Fix/bug en zh (#382)

* [fix]The log retains genuine alerts and errors, while filtering out unnecessary noise.

* [fix]Scenario English and Chinese, emotion specifications

* [fix]Change the "no data" scenario from 0.0 to None

api/app/controllers/emotion_controller.py

@@ -186,7 +186,7 @@ async def get_emotion_health(
             "情绪健康指数获取成功",
             extra={
                 "end_user_id": request.end_user_id,
-                "health_score": data.get("health_score", 0),
+                "health_score": data.get("health_score") or 0,
                 "level": data.get("level", "未知")
             }
         )

api/app/controllers/memory_episodic_controller.py

@@ -3,9 +3,10 @@
 包含情景记忆总览和详情查询接口
 """
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Header
 
 from app.core.error_codes import BizCode
+from app.core.language_utils import get_language_from_header
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import fail, success
 from app.dependencies import get_current_user
@@ -14,6 +15,7 @@ from app.schemas.response_schema import ApiResponse
 from app.schemas.memory_episodic_schema import (
     EpisodicMemoryOverviewRequest,
     EpisodicMemoryDetailsRequest,
+    translate_episodic_type,
 )
 from app.services.memory_episodic_service import memory_episodic_service
 
@@ -84,6 +86,7 @@ async def get_episodic_memory_overview_api(
 @router.post("/details", response_model=ApiResponse)
 async def get_episodic_memory_details_api(
     request: EpisodicMemoryDetailsRequest,
+    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
 ) -> dict:
     """
@@ -111,6 +114,11 @@ async def get_episodic_memory_details_api(
             summary_id=request.summary_id
         )
         
+        # 根据语言参数翻译 episodic_type
+        language = get_language_from_header(language_type)
+        if "episodic_type" in result:
+            result["episodic_type"] = translate_episodic_type(result["episodic_type"], language)
+        
         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}"
         )

api/app/controllers/ontology_controller.py

@@ -233,6 +233,14 @@ async def extract_ontology(
             language=language
         )
         
+        # 根据语言类型统一 name 字段
+        # zh: name 使用 name_chinese（中文名）
+        # en: name 保持原值（英文 PascalCase）
+        if language == "zh":
+            for cls in result.classes:
+                if cls.name_chinese:
+                    cls.name = cls.name_chinese
+        
         # 构建响应
         response = ExtractionResponse(
             classes=result.classes,

api/app/core/logging_config.py

@@ -70,8 +70,10 @@ class Neo4jSuccessNotificationFilter(logging.Filter):
         Returns:
             True表示允许记录，False表示拒绝（过滤掉）
         """
-        # 只处理 WARNING 级别的日志
-        if record.levelno != logging.WARNING:
+        # 只处理 INFO 和 WARNING 级别的日志
+        # Neo4j 驱动对 severity='INFORMATION' 的通知使用 INFO 级别，
+        # 对 severity='WARNING' 的通知使用 WARNING 级别
+        if record.levelno not in (logging.INFO, logging.WARNING):
             return True
         
         # 检查是否是 Neo4j 的成功通知
@@ -110,17 +112,25 @@ class LoggingConfig:
         root_logger = logging.getLogger()
         root_logger.setLevel(getattr(logging, settings.LOG_LEVEL.upper()))
         
-        # 为 Neo4j 驱动添加过滤器，过滤成功/信息性通知但保留真正的警告
-        # Neo4j 驱动会以 WARNING 级别记录所有数据库通知，包括成功(00000)和信息性(00NA0)通知
-        # 使用过滤器而不是改变日志级别，这样可以保留真正的警告和错误
-        neo4j_filter = Neo4jSuccessNotificationFilter()
-        for neo4j_logger_name in ["neo4j", "neo4j.io", "neo4j.pool"]:
-            neo4j_logger = logging.getLogger(neo4j_logger_name)
-            neo4j_logger.addFilter(neo4j_filter)
-        
         # 清除现有处理器
         root_logger.handlers.clear()
         
