435 lines
18 KiB
Python
435 lines
18 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
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import datetime
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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.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.core.api_key_utils import timestamp_to_datetime
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from app.services.memory_base_service import Translation_English
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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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)
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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.repositories.workspace_repository import WorkspaceRepository
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from app.schemas.end_user_schema import (
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EndUserProfileResponse,
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EndUserProfileUpdate,
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)
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from app.models.end_user_model import EndUser
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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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language_type: str = Header(default="zh", 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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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"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,model_id,language_type)
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if result["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=result, 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=result, 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="zh", 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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"""
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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_type)
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if result["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=result, 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=result, 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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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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"""
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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'}"
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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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# 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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# 构建响应
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result = {
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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": []
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}
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# 收集错误信息
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if not insight_result["success"]:
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result["errors"].append({
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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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result["errors"].append({
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"type": "summary",
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"error": summary_result.get("error")
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})
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# 记录结果
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if result["insight_success"] and result["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: {result['errors']}")
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return success(data=result, 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)
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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={result['total_users']}, 成功={result['successful']}, 失败={result['failed']}"
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)
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return success(data=result, 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(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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# 计算总数用于日志
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total_count = sum(item["count"] for item in result)
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api_logger.info(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(result)}")
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return success(data=result, 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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# 检查是否有错误消息
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if "message" in result and result["statistics"]["total_nodes"] == 0:
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api_logger.warning(f"图数据查询返回空结果: {result.get('message')}")
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return success(data=result, msg=result.get("message", "查询成功"))
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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={result['statistics']['total_nodes']}, "
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f"edges={result['statistics']['total_edges']}"
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)
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return success(data=result, 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("/read_end_user/profile", response_model=ApiResponse)
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async def get_end_user_profile(
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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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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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# 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} 尝试查询用户信息但未选择工作空间")
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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"用户信息查询请求: 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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# 查询终端用户
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end_user = db.query(EndUser).filter(EndUser.id == end_user_id).first()
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if not end_user:
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api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}")
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return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}")
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# 构建响应数据
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profile_data = EndUserProfileResponse(
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id=end_user.id,
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other_name=end_user.other_name,
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position=end_user.position,
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department=end_user.department,
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contact=end_user.contact,
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phone=end_user.phone,
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hire_date=end_user.hire_date,
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updatetime_profile=end_user.updatetime_profile
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)
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api_logger.info(f"成功获取用户信息: end_user_id={end_user_id}")
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return success(data=UserMemoryService.convert_profile_to_dict_with_timestamp(profile_data), msg="查询成功")
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except Exception as e:
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api_logger.error(f"用户信息查询失败: end_user_id={end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "用户信息查询失败", str(e))
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@router.post("/updated_end_user/profile", response_model=ApiResponse)
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async def update_end_user_profile(
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profile_update: EndUserProfileUpdate,
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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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更新终端用户的基本信息
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该接口可以更新用户的姓名、职位、部门、联系方式、电话和入职日期等信息。
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所有字段都是可选的，只更新提供的字段。
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"""
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workspace_id = current_user.current_workspace_id
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end_user_id = profile_update.end_user_id
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# 验证工作空间
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试更新用户信息但未选择工作空间")
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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"用户信息更新请求: 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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# 调用 Service 层处理业务逻辑
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result = user_memory_service.update_end_user_profile(db, end_user_id, profile_update)
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if result["success"]:
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api_logger.info(f"成功更新用户信息: end_user_id={end_user_id}")
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return success(data=result["data"], msg="更新成功")
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else:
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error_msg = result["error"]
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api_logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={error_msg}")
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# 根据错误类型映射到合适的业务错误码
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if error_msg == "终端用户不存在":
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return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", error_msg)
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elif error_msg == "无效的用户ID格式":
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return fail(BizCode.INVALID_USER_ID, "无效的用户ID格式", error_msg)
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else:
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# 只有未预期的错误才使用 INTERNAL_ERROR
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return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", error_msg)
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@router.get("/memory_space/timeline_memories", response_model=ApiResponse)
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async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str = Header(default="zh", 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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):
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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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MemoryEntity = MemoryEntityService(id, label)
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timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language_type)
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return success(data=timeline_memories_result, msg="共同记忆时间线")
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@router.get("/memory_space/relationship_evolution", response_model=ApiResponse)
|
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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()
|
||
|
||
result = {
|
||
"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=result, 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))
|