Fix/memory bug fix (#162)
* 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段
This commit is contained in:
lixinyue11
@@ -9,7 +9,7 @@ from app.db import get_db
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from app.dependencies import cur_workspace_access_guard, get_current_user
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from app.models import ModelApiKey
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from app.models.user_model import User
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from app.repositories import knowledge_repository
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from app.repositories import knowledge_repository, WorkspaceRepository
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from app.schemas.memory_agent_schema import UserInput, Write_UserInput
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from app.schemas.response_schema import ApiResponse
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from app.services import task_service, workspace_service
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@@ -616,8 +616,10 @@ async def get_knowledge_type_stats_api(
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@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse)
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async def get_hot_memory_tags_by_user_api(
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end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
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language_type: Optional[str] ="zh",
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limit: int = Query(20, description="返回标签数量限制"),
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current_user: User = Depends(get_current_user)
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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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"""
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获取指定用户的热门记忆标签
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@@ -628,10 +630,22 @@ async def get_hot_memory_tags_by_user_api(
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...
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]
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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"Hot memory tags by user requested: end_user_id={end_user_id}")
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try:
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result = await memory_agent_service.get_hot_memory_tags_by_user(
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end_user_id=end_user_id,
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language_type=language_type,
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model_id=model_id,
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limit=limit
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)
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return success(data=result, msg="获取热门记忆标签成功")
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@@ -20,6 +20,7 @@ router = APIRouter(
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@router.get("/short_term")
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async def short_term_configs(
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end_user_id: str,
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language_type:Optional[str] = "zh",
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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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@@ -12,6 +12,7 @@ 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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@@ -20,7 +21,7 @@ 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.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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@@ -44,6 +45,7 @@ router = APIRouter(
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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 = "zh",
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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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@@ -53,10 +55,18 @@ async def get_memory_insight_report_api(
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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)
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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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@@ -72,6 +82,7 @@ async def get_memory_insight_report_api(
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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="zh",
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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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@@ -81,10 +92,18 @@ async def get_user_summary_api(
