From 322b1dd845c7d55a87870c4102c0661fe87b999c Mon Sep 17 00:00:00 2001
From: zzlgreat <zzlgreat@gmail.com>
Date: Fri, 7 Nov 2025 20:23:54 +0800
Subject: [PATCH] =?UTF-8?q?agent=E5=8A=9F=E8=83=BD=E5=BC=80=E5=8F=91?=
 =?UTF-8?q?=E5=A2=9E=E5=8A=A0MCP=E5=90=8E=E7=AB=AF?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 __pycache__/mcp_server.cpython-310.pyc    | Bin 33180 -> 36620 bytes
 mcp_server.py                             | 177 +++++++++++++++++-
 src/components/ChatBot/ChatInterfaceV2.js | 213 ++++++++++++++++------
 3 files changed, 332 insertions(+), 58 deletions(-)

diff --git a/__pycache__/mcp_server.cpython-310.pyc b/__pycache__/mcp_server.cpython-310.pyc
index 0b667eb0441c9f64c3d41019872be0231120d681..ef7143306d19fc895d8714cc5ff4459c9d76a9d2 100644
GIT binary patch
delta 13523
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diff --git a/mcp_server.py b/mcp_server.py
index 534816b5..61ea6a53 100644
--- a/mcp_server.py
+++ b/mcp_server.py
@@ -6,9 +6,9 @@ MCP Server for Financial Data Search
 
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from pydantic import BaseModel, Field
-from typing import List, Dict, Any, Optional, Literal
+from typing import List, Dict, Any, Optional, Literal, AsyncGenerator
 from datetime import datetime, date
 import logging
 import httpx
@@ -16,6 +16,7 @@ from enum import Enum
 import mcp_database as db
 from openai import OpenAI
 import json
+import asyncio
 
 # 配置日志
 logging.basicConfig(level=logging.INFO)
@@ -1327,7 +1328,7 @@ class MCPAgentIntegrated:
         tools: List[dict],
         tool_handlers: Dict[str, Any],
     ) -> AgentResponse:
-        """主流程"""
+        """主流程（非流式）"""
         logger.info(f"[Agent] 处理查询: {user_query}")
 
         try:
@@ -1368,6 +1369,131 @@ class MCPAgentIntegrated:
                 message=f"处理失败: {str(e)}",
             )
 
