199 lines
7.4 KiB
Python
199 lines
7.4 KiB
Python
from app.config import (
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LLM_API_KEY, ZHIPUAI_API_KEY,
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VLLM_BASE_URL, QDRANT_URL, QDRANT_COLLECTION_NAME, QDRANT_API_KEY,
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LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY,
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ZHIPU_EMBEDDING_MODEL, ZHIPU_API_BASE
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)
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from ..model_services import get_embedding_service
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from app.logger import info, warning, error
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import time
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"""
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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 asyncio
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from typing import Optional, List, Dict
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from mem0 import AsyncMemory
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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，并进行实际连接测试"""
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if self._initialized:
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return
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try:
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# 获取可用的 embedding 服务并确定维度
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info("🔄 正在获取嵌入服务...")
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embeddings = get_embedding_service()
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test_embedding = embeddings.embed_query("test")
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embedding_dim = len(test_embedding)
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info(f"✅ 嵌入服务可用，向量维度: {embedding_dim}")
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# 构建 embedder 配置 - 改进的方法
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# 检查本地 provider
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from ..model_services.embedding_services import LocalLlamaCppEmbeddingProvider, ZhipuEmbeddingProvider
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embedder_config = None
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local_provider = LocalLlamaCppEmbeddingProvider()
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if local_provider.is_available():
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info("✅ 使用本地 llama.cpp 作为 mem0 embedder")
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embedder_config = {
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"provider": "openai",
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"config": {
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"model": "Qwen3-Embedding-0.6B-Q8_0",
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"api_key": LLAMACPP_API_KEY or "dummy-key",
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"openai_base_url": LLAMACPP_EMBEDDING_URL,
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}
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}
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else:
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# 检查智谱
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zhipu_provider = ZhipuEmbeddingProvider()
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if zhipu_provider.is_available():
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info("✅ 使用智谱 API 作为 mem0 embedder")
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# 使用自定义 embedder 或者 openai 兼容方式
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# 注意：这里我们使用一个特殊的配置方法
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embedder_config = {
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"provider": "openai",
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"config": {
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"model": ZHIPU_EMBEDDING_MODEL,
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"api_key": ZHIPUAI_API_KEY,
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"openai_base_url": ZHIPU_API_BASE,
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}
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}
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else:
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# 都不可用，使用 dummy 配置并警告
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warning("⚠️ 没有可用的 embedder，使用 dummy 配置")
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embedder_config = {
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"provider": "openai",
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"config": {
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"model": "text-embedding-ada-002",
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"api_key": "dummy-key",
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"openai_base_url": "http://localhost:8080/v1",
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}
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}
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# Mem0 配置 - 简化配置，先确保能启动
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info("🔄 正在构建 Mem0 配置...")
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config = {
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"vector_store": {
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"provider": "qdrant",
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"config": {
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"url": QDRANT_URL,
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"api_key": QDRANT_API_KEY,
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"collection_name": QDRANT_COLLECTION_NAME,
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"embedding_model_dims": embedding_dim,
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}
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},
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-3.5-turbo", # 使用一个通用的模型名
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"api_key": LLM_API_KEY or ZHIPUAI_API_KEY or "dummy-key",
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"openai_base_url": VLLM_BASE_URL or ZHIPU_API_BASE,
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"temperature": 0.1,
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"max_tokens": 2000,
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}
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},
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"embedder": embedder_config,
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"version": "v1.1"
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}
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info("🔄 正在初始化 Mem0 实例...")
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self.mem0 = AsyncMemory.from_config(config)
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info("✅ Mem0 配置加载成功")
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# 尝试进行连接测试，但失败不会阻止初始化
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try:
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info("🔄 正在测试 Mem0 连接...")
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# 使用短超时的测试
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await asyncio.wait_for(
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self.mem0.search("ping", user_id="test", limit=1),
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timeout=10.0
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)
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info("✅ Mem0 连接测试成功")
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except Exception as e:
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warning(f"⚠️ Mem0 连接测试遇到问题（但继续使用）: {e}")
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self._initialized = True
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info("🎉 Mem0 初始化完成")
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except asyncio.TimeoutError:
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error("❌ Mem0 初始化超时")
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self.mem0 = None
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self._initialized = False
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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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error(f"详细错误信息:\n{traceback.format_exc()}")
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self.mem0 = None
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self._initialized = False
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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 asyncio.wait_for(
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self.mem0.search(query, user_id=user_id, limit=limit),
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timeout=30.0
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)
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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 asyncio.TimeoutError:
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warning("⚠️ Mem0 检索超时 (30s)，跳过本次记忆检索")
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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, user_id):
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if not self.mem0:
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return False
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try:
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start = time.time()
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info(f"📝 开始 Mem0 add，消息数: {len(messages)}")
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await asyncio.wait_for(
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self.mem0.add(messages, user_id=user_id, metadata={"type": "conversation"}),
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timeout=60.0
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)
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info(f"✅ Mem0 add 完成，耗时: {time.time() - start:.2f}s")
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return True
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except asyncio.TimeoutError:
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error(f"❌ Mem0 记忆添加超时 (60s)，已等待 {time.time() - start:.2f}s")
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return False |