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backend/app/api/routes/ai_assistant.py
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backend/app/api/routes/ai_assistant.py
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"""
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AI 助手 API 路由
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"""
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from typing import Optional, List
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from pathlib import Path
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from pydantic import BaseModel
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from datetime import datetime
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from app.database import get_db
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from app.models.intent import Intent, AICheckRecord
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from app.services.ai_service import get_ai_service
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router = APIRouter(prefix="/ai", tags=["AI助手"])
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# ============ Pydantic Schemas ============
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class GenerateRequest(BaseModel):
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"""AI生成请求"""
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prompt: str
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context: Optional[str] = None
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class GenerateResponse(BaseModel):
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"""AI生成响应"""
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content: str
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success: bool
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error: Optional[str] = None
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class CheckRequest(BaseModel):
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"""AI检查请求"""
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content: str
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intent_id: Optional[int] = None # 如果提供，会保存检查记录
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requirements: Optional[List[str]] = None
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class CheckResult(BaseModel):
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"""AI检查结果"""
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passed: bool
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score: int
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issues: List[str]
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suggestions: List[str]
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raw_response: Optional[str] = None
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class CheckResponse(BaseModel):
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"""AI检查响应"""
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result: CheckResult
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record_id: Optional[int] = None # 如果保存了记录
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success: bool
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error: Optional[str] = None
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class IntentGenerateRequest(BaseModel):
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"""意图编制生成请求"""
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test_type: str # 测试类型：功能测试、性能测试、安全测试等
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test_target: str # 测试目标描述
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additional_requirements: Optional[str] = None
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class PlanGenerateRequest(BaseModel):
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"""测试规划生成请求"""
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requirement_text: str # 测试需求文本（前端文本框内容）
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source_name: Optional[str] = "用户输入" # 来源名称
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class ExtractStepsRequest(BaseModel):
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"""提取测试步骤请求"""
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content: str # 意图编制内容
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title: Optional[str] = "意图编制"
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class ExtractedStepData(BaseModel):
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"""提取的步骤数据"""
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id: int
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name: str
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purpose: str
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deviceParams: dict # {设备类别: {参数名: 参数值}}
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class ExtractStepsResponse(BaseModel):
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"""提取测试步骤响应"""
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success: bool
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steps: List[ExtractedStepData]
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error: Optional[str] = None
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# ============ API Endpoints ============
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@router.post("/generate", response_model=GenerateResponse)
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async def generate_content(request: GenerateRequest):
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"""AI 生成内容"""
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try:
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ai_service = get_ai_service()
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content = await ai_service.generate(
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prompt=request.prompt,
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context=request.context
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)
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return GenerateResponse(content=content, success=True)
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except Exception as e:
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return GenerateResponse(
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content="",
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success=False,
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error=str(e)
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)
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@router.post("/check", response_model=CheckResponse)
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async def check_content(
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request: CheckRequest,
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db: AsyncSession = Depends(get_db)
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):
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"""AI 检查内容"""
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try:
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ai_service = get_ai_service()
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result = await ai_service.check(
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content=request.content,
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requirements=request.requirements
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)
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check_result = CheckResult(
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passed=result.get("passed", False),
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score=result.get("score", 0),
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issues=result.get("issues", []),
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suggestions=result.get("suggestions", []),
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raw_response=result.get("raw_response")
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)
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record_id = None
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# 如果提供了 intent_id，保存检查记录
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if request.intent_id:
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# 验证意图存在
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query = select(Intent).where(Intent.id == request.intent_id)
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intent_result = await db.execute(query)
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intent = intent_result.scalar_one_or_none()
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if intent:
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record = AICheckRecord(
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intent_id=request.intent_id,
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check_result=result,
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suggestions="\n".join(result.get("suggestions", []))
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)
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db.add(record)
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await db.commit()
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await db.refresh(record)
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record_id = record.id
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return CheckResponse(
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result=check_result,
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record_id=record_id,
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success=True
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)
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except Exception as e:
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return CheckResponse(
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result=CheckResult(passed=False, score=0, issues=[str(e)], suggestions=[]),
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success=False,
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error=str(e)
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)
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@router.post("/generate-intent", response_model=GenerateResponse)
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async def generate_intent_content(request: IntentGenerateRequest):
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"""AI 生成意图编制初始内容"""
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prompt = f"""请帮我生成一份测试意图编制文档的初始内容。
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测试类型：{request.test_type}
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测试目标：{request.test_target}
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{"额外要求：" + request.additional_requirements if request.additional_requirements else ""}
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请按照以下格式生成内容：
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## 1. 测试目标
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[详细描述测试的目标和预期达成的效果]
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## 2. 测试范围
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[明确测试的边界和覆盖范围]
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## 3. 测试条件
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### 3.1 前置条件
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[列出测试开始前需要满足的条件]
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### 3.2 测试环境
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[描述测试所需的硬件、软件环境]
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### 3.3 测试数据
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[描述测试所需的数据准备]
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## 4. 测试用例概述
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[列出主要的测试场景和用例]
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## 5. 预期结果
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[描述测试成功的判定标准]
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## 6. 风险与注意事项
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[列出可能的风险和需要注意的事项]
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"""
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context = """你是一位专业的软件测试工程师，擅长编写测试意图编制文档。
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请生成规范、完整、专业的测试意图编制内容。"""
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try:
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ai_service = get_ai_service()
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content = await ai_service.generate(prompt=prompt, context=context)
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return GenerateResponse(content=content, success=True)
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except Exception as e:
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return GenerateResponse(
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content="",
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success=False,
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error=str(e)
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)
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@router.post("/generate-plan", response_model=GenerateResponse)
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async def generate_test_plan(request: PlanGenerateRequest):
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"""
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根据前端输入的测试需求文本，调用 planner 生成测试规划。
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直接返回生成的 Markdown 内容（不保存文件）。
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"""
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from planner.planning_agent.planner import build_plan_from_text
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try:
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# 直接调用规划生成函数，返回 Markdown 内容
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md_content = build_plan_from_text(
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requirement_text=request.requirement_text,
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source_doc=request.source_name or "用户输入"
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)
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if not md_content:
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return GenerateResponse(
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content="",
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success=False,
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error="生成内容为空，请检查输入内容或后端服务"
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)
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return GenerateResponse(content=md_content, success=True)
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except Exception as e:
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import traceback
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traceback.print_exc()
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return GenerateResponse(
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content="",
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success=False,
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error=f"规划生成失败: {str(e)}"
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)
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@router.post("/extract-steps", response_model=ExtractStepsResponse)
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async def extract_steps_from_intent(request: ExtractStepsRequest):
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"""
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从意图编制内容中提取测试步骤和参数。
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使用 LLM 解析文本并返回结构化的步骤数据。
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"""
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try:
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ai_service = get_ai_service()
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# 构建提取 prompt
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extract_prompt = f"""请从以下测试规划内容中提取测试步骤和仪器参数。
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请严格按照以下 JSON 格式返回，不要添加任何其他内容：
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```json
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{{
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"steps": [
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{{
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"id": 1,
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"name": "步骤名称",
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"purpose": "步骤目的",
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"deviceParams": {{
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"程控电源参数": {{
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"输出电压": "28V",
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"输出电流": "6A"
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}},
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"频谱分析仪参数": {{
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"中心频率": "2.7GHz",
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"扫宽Span": "500MHz"
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}}
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}}
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}}
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]
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}}
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```
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设备类别必须使用以下名称之一：
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- 程控电源参数
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- 功率探头参数
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- 频谱分析仪参数
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- 矢量网络分析仪参数
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- 矢量信号分析仪参数
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- 矢量信号源参数
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- 示波器参数
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- 信号源基础参数
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测试规划内容：
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{request.content}
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"""
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context = """你是一个专业的测试工程师，擅长从测试规划文档中提取结构化的测试步骤和仪器参数。
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请仔细分析文档，提取每个测试步骤及其涉及的仪器配置参数。
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只返回 JSON 格式的结果，不要添加任何解释或其他文字。"""
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response_text = await ai_service.generate(prompt=extract_prompt, context=context)
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# 解析 JSON 响应
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import json
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import re
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# 尝试从响应中提取 JSON
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json_match = re.search(r'```json\s*([\s\S]*?)\s*```', response_text)
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if json_match:
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json_str = json_match.group(1)
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else:
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# 尝试直接解析整个响应
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json_str = response_text.strip()
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# 清理可能的 markdown 代码块标记
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json_str = re.sub(r'^```\w*\n?', '', json_str)
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json_str = re.sub(r'\n?```$', '', json_str)
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try:
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parsed = json.loads(json_str)
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steps_data = parsed.get("steps", [])
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except json.JSONDecodeError:
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# 如果解析失败，返回空列表
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return ExtractStepsResponse(
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success=False,
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steps=[],
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error="无法解析 LLM 返回的 JSON 格式"
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)
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# 构建响应，过滤掉空值
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def filter_device_params(params: dict) -> dict:
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"""过滤掉空的设备参数"""
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if not params or not isinstance(params, dict):
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return {}
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filtered = {}
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for device_category, device_params in params.items():
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if not device_params or not isinstance(device_params, dict):
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continue
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# 过滤掉空值的参数
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non_empty_params = {
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k: v for k, v in device_params.items()
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if v and isinstance(v, str) and v.strip()
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}
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if non_empty_params:
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filtered[device_category] = non_empty_params
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return filtered
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steps = []
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for step in steps_data:
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filtered_params = filter_device_params(step.get("deviceParams", {}))
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steps.append(ExtractedStepData(
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id=step.get("id", len(steps) + 1),
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name=step.get("name", f"步骤 {len(steps) + 1}"),
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purpose=step.get("purpose", ""),
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deviceParams=filtered_params
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))
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return ExtractStepsResponse(
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success=True,
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steps=steps
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)
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except Exception as e:
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import traceback
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traceback.print_exc()
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return ExtractStepsResponse(
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success=False,
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steps=[],
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error=f"步骤提取失败: {str(e)}"
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)
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@router.get("/check-records/{intent_id}")
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async def get_check_records(
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intent_id: int,
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db: AsyncSession = Depends(get_db)
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):
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"""获取意图的所有检查记录"""
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query = select(AICheckRecord).where(
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AICheckRecord.intent_id == intent_id
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).order_by(AICheckRecord.checked_at.desc())
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result = await db.execute(query)
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records = result.scalars().all()
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return {
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"intent_id": intent_id,
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"total": len(records),
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"records": [
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{
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"id": r.id,
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"check_result": r.check_result,
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"suggestions": r.suggestions,
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"checked_at": r.checked_at.isoformat()
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}
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for r in records
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]
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}
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