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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)
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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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@@ -253,7 +272,6 @@ async def get_graph_data_api(
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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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@@ -278,7 +296,13 @@ async def get_end_user_profile(
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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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@@ -296,7 +320,6 @@ async def get_end_user_profile(
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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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@@ -396,12 +419,21 @@ async def update_end_user_profile(
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return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", str(e))
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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,
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async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str="zh",
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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()
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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,
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@@ -11,6 +11,7 @@ class EmotionTagsRequest(BaseModel):
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start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）")
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end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）")
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limit: int = Field(10, ge=1, le=100, description="返回数量限制")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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class EmotionWordcloudRequest(BaseModel):
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@@ -18,20 +19,24 @@ class EmotionWordcloudRequest(BaseModel):
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group_id: str = Field(..., description="组ID")
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emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）")
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limit: int = Field(50, ge=1, le=200, description="返回词语数量")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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class EmotionHealthRequest(BaseModel):
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"""获取情绪健康指数请求"""
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group_id: str = Field(..., description="组ID")
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time_range: str = Field("30d", description="时间范围（7d/30d/90d）")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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class EmotionSuggestionsRequest(BaseModel):
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"""获取个性化情绪建议请求"""
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group_id: str = Field(..., description="组ID")
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config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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class EmotionGenerateSuggestionsRequest(BaseModel):
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"""生成个性化情绪建议请求"""
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end_user_id: str = Field(..., description="终端用户ID")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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@@ -44,6 +44,7 @@ class EndUserProfileResponse(BaseModel):
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updatetime_profile: Optional[datetime.datetime] = Field(description="核心档案信息最后更新时间", default=None)
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class EndUserProfileUpdate(BaseModel):
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"""终端用户基本信息更新请求模型"""
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end_user_id: str = Field(description="终端用户ID")
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@@ -51,6 +51,7 @@ class EpisodicMemoryOverviewRequest(BaseModel):
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"""情景记忆总览查询请求"""
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end_user_id: str = Field(..., description="终端用户ID")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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time_range: str = Field(
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default="all",
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description="时间范围筛选，可选值：all, today, this_week, this_month"
