159 lines
4.3 KiB
Python
159 lines
4.3 KiB
Python
"""
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FastAPI application for Red Mountain Intelligent Development Assistant
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"""
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import Optional, List
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import logging
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logger = logging.getLogger(__name__)
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# Create FastAPI app
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app = FastAPI(
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title="Red Mountain Dev Assistant API",
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description="智能开发助手 API - 提供智能问答、代码分析等功能",
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version="0.1.0"
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)
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# Configure CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # In production, replace with specific origins
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# ==================== Data Models ====================
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class ChatRequest(BaseModel):
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"""Chat request model"""
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message: str
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conversation_id: Optional[str] = None
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class ChatResponse(BaseModel):
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"""Chat response model"""
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response: str
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conversation_id: str
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class RepoAnalysisRequest(BaseModel):
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"""Repository analysis request model"""
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repo_url: str
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repo_type: str = "github" # github, gitlab, bitbucket
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access_token: Optional[str] = None
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included_dirs: Optional[List[str]] = None
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excluded_dirs: Optional[List[str]] = None
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class RepoAnalysisResponse(BaseModel):
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"""Repository analysis response model"""
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status: str
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message: str
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analysis_id: Optional[str] = None
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# ==================== API Endpoints ====================
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@app.get("/")
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async def root():
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"""Root endpoint"""
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return {
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"name": "Red Mountain Dev Assistant API",
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"version": "0.1.0",
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"status": "running"
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}
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@app.get("/health")
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async def health_check():
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"""Health check endpoint"""
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return {"status": "healthy"}
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@app.post("/api/chat", response_model=ChatResponse)
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async def chat(request: ChatRequest):
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"""
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Chat endpoint for intelligent Q&A
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智能问答接口
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"""
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try:
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# TODO: Implement actual chat logic with LLM
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# This is a placeholder response
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logger.info(f"Received chat message: {request.message}")
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response_text = (
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f"你好！我收到了你的消息：\"{request.message}\"\n\n"
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"这是一个占位响应。要启用真实的 AI 对话功能，请：\n"
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"1. 配置 .env 文件中的 API 密钥\n"
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"2. 实现 RAG 和 LLM 集成逻辑\n"
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"3. 参考 DeepWiki 的 rag.py 实现"
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)
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return ChatResponse(
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response=response_text,
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conversation_id=request.conversation_id or "demo-conversation-id"
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)
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except Exception as e:
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logger.error(f"Error in chat endpoint: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/repo/analyze", response_model=RepoAnalysisResponse)
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async def analyze_repository(request: RepoAnalysisRequest):
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"""
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Repository analysis endpoint
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代码仓库分析接口
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"""
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try:
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logger.info(f"Analyzing repository: {request.repo_url}")
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# TODO: Implement actual repository analysis logic
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# Reference DeepWiki's data_pipeline.py for implementation
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return RepoAnalysisResponse(
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status="pending",
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message="Repository analysis started. This feature is under development.",
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analysis_id="demo-analysis-id"
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)
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except Exception as e:
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logger.error(f"Error in repository analysis: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/repo/status/{analysis_id}")
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async def get_analysis_status(analysis_id: str):
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"""
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Get repository analysis status
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获取分析状态
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"""
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# TODO: Implement status tracking
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return {
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"analysis_id": analysis_id,
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"status": "processing",
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"progress": 0
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}
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# ==================== Quality Analysis Endpoints (Placeholder) ====================
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@app.post("/api/quality/analyze")
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async def analyze_quality(request: RepoAnalysisRequest):
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"""
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Code quality analysis endpoint (to be implemented)
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代码质量分析接口（待实现）
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"""
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return {
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"status": "not_implemented",
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"message": "Quality analysis feature is planned for future development"
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8001)
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