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