal; diff --git a/web/src/views/ToolManagement/components/McpServiceModal.tsx b/web/src/views/ToolManagement/components/McpServiceModal.tsx index a104c2d6..bd97b876 100644 --- a/web/src/views/ToolManagement/components/McpServiceModal.tsx +++ b/web/src/views/ToolManagement/components/McpServiceModal.tsx @@ -9,6 +9,7 @@ import RequestHeaderModal from './RequestHeaderModal'; import Table from '@/components/Table'; import { addTool, updateTool, testConnection } from '@/api/tools' import type { McpServiceModalRef } from '../types' +import { stringRegExp } from '@/utils/validator'; const FormItem = Form.Item; @@ -168,14 +169,22 @@ const McpServiceModal = forwardRef(({ name={['config', "server_url"]} label={t('tool.serviceEndpoint')} extra={t('tool.serviceEndpointExtra')} - rules={[{ required: true, message: t('common.pleaseEnter') }]} + rules={[ + { required: true, message: t('common.pleaseEnter') }, + { max: 500 }, + { pattern: /^https?:\/\/\S+$/, message: t('tool.serverUrlInvalid') }, + ]} > @@ -201,6 +210,7 @@ const McpServiceModal = forwardRef(({ diff --git a/web/src/views/ToolManagement/components/RequestHeaderModal.tsx b/web/src/views/ToolManagement/components/RequestHeaderModal.tsx index 5e20120d..1f2bdff3 100644 --- a/web/src/views/ToolManagement/components/RequestHeaderModal.tsx +++ b/web/src/views/ToolManagement/components/RequestHeaderModal.tsx @@ -4,6 +4,7 @@ import { useTranslation } from 'react-i18next'; import type { RequestHeader, RequestHeaderModalRef } from './McpServiceModal' import RbModal from '@/components/RbModal' +import { stringRegExp } from '@/utils/validator'; const FormItem = Form.Item; @@ -82,7 +83,11 @@ const RequestHeaderModal = forwardRef @@ -90,7 +95,11 @@ const RequestHeaderModal = forwardRef diff --git a/web/src/views/ToolManagement/index.tsx b/web/src/views/ToolManagement/index.tsx index 9fa73067..c9383258 100644 --- a/web/src/views/ToolManagement/index.tsx +++ b/web/src/views/ToolManagement/index.tsx @@ -1,3 +1,11 @@ +/* + * @Description: + * @Version: 0.0.1 + * @Author: yujiangping + * @Date: 2026-01-05 17:22:23 + * @LastEditors: yujiangping + * @LastEditTime: 2026-03-04 15:12:48 + */ import React, { useState } from 'react'; import { Tabs } from 'antd'; import { useTranslation } from 'react-i18next'; @@ -5,9 +13,10 @@ import { useTranslation } from 'react-i18next'; import Mcp from './Mcp'; import Inner from './Inner'; import Custom from './Custom'; +import Market from './Market'; import Tag from '@/components/Tag' -const tabKeys = ['mcp', 'inner', 'custom'] +const tabKeys = ['mcp', 'inner', 'custom', 'market'] const ToolManagement: React.FC = () => { const { t } = useTranslation(); const [activeTab, setActiveTab] = useState('mcp'); @@ -45,6 +54,7 @@ const ToolManagement: React.FC = () => { {activeTab === 'mcp' && } {activeTab === 'inner' && } {activeTab === 'custom' && } + {/* {activeTab === 'market' && } */} ); }; diff --git a/web/src/views/UserMemoryDetail/pages/ShortTermDetail.tsx b/web/src/views/UserMemoryDetail/pages/ShortTermDetail.tsx index 6cc8eafc..f0f9ce02 100644 --- a/web/src/views/UserMemoryDetail/pages/ShortTermDetail.tsx +++ b/web/src/views/UserMemoryDetail/pages/ShortTermDetail.tsx @@ -6,6 +6,7 @@ import { getShortTerm, } from '@/api/memory' import Empty from '@/components/Empty' +import Markdown from '@/components/Markdown' interface ShortTermItem { retrieval: Array<{ query: string; retrieval: string[]; }>; @@ -85,7 +86,9 @@ const ShortTermDetail: FC = () => { ))}
{t('shortTermDetail.answer')}
-
{vo.answer}
+
+ +
@@ -103,7 +106,9 @@ const ShortTermDetail: FC = () => { : data.long_term?.map((vo, voIdx) => (
{vo.query}
-
{vo.retrieval}
+
+ +
)) } diff --git a/web/src/views/Workflow/components/Chat/Chat.tsx b/web/src/views/Workflow/components/Chat/Chat.tsx index f8049cb7..eb24a206 100644 --- a/web/src/views/Workflow/components/Chat/Chat.tsx +++ b/web/src/views/Workflow/components/Chat/Chat.tsx @@ -174,8 +174,8 @@ const Chat = forwardRef(({ appId */ const handleStreamMessage = (data: SSEMessage[]) => { data.forEach(item => { - const { chunk, conversation_id, node_id, cycle_id, cycle_idx, input, output, error, elapsed_time, status } = item.data as { - chunk: string; + const { content, conversation_id, node_id, cycle_id, cycle_idx, input, output, error, elapsed_time, status } = item.data as { + content: string; conversation_id: string | null; cycle_id: string; cycle_idx: number; @@ -202,7 +202,7 @@ const Chat = forwardRef(({ appId if (lastIndex >= 0) { newList[lastIndex] = { ...newList[lastIndex], - content: newList[lastIndex].content + chunk + content: newList[lastIndex].content + content } } return newList