feat: 修复数据库持久化，完善服务降级机制

- 恢复使用 AsyncPostgresSaver 持久化短期记忆
- 添加 LLM 作为 Rerank 服务的最后降级方案
- 完善降级链：Local llama.cpp → Zhipu Rerank → LLM Fallback

backend/app/backend.py

@@ -4,8 +4,7 @@ FastAPI 后端 - 支持动态模型切换，使用 PostgreSQL 持久化记忆
 """
 
 import os
-# from app.config import DB_URI, BACKEND_PORT
-from app.config import BACKEND_PORT
+from app.config import DB_URI, BACKEND_PORT
 import uuid
 import json
 from contextlib import asynccontextmanager
@@ -15,7 +14,7 @@ from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, Depe
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 from .agent.service import AIAgentService
 from .agent.history import ThreadHistoryService
 from app.core.human_review import (
@@ -27,42 +26,56 @@ from app.core.human_review import (
 from app.subgraphs.contact.api_client import ContactAPIClient
 from app.subgraphs.dictionary.api_client import DictionaryAPIClient
 from app.subgraphs.news_analysis.api_client import NewsAPIClient
-# from .db.init_db import init_subgraph_tables
-# from .db.models import ContactRepository, DictionaryRepository, NewsRepository
+from .db.init_db import init_subgraph_tables
+from .db.models import ContactRepository, DictionaryRepository, NewsRepository
 from app.logger import info, error
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    """应用生命周期管理：创建并注入全局服务（临时用内存 checkpoint）"""
-    # 1. 创建内存 checkpointer（临时测试）
-    checkpointer = MemorySaver()
-    
-    # 2. 构建 AI Agent 服务
-    agent_service = AIAgentService(checkpointer)
-    await agent_service.initialize()
-    
-    # 3. 创建历史查询服务（保持原有的 checkpointer 参数）
-    history_service = ThreadHistoryService(checkpointer)
-    
-    # 4. 创建审核管理器
-    review_manager = ReviewManager(InMemoryReviewStore())
-    
-    # 5. 将服务实例存入 app.state
-    app.state.agent_service = agent_service
-    app.state.history_service = history_service
-    app.state.review_manager = review_manager
-    app.state.contact_api = ContactAPIClient()
-    app.state.dictionary_api = DictionaryAPIClient()
-    app.state.news_api = NewsAPIClient()
-    app.state.contact_repo = None
-    app.state.dictionary_repo = None
-    app.state.news_repo = None
-    
-    # 应用运行中...
-    yield
-    
-    # 6. 关闭时清理
-    info("🛑 应用关闭")
+    """应用生命周期管理：创建并注入全局服务"""
+    # 1. 创建数据库连接池并初始化表（仅 checkpointer）
+    async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
+        await checkpointer.setup()
+        
+        # 1.5 初始化子图表
+        await init_subgraph_tables(checkpointer.conn)
+        
+        # 2. 构建 AI Agent 服务
+        agent_service = AIAgentService(checkpointer)
+        await agent_service.initialize()
+        
+        # 3. 创建历史查询服务（保持原有的 checkpointer 参数）
+        history_service = ThreadHistoryService(checkpointer)
+        
+        # 3.5 创建子图 Repositories
+        contact_repo = ContactRepository(checkpointer.conn)
+        dictionary_repo = DictionaryRepository(checkpointer.conn)
+        news_repo = NewsRepository(checkpointer.conn)
+        
+        # 3.6 创建子图 API 客户端（真实数据库模式）
+        contact_api = ContactAPIClient(checkpointer.conn)
+        dictionary_api = DictionaryAPIClient(word_repository=dictionary_repo)
+        news_api = NewsAPIClient(news_repository=news_repo)
+        
+        # 4. 创建审核管理器
+        review_manager = ReviewManager(InMemoryReviewStore())
+        
+        # 5. 将服务实例存入 app.state
+        app.state.agent_service = agent_service
+        app.state.history_service = history_service
+        app.state.review_manager = review_manager
+        app.state.contact_api = contact_api
+        app.state.dictionary_api = dictionary_api
+        app.state.news_api = news_api
+        app.state.contact_repo = contact_repo
+        app.state.dictionary_repo = dictionary_repo
+        app.state.news_repo = news_repo
+        
+        # 应用运行中...
+        yield
+        
+        # 6. 关闭时自动清理数据库连接（async with 负责）
+        info("🛑 应用关闭，数据库连接池已释放")
 
 app = FastAPI(lifespan=lifespan)
 

backend/app/model_services/rerank_services.py

@@ -136,6 +136,92 @@ class ZhipuRerankService(BaseRerankService):
             raise
 
