Refactor/episodic explicit (#93)
* [refactor]Reconstruct episodic memory * [refactor]Reconstructing explicit memory * [refactor]Reconstruct episodic memory * [refactor]Reconstructing explicit memory * [changes]Based on the improvement of AI review * [changes]Modify the routing * [changes]Uniform routing format * [fix]Fix the failure in parsing the timestamp. * [refactor]Reconstruct episodic memory * [refactor]Reconstructing explicit memory * [changes]Based on the improvement of AI review * [changes]Modify the routing * [changes]Uniform routing format * [fix]Fix the failure in parsing the timestamp. * [deleted]Delete migration files * [refactor]Reconstruct episodic memory * [refactor]Reconstructing explicit memory * [changes]Based on the improvement of AI review * [changes]Modify the routing * [changes]Uniform routing format * [fix]Fix the failure in parsing the timestamp. * [deleted]Delete migration files * feat: add database migration 9ab9b6393f32_20261511
This commit is contained in:
乐力齐
@@ -20,6 +20,8 @@ from . import (
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knowledgeshare_controller,
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memory_agent_controller,
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memory_dashboard_controller,
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memory_episodic_controller,
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memory_explicit_controller,
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memory_forget_controller,
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memory_reflection_controller,
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memory_short_term_controller,
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@@ -67,6 +69,8 @@ manager_router.include_router(memory_agent_controller.router)
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manager_router.include_router(memory_dashboard_controller.router)
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manager_router.include_router(memory_storage_controller.router)
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manager_router.include_router(user_memory_controllers.router)
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manager_router.include_router(memory_episodic_controller.router)
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manager_router.include_router(memory_explicit_controller.router)
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manager_router.include_router(api_key_controller.router)
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manager_router.include_router(release_share_controller.router)
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manager_router.include_router(public_share_controller.router) # 公开路由（无需认证）
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125
api/app/controllers/memory_episodic_controller.py
Normal file
125
api/app/controllers/memory_episodic_controller.py
Normal file
@@ -0,0 +1,125 @@
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"""
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情景记忆相关的控制器
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包含情景记忆总览和详情查询接口
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"""
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from fastapi import APIRouter, Depends
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from app.core.error_codes import BizCode
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from app.core.logging_config import get_api_logger
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from app.core.response_utils import fail, success
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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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from app.schemas.response_schema import ApiResponse
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from app.schemas.memory_episodic_schema import (
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EpisodicMemoryOverviewRequest,
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EpisodicMemoryDetailsRequest,
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)
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from app.services.memory_episodic_service import memory_episodic_service
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# Get API logger
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api_logger = get_api_logger()
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router = APIRouter(
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prefix="/memory/episodic-memory",
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tags=["Episodic Memory"],
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)
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@router.post("/overview", response_model=ApiResponse)
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async def get_episodic_memory_overview_api(
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request: EpisodicMemoryOverviewRequest,
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current_user: User = Depends(get_current_user),
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) -> dict:
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"""
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获取情景记忆总览
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返回指定用户的所有情景记忆列表，包括标题和创建时间。
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支持通过时间范围、情景类型和标题关键词进行筛选。
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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} 尝试查询情景记忆总览但未选择工作空间")
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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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# 验证参数
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valid_time_ranges = ["all", "today", "this_week", "this_month"]
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valid_episodic_types = ["all", "conversation", "project_work", "learning", "decision", "important_event"]
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if request.time_range not in valid_time_ranges:
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return fail(BizCode.INVALID_PARAMETER, f"无效的时间范围参数，可选值：{', '.join(valid_time_ranges)}")
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if request.episodic_type not in valid_episodic_types:
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return fail(BizCode.INVALID_PARAMETER, f"无效的情景类型参数，可选值：{', '.join(valid_episodic_types)}")
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# 处理 title_keyword（去除首尾空格）
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title_keyword = request.title_keyword.strip() if request.title_keyword else None
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api_logger.info(
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f"情景记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
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f"workspace={workspace_id}, time_range={request.time_range}, episodic_type={request.episodic_type}, "
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f"title_keyword={title_keyword}"
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)
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try:
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# 调用Service层方法
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result = await memory_episodic_service.get_episodic_memory_overview(
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request.end_user_id, request.time_range, request.episodic_type, title_keyword
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)
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api_logger.info(
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f"成功获取情景记忆总览: end_user_id={request.end_user_id}, "
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f"total={result['total']}"
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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"情景记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "情景记忆总览查询失败", str(e))
