From 8466c8e0192c84641ec2bd8f088075604b814bb2 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 3 Mar 2026 23:30:54 +0800 Subject: [PATCH 1/6] [fix] Revising the judgment method for the interest analysis tags --- .../controllers/memory_agent_controller.py | 32 ++--- .../core/memory/analytics/hot_memory_tags.py | 112 ++++++++++++++++++ .../core/memory/utils/prompt/prompt_utils.py | 17 +++ .../prompt/prompts/interest_filter.jinja2 | 47 ++++++++ api/app/services/memory_agent_service.py | 32 ++--- 5 files changed, 210 insertions(+), 30 deletions(-) create mode 100644 api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index b88e65ff..8f2e5c31 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -661,34 +661,38 @@ async def get_knowledge_type_stats_api( return fail(BizCode.INTERNAL_ERROR, "获取知识库类型统计失败", str(e)) -@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse) -async def get_hot_memory_tags_by_user_api( - end_user_id: Optional[str] = Query(None, description="用户ID（可选）"), - limit: int = Query(20, description="返回标签数量限制"), +@router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse) +async def get_interest_distribution_by_user_api( + end_user_id: Optional[str] = Query(None, description="用户ID（必填）"), + limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"), + language_type: str = Header(default=None, alias="X-Language-Type"), current_user: User = Depends(get_current_user), - db: Session=Depends(get_db), + db: Session = Depends(get_db), ): """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签 - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习、创作等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] """ - api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}") + 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: - result = await memory_agent_service.get_hot_memory_tags_by_user( + result = await memory_agent_service.get_interest_distribution_by_user( end_user_id=end_user_id, - limit=limit + limit=limit, + language=language ) - return success(data=result, msg="获取热门记忆标签成功") + return success(data=result, msg="获取兴趣分布标签成功") except Exception as e: - api_logger.error(f"Hot memory tags by user failed: {str(e)}") - return fail(BizCode.INTERNAL_ERROR, "获取热门记忆标签失败", str(e)) + api_logger.error(f"Interest distribution by user failed: {str(e)}") + return fail(BizCode.INTERNAL_ERROR, "获取兴趣分布标签失败", str(e)) @router.get("/analytics/user_profile", response_model=ApiResponse) diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index abb0f138..da08e88e 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -16,6 +16,10 @@ class FilteredTags(BaseModel): """用于接收LLM筛选后的核心标签列表的模型。""" meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。") +class InterestTags(BaseModel): + """用于接收LLM筛选后的兴趣活动标签列表的模型。""" + interest_tags: List[str] = Field(..., description="从原始列表中筛选出的代表用户兴趣活动的标签列表。") + async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: """ 使用LLM筛选标签列表，仅保留具有代表性的核心名词。 @@ -89,6 +93,70 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: # 在LLM失败时返回原始标签，确保流程继续 return tags +async def filter_interests_with_llm(tags: List[str], end_user_id: str, language: str = "zh") -> List[str]: + """ + 使用LLM从标签列表中筛选出代表用户兴趣活动的标签。 + + 与 filter_tags_with_llm 不同，此函数专注于识别"活动/行为"类兴趣， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 + + Args: + tags: 原始标签列表 + end_user_id: 用户ID，用于获取LLM配置 + + Returns: + 筛选后的兴趣活动标签列表 + """ + try: + with get_db_context() as db: + from app.services.memory_agent_service import ( + get_end_user_connected_config, + ) + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") + workspace_id = connected_config.get("workspace_id") + + if not config_id and not workspace_id: + raise ValueError( + f"No memory_config_id found for end_user_id: {end_user_id}." + ) + + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + + if not memory_config.llm_model_id: + raise ValueError( + f"No llm_model_id found in memory config {config_id}." + ) + + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(memory_config.llm_model_id) + + tag_list_str = ", ".join(tags) + from app.core.memory.utils.prompt.prompt_utils import render_interest_filter_prompt + rendered_prompt = render_interest_filter_prompt(tag_list_str, language=language) + messages = [ + { + "role": "user", + "content": rendered_prompt + } + ] + + structured_response = await llm_client.response_structured( + messages=messages, + response_model=InterestTags + ) + + return structured_response.interest_tags + + except Exception as e: + print(f"兴趣标签LLM筛选过程中发生错误: {e}") + return tags + + async def get_raw_tags_from_db( connector: Neo4jConnector, end_user_id: str, @@ -183,3 +251,47 @@ async def get_hot_memory_tags(end_user_id: str, limit: int = 10, by_user: bool = finally: # 确保关闭连接 await connector.close() + +async def get_interest_distribution(end_user_id: str, limit: int = 10, by_user: bool = False, language: str = "zh") -> List[Tuple[str, int]]: + """ + 获取用户的兴趣分布标签。 + + 与 get_hot_memory_tags 不同，此函数使用专门针对"活动/行为"的LLM prompt， + 过滤掉纯物品、工具、地点等，只保留能代表用户兴趣爱好的活动类标签。 + + Args: + end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id + limit: 最终返回的标签数量限制（默认10） + by_user: 是否按user_id查询（默认False，按end_user_id查询） + + Raises: + ValueError: 如果end_user_id未提供或为空 + """ + if not end_user_id or not end_user_id.strip(): + raise ValueError( + "end_user_id is required. Please provide a valid end_user_id or user_id." + ) + + connector = Neo4jConnector() + try: + # 查询更多原始标签，给LLM提供充足上下文 + query_limit = 40 + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, query_limit, by_user=by_user) + if not raw_tags_with_freq: + return [] + + raw_tag_names = [tag for tag, freq in raw_tags_with_freq] + + # 使用兴趣活动专用prompt进行筛选 + 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 + final_tags = [ + (tag, freq) + for tag, freq in raw_tags_with_freq + if tag in interest_tag_names + ] + + return final_tags[:limit] + finally: + await connector.close() diff --git a/api/app/core/memory/utils/prompt/prompt_utils.py b/api/app/core/memory/utils/prompt/prompt_utils.py index d88f50cf..0cea98f2 100644 --- a/api/app/core/memory/utils/prompt/prompt_utils.py +++ b/api/app/core/memory/utils/prompt/prompt_utils.py @@ -548,3 +548,20 @@ async def render_ontology_extraction_prompt( }) return rendered_prompt + + +def render_interest_filter_prompt(tag_list: str, language: str = "zh") -> str: + """ + Renders the interest filter prompt using the interest_filter.jinja2 template. + + Args: + tag_list: Comma-separated string of raw tags to filter + language: Output language ("zh" for Chinese, "en" for English) + + Returns: + Rendered prompt content as string + """ + template = prompt_env.get_template("interest_filter.jinja2") + rendered_prompt = template.render(tag_list=tag_list, language=language) + log_prompt_rendering('interest filter', rendered_prompt) + return rendered_prompt diff --git a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 new file mode 100644 index 00000000..1e3aac55 --- /dev/null +++ b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 @@ -0,0 +1,47 @@ +{% 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. + +**Keep Rules** (keep if any condition is met): +- Tags representing sports or physical activities the user actively participates in (e.g., '攀岩', '篮球', '游泳', '跑步') +- 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): +- Pure object or tool names that do not represent an activity (e.g., '篮球鞋', '相机', '书桌') +- Pure location or venue names (e.g., '篮球场', '图书馆', '健身房') +- Abstract concepts or quality descriptions (e.g., '核心力量', '团队合作', '专注力') +- Person names, brand names, or proper nouns (e.g., '乔丹', 'Nike') + +**Merge Rules**: For semantically similar tags, keep only the most representative one. +For example: keep '篮球' over '打篮球'; keep '读书' over '阅读'. + +**Example**: +Input: ['攀岩', '篮球场', '篮球鞋', '篮球', '《三体》', '历史', '核心力量', '烹饪', '菜刀'] +Output: ['攀岩', '篮球', '历史', '烹饪'] + +Please filter the following tag list and return only the tags that represent user 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. + +**Keep Rules** (keep if any condition is met): +- Tags representing sports or physical activities the user actively participates in (e.g., 'rock climbing', 'basketball', 'swimming', 'running') +- 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): +- Pure object or tool names that do not represent an activity (e.g., 'basketball shoes', 'camera', 'desk') +- Pure location or venue names (e.g., 'basketball court', 'library', 'gym') +- Abstract concepts or quality descriptions (e.g., 'core strength', 'teamwork', 'focus') +- Person names, brand names, or proper nouns (e.g., 'Jordan', 'Nike') + +**Merge Rules**: For semantically similar tags, keep only the most representative one. +For example: keep 'basketball' over 'playing basketball'; keep 'reading' over 'reading books'. + +**Example**: +Input: ['rock climbing', 'basketball court', 'basketball shoes', 'basketball', 'The Three-Body Problem', 'history', 'core strength', 'cooking', 'kitchen knife'] +Output: ['rock climbing', 'basketball', 'history', 'cooking'] + +Please filter the following tag list and return only the tags that represent user interest activities in English: {{ tag_list }} +{% endif %} diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 1f3667a6..16aee283 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -36,7 +36,7 @@ from app.core.memory.agent.utils.messages_tools import ( ) from app.core.memory.agent.utils.type_classifier import status_typle from app.core.memory.agent.utils.write_tools import write # 新增：直接导入 write 函数 -from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags +from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags, get_interest_distribution from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType @@ -890,36 +890,36 @@ class MemoryAgentService: return result - async def get_hot_memory_tags_by_user( + + async def get_interest_distribution_by_user( self, end_user_id: Optional[str] = None, - limit: int = 20 + limit: int = 5, + language: str = "zh" ) -> List[Dict[str, Any]]: """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签。 + + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 + - end_user_id: 用户ID（必填） - limit: 返回标签数量限制 + - language: 输出语言（"zh" 中文, "en" 英文） 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] - - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 """ try: - # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度） - tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) - payload = [] - for tag, freq in tags: - payload.append({"name": tag, "frequency": freq}) - return payload + tags = await get_interest_distribution(end_user_id, limit=limit, by_user=False, language=language) + return [{"name": tag, "frequency": freq} for tag, freq in tags] except Exception as e: - logger.error(f"热门记忆标签查询失败: {e}") - raise Exception(f"热门记忆标签查询失败: {e}") + logger.error(f"兴趣分布标签查询失败: {e}") + raise Exception(f"兴趣分布标签查询失败: {e}") async def get_user_profile( From 9115ad6950dc30c5fa20aaa0c0d9f3aa23cca7a9 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 3 Mar 2026 23:30:54 +0800 Subject: [PATCH 2/6] [fix] Revising the judgment method for the interest analysis tags --- .../controllers/memory_agent_controller.py | 32 ++--- .../core/memory/analytics/hot_memory_tags.py | 112 ++++++++++++++++++ .../core/memory/utils/prompt/prompt_utils.py | 17 +++ .../prompt/prompts/interest_filter.jinja2 | 47 ++++++++ api/app/services/memory_agent_service.py | 32 ++--- 5 files changed, 210 insertions(+), 30 deletions(-) create mode 100644 api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index b88e65ff..8f2e5c31 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -661,34 +661,38 @@ async def get_knowledge_type_stats_api( return fail(BizCode.INTERNAL_ERROR, "获取知识库类型统计失败", str(e)) -@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse) -async def get_hot_memory_tags_by_user_api( - end_user_id: Optional[str] = Query(None, description="用户ID（可选）"), - limit: int = Query(20, description="返回标签数量限制"), +@router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse) +async def get_interest_distribution_by_user_api( + end_user_id: Optional[str] = Query(None, description="用户ID（必填）"), + limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"), + language_type: str = Header(default=None, alias="X-Language-Type"), current_user: User = Depends(get_current_user), - db: Session=Depends(get_db), + db: Session = Depends(get_db), ): """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签 - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习、创作等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] """ - api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}") + 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: - result = await memory_agent_service.get_hot_memory_tags_by_user( + result = await memory_agent_service.get_interest_distribution_by_user( end_user_id=end_user_id, - limit=limit + limit=limit, + language=language ) - return success(data=result, msg="获取热门记忆标签成功") + return success(data=result, msg="获取兴趣分布标签成功") except Exception as e: - api_logger.error(f"Hot memory tags by user failed: {str(e)}") - return fail(BizCode.INTERNAL_ERROR, "获取热门记忆标签失败", str(e)) + api_logger.error(f"Interest distribution by user failed: {str(e)}") + return fail(BizCode.INTERNAL_ERROR, "获取兴趣分布标签失败", str(e)) @router.get("/analytics/user_profile", response_model=ApiResponse) diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index abb0f138..da08e88e 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -16,6 +16,10 @@ class FilteredTags(BaseModel): """用于接收LLM筛选后的核心标签列表的模型。""" meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。") +class InterestTags(BaseModel): + """用于接收LLM筛选后的兴趣活动标签列表的模型。""" + interest_tags: List[str] = Field(..., description="从原始列表中筛选出的代表用户兴趣活动的标签列表。") + async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: """ 使用LLM筛选标签列表，仅保留具有代表性的核心名词。 @@ -89,6 +93,70 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: # 在LLM失败时返回原始标签，确保流程继续 return tags +async def filter_interests_with_llm(tags: List[str], end_user_id: str, language: str = "zh") -> List[str]: + """ + 使用LLM从标签列表中筛选出代表用户兴趣活动的标签。 + + 与 filter_tags_with_llm 不同，此函数专注于识别"活动/行为"类兴趣， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 + + Args: + tags: 原始标签列表 + end_user_id: 用户ID，用于获取LLM配置 + + Returns: + 筛选后的兴趣活动标签列表 + """ + try: + with get_db_context() as db: + from app.services.memory_agent_service import ( + get_end_user_connected_config, + ) + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") + workspace_id = connected_config.get("workspace_id") + + if not config_id and not workspace_id: + raise ValueError( + f"No memory_config_id found for end_user_id: {end_user_id}." + ) + + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + + if not memory_config.llm_model_id: + raise ValueError( + f"No llm_model_id found in memory config {config_id}." + ) + + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(memory_config.llm_model_id) + + tag_list_str = ", ".join(tags) + from app.core.memory.utils.prompt.prompt_utils import render_interest_filter_prompt + rendered_prompt = render_interest_filter_prompt(tag_list_str, language=language) + messages = [ + { + "role": "user", + "content": rendered_prompt + } + ] + + structured_response = await llm_client.response_structured( + messages=messages, + response_model=InterestTags + ) + + return structured_response.interest_tags + + except Exception as e: + print(f"兴趣标签LLM筛选过程中发生错误: {e}") + return tags + + async def get_raw_tags_from_db( connector: Neo4jConnector, end_user_id: str, @@ -183,3 +251,47 @@ async def get_hot_memory_tags(end_user_id: str, limit: int = 10, by_user: bool = finally: # 确保关闭连接 await connector.close() + +async def get_interest_distribution(end_user_id: str, limit: int = 10, by_user: bool = False, language: str = "zh") -> List[Tuple[str, int]]: + """ + 获取用户的兴趣分布标签。 + + 与 get_hot_memory_tags 不同，此函数使用专门针对"活动/行为"的LLM prompt， + 过滤掉纯物品、工具、地点等，只保留能代表用户兴趣爱好的活动类标签。 + + Args: + end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id + limit: 最终返回的标签数量限制（默认10） + by_user: 是否按user_id查询（默认False，按end_user_id查询） + + Raises: + ValueError: 如果end_user_id未提供或为空 + """ + if not end_user_id or not end_user_id.strip(): + raise ValueError( + "end_user_id is required. Please provide a valid end_user_id or user_id." + ) + + connector = Neo4jConnector() + try: + # 查询更多原始标签，给LLM提供充足上下文 + query_limit = 40 + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, query_limit, by_user=by_user) + if not raw_tags_with_freq: + return [] + + raw_tag_names = [tag for tag, freq in raw_tags_with_freq] + + # 使用兴趣活动专用prompt进行筛选 + 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 + final_tags = [ + (tag, freq) + for tag, freq in raw_tags_with_freq + if tag in interest_tag_names + ] + + return final_tags[:limit] + finally: + await connector.close() diff --git a/api/app/core/memory/utils/prompt/prompt_utils.py b/api/app/core/memory/utils/prompt/prompt_utils.py index d88f50cf..0cea98f2 100644 --- a/api/app/core/memory/utils/prompt/prompt_utils.py +++ b/api/app/core/memory/utils/prompt/prompt_utils.py @@ -548,3 +548,20 @@ async def render_ontology_extraction_prompt( }) return rendered_prompt + + +def render_interest_filter_prompt(tag_list: str, language: str = "zh") -> str: + """ + Renders the interest filter prompt using the interest_filter.jinja2 template. + + Args: + tag_list: Comma-separated string of raw tags to filter + language: Output language ("zh" for Chinese, "en" for English) + + Returns: + Rendered prompt content as string + """ + template = prompt_env.get_template("interest_filter.jinja2") + rendered_prompt = template.render(tag_list=tag_list, language=language) + log_prompt_rendering('interest filter', rendered_prompt) + return rendered_prompt diff --git a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 new file mode 100644 index 00000000..1e3aac55 --- /dev/null +++ b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 @@ -0,0 +1,47 @@ +{% 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. + +**Keep Rules** (keep if any condition is met): +- Tags representing sports or physical activities the user actively participates in (e.g., '攀岩', '篮球', '游泳', '跑步') +- 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): +- Pure object or tool names that do not represent an activity (e.g., '篮球鞋', '相机', '书桌') +- Pure location or venue names (e.g., '篮球场', '图书馆', '健身房') +- Abstract concepts or quality descriptions (e.g., '核心力量', '团队合作', '专注力') +- Person names, brand names, or proper nouns (e.g., '乔丹', 'Nike') + +**Merge Rules**: For semantically similar tags, keep only the most representative one. +For example: keep '篮球' over '打篮球'; keep '读书' over '阅读'. + +**Example**: +Input: ['攀岩', '篮球场', '篮球鞋', '篮球', '《三体》', '历史', '核心力量', '烹饪', '菜刀'] +Output: ['攀岩', '篮球', '历史', '烹饪'] + +Please filter the following tag list and return only the tags that represent user 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. + +**Keep Rules** (keep if any condition is met): +- Tags representing sports or physical activities the user actively participates in (e.g., 'rock climbing', 'basketball', 'swimming', 'running') +- 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): +- Pure object or tool names that do not represent an activity (e.g., 'basketball shoes', 'camera', 'desk') +- Pure location or venue names (e.g., 'basketball court', 'library', 'gym') +- Abstract concepts or quality descriptions (e.g., 'core strength', 'teamwork', 'focus') +- Person names, brand names, or proper nouns (e.g., 'Jordan', 'Nike') + +**Merge Rules**: For semantically similar tags, keep only the most representative one. +For example: keep 'basketball' over 'playing basketball'; keep 'reading' over 'reading books'. + +**Example**: +Input: ['rock climbing', 'basketball court', 'basketball shoes', 'basketball', 'The Three-Body Problem', 'history', 'core strength', 'cooking', 'kitchen knife'] +Output: ['rock climbing', 'basketball', 'history', 'cooking'] + +Please filter the following tag list and return only the tags that represent user interest activities in English: {{ tag_list }} +{% endif %} diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 1f3667a6..16aee283 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -36,7 +36,7 @@ from app.core.memory.agent.utils.messages_tools import ( ) from app.core.memory.agent.utils.type_classifier import status_typle from app.core.memory.agent.utils.write_tools import write # 新增：直接导入 write 函数 -from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags +from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags, get_interest_distribution from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType @@ -890,36 +890,36 @@ class MemoryAgentService: return result - async def get_hot_memory_tags_by_user( + + async def get_interest_distribution_by_user( self, end_user_id: Optional[str] = None, - limit: int = 20 + limit: int = 5, + language: str = "zh" ) -> List[Dict[str, Any]]: """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签。 + + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 + - end_user_id: 用户ID（必填） - limit: 返回标签数量限制 + - language: 输出语言（"zh" 中文, "en" 英文） 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] - - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 """ try: - # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度） - tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) - payload = [] - for tag, freq in tags: - payload.append({"name": tag, "frequency": freq}) - return payload + tags = await get_interest_distribution(end_user_id, limit=limit, by_user=False, language=language) + return [{"name": tag, "frequency": freq} for tag, freq in tags] except Exception as e: - logger.error(f"热门记忆标签查询失败: {e}") - raise Exception(f"热门记忆标签查询失败: {e}") + logger.error(f"兴趣分布标签查询失败: {e}") + raise Exception(f"兴趣分布标签查询失败: {e}") async def get_user_profile( From df34735a9bb2f15f7d9d19e92edbe857eaf75c5d Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Wed, 4 Mar 2026 12:08:57 +0800 Subject: [PATCH 3/6] [add] Set cache for the distribution of interest tags --- api/app/cache/__init__.py | 3 +- api/app/cache/memory/__init__.py | 2 + api/app/cache/memory/interest_memory.py | 122 ++++++++++++++++++ .../controllers/memory_agent_controller.py | 19 +++ api/app/core/config.py | 2 +- .../core/memory/analytics/hot_memory_tags.py | 23 +++- .../prompt/prompts/interest_filter.jinja2 | 84 +++++++----- api/env.example | 2 +- 8 files changed, 215 insertions(+), 42 deletions(-) create mode 100644 api/app/cache/memory/interest_memory.py diff --git a/api/app/cache/__init__.py b/api/app/cache/__init__.py index a79d4cb2..46d1c959 