[add] Set cache for the distribution of interest tags

api/app/cache/__init__.py

@@ -3,9 +3,10 @@ Cache 缓存模块
 
 提供各种缓存功能的统一入口
 """
-from .memory import EmotionMemoryCache, ImplicitMemoryCache
+from .memory import EmotionMemoryCache, ImplicitMemoryCache, InterestMemoryCache
 
 __all__ = [
     "EmotionMemoryCache",
     "ImplicitMemoryCache",
+    "InterestMemoryCache",
 ]

api/app/cache/memory/__init__.py

@@ -5,8 +5,10 @@ Memory 缓存模块
 """
 from .emotion_memory import EmotionMemoryCache
 from .implicit_memory import ImplicitMemoryCache
+from .interest_memory import InterestMemoryCache
 
 __all__ = [
     "EmotionMemoryCache",
     "ImplicitMemoryCache",
+    "InterestMemoryCache",
 ]

api/app/cache/memory/interest_memory.py

@@ -0,0 +1,122 @@
+"""
+Interest Distribution Cache
+
+兴趣分布缓存模块
+用于缓存用户的兴趣分布标签数据，避免重复调用模型生成
+"""
+import json
+import logging
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+
+from app.aioRedis import aio_redis
+
+logger = logging.getLogger(__name__)
+
+# 缓存过期时间：24小时
+INTEREST_CACHE_EXPIRE = 86400
+
+
+class InterestMemoryCache:
+    """兴趣分布缓存类"""
+
+    PREFIX = "cache:memory:interest_distribution"
+
+    @classmethod
+    def _get_key(cls, end_user_id: str, language: str) -> str:
+        """生成 Redis key
+
+        Args:
+            end_user_id: 用户ID
+            language: 语言类型
+
+        Returns:
+            完整的 Redis key
+        """
+        return f"{cls.PREFIX}:by_user:{end_user_id}:{language}"
+
+    @classmethod
+    async def set_interest_distribution(
+        cls,
+        end_user_id: str,
+        language: str,
+        data: List[Dict[str, Any]],
+        expire: int = INTEREST_CACHE_EXPIRE,
+    ) -> bool:
+        """设置用户兴趣分布缓存
+
+        Args:
+            end_user_id: 用户ID
+            language: 语言类型
+            data: 兴趣分布列表，格式 [{"name": "...", "frequency": ...}, ...]
+            expire: 过期时间（秒），默认24小时
+
+        Returns:
+            是否设置成功
+        """
+        try:
+            key = cls._get_key(end_user_id, language)
+            payload = {
+                "data": data,
+                "generated_at": datetime.now().isoformat(),
+                "cached": True,
+            }
+            value = json.dumps(payload, ensure_ascii=False)
+            await aio_redis.set(key, value, ex=expire)
+            logger.info(f"设置兴趣分布缓存成功: {key}, 过期时间: {expire}秒")
+            return True
+        except Exception as e:
+            logger.error(f"设置兴趣分布缓存失败: {e}", exc_info=True)
+            return False
+
+    @classmethod
+    async def get_interest_distribution(
+        cls,
+        end_user_id: str,
+        language: str,
+    ) -> Optional[List[Dict[str, Any]]]:
+        """获取用户兴趣分布缓存
+
+        Args:
+            end_user_id: 用户ID
+            language: 语言类型
+
+        Returns:
+            兴趣分布列表，缓存不存在或已过期返回 None
+        """
+        try:
+            key = cls._get_key(end_user_id, language)
+            value = await aio_redis.get(key)
+            if value:
+                payload = json.loads(value)
+                logger.info(f"命中兴趣分布缓存: {key}")
+                return payload.get("data")
+            logger.info(f"兴趣分布缓存不存在或已过期: {key}")
+            return None
+        except Exception as e:
+            logger.error(f"获取兴趣分布缓存失败: {e}", exc_info=True)
+            return None
+
+    @classmethod
+    async def delete_interest_distribution(
+        cls,
+        end_user_id: str,
+        language: str,
+    ) -> bool:
+        """删除用户兴趣分布缓存
+
+        Args:
+            end_user_id: 用户ID
+            language: 语言类型
+
+        Returns:
+            是否删除成功
+        """
+        try:
+            key = cls._get_key(end_user_id, language)
+            result = await aio_redis.delete(key)
+            logger.info(f"删除兴趣分布缓存: {key}, 结果: {result}")
+            return result > 0
+        except Exception as e:
+            logger.error(f"删除兴趣分布缓存失败: {e}", exc_info=True)
+            return False

