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agent功能开发增加MCP后端

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zzlgreat
2025-11-07 23:51:18 +08:00
parent 6899b9d0d2
commit 95eb86c06a
2 changed files with 279 additions and 61 deletions
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157
mcp_server.py
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@@ -1439,12 +1439,68 @@ class MCPAgentIntegrated:
# 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
# 阶段1: Kimi 制定计划（流式）
yield self._format_sse("status", {"stage": "planning", "message": "正在制定执行计划..."})
plan = await self.create_plan(user_query, tools)
messages = [
{"role": "system", "content": self.get_planning_prompt(tools)},
{"role": "user", "content": user_query},
]
# 发送计划
# 使用流式 API 调用 Kimi
stream = self.kimi_client.chat.completions.create(
model=self.kimi_model,
messages=messages,
temperature=1.0,
max_tokens=16000,
stream=True, # 启用流式输出
)
reasoning_content = ""
plan_content = ""
# 逐块接收 Kimi 的响应
for chunk in stream:
if chunk.choices[0].delta.content:
content_chunk = chunk.choices[0].delta.content
plan_content += content_chunk
# 发送思考过程片段
yield self._format_sse("thinking", {
"content": content_chunk,
"stage": "planning"
})
# 提取 reasoning_content（如果有）
if hasattr(chunk.choices[0], 'delta') and hasattr(chunk.choices[0].delta, 'reasoning_content'):
reasoning_chunk = chunk.choices[0].delta.reasoning_content
if reasoning_chunk:
reasoning_content += reasoning_chunk
# 发送推理过程片段
yield self._format_sse("reasoning", {
"content": reasoning_chunk
})
# 解析完整的计划
plan_json = plan_content.strip()
# 清理可能的代码块标记
if "```json" in plan_json:
plan_json = plan_json.split("```json")[1].split("```")[0].strip()
elif "```" in plan_json:
plan_json = plan_json.split("```")[1].split("```")[0].strip()
plan_data = json.loads(plan_json)
plan = ExecutionPlan(
goal=plan_data["goal"],
reasoning=plan_data.get("reasoning", "") + "\n\n" + (reasoning_content[:500] if reasoning_content else ""),
steps=[ToolCall(**step) for step in plan_data["steps"]],
)
logger.info(f"[Planning] 计划制定完成: {len(plan.steps)} 步")
# 发送完整计划
yield self._format_sse("plan", {
"goal": plan.goal,
"reasoning": plan.reasoning,
@@ -1524,17 +1580,104 @@ class MCPAgentIntegrated:
"execution_time": execution_time,
})
# 阶段3: Kimi 生成总结
# 阶段3: Kimi 生成总结（流式）
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)
# 收集成功的结果
successful_results = [r for r in step_results if r.status == "success"]
# 发送最终总结
if not successful_results:
yield self._format_sse("summary", {
"content": "很抱歉，所有步骤都执行失败，无法生成分析报告。",
"metadata": {
"total_steps": len(plan.steps),
"successful_steps": 0,
"failed_steps": len(step_results),
"total_execution_time": sum(r.execution_time for r in step_results),
},
})
else:
# 构建结果文本（精简版）
results_text = "\n\n".join([
f"**步骤 {r.step_index + 1}: {r.tool}**\n"
f"结果: {str(r.result)[:800]}..."
for r in successful_results[:3] # 只取前3个，避免超长
])
messages = [
{
"role": "system",
"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。
## 数据可视化能力
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。
支持的图表类型：
- 折线图（line）：适合时间序列数据（如股价走势、财务指标趋势）
- 柱状图（bar）：适合对比数据（如不同年份的收入、利润对比）
- 饼图（pie）：适合占比数据（如业务结构、资产分布）
### 图表格式（使用 Markdown 代码块）
在报告中插入图表时，使用以下格式：
```echarts
{
"title": {"text": "图表标题"},
"tooltip": {},
"xAxis": {"type": "category", "data": ["类别1", "类别2"]},
"yAxis": {"type": "value"},
"series": [{"name": "数据系列", "type": "line", "data": [100, 200]}]
}
```
**重要提示**：
- ECharts 配置必须是合法的 JSON 格式
- 只在有明确数值数据时才生成图表
- 不要凭空捏造数据"""
},
{
"role": "user",
"content": f"""用户问题：{user_query}
执行计划：{plan.goal}
执行结果：
{results_text}
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。"""
},
]
# 使用流式 API 生成总结
summary_stream = self.kimi_client.chat.completions.create(
model="kimi-k2-turbo-preview",
messages=messages,
temperature=0.7,
max_tokens=2000,
stream=True, # 启用流式输出
)
final_summary = ""
# 逐块发送总结内容
for chunk in summary_stream:
if chunk.choices[0].delta.content:
content_chunk = chunk.choices[0].delta.content
final_summary += content_chunk
# 发送总结片段
yield self._format_sse("summary_chunk", {
"content": content_chunk
})
logger.info("[Summary] 流式总结完成")
# 发送完整的总结和元数据
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"]),
"successful_steps": len(successful_results),
"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),
},