100644 --- a/api/app/cache/__init__.py +++ b/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", ] diff --git a/api/app/cache/memory/__init__.py b/api/app/cache/memory/__init__.py index 4ada3153..0e21df0f 100644 --- a/api/app/cache/memory/__init__.py +++ b/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", ] diff --git a/api/app/cache/memory/interest_memory.py b/api/app/cache/memory/interest_memory.py new file mode 100644 index 00000000..108e2a37 --- /dev/null +++ b/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 diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 8f2e5c31..1f070eb6 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/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)}") diff --git a/api/app/core/config.py b/api/app/core/config.py index 6a2cf206..d9132be2 100644 --- a/api/app/core/config.py +++ b/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" diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index da08e88e..1d2d5259 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/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 - final_tags = [ - (tag, freq) - for tag, freq in raw_tags_with_freq - if tag in interest_tag_names - ] + # 构建最终标签列表： + # - 原始标签中存在的，保留原始频率 + # - LLM推断出的新标签（不在原始列表中），赋予默认频率1 + final_tags = [] + 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: diff --git a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 index 1e3aac55..7957bf1c 100644 --- a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 +++ b/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): -- Tags representing sports or physical activities the user actively participates in (e.g., '攀岩', '篮球', '游泳', '跑步') -- 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') +**Step 1 - Infer the underlying interest from each tag**: +Look at each tag and ask: "What hobby or interest does this tag suggest the user has?" -**Filter Rules** (remove if any condition is met): -- Pure object or tool names that do not represent an activity (e.g., '篮球鞋', '相机', '书桌') -- Pure location or venue names (e.g., '篮球场', '图书馆', '健身房') -- Abstract concepts or quality descriptions (e.g., '核心力量', '团队合作', '专注力') -- Person names, brand names, or proper nouns (e.g., '乔丹', 'Nike') +Examples of inference: +- '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' +- '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' +- '晨间冥想坚持天数', '身心协同峰值' → '冥想' +- '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. -For example: keep '篮球' over '打篮球'; keep '读书' over '阅读'. +**Step 2 - Consolidate and deduplicate**: +- 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'] -Output: ['攀岩', '篮球', '历史', '烹饪'] +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', '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): -- Tags representing sports or physical activities the user actively participates in (e.g., 'rock climbing', 'basketball', 'swimming', 'running') -- 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') +**Step 1 - Infer the underlying interest from each tag**: +Look at each tag and ask: "What hobby or interest does this tag suggest the user has?" -**Filter Rules** (remove if any condition is met): -- Pure object or tool names that do not represent an activity (e.g., 'basketball shoes', 'camera', 'desk') -- Pure location or venue names (e.g., 'basketball court', 'library', 'gym') -- Abstract concepts or quality descriptions (e.g., 'core strength', 'teamwork', 'focus') -- Person names, brand names, or proper nouns (e.g., 'Jordan', 'Nike') +Examples of inference: +- 'rock climbing', 'indoor climbing gym', 'climber dashboard', 'route map', 'finger strength' → 'rock climbing' +- 'landscape photography metadata enhancer', 'EXIF data', 'sunrise shooting spot', 'exposure compensation' → 'photography' +- 'morning meditation streak', 'mind-body peak' → 'meditation' +- '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. -For example: keep 'basketball' over 'playing basketball'; keep 'reading' over 'reading books'. +**Step 2 - Consolidate and deduplicate**: +- 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'] -Output: ['rock climbing', 'basketball', 'history', 'cooking'] +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', '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 %} diff --git a/api/env.example b/api/env.example index d67bbf7c..1dc4536c 100644 --- a/api/env.example +++ b/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 From b5703c1b8282e3e8472e897968c76cec7ac0430b Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 3 Mar 2026 23:30:54 +0800 Subject: [PATCH 4/6] [fix] Revising the judgment method for the interest analysis tags --- .../controllers/memory_agent_controller.py | 32 ++--- .../core/memory/analytics/hot_memory_tags.py | 112 ++++++++++++++++++ .../core/memory/utils/prompt/prompt_utils.py | 17 +++ .../prompt/prompts/interest_filter.jinja2 | 47 ++++++++ api/app/services/memory_agent_service.py | 32 ++--- 5 files changed, 210 insertions(+), 30 deletions(-) create mode 100644 api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index b88e65ff..8f2e5c31 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -661,34 +661,38 @@ async def get_knowledge_type_stats_api( return fail(BizCode.INTERNAL_ERROR, "获取知识库类型统计失败", str(e)) -@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse) -async def get_hot_memory_tags_by_user_api( - end_user_id: Optional[str] = Query(None, description="用户ID（可选）"), - limit: int = Query(20, description="返回标签数量限制"), +@router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse) +async def get_interest_distribution_by_user_api( + end_user_id: Optional[str] = Query(None, description="用户ID（必填）"), + limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"), + language_type: str = Header(default=None, alias="X-Language-Type"), current_user: User = Depends(get_current_user), - db: Session=Depends(get_db), + db: Session = Depends(get_db), ): """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签 - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习、创作等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] """ - api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}") + 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: - result = await memory_agent_service.get_hot_memory_tags_by_user( + result = await memory_agent_service.get_interest_distribution_by_user( end_user_id=end_user_id, - limit=limit + limit=limit, + language=language ) - return success(data=result, msg="获取热门记忆标签成功") + return success(data=result, msg="获取兴趣分布标签成功") except Exception as e: - api_logger.error(f"Hot memory tags by user failed: {str(e)}") - return fail(BizCode.INTERNAL_ERROR, "获取热门记忆标签失败", str(e)) + api_logger.error(f"Interest distribution by user failed: {str(e)}") + return fail(BizCode.INTERNAL_ERROR, "获取兴趣分布标签失败", str(e)) @router.get("/analytics/user_profile", response_model=ApiResponse) diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index abb0f138..da08e88e 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -16,6 +16,10 @@ class FilteredTags(BaseModel): """用于接收LLM筛选后的核心标签列表的模型。""" meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。") +class InterestTags(BaseModel): + """用于接收LLM筛选后的兴趣活动标签列表的模型。""" + interest_tags: List[str] = Field(..., description="从原始列表中筛选出的代表用户兴趣活动的标签列表。") + async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: """ 使用LLM筛选标签列表，仅保留具有代表性的核心名词。 @@ -89,6 +93,70 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: # 在LLM失败时返回原始标签，确保流程继续 return tags +async def filter_interests_with_llm(tags: List[str], end_user_id: str, language: str = "zh") -> List[str]: + """ + 使用LLM从标签列表中筛选出代表用户兴趣活动的标签。 + + 与 filter_tags_with_llm 不同，此函数专注于识别"活动/行为"类兴趣， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 + + Args: + tags: 原始标签列表 + end_user_id: 用户ID，用于获取LLM配置 + + Returns: + 筛选后的兴趣活动标签列表 + """ + try: + with get_db_context() as db: + from app.services.memory_agent_service import ( + get_end_user_connected_config, + ) + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") + workspace_id = connected_config.get("workspace_id") + + if not config_id and not workspace_id: + raise ValueError( + f"No memory_config_id found for end_user_id: {end_user_id}." + ) + + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + + if not memory_config.llm_model_id: + raise ValueError( + f"No llm_model_id found in memory config {config_id}." + ) + + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(memory_config.llm_model_id) + + tag_list_str = ", ".join(tags) + from app.core.memory.utils.prompt.prompt_utils import render_interest_filter_prompt + rendered_prompt = render_interest_filter_prompt(tag_list_str, language=language) + messages = [ + { + "role": "user", + "content": rendered_prompt + } + ] + + structured_response = await llm_client.response_structured( + messages=messages, + response_model=InterestTags + ) + + return structured_response.interest_tags + + except Exception as e: + print(f"兴趣标签LLM筛选过程中发生错误: {e}") + return tags + + async def get_raw_tags_from_db( connector: Neo4jConnector, end_user_id: str, @@ -183,3 +251,47 @@ async def get_hot_memory_tags(end_user_id: str, limit: int = 10, by_user: bool = finally: # 确保关闭连接 await connector.close() + +async def get_interest_distribution(end_user_id: str, limit: int = 10, by_user: bool = False, language: str = "zh") -> List[Tuple[str, int]]: + """ + 获取用户的兴趣分布标签。 + + 与 get_hot_memory_tags 不同，此函数使用专门针对"活动/行为"的LLM prompt， + 过滤掉纯物品、工具、地点等，只保留能代表用户兴趣爱好的活动类标签。 + + Args: + end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id + limit: 最终返回的标签数量限制（默认10） + by_user: 是否按user_id查询（默认False，按end_user_id查询） + + Raises: + ValueError: 如果end_user_id未提供或为空 + """ + if not end_user_id or not end_user_id.strip(): + raise ValueError( + "end_user_id is required. Please provide a valid end_user_id or user_id." + ) + + connector = Neo4jConnector() + try: + # 查询更多原始标签，给LLM提供充足上下文 + query_limit = 40 + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, query_limit, by_user=by_user) + if not raw_tags_with_freq: + return [] + + raw_tag_names = [tag for tag, freq in raw_tags_with_freq] + + # 使用兴趣活动专用prompt进行筛选 + 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 + final_tags = [ + (tag, freq) + for tag, freq in raw_tags_with_freq + if tag in interest_tag_names + ] + + return final_tags[:limit] + finally: + await connector.close() diff --git a/api/app/core/memory/utils/prompt/prompt_utils.py b/api/app/core/memory/utils/prompt/prompt_utils.py index d88f50cf..0cea98f2 100644 --- a/api/app/core/memory/utils/prompt/prompt_utils.py +++ b/api/app/core/memory/utils/prompt/prompt_utils.py @@ -548,3 +548,20 @@ async def render_ontology_extraction_prompt( }) return rendered_prompt + + +def render_interest_filter_prompt(tag_list: str, language: str = "zh") -> str: + """ + Renders the interest filter prompt using the interest_filter.jinja2 template. + + Args: + tag_list: Comma-separated string of raw tags to filter + language: Output language ("zh" for Chinese, "en" for English) + + Returns: + Rendered prompt content as string + """ + template = prompt_env.get_template("interest_filter.jinja2") + rendered_prompt = template.render(tag_list=tag_list, language=language) + log_prompt_rendering('interest filter', rendered_prompt) + return rendered_prompt diff --git a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 