添加长期存储,流式检查
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2026-04-17 01:26:05 +08:00
parent 602d551fd1
commit 404efde282
37 changed files with 794 additions and 2095 deletions

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@@ -3,142 +3,151 @@ 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
import asyncio
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.config import QDRANT_URL, QDRANT_COLLECTION_NAME, LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
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"""
"""异步初始化 Mem0 客户端,并进行实际连接测试"""
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")
try:
# Mem0 配置
config = {
# 向量存储:复用 Qdrant 实例
"vector_store": {
"provider": "qdrant",
"config": {
"url": QDRANT_URL, # 直接使用完整 URL
"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
"embedding_model_dims": 768,
}
},
# 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
"model": self.llm
}
},
# 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
}
"model": "embeddinggemma-300M-Q8_0",
"api_key": LLAMACPP_API_KEY,
"openai_base_url": LLAMACPP_EMBEDDING_URL,
},
},
"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)")
info("✅ Mem0 配置加载成功,开始连接测试...")
except Exception as e:
error(f"❌ Mem0 初始化失败: {e}")
import traceback
traceback.print_exc()
# 实际连接测试:调用一次 search 确保 Qdrant 和 Embedding 都可达
await asyncio.wait_for(
self.mem0.search("ping", user_id="test", limit=1),
timeout=60.0
)
info("✅ Mem0 实际连接测试成功,初始化完成")
self._initialized = True
except asyncio.TimeoutError:
error("❌ Mem0 连接测试超时 (10s),请检查 Qdrant 或 Embedding 服务响应")
self.mem0 = None
self._initialized = False
except Exception as e:
error(f"❌ Mem0 初始化或连接测试失败: {e}")
import traceback
error(f"详细错误信息:\n{traceback.format_exc()}")
self.mem0 = None
self._initialized = False
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)
memories = await asyncio.wait_for(
self.mem0.search(query, user_id=user_id, limit=limit),
timeout=30.0
)
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 asyncio.TimeoutError:
warning("⚠️ Mem0 检索超时 (30s),跳过本次记忆检索")
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"}
await asyncio.wait_for(
self.mem0.add(
messages,
user_id=user_id,
metadata={"type": "conversation"}
),
timeout=60.0
)
info(f"📝 [记忆添加] 已提交给 Mem0 进行事实提取")
info("📝 [记忆添加] 已提交给 Mem0 进行事实提取")
return True
except asyncio.TimeoutError:
error("❌ Mem0 记忆添加超时 (60s)")
return False
except Exception as e:
error(f"❌ Mem0 记忆添加失败: {e}")
return False
return False