9a5ce7f7c6
- Add user_memory_schema.py with typed Pydantic models for all user memory API responses: MemoryInsightReportData, UserSummaryData, GraphData, MemoryTypeStatItem, cache result models, and RelationshipEvolutionData - Refactor user_memory_controllers.py to construct schema instances and return model_dump() instead of raw dicts - Remove unused imports (datetime, timestamp_to_datetime, EndUserInfoResponse, EndUserInfoCreate, EndUser)
580 lines
23 KiB
Python
580 lines
23 KiB
Python
"""
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用户记忆相关的控制器
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包含用户摘要、记忆洞察、节点统计、图数据和用户档案等接口
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"""
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from typing import Optional, List
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from sqlalchemy.orm import Session
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from fastapi import APIRouter, Depends, Header
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from app.db import get_db
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from app.core.language_utils import get_language_from_header
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from app.core.logging_config import get_api_logger
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from app.core.response_utils import success, fail
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from app.core.error_codes import BizCode
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from app.services.user_memory_service import (
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UserMemoryService,
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analytics_memory_types,
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analytics_graph_data,
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analytics_community_graph_data,
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)
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from app.services.memory_entity_relationship_service import MemoryEntityService, MemoryEmotion, MemoryInteraction
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from app.schemas.response_schema import ApiResponse
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from app.schemas.memory_storage_schema import GenerateCacheRequest
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from app.schemas.user_memory_schema import (
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MemoryInsightReportData,
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UserSummaryData,
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SingleUserCacheResultData,
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GenerateCacheErrorItem,
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WorkspaceCacheResultData,
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WorkspaceCacheErrorItem,
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MemoryTypeStatItem,
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GraphData,
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GraphNodeData,
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GraphEdgeData,
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GraphStatistics,
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RelationshipEvolutionData,
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)
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from app.repositories.workspace_repository import WorkspaceRepository
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from app.repositories.end_user_repository import EndUserRepository
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from app.schemas.end_user_info_schema import (
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EndUserInfoUpdate,
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)
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from app.dependencies import get_current_user
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from app.models.user_model import User
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# Get API logger
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api_logger = get_api_logger()
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# Initialize service
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user_memory_service = UserMemoryService()
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router = APIRouter(
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prefix="/memory-storage",
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tags=["User Memory"],
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)
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@router.get("/analytics/memory_insight/report", response_model=ApiResponse)
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async def get_memory_insight_report_api(
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end_user_id: str,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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"""
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获取缓存的记忆洞察报告
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此接口仅查询数据库中已缓存的记忆洞察数据，不执行生成操作。
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如需生成新的洞察报告，请使用专门的生成接口。
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"""
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api_logger.info(f"记忆洞察报告查询请求: end_user_id={end_user_id}, user={current_user.username}")
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try:
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# 调用服务层获取缓存数据
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result = await user_memory_service.get_cached_memory_insight(db, end_user_id)
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data = MemoryInsightReportData(
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memory_insight=result.get("memory_insight"),
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behavior_pattern=result.get("behavior_pattern"),
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key_findings=result.get("key_findings"),
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growth_trajectory=result.get("growth_trajectory"),
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updated_at=result.get("updated_at"),
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is_cached=result["is_cached"],
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message=result.get("message"),
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)
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if data.is_cached:
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api_logger.info(f"成功返回缓存的记忆洞察报告: end_user_id={end_user_id}")
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return success(data=data.model_dump(), msg="查询成功")
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else:
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api_logger.info(f"记忆洞察报告缓存不存在: end_user_id={end_user_id}")
