refactor: 将生成式大模型提取为服务层架构，移除 llm_factory

backend/app/agent/llm_factory.py

@@ -1,57 +0,0 @@
-# app/llm_factory.py
-import os
-from ..config import ZHIPUAI_API_KEY, DEEPSEEK_API_KEY, VLLM_BASE_URL, LLAMACPP_API_KEY
-from langchain_community.chat_models import ChatZhipuAI
-from langchain_openai import ChatOpenAI
-from pydantic import SecretStr
-
-class LLMFactory:
-    @staticmethod
-    def create_zhipu():
-        api_key = ZHIPUAI_API_KEY
-        if not api_key:
-            raise ValueError("ZHIPUAI_API_KEY not set")
-        return ChatZhipuAI(
-            model="glm-4.7-flash",
-            api_key=api_key,
-            temperature=0.1,
-            max_tokens=4096,
-            timeout=120.0,
-            max_retries=3,
-            streaming=True,
-        )
-
-    @staticmethod
-    def create_deepseek():
-        api_key = DEEPSEEK_API_KEY
-        if not api_key:
-            raise ValueError("DEEPSEEK_API_KEY not set")
-        return ChatOpenAI(
-            base_url="https://api.deepseek.com",
-            api_key=SecretStr(api_key),
-            model="deepseek-reasoner",
-            temperature=0.1,
-            max_tokens=4096,
-            timeout=60.0,
-            max_retries=2,
-            streaming=True,
-        )
-
-    @staticmethod
-    def create_local():
-        base_url = VLLM_BASE_URL
-        return ChatOpenAI(
-            base_url=base_url,
-            api_key=SecretStr(LLAMACPP_API_KEY),
-            model="gemma-4-E4B-it",
-            timeout=60.0,
-            max_retries=2,
-            streaming=True,
-        )
-
-    # 模型创建器映射
-    CREATORS = {
-        "zhipu": create_zhipu,
-        "local": create_local,
-        "deepseek": create_deepseek,
-    }

backend/app/agent/service.py

@@ -8,7 +8,7 @@ import json
 # 本地模块
 from ..graph.graph_builder import GraphBuilder, GraphContext
 from ..graph.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
-from .llm_factory import LLMFactory
+from ..model_services.chat_services import get_all_chat_services, LocalVLLMChatProvider
 from .rag_initializer import init_rag_tool
 from ..logger import info, warning
 
@@ -21,16 +21,19 @@ class AIAgentService:
 
     async def initialize(self):
         # 1. 初始化 RAG 工具（如果需要）
-        rag_tool = await init_rag_tool(LLMFactory.create_local)
+        def create_local_llm():
+            provider = LocalVLLMChatProvider()
+            return provider.get_service()
+        rag_tool = await init_rag_tool(create_local_llm)
         if rag_tool:
             self.tools.append(rag_tool)
             self.tools_by_name[rag_tool.name] = rag_tool
 
         # 2. 构建各模型的 Graph
-        for name, creator in LLMFactory.CREATORS.items():
+        chat_services = get_all_chat_services()
+        for name, llm in chat_services.items():
             try:
                 info(f"🔄 初始化模型 '{name}'...")
-                llm = creator()
                 builder = GraphBuilder(llm, self.tools, self.tools_by_name).build()
                 graph = builder.compile(checkpointer=self.checkpointer)
                 self.graphs[name] = graph