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
2026-02-03 16:03:08 +08:00
parent 63fa4dc8ec
commit 8670aaba1e
30 changed files with 1033 additions and 810 deletions
--- a/api/app/services/user_memory_service.py
+++ b/api/app/services/user_memory_service.py
@@ -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, MemoryTransService, Translation_English
+from app.services.memory_base_service import MemoryBaseService, MemoryTransService
 from app.services.memory_config_service import MemoryConfigService
 from app.services.memory_perceptual_service import MemoryPerceptualService
 from app.services.memory_short_service import ShortService
@@ -455,9 +455,7 @@ class UserMemoryService:
    async def get_cached_memory_insight(
        self, 
        db: Session, 
-        end_user_id: str,
-        model_id: str,
-        language_type: str
+        end_user_id: str
    ) -> Dict[str, Any]:
        """
        从数据库获取缓存的记忆洞察（四个维度）
@@ -465,7 +463,7 @@ class UserMemoryService:
        Args:
            db: 数据库会话
            end_user_id: 终端用户ID (UUID)
-            
+        
        Returns:
            {
                "memory_insight": str,           # 总体概述
@@ -519,10 +517,6 @@ class UserMemoryService:
                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":memory_insight,  # 总体概述存储在 memory_insight
                    "behavior_pattern":behavior_pattern,
@@ -571,6 +565,8 @@ class UserMemoryService:
        Args:
            db: 数据库会话
            end_user_id: 终端用户ID (UUID)
+            model_id: 模型ID（用于翻译）
+            language_type: 语言类型 ("zh" 中文, "en" 英文)
            
        Returns:
            {
@@ -604,11 +600,10 @@ class UserMemoryService:
            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)
+            
+            # 直接返回数据库中的数据，不进行二次翻译
+            # 语言由生成时的 X-Language-Type 决定
+            
            has_cache = any([
                user_summary,
                personality_traits,
@@ -658,7 +653,8 @@ class UserMemoryService:
        self, 
        db: Session, 
        end_user_id: str,
-        workspace_id: Optional[uuid.UUID] = None
+        workspace_id: Optional[uuid.UUID] = None,
+        language: str = "zh"
    ) -> Dict[str, Any]:
        """
        生成并缓存记忆洞察
@@ -667,6 +663,7 @@ class UserMemoryService:
            db: 数据库会话
            end_user_id: 终端用户ID (UUID)
            workspace_id: 工作空间ID (可选)
+            language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
            
        Returns:
            {
@@ -679,7 +676,7 @@ class UserMemoryService:
            }
        """
        try:
-            logger.info(f"开始为 end_user_id {end_user_id} 生成记忆洞察")
+            logger.info(f"开始为 end_user_id {end_user_id} 生成记忆洞察, language={language}")
            
            # 转换为UUID并查询用户
            user_uuid = uuid.UUID(end_user_id)
@@ -700,7 +697,7 @@ class UserMemoryService:
            # 使用 end_user_id 调用分析函数
            try:
                logger.info(f"使用 end_user_id={end_user_id} 生成记忆洞察")
-                result = await analytics_memory_insight_report(end_user_id)
+                result = await analytics_memory_insight_report(end_user_id, language=language)
                
                memory_insight = result.get("memory_insight", "")
                behavior_pattern = result.get("behavior_pattern", "")
@@ -789,7 +786,8 @@ class UserMemoryService:
        self, 
        db: Session, 
        end_user_id: str,
-        workspace_id: Optional[uuid.UUID] = None
+        workspace_id: Optional[uuid.UUID] = None,
+        language: str = "zh"
    ) -> Dict[str, Any]:
        """
        生成并缓存用户摘要（四个部分）
@@ -798,6 +796,7 @@ class UserMemoryService:
            db: 数据库会话
            end_user_id: 终端用户ID (UUID)
            workspace_id: 工作空间ID (可选)
+            language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
            
        Returns:
            {
@@ -810,7 +809,7 @@ class UserMemoryService:
            }
        """
        try:
-            logger.info(f"开始为 end_user_id {end_user_id} 生成用户摘要")
+            logger.info(f"开始为 end_user_id {end_user_id} 生成用户摘要, language={language}")
            
            # 转换为UUID并查询用户
            user_uuid = uuid.UUID(end_user_id)
@@ -831,7 +830,7 @@ class UserMemoryService:
            # 使用 end_user_id 调用分析函数
            try:
                logger.info(f"使用 end_user_id={end_user_id} 生成用户摘要")
-                result = await analytics_user_summary(end_user_id)
+                result = await analytics_user_summary(end_user_id, language=language)
                
                user_summary = result.get("user_summary", "")
                personality = result.get("personality", "")
@@ -915,7 +914,8 @@ class UserMemoryService:
    async def generate_cache_for_workspace(
        self, 
        db: Session, 
-        workspace_id: uuid.UUID
+        workspace_id: uuid.UUID,
+        language: str = "zh"
    ) -> Dict[str, Any]:
        """
        为整个工作空间生成缓存
@@ -923,6 +923,7 @@ class UserMemoryService:
        Args:
            db: 数据库会话
            workspace_id: 工作空间ID
+            language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
            
