本地RAG尝试

app/agent.py

@@ -6,14 +6,40 @@ AI Agent 服务类 - 支持多模型动态切换
 import os
 import json
 from dotenv import load_dotenv
-from langchain_community.chat_models import ChatZhipuAI
-from langchain_openai import ChatOpenAI
+try:
+    from langchain_community.chat_models import ChatZhipuAI
+    HAS_ZHIPUAI = True
+except ImportError:
+    HAS_ZHIPUAI = False
+    ChatZhipuAI = None
+
+try:
+    from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+    HAS_OPENAI = True
+except ImportError:
+    HAS_OPENAI = False
+    ChatOpenAI = None
+    OpenAIEmbeddings = None
+
 from pydantic import SecretStr
-from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+try:
+    from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+    HAS_POSTGRES_CHECKPOINT = True
+except ImportError:
+    HAS_POSTGRES_CHECKPOINT = False
+    AsyncPostgresSaver = None
 
 # 本地模块
 from app.graph_builder import GraphBuilder, GraphContext
 from app.tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
+try:
+    from app.rag import RAGPipeline
+    from app.rag.tools import RAGTool
+    HAS_RAG = True
+except ImportError as e:
+    HAS_RAG = False
+    RAGPipeline = None
+    RAGTool = None
 from app.logger import debug, info, warning, error
 
 
@@ -31,9 +57,13 @@ class AIAgentService:
         """
         self.checkpointer = checkpointer
         self.graphs = {}  # 存储不同模型对应的 graph 实例
+        self.rag = None  # RAG 检索实例
+        self.rag_tool = None  # RAG 工具实例
 
     def _create_zhipu_llm(self):
         """创建智谱在线 LLM"""
+        if not HAS_ZHIPUAI:
+            raise ImportError("智谱AI支持不可用，请安装langchain-community包")
         api_key = os.getenv("ZHIPUAI_API_KEY")
         if not api_key:
             raise ValueError("ZHIPUAI_API_KEY not set in environment")
@@ -49,6 +79,8 @@ class AIAgentService:
 
     def _create_deepseek_llm(self):
         """创建 DeepSeek LLM（使用 OpenAI 兼容 API）"""
+        if not HAS_OPENAI:
+            raise ImportError("OpenAI兼容支持不可用，请安装langchain-openai包")
         api_key = os.getenv("DEEPSEEK_API_KEY")
         if not api_key:
             raise ValueError("DEEPSEEK_API_KEY not set in environment")
@@ -65,6 +97,8 @@ class AIAgentService:
 
     def _create_local_llm(self):
         """创建本地 vLLM 服务 LLM"""
+        if not HAS_OPENAI:
+            raise ImportError("OpenAI兼容支持不可用，请安装langchain-openai包")
         # vLLM 服务地址：优先从环境变量读取，适配 Docker、FRP 穿透和本地开发
         vllm_base_url = os.getenv(
             "VLLM_BASE_URL",
@@ -80,8 +114,39 @@ class AIAgentService:
             streaming=True,         # 确保开启流式输出
         )
 
+    def _create_embeddings(self):
+        """创建嵌入模型"""
+        if not HAS_OPENAI:
+            raise ImportError("OpenAI兼容支持不可用，请安装langchain-openai包")
+        embedding_url = os.getenv(
+            "LLAMACPP_EMBEDDING_URL",
+            "http://127.0.0.1:8082/v1"
+        )
+        return OpenAIEmbeddings(
+            openai_api_base=embedding_url,
+            openai_api_key=os.getenv("LLAMACPP_API_KEY", "token-abc123"),
+            model="text-embedding-ada-002",  # 模型名称不重要，兼容即可
+        )
+
     async def initialize(self):
         """预编译所有模型的 graph（使用传入的 checkpointer）"""
+        # 先初始化 RAG 检索系统
+        if HAS_RAG and RAGPipeline is not None and RAGTool is not None:
+            try:
+                info("🔄 正在初始化 RAG 检索系统...")
+                embeddings = self._create_embeddings()
+                self.rag = RAGPipeline(embeddings=embeddings)
+                self.rag_tool = RAGTool(self.rag).get_tool()
+                info("✅ RAG 检索系统初始化成功")
+            except Exception as e:
+                warning(f"⚠️ RAG 检索系统初始化失败: {e}")
+                self.rag = None
+                self.rag_tool = None
+        else:
+            info("⏭️  RAG 检索系统不可用，跳过初始化")
+            self.rag = None
+            self.rag_tool = None
+
         model_configs = {
             "local": self._create_local_llm,      # 本地模型作为第一个
             "deepseek": self._create_deepseek_llm, # DeepSeek 作为中间
@@ -92,7 +157,16 @@ class AIAgentService:
             try:
                 info(f"🔄 正在初始化模型 '{model_name}'...")
                 llm = llm_creator()
-                builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
+                
+                # 构建工具列表：基础工具 + RAG工具（如果可用）
+                tools = AVAILABLE_TOOLS.copy()
+                tools_by_name = TOOLS_BY_NAME.copy()
+                
+                if self.rag_tool is not None:
+                    tools.append(self.rag_tool)
+                    tools_by_name[self.rag_tool.name] = self.rag_tool
+                
+                builder = GraphBuilder(llm, tools, tools_by_name).build()
                 graph = builder.compile(checkpointer=self.checkpointer)
                 self.graphs[model_name] = graph
                 info(f"✅ 模型 '{model_name}' 初始化成功")