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@@ -70,3 +71,4 @@ class EpisodicMemoryDetailsRequest(BaseModel):
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end_user_id: str = Field(..., description="终端用户ID")
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summary_id: str = Field(..., description="情景记忆摘要ID")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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@@ -1,15 +1,19 @@
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"""
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显性记忆的请求和响应模型
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"""
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from typing import Optional
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from pydantic import BaseModel, Field
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class ExplicitMemoryOverviewRequest(BaseModel):
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"""显性记忆总览查询请求"""
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end_user_id: str = Field(..., description="终端用户ID")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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class ExplicitMemoryDetailsRequest(BaseModel):
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"""显性记忆详情查询请求"""
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end_user_id: str = Field(..., description="终端用户ID")
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memory_id: str = Field(..., description="记忆ID（情景记忆或语义记忆的ID）")
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language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
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@@ -26,6 +26,7 @@ from app.db import get_db_context
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from app.models.knowledge_model import Knowledge, KnowledgeType
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.schemas.memory_config_schema import ConfigurationError
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from app.services.memory_base_service import Translation_English
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from app.services.memory_config_service import MemoryConfigService
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from app.services.memory_konwledges_server import (
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write_rag,
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@@ -692,7 +693,9 @@ class MemoryAgentService:
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async def get_hot_memory_tags_by_user(
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self,
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end_user_id: Optional[str] = None,
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limit: int = 20
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limit: int = 20,
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model_id: Optional[str] = None,
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language_type: Optional[str] = "zh"
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) -> List[Dict[str, Any]]:
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"""
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获取指定用户的热门记忆标签
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@@ -710,7 +713,13 @@ class MemoryAgentService:
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try:
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# by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度）
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tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False)
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payload = [{"name": t, "frequency": f} for t, f in tags]
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payload=[]
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for tag, freq in tags:
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if language_type!="zh":
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tag=await Translation_English(model_id, tag)
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payload.append({"name": tag, "frequency": freq})
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else:
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payload.append({"name": tag, "frequency": freq})
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return payload
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except Exception as e:
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logger.error(f"热门记忆标签查询失败: {e}")
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@@ -3,17 +3,268 @@ Memory Base Service
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提供记忆服务的基础功能和共享辅助方法。
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"""
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import asyncio
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import re