new file mode 100644 index 00000000..1e3aac55 --- /dev/null +++ b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 @@ -0,0 +1,47 @@ +{% 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. + +**Keep Rules** (keep if any condition is met): +- Tags representing sports or physical activities the user actively participates in (e.g., '攀岩', '篮球', '游泳', '跑步') +- 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): +- Pure object or tool names that do not represent an activity (e.g., '篮球鞋', '相机', '书桌') +- Pure location or venue names (e.g., '篮球场', '图书馆', '健身房') +- Abstract concepts or quality descriptions (e.g., '核心力量', '团队合作', '专注力') +- Person names, brand names, or proper nouns (e.g., '乔丹', 'Nike') + +**Merge Rules**: For semantically similar tags, keep only the most representative one. +For example: keep '篮球' over '打篮球'; keep '读书' over '阅读'. + +**Example**: +Input: ['攀岩', '篮球场', '篮球鞋', '篮球', '《三体》', '历史', '核心力量', '烹饪', '菜刀'] +Output: ['攀岩', '篮球', '历史', '烹饪'] + +Please filter the following tag list and return only the tags that represent user 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. + +**Keep Rules** (keep if any condition is met): +- Tags representing sports or physical activities the user actively participates in (e.g., 'rock climbing', 'basketball', 'swimming', 'running') +- 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): +- Pure object or tool names that do not represent an activity (e.g., 'basketball shoes', 'camera', 'desk') +- Pure location or venue names (e.g., 'basketball court', 'library', 'gym') +- Abstract concepts or quality descriptions (e.g., 'core strength', 'teamwork', 'focus') +- Person names, brand names, or proper nouns (e.g., 'Jordan', 'Nike') + +**Merge Rules**: For semantically similar tags, keep only the most representative one. +For example: keep 'basketball' over 'playing basketball'; keep 'reading' over 'reading books'. + +**Example**: +Input: ['rock climbing', 'basketball court', 'basketball shoes', 'basketball', 'The Three-Body Problem', 'history', 'core strength', 'cooking', 'kitchen knife'] +Output: ['rock climbing', 'basketball', 'history', 'cooking'] + +Please filter the following tag list and return only the tags that represent user interest activities in English: {{ tag_list }} +{% endif %} diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 1f3667a6..16aee283 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -36,7 +36,7 @@ from app.core.memory.agent.utils.messages_tools import ( ) from app.core.memory.agent.utils.type_classifier import status_typle from app.core.memory.agent.utils.write_tools import write # 新增：直接导入 write 函数 -from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags +from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags, get_interest_distribution from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType @@ -890,36 +890,36 @@ class MemoryAgentService: return result - async def get_hot_memory_tags_by_user( + + async def get_interest_distribution_by_user( self, end_user_id: Optional[str] = None, - limit: int = 20 + limit: int = 5, + language: str = "zh" ) -> List[Dict[str, Any]]: """ - 获取指定用户的热门记忆标签 + 获取指定用户的兴趣分布标签。 + + 与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习等）， + 过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 + - end_user_id: 用户ID（必填） - limit: 返回标签数量限制 + - language: 输出语言（"zh" 中文, "en" 英文） 返回格式： [ - {"name": "标签名", "frequency": 频次}, + {"name": "兴趣活动名", "frequency": 频次}, ... ] - - 注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译 """ try: - # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度） - tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) - payload = [] - for tag, freq in tags: - payload.append({"name": tag, "frequency": freq}) - return payload + tags = await get_interest_distribution(end_user_id, limit=limit, by_user=False, language=language) + return [{"name": tag, "frequency": freq} for tag, freq in tags] except Exception as e: - logger.error(f"热门记忆标签查询失败: {e}") - raise Exception(f"热门记忆标签查询失败: {e}") + logger.error(f"兴趣分布标签查询失败: {e}") + raise Exception(f"兴趣分布标签查询失败: {e}") async def get_user_profile( From c31a92bf01a87721afb0c87272975704b0322ad7 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Wed, 4 Mar 2026 12:08:57 +0800 Subject: [PATCH 5/6] [add] Set cache for the distribution of interest tags --- api/app/cache/__init__.py | 3 +- api/app/cache/memory/__init__.py | 2 + api/app/cache/memory/interest_memory.py | 122 ++++++++++++++++++ .../controllers/memory_agent_controller.py | 19 +++ api/app/core/config.py | 2 +- .../core/memory/analytics/hot_memory_tags.py | 23 +++- .../prompt/prompts/interest_filter.jinja2 | 84 +++++++----- api/env.example | 2 +- 8 files changed, 215 insertions(+), 42 deletions(-) create mode 100644 api/app/cache/memory/interest_memory.py diff --git a/api/app/cache/__init__.py b/api/app/cache/__init__.py index a79d4cb2..46d1c959 100644 --- a/api/app/cache/__init__.py +++ b/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", ] diff --git a/api/app/cache/memory/__init__.py b/api/app/cache/memory/__init__.py index 4ada3153..0e21df0f 100644 --- a/api/app/cache/memory/__init__.py +++ b/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", ] diff --git a/api/app/cache/memory/interest_memory.py b/api/app/cache/memory/interest_memory.py new file mode 100644 index 00000000..108e2a37 --- /dev/null +++ b/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 diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 8f2e5c31..1f070eb6 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/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)}") diff --git a/api/app/core/config.py