+    async def process_query_stream(
+        self,
+        user_query: str,
+        tools: List[dict],
+        tool_handlers: Dict[str, Any],
+    ) -> AsyncGenerator[str, None]:
+        """主流程（流式输出）- 逐步返回执行结果"""
+        logger.info(f"[Agent Stream] 处理查询: {user_query}")
+
+        try:
+            # 发送开始事件
+            yield self._format_sse("status", {"stage": "start", "message": "开始处理查询"})
+
+            # 阶段1: Kimi 制定计划
+            yield self._format_sse("status", {"stage": "planning", "message": "正在制定执行计划..."})
+
+            plan = await self.create_plan(user_query, tools)
+
+            # 发送计划
+            yield self._format_sse("plan", {
+                "goal": plan.goal,
+                "reasoning": plan.reasoning,
+                "steps": [
+                    {"tool": step.tool, "arguments": step.arguments, "reason": step.reason}
+                    for step in plan.steps
+                ],
+            })
+
+            # 阶段2: 执行工具（逐步返回）
+            yield self._format_sse("status", {"stage": "executing", "message": f"开始执行 {len(plan.steps)} 个步骤"})
+
+            step_results = []
+            collected_data = {}
+
+            for i, step in enumerate(plan.steps):
+                # 发送步骤开始事件
+                yield self._format_sse("step_start", {
+                    "step_index": i,
+                    "tool": step.tool,
+                    "arguments": step.arguments,
+                    "reason": step.reason,
+                })
+
+                start_time = datetime.now()
+
+                try:
+                    # 替换占位符
+                    arguments = step.arguments.copy()
+                    if step.tool == "summarize_news":
+                        if arguments.get("data") in ["前面的新闻数据", "前面收集的所有数据"]:
+                            arguments["data"] = json.dumps(collected_data, ensure_ascii=False, indent=2)
+
+                    # 执行工具
+                    result = await self.execute_tool(step.tool, arguments, tool_handlers)
+                    execution_time = (datetime.now() - start_time).total_seconds()
+
+                    step_result = StepResult(
+                        step_index=i,
+                        tool=step.tool,
+                        arguments=arguments,
+                        status="success",
+                        result=result,
+                        execution_time=execution_time,
+                    )
+                    step_results.append(step_result)
+                    collected_data[f"step_{i+1}_{step.tool}"] = result
+
+                    # 发送步骤完成事件（包含结果）
+                    yield self._format_sse("step_complete", {
+                        "step_index": i,
+                        "tool": step.tool,
+                        "status": "success",
+                        "result": result,
+                        "execution_time": execution_time,
+                    })
+
+                except Exception as e:
+                    execution_time = (datetime.now() - start_time).total_seconds()
+
+                    step_result = StepResult(
+                        step_index=i,
+                        tool=step.tool,
+                        arguments=step.arguments,
+                        status="failed",
+                        error=str(e),
+                        execution_time=execution_time,
+                    )
+                    step_results.append(step_result)
+
+                    # 发送步骤失败事件
+                    yield self._format_sse("step_complete", {
+                        "step_index": i,
+                        "tool": step.tool,
+                        "status": "failed",
+                        "error": str(e),
+                        "execution_time": execution_time,
+                    })
+
+            # 阶段3: Kimi 生成总结
+            yield self._format_sse("status", {"stage": "summarizing", "message": "正在生成最终总结..."})
+
+            final_summary = await self.generate_final_summary(user_query, plan, step_results)
+
+            # 发送最终总结
+            yield self._format_sse("summary", {
+                "content": final_summary,
+                "metadata": {
+                    "total_steps": len(plan.steps),
+                    "successful_steps": len([r for r in step_results if r.status == "success"]),
+                    "failed_steps": len([r for r in step_results if r.status == "failed"]),
+                    "total_execution_time": sum(r.execution_time for r in step_results),
+                },
+            })
+
+            # 发送完成事件
+            yield self._format_sse("done", {"message": "处理完成"})
+
+        except Exception as e:
+            logger.error(f"[Agent Stream] 错误: {str(e)}", exc_info=True)
+            yield self._format_sse("error", {"message": f"处理失败: {str(e)}"})
+
+    def _format_sse(self, event: str, data: dict) -> str:
+        """格式化 SSE 消息"""
+        return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+
 # 创建 Agent 实例（全局）
 agent = MCPAgentIntegrated()
 
@@ -1406,7 +1532,7 @@ async def chat(request: ChatRequest):
 
 @app.post("/agent/chat", response_model=AgentResponse)
 async def agent_chat(request: AgentChatRequest):
-    """智能代理对话端点"""
+    """智能代理对话端点（非流式）"""
     logger.info(f"Agent chat: {request.message}")
 
     # 获取工具列表
@@ -1441,6 +1567,49 @@ async def agent_chat(request: AgentChatRequest):
 
     return response
 
+@app.post("/agent/chat/stream")
+async def agent_chat_stream(request: AgentChatRequest):
+    """智能代理对话端点（流式 SSE）"""
+    logger.info(f"Agent chat stream: {request.message}")
+
+    # 获取工具列表
+    tools = [tool.dict() for tool in TOOLS]
+
+    # 添加特殊工具：summarize_news
+    tools.append({
+        "name": "summarize_news",
+        "description": "使用 DeepMoney 模型总结新闻数据，提取关键信息",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "data": {
+                    "type": "string",
+                    "description": "要总结的新闻数据（JSON格式）"
+                },
+                "focus": {
+                    "type": "string",
+                    "description": "关注点，例如：'市场影响'、'投资机会'等"
+                }
+            },
+            "required": ["data"]
+        }
+    })
+
+    # 返回流式响应
+    return StreamingResponse(
+        agent.process_query_stream(
+            user_query=request.message,
+            tools=tools,
+            tool_handlers=TOOL_HANDLERS,
+        ),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",  # 禁用 Nginx 缓冲
+        },
+    )
+
 # ==================== 健康检查 ====================
 