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from datetime import datetime
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from typing import Optional
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from pydantic import BaseModel
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from app.core.logging_config import get_logger
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.services.emotion_analytics_service import EmotionAnalyticsService
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from app.core.memory.llm_tools.openai_client import OpenAIClient
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from app.core.models.base import RedBearModelConfig
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from app.services.memory_config_service import MemoryConfigService
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from app.db import get_db_context
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logger = get_logger(__name__)
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class TranslationResponse(BaseModel):
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"""翻译响应模型"""
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data: str
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class MemoryTransService:
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"""记忆翻译服务，提供中英文翻译功能"""
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def __init__(self, llm_client=None, model_id: Optional[str] = None):
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"""
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初始化翻译服务
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Args:
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llm_client: LLM客户端实例或模型ID字符串（可选）
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model_id: 模型ID，用于初始化LLM客户端（可选）
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|
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Note:
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- 如果llm_client是字符串，会被当作model_id使用
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- 如果同时提供llm_client和model_id，优先使用llm_client
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"""
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# 处理llm_client参数：如果是字符串，当作model_id
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if isinstance(llm_client, str):
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self.model_id = llm_client
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self.llm_client = None
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else:
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self.llm_client = llm_client
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self.model_id = model_id
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self._initialized = False
|
||||
|
||||
def _ensure_llm_client(self):
|
||||
"""确保LLM客户端已初始化"""
|
||||
if self._initialized:
|
||||
return
|
||||
|
||||
if self.llm_client is None:
|
||||
if self.model_id:
|
||||
with get_db_context() as db:
|
||||
config_service = MemoryConfigService(db)
|
||||
model_config = config_service.get_model_config(self.model_id)
|
||||
|
||||
extra_params = {
|
||||
"temperature": 0.2,
|
||||
"max_tokens": 400,
|
||||
"top_p": 0.8,
|
||||
"stream": False,
|
||||
}
|
||||
|
||||
self.llm_client = OpenAIClient(
|
||||
RedBearModelConfig(
|
||||
model_name=model_config.get("model_name"),
|
||||
provider=model_config.get("provider"),
|
||||
api_key=model_config.get("api_key"),
|
||||
base_url=model_config.get("base_url"),
|
||||
timeout=model_config.get("timeout", 30),
|
||||
max_retries=model_config.get("max_retries", 3),
|
||||
extra_params=extra_params
|
||||
),
|
||||
type_=model_config.get("type")
|
||||
)
|
||||
else:
|
||||
raise ValueError("必须提供 llm_client 或 model_id 之一")
|
||||
|
||||
self._initialized = True
|
||||
|
||||
async def translate_to_english(self, text: str) -> str:
|
||||
"""
|
||||
将中文翻译为英文
|
||||
|
||||
Args:
|
||||
text: 要翻译的中文文本
|
||||
|
||||
Returns:
|
||||
翻译后的英文文本
|
||||
"""
|
||||
self._ensure_llm_client()
|
||||
|
||||
translation_messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"{text}\n\n中文翻译为英文，输出格式为{{\"data\":\"翻译后的内容\"}}"
|
||||
}
|
||||
]
|
||||
|
||||
try:
|
||||
response = await self.llm_client.response_structured(
|
||||
messages=translation_messages,
|
||||
response_model=TranslationResponse
|
||||
)
|
||||
return response.data
|
||||
except Exception as e:
|
||||
logger.error(f"翻译失败: {str(e)}")
|
||||
return text # 翻译失败时返回原文
|
||||
|
||||
async def is_english(self, text: str) -> bool:
|
||||
"""
|
||||
检查文本是否为英文
|
||||
|
||||
Args:
|
||||
text: 要检查的文本（必须是字符串）
|
||||
|
||||
Returns:
|
||||
True 如果文本主要是英文，False 否则
|
||||
|
||||
Note:
|
||||
- 只接受字符串类型
|
||||
- 检查是否主要由英文字母和常见标点组成
|
||||
- 允许数字、空格和常见标点符号
|
||||
"""
|
||||
if not isinstance(text, str):
|
||||
raise TypeError(f"is_english 只接受字符串类型，收到: {type(text).__name__}")
|
||||
|
||||