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@router.post("/details", response_model=ApiResponse)
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async def get_episodic_memory_details_api(
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request: EpisodicMemoryDetailsRequest,
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current_user: User = Depends(get_current_user),
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) -> dict:
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"""
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获取情景记忆详情
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返回指定情景记忆的详细信息，包括涉及对象、情景类型、内容记录和情绪。
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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} 尝试查询情景记忆详情但未选择工作空间")
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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"情景记忆详情查询请求: end_user_id={request.end_user_id}, summary_id={request.summary_id}, "
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f"user={current_user.username}, workspace={workspace_id}"
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)
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try:
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# 调用Service层方法
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result = await memory_episodic_service.get_episodic_memory_details(
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end_user_id=request.end_user_id,
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summary_id=request.summary_id
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)
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api_logger.info(
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f"成功获取情景记忆详情: end_user_id={request.end_user_id}, summary_id={request.summary_id}"
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)
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return success(data=result, msg="查询成功")
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except ValueError as e:
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# 处理情景记忆不存在的情况
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api_logger.warning(f"情景记忆不存在: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
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return fail(BizCode.INVALID_PARAMETER, "情景记忆不存在", str(e))
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except Exception as e:
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api_logger.error(f"情景记忆详情查询失败: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "情景记忆详情查询失败", str(e))
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115
api/app/controllers/memory_explicit_controller.py
Normal file
115
api/app/controllers/memory_explicit_controller.py
Normal file
@@ -0,0 +1,115 @@
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"""
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显性记忆控制器
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处理显性记忆相关的API接口，包括情景记忆和语义记忆的查询。
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"""
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from fastapi import APIRouter, Depends
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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.memory_explicit_service import MemoryExplicitService
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from app.schemas.response_schema import ApiResponse
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from app.schemas.memory_explicit_schema import (
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ExplicitMemoryOverviewRequest,
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ExplicitMemoryDetailsRequest,
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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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memory_explicit_service = MemoryExplicitService()
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router = APIRouter(
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prefix="/memory/explicit-memory",
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tags=["Explicit Memory"],
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)
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@router.post("/overview", response_model=ApiResponse)
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async def get_explicit_memory_overview_api(
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request: ExplicitMemoryOverviewRequest,
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current_user: User = Depends(get_current_user),
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) -> dict:
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"""
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获取显性记忆总览
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返回指定用户的所有显性记忆列表，包括标题、完整内容、创建时间和情绪信息。
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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} 尝试查询显性记忆总览但未选择工作空间")
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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"显性记忆总览查询请求: end_user_id={request.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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# 调用Service层方法
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result = await memory_explicit_service.get_explicit_memory_overview(
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request.end_user_id
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)
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api_logger.info(
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f"成功获取显性记忆总览: end_user_id={request.end_user_id}, "
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f"total={result['total']}"
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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"显性记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "显性记忆总览查询失败", str(e))
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@router.post("/details", response_model=ApiResponse)
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async def get_explicit_memory_details_api(
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request: ExplicitMemoryDetailsRequest,
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current_user: User = Depends(get_current_user),
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) -> dict:
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"""
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获取显性记忆详情
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根据 memory_id 返回情景记忆或语义记忆的详细信息。
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- 情景记忆：包括标题、内容、情绪、创建时间
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- 语义记忆：包括名称、核心定义、详细笔记、创建时间
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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} 尝试查询显性记忆详情但未选择工作空间")
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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"显性记忆详情查询请求: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
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f"user={current_user.username}, workspace={workspace_id}"
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)
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try:
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# 调用Service层方法
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result = await memory_explicit_service.get_explicit_memory_details(
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end_user_id=request.end_user_id,
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memory_id=request.memory_id
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)
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api_logger.info(
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f"成功获取显性记忆详情: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
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f"memory_type={result.get('memory_type')}"
