dazhuang
acd73431ae
Backend (FastAPI + SQLAlchemy + Claude API + RAG): - Config management with Pydantic v2 - Database engine with connection pooling and SQL injection prevention - AI engine with Claude API integration (support custom base URL) - RAG engine with ChromaDB and sentence-transformers - Requirement analysis service - Config generation service - Executor engine with SQL validation - REST API endpoints: /analyze, /generate, /execute Frontend (Vue 3 + Element Plus + Pinia): - Complete 3-step workflow: analyze → generate → execute - Step indicator with progress visualization - Analysis result display with field table - SQL preview with monospace font - Execute confirmation dialog with safety warning - Execution result display - State management with Pinia - API service integration Security: - SQL injection prevention with parameterized queries - Dangerous SQL operation blocking - Database password URL encoding - Transaction auto-rollback - Pydantic config validation Features: - Natural language requirement analysis - Automated SQL configuration generation - Safe execution with human review - LAN access support - Custom Claude API endpoint support Documentation: - README with quick start guide - Quick start guide - LAN access configuration - Dependency fixes guide - Claude API configuration - Git operation guide - Implementation report Dependencies fixed: - numpy<2.0.0 for chromadb compatibility - sentence-transformers==2.7.0 for huggingface_hub compatibility Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
"""Analyze API endpoint for requirement analysis.
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This module provides the /analyze endpoint for analyzing user requirements.
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"""
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import uuid
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from typing import Dict
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from fastapi import APIRouter, HTTPException, status
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from loguru import logger
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from app.models.request import AnalyzeRequest
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from app.models.response import AnalyzeResponse, ErrorResponse
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from app.services.requirement_service import RequirementService
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# Create router
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router = APIRouter()
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@router.post(
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"/analyze",
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response_model=AnalyzeResponse,
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responses={
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400: {"model": ErrorResponse, "description": "Invalid request"},
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500: {"model": ErrorResponse, "description": "Internal server error"}
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},
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summary="Analyze user requirement",
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description="Analyze natural language or structured requirement and return structured specification"
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)
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async def analyze_requirement(request: AnalyzeRequest) -> AnalyzeResponse:
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"""Analyze user requirement and return structured specification.
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This endpoint accepts either natural language or structured input,
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processes it through Claude AI with RAG knowledge retrieval, and
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returns a structured requirement specification.
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Args:
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request: AnalyzeRequest containing input_type, content, and optional session_id
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Returns:
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AnalyzeResponse with session_id, status, and structured data
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Raises:
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HTTPException: 400 for invalid input, 500 for processing errors
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"""
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# Generate session ID if not provided
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session_id = request.session_id or str(uuid.uuid4())
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try:
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# Validate input type
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if request.input_type not in ["natural_language", "structured"]:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail={
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"code": "INVALID_INPUT_TYPE",
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"message": "input_type must be 'natural_language' or 'structured'",
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"session_id": session_id
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}
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)
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# Validate content
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if not request.content or not request.content.strip():
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail={
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"code": "EMPTY_CONTENT",
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"message": "content cannot be empty",
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"session_id": session_id
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}
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)
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logger.info(f"[{session_id}] Processing analyze request: {request.content[:50]}...")
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# Create service and analyze
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service = RequirementService()
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result = await service.analyze(
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user_input=request.content,
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session_id=session_id
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)
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logger.success(f"[{session_id}] Analysis completed successfully")
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return AnalyzeResponse(
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session_id=session_id,
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status="success",
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data=result
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)
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except HTTPException:
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# Re-raise HTTP exceptions
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raise
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except ValueError as e:
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logger.error(f"[{session_id}] Validation error: {e}")
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail={
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"code": "VALIDATION_ERROR",
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"message": str(e),
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"session_id": session_id
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}
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)
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except Exception as e:
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logger.error(f"[{session_id}] Analysis failed: {e}")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail={
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"code": "ANALYSIS_FAILED",
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"message": f"Failed to analyze requirement: {str(e)}",
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"session_id": session_id
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}
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) |