131
src/views/AgentChat/index.js
View File

@@ -307,6 +307,12 @@ const AgentChatV3 = () => {
const decoder = new TextDecoder();
let buffer = '';
// 流式状态变量
let thinkingMessageId = null;
let thinkingContent = '';
let summaryMessageId = null;
let summaryContent = '';
// 读取流式数据
while (true) {
const { done, value } = await reader.read();
@@ -316,11 +322,14 @@ const AgentChatV3 = () => {
const lines = buffer.split('\n');
buffer = lines.pop(); // 保留不完整的行
let currentEvent = null;
for (const line of lines) {
if (!line.trim() || line.startsWith(':')) continue;
if (line.startsWith('event:')) {
// 忽略事件类型行
// 提取事件类型
currentEvent = line.substring(6).trim();
continue;
}
@@ -328,19 +337,37 @@ const AgentChatV3 = () => {
try {
const data = JSON.parse(line.substring(5).trim());
// 处理不同类型的事件
if (data.stage === 'planning') {
// 正在制定计划
// 根据事件类型处理数据
if (currentEvent === 'thinking') {
// Kimi 流式思考过程
if (!thinkingMessageId) {
thinkingMessageId = Date.now();
thinkingContent = '';
setMessages((prev) => prev.filter((m) => m.type !== MessageTypes.AGENT_THINKING));
addMessage({
id: thinkingMessageId,
type: MessageTypes.AGENT_THINKING,
content: '正在制定执行计划...',
content: '',
timestamp: new Date().toISOString(),
});
setCurrentProgress(20);
} else if (data.goal) {
}
thinkingContent += data.content;
// 实时更新思考内容
setMessages((prev) =>
prev.map((m) =>
m.id === thinkingMessageId
? { ...m, content: thinkingContent }
: m
)
);
} else if (currentEvent === 'reasoning') {
// Kimi 推理过程（可选显示）
logger.info('Kimi reasoning:', data.content);
} else if (currentEvent === 'plan') {
// 收到执行计划
currentPlan = data;
thinkingMessageId = null;
thinkingContent = '';
setMessages((prev) => prev.filter((m) => m.type !== MessageTypes.AGENT_THINKING));
addMessage({
type: MessageTypes.AGENT_PLAN,
@@ -349,20 +376,7 @@ const AgentChatV3 = () => {
timestamp: new Date().toISOString(),
});
setCurrentProgress(30);
} else if (data.stage === 'executing') {
// 开始执行步骤
const msgId = Date.now();
executingMessageId = msgId;
addMessage({
id: msgId,
type: MessageTypes.AGENT_EXECUTING,
content: `正在执行 ${data.message}`,
plan: currentPlan,
stepResults: [],
timestamp: new Date().toISOString(),
});
setCurrentProgress(40);
} else if (data.step_index !== undefined && data.tool) {
} else if (currentEvent === 'step_complete') {
// 收到步骤完成事件
const stepResult = {
step_index: data.step_index,
@@ -386,17 +400,46 @@ const AgentChatV3 = () => {
// 更新进度
const progress = 40 + (stepResults.length / (currentPlan?.steps?.length || 5)) * 40;
setCurrentProgress(Math.min(progress, 80));
} else if (data.stage === 'summarizing') {
// 正在生成总结
setMessages((prev) => prev.filter((m) => m.type !== MessageTypes.AGENT_EXECUTING));
} else if (currentEvent === 'summary_chunk') {
// 流式总结内容
if (!summaryMessageId) {
summaryMessageId = Date.now();
summaryContent = '';
setMessages((prev) =>
prev.filter((m) => m.type !== MessageTypes.AGENT_THINKING && m.type !== MessageTypes.AGENT_EXECUTING)
);
addMessage({
type: MessageTypes.AGENT_THINKING,
content: '正在生成分析报告...',
id: summaryMessageId,
type: MessageTypes.AGENT_RESPONSE,
content: '',
plan: currentPlan,
stepResults: stepResults,
timestamp: new Date().toISOString(),
});
setCurrentProgress(85);
} else if (data.content) {
// 收到最终总结
}
summaryContent += data.content;
// 实时更新总结内容
setMessages((prev) =>
prev.map((m) =>
m.id === summaryMessageId
? { ...m, content: summaryContent }
: m
)
);
} else if (currentEvent === 'summary') {
// 收到完整总结（包含元数据）
if (summaryMessageId) {
// 更新已有消息的元数据
setMessages((prev) =>
prev.map((m) =>
m.id === summaryMessageId
? { ...m, metadata: data.metadata }
: m
)
);
} else {
// 如果没有流式片段，直接显示完整总结
setMessages((prev) =>
prev.filter((m) => m.type !== MessageTypes.AGENT_THINKING && m.type !== MessageTypes.AGENT_EXECUTING)
);
@@ -408,7 +451,39 @@ const AgentChatV3 = () => {
metadata: data.metadata,
timestamp: new Date().toISOString(),
});
}
setCurrentProgress(100);
} else if (currentEvent === 'status') {
// 状态更新
if (data.stage === 'planning') {
setMessages((prev) => prev.filter((m) => m.type !== MessageTypes.AGENT_THINKING));
addMessage({
type: MessageTypes.AGENT_THINKING,
content: data.message,
timestamp: new Date().toISOString(),
});
setCurrentProgress(10);
} else if (data.stage === 'executing') {
const msgId = Date.now();
executingMessageId = msgId;
addMessage({
id: msgId,
type: MessageTypes.AGENT_EXECUTING,
content: data.message,
plan: currentPlan,
stepResults: [],
timestamp: new Date().toISOString(),
});
setCurrentProgress(40);
} else if (data.stage === 'summarizing') {
setMessages((prev) => prev.filter((m) => m.type !== MessageTypes.AGENT_EXECUTING));
addMessage({
type: MessageTypes.AGENT_THINKING,
content: data.message,
timestamp: new Date().toISOString(),
});
setCurrentProgress(80);
}
}
} catch (e) {
logger.error('解析 SSE 数据失败', e);