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return success(data=data.model_dump(), msg="数据尚未生成")
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except Exception as e:
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api_logger.error(f"记忆洞察报告查询失败: end_user_id={end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "记忆洞察报告查询失败", str(e))
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@router.get("/analytics/user_summary", response_model=ApiResponse)
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async def get_user_summary_api(
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end_user_id: str,
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language_type: str = Header(default=None, alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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"""
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获取缓存的用户摘要
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此接口仅查询数据库中已缓存的用户摘要数据，不执行生成操作。
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如需生成新的用户摘要，请使用专门的生成接口。
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语言控制：
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- 使用 X-Language-Type Header 指定语言
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- 如果未传 Header，默认使用中文 (zh)
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"""
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# 使用集中化的语言校验
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language = get_language_from_header(language_type)
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workspace_id = current_user.current_workspace_id
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workspace_repo = WorkspaceRepository(db)
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workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
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if workspace_models:
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model_id = workspace_models.get("llm", None)
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else:
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model_id = None
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api_logger.info(f"用户摘要查询请求: end_user_id={end_user_id}, user={current_user.username}")
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try:
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# 调用服务层获取缓存数据
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result = await user_memory_service.get_cached_user_summary(db, end_user_id, model_id, language)
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data = UserSummaryData(
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user_summary=result.get("user_summary"),
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personality=result.get("personality"),
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core_values=result.get("core_values"),
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one_sentence=result.get("one_sentence"),
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updated_at=result.get("updated_at"),
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is_cached=result["is_cached"],
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message=result.get("message"),
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)
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if data.is_cached:
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api_logger.info(f"成功返回缓存的用户摘要: end_user_id={end_user_id}")
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return success(data=data.model_dump(), msg="查询成功")
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else:
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api_logger.info(f"用户摘要缓存不存在: end_user_id={end_user_id}")
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return success(data=data.model_dump(), msg="数据尚未生成")
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except Exception as e:
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api_logger.error(f"用户摘要查询失败: end_user_id={end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "用户摘要查询失败", str(e))
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@router.post("/analytics/generate_cache", response_model=ApiResponse)
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async def generate_cache_api(
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request: GenerateCacheRequest,
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language_type: str = Header(default=None, alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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"""
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手动触发缓存生成
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- 如果提供 end_user_id，只为该用户生成
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- 如果不提供，为当前工作空间的所有用户生成
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语言控制：
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- 使用 X-Language-Type Header 指定语言 ("zh" 中文, "en" 英文)
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- 如果未传 Header，默认使用中文 (zh)
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"""
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# 使用集中化的语言校验
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language = get_language_from_header(language_type)
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workspace_id = current_user.current_workspace_id
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# 检查用户是否已选择工作空间
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间")
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return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
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end_user_id = request.end_user_id
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api_logger.info(
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f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, "
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f"end_user_id={end_user_id if end_user_id else '全部用户'}, language={language}"
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)
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try:
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if end_user_id:
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# 为单个用户生成
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api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}")
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# 生成记忆洞察
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insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id,
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language=language)
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# 生成用户摘要
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summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id,
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language=language)
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# 构建响应