+        # Neo4j 通知过滤器 - 挂在 handler 上确保所有传播上来的日志都能被过滤
+        neo4j_filter = Neo4jSuccessNotificationFilter()
+        
+        # 抑制 Neo4j 通知日志
+        # Neo4j 驱动内部会给 neo4j.notifications logger 配置自己的 handler，
+        # 导致日志绕过根 logger 的 filter 直接输出。
+        # 多管齐下确保过滤生效：
+        # 1. 设置 neo4j.notifications 级别为 WARNING（过滤 INFO 级别的 00NA0 通知）
+        # 2. 在所有 neo4j logger 上添加 filter（过滤 WARNING 级别的成功通知）
+        # 3. 在根 handler 上也添加 filter（兜底）
+        neo4j_notifications_logger = logging.getLogger("neo4j.notifications")
+        neo4j_notifications_logger.setLevel(logging.WARNING)
+        for neo4j_logger_name in ["neo4j", "neo4j.io", "neo4j.pool", "neo4j.notifications"]:
+            neo4j_logger = logging.getLogger(neo4j_logger_name)
+            neo4j_logger.addFilter(neo4j_filter)
+        
         # 创建格式化器
         formatter = logging.Formatter(
             fmt=settings.LOG_FORMAT,
@@ -136,6 +146,7 @@ class LoggingConfig:
             console_handler.setFormatter(formatter)
             console_handler.setLevel(getattr(logging, settings.LOG_LEVEL.upper()))
             console_handler.addFilter(sensitive_filter)
+            console_handler.addFilter(neo4j_filter)
             root_logger.addHandler(console_handler)
         
         # 文件处理器（带轮转）
@@ -149,6 +160,7 @@ class LoggingConfig:
             file_handler.setFormatter(formatter)
             file_handler.setLevel(getattr(logging, settings.LOG_LEVEL.upper()))
             file_handler.addFilter(sensitive_filter)
+            file_handler.addFilter(neo4j_filter)
             root_logger.addHandler(file_handler)
         
         cls._initialized = True

api/app/core/memory/utils/prompt/prompt_utils.py

@@ -349,19 +349,39 @@ async def render_emotion_suggestions_prompt(
     import json
     
     # 预处理 emotion_distribution 为 JSON 字符串
+    # 如果是中文，将 emotion_distribution 的 key 翻译为中文
+    emotion_distribution = health_data.get('emotion_distribution', {})
+    if language == "zh":
+        emotion_type_zh = {
+            'joy': '喜悦', 'sadness': '悲伤', 'anger': '愤怒',
+            'fear': '恐惧', 'surprise': '惊讶', 'neutral': '中性'
+        }
+        emotion_distribution = {
+            emotion_type_zh.get(k, k): v for k, v in emotion_distribution.items()
+        }
     emotion_distribution_json = json.dumps(
-        health_data.get('emotion_distribution', {}), 
+        emotion_distribution, 
         ensure_ascii=False, 
         indent=2
     )
     
+    # 翻译 dominant_negative_emotion
+    dominant_negative_translated = None
+    dominant_neg = patterns.get('dominant_negative_emotion')
+    if dominant_neg and language == "zh":
+        emotion_type_zh_map = {
+            'sadness': '悲伤', 'anger': '愤怒', 'fear': '恐惧'
+        }
+        dominant_negative_translated = emotion_type_zh_map.get(dominant_neg, dominant_neg)
+    
     template = prompt_env.get_template("generate_emotion_suggestions.jinja2")
     rendered_prompt = template.render(
         health_data=health_data,
         patterns=patterns,
         user_profile=user_profile,
         emotion_distribution_json=emotion_distribution_json,
-        language=language
+        language=language,
+        dominant_negative_translated=dominant_negative_translated
     )
     
     # 记录渲染结果到提示日志

api/app/core/memory/utils/prompt/prompts/generate_emotion_suggestions.jinja2

@@ -1,10 +1,23 @@
 {% if language == "en" %}
 You are a professional mental health consultant. Based on the following user's emotional health data and personal information, generate 3-5 personalized emotional improvement suggestions.
 