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)
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return success(data=result, msg="查询成功")
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except ValueError as e:
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# 处理记忆不存在的情况
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api_logger.warning(f"显性记忆不存在: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
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return fail(BizCode.INVALID_PARAMETER, "显性记忆不存在", str(e))
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except Exception as e:
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api_logger.error(f"显性记忆详情查询失败: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "显性记忆详情查询失败", str(e))
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@@ -20,12 +20,6 @@ from app.services.user_memory_service import (
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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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EpisodicMemoryOverviewRequest,
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EpisodicMemoryDetailsRequest,
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ExplicitMemoryOverviewRequest,
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ExplicitMemoryDetailsRequest,
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)
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from app.schemas.end_user_schema import (
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EndUserProfileResponse,
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@@ -440,195 +434,3 @@ async def memory_space_relationship_evolution(id: str, label: str,
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except Exception as e:
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api_logger.error(f"关系演变查询失败: id={id}, table={label}, error={str(e)}", exc_info=True)
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return fail(BizCode.INTERNAL_ERROR, "关系演变查询失败", str(e))
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@router.post("/classifications/episodic-memory", response_model=ApiResponse)
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async def get_episodic_memory_overview_api(
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request: EpisodicMemoryOverviewRequest,
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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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获取情景记忆总览
|
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|
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返回指定用户的所有情景记忆列表，包括标题和创建时间。
|
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支持通过时间范围、情景类型和标题关键词进行筛选。
|
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|
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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} 尝试查询情景记忆总览但未选择工作空间")
|
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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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|
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# 验证参数
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valid_time_ranges = ["all", "today", "this_week", "this_month"]
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valid_episodic_types = ["all", "conversation", "project_work", "learning", "decision", "important_event"]
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if request.time_range not in valid_time_ranges:
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return fail(BizCode.INVALID_PARAMETER, f"无效的时间范围参数，可选值：{', '.join(valid_time_ranges)}")
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|
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if request.episodic_type not in valid_episodic_types:
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return fail(BizCode.INVALID_PARAMETER, f"无效的情景类型参数，可选值：{', '.join(valid_episodic_types)}")
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# 处理 title_keyword（去除首尾空格）
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title_keyword = request.title_keyword.strip() if request.title_keyword else None
|
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|
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api_logger.info(
|
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f"情景记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
|
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f"workspace={workspace_id}, time_range={request.time_range}, episodic_type={request.episodic_type}, "
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f"title_keyword={title_keyword}"
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)
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try:
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# 调用Service层方法
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result = await user_memory_service.get_episodic_memory_overview(
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db, request.end_user_id, request.time_range, request.episodic_type, title_keyword
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)
|
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api_logger.info(
|
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f"成功获取情景记忆总览: end_user_id={request.end_user_id}, "
|
||||
f"total={result['total']}"
|
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)
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return success(data=result, msg="查询成功")
|
||||
|
||||
except Exception as e:
|
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api_logger.error(f"情景记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "情景记忆总览查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/episodic-memory-details", response_model=ApiResponse)
|
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async def get_episodic_memory_details_api(
|
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request: EpisodicMemoryDetailsRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取情景记忆详情
|
||||
|
||||
返回指定情景记忆的详细信息，包括涉及对象、情景类型、内容记录和情绪。
|
||||
|
||||
"""
|
||||
workspace_id = current_user.current_workspace_id
|
||||
|
||||
# 检查用户是否已选择工作空间
|
||||
if workspace_id is None:
|
||||
api_logger.warning(f"用户 {current_user.username} 尝试查询情景记忆详情但未选择工作空间")
|
||||
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"情景记忆详情查询请求: end_user_id={request.end_user_id}, summary_id={request.summary_id}, "
|
||||
f"user={current_user.username}, workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_episodic_memory_details(
|
||||
db=db,
|
||||
end_user_id=request.end_user_id,
|
||||
summary_id=request.summary_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取情景记忆详情: end_user_id={request.end_user_id}, summary_id={request.summary_id}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except ValueError as e:
|
||||
# 处理情景记忆不存在的情况
|
||||
api_logger.warning(f"情景记忆不存在: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
|
||||
return fail(BizCode.INVALID_PARAMETER, "情景记忆不存在", str(e))
|
||||
except Exception as e:
|
||||
api_logger.error(f"情景记忆详情查询失败: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "情景记忆详情查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/explicit-memory", response_model=ApiResponse)
|
||||
async def get_explicit_memory_overview_api(
|
||||
request: ExplicitMemoryOverviewRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取显性记忆总览
|
||||
|
||||
返回指定用户的所有显性记忆列表，包括标题、完整内容、创建时间和情绪信息。
|
||||
"""
|
||||
workspace_id = current_user.current_workspace_id
|
||||
|
||||
# 检查用户是否已选择工作空间
|
||||
if workspace_id is None:
|
||||
api_logger.warning(f"用户 {current_user.username} 尝试查询显性记忆总览但未选择工作空间")
|
||||
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"显性记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
|
||||
f"workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_explicit_memory_overview(
|
||||
db, request.end_user_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取显性记忆总览: end_user_id={request.end_user_id}, "