if not text.strip():
|
||||
return True # 空字符串视为英文
|
||||
|
||||
# 更宽松的英文检查：允许字母、数字、空格和常见标点
|
||||
# 如果文本中英文字符占比超过 80%，认为是英文
|
||||
english_chars = sum(1 for c in text if c.isascii() and (c.isalnum() or c.isspace() or c in '.,!?;:\'"()-'))
|
||||
total_chars = len(text)
|
||||
|
||||
if total_chars == 0:
|
||||
return True
|
||||
|
||||
return (english_chars / total_chars) >= 0.8
|
||||
async def Translate(self, text: str, target_language: str = "en") -> str:
|
||||
"""
|
||||
通用翻译方法（保持向后兼容）
|
||||
|
||||
Args:
|
||||
text: 要翻译的文本
|
||||
target_language: 目标语言，"en"表示英文，"zh"表示中文
|
||||
|
||||
Returns:
|
||||
翻译后的文本
|
||||
"""
|
||||
if target_language == "en":
|
||||
return await self.translate_to_english(text)
|
||||
else:
|
||||
logger.warning(f"不支持的目标语言: {target_language}，返回原文")
|
||||
return text
|
||||
|
||||
|
||||
|
||||
# 测试翻译服务
|
||||
async def Translation_English(modid, text, fields=None):
|
||||
"""
|
||||
将数据翻译为英文（支持字段级翻译）
|
||||
|
||||
Args:
|
||||
modid: 模型ID
|
||||
text: 要翻译的数据（可以是字符串、字典或列表）
|
||||
fields: 需要翻译的字段列表（可选）
|
||||
如果为None，默认翻译: ['content', 'summary', 'statement', 'description',
|
||||
'name', 'aliases', 'caption', 'emotion_keywords']
|
||||
|
||||
Returns:
|
||||
翻译后的数据，保持原有结构
|
||||
|
||||
Note:
|
||||
- 对于字符串：直接翻译
|
||||
- 对于列表：递归处理每个元素，保持列表长度和索引不变
|
||||
- 对于字典：只翻译指定字段（fields参数）
|
||||
- 对于其他类型：原样返回
|
||||
"""
|
||||
trans_service = MemoryTransService(modid)
|
||||
|
||||
# 处理字符串类型
|
||||
if isinstance(text, str):
|
||||
# 空字符串直接返回
|
||||
if not text.strip():
|
||||
return text
|
||||
|
||||
try:
|
||||
is_eng = await trans_service.is_english(text)
|
||||
if not is_eng:
|
||||
english_result = await trans_service.Translate(text)
|
||||
return english_result
|
||||
return text
|
||||
except Exception as e:
|
||||
logger.warning(f"翻译字符串失败: {e}")
|
||||
return text
|
||||
|
||||
# 处理列表类型
|
||||
elif isinstance(text, list):
|
||||
english_result = []
|
||||
for item in text:
|
||||
# 递归处理列表中的每个元素
|
||||
if isinstance(item, str):
|
||||
# 字符串元素：检查是否需要翻译
|
||||
if not item.strip():
|
||||
english_result.append(item)
|
||||
continue
|
||||
|
||||
try:
|
||||
is_eng = await trans_service.is_english(item)
|
||||
if not is_eng:
|
||||
translated = await trans_service.Translate(item)
|
||||
english_result.append(translated)
|
||||
else:
|
||||
# 保留英文项，不改变列表长度
|
||||
english_result.append(item)
|
||||
except Exception as e:
|
||||
logger.warning(f"翻译列表项失败: {e}")
|
||||
english_result.append(item)
|
||||
|
||||
elif isinstance(item, dict):
|
||||
# 字典元素：递归调用自己处理字典
|
||||
translated_dict = await Translation_English(modid, item, fields)
|
||||
english_result.append(translated_dict)
|
||||
|
||||
elif isinstance(item, list):
|
||||
# 嵌套列表：递归处理
|
||||
translated_list = await Translation_English(modid, item, fields)
|
||||
english_result.append(translated_list)
|
||||
|
||||
else:
|
||||
# 其他类型（数字、布尔值等）：原样保留
|
||||
english_result.append(item)
|
||||
|
||||
return english_result
|
||||
|
||||
# 处理字典类型
|
||||
elif isinstance(text, dict):
|
||||
# 确定要翻译的字段
|
||||
if fields is None:
|
||||
# 默认翻译字段
|
||||
fields = [
|
||||
'content', 'summary', 'statement', 'description',
|
||||
'name', 'aliases', 'caption', 'emotion_keywords',
|
||||
'text', 'title', 'label', 'type' # 添加常用字段
|
||||
]
|
||||
|
||||
# 创建副本，避免修改原始数据
|
||||
result = text.copy()
|
||||
|
||||
for field in fields:
|
||||
if field in result and result[field] is not None:
|
||||
# 递归翻译字段值（可能是字符串、列表或嵌套字典）
|
||||
try:
|
||||
result[field] = await Translation_English(modid, result[field], fields)
|
||||
except Exception as e:
|
||||
logger.warning(f"翻译字段 {field} 失败: {e}")
|
||||
# 翻译失败时保留原值
|
||||
continue
|
||||
|
||||
return result
|
||||
|
||||
# 其他类型（数字、布尔值、None等）：原样返回
|
||||
else:
|
||||
return text
|
||||
class MemoryBaseService:
|
||||
"""记忆服务基类，提供共享的辅助方法"""
|
||||
|
||||
@@ -294,4 +545,4 @@ class MemoryBaseService:
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取遗忘记忆数量时出错: {str(e)}", exc_info=True)
|
||||
return 0
|
||||
return 0
|
||||
@@ -16,6 +16,7 @@ import json
|
||||
from datetime import datetime
|
||||
|
||||
from app.schemas.memory_episodic_schema import EmotionType
|
||||
from app.services.memory_base_service import Translation_English
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -24,7 +25,7 @@ class MemoryEntityService:
|
||||
self.id = id
|
||||
self.table = table
|
||||
self.connector = Neo4jConnector()
|
||||
async def get_timeline_memories_server(self):
|
||||
async def get_timeline_memories_server(self,model_id, language_type):
|
||||
"""
|
||||
获取时间线记忆数据
|
||||
|
||||
@@ -48,10 +49,10 @@ class MemoryEntityService:
|
||||
logger.info(f"获取时间线记忆数据 - ID: {self.id}, Table: {self.table}")
|
||||
|
||||
# 根据表类型选择查询
|
||||
if self.table == 'Statement':
|
||||
if self.table == 'Statement':
|
||||
# Statement只需要输入ID，使用简化查询
|
||||
results = await self.connector.execute_query(Memory_Timeline_Statement, id=self.id)
|
||||