        Returns:
            {
@@ -932,7 +933,7 @@ class UserMemoryService:
                "errors": List[Dict]
            }
        """
-        logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存")
+        logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存, language={language}")
        
        total_users = 0
        successful = 0
@@ -953,10 +954,10 @@ class UserMemoryService:
                
                try:
                    # 生成记忆洞察
-                    insight_result = await self.generate_and_cache_insight(db, end_user_id)
+                    insight_result = await self.generate_and_cache_insight(db, end_user_id, language=language)
                    
                    # 生成用户摘要
-                    summary_result = await self.generate_and_cache_summary(db, end_user_id)
+                    summary_result = await self.generate_and_cache_summary(db, end_user_id, language=language)
                    
                    # 检查是否都成功
                    if insight_result["success"] and summary_result["success"]:
@@ -1007,7 +1008,7 @@ class UserMemoryService:

 # 独立的分析函数

-async def analytics_memory_insight_report(end_user_id: Optional[str] = None) -> Dict[str, Any]:
+async def analytics_memory_insight_report(end_user_id: Optional[str] = None, language: str = "zh") -> Dict[str, Any]:
    """
    生成记忆洞察报告（四个维度）
    
@@ -1019,6 +1020,7 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
    
    Args:
        end_user_id: 可选的终端用户ID
+        language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
        
    Returns:
        包含四个维度报告的字典: {
@@ -1029,8 +1031,12 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
        }
    """
    from app.core.memory.utils.prompt.prompt_utils import render_memory_insight_prompt
+    from app.core.language_utils import validate_language
    import re
    
+    # 验证语言参数
+    language = validate_language(language)
+    
    insight = MemoryInsightHelper(end_user_id)     
    
    try:
@@ -1070,7 +1076,8 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
        user_prompt = await render_memory_insight_prompt(
            domain_distribution=domain_distribution_str,
            active_periods=active_periods_str,
-            social_connections=social_connections_str
+            social_connections=social_connections_str,
+            language=language
        )
        
        messages = [
@@ -1097,11 +1104,11 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
            full_response = str(content) if content is not None else ""
        
        # 7. 解析四个部分
-        # 使用正则表达式提取四个部分
-        memory_insight_match = re.search(r'【总体概述】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
-        behavior_match = re.search(r'【行为模式】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
-        findings_match = re.search(r'【关键发现】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
-        trajectory_match = re.search(r'【成长轨迹】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        # 使用正则表达式提取四个部分（支持中英文双语标题）
+        memory_insight_match = re.search(r'【(?:总体概述|Overview)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        behavior_match = re.search(r'【(?:行为模式|Behavior Pattern)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        findings_match = re.search(r'【(?:关键发现|Key Findings)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        trajectory_match = re.search(r'【(?:成长轨迹|Growth Trajectory)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
        
        memory_insight = memory_insight_match.group(1).strip() if memory_insight_match else ""
        behavior_pattern = behavior_match.group(1).strip() if behavior_match else ""
@@ -1128,7 +1135,7 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
        await insight.close()


-async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, Any]:
+async def analytics_user_summary(end_user_id: Optional[str] = None, language: str = "zh") -> Dict[str, Any]:
    """
    生成用户摘要（包含四个部分）
    
@@ -1139,6 +1146,7 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
    
    Args:
        end_user_id: 可选的终端用户ID
+        language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
        
    Returns:
        包含四部分摘要的字典: {
@@ -1149,8 +1157,12 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
        }
    """
    from app.core.memory.utils.prompt.prompt_utils import render_user_summary_prompt
+    from app.core.language_utils import validate_language
    import re
    
+    # 验证语言参数
+    language = validate_language(language)
+    
    # 创建 UserSummaryHelper 实例
    user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_end_user_id", "group_123"))
    
@@ -1165,8 +1177,9 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
        # 2) 使用 prompt_utils 渲染提示词
        user_prompt = await render_user_summary_prompt(
            user_id=user_summary_tool.user_id,
-            entities=", ".join(entity_lines) if entity_lines else "(空)",
-            statements=" | ".join(statement_samples) if statement_samples else "(空)"
+            entities=", ".join(entity_lines) if entity_lines else "(空)" if language == "zh" else "(empty)",
+            statements=" | ".join(statement_samples) if statement_samples else "(空)" if language == "zh" else "(empty)",
+            language=language
        )

        messages = [
@@ -1193,11 +1206,11 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
            full_response = str(content) if content is not None else ""
        
        # 5) 解析四个部分
-        # 使用正则表达式提取四个部分
-        user_summary_match = re.search(r'【基本介绍】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
-        personality_match = re.search(r'【性格特点】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
-        core_values_match = re.search(r'【核心价值观】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
-        one_sentence_match = re.search(r'【一句话总结】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        # 使用正则表达式提取四个部分（支持中英文标题）
+        user_summary_match = re.search(r'【(?:基本介绍|Basic Introduction)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        personality_match = re.search(r'【(?:性格特点|Personality Traits)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        core_values_match = re.search(r'【(?:核心价值观|Core Values)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
+        one_sentence_match = re.search(r'【(?:一句话总结|One-Sentence Summary)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
        
        user_summary = user_summary_match.group(1).strip() if user_summary_match else ""
        personality = personality_match.group(1).strip() if personality_match else ""