|
||||
f"total={result['total']}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except Exception as e:
|
||||
api_logger.error(f"显性记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "显性记忆总览查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/explicit-memory-details", response_model=ApiResponse)
|
||||
async def get_explicit_memory_details_api(
|
||||
request: ExplicitMemoryDetailsRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 返回情景记忆或语义记忆的详细信息。
|
||||
- 情景记忆：包括标题、内容、情绪、创建时间
|
||||
- 语义记忆：包括名称、核心定义、详细笔记、创建时间
|
||||
"""
|
||||
workspace_id = current_user.current_workspace_id
|
||||
|
||||
# 检查用户是否已选择工作空间
|
||||
if workspace_id is None:
|
||||
api_logger.warning(f"用户 {current_user.username} 尝试查询显性记忆详情但未选择工作空间")
|
||||
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"显性记忆详情查询请求: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
|
||||
f"user={current_user.username}, workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_explicit_memory_details(
|
||||
db=db,
|
||||
end_user_id=request.end_user_id,
|
||||
memory_id=request.memory_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取显性记忆详情: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
|
||||
f"memory_type={result.get('memory_type')}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except ValueError as e:
|
||||
# 处理记忆不存在的情况
|
||||
api_logger.warning(f"显性记忆不存在: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
|
||||
return fail(BizCode.INVALID_PARAMETER, "显性记忆不存在", str(e))
|
||||
except Exception as e:
|
||||
api_logger.error(f"显性记忆详情查询失败: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "显性记忆详情查询失败", str(e))
|
||||
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
"""
|
||||
用户记忆相关的请求和响应模型
|
||||
情景记忆的请求和响应模型
|
||||
"""
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Optional
|
||||
@@ -28,16 +28,3 @@ class EpisodicMemoryDetailsRequest(BaseModel):
|
||||
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
summary_id: str = Field(..., description="情景记忆摘要ID")
|
||||
|
||||
|
||||
class ExplicitMemoryOverviewRequest(BaseModel):
|
||||
"""显性记忆总览查询请求"""
|
||||
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
|
||||
|
||||
class ExplicitMemoryDetailsRequest(BaseModel):
|
||||
"""显性记忆详情查询请求"""
|
||||
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
memory_id: str = Field(..., description="记忆ID（情景记忆或语义记忆的ID）")
|
||||
15
api/app/schemas/memory_explicit_schema.py
Normal file
15
api/app/schemas/memory_explicit_schema.py
Normal file
@@ -0,0 +1,15 @@
|
||||
"""
|
||||
显性记忆的请求和响应模型
|
||||
"""
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class ExplicitMemoryOverviewRequest(BaseModel):
|
||||
"""显性记忆总览查询请求"""
|
||||
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
|
||||
class ExplicitMemoryDetailsRequest(BaseModel):
|
||||
"""显性记忆详情查询请求"""
|
||||
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
memory_id: str = Field(..., description="记忆ID（情景记忆或语义记忆的ID）")
|
||||
111
api/app/services/memory_base_service.py
Normal file
111
api/app/services/memory_base_service.py
Normal file
@@ -0,0 +1,111 @@
|
||||
"""
|
||||
Memory Base Service
|
||||
|
||||
提供记忆服务的基础功能和共享辅助方法。
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from app.core.logging_config import get_logger
|
||||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryBaseService:
|
||||
"""记忆服务基类，提供共享的辅助方法"""
|
||||
|
||||
def __init__(self):
|
||||
self.neo4j_connector = Neo4jConnector()
|
||||
|
||||
@staticmethod
|
||||
def parse_timestamp(timestamp_value) -> Optional[int]:
|
||||
"""
|
||||
将时间戳转换为毫秒级时间戳
|
||||
|
||||
支持多种输入格式：
|
||||
- Neo4j DateTime 对象
|
||||
- ISO格式的时间戳字符串
|
||||
- Python datetime 对象
|
||||
|
||||
Args:
|
||||
timestamp_value: 时间戳值（可以是多种类型）
|
||||
|
||||
Returns:
|
||||
毫秒级时间戳，如果解析失败则返回None
|
||||
"""
|
||||
if not timestamp_value:
|
||||
return None
|
||||
|
||||
try:
|
||||
# 处理 Neo4j DateTime 对象
|
||||
if hasattr(timestamp_value, 'to_native'):
|
||||
dt_object = timestamp_value.to_native()
|
||||
return int(dt_object.timestamp() * 1000)
|
||||
|
||||
# 处理 Python datetime 对象
|
||||
if isinstance(timestamp_value, datetime):
|
||||
return int(timestamp_value.timestamp() * 1000)
|
||||
|
||||
# 处理字符串格式
|
||||
if isinstance(timestamp_value, str):
|
||||
dt_object = datetime.fromisoformat(timestamp_value.replace("Z", "+00:00"))
|
||||
return int(dt_object.timestamp() * 1000)
|
||||
|
||||
# 其他情况尝试转换为字符串再解析
|
||||
dt_object = datetime.fromisoformat(str(timestamp_value).replace("Z", "+00:00"))
|
||||
return int(dt_object.timestamp() * 1000)
|
||||
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {timestamp_value}, error={str(e)}")
|
||||
return None
|
||||
|
||||
async def extract_episodic_emotion(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
提取情景记忆的主要情绪
|
||||
|
||||
查询MemorySummary节点关联的Statement节点，
|
||||
返回emotion_intensity最大的emotion_type。
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
最大emotion_intensity对应的emotion_type，如果没有则返回None
|
||||
"""
|
||||
try:
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.emotion_type IS NOT NULL
|
||||
AND stmt.emotion_intensity IS NOT NULL
|
||||
RETURN stmt.emotion_type AS emotion_type,
|
||||
stmt.emotion_intensity AS emotion_intensity
|
||||
ORDER BY emotion_intensity DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if result and len(result) > 0:
|
||||
emotion_type = result[0].get("emotion_type")
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的情绪: {emotion_type}")
|
||||
return emotion_type
|
||||
else:
|
||||
logger.info(f"summary_id={summary_id} 没有情绪信息")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取情景记忆情绪时出错: {str(e)}", exc_info=True)
|
||||
return None
|
||||
405
api/app/services/memory_episodic_service.py
Normal file
405
api/app/services/memory_episodic_service.py
Normal file
@@ -0,0 +1,405 @@
|
||||
"""
|
||||
Episodic Memory Service
|
||||
|
||||
处理情景记忆相关的业务逻辑，包括情景记忆总览、详情查询等。
|
||||
"""
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import pytz
|
||||
from app.core.logging_config import get_logger
|
||||
from app.services.memory_base_service import MemoryBaseService
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryEpisodicService(MemoryBaseService):
|
||||
"""情景记忆服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
logger.info("MemoryEpisodicService initialized")
|
||||
|
||||
async def _get_title_and_type(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
读取情景记忆的标题(title)和类型(type)
|
||||
|
||||
仅负责读取已存在的title和type，不进行生成
|
||||
title从name属性读取，type从memory_type属性读取
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组，如果不存在则返回默认值
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点的name(作为title)和memory_type(作为type)
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
RETURN s.name AS title, s.memory_type AS type
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的节点")
|
||||
return ("未知标题", "其他")
|
||||
|
||||
record = result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
episodic_type = record.get("type") or "其他"
|
||||
|
||||
return (title, episodic_type)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"读取标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", "其他")
|
||||
|
||||
async def _extract_involved_objects(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆涉及的前3个最重要实体
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
前3个实体的name属性列表
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的Statement节点,再查询Statement指向的ExtractedEntity节点
|
||||
# 按activation_value降序排序,返回前3个
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity)
|
||||
WHERE entity.activation_value IS NOT NULL
|
||||
RETURN DISTINCT entity.name AS name, entity.activation_value AS activation
|
||||
ORDER BY activation DESC
|
||||
LIMIT 3
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取实体名称
|
||||
involved_objects = [record["name"] for record in result if record.get("name")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的涉及对象: {involved_objects}")
|
||||
|
||||
return involved_objects