 
+class LLMFallbackRerankService(BaseRerankService):
+    """
+    使用 LLM 作为最后的降级方案进行重排
+    通过让 LLM 评估文档相关性并给出分数
+    """
+
+    def __init__(self, llm=None):
+        from .chat_services import get_chat_service
+        self.llm = llm or get_chat_service()
+
+    def compute_scores(self, query: str, documents: List[str]) -> List[float]:
+        """
+        使用 LLM 评估文档相关性并打分
+        """
+        if not documents:
+            return []
+
+        scores = []
+        for doc in documents:
+            score = self._score_single_document(query, doc)
+            scores.append(score)
+        
+        return scores
+
+    def _score_single_document(self, query: str, document: str) -> float:
+        """
+        让 LLM 为单个文档的相关性打分 (0.0-1.0)
+        """
+        prompt = f"""你是一个文档相关性评分专家。请评估以下文档与查询的相关性，返回一个0到1之间的分数：
+- 1.0表示完全相关
+- 0.0表示完全不相关
+
+查询: {query}
+
+文档: {document}
+
+请只返回一个数字，不要解释。"""
+
+        try:
+            result = self.llm.invoke(prompt)
+            content = result.content if hasattr(result, 'content') else str(result)
+            # 尝试提取数字
+            import re
+            match = re.search(r'(\d+\.?\d*)', content)
+            if match:
+                score = float(match.group(1))
+                # 确保在 0-1 之间
+                return max(0.0, min(1.0, score))
+            # 如果没有找到数字，返回0.5作为默认值
+            return 0.5
+        except Exception as e:
+            logger.warning(f"LLM 打分失败，返回默认分数 0.5: {e}")
+            return 0.5
+
+
+class LLMFallbackRerankProvider(BaseServiceProvider[BaseRerankService]):
+    """
+    LLM 降级重排服务提供者
+    """
+
+    def __init__(self, llm=None):
+        super().__init__("llm_fallback_rerank")
+        self._llm = llm
+
+    def is_available(self) -> bool:
+        """
+        LLM 降级方案总是可用（只要 LLM 服务可用）
+        """
+        try:
+            from .chat_services import get_chat_service
+            get_chat_service()
+            logger.info("LLM 降级重排服务可用")
+            return True
+        except Exception as e:
+            logger.warning(f"LLM 降级重排服务不可用: {e}")
+            return False
+
+    def get_service(self) -> BaseRerankService:
+        """
+        获取 LLM 降级重排服务
+        """
+        if self._service_instance is None:
+            self._service_instance = LLMFallbackRerankService(self._llm)
+        return self._service_instance
+
+
 class LocalLlamaCppRerankProvider(BaseServiceProvider[BaseRerankService]):
     """
     本地 llama.cpp 重排服务提供者
@@ -221,13 +307,15 @@ def get_rerank_service() -> BaseRerankService:
     """
     获取重排服务（带自动降级）- 纯服务层
 
+    降级链: Local llama.cpp -> Zhipu Rerank -> LLM Fallback
+
     Returns:
         BaseRerankService: 重排服务实例
     """
     def _create_chain():
         primary = LocalLlamaCppRerankProvider()
-        fallback = ZhipuRerankProvider()
-        return FallbackServiceChain(primary, [fallback])
+        fallbacks = [ZhipuRerankProvider(), LLMFallbackRerankProvider()]
+        return FallbackServiceChain(primary, fallbacks)
 
     chain = SingletonServiceManager.get_or_create("rerank_service_chain", _create_chain)
     return chain.get_available_service()