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errors: List[GenerateCacheErrorItem] = []
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if not insight_result["success"]:
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errors.append(GenerateCacheErrorItem(
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type="insight",
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error=insight_result.get("error"),
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))
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if not summary_result["success"]:
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errors.append(GenerateCacheErrorItem(
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type="summary",
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error=summary_result.get("error"),
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))
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data = SingleUserCacheResultData(
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end_user_id=end_user_id,
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insight_success=insight_result["success"],
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summary_success=summary_result["success"],
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errors=errors,
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)
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# 记录结果
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if data.insight_success and data.summary_success:
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api_logger.info(f"成功为用户 {end_user_id} 生成缓存")
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else:
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api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {[e.model_dump() for e in errors]}")
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return success(data=data.model_dump(), msg="生成完成")
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else:
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# 为整个工作空间生成
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api_logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存")
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result = await user_memory_service.generate_cache_for_workspace(db, workspace_id, language=language)
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ws_errors = [
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WorkspaceCacheErrorItem(
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end_user_id=e.get("end_user_id"),
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insight_error=e.get("insight_error"),
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summary_error=e.get("summary_error"),
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error=e.get("error"),
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)
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for e in result.get("errors", [])
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]
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data = WorkspaceCacheResultData(
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total_users=result["total_users"],
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successful=result["successful"],
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failed=result["failed"],
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errors=ws_errors,
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)
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# 记录统计信息
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api_logger.info(
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f"工作空间 {workspace_id} 批量生成完成: "
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f"总数={data.total_users}, 成功={data.successful}, 失败={data.failed}"
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)
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return success(data=data.model_dump(), msg="批量生成完成")
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except Exception as e:
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api_logger.error(f"缓存生成失败: user={current_user.username}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "缓存生成失败", str(e))
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@router.get("/analytics/node_statistics", response_model=ApiResponse)
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async def get_node_statistics_api(
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end_user_id: str,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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workspace_id = current_user.current_workspace_id
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# 检查用户是否已选择工作空间
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试查询节点统计但未选择工作空间")
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return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
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api_logger.info(
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f"记忆类型统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
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try:
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# 调用新的记忆类型统计函数
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result = await analytics_memory_types(db, end_user_id)
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# 使用 schema 模型构建响应
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stat_items = [
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MemoryTypeStatItem(
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type=item["type"],
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count=item["count"],
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percentage=item["percentage"],
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)
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for item in result
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]
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# 计算总数用于日志
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total_count = sum(item.count for item in stat_items)
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api_logger.info(
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f"成功获取记忆类型统计: end_user_id={end_user_id}, 总记忆数={total_count}, 类型数={len(stat_items)}")
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return success(data=[item.model_dump() for item in stat_items], msg="查询成功")
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except Exception as e:
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api_logger.error(f"记忆类型查询失败: end_user_id={end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "记忆类型查询失败", str(e))
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@router.get("/analytics/graph_data", response_model=ApiResponse)
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async def get_graph_data_api(
