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OnboardMeAI — Understand Repo with AI chat

Lightweight FastAPI service to ingest a GitHub repo and answer developer questions using RAG.

Quick start

  1. Create a virtualenv and install:
python -m venv .venv
source .venv/bin/activate   # or .\.venv\Scripts\activate on Windows
pip install -r requirements.txt
  1. Copy .env.example to .env and set GOOGLE_API_KEY or GEMINI_API_KEY.

    To get a free Gemini API key:

    • Sign in at https://aistudio.google.com/app/apikey
    • Create a new API key in Google AI Studio
    • Copy the key into your .env file as GOOGLE_API_KEY=...
    • If you are using the free-tier Gemini access, keep the key in local development only and avoid committing it to git
    • Optional: set GEMINI_MODEL=gemini-3.6-flash in .env if you want the recommended standard text-generation model
  2. Run the app:

uvicorn app.main:app --reload --port 8000

Usage

  • Ingest a repo:
curl -X POST "http://localhost:8000/ingest" -H "Content-Type: application/json" -d '{"repo_url":"https://github.com/owner/repo"}'
  • Query the ingested repo:
curl -X POST "http://localhost:8000/query" -H "Content-Type: application/json" -d '{"repo_url":"https://github.com/owner/repo", "question":"Where is the database configuration?"}'

Notes & next steps

  • FAISS and sentence-transformers may need platform-specific wheels on Windows. If faiss-cpu fails to install, consider using Linux or WSL.

Frontend

  1. Change into the frontend folder and install deps:
cd frontend
npm install
npm run dev
  1. Open http://localhost:5173 and use the simple UI to Ingest a repo and ask questions.

Git & GitHub

  1. Initialize git and push to your GitHub repo (replace URL):
git init
git add .
git commit -m "Initial commit"
git remote add origin git@github.com:youruser/yourrepo.git
git branch -M main
git push -u origin main
  1. Ensure you do not commit secrets like .env. .gitignore is present to help.

About

OnboardMeAI is an AI-powered developer onboarding assistant that ingests GitHub repositories and uses RAG to answer context-aware questions about large codebases. It helps developers quickly understand project architecture, code, and documentation, reducing onboarding time and accelerating meaningful contributions.

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