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取涉及对象时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def _extract_content_records(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆的内容记录
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
所有Statement节点的statement属性内容列表
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的所有Statement节点
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.statement IS NOT NULL AND stmt.statement <> ''
|
||||
RETURN stmt.statement AS statement
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取statement内容
|
||||
content_records = [record["statement"] for record in result if record.get("statement")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的内容记录,共 {len(content_records)} 条")
|
||||
|
||||
return content_records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取内容记录时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
def _calculate_time_filter(self, time_range: str) -> Optional[str]:
|
||||
"""
|
||||
根据时间范围计算过滤的起始时间
|
||||
|
||||
Args:
|
||||
time_range: 时间范围 (all/today/this_week/this_month)
|
||||
|
||||
Returns:
|
||||
ISO格式的时间字符串，如果是"all"则返回None
|
||||
"""
|
||||
if time_range == "all":
|
||||
return None
|
||||
|
||||
# 获取当前时间（UTC）
|
||||
now = datetime.now(pytz.UTC)
|
||||
|
||||
if time_range == "today":
|
||||
# 今天的开始时间（00:00:00）
|
||||
start_time = now.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
elif time_range == "this_week":
|
||||
# 本周的开始时间（周一00:00:00）
|
||||
days_since_monday = now.weekday()
|
||||
start_time = (now - timedelta(days=days_since_monday)).replace(
|
||||
hour=0, minute=0, second=0, microsecond=0
|
||||
)
|
||||
elif time_range == "this_month":
|
||||
# 本月的开始时间（1号00:00:00）
|
||||
start_time = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
|
||||
else:
|
||||
return None
|
||||
|
||||
# 返回ISO格式字符串
|
||||
return start_time.isoformat()
|
||||
|
||||
async def get_episodic_memory_overview(
|
||||
self,
|
||||
end_user_id: str,
|
||||
time_range: str = "all",
|
||||
episodic_type: str = "all",
|
||||
title_keyword: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取情景记忆总览信息
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
time_range: 时间范围筛选
|
||||
episodic_type: 情景类型筛选
|
||||
title_keyword: 标题关键词（可选，用于模糊搜索）
|
||||
"""
|
||||
try:
|
||||
logger.info(
|
||||
f"开始查询 end_user_id={end_user_id} 的情景记忆总览, "
|
||||
f"time_range={time_range}, episodic_type={episodic_type}, title_keyword={title_keyword}"
|
||||
)
|
||||
|
||||
# 1. 先查询所有情景记忆的总数（不受筛选条件限制）
|
||||
total_all_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
RETURN count(s) AS total_all
|
||||
"""
|
||||
total_all_result = await self.neo4j_connector.execute_query(
|
||||
total_all_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
total_all = total_all_result[0]["total_all"] if total_all_result else 0
|
||||
|
||||
# 2. 计算时间范围的起始时间戳
|
||||
time_filter = self._calculate_time_filter(time_range)
|
||||
|
||||
# 3. 构建Cypher查询
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
"""
|
||||
|
||||
# 添加时间范围过滤
|
||||
if time_filter:
|
||||
query += " AND s.created_at >= $time_filter"
|
||||
|
||||
# 添加标题关键词过滤（如果提供了title_keyword）
|
||||
if title_keyword:
|
||||
query += " AND toLower(s.name) CONTAINS toLower($title_keyword)"
|
||||
|
||||
query += """
|
||||
RETURN elementId(s) AS id,
|
||||
s.created_at AS created_at,
|
||||
s.memory_type AS type,
|
||||
s.name AS title
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
params = {"group_id": end_user_id}
|
||||
if time_filter:
|
||||
params["time_filter"] = time_filter
|
||||
if title_keyword:
|
||||
params["title_keyword"] = title_keyword
|
||||
|
||||
result = await self.neo4j_connector.execute_query(query, **params)
|
||||
|
||||
# 4. 如果没有数据，返回空列表
|
||||
if not result:
|
||||
logger.info(f"end_user_id={end_user_id} 没有情景记忆数据")
|
||||
return {
|
||||
"total": 0,
|
||||
"total_all": total_all,
|
||||
"episodic_memories": []
|
||||
}
|
||||
|
||||
# 5. 对每个节点读取标题和类型，并应用类型筛选
|
||||
episodic_memories = []
|
||||
for record in result:
|
||||
summary_id = record["id"]
|
||||
created_at_str = record.get("created_at")
|
||||
memory_type = record.get("type", "其他")
|
||||
title = record.get("title") or "未命名" # 直接从查询结果获取标题
|
||||
|
||||
# 应用情景类型筛选
|
||||
if episodic_type != "all":
|
||||
# 检查类型是否匹配
|
||||
# 注意：Neo4j 中存储的 memory_type 现在应该是英文格式（如 "conversation", "project_work"）
|
||||
# 但为了兼容旧数据，我们也支持中文格式的匹配
|
||||
type_mapping = {
|
||||
"conversation": "对话",
|
||||
"project_work": "项目/工作",
|
||||
"learning": "学习",
|
||||
"decision": "决策",
|
||||
"important_event": "重要事件"
|
||||
}
|
||||
|
||||
# 获取对应的中文类型（用于兼容旧数据）
|
||||
chinese_type = type_mapping.get(episodic_type)
|
||||
|
||||
# 检查类型是否匹配（支持新的英文格式和旧的中文格式）
|
||||
if memory_type != episodic_type and memory_type != chinese_type:
|
||||
continue
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"type": memory_type,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的情景记忆总览,"
|
||||
f"筛选后 {len(episodic_memories)} 条，总共 {total_all} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": len(episodic_memories),
|
||||
"total_all": total_all,
|
||||
"episodic_memories": episodic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_episodic_memory_details(
|
||||
self,
|
||||
end_user_id: str,
|
||||
summary_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取单个情景记忆的详细信息
|
||||
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id}, summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
# 1. 查询指定的MemorySummary节点
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
RETURN elementId(s) AS id, s.created_at AS created_at
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 2. 如果节点不存在，返回错误
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的情景记忆")
|
||||
raise ValueError(f"情景记忆不存在: summary_id={summary_id}")
|
||||
|
||||
# 3. 获取基本信息
|
||||
record = result[0]
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
# 4. 调用_get_title_and_type读取标题和类型
|
||||
title, episodic_type = await self._get_title_and_type(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 5. 调用_extract_involved_objects提取涉及对象
|
||||
involved_objects = await self._extract_involved_objects(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 6. 调用_extract_content_records提取内容记录
|
||||
content_records = await self._extract_content_records(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 7. 使用基类方法提取情绪
|
||||
emotion = await self.extract_episodic_emotion(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 8. 返回完整的详情信息
|
||||
details = {
|
||||
"id": summary_id,
|
||||
"created_at": created_at_timestamp,
|
||||
"involved_objects": involved_objects,
|
||||
"episodic_type": episodic_type,
|
||||
"content_records": content_records,
|
||||
"emotion": emotion
|
||||
}
|
||||
|
||||
logger.info(f"成功获取 summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
return details
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出ValueError，让Controller层处理
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
# 创建全局服务实例
|
||||
memory_episodic_service = MemoryEpisodicService()
|
||||
274
api/app/services/memory_explicit_service.py
Normal file
274
api/app/services/memory_explicit_service.py
Normal file
@@ -0,0 +1,274 @@
|
||||
"""
|
||||
显性记忆服务
|
||||
|
||||
处理显性记忆相关的业务逻辑，包括情景记忆和语义记忆的查询。
|
||||
"""
|
||||
|
||||
from typing import Any, Dict
|
||||
|
||||
from app.core.logging_config import get_logger
|
||||
from app.services.memory_base_service import MemoryBaseService
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryExplicitService(MemoryBaseService):
|
||||
"""显性记忆服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
logger.info("MemoryExplicitService initialized")
|
||||
|
||||
async def get_explicit_memory_overview(
|
||||
self,
|
||||
end_user_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆总览信息
|
||||
|
||||
返回两部分：
|
||||
1. 情景记忆（episodic_memories）- 来自MemorySummary节点
|
||||
2. 语义记忆（semantic_memories）- 来自ExtractedEntity节点（is_explicit_memory=true）
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
|
||||
Returns:
|
||||
{
|
||||
"total": int,
|
||||