elif self.table == 'ExtractedEntity':
|
||||
elif self.table == 'ExtractedEntity':
|
||||
# ExtractedEntity类型查询
|
||||
results = await self.connector.execute_query(Memory_Timeline_ExtractedEntity, id=self.id)
|
||||
else:
|
||||
@@ -62,7 +63,7 @@ class MemoryEntityService:
|
||||
logger.info(f"时间线查询结果类型: {type(results)}, 长度: {len(results) if isinstance(results, list) else 'N/A'}")
|
||||
|
||||
# 处理查询结果
|
||||
timeline_data = self._process_timeline_results(results)
|
||||
timeline_data =await self._process_timeline_results(results, model_id, language_type)
|
||||
|
||||
logger.info(f"成功获取时间线记忆数据: 总计 {len(timeline_data.get('timelines_memory', []))} 条")
|
||||
|
||||
@@ -71,12 +72,14 @@ class MemoryEntityService:
|
||||
except Exception as e:
|
||||
logger.error(f"获取时间线记忆数据失败: {str(e)}", exc_info=True)
|
||||
return str(e)
|
||||
def _process_timeline_results(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
async def _process_timeline_results(self, results: List[Dict[str, Any]], model_id: str, language_type: str) -> Dict[str, Any]:
|
||||
"""
|
||||
处理时间线查询结果
|
||||
|
||||
Args:
|
||||
results: Neo4j查询结果
|
||||
model_id: 模型ID用于翻译
|
||||
language_type: 语言类型 ('zh' 或其他)
|
||||
|
||||
Returns:
|
||||
处理后的时间线数据字典
|
||||
@@ -104,19 +107,19 @@ class MemoryEntityService:
|
||||
# 处理MemorySummary
|
||||
summary = data.get('MemorySummary')
|
||||
if summary is not None:
|
||||
processed_summary = self._process_field_value(summary, "MemorySummary")
|
||||
processed_summary = await self._process_field_value(summary, "MemorySummary")
|
||||
memory_summary_list.extend(processed_summary)
|
||||
|
||||
# 处理Statement
|
||||
statement = data.get('statement')
|
||||
if statement is not None:
|
||||
processed_statement = self._process_field_value(statement, "Statement")
|
||||
processed_statement = await self._process_field_value(statement, "Statement")
|
||||
statement_list.extend(processed_statement)
|
||||
|
||||
# 处理ExtractedEntity
|
||||
extracted_entity = data.get('ExtractedEntity')
|
||||
if extracted_entity is not None:
|
||||
processed_entity = self._process_field_value(extracted_entity, "ExtractedEntity")
|
||||
processed_entity = await self._process_field_value(extracted_entity, "ExtractedEntity")
|
||||
extracted_entity_list.extend(processed_entity)
|
||||
|
||||
# 去重 - 现在处理的是字典列表，需要更智能的去重
|
||||
@@ -128,6 +131,8 @@ class MemoryEntityService:
|
||||
all_timeline_data = memory_summary_list + statement_list
|
||||
all_timeline_data = self._merge_same_text_items(all_timeline_data)
|
||||
|
||||
# 如果需要翻译（非中文），对整个结果进行翻译
|
||||
|
||||
result = {
|
||||
"MemorySummary": memory_summary_list,
|
||||
"Statement": statement_list,
|
||||
@@ -233,7 +238,7 @@ class MemoryEntityService:
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _process_field_value(self, value: Any, field_name: str) -> List[Dict[str, Any]]:
|
||||
async def _process_field_value(self, value: Any, field_name: str) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
处理字段值，支持字符串、列表等类型
|
||||
|
||||
@@ -251,13 +256,13 @@ class MemoryEntityService:
|
||||
# 如果是列表，处理每个元素
|
||||
for item in value:
|
||||
if self._is_valid_item(item):
|
||||
processed_item = self._process_single_item(item)
|
||||
processed_item = await self._process_single_item(item)
|
||||
if processed_item:
|
||||
processed_values.append(processed_item)
|
||||
elif isinstance(value, dict):
|
||||
# 如果是字典，直接处理
|
||||
if self._is_valid_item(value):
|
||||
processed_item = self._process_single_item(value)
|
||||
processed_item = await self._process_single_item(value)
|
||||
if processed_item:
|
||||
processed_values.append(processed_item)
|
||||
elif isinstance(value, str):
|
||||
@@ -304,7 +309,7 @@ class MemoryEntityService:
|
||||
return (str(item).strip() != '' and
|
||||
"MemorySummaryChunk" not in str(item))
|
||||
|
||||
def _process_single_item(self, item: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
||||
async def _process_single_item(self, item: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
处理单个项目
|
||||
|
||||
@@ -369,6 +374,117 @@ class MemoryEntityService:
|
||||
logger.warning(f"转换时间格式失败: {e}, 原始值: {dt}")
|
||||
return str(dt) if dt is not None else None
|
||||
|
||||
async def _translate_list(
|
||||
self,
|
||||
data_list: List[Dict[str, Any]],
|
||||
model_id: str,
|
||||
fields: List[str]
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
翻译列表中每个字典的指定字段（并发有限度以降低整体延迟）
|
||||
|
||||
Args:
|
||||
data_list: 要翻译的字典列表
|
||||
model_id: 模型ID
|
||||
fields: 需要翻译的字段列表
|
||||
|
||||
Returns:
|
||||
翻译后的字典列表
|
||||
"""
|
||||
# 空列表或无字段时直接返回
|
||||
if not data_list or not fields:
|
||||
return data_list
|
||||
|
||||
import asyncio
|
||||
|
||||
# 并发限制，避免一次性发起过多请求
|
||||
# 可根据实际情况调整（建议 5-10）
|
||||
concurrency_limit = 5
|
||||
semaphore = asyncio.Semaphore(concurrency_limit)
|
||||
|
||||
async def translate_single_field(
|
||||
index: int,
|
||||
field: str,
|
||||
value: Any,
