🐛 fix(mem0): fix connection pool exhausted error

修复 Mem0 连接池耗尽错误，问题根因是 CustomMem0Embedding.embed()
方法中使用 asyncio.run() 导致事件循环泄漏。

主要修改：
- 使用线程池替代 asyncio.run() 避免事件循环泄漏
- 添加线程安全的模型缓存机制
- 为 Mem0 实例缓存添加 LRU 机制，最多保留 50 个实例
- 在 GlobalModelManager 中添加 get_model_sync() 同步方法

Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>

agent/mem0_manager.py

@@ -5,7 +5,10 @@ Mem0 连接和实例管理器
 
 import logging
 import asyncio
+import threading
+import concurrent.futures
 from typing import Any, Dict, List, Optional, Literal
+from collections import OrderedDict
 from embedding.manager import GlobalModelManager, get_model_manager
 import json_repair
 from psycopg2 import pool
@@ -27,7 +30,9 @@ class CustomMem0Embedding:
     这样 Mem0 就不需要再次加载同一个模型，节省内存
     """
 
-    _model_manager = None  # 缓存 GlobalModelManager 实例
+    _model = None  # 类变量，缓存模型实例
+    _lock = threading.Lock()  # 线程安全锁
+    _executor = None  # 线程池执行器
 
     def __init__(self, config: Optional[Any] = None):
         """初始化自定义 Embedding"""
@@ -41,6 +46,41 @@ class CustomMem0Embedding:
         """获取 embedding 维度"""
         return 384  # gte-tiny 的维度
 
+    def _get_model_sync(self):
+        """同步获取模型，避免 asyncio.run()"""
+        # 首先尝试从 manager 获取已加载的模型
+        manager = get_model_manager()
+        model = manager.get_model_sync()
+
+        if model is not None:
+            # 缓存模型
+            CustomMem0Embedding._model = model
+            return model
+
+        # 如果模型未加载，使用线程池运行异步初始化
+        if CustomMem0Embedding._executor is None:
+            CustomMem0Embedding._executor = concurrent.futures.ThreadPoolExecutor(
+                max_workers=1,
+                thread_name_prefix="mem0_embed"
+            )
+
+        # 在独立线程中运行异步代码
+        def run_async_in_thread():
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            try:
+                result = loop.run_until_complete(manager.get_model())
+                return result
+            finally:
+                loop.close()
+
+        future = CustomMem0Embedding._executor.submit(run_async_in_thread)
+        model = future.result(timeout=30)  # 30秒超时
+
+        # 缓存模型
+        CustomMem0Embedding._model = model
+        return model
+
     def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
         """
         获取文本的 embedding 向量（同步方法，供 Mem0 调用）
@@ -52,8 +92,13 @@ class CustomMem0Embedding:
         Returns:
             list: embedding 向量
         """
-        manager = get_model_manager()
-        model = asyncio.run(manager.get_model())
+        # 线程安全地获取模型
+        if CustomMem0Embedding._model is None:
+            with CustomMem0Embedding._lock:
+                if CustomMem0Embedding._model is None:
+                    self._get_model_sync()
+
+        model = CustomMem0Embedding._model
         embeddings = model.encode(text, convert_to_numpy=True)
         return embeddings.tolist()
 
@@ -136,8 +181,9 @@ class Mem0Manager:
         """
         self._sync_pool = sync_pool
 
-        # 缓存 Mem0 实例: key = f"{user_id}:{agent_id}"
-        self._instances: Dict[str, Any] = {}
+        # 使用 OrderedDict 实现 LRU 缓存，最多保留 50 个实例
+        self._instances: OrderedDict[str, Any] = OrderedDict()
+        self._max_instances = 50  # 最大缓存实例数
         self._initialized = False
 
     async def initialize(self) -> None:
@@ -194,10 +240,16 @@ class Mem0Manager:
             llm_suffix = f":{id(config.llm_instance)}"
         cache_key = f"{user_id}:{agent_id}{llm_suffix}"
 
-        # 检查缓存
+        # 检查缓存（同时移动到末尾表示最近使用）
         if cache_key in self._instances:
+            self._instances.move_to_end(cache_key)
             return self._instances[cache_key]
 
+        # 检查缓存大小，超过则移除最旧的
+        if len(self._instances) >= self._max_instances:
+            removed_key, _ = self._instances.popitem(last=False)
+            logger.debug(f"Mem0 instance cache full, removed oldest entry: {removed_key}")
+
         # 创建新实例
         mem0_instance = await self._create_mem0_instance(
             user_id=user_id,
@@ -206,7 +258,7 @@ class Mem0Manager:
             config=config,
         )
 
-        # 缓存实例
+        # 缓存实例（新实例自动在末尾）
         self._instances[cache_key] = mem0_instance
         return mem0_instance
 

embedding/manager.py

@@ -108,6 +108,16 @@ class GlobalModelManager:
             logger.error(f"文本编码失败: {e}")
             raise
     
+    def get_model_sync(self) -> Optional[SentenceTransformer]:
+        """同步获取模型实例（供同步上下文使用）
+
+        如果模型未加载，返回 None。调用者应确保先通过异步方法初始化模型。
+
+        Returns:
+            已加载的 SentenceTransformer 模型，或 None
+        """
+        return self._model
+
     def get_model_info(self) -> Dict[str, Any]:
         """获取模型信息"""
         return {