+## Core Principle (Highest Priority)
+
+**You must strictly base your suggestions on the emotion distribution data provided below. As long as any emotion type has a count ≥ 1, that emotion EXISTS and you must acknowledge and address it in your suggestions. You must NEVER claim an emotion is "zero" or "absent" when its count is ≥ 1.**
+
+Specific rules:
+1. Carefully check the count for each emotion type in "Emotion Distribution" — count ≥ 1 means the emotion exists
+2. Even if an emotion appeared only once, you must mention it in health_summary or suggestions and provide targeted advice
+3. Never state that an emotion is "zero" or "non-existent" unless its count in the distribution data is truly 0
+4. If positive emotions (e.g., Joy) exist, health_summary must affirm this positive signal
+5. If negative emotions (e.g., Sadness, Anger, Fear) exist even once, you must provide targeted improvement suggestions
+6. A high proportion of neutral emotions does NOT mean other emotions are absent — address all non-zero emotions
+
 ## User Emotional Health Data
 
 Health Score: {{ health_data.health_score }}/100
 Health Level: {{ health_data.level }}
+Total Emotion Records: {{ health_data.dimensions.positivity_rate.positive_count + health_data.dimensions.positivity_rate.negative_count + health_data.dimensions.positivity_rate.neutral_count }}
 
 Dimension Analysis:
 - Positivity Rate: {{ health_data.dimensions.positivity_rate.score }}/100
@@ -18,7 +31,7 @@ Dimension Analysis:
 - Resilience: {{ health_data.dimensions.resilience.score }}/100
   - Recovery Rate: {{ health_data.dimensions.resilience.recovery_rate }}
 
-Emotion Distribution:
+Emotion Distribution (check each item — every emotion with count ≥ 1 must be reflected in suggestions):
 {{ emotion_distribution_json }}
 
 ## Emotion Pattern Analysis
@@ -41,6 +54,7 @@ Please generate 3-5 personalized suggestions, each containing:
 5. actionable_steps: 3 specific executable steps
 
 Also provide a health_summary (no more than 50 words) summarizing the user's overall emotional state.
+**The health_summary must truthfully reflect ALL non-zero emotions from the distribution data. Do not omit any emotion type that has appeared.**
 
 Please return in JSON format as follows:
 {
@@ -57,6 +71,7 @@ Please return in JSON format as follows:
 }
 
 Notes:
+- CRITICAL: Any emotion with count ≥ 1 in the distribution MUST be acknowledged and addressed — never ignore or claim it is zero
 - Suggestions should be specific and actionable, avoid vague advice
 - Provide personalized suggestions based on user's interests and hobbies
 - Provide targeted suggestions for main issues (such as dominant negative emotions)
@@ -66,10 +81,23 @@ Notes:
 {% else %}
 你是一位专业的心理健康顾问。请根据以下用户的情绪健康数据和个人信息，生成3-5条个性化的情绪改善建议。
 
+## 核心原则（最高优先级）
+
+**你必须严格基于下方提供的情绪分布数据来生成建议。只要某种情绪的出现次数 ≥ 1，就代表该情绪确实存在，你必须在建议中承认并回应这一情绪，绝对不能说"该情绪为零"或"没有该情绪"。**
+
+具体规则：
+1. 仔细查看"情绪分布"中每种情绪的出现次数，次数 ≥ 1 即表示该情绪存在
+2. 即使某种情绪只出现了1次，也必须在 health_summary 或建议中提及并给出针对性建议
+3. 严禁在输出中声称某种情绪"为零"或"不存在"，除非该情绪在分布数据中确实为0次
+4. 如果正面情绪（如喜悦）存在，health_summary 中必须肯定这一积极信号
+5. 如果负面情绪（如悲伤、愤怒、恐惧）存在，即使只有1次，也必须给出针对性的改善建议
+6. 中性情绪占比高不代表没有其他情绪，必须同时关注所有非零情绪
+
 ## 用户情绪健康数据
 
