Fix/language unification (#283)
* [changes]add user_summary language unification * [add]Entity extraction, user memory, emotion suggestions, unified language type for writing * [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields. * [changes]add user_summary language unification * [add]Entity extraction, user memory, emotion suggestions, unified language type for writing * [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields. * [changes]Modify the code based on the AI review
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乐力齐
@@ -8,11 +8,11 @@ from sqlalchemy.orm import Session
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from fastapi import APIRouter, Depends,Header
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from app.db import get_db
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from app.core.language_utils import get_language_from_header
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from app.core.logging_config import get_api_logger
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from app.core.response_utils import success, fail
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from app.core.error_codes import BizCode
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from app.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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@@ -45,7 +45,6 @@ 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 = Header(default="zh", alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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@@ -55,18 +54,10 @@ 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,model_id,language_type)
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result = await user_memory_service.get_cached_memory_insight(db, end_user_id)
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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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@@ -82,7 +73,7 @@ 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 = Header(default="zh", alias="X-Language-Type"),
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language_type: str = Header(default=None, alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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@@ -91,7 +82,14 @@ 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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语言控制：
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- 使用 X-Language-Type Header 指定语言
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- 如果未传 Header，默认使用中文 (zh)
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"""
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# 使用集中化的语言校验
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language = get_language_from_header(language_type)
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workspace_id = current_user.current_workspace_id
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workspace_repo = WorkspaceRepository(db)
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workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
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@@ -103,7 +101,7 @@ async def get_user_summary_api(
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api_logger.info(f"用户摘要查询请求: end_user_id={end_user_id}, user={current_user.username}")
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try:
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# 调用服务层获取缓存数据
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result = await user_memory_service.get_cached_user_summary(db, end_user_id,model_id,language_type)
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result = await user_memory_service.get_cached_user_summary(db, end_user_id,model_id,language)
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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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@@ -119,6 +117,7 @@ async def get_user_summary_api(
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@router.post("/analytics/generate_cache", response_model=ApiResponse)
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async def generate_cache_api(
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request: GenerateCacheRequest,
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language_type: str = Header(default=None, alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> dict:
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@@ -127,7 +126,14 @@ async def generate_cache_api(
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- 如果提供 end_user_id，只为该用户生成
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- 如果不提供，为当前工作空间的所有用户生成
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语言控制：
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- 使用 X-Language-Type Header 指定语言 ("zh" 中文, "en" 英文)
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- 如果未传 Header，默认使用中文 (zh)
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"""
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# 使用集中化的语言校验
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language = get_language_from_header(language_type)
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workspace_id = current_user.current_workspace_id
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# 检查用户是否已选择工作空间
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@@ -139,7 +145,7 @@ async def generate_cache_api(
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api_logger.info(
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f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, "
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f"end_user_id={end_user_id if end_user_id else '全部用户'}"
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f"end_user_id={end_user_id if end_user_id else '全部用户'}, language={language}"
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)
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try:
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@@ -148,10 +154,10 @@ async def generate_cache_api(
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api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}")
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# 生成记忆洞察
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insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id)
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insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id, language=language)
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# 生成用户摘要
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summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id)
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summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id, language=language)
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# 构建响应
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result = {
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@@ -185,7 +191,7 @@ async def generate_cache_api(
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# 为整个工作空间生成
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api_logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存")
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result = await user_memory_service.generate_cache_for_workspace(db, workspace_id)
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result = await user_memory_service.generate_cache_for_workspace(db, workspace_id, language=language)
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# 记录统计信息
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api_logger.info(
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@@ -385,10 +391,13 @@ async def update_end_user_profile(
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return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", error_msg)
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@router.get("/memory_space/timeline_memories", response_model=ApiResponse)
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async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str = Header(default="zh", alias="X-Language-Type"),
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async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str = Header(default=None, alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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# 使用集中化的语言校验
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language = get_language_from_header(language_type)
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workspace_id=current_user.current_workspace_id
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workspace_repo = WorkspaceRepository(db)
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workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
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@@ -398,7 +407,7 @@ async def memory_space_timeline_of_shared_memories(id: str, label: str,language_
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else:
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model_id = None
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MemoryEntity = MemoryEntityService(id, label)
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timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language_type)
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timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language)
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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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