Initial commit: 国土空间规划课程智能体 v1.0

单容器 Docker 架构的国土空间规划课程智能问答系统，集成 FastAPI 后端与 Next.js 前端。

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/src/llm/siliconflow.py

@@ -0,0 +1,108 @@
+"""
+硅基流动大模型API集成
+"""
+import os
+from typing import List, Dict, Any, Optional, AsyncGenerator
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
+from langchain_core.callbacks.base import BaseCallbackHandler
+from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+from ..core.config import get_settings
+
+settings = get_settings()
+
+
+class SiliconFlowLLM:
+    """硅基流动大模型客户端"""
+    
+    def __init__(self, model: Optional[str] = None):
+        """初始化LLM客户端"""
+        # 设置环境变量
+        os.environ["OPENAI_API_KEY"] = settings.siliconflow_api_key
+        os.environ["OPENAI_API_BASE"] = settings.siliconflow_base_url
+        
+        # 使用传入的模型或默认模型
+        self.model_name = model or settings.siliconflow_model
+        print(f"[DEBUG-LLM] 初始化LLM客户端，使用模型: {self.model_name} (传入参数: {model}, 默认配置: {settings.siliconflow_model})")
+        
+        # 创建LLM实例
+        self.llm = ChatOpenAI(
+            model=self.model_name,
+            api_key=settings.siliconflow_api_key,
+            base_url=settings.siliconflow_base_url,
+            temperature=0.7,
+            max_tokens=2000,
+            streaming=True
+        )
+        # 检查实际使用的模型名称
+        actual_model = getattr(self.llm, 'model_name', None) or getattr(self.llm, 'model', None) or str(self.llm)
+        print(f"[DEBUG-LLM] ChatOpenAI实例创建完成，实际模型: {actual_model}")
+    
+    def chat(self, messages: List[BaseMessage], **kwargs) -> str:
+        """同步聊天"""
+        try:
+            response = self.llm.invoke(messages, **kwargs)
+            return response.content
+        except Exception as e:
+            raise Exception(f"LLM调用失败: {str(e)}")
+    
+    async def achat(self, messages: List[BaseMessage], **kwargs) -> str:
+        """异步聊天"""
+        try:
+            response = await self.llm.ainvoke(messages, **kwargs)
+            return response.content
+        except Exception as e:
+            raise Exception(f"LLM异步调用失败: {str(e)}")
+    
+    async def stream_chat(self, messages: List[BaseMessage], **kwargs) -> AsyncGenerator[str, None]:
+        """流式聊天"""
+        try:
+            async for chunk in self.llm.astream(messages, **kwargs):
+                if hasattr(chunk, 'content') and chunk.content:
+                    yield chunk.content
+        except Exception as e:
+            raise Exception(f"LLM流式调用失败: {str(e)}")
+    
+    def create_messages(
+        self, 
+        user_message: str, 
+        system_prompt: Optional[str] = None,
+        chat_history: Optional[List[Dict[str, str]]] = None
+    ) -> List[BaseMessage]:
+        """创建消息列表"""
+        messages = []
+        
+        # 添加系统提示
+        if system_prompt:
+            messages.append(SystemMessage(content=system_prompt))
+        
+        # 添加聊天历史
+        if chat_history:
+            for msg in chat_history:
+                if msg["role"] == "user":
+                    messages.append(HumanMessage(content=msg["content"]))
+                elif msg["role"] == "assistant":
+                    messages.append(AIMessage(content=msg["content"]))
+        
+        # 添加当前用户消息
+        messages.append(HumanMessage(content=user_message))
+        
+        return messages
+
+
+# 全局LLM实例（使用默认模型）
+llm_client = SiliconFlowLLM()
+
+
+def get_llm_client(model: Optional[str] = None) -> SiliconFlowLLM:
+    """获取LLM客户端实例
+    
+    Args:
+        model: 可选的模型名称，如果提供则创建新的实例，否则返回默认实例
+    """
+    if model is None:
+        return llm_client
+    else:
+        # 为指定模型创建新实例
+        return SiliconFlowLLM(model=model)