SLA-RedM/api/api.py

"""
FastAPI application for Red Mountain Intelligent Development Assistant
"""
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional, List
import logging

logger = logging.getLogger(__name__)

# Create FastAPI app
app = FastAPI(
    title="Red Mountain Dev Assistant API",
    description="智能开发助手 API - 提供智能问答、代码分析等功能",
    version="0.1.0"
)

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, replace with specific origins
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# ==================== Data Models ====================

class ChatRequest(BaseModel):
    """Chat request model"""
    message: str
    conversation_id: Optional[str] = None


class ChatResponse(BaseModel):
    """Chat response model"""
    response: str
    conversation_id: str


class RepoAnalysisRequest(BaseModel):
    """Repository analysis request model"""
    repo_url: str
    repo_type: str = "github"  # github, gitlab, bitbucket
    access_token: Optional[str] = None
    included_dirs: Optional[List[str]] = None
    excluded_dirs: Optional[List[str]] = None


class RepoAnalysisResponse(BaseModel):
    """Repository analysis response model"""
    status: str
    message: str
    analysis_id: Optional[str] = None


# ==================== API Endpoints ====================

@app.get("/")
async def root():
    """Root endpoint"""
    return {
        "name": "Red Mountain Dev Assistant API",
        "version": "0.1.0",
        "status": "running"
    }


@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy"}


@app.post("/api/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    """
    Chat endpoint for intelligent Q&A
    智能问答接口
    """
    try:
        # TODO: Implement actual chat logic with LLM
        # This is a placeholder response
        logger.info(f"Received chat message: {request.message}")
        
        response_text = (
            f"你好！我收到了你的消息：\"{request.message}\"\n\n"
            "这是一个占位响应。要启用真实的 AI 对话功能，请：\n"
            "1. 配置 .env 文件中的 API 密钥\n"
            "2. 实现 RAG 和 LLM 集成逻辑\n"
            "3. 参考 DeepWiki 的 rag.py 实现"
        )
        
        return ChatResponse(
            response=response_text,
            conversation_id=request.conversation_id or "demo-conversation-id"
        )
    except Exception as e:
        logger.error(f"Error in chat endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/repo/analyze", response_model=RepoAnalysisResponse)
async def analyze_repository(request: RepoAnalysisRequest):
    """
    Repository analysis endpoint
    代码仓库分析接口
    """
    try:
        logger.info(f"Analyzing repository: {request.repo_url}")
        
        # TODO: Implement actual repository analysis logic
        # Reference DeepWiki's data_pipeline.py for implementation
        
        return RepoAnalysisResponse(
            status="pending",
            message="Repository analysis started. This feature is under development.",
            analysis_id="demo-analysis-id"
        )
    except Exception as e:
        logger.error(f"Error in repository analysis: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/api/repo/status/{analysis_id}")
async def get_analysis_status(analysis_id: str):
    """
    Get repository analysis status
    获取分析状态
    """
    # TODO: Implement status tracking
    return {
        "analysis_id": analysis_id,
        "status": "processing",
        "progress": 0
    }


# ==================== Quality Analysis Endpoints (Placeholder) ====================

@app.post("/api/quality/analyze")
async def analyze_quality(request: RepoAnalysisRequest):
    """
    Code quality analysis endpoint (to be implemented)
    代码质量分析接口（待实现）
    """
    return {
        "status": "not_implemented",
        "message": "Quality analysis feature is planned for future development"
    }


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)