"episodic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"title": str,
|
||||
"content": str,
|
||||
"created_at": int
|
||||
}
|
||||
],
|
||||
"semantic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"name": str,
|
||||
"entity_type": str,
|
||||
"core_definition": str
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id} 的显性记忆总览（情景记忆+语义记忆）")
|
||||
|
||||
# ========== 1. 查询情景记忆（MemorySummary节点） ==========
|
||||
episodic_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
RETURN elementId(s) AS id,
|
||||
s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理情景记忆数据
|
||||
episodic_memories = []
|
||||
if episodic_result:
|
||||
for record in episodic_result:
|
||||
summary_id = record["id"]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
# 注意：总览接口不返回 emotion 字段
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"content": content,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
# ========== 2. 查询语义记忆（ExtractedEntity节点） ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN elementId(e) AS id,
|
||||
e.name AS name,
|
||||
e.entity_type AS entity_type,
|
||||
e.description AS core_definition
|
||||
ORDER BY e.name ASC
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理语义记忆数据
|
||||
semantic_memories = []
|
||||
if semantic_result:
|
||||
for record in semantic_result:
|
||||
entity_id = record["id"]
|
||||
name = record.get("name") or "未命名"
|
||||
entity_type = record.get("entity_type") or "未分类"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
|
||||
# 注意：总览接口不返回 detailed_notes 和 created_at 字段
|
||||
semantic_memories.append({
|
||||
"id": entity_id,
|
||||
"name": name,
|
||||
"entity_type": entity_type,
|
||||
"core_definition": core_definition
|
||||
})
|
||||
|
||||
# ========== 3. 返回结果 ==========
|
||||
total_count = len(episodic_memories) + len(semantic_memories)
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的显性记忆总览，"
|
||||
f"情景记忆={len(episodic_memories)} 条，语义记忆={len(semantic_memories)} 条，"
|
||||
f"总计 {total_count} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": total_count,
|
||||
"episodic_memories": episodic_memories,
|
||||
"semantic_memories": semantic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_explicit_memory_details(
|
||||
self,
|
||||
end_user_id: str,
|
||||
memory_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 查询情景记忆或语义记忆的详细信息。
|
||||
先尝试查询情景记忆，如果找不到再查询语义记忆。
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
memory_id: 记忆ID（可以是情景记忆或语义记忆的ID）
|
||||
|
||||
Returns:
|
||||
情景记忆返回：
|
||||
{
|
||||
"memory_type": "episodic",
|
||||
"title": str,
|
||||
"content": str,
|
||||
"emotion": Dict,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
语义记忆返回：
|
||||
{
|
||||
"memory_type": "semantic",
|
||||
"name": str,
|
||||
"core_definition": str,
|
||||
"detailed_notes": str,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
Raises:
|
||||
ValueError: 当记忆不存在时
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询显性记忆详情: end_user_id={end_user_id}, memory_id={memory_id}")
|
||||
|
||||
# ========== 1. 先尝试查询情景记忆 ==========
|
||||
episodic_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $memory_id AND s.group_id = $group_id
|
||||
RETURN s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if episodic_result and len(episodic_result) > 0:
|
||||
record = episodic_result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
# 使用基类方法获取情绪信息
|
||||
emotion = await self.extract_episodic_emotion(
|
||||
summary_id=memory_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
logger.info(f"成功获取情景记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "episodic",
|
||||
"title": title,
|
||||
"content": content,
|
||||
"emotion": emotion,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 2. 如果不是情景记忆，尝试查询语义记忆 ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE elementId(e) = $memory_id
|
||||
AND e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN e.name AS name,
|
||||
e.description AS core_definition,
|
||||
e.example AS detailed_notes,
|
||||
e.created_at AS created_at
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if semantic_result and len(semantic_result) > 0:
|
||||
record = semantic_result[0]
|
||||
name = record.get("name") or "未命名"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
detailed_notes = record.get("detailed_notes") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
logger.info(f"成功获取语义记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "semantic",
|
||||
"name": name,
|
||||
"core_definition": core_definition,
|
||||
"detailed_notes": detailed_notes,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 3. 两种记忆都找不到 ==========
|
||||
logger.warning(f"记忆不存在: memory_id={memory_id}, end_user_id={end_user_id}")
|
||||
raise ValueError(f"记忆不存在: memory_id={memory_id}")
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出 ValueError（记忆不存在）
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
@@ -883,866 +883,6 @@ class UserMemoryService:
|
||||
"failed": failed,
|
||||
"errors": errors + [{"error": f"批量处理失败: {str(e)}"}]
|
||||
}
|
||||
|
||||
async def _get_title_and_type(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
读取情景记忆的标题(title)和类型(type)
|
||||
|
||||
仅负责读取已存在的title和type，不进行生成
|
||||
title从name属性读取，type从memory_type属性读取
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组，如果不存在则返回默认值
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点的name(作为title)和memory_type(作为type)
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
RETURN s.name AS title, s.memory_type AS type
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的节点")
|
||||
return ("未知标题", "其他")
|
||||
|
||||
record = result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
episodic_type = record.get("type") or "其他"
|
||||
|
||||
return (title, episodic_type)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"读取标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", "其他")
|
||||
|
||||
@staticmethod
|
||||
async def generate_title_and_type_for_summary(
|
||||
content: str,
|
||||
end_user_id: str
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
为MemorySummary生成标题和类型（静态方法，用于创建节点时调用）
|
||||
|
||||
此方法应该在创建MemorySummary节点时调用，生成title和type
|
||||
|
||||
Args:
|
||||
content: Summary的内容文本
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组
|
||||
"""
|
||||
from app.core.memory.utils.prompt.prompt_utils import render_episodic_title_and_type_prompt
|
||||
import json
|
||||
|
||||
# 定义有效的类型集合
|
||||
VALID_TYPES = {
|
||||
"conversation", # 对话
|
||||
"project_work", # 项目/工作
|
||||
"learning", # 学习
|
||||
"decision", # 决策
|
||||
"important_event" # 重要事件
|
||||
}
|
||||
DEFAULT_TYPE = "conversation" # 默认类型
|
||||
|
||||
try:
|
||||
if not content:
|
||||
logger.warning("content为空，无法生成标题和类型")
|
||||
return ("空内容", DEFAULT_TYPE)
|
||||
|
||||
# 1. 渲染Jinja2提示词模板
|
||||
prompt = await render_episodic_title_and_type_prompt(content)
|
||||
|
||||
# 2. 调用LLM生成标题和类型
|
||||
llm_client = _get_llm_client_for_user(end_user_id)
|
||||
messages = [
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
response = await llm_client.chat(messages=messages)
|
||||
|
||||
# 3. 解析LLM响应
|
||||
content_response = response.content
|
||||
if isinstance(content_response, list):
|
||||
if len(content_response) > 0:
|
||||
if isinstance(content_response[0], dict):
|
||||
text = content_response[0].get('text', content_response[0].get('content', str(content_response[0])))
|
||||
full_response = str(text)
|
||||
else:
|
||||
full_response = str(content_response[0])
|
||||
else:
|
||||
full_response = ""
|
||||
elif isinstance(content_response, dict):
|
||||
full_response = str(content_response.get('text', content_response.get('content', str(content_response))))
|
||||
else:
|
||||
full_response = str(content_response) if content_response is not None else ""
|
||||
|
||||
# 4. 解析JSON响应
|
||||
try:
|
||||
# 尝试从响应中提取JSON
|
||||
# 移除可能的markdown代码块标记
|
||||
json_str = full_response.strip()
|
||||
if json_str.startswith("```json"):
|
||||
json_str = json_str[7:]
|
||||
if json_str.startswith("```"):
|
||||
json_str = json_str[3:]
|
||||