api/app/controllers/memory_agent_controller.py

@@ -1,5 +1,6 @@
 from typing import List, Optional
 
+from app.cache.memory.interest_memory import InterestMemoryCache
 from app.celery_app import celery_app
 from app.core.error_codes import BizCode
 from app.core.language_utils import get_language_from_header
@@ -684,11 +685,29 @@ async def get_interest_distribution_by_user_api(
     language = get_language_from_header(language_type)
     api_logger.info(f"Interest distribution by user requested: end_user_id={end_user_id}, language={language}")
     try:
+        # 优先读取缓存
+        cached = await InterestMemoryCache.get_interest_distribution(
+            end_user_id=end_user_id,
+            language=language,
+        )
+        if cached is not None:
+            api_logger.info(f"Interest distribution cache hit: end_user_id={end_user_id}")
+            return success(data=cached, msg="获取兴趣分布标签成功")
+
+        # 缓存未命中，调用模型生成
         result = await memory_agent_service.get_interest_distribution_by_user(
             end_user_id=end_user_id,
             limit=limit,
             language=language
         )
+
+        # 写入缓存，24小时过期
+        await InterestMemoryCache.set_interest_distribution(
+            end_user_id=end_user_id,
+            language=language,
+            data=result,
+        )
+
         return success(data=result, msg="获取兴趣分布标签成功")
     except Exception as e:
         api_logger.error(f"Interest distribution by user failed: {str(e)}")

api/app/core/config.py

@@ -230,7 +230,7 @@ class Settings:
     # General Ontology Type Configuration
     # ========================================================================
     # 通用本体文件路径列表（逗号分隔）
-    GENERAL_ONTOLOGY_FILES: str = os.getenv("GENERAL_ONTOLOGY_FILES", "app/core/memory/ontology_services/General_purpose_entity.ttl")
+    GENERAL_ONTOLOGY_FILES: str = os.getenv("GENERAL_ONTOLOGY_FILES", "api/app/core/memory/ontology_services/General_purpose_entity.ttl")
 
     # 是否启用通用本体类型功能
     ENABLE_GENERAL_ONTOLOGY_TYPES: bool = os.getenv("ENABLE_GENERAL_ONTOLOGY_TYPES", "true").lower() == "true"

api/app/core/memory/analytics/hot_memory_tags.py

@@ -281,16 +281,25 @@ async def get_interest_distribution(end_user_id: str, limit: int = 10, by_user:
             return []
 
         raw_tag_names = [tag for tag, freq in raw_tags_with_freq]
+        raw_freq_map = {tag: freq for tag, freq in raw_tags_with_freq}
 
-        # 使用兴趣活动专用prompt进行筛选
+        # 使用兴趣活动专用prompt进行筛选（支持语义推断出新标签）
         interest_tag_names = await filter_interests_with_llm(raw_tag_names, end_user_id, language=language)
 
-        # 保留原始频率，按兴趣筛选结果过滤
+        # 构建最终标签列表：
-        final_tags = [
+        # - 原始标签中存在的，保留原始频率
-            (tag, freq)
+        # - LLM推断出的新标签（不在原始列表中），赋予默认频率1
-            for tag, freq in raw_tags_with_freq
+        final_tags = []
-            if tag in interest_tag_names
+        seen = set()
-        ]
+        for tag in interest_tag_names:
+            if tag in seen:
+                continue
+            seen.add(tag)
+            freq = raw_freq_map.get(tag, 1)
+            final_tags.append((tag, freq))
+
+        # 按频率降序排列
+        final_tags.sort(key=lambda x: x[1], reverse=True)
 
         return final_tags[:limit]
     finally:

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

@@ -1,47 +1,67 @@
 {% if language == "zh" %}
-You are a user interest analysis expert. Your task is to identify activity-based tags from a tag list that represent the user's hobbies and interests. Please output the results in Chinese.
+You are a user interest analysis expert. Your task is to infer and extract the user's core hobby/interest activities from a tag list. The tags may be specific project names, tool names, or compound nouns — your job is to identify the underlying interest they represent.
 