 健康分数：{{ health_data.health_score }}/100
 健康等级：{{ health_data.level }}
+情绪记录总数：{{ health_data.dimensions.positivity_rate.positive_count + health_data.dimensions.positivity_rate.negative_count + health_data.dimensions.positivity_rate.neutral_count }}条
 
 维度分析：
 - 积极率：{{ health_data.dimensions.positivity_rate.score }}/100
@@ -83,12 +111,12 @@ Notes:
 - 恢复力：{{ health_data.dimensions.resilience.score }}/100
   - 恢复率：{{ health_data.dimensions.resilience.recovery_rate }}
 
-情绪分布：
+情绪分布（请逐项检查，次数≥1的情绪都必须在建议中体现）：
 {{ emotion_distribution_json }}
 
 ## 情绪模式分析
 
-主要负面情绪：{{ patterns.dominant_negative_emotion|default('无') }}
+主要负面情绪：{{ dominant_negative_translated|default(patterns.dominant_negative_emotion)|default('无') }}
 情绪波动性：{{ patterns.emotion_volatility|default('未知') }}
 高强度情绪次数：{{ patterns.high_intensity_emotions|default([])|length }}
 
@@ -106,6 +134,7 @@ Notes:
 5. actionable_steps: 3个可执行的具体步骤
 
 同时提供一个health_summary（不超过50字），概括用户的整体情绪状态。
+**health_summary 必须如实反映情绪分布中所有非零情绪的存在，不得遗漏任何已出现的情绪类型。**
 
 请以JSON格式返回，格式如下：
 {
@@ -122,9 +151,11 @@ Notes:
 }
 
 注意事项：
+- 所有输出内容必须完全使用中文，严禁出现任何英文单词或短语（包括情绪类型名称如fear、sadness、anger等，必须使用对应的中文：恐惧、悲伤、愤怒等）
+- 再次强调：情绪分布中出现次数≥1的情绪必须在建议中被提及和回应，绝不能忽略或声称为零
 - 建议要具体、可执行，避免空泛
 - 结合用户的兴趣爱好提供个性化建议
 - 针对主要问题（如主要负面情绪）提供针对性建议
-- 优先级要合理分配（至少1个high，1-2个medium，其余low）
+- 优先级要合理分配（至少1个高，1-2个中，其余低）
 - 每个建议的3个步骤要循序渐进、易于实施
 {% endif %}

api/app/schemas/memory_episodic_schema.py

@@ -25,6 +25,31 @@ type_mapping = {
             "Condition": "条件实体节点",
             "Numeric": "数值实体节点"
         }
+EPISODIC_TYPE_MAPPING = {
+    "conversation": "对话",
+    "project_work": "项目/工作",
+    "learning": "学习",
+    "decision": "决策",
+    "important_event": "重要事件",
+}
+
+
+def translate_episodic_type(episodic_type: str, language: str = "zh") -> str:
+    """
+    根据语言参数翻译情景类型
+    
+    Args:
+        episodic_type: 英文枚举值 (conversation, project_work, etc.)
+        language: 语言类型 ("zh" 中文, "en" 英文)
+        
+    Returns:
+        翻译后的类型字符串
+    """
+    if language == "en":
+        return episodic_type
+    return EPISODIC_TYPE_MAPPING.get(episodic_type, episodic_type)
+
+
 class EmotionType(ABC):
     JOY_TYPE = "joy"
     SURPRISE_TYPE = "surprise"
@@ -51,7 +76,6 @@ class EpisodicMemoryOverviewRequest(BaseModel):
     """情景记忆总览查询请求"""
     
