agent功能开发增加MCP后端
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173
mcp_server.py
173
mcp_server.py
@@ -1439,12 +1439,68 @@ class MCPAgentIntegrated:
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# 发送开始事件
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yield self._format_sse("status", {"stage": "start", "message": "开始处理查询"})
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# 阶段1: Kimi 制定计划
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# 阶段1: Kimi 制定计划(流式)
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yield self._format_sse("status", {"stage": "planning", "message": "正在制定执行计划..."})
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plan = await self.create_plan(user_query, tools)
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messages = [
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{"role": "system", "content": self.get_planning_prompt(tools)},
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{"role": "user", "content": user_query},
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]
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# 发送计划
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# 使用流式 API 调用 Kimi
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stream = self.kimi_client.chat.completions.create(
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model=self.kimi_model,
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messages=messages,
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temperature=1.0,
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max_tokens=16000,
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stream=True, # 启用流式输出
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)
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reasoning_content = ""
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plan_content = ""
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# 逐块接收 Kimi 的响应
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for chunk in stream:
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if chunk.choices[0].delta.content:
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content_chunk = chunk.choices[0].delta.content
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plan_content += content_chunk
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# 发送思考过程片段
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yield self._format_sse("thinking", {
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"content": content_chunk,
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"stage": "planning"
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})
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# 提取 reasoning_content(如果有)
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if hasattr(chunk.choices[0], 'delta') and hasattr(chunk.choices[0].delta, 'reasoning_content'):
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reasoning_chunk = chunk.choices[0].delta.reasoning_content
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if reasoning_chunk:
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reasoning_content += reasoning_chunk
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# 发送推理过程片段
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yield self._format_sse("reasoning", {
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"content": reasoning_chunk
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})
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# 解析完整的计划
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plan_json = plan_content.strip()
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# 清理可能的代码块标记
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if "```json" in plan_json:
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plan_json = plan_json.split("```json")[1].split("```")[0].strip()
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elif "```" in plan_json:
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plan_json = plan_json.split("```")[1].split("```")[0].strip()
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plan_data = json.loads(plan_json)
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plan = ExecutionPlan(
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goal=plan_data["goal"],
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reasoning=plan_data.get("reasoning", "") + "\n\n" + (reasoning_content[:500] if reasoning_content else ""),
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steps=[ToolCall(**step) for step in plan_data["steps"]],
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)
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logger.info(f"[Planning] 计划制定完成: {len(plan.steps)} 步")
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# 发送完整计划
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yield self._format_sse("plan", {
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"goal": plan.goal,
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"reasoning": plan.reasoning,
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@@ -1524,21 +1580,108 @@ class MCPAgentIntegrated:
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"execution_time": execution_time,
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})
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# 阶段3: Kimi 生成总结
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# 阶段3: Kimi 生成总结(流式)
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yield self._format_sse("status", {"stage": "summarizing", "message": "正在生成最终总结..."})
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final_summary = await self.generate_final_summary(user_query, plan, step_results)
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# 收集成功的结果
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successful_results = [r for r in step_results if r.status == "success"]
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# 发送最终总结
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yield self._format_sse("summary", {
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"content": final_summary,
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"metadata": {
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"total_steps": len(plan.steps),
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"successful_steps": len([r for r in step_results if r.status == "success"]),
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"failed_steps": len([r for r in step_results if r.status == "failed"]),
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"total_execution_time": sum(r.execution_time for r in step_results),
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},
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})
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if not successful_results:
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yield self._format_sse("summary", {
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"content": "很抱歉,所有步骤都执行失败,无法生成分析报告。",
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"metadata": {
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"total_steps": len(plan.steps),
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"successful_steps": 0,
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"failed_steps": len(step_results),
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"total_execution_time": sum(r.execution_time for r in step_results),
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},
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})
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else:
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# 构建结果文本(精简版)
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results_text = "\n\n".join([
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f"**步骤 {r.step_index + 1}: {r.tool}**\n"
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f"结果: {str(r.result)[:800]}..."
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for r in successful_results[:3] # 只取前3个,避免超长
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])
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messages = [
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{
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"role": "system",
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"content": """你是专业的金融研究助手。根据执行结果,生成简洁清晰的报告。
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## 数据可视化能力
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如果执行结果中包含数值型数据(如财务指标、交易数据、时间序列等),你可以使用 ECharts 生成图表来增强报告的可读性。
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支持的图表类型:
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- 折线图(line):适合时间序列数据(如股价走势、财务指标趋势)
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- 柱状图(bar):适合对比数据(如不同年份的收入、利润对比)
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- 饼图(pie):适合占比数据(如业务结构、资产分布)
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### 图表格式(使用 Markdown 代码块)
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在报告中插入图表时,使用以下格式:
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```echarts
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{
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"title": {"text": "图表标题"},
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"tooltip": {},
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"xAxis": {"type": "category", "data": ["类别1", "类别2"]},
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"yAxis": {"type": "value"},
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"series": [{"name": "数据系列", "type": "line", "data": [100, 200]}]
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}
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```
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**重要提示**:
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- ECharts 配置必须是合法的 JSON 格式
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- 只在有明确数值数据时才生成图表
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- 不要凭空捏造数据"""
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},
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{
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"role": "user",
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"content": f"""用户问题:{user_query}
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执行计划:{plan.goal}
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执行结果:
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{results_text}
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请生成专业的分析报告(500字以内)。如果结果中包含数值型数据,请使用 ECharts 图表进行可视化展示。"""
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},
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]
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# 使用流式 API 生成总结
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summary_stream = self.kimi_client.chat.completions.create(
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model="kimi-k2-turbo-preview",
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messages=messages,
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temperature=0.7,
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max_tokens=2000,
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stream=True, # 启用流式输出
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)
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final_summary = ""
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# 逐块发送总结内容
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for chunk in summary_stream:
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if chunk.choices[0].delta.content:
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content_chunk = chunk.choices[0].delta.content
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final_summary += content_chunk
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# 发送总结片段
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yield self._format_sse("summary_chunk", {
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"content": content_chunk
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})
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logger.info("[Summary] 流式总结完成")
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# 发送完整的总结和元数据
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yield self._format_sse("summary", {
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"content": final_summary,
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"metadata": {
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"total_steps": len(plan.steps),
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"successful_steps": len(successful_results),
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"failed_steps": len([r for r in step_results if r.status == "failed"]),
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"total_execution_time": sum(r.execution_time for r in step_results),
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},
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})
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# 发送完成事件
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yield self._format_sse("done", {"message": "处理完成"})
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