-**Keep Rules** (keep if any condition is met):
+**Step 1 - Infer the underlying interest from each tag**:
-- Tags representing sports or physical activities the user actively participates in (e.g., '攀岩', '篮球', '游泳', '跑步')
+Look at each tag and ask: "What hobby or interest does this tag suggest the user has?"
-- Tags representing cultural or entertainment hobbies (e.g., '读书', '看电影', '听音乐', '摄影')
-- Tags representing learning or creative activities (e.g., '编程', '绘画', '写作', '烹饪')
-- Tags representing specific interest domains or hobby categories (e.g., '历史', '天文', '园艺')
 
-**Filter Rules** (remove if any condition is met):
+Examples of inference:
-- Pure object or tool names that do not represent an activity (e.g., '篮球鞋', '相机', '书桌')
+- '攀岩', '室内攀岩馆', '攀岩者数据仪表盘', '路线解锁地图', '指力', '路线等级', '当日攀岩流畅度' → '攀岩'
-- Pure location or venue names (e.g., '篮球场', '图书馆', '健身房')
+- '风光摄影元数据增强器', 'EXIF数据', '.CR2文件', '.NEF文件', '日出拍摄点', '曝光补偿', '光圈', '太阳高度角', '云量预测图层' → '摄影'
-- Abstract concepts or quality descriptions (e.g., '核心力量', '团队合作', '专注力')
+- '晨间冥想坚持天数', '身心协同峰值' → '冥想'
-- Person names, brand names, or proper nouns (e.g., '乔丹', 'Nike')
+- '川味可视化', '川菜' → '烹饪'
+- '开源项目命名建议', 'climbviz', '可视化', '力量增长雷达图' → '编程' 或 '数据可视化'
+- '吉他', '指弹', '琴谱' → '吉他'
+- '跑步', '5公里', '跑鞋' → '跑步'
+- '瑜伽垫', '瑜伽课' → '瑜伽'
 
-**Merge Rules**: For semantically similar tags, keep only the most representative one.
+**Step 2 - Consolidate and deduplicate**:
-For example: keep '篮球' over '打篮球'; keep '读书' over '阅读'.
+- Merge tags that point to the same interest into one representative label
+- Use concise, standard hobby names (e.g., '攀岩', '摄影', '编程', '烹饪', '冥想', '吉他', '跑步')
+- If multiple tags all point to '攀岩', output '攀岩' only once
+
+**Step 3 - Filter out non-interest tags**:
+Remove tags that do NOT suggest any hobby or interest:
+- Generic system/assistant terms (e.g., '助手', '用户', 'AI')
+- Pure abstract metrics with no clear hobby link (e.g., '完成时间', '日期', '自我评分')
+- Location names with no clear hobby link (e.g., '青城山后山' alone — but if combined with photography context, infer '摄影')
+
+**Output format**: Return a list of concise interest activity names in Chinese.
 
 **Example**:
-Input: ['攀岩', '篮球场', '篮球鞋', '篮球', '《三体》', '历史', '核心力量', '烹饪', '菜刀']
+Input: ['攀岩', '攀岩者数据仪表盘', '路线解锁地图', '指力', '风光摄影元数据增强器', 'EXIF数据', '晨间冥想坚持天数', '川味可视化', '可视化', '助手', '完成时间']
-Output: ['攀岩', '篮球', '历史', '烹饪']
+Output: ['攀岩', '摄影', '冥想', '烹饪', '编程']
 
-Please filter the following tag list and return only the tags that represent user interest activities in Chinese: {{ tag_list }}
+Now process the following tag list and return the inferred interest activities in Chinese: {{ tag_list }}
 {% else %}
-You are a user interest analysis expert. Your task is to identify activity-based tags from a tag list that represent the user's hobbies and interests. Please output the results in English.
+You are a user interest analysis expert. Your task is to infer and extract the user's core hobby/interest activities from a tag list. The tags may be specific project names, tool names, or compound nouns — your job is to identify the underlying interest they represent.
 