if json_str.endswith("```"):
|
||||
json_str = json_str[:-3]
|
||||
json_str = json_str.strip()
|
||||
|
||||
result_data = json.loads(json_str)
|
||||
title = result_data.get("title", "未知标题")
|
||||
episodic_type_raw = result_data.get("type", DEFAULT_TYPE)
|
||||
|
||||
# 5. 校验和归一化类型
|
||||
# 将类型转换为小写并去除空格
|
||||
episodic_type_normalized = str(episodic_type_raw).lower().strip()
|
||||
|
||||
# 检查是否在有效类型集合中
|
||||
if episodic_type_normalized in VALID_TYPES:
|
||||
episodic_type = episodic_type_normalized
|
||||
else:
|
||||
# 尝试映射常见的中文类型到英文
|
||||
type_mapping = {
|
||||
"对话": "conversation",
|
||||
"项目": "project_work",
|
||||
"工作": "project_work",
|
||||
"项目/工作": "project_work",
|
||||
"学习": "learning",
|
||||
"决策": "decision",
|
||||
"重要事件": "important_event",
|
||||
"事件": "important_event"
|
||||
}
|
||||
episodic_type = type_mapping.get(episodic_type_raw, DEFAULT_TYPE)
|
||||
logger.warning(
|
||||
f"LLM返回的类型 '{episodic_type_raw}' 不在有效集合中，"
|
||||
f"已归一化为 '{episodic_type}'"
|
||||
)
|
||||
|
||||
logger.info(f"成功生成标题和类型: title={title}, type={episodic_type}")
|
||||
return (title, episodic_type)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
logger.error(f"无法解析LLM响应为JSON: {full_response}")
|
||||
return ("解析失败", DEFAULT_TYPE)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", DEFAULT_TYPE)
|
||||
|
||||
async def _extract_involved_objects(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆涉及的前3个最重要实体
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
前3个实体的name属性列表
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的Statement节点,再查询Statement指向的ExtractedEntity节点
|
||||
# 按activation_value降序排序,返回前3个
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity)
|
||||
WHERE entity.activation_value IS NOT NULL
|
||||
RETURN DISTINCT entity.name AS name, entity.activation_value AS activation
|
||||
ORDER BY activation DESC
|
||||
LIMIT 3
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取实体名称
|
||||
involved_objects = [record["name"] for record in result if record.get("name")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的涉及对象: {involved_objects}")
|
||||
|
||||
return involved_objects
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取涉及对象时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def _extract_content_records(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆的内容记录
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
所有Statement节点的statement属性内容列表
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的所有Statement节点
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.statement IS NOT NULL AND stmt.statement <> ''
|
||||
RETURN stmt.statement AS statement
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取statement内容
|
||||
content_records = [record["statement"] for record in result if record.get("statement")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的内容记录,共 {len(content_records)} 条")
|
||||
|
||||
return content_records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取内容记录时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def _extract_episodic_emotion(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
提取情景记忆的主要情绪
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
最大emotion_intensity对应的emotion_type,如果没有则返回None
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的所有Statement节点
|
||||
# 筛选具有emotion_type属性的节点
|
||||
# 按emotion_intensity降序排序,返回第一个
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.emotion_type IS NOT NULL
|
||||
AND stmt.emotion_intensity IS NOT NULL
|
||||
RETURN stmt.emotion_type AS emotion_type,
|
||||
stmt.emotion_intensity AS emotion_intensity
|
||||
ORDER BY emotion_intensity DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取emotion_type
|
||||
if result and len(result) > 0:
|
||||
emotion_type = result[0].get("emotion_type")
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的情绪: {emotion_type}")
|
||||
return emotion_type
|
||||
else:
|
||||
logger.info(f"summary_id={summary_id} 没有情绪信息")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取情景记忆情绪时出错: {str(e)}", exc_info=True)
|
||||
return None
|
||||
|
||||
async def get_episodic_memory_overview(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
time_range: str = "all",
|
||||
episodic_type: str = "all",
|
||||
title_keyword: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取情景记忆总览信息
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
time_range: 时间范围筛选
|
||||
episodic_type: 情景类型筛选
|
||||
title_keyword: 标题关键词（可选，用于模糊搜索）
|
||||
"""
|
||||
try:
|
||||
logger.info(
|
||||
f"开始查询 end_user_id={end_user_id} 的情景记忆总览, "
|
||||
f"time_range={time_range}, episodic_type={episodic_type}, title_keyword={title_keyword}"
|
||||
)
|
||||
|
||||
# 1. 先查询所有情景记忆的总数（不受筛选条件限制）
|
||||
total_all_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
RETURN count(s) AS total_all
|
||||
"""
|
||||
total_all_result = await self.neo4j_connector.execute_query(
|
||||
total_all_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
total_all = total_all_result[0]["total_all"] if total_all_result else 0
|
||||
|
||||
# 2. 计算时间范围的起始时间戳
|
||||
time_filter = self._calculate_time_filter(time_range)
|
||||
|
||||
# 3. 构建Cypher查询
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
"""
|
||||
|
||||
# 添加时间范围过滤
|
||||
if time_filter:
|
||||
query += " AND s.created_at >= $time_filter"
|
||||
|
||||
# 添加标题关键词过滤（如果提供了title_keyword）
|
||||
if title_keyword:
|
||||
query += " AND toLower(s.name) CONTAINS toLower($title_keyword)"
|
||||
|
||||
query += """
|
||||
RETURN elementId(s) AS id,
|
||||
s.created_at AS created_at,
|
||||
s.memory_type AS type,
|
||||
s.name AS title
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
params = {"group_id": end_user_id}
|
||||
if time_filter:
|
||||
params["time_filter"] = time_filter
|
||||
if title_keyword:
|
||||
params["title_keyword"] = title_keyword
|
||||
|
||||
result = await self.neo4j_connector.execute_query(query, **params)
|
||||
|
||||
# 4. 如果没有数据，返回空列表
|
||||
if not result:
|
||||
logger.info(f"end_user_id={end_user_id} 没有情景记忆数据")
|
||||
return {
|
||||
"total": 0,
|
||||
"total_all": total_all,
|
||||
"episodic_memories": []
|
||||
}
|
||||
|
||||
# 5. 对每个节点读取标题和类型，并应用类型筛选
|
||||
episodic_memories = []
|
||||
for record in result:
|
||||
summary_id = record["id"]
|
||||
created_at_str = record.get("created_at")
|
||||
memory_type = record.get("type", "其他")
|
||||
title = record.get("title") or "未命名" # 直接从查询结果获取标题
|
||||
|
||||
# 应用情景类型筛选
|
||||
if episodic_type != "all":
|
||||
# 检查类型是否匹配
|
||||
# 注意：Neo4j 中存储的 memory_type 现在应该是英文格式（如 "conversation", "project_work"）
|
||||
# 但为了兼容旧数据，我们也支持中文格式的匹配
|
||||
type_mapping = {
|
||||
"conversation": "对话",
|
||||
"project_work": "项目/工作",
|
||||
"learning": "学习",
|
||||
"decision": "决策",
|
||||
"important_event": "重要事件"
|
||||
}
|
||||
|
||||
# 获取对应的中文类型（用于兼容旧数据）
|
||||
chinese_type = type_mapping.get(episodic_type)
|
||||
|
||||
# 检查类型是否匹配（支持新的英文格式和旧的中文格式）
|
||||
if memory_type != episodic_type and memory_type != chinese_type:
|
||||
continue
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"type": memory_type,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的情景记忆总览,"
|
||||
f"筛选后 {len(episodic_memories)} 条，总共 {total_all} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": len(episodic_memories),
|
||||
"total_all": total_all,
|
||||
"episodic_memories": episodic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
def _calculate_time_filter(self, time_range: str) -> Optional[str]:
|
||||
"""
|
||||
根据时间范围计算过滤的起始时间
|
||||
|
||||
Args:
|
||||
time_range: 时间范围 (all/today/this_week/this_month)
|
||||
|
||||
Returns:
|
||||
ISO格式的时间字符串，如果是"all"则返回None
|
||||
"""
|
||||
from datetime import datetime, timedelta
|
||||
import pytz
|
||||
|
||||
if time_range == "all":
|
||||
return None
|
||||
|
||||
# 获取当前时间（UTC）
|
||||
now = datetime.now(pytz.UTC)
|
||||
|
||||
if time_range == "today":
|
||||
# 今天的开始时间（00:00:00）
|
||||
start_time = now.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
elif time_range == "this_week":
|
||||
# 本周的开始时间（周一00:00:00）
|
