采用向量数据库实现长期记忆

app/memory/__init__.py

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+"""
+Mem0 记忆层模块
+"""
+
+from app.memory.mem0_client import Mem0Client
+
+__all__ = ["Mem0Client"]

app/memory/mem0_client.py

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+"""
+Mem0 记忆层客户端封装模块
+负责 Mem0 的初始化、检索和存储
+"""
+
+import os
+from typing import Optional, List, Dict, Any
+from mem0 import AsyncMemory
+
+# 本地模块
+from app.config import QDRANT_URL, QDRANT_COLLECTION_NAME, VLLM_EMBEDDING_URL
+from app.logger import info, warning, error
+
+
+class Mem0Client:
+    """Mem0 异步客户端封装类"""
+    
+    def __init__(self, llm_instance):
+        """
+        初始化 Mem0 客户端
+        
+        Args:
+            llm_instance: LangChain LLM 实例（用于事实提取）
+        """
+        self.llm = llm_instance
+        self.mem0: Optional[AsyncMemory] = None
+        self._initialized = False
+    
+    async def initialize(self):
+        """异步初始化 Mem0 客户端"""
+        if self._initialized:
+            return
+        
+        try:
+            # 检查 Qdrant 是否可达 (可选)
+            import requests
+            try:
+                resp = requests.get(f"{QDRANT_URL}/collections", timeout=2)
+                if resp.status_code == 200:
+                    info(f"✅ Qdrant 服务正常: {QDRANT_URL}")
+            except Exception:
+                warning(f"⚠️ 无法连接到 Qdrant: {QDRANT_URL}，Mem0 将尝试自动连接")
+
+            config = {
+                # 向量存储：复用 Qdrant 实例
+                "vector_store": {
+                    "provider": "qdrant",
+                    "config": {
+                        "collection_name": QDRANT_COLLECTION_NAME,
+                        "host": QDRANT_URL.split("://")[1].split(":")[0] if "://" in QDRANT_URL else "localhost",
+                        "port": int(QDRANT_URL.split(":")[-1]) if ":" in QDRANT_URL.split("://")[-1] else 6333,
+                        "embedding_model_dims": 768,  # embeddinggemma-300m 输出 768 维
+                    }
+                },
+                # 事实提取 LLM：直接复用传入的 LangChain 实例
+                "llm": {
+                    "provider": "langchain",
+                    "config": {
+                        "model": self.llm  # 直接传入 LangChain 模型实例
+                    }
+                },
+                # Embedding：指向 vLLM 服务
+                "embedder": {
+                    "provider": "openai",
+                    "embedding_dims": 768,  # 关键：将维度参数提升到顶层
+                    "config": {
+                        "model": "google/embeddinggemma-300m",
+                        "api_key": "EMPTY",
+                        "api_base": VLLM_EMBEDDING_URL,
+                        # 注意：不要在此处传递 dimensions 参数，避免与 vLLM v0.7.2 不兼容
+                    }
+                },
+                "version": "v1.1"
+            }
+
+            self.mem0 = AsyncMemory.from_config(config)
+            self._initialized = True
+            info(f"✅ Mem0 初始化成功 (Embedding: vLLM@8002, Vector: Qdrant, LLM: 复用现有实例)")
+
+        except Exception as e:
+            error(f"❌ Mem0 初始化失败: {e}")
+            import traceback
+            traceback.print_exc()
+            self.mem0 = None
+    
+    async def search_memories(self, query: str, user_id: str, limit: int = 5) -> List[str]:
+        """
+        检索相关记忆
+        
+        Args:
+            query: 查询文本
+            user_id: 用户 ID
+            limit: 返回结果数量限制
+            
+        Returns:
+            List[str]: 记忆事实列表
+        """
+        if not self.mem0:
+            warning("⚠️ Mem0 未初始化，跳过记忆检索")
+            return []
+        
+        try:
+            memories = await self.mem0.search(query, user_id=user_id, limit=limit)
+            
+            if memories and "results" in memories:
+                facts = [m["memory"] for m in memories["results"] if m.get("memory")]
+                if facts:
+                    info(f"🔍 [记忆检索] Mem0 返回 {len(facts)} 条记忆")
+                    return facts
+            
+            info("🔍 [记忆检索] 未找到相关记忆")
+            return []
+            
+        except Exception as e:
+            warning(f"⚠️ Mem0 检索失败: {e}")
+            return []
+    
+    async def add_memories(self, messages: List[Dict[str, str]], user_id: str) -> bool:
+        """
+        添加记忆（自动提取事实并存储）
+        
+        Args:
+            messages: 消息列表，格式为 [{"role": "user/assistant/system", "content": "..."}]
+            user_id: 用户 ID
+            
+        Returns:
+            bool: 是否成功
+        """
+        if not self.mem0:
+            warning("⚠️ Mem0 未初始化，跳过记忆添加")
+            return False
+        
+        try:
+            result = await self.mem0.add(
+                messages,
+                user_id=user_id,
+                metadata={"type": "conversation"}
+            )
+            info(f"📝 [记忆添加] 已提交给 Mem0 进行事实提取")
+            return True
+            
+        except Exception as e:
+            error(f"❌ Mem0 记忆添加失败: {e}")
+            return False