-**Keep Rules** (keep if any condition is met):
+**Step 1 - Infer the underlying interest from each tag**:
-- Tags representing sports or physical activities the user actively participates in (e.g., 'rock climbing', 'basketball', 'swimming', 'running')
+Look at each tag and ask: "What hobby or interest does this tag suggest the user has?"
-- Tags representing cultural or entertainment hobbies (e.g., 'reading', 'watching movies', 'listening to music', 'photography')
-- Tags representing learning or creative activities (e.g., 'programming', 'painting', 'writing', 'cooking')
-- Tags representing specific interest domains or hobby categories (e.g., 'history', 'astronomy', 'gardening')
 
-**Filter Rules** (remove if any condition is met):
+Examples of inference:
-- Pure object or tool names that do not represent an activity (e.g., 'basketball shoes', 'camera', 'desk')
+- 'rock climbing', 'indoor climbing gym', 'climber dashboard', 'route map', 'finger strength' → 'rock climbing'
-- Pure location or venue names (e.g., 'basketball court', 'library', 'gym')
+- 'landscape photography metadata enhancer', 'EXIF data', 'sunrise shooting spot', 'exposure compensation' → 'photography'
-- Abstract concepts or quality descriptions (e.g., 'core strength', 'teamwork', 'focus')
+- 'morning meditation streak', 'mind-body peak' → 'meditation'
-- Person names, brand names, or proper nouns (e.g., 'Jordan', 'Nike')
+- 'Sichuan cuisine visualization', 'Sichuan food' → 'cooking'
+- 'open source project', 'data visualization tool', 'Python' → 'programming'
+- 'guitar', 'fingerpicking', 'sheet music' → 'guitar'
+- 'running', '5km', 'running shoes' → 'running'
 
-**Merge Rules**: For semantically similar tags, keep only the most representative one.
+**Step 2 - Consolidate and deduplicate**:
-For example: keep 'basketball' over 'playing basketball'; keep 'reading' over 'reading books'.
+- Merge tags that point to the same interest into one representative label
+- Use concise, standard hobby names (e.g., 'rock climbing', 'photography', 'programming', 'cooking', 'meditation')
+- If multiple tags all point to 'rock climbing', output 'rock climbing' only once
+
+**Step 3 - Filter out non-interest tags**:
+Remove tags that do NOT suggest any hobby or interest:
+- Generic system/assistant terms (e.g., 'assistant', 'user', 'AI')
+- Pure abstract metrics with no clear hobby link (e.g., 'completion time', 'date', 'self-rating')
+
+**Output format**: Return a list of concise interest activity names in English.
 
 **Example**:
-Input: ['rock climbing', 'basketball court', 'basketball shoes', 'basketball', 'The Three-Body Problem', 'history', 'core strength', 'cooking', 'kitchen knife']
+Input: ['rock climbing', 'climber dashboard', 'route map', 'finger strength', 'landscape photography metadata enhancer', 'EXIF data', 'morning meditation streak', 'Sichuan cuisine visualization', 'visualization', 'assistant', 'completion time']
-Output: ['rock climbing', 'basketball', 'history', 'cooking']
+Output: ['rock climbing', 'photography', 'meditation', 'cooking', 'programming']
 
-Please filter the following tag list and return only the tags that represent user interest activities in English: {{ tag_list }}
+Now process the following tag list and return the inferred interest activities in English: {{ tag_list }}
 {% endif %}

api/env.example

@@ -139,7 +139,7 @@ SMTP_USER=
 SMTP_PASSWORD=
 
 # 本体类型融合配置 (记得写入env_example)
-GENERAL_ONTOLOGY_FILES=app/core/memory/ontology_services/General_purpose_entity.ttl  # 指定要加载的本体文件路径，多个文件用逗号分隔
+GENERAL_ONTOLOGY_FILES=api/app/core/memory/ontology_services/General_purpose_entity.ttl  # 指定要加载的本体文件路径，多个文件用逗号分隔
 ENABLE_GENERAL_ONTOLOGY_TYPES=true # 总开关，控制是否启用通用本体类型融合功能(false = 不使用任何本体类型指导)
 MAX_ONTOLOGY_TYPES_IN_PROMPT=100 # 限制传给 LLM 的类型数量，防止 Prompt 过长
 CORE_GENERAL_TYPES=Person,Organization,Place,Event,Work,Concept # 	定义核心类型列表，这些类型会优先包含在合并结果中