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Generate and maintain the FastAPI backend structure for the RAG chatbot with proper project organization.

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SKILL.md

name FastAPI Scaffolding
description Generate and maintain the FastAPI backend structure for the RAG chatbot with proper project organization.

FastAPI Scaffolding

Instructions

  1. Create or update the backend folder structure with these directories:

    • app/
      • main.py (entry point with health endpoint)
      • routers/ (for API endpoints)
      • services/ (for business logic)
      • schemas/ (for Pydantic models)
      • deps/ (for dependency injection)
  2. Generate main.py with:

    • FastAPI app initialization
    • Health check endpoint at GET /health
    • CORS middleware configuration
    • Include routers setup
  3. Create a chat router placeholder in routers/chat.py

    • Include basic route registration
    • Setup for POST /chat endpoint
  4. Ensure proper Uvicorn configuration in requirements.txt and startup command

    • Include uvicorn, fastapi in dependencies
    • Provide proper startup command: uvicorn app.main:app --host 0.0.0.0 --port 7860
  5. Follow deterministic, clean Python code standards

    • Use proper type hints
    • Follow PEP 8 style guidelines
    • Include proper error handling

Examples

Input: "Create FastAPI backend structure" Output: Creates the complete directory structure with main.py containing:

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.routers import chat

app = FastAPI(title="Physical AI & Humanoid Robotics RAG API")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Configure appropriately for production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

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

app.include_router(chat.router, prefix="/api/v1")