     end_user_id: str = Field(..., description="终端用户ID")
-    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
     time_range: str = Field(
         default="all",
         description="时间范围筛选，可选值：all, today, this_week, this_month"
@@ -71,4 +95,3 @@ 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")
-    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")

api/app/services/emotion_analytics_service.py

@@ -220,14 +220,16 @@ class EmotionAnalyticsService:
         """计算积极率
 
         根据情绪类型分类正面、负面和中性情绪，计算积极率。
-        公式：(正面数 / (正面数 + 负面数)) * 100
+        当存在非中性情绪时：(正面数 / (正面数 + 负面数)) * 100
+        当只有中性情绪时：基于中性情绪的存在给出基准分数
+        当完全没有情绪数据时：score 为 None，表示无法计算
 
         Args:
             emotions: 情绪数据列表，每个包含 emotion_type 字段
 
         Returns:
             Dict: 包含积极率计算结果：
-                - score: 积极率分数（0-100）
+                - score: 积极率分数（0-100），无数据时为 None
                 - positive_count: 正面情绪数量
                 - negative_count: 负面情绪数量
                 - neutral_count: 中性情绪数量
@@ -245,14 +247,19 @@ class EmotionAnalyticsService:
         total_non_neutral = positive_count + negative_count
         if total_non_neutral > 0:
             score = (positive_count / total_non_neutral) * 100
+        elif neutral_count > 0:
+            # 只有中性情绪，说明情绪状态平稳，给予基准分 50
+            score = 50.0
         else:
-            score = 50.0  # 如果没有非中性情绪，默认为50
+            # 完全没有情绪数据，无法计算积极率
+            score = None
 
+        score_display = f"{score:.2f}" if score is not None else "N/A"
         logger.debug(f"积极率计算: positive={positive_count}, negative={negative_count}, "
-                     f"neutral={neutral_count}, score={score:.2f}")
+                     f"neutral={neutral_count}, score={score_display}")
 
         return {
-            "score": round(score, 2),
+            "score": round(score, 2) if score is not None else None,
             "positive_count": positive_count,
             "negative_count": negative_count,
             "neutral_count": neutral_count
@@ -385,12 +392,12 @@ class EmotionAnalyticsService:
             if not emotions:
                 logger.warning(f"用户 {end_user_id} 在时间范围 {time_range} 内没有情绪数据")
                 return {
-                    "health_score": 0.0,
+                    "health_score": None,
                     "level": "无数据",
                     "dimensions": {
-                        "positivity_rate": {"score": 0.0, "positive_count": 0, "negative_count": 0, "neutral_count": 0},
-                        "stability": {"score": 0.0, "std_deviation": 0.0},
-                        "resilience": {"score": 0.0, "recovery_rate": 0.0}
+                        "positivity_rate": {"score": None, "positive_count": 0, "negative_count": 0, "neutral_count": 0},
+                        "stability": {"score": None, "std_deviation": 0.0},
+                        "resilience": {"score": None, "recovery_rate": 0.0}
                     },
                     "emotion_distribution": {},
                     "time_range": time_range
@@ -403,8 +410,10 @@ class EmotionAnalyticsService:
 
             # 计算综合健康分数
             # 公式：positivity_rate * 0.4 + stability * 0.3 + resilience * 0.3
+            # 如果积极率无法计算（无数据），视为 0 参与加权
+            positivity_score = positivity_rate["score"] if positivity_rate["score"] is not None else 0.0
             health_score = (
-                    positivity_rate["score"] * 0.4 +
+                    positivity_score * 0.4 +
                     stability["score"] * 0.3 +
                     resilience["score"] * 0.3
             )
@@ -700,7 +709,7 @@ class EmotionAnalyticsService:
         Returns:
             EmotionSuggestionsResponse: 默认建议
         """
-        health_score = health_data.get('health_score', 0)
+        health_score = health_data.get('health_score') or 0
 
         if language == "en":
             if health_score >= 80: