Merge branch 'develop' of github.com:SuanmoSuanyangTechnology/MemoryBear into develop
This commit is contained in:
yujiangping
3
api/app/cache/__init__.py
vendored
3
api/app/cache/__init__.py
vendored
@@ -3,9 +3,10 @@ Cache 缓存模块
|
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提供各种缓存功能的统一入口
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"""
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from .memory import EmotionMemoryCache, ImplicitMemoryCache
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from .memory import EmotionMemoryCache, ImplicitMemoryCache, InterestMemoryCache
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__all__ = [
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"EmotionMemoryCache",
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"ImplicitMemoryCache",
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"InterestMemoryCache",
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]
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2
api/app/cache/memory/__init__.py
vendored
2
api/app/cache/memory/__init__.py
vendored
@@ -5,8 +5,10 @@ Memory 缓存模块
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"""
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from .emotion_memory import EmotionMemoryCache
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from .implicit_memory import ImplicitMemoryCache
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from .interest_memory import InterestMemoryCache
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__all__ = [
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"EmotionMemoryCache",
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"ImplicitMemoryCache",
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"InterestMemoryCache",
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]
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122
api/app/cache/memory/interest_memory.py
vendored
Normal file
122
api/app/cache/memory/interest_memory.py
vendored
Normal file
@@ -0,0 +1,122 @@
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"""
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Interest Distribution Cache
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兴趣分布缓存模块
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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
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"""
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import json
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import logging
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from typing import Optional, List, Dict, Any
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from datetime import datetime
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from app.aioRedis import aio_redis
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logger = logging.getLogger(__name__)
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# 缓存过期时间：24小时
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INTEREST_CACHE_EXPIRE = 86400
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class InterestMemoryCache:
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"""兴趣分布缓存类"""
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PREFIX = "cache:memory:interest_distribution"
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@classmethod
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def _get_key(cls, end_user_id: str, language: str) -> str:
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"""生成 Redis key
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Args:
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end_user_id: 用户ID
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language: 语言类型
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Returns:
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完整的 Redis key
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"""
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return f"{cls.PREFIX}:by_user:{end_user_id}:{language}"
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@classmethod
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async def set_interest_distribution(
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cls,
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end_user_id: str,
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language: str,
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data: List[Dict[str, Any]],
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expire: int = INTEREST_CACHE_EXPIRE,
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) -> bool:
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"""设置用户兴趣分布缓存
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Args:
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end_user_id: 用户ID
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language: 语言类型
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data: 兴趣分布列表，格式 [{"name": "...", "frequency": ...}, ...]
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expire: 过期时间（秒），默认24小时
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Returns:
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是否设置成功
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"""
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try:
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key = cls._get_key(end_user_id, language)
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payload = {
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"data": data,
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"generated_at": datetime.now().isoformat(),
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"cached": True,
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}
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value = json.dumps(payload, ensure_ascii=False)
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await aio_redis.set(key, value, ex=expire)
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logger.info(f"设置兴趣分布缓存成功: {key}, 过期时间: {expire}秒")
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return True
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except Exception as e:
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logger.error(f"设置兴趣分布缓存失败: {e}", exc_info=True)
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return False
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@classmethod
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async def get_interest_distribution(
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cls,
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end_user_id: str,
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language: str,
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) -> Optional[List[Dict[str, Any]]]:
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"""获取用户兴趣分布缓存
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Args:
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end_user_id: 用户ID
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language: 语言类型
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Returns:
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兴趣分布列表，缓存不存在或已过期返回 None
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"""
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try:
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key = cls._get_key(end_user_id, language)
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value = await aio_redis.get(key)
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if value:
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payload = json.loads(value)
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logger.info(f"命中兴趣分布缓存: {key}")
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return payload.get("data")
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logger.info(f"兴趣分布缓存不存在或已过期: {key}")
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return None
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except Exception as e:
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logger.error(f"获取兴趣分布缓存失败: {e}", exc_info=True)
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return None
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@classmethod
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async def delete_interest_distribution(
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cls,
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end_user_id: str,
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language: str,
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) -> bool:
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"""删除用户兴趣分布缓存
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Args:
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end_user_id: 用户ID
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language: 语言类型
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Returns:
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是否删除成功
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"""
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try:
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key = cls._get_key(end_user_id, language)
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result = await aio_redis.delete(key)
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logger.info(f"删除兴趣分布缓存: {key}, 结果: {result}")
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return result > 0
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except Exception as e:
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logger.error(f"删除兴趣分布缓存失败: {e}", exc_info=True)
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return False
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@@ -1,6 +1,7 @@
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import os
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import platform
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from datetime import timedelta
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from celery.schedules import crontab
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from urllib.parse import quote
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from celery import Celery
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@@ -90,11 +91,10 @@ celery_app.conf.update(
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celery_app.autodiscover_tasks(['app'])
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# Celery Beat schedule for periodic tasks
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memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
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memory_increment_schedule = crontab(hour=settings.MEMORY_INCREMENT_HOUR, minute=settings.MEMORY_INCREMENT_MINUTE)
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memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
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# 这个30秒的设计不合理
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workspace_reflection_schedule = timedelta(seconds=30) # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
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forgetting_cycle_schedule = timedelta(hours=24) # 每24小时运行一次遗忘周期
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workspace_reflection_schedule = timedelta(seconds=settings.WORKSPACE_REFLECTION_INTERVAL_SECONDS)
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forgetting_cycle_schedule = timedelta(hours=settings.FORGETTING_CYCLE_INTERVAL_HOURS)
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#构建定时任务配置
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beat_schedule_config = {
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@@ -1,5 +1,6 @@
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from typing import List, Optional
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from app.cache.memory.interest_memory import InterestMemoryCache
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from app.celery_app import celery_app
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from app.core.error_codes import BizCode
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from app.core.language_utils import get_language_from_header
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@@ -661,34 +662,56 @@ async def get_knowledge_type_stats_api(
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return fail(BizCode.INTERNAL_ERROR, "获取知识库类型统计失败", str(e))
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@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse)
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async def get_hot_memory_tags_by_user_api(
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end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
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limit: int = Query(20, description="返回标签数量限制"),
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@router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse)
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async def get_interest_distribution_by_user_api(
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end_user_id: str = Query(..., description="用户ID（必填）"),
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limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"),
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language_type: str = Header(default=None, alias="X-Language-Type"),
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current_user: User = Depends(get_current_user),
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db: Session=Depends(get_db),
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db: Session = Depends(get_db),
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||||
):
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"""
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获取指定用户的热门记忆标签
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获取指定用户的兴趣分布标签
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注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译
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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），
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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。
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|
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返回格式：
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[
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{"name": "标签名", "frequency": 频次},
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{"name": "兴趣活动名", "frequency": 频次},
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...
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]
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"""
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api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}")
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||||
language = get_language_from_header(language_type)
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api_logger.info(f"Interest distribution by user requested: end_user_id={end_user_id}, language={language}")
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try:
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result = await memory_agent_service.get_hot_memory_tags_by_user(
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# 优先读取缓存
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cached = await InterestMemoryCache.get_interest_distribution(
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end_user_id=end_user_id,
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limit=limit
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language=language,
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)
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return success(data=result, msg="获取热门记忆标签成功")
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||||
if cached is not None:
|
||||
api_logger.info(f"Interest distribution cache hit: end_user_id={end_user_id}")
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||||
return success(data=cached, msg="获取兴趣分布标签成功")
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||||
|
||||
# 缓存未命中，调用模型生成
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||||
result = await memory_agent_service.get_interest_distribution_by_user(
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end_user_id=end_user_id,
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limit=limit,
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||||
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"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)
|
||||
|
||||
@@ -1,9 +1,10 @@
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Optional
|
||||
from typing import Annotated, Any, Dict, Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from pydantic import Field, TypeAdapter
|
||||
|
||||
load_dotenv()
|
||||
|
||||
@@ -200,12 +201,25 @@ class Settings:
|
||||
|
||||
REFLECTION_INTERVAL_SECONDS: float = float(os.getenv("REFLECTION_INTERVAL_SECONDS", "300"))
|
||||
HEALTH_CHECK_SECONDS: float = float(os.getenv("HEALTH_CHECK_SECONDS", "600"))
|
||||
MEMORY_INCREMENT_INTERVAL_HOURS: float = float(os.getenv("MEMORY_INCREMENT_INTERVAL_HOURS", "24"))
|
||||
REFLECTION_INTERVAL_TIME: Optional[str] = int(os.getenv("REFLECTION_INTERVAL_TIME", 30))
|
||||
|
||||
# Memory Cache Regeneration Configuration
|
||||
MEMORY_CACHE_REGENERATION_HOURS: int = int(os.getenv("MEMORY_CACHE_REGENERATION_HOURS", "24"))
|
||||
|
||||
# Celery Beat Schedule Configuration (定时任务执行频率)
|
||||
MEMORY_INCREMENT_HOUR: int = TypeAdapter(
|
||||
Annotated[int, Field(ge=0, le=23, description="cron hour [0, 23]")]
|
||||
).validate_python(int(os.getenv("MEMORY_INCREMENT_HOUR", "2")))
|
||||
MEMORY_INCREMENT_MINUTE: int = TypeAdapter(
|
||||
Annotated[int, Field(ge=0, le=59, description="cron minute [0, 59]")]
|
||||
).validate_python(int(os.getenv("MEMORY_INCREMENT_MINUTE", "0")))
|
||||
WORKSPACE_REFLECTION_INTERVAL_SECONDS: int = TypeAdapter(
|
||||
Annotated[int, Field(ge=1, description="reflection interval in seconds, must be >= 1")]
|
||||
).validate_python(int(os.getenv("WORKSPACE_REFLECTION_INTERVAL_SECONDS", "30")))
|
||||
FORGETTING_CYCLE_INTERVAL_HOURS: int = TypeAdapter(
|
||||
Annotated[int, Field(ge=1, description="forgetting cycle interval in hours, must be >= 1")]
|
||||
).validate_python(int(os.getenv("FORGETTING_CYCLE_INTERVAL_HOURS", "24")))
|
||||
|
||||
# Memory Module Configuration (internal)
|
||||
MEMORY_OUTPUT_DIR: str = os.getenv("MEMORY_OUTPUT_DIR", "logs/memory-output")
|
||||
MEMORY_CONFIG_DIR: str = os.getenv("MEMORY_CONFIG_DIR", "app/core/memory")
|
||||
@@ -230,7 +244,7 @@ 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"
|
||||
|
||||
@@ -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
|
||||
@@ -16,6 +19,10 @@ 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。
|
||||
@@ -85,10 +92,74 @@ 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
|
||||
|
||||
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:
|
||||
logger.error(f"兴趣标签LLM筛选过程中发生错误: {e}", exc_info=True)
|
||||
return tags
|
||||
|
||||
|
||||
async def get_raw_tags_from_db(
|
||||
connector: Neo4jConnector,
|
||||
end_user_id: str,
|
||||
@@ -183,3 +254,56 @@ 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]
|
||||
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（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)
|
||||
|
||||
# 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:
|
||||
await connector.close()
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
{% if language == "zh" %}
|
||||
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.
|
||||
|
||||
**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?"
|
||||
|
||||
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'
|
||||
|
||||
**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', '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']
|
||||
|
||||
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 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.
|
||||
|
||||
**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?"
|
||||
|
||||
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'
|
||||
|
||||
**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', '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']
|
||||
|
||||
Now process the following tag list and return the inferred interest activities in English: {{ tag_list }}
|
||||
{% endif %}
|
||||
@@ -127,7 +127,7 @@ class EventStreamHandler:
|
||||
yield {
|
||||
"event": "message",
|
||||
"data": {
|
||||
"chunk": data.get("chunk")
|
||||
"content": data.get("chunk")
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -274,7 +274,7 @@ class StreamOutputCoordinator:
|
||||
yield {
|
||||
"event": "message",
|
||||
"data": {
|
||||
"chunk": final_chunk
|
||||
"content": final_chunk
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -272,7 +272,7 @@ class WorkflowExecutor:
|
||||
event_type = data.get("type", "node_chunk") # "message" or "node_chunk"
|
||||
if event_type == "node_chunk":
|
||||
async for msg_event in self.event_handler.handle_node_chunk_event(data):
|
||||
full_content += msg_event["data"]["chunk"]
|
||||
full_content += msg_event["data"]["content"]
|
||||
yield msg_event
|
||||
|
||||
elif event_type == "node_error":
|
||||
@@ -295,12 +295,12 @@ class WorkflowExecutor:
|
||||
self.graph,
|
||||
self.execution_context.checkpoint_config
|
||||
):
|
||||
full_content += msg_event["data"]['chunk']
|
||||
full_content += msg_event["data"]['content']
|
||||
yield msg_event
|
||||
|
||||
# Flush any remaining chunks
|
||||
async for msg_event in self.stream_coordinator.flush_remaining_chunk(self.variable_pool):
|
||||
full_content += msg_event["data"]['chunk']
|
||||
full_content += msg_event["data"]['content']
|
||||
yield msg_event
|
||||
|
||||
result = graph.get_state(self.execution_context.checkpoint_config).values
|
||||
|
||||
@@ -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(
|
||||
|
||||
@@ -13,6 +13,7 @@ from sqlalchemy.orm import Session
|
||||
from app.core.error_codes import BizCode
|
||||
from app.core.exceptions import BusinessException
|
||||
from app.core.workflow.adapters.registry import PlatformAdapterRegistry
|
||||
from app.core.workflow.executor import execute_workflow, execute_workflow_stream
|
||||
from app.core.workflow.nodes.enums import NodeType
|
||||
from app.core.workflow.validator import validate_workflow_config
|
||||
from app.db import get_db
|
||||
@@ -23,7 +24,7 @@ from app.repositories.workflow_repository import (
|
||||
WorkflowExecutionRepository,
|
||||
WorkflowNodeExecutionRepository
|
||||
)
|
||||
from app.schemas import DraftRunRequest
|
||||
from app.schemas import DraftRunRequest, FileInput
|
||||
from app.services.conversation_service import ConversationService
|
||||
from app.services.multi_agent_service import convert_uuids_to_str
|
||||
from app.services.multimodal_service import MultimodalService
|
||||
@@ -445,6 +446,91 @@ class WorkflowService:
|
||||
"success_rate": completed / total if total > 0 else 0
|
||||
}
|
||||
|
||||
async def _handle_file_input(self, files: list[FileInput]):
|
||||
if not files:
|
||||
return []
|
||||
|
||||
files_struct = []
|
||||
for file in files:
|
||||
files_struct.append(
|
||||
{
|
||||
"type": file.type,
|
||||
"url": await self.multimodal_service.get_file_url(file),
|
||||
"__file": True
|
||||
}
|
||||
)
|
||||
return files_struct
|
||||
|
||||
@staticmethod
|
||||
def _map_public_event(event: dict) -> dict | None:
|
||||
"""
|
||||
Map internal workflow events to public-facing event formats.
|
||||
|
||||
Purpose:
|
||||
- Hide internal execution details
|
||||
- Expose a stable and simplified public event schema
|
||||
- Filter out non-public events
|
||||
- Maintain backward compatibility when possible
|
||||
|
||||
Args:
|
||||
event (dict): Internal event object, e.g.:
|
||||
{
|
||||
"event": "workflow_start",
|
||||
"data": {...}
|
||||
}
|
||||
|
||||
Returns:
|
||||
dict | None:
|
||||
- Returns the mapped public event
|
||||
- Returns None if the event should not be exposed
|
||||
"""
|
||||
event_type = event.get("event")
|
||||
payload = event.get("data")
|
||||
match event_type:
|
||||
case "workflow_start":
|
||||
return {
|
||||
"event": "start",
|
||||
"data": {
|
||||
"conversation_id": payload.get("conversation_id"),
|
||||
}
|
||||
}
|
||||
case "workflow_end":
|
||||
return {
|
||||
"event": "end",
|
||||
"data": {
|
||||
"elapsed_time": payload.get("elapsed_time"),
|
||||
"message_length": len(payload.get("output", "")),
|
||||
"error": payload.get("error", "")
|
||||
}
|
||||
}
|
||||
case "node_start" | "node_end" | "node_error" | "cycle_item":
|
||||
return None
|
||||
case _:
|
||||
return event
|
||||
|
||||
def _emit(self, public: bool, internal_event: dict):
|
||||
"""
|
||||
Unified event emission entry.
|
||||
|
||||
Args:
|
||||
public (bool):
|
||||
- True -> Emit mapped public event
|
||||
- False -> Emit raw internal event
|
||||
|
||||
internal_event (dict):
|
||||
The original internal event object
|
||||
|
||||
Returns:
|
||||
dict | None:
|
||||
- The mapped event
|
||||
- Or None if the event is filtered out
|
||||
"""
|
||||
if public:
|
||||
mapped = self._map_public_event(internal_event)
|
||||
else:
|
||||
mapped = internal_event
|
||||
return mapped
|
||||
|
||||
# ==================== 工作流执行 ====================
|
||||
|
||||
async def run(
|
||||
@@ -479,10 +565,11 @@ class WorkflowService:
|
||||
message=f"工作流配置不存在: app_id={app_id}"
|
||||
)
|
||||
|
||||
input_data = {"message": payload.message, "variables": payload.variables,
|
||||
"conversation_id": payload.conversation_id,
|
||||
"files": [file.model_dump(mode='json') for file in payload.files]
|
||||
}
|
||||
input_data = {
|
||||
"message": payload.message, "variables": payload.variables,
|
||||
"conversation_id": payload.conversation_id,
|
||||
"files": [file.model_dump(mode='json') for file in payload.files]
|
||||
}
|
||||
|
||||
# 转换 conversation_id 为 UUID
|
||||
conversation_id_uuid = uuid.UUID(payload.conversation_id) if payload.conversation_id else None
|
||||
@@ -506,22 +593,8 @@ class WorkflowService:
|
||||
"execution_config": config.execution_config
|
||||
}
|
||||
|
||||
# 4. 获取工作空间 ID（从 app 获取）
|
||||
|
||||
# 5. 执行工作流
|
||||
from app.core.workflow.executor import execute_workflow
|
||||
|
||||
try:
|
||||
files = []
|
||||
if payload.files:
|
||||
for file in payload.files:
|
||||
files.append(
|
||||
{
|
||||
"type": file.type,
|
||||
"url": await self.multimodal_service.get_file_url(file),
|
||||
"__file": True
|
||||
}
|
||||
)
|
||||
files = await self._handle_file_input(payload.files)
|
||||
input_data["files"] = files
|
||||
# 更新状态为运行中
|
||||
self.update_execution_status(execution.execution_id, "running")
|
||||
@@ -601,42 +674,6 @@ class WorkflowService:
|
||||
message=f"工作流执行失败: {str(e)}"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _map_public_event(event: dict) -> dict | None:
|
||||
event_type = event.get("event")
|
||||
payload = event.get("data")
|
||||
match event_type:
|
||||
case "workflow_start":
|
||||
return {
|
||||
"event": "start",
|
||||
"data": {
|
||||
"conversation_id": payload.get("conversation_id"),
|
||||
}
|
||||
}
|
||||
case "workflow_end":
|
||||
return {
|
||||
"event": "end",
|
||||
"data": {
|
||||
"elapsed_time": payload.get("elapsed_time"),
|
||||
"message_length": len(payload.get("output", "")),
|
||||
"error": payload.get("error", "")
|
||||
}
|
||||
}
|
||||
case "node_start" | "node_end" | "node_error" | "cycle_item":
|
||||
return None
|
||||
case _:
|
||||
return event
|
||||
|
||||
def _emit(self, public: bool, internal_event: dict):
|
||||
"""
|
||||
decide
|
||||
"""
|
||||
if public:
|
||||
mapped = self._map_public_event(internal_event)
|
||||
else:
|
||||
mapped = internal_event
|
||||
return mapped
|
||||
|
||||
async def run_stream(
|
||||
self,
|
||||
app_id: uuid.UUID,
|
||||
@@ -671,10 +708,11 @@ class WorkflowService:
|
||||
message=f"工作流配置不存在: app_id={app_id}"
|
||||
)
|
||||
|
||||
input_data = {"message": payload.message, "variables": payload.variables,
|
||||
"conversation_id": payload.conversation_id,
|
||||
"files": [file.model_dump(mode='json') for file in payload.files]
|
||||
}
|
||||
input_data = {
|
||||
"message": payload.message, "variables": payload.variables,
|
||||
"conversation_id": payload.conversation_id,
|
||||
"files": [file.model_dump(mode='json') for file in payload.files]
|
||||
}
|
||||
|
||||
# 转换 conversation_id 为 UUID
|
||||
conversation_id_uuid = uuid.UUID(payload.conversation_id) if payload.conversation_id else None
|
||||
@@ -699,16 +737,7 @@ class WorkflowService:
|
||||
}
|
||||
|
||||
try:
|
||||
files = []
|
||||
if payload.files:
|
||||
for file in payload.files:
|
||||
files.append(
|
||||
{
|
||||
"type": file.type,
|
||||
"url": await self.multimodal_service.get_file_url(file),
|
||||
"__file": True
|
||||
}
|
||||
)
|
||||
files = await self._handle_file_input(payload.files)
|
||||
input_data["files"] = files
|
||||
self.update_execution_status(execution.execution_id, "running")
|
||||
executions = self.execution_repo.get_by_conversation_id(conversation_id=conversation_id_uuid)
|
||||
@@ -723,7 +752,6 @@ class WorkflowService:
|
||||
input_data["conv_messages"] = last_state.get("messages") or []
|
||||
break
|
||||
init_message_length = len(input_data.get("conv_messages", []))
|
||||
from app.core.workflow.executor import execute_workflow_stream
|
||||
|
||||
async for event in execute_workflow_stream(
|
||||
workflow_config=workflow_config_dict,
|
||||
@@ -789,37 +817,6 @@ class WorkflowService:
|
||||
return node.get("config", {}).get("variables", [])
|
||||
raise BusinessException("workflow config error - start node not found")
|
||||
|
||||
def _clean_event_for_json(self, event: dict[str, Any]) -> dict[str, Any]:
|
||||
"""清理事件数据，移除不可序列化的对象
|
||||
|
||||
Args:
|
||||
event: 原始事件数据
|
||||
|
||||
Returns:
|
||||
可序列化的事件数据
|
||||
"""
|
||||
from langchain_core.messages import BaseMessage
|
||||
|
||||
def clean_value(value):
|
||||
"""递归清理值"""
|
||||
if isinstance(value, BaseMessage):
|
||||
# 将 Message 对象转换为字典
|
||||
return {
|
||||
"type": value.__class__.__name__,
|
||||
"content": value.content,
|
||||
}
|
||||
elif isinstance(value, dict):
|
||||
return {k: clean_value(v) for k, v in value.items()}
|
||||
elif isinstance(value, list):
|
||||
return [clean_value(item) for item in value]
|
||||
elif isinstance(value, (str, int, float, bool, type(None))):
|
||||
return value
|
||||
else:
|
||||
# 其他不可序列化的对象转换为字符串
|
||||
return str(value)
|
||||
|
||||
return clean_value(event)
|
||||
|
||||
|
||||
# ==================== 依赖注入函数 ====================
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
/*
|
||||
* @Author: ZhaoYing
|
||||
* @Date: 2026-02-03 14:00:06
|
||||
* @Last Modified by: ZhaoYing
|
||||
* @Last Modified time: 2026-02-03 14:00:06
|
||||
* @Last Modified by: ZhaoYing
|
||||
* @Last Modified time: 2026-03-04 10:58:41
|
||||
*/
|
||||
import { request } from '@/utils/request'
|
||||
import type {
|
||||
@@ -98,8 +98,8 @@ export const getMemorySearchEdges = (end_user_id: string) => {
|
||||
return request.get(`/memory-storage/analytics/graph_data`, { end_user_id })
|
||||
}
|
||||
// User Memory - User interest distribution
|
||||
export const getHotMemoryTagsByUser = (end_user_id: string) => {
|
||||
return request.get(`/memory/analytics/hot_memory_tags/by_user`, { end_user_id })
|
||||
export const getInterestDistributionByUser = (end_user_id: string) => {
|
||||
return request.get(`/memory/analytics/interest_distribution/by_user`, { end_user_id })
|
||||
}
|
||||
// User Memory - Total memory count
|
||||
export const getTotalMemoryCountByUser = (end_user_id: string) => {
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
* @Author: ZhaoYing
|
||||
* @Date: 2026-02-03 16:58:03
|
||||
* @Last Modified by: ZhaoYing
|
||||
* @Last Modified time: 2026-03-03 13:46:22
|
||||
* @Last Modified time: 2026-03-04 12:10:44
|
||||
*/
|
||||
/**
|
||||
* Conversation Page
|
||||
@@ -267,8 +267,8 @@ const Conversation: FC = () => {
|
||||
currentConversationId = newId
|
||||
break
|
||||
case 'message':
|
||||
const { content, chunk, conversation_id: curId } = item.data as { content: string; chunk: string; conversation_id: string; }
|
||||
updateAssistantMessage(content ?? chunk)
|
||||
