feat: 重构MCP功能和AI服务提供者架构

backend/app/services/ai_providers/gemini_provider.py

@@ -1,8 +1,12 @@
 """Gemini Provider"""
 from typing import Any, AsyncGenerator, Dict, List, Optional
+
+from app.logger import get_logger
 from app.services.ai_clients.gemini_client import GeminiClient
 from .base_provider import BaseAIProvider
 
+logger = get_logger(__name__)
+
 
 class GeminiProvider(BaseAIProvider):
     def __init__(self, client: GeminiClient):
@@ -36,7 +40,62 @@ class GeminiProvider(BaseAIProvider):
         temperature: float,
         max_tokens: int,
         system_prompt: Optional[str] = None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> AsyncGenerator[str, None]:
+        # 如果有工具，使用真正的流式工具调用
+        if tools:
+            logger.debug(f"🔧 GeminiProvider: 有 {len(tools)} 个工具，使用流式处理")
+            messages = [{"role": "user", "content": prompt}]
+            actual_tool_choice = tool_choice if tool_choice else "auto"
+            
+            tool_calls_buffer = []
+            
+            async for chunk in self.client.chat_completion_stream(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                system_prompt=system_prompt,
+                tools=tools,
+                tool_choice=actual_tool_choice,
+            ):
+                # 检查是否有工具调用
+                if chunk.get("tool_calls"):
+                    tool_calls_buffer.extend(chunk["tool_calls"])
+                    logger.debug(f"🔧 收到工具调用: {len(chunk['tool_calls'])} 个")
+                
+                # 检查是否结束
+                if chunk.get("done"):
+                    if tool_calls_buffer:
+                        logger.info(f"🔧 流式结束，处理 {len(tool_calls_buffer)} 个工具调用")
+                        from app.mcp import mcp_client
+                        actual_user_id = user_id or ""
+                        tool_results = await mcp_client.batch_call_tools(
+                            user_id=actual_user_id,
+                            tool_calls=tool_calls_buffer
+                        )
+                        # 将工具结果注入到上下文中
+                        tool_context = mcp_client.build_tool_context(tool_results, format="markdown")
+                        
+                        # 构建最终提示词，要求AI基于工具结果回答
+                        final_prompt = f"{prompt}\n\n{tool_context}\n\n请基于以上工具查询结果，给出完整详细的回答。"
+                        final_messages = [{"role": "user", "content": final_prompt}]
+                        
+                        # 递归调用生成最终结果
+                        async for final_chunk in self._generate_with_tools(
+                            final_messages, model, temperature, max_tokens, system_prompt, tools, user_id
+                        ):
+                            yield final_chunk
+                    break
+                
+                # 输出文本内容
+                if chunk.get("content"):
+                    yield chunk["content"]
+            return
+        
+        # 无工具时普通流式生成
         messages = [{"role": "user", "content": prompt}]
         async for chunk in self.client.chat_completion_stream(
             messages=messages,
@@ -45,4 +104,56 @@ class GeminiProvider(BaseAIProvider):
             max_tokens=max_tokens,
             system_prompt=system_prompt,
         ):
-            yield chunk
+            # 确保只 yield 字符串内容，避免 yield 字典导致类型错误
+            if isinstance(chunk, dict):
+                if chunk.get("content"):
+                    yield chunk["content"]
+            else:
+                yield chunk
+
+    async def _generate_with_tools(
+        self,
+        messages: list,
+        model: str,
+        temperature: float,
+        max_tokens: int,
+        system_prompt: Optional[str] = None,
+        tools: list = None,
+        user_id: Optional[str] = None,
+    ) -> AsyncGenerator[str, None]:
+        """辅助方法：带工具的流式生成"""
+        tool_calls_buffer = []
+        
+        async for chunk in self.client.chat_completion_stream(
+            messages=messages,
+            model=model,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            system_prompt=system_prompt,
+            tools=tools,
+            tool_choice="auto",
+        ):
+            if chunk.get("tool_calls"):
+                tool_calls_buffer.extend(chunk["tool_calls"])
+                logger.debug(f"🔧 _generate_with_tools 收到工具调用: {len(chunk['tool_calls'])} 个")
+            
+            if chunk.get("done"):
+                if tool_calls_buffer:
+                    from app.mcp import mcp_client
+                    actual_user_id = user_id or ""
+                    tool_results = await mcp_client.batch_call_tools(
+                        user_id=actual_user_id,
+                        tool_calls=tool_calls_buffer
+                    )
+                    tool_context = mcp_client.build_tool_context(tool_results, format="markdown")
+                    
+                    messages.append({"role": "user", "content": f"{tool_context}\n\n请基于以上工具查询结果，给出完整详细的回答。"})
+                    
+                    async for final_chunk in self._generate_with_tools(
+                        messages, model, temperature, max_tokens, system_prompt, tools, user_id
+                    ):
+                        yield final_chunk
+                break
+            
+            if chunk.get("content"):
+                yield chunk["content"]