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end_user_id: str,
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node_types: Optional[str] = None,
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limit: int = 100,
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depth: int = 1,
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center_node_id: Optional[str] = None,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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workspace_id = current_user.current_workspace_id
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# 检查用户是否已选择工作空间
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试查询图数据但未选择工作空间")
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return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
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# 参数验证
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if limit > 1000:
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limit = 1000
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api_logger.warning("limit 参数超过最大值，已调整为 1000")
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if depth > 3:
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depth = 3
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api_logger.warning("depth 参数超过最大值，已调整为 3")
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# 解析 node_types 参数
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node_types_list = None
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if node_types:
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node_types_list = [t.strip() for t in node_types.split(",") if t.strip()]
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api_logger.info(
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f"图数据查询请求: end_user_id={end_user_id}, user={current_user.username}, "
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f"workspace={workspace_id}, node_types={node_types_list}, limit={limit}, depth={depth}"
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)
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try:
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result = await analytics_graph_data(
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db=db,
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end_user_id=end_user_id,
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node_types=node_types_list,
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limit=limit,
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depth=depth,
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center_node_id=center_node_id
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)
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# 使用 schema 模型构建响应
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data = GraphData(
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nodes=[GraphNodeData(**n) for n in result.get("nodes", [])],
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edges=[GraphEdgeData(**e) for e in result.get("edges", [])],
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statistics=GraphStatistics(**result.get("statistics", {})),
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message=result.get("message"),
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)
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# 检查是否有错误消息
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if data.message and data.statistics.total_nodes == 0:
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api_logger.warning(f"图数据查询返回空结果: {data.message}")
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return success(data=data.model_dump(), msg=data.message)
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api_logger.info(
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f"成功获取图数据: end_user_id={end_user_id}, "
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f"nodes={data.statistics.total_nodes}, "
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f"edges={data.statistics.total_edges}"
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)
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return success(data=data.model_dump(), msg="查询成功")
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except Exception as e:
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api_logger.error(f"图数据查询失败: end_user_id={end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e))
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@router.get("/analytics/community_graph", response_model=ApiResponse)
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async def get_community_graph_data_api(
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end_user_id: str,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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workspace_id = current_user.current_workspace_id
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试查询社区图谱但未选择工作空间")
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return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
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api_logger.info(
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f"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, "
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f"workspace={workspace_id}"
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)
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try:
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result = await analytics_community_graph_data(db=db, end_user_id=end_user_id)
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# 使用 schema 模型构建响应
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data = GraphData(
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nodes=[GraphNodeData(**n) for n in result.get("nodes", [])],
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edges=[GraphEdgeData(**e) for e in result.get("edges", [])],
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statistics=GraphStatistics(**result.get("statistics", {})),
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message=result.get("message"),
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)
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if data.message and data.statistics.total_nodes == 0:
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api_logger.warning(f"社区图谱查询返回空结果: {data.message}")
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return success(data=data.model_dump(), msg=data.message)
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api_logger.info(
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f"成功获取社区图谱: end_user_id={end_user_id}, "
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f"nodes={data.statistics.total_nodes}, "
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f"edges={data.statistics.total_edges}"
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)