||||
days_since_monday = now.weekday()
|
||||
start_time = (now - timedelta(days=days_since_monday)).replace(
|
||||
hour=0, minute=0, second=0, microsecond=0
|
||||
)
|
||||
elif time_range == "this_month":
|
||||
# 本月的开始时间（1号00:00:00）
|
||||
start_time = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
|
||||
else:
|
||||
return None
|
||||
|
||||
# 返回ISO格式字符串
|
||||
return start_time.isoformat()
|
||||
|
||||
async def get_episodic_memory_details(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
summary_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取单个情景记忆的详细信息
|
||||
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id}, summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
# 1. 查询指定的MemorySummary节点
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
RETURN elementId(s) AS id, s.created_at AS created_at
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 2. 如果节点不存在，返回错误
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的情景记忆")
|
||||
raise ValueError(f"情景记忆不存在: summary_id={summary_id}")
|
||||
|
||||
# 3. 获取基本信息
|
||||
record = result[0]
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 4. 调用_get_title_and_type读取标题和类型
|
||||
title, episodic_type = await self._get_title_and_type(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 5. 调用_extract_involved_objects提取涉及对象
|
||||
involved_objects = await self._extract_involved_objects(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 6. 调用_extract_content_records提取内容记录
|
||||
content_records = await self._extract_content_records(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 7. 调用_extract_episodic_emotion提取情绪
|
||||
emotion = await self._extract_episodic_emotion(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 8. 返回完整的详情信息
|
||||
details = {
|
||||
"id": summary_id,
|
||||
"created_at": created_at_timestamp,
|
||||
"involved_objects": involved_objects,
|
||||
"episodic_type": episodic_type,
|
||||
"content_records": content_records,
|
||||
"emotion": emotion
|
||||
}
|
||||
|
||||
logger.info(f"成功获取 summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
return details
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出ValueError，让Controller层处理
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_explicit_memory_overview(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆总览信息
|
||||
|
||||
返回两部分：
|
||||
1. 情景记忆（episodic_memories）- 来自MemorySummary节点
|
||||
2. 语义记忆（semantic_memories）- 来自ExtractedEntity节点（is_explicit_memory=true）
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
|
||||
Returns:
|
||||
{
|
||||
"total": int,
|
||||
"episodic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"title": str,
|
||||
"content": str,
|
||||
"created_at": int,
|
||||
"emotion": Dict
|
||||
}
|
||||
],
|
||||
"semantic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"name": str,
|
||||
"entity_type": str,
|
||||
"core_definition": str,
|
||||
"detailed_notes": str,
|
||||
"created_at": int
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id} 的显性记忆总览（情景记忆+语义记忆）")
|
||||
|
||||
# ========== 1. 查询情景记忆（MemorySummary节点） ==========
|
||||
episodic_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
RETURN elementId(s) AS id,
|
||||
s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理情景记忆数据
|
||||
episodic_memories = []
|
||||
if episodic_result:
|
||||
for record in episodic_result:
|
||||
summary_id = record["id"]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 注意：总览接口不返回 emotion 字段
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"content": content,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
# ========== 2. 查询语义记忆（ExtractedEntity节点） ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN elementId(e) AS id,
|
||||
e.name AS name,
|
||||
e.entity_type AS entity_type,
|
||||
e.description AS core_definition,
|
||||
e.example AS detailed_notes,
|
||||
e.created_at AS created_at
|
||||
ORDER BY e.created_at DESC
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理语义记忆数据
|
||||
semantic_memories = []
|
||||
if semantic_result:
|
||||
for record in semantic_result:
|
||||
entity_id = record["id"]
|
||||
name = record.get("name") or "未命名"
|
||||
entity_type = record.get("entity_type") or "未分类"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 注意：总览接口不返回 detailed_notes 字段
|
||||
semantic_memories.append({
|
||||
"id": entity_id,
|
||||
"name": name,
|
||||
"entity_type": entity_type,
|
||||
"core_definition": core_definition,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
# ========== 3. 返回结果 ==========
|
||||
total_count = len(episodic_memories) + len(semantic_memories)
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的显性记忆总览，"
|
||||
f"情景记忆={len(episodic_memories)} 条，语义记忆={len(semantic_memories)} 条，"
|
||||
f"总计 {total_count} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": total_count,
|
||||
"episodic_memories": episodic_memories,
|
||||
"semantic_memories": semantic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_explicit_memory_details(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
memory_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 查询情景记忆或语义记忆的详细信息。
|
||||
先尝试查询情景记忆，如果找不到再查询语义记忆。
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
memory_id: 记忆ID（可以是情景记忆或语义记忆的ID）
|
||||
|
||||
Returns:
|
||||
情景记忆返回：
|
||||
{
|
||||
"memory_type": "episodic",
|
||||
"title": str,
|
||||
"content": str,
|
||||
"emotion": Dict,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
语义记忆返回：
|
||||
{
|
||||
"memory_type": "semantic",
|
||||
"name": str,
|
||||
"core_definition": str,
|
||||
"detailed_notes": str,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
Raises:
|
||||
ValueError: 当记忆不存在时
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询显性记忆详情: end_user_id={end_user_id}, memory_id={memory_id}")
|
||||
|
||||
# ========== 1. 先尝试查询情景记忆 ==========
|
||||
episodic_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $memory_id AND s.group_id = $group_id
|
||||
RETURN s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if episodic_result and len(episodic_result) > 0:
|
||||
record = episodic_result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 获取情绪信息
|
||||
emotion = await self._extract_episodic_emotion(
|
||||
summary_id=memory_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
logger.info(f"成功获取情景记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "episodic",
|
||||
"title": title,
|
||||
"content": content,
|
||||
"emotion": emotion,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 2. 如果不是情景记忆，尝试查询语义记忆 ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE elementId(e) = $memory_id
|
||||
AND e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN e.name AS name,
|
||||
e.description AS core_definition,
|
||||
e.example AS detailed_notes,
|
||||
e.created_at AS created_at
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if semantic_result and len(semantic_result) > 0:
|
||||
record = semantic_result[0]
|
||||
name = record.get("name") or "未命名"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
detailed_notes = record.get("detailed_notes") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
logger.info(f"成功获取语义记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "semantic",
|
||||
"name": name,
|
||||
"core_definition": core_definition,
|
||||
"detailed_notes": detailed_notes,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 3. 两种记忆都找不到 ==========
|
||||
logger.warning(f"记忆不存在: memory_id={memory_id}, end_user_id={end_user_id}")
|
||||
raise ValueError(f"记忆不存在: memory_id={memory_id}")
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出 ValueError（记忆不存在）
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
# 独立的分析函数
|
||||
|
||||
@@ -27,4 +27,4 @@ def upgrade() -> None:
|
||||
def downgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.create_foreign_key(op.f('tool_executions_user_id_fkey'), 'tool_executions', 'users', ['user_id'], ['id'])
|
||||
# ### end Alembic commands ###
|
||||
# ### end Alembic commands ###
|
||||
Reference in New Issue
Block a user