const { content, conversation_id: curId } = item.data as { content: string; conversation_id: string; }
|
||||
updateAssistantMessage(content)
|
||||
|
||||
if (curId) {
|
||||
currentConversationId = curId;
|
||||
|
||||
@@ -15,7 +15,7 @@ import { useParams } from 'react-router-dom'
|
||||
import ReactEcharts from 'echarts-for-react';
|
||||
import { Space } from 'antd'
|
||||
|
||||
import { getHotMemoryTagsByUser } from '@/api/memory';
|
||||
import { getInterestDistributionByUser } from '@/api/memory';
|
||||
import Empty from '@/components/Empty';
|
||||
import Loading from '@/components/Empty/Loading';
|
||||
import RbCard from '@/components/RbCard/Card';
|
||||
@@ -38,7 +38,7 @@ const InterestDistribution: FC = () => {
|
||||
/** Fetch interest distribution data */
|
||||
const getData = () => {
|
||||
setLoading(true)
|
||||
getHotMemoryTagsByUser(id as string).then(res => {
|
||||
getInterestDistributionByUser(id as string).then(res => {
|
||||
const response = res as { name: string; frequency: number }[]
|
||||
setData(response.map(item => ({
|
||||
...item,
|
||||
|
||||
@@ -6,6 +6,7 @@ import {
|
||||
getShortTerm,
|
||||
} from '@/api/memory'
|
||||
import Empty from '@/components/Empty'
|
||||
import Markdown from '@/components/Markdown'
|
||||
|
||||
interface ShortTermItem {
|
||||
retrieval: Array<{ query: string; retrieval: string[]; }>;
|
||||
@@ -85,7 +86,9 @@ const ShortTermDetail: FC = () => {
|
||||
))}
|
||||
<div>
|
||||
<div className="rb:font-medium rb:leading-5 rb:mb-1">{t('shortTermDetail.answer')}</div>
|
||||
<div className="rb:bg-[#FFFFFF] rb:border rb:border-[#DFE4ED] rb:rounded-md rb:px-3 rb:py-2.5 rb:leading-5">{vo.answer}</div>
|
||||
<div className="rb:bg-[#FFFFFF] rb:border rb:border-[#DFE4ED] rb:rounded-md rb:px-3 rb:py-2.5 rb:leading-5">
|
||||
<Markdown content={vo.answer} />
|
||||
</div>
|
||||
</div>
|
||||
</Space>
|
||||
</div>
|
||||
@@ -103,7 +106,9 @@ const ShortTermDetail: FC = () => {
|
||||
: data.long_term?.map((vo, voIdx) => (
|
||||
<div key={voIdx} className="rb:leading-5 rb:shadow-[inset_3px_0px_0px_0px_#155EEF] rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg rb:px-6 rb:py-3">
|
||||
<div className="rb:mb-1 rb:font-medium rb:leading-5.5">{vo.query}</div>
|
||||
<div className="rb:mt-1 rb:leading-5 rb:text-[#5B6167] rb:text-[12px]">{vo.retrieval}</div>
|
||||
<div className="rb:mt-1 rb:leading-5 rb:text-[#5B6167] rb:text-[12px]">
|
||||
<Markdown content={vo.retrieval} />
|
||||
</div>
|
||||
</div>
|
||||
))
|
||||
}
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
* @Author: ZhaoYing
|
||||
* @Date: 2026-02-06 21:10:56
|
||||
* @Last Modified by: ZhaoYing
|
||||
* @Last Modified time: 2026-02-28 16:43:06
|
||||
* @Last Modified time: 2026-03-04 12:10:17
|
||||
*/
|
||||
/**
|
||||
* Workflow Chat Component
|
||||
@@ -174,8 +174,8 @@ const Chat = forwardRef<ChatRef, { appId: string; graphRef: GraphRef }>(({ appId
|
||||
*/
|
||||
const handleStreamMessage = (data: SSEMessage[]) => {
|
||||
data.forEach(item => {
|
||||
const { chunk, conversation_id, node_id, cycle_id, cycle_idx, input, output, error, elapsed_time, status } = item.data as {
|
||||
chunk: string;
|
||||
const { content, conversation_id, node_id, cycle_id, cycle_idx, input, output, error, elapsed_time, status } = item.data as {
|
||||
content: string;
|
||||
conversation_id: string | null;
|
||||
cycle_id: string;
|
||||
cycle_idx: number;
|
||||
@@ -202,7 +202,7 @@ const Chat = forwardRef<ChatRef, { appId: string; graphRef: GraphRef }>(({ appId
|
||||
if (lastIndex >= 0) {
|
||||
newList[lastIndex] = {
|
||||
...newList[lastIndex],
|
||||
content: newList[lastIndex].content + chunk
|
||||
content: newList[lastIndex].content + content
|
||||
}
|
||||
}
|
||||
return newList
|
||||
|
||||
Reference in New Issue
Block a user