|
||||
) -> Optional[tuple]:
|
||||
"""
|
||||
翻译单个字段并返回 (索引, 字段名, 翻译结果)
|
||||
|
||||
Returns:
|
||||
(index, field, translated_value) 或 None（如果跳过）
|
||||
"""
|
||||
# 跳过空值
|
||||
if value is None or value == "":
|
||||
return None
|
||||
|
||||
# 统一转成字符串再翻译，防止非字符串类型导致错误
|
||||
text = str(value)
|
||||
|
||||
try:
|
||||
async with semaphore:
|
||||
# 调用 Translation_English 进行翻译
|
||||
# 注意：Translation_English 的参数顺序是 (model_id, text)
|
||||
translated = await Translation_English(model_id, text)
|
||||
|
||||
# 如果翻译结果为空，保留原值
|
||||
if translated is None or translated == "":
|
||||
return None
|
||||
|
||||
return index, field, translated
|
||||
except Exception as e:
|
||||
logger.warning(f"翻译字段 {field} (索引 {index}) 失败: {e}")
|
||||
return None
|
||||
|
||||
# 构造所有需要翻译的任务
|
||||
tasks = []
|
||||
for idx, item in enumerate(data_list):
|
||||
# 防御性检查：确保 item 是字典
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
|
||||
for field in fields:
|
||||
if field not in item:
|
||||
continue
|
||||
|
||||
value = item.get(field)
|
||||
|
||||
# 对于 None 或空字符串的值，直接跳过，不创建任务
|
||||
if value is None or value == "":
|
||||
continue
|
||||
|
||||
tasks.append(
|
||||
asyncio.create_task(
|
||||
translate_single_field(idx, field, value)
|
||||
)
|
||||
)
|
||||
|
||||
# 如果没有需要翻译的任务，直接返回原列表
|
||||
if not tasks:
|
||||
return data_list
|
||||
|
||||
# 使用 gather 并发执行翻译任务（受 semaphore 限制）
|
||||
# return_exceptions=True 可以防止单个任务失败导致整体失败
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
|
||||
# 创建深拷贝以避免修改原始数据
|
||||
translated_list = [item.copy() if isinstance(item, dict) else item for item in data_list]
|
||||
|
||||
# 将翻译结果回填到列表
|
||||
for result in results:
|
||||
# 跳过 None 结果和异常
|
||||
if result is None or isinstance(result, Exception):
|
||||
if isinstance(result, Exception):
|
||||
logger.warning(f"翻译任务异常: {result}")
|
||||
continue
|
||||
|
||||
idx, field, translated = result
|
||||
|
||||
# 防御性检查索引范围
|
||||
if 0 <= idx < len(translated_list) and isinstance(translated_list[idx], dict):
|
||||
translated_list[idx][field] = translated
|
||||
|
||||
return translated_list
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -426,15 +542,19 @@ class MemoryEmotion:
|
||||
# 如果解析失败，返回原始字符串
|
||||
return iso_string
|
||||
|
||||
async def get_emotion(self) -> Dict[str, Any]:
|
||||
async def get_emotion(self, model_id: str = None, language_type: str = 'zh') -> Dict[str, Any]:
|
||||
"""
|
||||
获取情绪随时间变化数据
|
||||
|
||||
Args:
|
||||
model_id: 模型ID用于翻译
|
||||
language_type: 语言类型 ('zh' 或其他)
|
||||
|
||||
Returns:
|
||||
包含情绪数据的字典
|
||||
"""
|
||||
try:
|
||||
logger.info(f"获取情绪数据 - ID: {self.id}, Table: {self.table}")
|
||||
logger.info(f"获取情绪数据 - ID: {self.id}, Table: {self.table}, language_type={language_type}")
|
||||
|
||||
if self.table == 'Statement':
|
||||
results = await self.connector.execute_query(Memory_Space_Emotion_Statement, id=self.id)
|
||||
@@ -450,6 +570,10 @@ class MemoryEmotion:
|
||||
# 转换Neo4j类型
|
||||
final_data = self._convert_neo4j_types(emotion_data)
|
||||
|
||||
# 如果需要翻译（非中文）
|
||||
if language_type != 'zh' and model_id and final_data:
|
||||
final_data = await self._translate_emotion_data(final_data, model_id)
|
||||
|
||||
logger.info(f"成功获取 {len(final_data)} 条情绪数据")
|
||||
|
||||
return final_data
|
||||
@@ -590,16 +714,14 @@ class MemoryInteraction:
|
||||
"""
|
||||
try:
|
||||
logger.info(f"获取交互数据 - ID: {self.id}, Table: {self.table}")
|
||||
|
||||
ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id)
|
||||
if ori_data!=[]:
|
||||
# name = ori_data[0]['name']
|
||||
group_id = ori_data[0]['group_id']
|
||||
group_id = [i['group_id'] for i in ori_data][0]
|
||||
Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id)
|
||||
if not Space_User:
|
||||
return []
|
||||
user_id=Space_User[0]['id']
|
||||
|
||||
results = await self.connector.execute_query(Memory_Space_Associative, id=self.id,user_id=user_id)
|
||||
|
||||
|
||||
|
||||
@@ -18,7 +18,7 @@ from app.repositories.end_user_repository import EndUserRepository
|
||||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||||
from app.schemas.memory_episodic_schema import EmotionSubject, EmotionType, type_mapping
|
||||
from app.services.implicit_memory_service import ImplicitMemoryService
|
||||
from app.services.memory_base_service import MemoryBaseService
|
||||
from app.services.memory_base_service import MemoryBaseService, MemoryTransService, Translation_English
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
from app.services.memory_perceptual_service import MemoryPerceptualService
|
||||
from app.services.memory_short_service import ShortService
|
||||
@@ -360,7 +360,9 @@ class UserMemoryService:
|
||||
async def get_cached_memory_insight(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str
|
||||
end_user_id: str,
|
||||
model_id: str,
|
||||
language_type: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
