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feat: 展示 AI 分析过程

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cnbugs
2026-07-25 11:47:07 +08:00
parent 8109204843
commit 26ae846501
4 changed files with 389 additions and 95 deletions
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backend/ai_agent.py
+94 -50
View File
@@ -1,4 +1,5 @@
"""AI Agent 分析模块 - 调用 OpenAI 兼容接口分析诊断结果"""
"""AI Agent 分析模块 - 调用 OpenAI 兼容接口分析诊断结果 (支持流式输出)"""
import json
import httpx
from backend.config import AI_API_BASE, AI_API_KEY, AI_MODEL
@@ -13,68 +14,111 @@ SYSTEM_PROMPT = """你是一位资深的 Kubernetes 运维专家和 SRE 工程
输出格式：使用清晰的 Markdown，包含标题、列表、代码块。语言使用中文。"""
_HEADERS = {
"Authorization": f"Bearer {AI_API_KEY}",
"Content-Type": "application/json",
}
async def analyze_with_ai(report: str, question: str = "") -> dict:
"""调用 AI 分析诊断报告"""
async def _stream_chat(messages: list[dict]):
"""通用流式请求 - yield 文本 chunk"""
# connect=10s 快速失败; read=90s 防止 AI 服务无响应时前端无限卡死
timeout = httpx.Timeout(90, connect=10)
async with httpx.AsyncClient(timeout=timeout) as client:
async with client.stream(
"POST",
f"{AI_API_BASE}/chat/completions",
headers=_HEADERS,
json={
"model": AI_MODEL,
"messages": messages,
"temperature": 0.3,
"max_tokens": 4096,
"stream": True,
},
) as resp:
# 非 2xx 直接抛异常 (由上层转成 error 事件)
resp.raise_for_status()
# 有些服务认证失败时返回 200 + JSON 错误体 (非 SSE)，需要识别
ctype = resp.headers.get("content-type", "")
if "text/event-stream" not in ctype and "application/json" in ctype:
body = await resp.aread()
try:
err = json.loads(body.decode())
raise httpx.HTTPStatusError(
f"AI 服务返回错误: {err.get('error', body.decode()[:300])}",
request=resp.request,
response=resp,
)
except (json.JSONDecodeError, UnicodeDecodeError):
raise httpx.HTTPStatusError(
f"AI 服务返回非流式响应: {body.decode()[:300]}",
request=resp.request,
response=resp,
)
async for line in resp.aiter_lines():
if not line.startswith("data: "):
continue
data_str = line[6:].strip()
if data_str == "[DONE]":
break
try:
chunk = json.loads(data_str)
delta = chunk["choices"][0].get("delta", {})
content = delta.get("content", "")
if content:
yield content
except (json.JSONDecodeError, KeyError, IndexError):
continue
async def analyze_with_ai_stream(report: str, question: str = ""):
"""流式 AI 分析诊断报告 - yield 文本 chunk"""
user_msg = f"以下是 K8S 集群诊断报告，请进行详细分析：\n\n{report}"
if question:
user_msg += f"\n\n用户额外问题：{question}"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_msg},
]
async for chunk in _stream_chat(messages):
yield chunk
async def chat_with_ai_stream(messages: list[dict], context: str = ""):
"""流式多轮对话 - yield 文本 chunk"""
msgs = [{"role": "system", "content": SYSTEM_PROMPT}]
if context:
msgs.append({"role": "system", "content": f"当前诊断上下文：\n{context}"})
msgs.extend(messages)
async for chunk in _stream_chat(msgs):
yield chunk
async def analyze_with_ai(report: str, question: str = "") -> dict:
"""非流式 AI 分析 (兼容旧接口)"""
try:
async with httpx.AsyncClient(timeout=120) as client:
resp = await client.post(
f"{AI_API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {AI_API_KEY}",
"Content-Type": "application/json",
},
json={
"model": AI_MODEL,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_msg},
],
"temperature": 0.3,
"max_tokens": 4096,
},
)
resp.raise_for_status()
data = resp.json()
content = data["choices"][0]["message"]["content"]
return {"success": True, "analysis": content, "model": AI_MODEL}
content = ""
async for chunk in analyze_with_ai_stream(report, question):
content += chunk
return {"success": True, "analysis": content, "model": AI_MODEL}
except httpx.HTTPStatusError as e:
return {"success": False, "analysis": f"AI 接口返回错误: {e.response.status_code} - {e.response.text[:500]}", "model": AI_MODEL}
except httpx.ConnectError:
return {"success": False, "analysis": f"无法连接 AI 服务 ({AI_API_BASE})，请检查配置", "model": AI_MODEL}
return {"success": False, "analysis": f"无法连接 AI 服务 ({AI_API_BASE})，请检查 .env 中的 AI_API_BASE 配置", "model": AI_MODEL}
except Exception as e:
return {"success": False, "analysis": f"AI 分析失败: {str(e)}", "model": AI_MODEL}
async def chat_with_ai(messages: list[dict], context: str = "") -> dict:
"""多轮对话 - 基于诊断上下文追问"""
msgs = [{"role": "system", "content": SYSTEM_PROMPT}]
if context:
msgs.append({"role": "system", "content": f"当前诊断上下文：\n{context}"})
msgs.extend(messages)
"""非流式多轮对话 (兼容旧接口)"""
try:
async with httpx.AsyncClient(timeout=120) as client:
resp = await client.post(
f"{AI_API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {AI_API_KEY}",
"Content-Type": "application/json",
},
json={
"model": AI_MODEL,
"messages": msgs,
"temperature": 0.3,
"max_tokens": 4096,
},
)
resp.raise_for_status()
data = resp.json()
content = data["choices"][0]["message"]["content"]
return {"success": True, "reply": content}
content = ""
async for chunk in chat_with_ai_stream(messages, context):
content += chunk
return {"success": True, "reply": content}
except Exception as e:
return {"success": False, "reply": f"AI 对话失败: {str(e)}"}
backend/main.py
+68 -4
View File
@@ -2,6 +2,7 @@
import os
import json
import asyncio
import httpx
from datetime import datetime
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
@@ -10,7 +11,7 @@ from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import Optional
from backend.config import PORT
from backend.config import PORT, AI_API_BASE, AI_MODEL
from backend.diagnosis import (
run_full_diagnosis, build_diagnosis_report, ALL_CHECKS,
check_connectivity,
@@ -18,7 +19,10 @@ from backend.diagnosis import (
check_deployments, check_services, check_pvc, check_resource_usage,
check_network_policies,
)
from backend.ai_agent import analyze_with_ai, chat_with_ai
from backend.ai_agent import (
analyze_with_ai, chat_with_ai,
analyze_with_ai_stream, chat_with_ai_stream,
)
app = FastAPI(title="K8S 智能诊断平台", version="1.0.0")
@@ -196,7 +200,7 @@ async def get_latest():
@app.post("/api/analyze")
async def analyze(req: AnalyzeRequest):
"""AI 分析诊断结果"""
"""AI 分析诊断结果 (非流式，兼容旧接口)"""
global _last_diagnosis, _last_report
if _last_diagnosis is None:
_last_diagnosis = await run_full_diagnosis()
@@ -205,13 +209,73 @@ async def analyze(req: AnalyzeRequest):
return result
@app.get("/api/analyze/stream")
async def analyze_stream(question: str = ""):
"""SSE 流式 AI 分析 - 逐字推送分析内容"""
global _last_diagnosis, _last_report
if _last_diagnosis is None:
_last_diagnosis = await run_full_diagnosis()
_last_report = build_diagnosis_report(_last_diagnosis)
async def event_generator():
process_steps = [
("report", "读取诊断报告", "已加载本次集群诊断上下文"),
("risk", "识别关键风险", "正在梳理严重问题、警告和潜在影响"),
("solution", "生成修复方案", "正在为高优先级问题生成可执行操作"),
("summary", "整理分析结论", "正在组织根因、影响和预防建议"),
]
yield f"data: {json.dumps({'type': 'start', 'model': AI_MODEL}, ensure_ascii=False)}\n\n"
try:
for step, title, detail in process_steps:
yield f"data: {json.dumps({'type': 'process', 'step': step, 'title': title, 'detail': detail, 'status': 'running'}, ensure_ascii=False)}\n\n"
await asyncio.sleep(0)
yield f"data: {json.dumps({'type': 'process', 'step': step, 'title': title, 'detail': detail, 'status': 'done'}, ensure_ascii=False)}\n\n"
async for chunk in analyze_with_ai_stream(_last_report, question=question):
yield f"data: {json.dumps({'type': 'chunk', 'content': chunk}, ensure_ascii=False)}\n\n"
yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n"
except httpx.HTTPStatusError as e:
err = f"AI 接口返回错误: {e.response.status_code} - {str(e)[:300]}"
yield f"data: {json.dumps({'type': 'error', 'message': err}, ensure_ascii=False)}\n\n"
except httpx.ConnectError:
yield f"data: {json.dumps({'type': 'error', 'message': f'无法连接 AI 服务 ({AI_API_BASE})，请检查 .env 中的 AI_API_BASE 配置'}, ensure_ascii=False)}\n\n"
except Exception as e:
yield f"data: {json.dumps({'type': 'error', 'message': f'AI 分析失败: {str(e)}'}, ensure_ascii=False)}\n\n"
return StreamingResponse(
event_generator(),
media_type="text/event-stream",
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
)
@app.post("/api/chat")
async def chat(req: ChatRequest):
"""AI 多轮对话 (基于诊断上下文追问)"""
"""AI 多轮对话 (非流式，兼容旧接口)"""
result = await chat_with_ai(req.messages, context=_last_report)
return result
@app.post("/api/chat/stream")
async def chat_stream(req: ChatRequest):
"""SSE 流式 AI 对话 - 逐字推送回复"""
async def event_generator():
yield f"data: {json.dumps({'type': 'start'}, ensure_ascii=False)}\n\n"
try:
async for chunk in chat_with_ai_stream(req.messages, context=_last_report):
yield f"data: {json.dumps({'type': 'chunk', 'content': chunk}, ensure_ascii=False)}\n\n"
yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n"
except Exception as e:
yield f"data: {json.dumps({'type': 'error', 'message': f'AI 对话失败: {str(e)}'}, ensure_ascii=False)}\n\n"
return StreamingResponse(
event_generator(),
media_type="text/event-stream",
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
)
@app.get("/api/report")
async def get_report():
"""获取诊断报告文本"""