 @app.get("/health")
diff --git a/src/components/ChatBot/ChatInterfaceV2.js b/src/components/ChatBot/ChatInterfaceV2.js
index d617d729..b9785e34 100644
--- a/src/components/ChatBot/ChatInterfaceV2.js
+++ b/src/components/ChatBot/ChatInterfaceV2.js
@@ -95,7 +95,7 @@ export const ChatInterfaceV2 = () => {
     });
   };
 
-  // 发送消息（Agent模式）
+  // 发送消息（Agent模式 - 流式）
   const handleSendMessage = async () => {
     if (!inputValue.trim() || isProcessing) return;
 
@@ -106,10 +106,16 @@ export const ChatInterfaceV2 = () => {
     };
 
     addMessage(userMessage);
+    const userInput = inputValue; // 保存输入值
     setInputValue('');
     setIsProcessing(true);
     setCurrentProgress(0);
 
+    // 用于存储步骤结果
+    let currentPlan = null;
+    let stepResults = [];
+    let executingMessageId = null;
+
     try {
       // 1. 显示思考状态
       addMessage({
@@ -120,18 +126,40 @@ export const ChatInterfaceV2 = () => {
 
       setCurrentProgress(10);
 
-      // 调用 Agent API
-      const response = await fetch(`${mcpService.baseURL.replace('/mcp', '')}/mcp/agent/chat`, {
+      // 使用 EventSource 接收流式数据
+      const eventSource = new EventSource(
+        `${mcpService.baseURL.replace('/mcp', '')}/mcp/agent/chat/stream`,
+        {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+          },
+          body: JSON.stringify({
+            message: userInput,
+            conversation_history: messages
+              .filter(m => m.type === MessageTypes.USER || m.type === MessageTypes.AGENT_RESPONSE)
+              .map(m => ({
+                isUser: m.type === MessageTypes.USER,
+                content: m.content,
+              })),
+          }),
+        }
+      );
+
+      // 由于 EventSource 不支持 POST，我们使用 fetch + ReadableStream
+      const response = await fetch(`${mcpService.baseURL.replace('/mcp', '')}/mcp/agent/chat/stream`, {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
         },
         body: JSON.stringify({
-          message: inputValue,
-          conversation_history: messages.filter(m => m.type === MessageTypes.USER || m.type === MessageTypes.AGENT_RESPONSE).map(m => ({
-            isUser: m.type === MessageTypes.USER,
-            content: m.content,
-          })),
+          message: userInput,
+          conversation_history: messages
+            .filter(m => m.type === MessageTypes.USER || m.type === MessageTypes.AGENT_RESPONSE)
+            .map(m => ({
+              isUser: m.type === MessageTypes.USER,
+              content: m.content,
+            })),
         }),
       });
 
@@ -139,62 +167,139 @@ export const ChatInterfaceV2 = () => {
         throw new Error('Agent请求失败');
       }
 
-      const agentResponse = await response.json();
-      logger.info('Agent response', agentResponse);
+      const reader = response.body.getReader();
+      const decoder = new TextDecoder();
+      let buffer = '';
 
-      // 移除思考消息
-      setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_THINKING));
+      // 读取流式数据
+      while (true) {
+        const { done, value } = await reader.read();
+        if (done) break;
 
-      if (!agentResponse.success) {
-        throw new Error(agentResponse.message || '处理失败');
-      }
+        buffer += decoder.decode(value, { stream: true });
+        const lines = buffer.split('\n\n');
+        buffer = lines.pop(); // 保留不完整的行
 
-      setCurrentProgress(30);
+        for (const line of lines) {
+          if (!line.trim()) continue;
 
-      // 2. 显示执行计划
-      if (agentResponse.plan) {
-        addMessage({
-          type: MessageTypes.AGENT_PLAN,
-          content: '已制定执行计划',
-          plan: agentResponse.plan,
-          timestamp: new Date().toISOString(),
-        });
-      }
+          // 解析 SSE 消息
+          const eventMatch = line.match(/^event: (.+)$/m);
+          const dataMatch = line.match(/^data: (.+)$/m);
 
-      setCurrentProgress(40);
+          if (!eventMatch || !dataMatch) continue;
 
-      // 3. 显示执行过程
-      if (agentResponse.step_results && agentResponse.step_results.length > 0) {
-        addMessage({
-          type: MessageTypes.AGENT_EXECUTING,
-          content: '正在执行步骤...',
-          plan: agentResponse.plan,
-          stepResults: agentResponse.step_results,
-          timestamp: new Date().toISOString(),
-        });
+          const event = eventMatch[1];
+          const data = JSON.parse(dataMatch[1]);
 
-        // 模拟进度更新
-        for (let i = 0; i < agentResponse.step_results.length; i++) {
-          setCurrentProgress(40 + (i + 1) / agentResponse.step_results.length * 50);
-          await new Promise(resolve => setTimeout(resolve, 100));
+          logger.info(`SSE Event: ${event}`, data);
+
+          // 处理不同类型的事件
+          switch (event) {
+            case 'status':
+              if (data.stage === 'planning') {
+                // 移除思考消息，显示规划中
+                setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_THINKING));
+                addMessage({
+                  type: MessageTypes.AGENT_THINKING,
+                  content: data.message,
+                  timestamp: new Date().toISOString(),
+                });
+                setCurrentProgress(20);
+              } else if (data.stage === 'executing') {
+                setCurrentProgress(30);
+              } else if (data.stage === 'summarizing') {
+                setCurrentProgress(90);
+              }
+              break;
+
+            case 'plan':
+              // 移除思考消息
+              setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_THINKING));
+
+              // 显示执行计划
+              currentPlan = data;
+              addMessage({
+                type: MessageTypes.AGENT_PLAN,
+                content: '已制定执行计划',
+                plan: data,
+                timestamp: new Date().toISOString(),
+              });
+              setCurrentProgress(30);
+              break;
+
+            case 'step_start':
+              // 如果还没有执行中消息，创建一个
+              if (!executingMessageId) {
+                const executingMsg = {
+                  type: MessageTypes.AGENT_EXECUTING,
+                  content: '正在执行步骤...',
+                  plan: currentPlan,
+                  stepResults: [],
+                  timestamp: new Date().toISOString(),
+                };
+                addMessage(executingMsg);
+                executingMessageId = Date.now();
+              }
+              break;
+
+            case 'step_complete':
+              // 添加步骤结果
+              stepResults.push({
+                step_index: data.step_index,
+                tool: data.tool,
+                status: data.status,
+                result: data.result,
+                error: data.error,
+                execution_time: data.execution_time,
+                arguments: data.arguments,
+              });
+
+              // 更新执行中消息
+              setMessages(prev =>
+                prev.map(msg =>
+                  msg.type === MessageTypes.AGENT_EXECUTING
+                    ? { ...msg, stepResults: [...stepResults] }
+                    : msg
+                )
+              );
+
+              // 更新进度
+              if (currentPlan) {
+                const progress = 30 + ((data.step_index + 1) / currentPlan.steps.length) * 60;
+                setCurrentProgress(progress);
+              }
+              break;
+
+            case 'summary':
+              // 移除执行中消息
+              setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_EXECUTING));
+
+              // 显示最终结果
+              addMessage({
+                type: MessageTypes.AGENT_RESPONSE,
+                content: data.content,
+                plan: currentPlan,
+                stepResults: stepResults,
+                metadata: data.metadata,
+                timestamp: new Date().toISOString(),
+              });
+              setCurrentProgress(100);
+              break;
+
+            case 'error':
+              throw new Error(data.message);
+
+            case 'done':
+              logger.info('Stream完成');
+              break;
+
+            default:
+              logger.warn('未知事件类型:', event);
+          }
         }
       }
 
-      setCurrentProgress(100);
-
-      // 移除执行中消息
-      setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_EXECUTING));
-
-      // 4. 显示最终结果
-      addMessage({
-        type: MessageTypes.AGENT_RESPONSE,
-        content: agentResponse.message || agentResponse.final_summary,
-        plan: agentResponse.plan,
-        stepResults: agentResponse.step_results,
-        metadata: agentResponse.metadata,
-        timestamp: new Date().toISOString(),
-      });
-
     } catch (error) {
       logger.error('Agent chat error', error);