从数据库获取缓存的记忆洞察（四个维度）
|
||||
@@ -419,11 +421,18 @@ class UserMemoryService:
|
||||
key_findings_array = []
|
||||
|
||||
logger.info(f"成功获取 end_user_id {end_user_id} 的缓存记忆洞察（四维度）")
|
||||
memory_insight=end_user.memory_insight
|
||||
behavior_pattern=end_user.behavior_pattern
|
||||
growth_trajectory=end_user.growth_trajectory
|
||||
if language_type!='zh':
|
||||
memory_insight=await Translation_English(model_id,memory_insight)
|
||||
behavior_pattern=await Translation_English(model_id,behavior_pattern)
|
||||
growth_trajectory=await Translation_English(model_id,growth_trajectory)
|
||||
return {
|
||||
"memory_insight": end_user.memory_insight, # 总体概述存储在 memory_insight
|
||||
"behavior_pattern": end_user.behavior_pattern,
|
||||
"memory_insight":memory_insight, # 总体概述存储在 memory_insight
|
||||
"behavior_pattern":behavior_pattern,
|
||||
"key_findings": key_findings_array, # 返回数组
|
||||
"growth_trajectory": end_user.growth_trajectory,
|
||||
"growth_trajectory": growth_trajectory,
|
||||
"updated_at": self._datetime_to_timestamp(end_user.memory_insight_updated_at),
|
||||
"is_cached": True
|
||||
}
|
||||
@@ -457,7 +466,9 @@ class UserMemoryService:
|
||||
async def get_cached_user_summary(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str
|
||||
end_user_id: str,
|
||||
model_id:str,
|
||||
language_type:str="zh"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
从数据库获取缓存的用户摘要（四个部分）
|
||||
@@ -481,7 +492,6 @@ class UserMemoryService:
|
||||
user_uuid = uuid.UUID(end_user_id)
|
||||
repo = EndUserRepository(db)
|
||||
end_user = repo.get_by_id(user_uuid)
|
||||
|
||||
if not end_user:
|
||||
logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户")
|
||||
return {
|
||||
@@ -495,20 +505,29 @@ class UserMemoryService:
|
||||
}
|
||||
|
||||
# 检查是否有缓存数据（至少有一个字段不为空）
|
||||
user_summary=end_user.user_summary
|
||||
personality_traits=end_user.personality_traits
|
||||
core_values=end_user.core_values
|
||||
one_sentence_summary=end_user.one_sentence_summary
|
||||
if language_type!='zh':
|
||||
user_summary=await Translation_English(model_id, user_summary)
|
||||
personality_traits = await Translation_English(model_id, personality_traits)
|
||||
core_values = await Translation_English(model_id, core_values)
|
||||
one_sentence_summary = await Translation_English(model_id, one_sentence_summary)
|
||||
has_cache = any([
|
||||
end_user.user_summary,
|
||||
end_user.personality_traits,
|
||||
end_user.core_values,
|
||||
end_user.one_sentence_summary
|
||||
user_summary,
|
||||
personality_traits,
|
||||
core_values,
|
||||
one_sentence_summary
|
||||
])
|
||||
|
||||
if has_cache:
|
||||
logger.info(f"成功获取 end_user_id {end_user_id} 的缓存用户摘要")
|
||||
return {
|
||||
"user_summary": end_user.user_summary,
|
||||
"personality": end_user.personality_traits,
|
||||
"core_values": end_user.core_values,
|
||||
"one_sentence": end_user.one_sentence_summary,
|
||||
"user_summary": user_summary,
|
||||
"personality": personality_traits,
|
||||
"core_values":core_values,
|
||||
"one_sentence": one_sentence_summary,
|
||||
"updated_at": self._datetime_to_timestamp(end_user.user_summary_updated_at),
|
||||
"is_cached": True
|
||||
}
|
||||
@@ -1367,7 +1386,6 @@ async def analytics_memory_types(
|
||||
|
||||
return memory_types
|
||||
|
||||
|
||||
async def analytics_graph_data(
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
@@ -1557,7 +1575,7 @@ async def analytics_graph_data(
|
||||
f"成功获取图数据: end_user_id={end_user_id}, "
|
||||
f"nodes={len(nodes)}, edges={len(edges)}"
|
||||
)
|
||||
|
||||
|
||||
return {
|
||||
"nodes": nodes,
|
||||
"edges": edges,
|
||||
@@ -1606,11 +1624,7 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_
|
||||
|
||||
# 获取该节点类型的白名单字段
|
||||
allowed_fields = field_whitelist.get(label, [])
|
||||
|
||||
# 如果没有定义白名单，返回空字典（或者可以返回所有字段）
|
||||
# if not allowed_fields:
|
||||
# # 对于未定义的节点类型，只返回基本字段
|
||||
# allowed_fields = ["name", "created_at", "caption"]
|
||||
|
||||
count_neo4j=f"""MATCH (n)-[r]-(m) WHERE elementId(n) ="{node_id}" RETURN count(r) AS rel_count;"""
|
||||
node_results = await (_neo4j_connector.execute_query(count_neo4j))
|
||||
# 提取白名单中的字段
|
||||
@@ -1618,13 +1632,12 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_
|
||||
for field in allowed_fields:
|
||||
if field in properties:
|
||||
value = properties[field]
|
||||
if str(field) == 'entity_type':
|
||||
if str(field) == 'entity_type':
|
||||
value=type_mapping.get(value,'')
|
||||
if str(field)=="emotion_type":
|
||||
value=EmotionType.EMOTION_MAPPING.get(value)
|
||||
if str(field)=="emotion_subject":
|
||||
if str(field)=="emotion_subject":
|
||||
value=EmotionSubject.SUBJECT_MAPPING.get(value)
|
||||
# 清理 Neo4j 特殊类型
|
||||
filtered_props[field] = _clean_neo4j_value(value)
|
||||
filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0]
|
||||
return filtered_props
|
||||
|
||||
Reference in New Issue
Block a user