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return success(data=data.model_dump(), msg="查询成功")
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except Exception as e:
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api_logger.error(f"社区图谱查询失败: end_user_id={end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "社区图谱查询失败", str(e))
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#=======================终端用户信息接口=======================
|
||
|
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@router.get("/end_user_info", response_model=ApiResponse)
|
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async def get_end_user_info(
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end_user_id: str,
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current_user: User = Depends(get_current_user),
|
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db: Session = Depends(get_db),
|
||
) -> dict:
|
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"""
|
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查询终端用户信息记录
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根据 end_user_id 查询单条终端用户信息记录。
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"""
|
||
workspace_id = current_user.current_workspace_id
|
||
|
||
if workspace_id is None:
|
||
api_logger.warning(f"用户 {current_user.username} 尝试查询终端用户信息但未选择工作空间")
|
||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||
|
||
api_logger.info(
|
||
f"查询终端用户信息请求: end_user_id={end_user_id}, user={current_user.username}, "
|
||
f"workspace={workspace_id}"
|
||
)
|
||
|
||
# 校验 end_user 是否属于当前工作空间
|
||
end_user_repo = EndUserRepository(db)
|
||
end_user = end_user_repo.get_end_user_by_id(end_user_id)
|
||
if end_user is None:
|
||
return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", "end_user not found")
|
||
if str(end_user.workspace_id) != str(workspace_id):
|
||
api_logger.warning(
|
||
f"用户 {current_user.username} 尝试查询不属于工作空间 {workspace_id} 的终端用户 {end_user_id}"
|
||
)
|
||
return fail(BizCode.PERMISSION_DENIED, "该终端用户不属于当前工作空间", "end_user workspace mismatch")
|
||
|
||
result = user_memory_service.get_end_user_info(db, end_user_id)
|
||
|
||
if result["success"]:
|
||
api_logger.info(f"成功查询终端用户信息: end_user_id={end_user_id}")
|
||
return success(data=result["data"], msg="查询成功")
|
||
else:
|
||
error_msg = result["error"]
|
||
api_logger.error(f"查询终端用户信息失败: end_user_id={end_user_id}, error={error_msg}")
|
||
|
||
if error_msg == "终端用户信息记录不存在":
|
||
return fail(BizCode.USER_NOT_FOUND, "终端用户信息记录不存在", error_msg)
|
||
elif error_msg == "无效的终端用户ID格式":
|
||
return fail(BizCode.INVALID_USER_ID, "无效的终端用户ID格式", error_msg)
|
||
else:
|
||
return fail(BizCode.INTERNAL_ERROR, "查询终端用户信息失败", error_msg)
|
||
|
||
|
||
@router.post("/end_user_info/updated", response_model=ApiResponse)
|
||
async def update_end_user_info(
|
||
info_update: EndUserInfoUpdate,
|
||
current_user: User = Depends(get_current_user),
|
||
db: Session = Depends(get_db),
|
||
) -> dict:
|
||
"""
|
||
更新终端用户信息记录
|
||
|
||
根据 end_user_id 更新终端用户信息记录，支持批量更新多个别名。
|
||
|
||
示例请求体：
|
||
{
|
||
"end_user_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
|
||
"other_name": "张三1",
|
||
"aliases": ["小张", "张工"],
|
||
"meta_data": {"position": "工程师", "department": "技术部"}
|
||
}
|
||
"""
|
||
workspace_id = current_user.current_workspace_id
|
||
end_user_id = info_update.end_user_id
|
||
|
||
if workspace_id is None:
|
||
api_logger.warning(f"用户 {current_user.username} 尝试更新终端用户信息但未选择工作空间")
|
||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||
|
||
api_logger.info(
|
||
f"更新终端用户信息请求: end_user_id={end_user_id}, user={current_user.username}, "
|
||
f"workspace={workspace_id}"
|
||
)
|
||
|
||
# 校验 end_user 是否属于当前工作空间
|
||
end_user_repo = EndUserRepository(db)
|
||
end_user = end_user_repo.get_end_user_by_id(end_user_id)
|
||
if end_user is None:
|
||
return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", "end_user not found")
|
||
if str(end_user.workspace_id) != str(workspace_id):
|
||
api_logger.warning(
|
||
f"用户 {current_user.username} 尝试更新不属于工作空间 {workspace_id} 的终端用户 {end_user_id}"
|
||
)
|
||
return fail(BizCode.PERMISSION_DENIED, "该终端用户不属于当前工作空间", "end_user workspace mismatch")
|
||
|
||
# 获取更新数据（排除 end_user_id）
|
||
update_data = info_update.model_dump(exclude_unset=True, exclude={'end_user_id'})
|
||
|
||
result = user_memory_service.update_end_user_info(db, end_user_id, update_data)
|
||
|
||
if result["success"]:
|
||
api_logger.info(f"成功更新终端用户信息: end_user_id={end_user_id}")
|
||
return success(data=result["data"], msg="更新成功")
|
||
else:
|
||
error_msg = result["error"]
|
||
api_logger.error(f"终端用户信息更新失败: end_user_id={end_user_id}, error={error_msg}")
|
||
|
||
if error_msg == "终端用户信息记录不存在":
|
||
return fail(BizCode.USER_NOT_FOUND, "终端用户信息记录不存在", error_msg)
|
||
elif error_msg == "无效的终端用户ID格式":
|
||
return fail(BizCode.INVALID_USER_ID, "无效的终端用户ID格式", error_msg)
|
||
else:
|
||
return fail(BizCode.INTERNAL_ERROR, "终端用户信息更新失败", error_msg)
|
||
|
||
@router.get("/memory_space/timeline_memories", response_model=ApiResponse)
|
||
async def memory_space_timeline_of_shared_memories(
|
||
id: str, label: str,
|
||
language_type: str = Header(default=None, alias="X-Language-Type"),
|
||
current_user: User = Depends(get_current_user),
|
||
db: Session = Depends(get_db),
|
||
):
|
||
# 使用集中化的语言校验
|
||
language = get_language_from_header(language_type)
|
||
|
||
workspace_id = current_user.current_workspace_id
|
||
workspace_repo = WorkspaceRepository(db)
|
||
workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
|
||
|
||
if workspace_models:
|
||
model_id = workspace_models.get("llm", None)
|
||
else:
|
||
model_id = None
|
||
MemoryEntity = MemoryEntityService(id, label)
|
||
timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language)
|
||
|
||
return success(data=timeline_memories_result, msg="共同记忆时间线")
|
||
|
||
|
||
@router.get("/memory_space/relationship_evolution", response_model=ApiResponse)
|
||
async def memory_space_relationship_evolution(id: str, label: str,
|
||
current_user: User = Depends(get_current_user),
|
||
db: Session = Depends(get_db),
|
||
):
|
||
try:
|
||
api_logger.info(f"关系演变查询请求: id={id}, table={label}, user={current_user.username}")
|
||
|
||
# 获取情绪数据
|
||
emotion = MemoryEmotion(id, label)
|
||
emotion_result = await emotion.get_emotion()
|
||
|
||
# 获取交互数据
|
||
interaction = MemoryInteraction(id, label)
|
||
interaction_result = await interaction.get_interaction_frequency()
|
||
|
||
# 关闭连接
|
||
await emotion.close()
|
||
await interaction.close()
|
||
|
||
data = RelationshipEvolutionData(
|
||
emotion=emotion_result,
|
||
interaction=interaction_result,
|
||
)
|
||
|
||
api_logger.info(f"关系演变查询成功: id={id}, table={label}")
|
||
return success(data=data.model_dump(), msg="关系演变")
|
||
|
||
except Exception as e:
|
||
api_logger.error(f"关系演变查询失败: id={id}, table={label}, error={str(e)}", exc_info=True)
|
||
return fail(BizCode.INTERNAL_ERROR, "关系演变查询失败", str(e))
|