b/api/app/core/config.py index 4472d373..d04e2a43 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -229,7 +229,7 @@ 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" diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index da08e88e..1d2d5259 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/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 - final_tags = [ - (tag, freq) - for tag, freq in raw_tags_with_freq - if tag in interest_tag_names - ] + # 构建最终标签列表： + # - 原始标签中存在的，保留原始频率 + # - LLM推断出的新标签（不在原始列表中），赋予默认频率1 + final_tags = [] + 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: diff --git a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 b/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 index 1e3aac55..7957bf1c 100644 --- a/api/app/core/memory/utils/prompt/prompts/interest_filter.jinja2 +++ b/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): -- Tags representing sports or physical activities the user actively participates in (e.g., '攀岩', '篮球', '游泳', '跑步') -- 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') +**Step 1 - Infer the underlying interest from each tag**: +Look at each tag and ask: "What hobby or interest does this tag suggest the user has?" -**Filter Rules** (remove if any condition is met): -- Pure object or tool names that do not represent an activity (e.g., '篮球鞋', '相机', '书桌') -- Pure location or venue names (e.g., '篮球场', '图书馆', '健身房') -- Abstract concepts or quality descriptions (e.g., '核心力量', '团队合作', '专注力') -- Person names, brand names, or proper nouns (e.g., '乔丹', 'Nike') +Examples of inference: +- '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' +- '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' +- '晨间冥想坚持天数', '身心协同峰值' → '冥想' +- '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. -For example: keep '篮球' over '打篮球'; keep '读书' over '阅读'. +**Step 2 - Consolidate and deduplicate**: +- 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'] -Output: ['攀岩', '篮球', '历史', '烹饪'] +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', '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): -- Tags representing sports or physical activities the user actively participates in (e.g., 'rock climbing', 'basketball', 'swimming', 'running') -- 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') +**Step 1 - Infer the underlying interest from each tag**: +Look at each tag and ask: "What hobby or interest does this tag suggest the user has?" -**Filter Rules** (remove if any condition is met): -- Pure object or tool names that do not represent an activity (e.g., 'basketball shoes', 'camera', 'desk') -- Pure location or venue names (e.g., 'basketball court', 'library', 'gym') -- Abstract concepts or quality descriptions (e.g., 'core strength', 'teamwork', 'focus') -- Person names, brand names, or proper nouns (e.g., 'Jordan', 'Nike') +Examples of inference: +- 'rock climbing', 'indoor climbing gym', 'climber dashboard', 'route map', 'finger strength' → 'rock climbing' +- 'landscape photography metadata enhancer', 'EXIF data', 'sunrise shooting spot', 'exposure compensation' → 'photography' +- 'morning meditation streak', 'mind-body peak' → 'meditation' +- '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. -For example: keep 'basketball' over 'playing basketball'; keep 'reading' over 'reading books'. +**Step 2 - Consolidate and deduplicate**: +- 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'] -Output: ['rock climbing', 'basketball', 'history', 'cooking'] +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', '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 %} diff --git a/api/env.example b/api/env.example index d67bbf7c..1dc4536c 100644 --- a/api/env.example +++ b/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 From c488eb0cd00e3fccc6f0ce090b6fc909597fd441 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Wed, 4 Mar 2026 12:17:34 +0800 Subject: [PATCH 6/6] [changes] 1.Use structured logs; 2.Align the type and default value of "end_user_id" with the semantic meaning of "required". --- api/app/controllers/memory_agent_controller.py | 2 +- api/app/core/memory/analytics/hot_memory_tags.py | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 1f070eb6..ccf93d68 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -664,7 +664,7 @@ async def get_knowledge_type_stats_api( @router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse) async def get_interest_distribution_by_user_api( - end_user_id: Optional[str] = Query(None, description="用户ID（必填）"), + end_user_id: str = Query(..., description="用户ID（必填）"), limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"), language_type: str = Header(default=None, alias="X-Language-Type"), current_user: User = Depends(get_current_user), diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index 1d2d5259..6afcec6d 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -1,9 +1,12 @@ import asyncio import json +import logging import os from typing import List, Tuple from app.core.config import settings + +logger = logging.getLogger(__name__) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -89,7 +92,7 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: return structured_response.meaningful_tags except Exception as e: - print(f"LLM筛选过程中发生错误: {e}") + logger.error(f"LLM筛选过程中发生错误: {e}", exc_info=True) # 在LLM失败时返回原始标签，确保流程继续 return tags @@ -153,7 +156,7 @@ async def filter_interests_with_llm(tags: List[str], end_user_id: str, language: return structured_response.interest_tags except Exception as e: - print(f"兴趣标签LLM筛选过程中发生错误: {e}") + logger.error(f"兴趣标签LLM筛选过程中发生错误: {e}", exc_info=True) return tags