CodeMind AI

CodeMind AI is a local-first GitHub repository intelligence MVP. It clones a public repository, identifies semantic code sections, embeds them with sentence-transformers/all-MiniLM-L6-v2, persists them in FAISS, and answers questions with source citations.

Tech Stack

• Languages: HTML, Python, TypeScript, Dockerfile, JavaScript, CSS
• Frameworks & Libraries: Docker

Architecture

GitHub URL -> safe clone -> file scanner -> semantic parser/chunker
           -> embeddings -> persisted FAISS index -> retrieval
           -> grounded prompt -> Groq / OpenAI / Grok -> answer + citations

The backend uses small provider interfaces for embeddings and LLMs. FaissVectorStore, SentenceTransformerEmbeddings, and OpenAICompatibleProvider can be replaced later with Qdrant/Pinecone, another embedding model, or another model API without changing the API layer.

Run with Docker

1. Copy .env.example to .env and add GROQ_API_KEY, OPENAI_API_KEY, or XAI_API_KEY.
2. Run:

docker compose up --build

Open http://localhost:5173. The backend API and Swagger documentation are available at http://localhost:8000 and http://localhost:8000/docs.

An API key is optional. Without one, retrieval and citations still work, and CodeMind returns the most relevant code sections instead of a generated explanation.

To use Groq, set:

LLM_PROVIDER=groq
GROQ_API_KEY=your-groq-api-key
GROQ_MODEL=llama-3.3-70b-versatile

Run locally

Backend:

python -m venv .venv
.venv/Scripts/activate
pip install -r backend/requirements.txt
uvicorn backend.app:app --reload

Frontend:

cd frontend
npm install
npm run dev

The embedding model downloads on the first indexing request. Repository clones, indexes, model cache, and state are stored under backend/repositories, backend/indexes, and backend/data.

API

• POST /clone with {"repo_url":"https://github.com/owner/repository.git"}
• POST /index with {"repo_id":"..."}; omitting repo_id uses the active repository
• POST /ask with {"repo_id":"...","question":"Where is JWT implemented?","top_k":5}
• GET /health

Tests

pip install pytest
pytest backend/tests

The MVP deliberately leaves hybrid search, reranking, dependency graphs, visualizations, private repositories, and multi-repository retrieval as extension points rather than partially implementing them.

Deploy Live on AWS (Elastic Beanstalk)

CodeMind AI is deployed live on AWS using Elastic Beanstalk (Single Container Docker platform).

For a complete guide of the commands, optimizations, and troubleshooting steps applied during deployment, see AWS_DEPLOYMENT.md.

Quick Deploy Command

To deploy any local updates to your live Beanstalk environment:

git add .
git commit -m "Describe updates"
eb deploy

You can view logs using:

eb logs

And open your live app using:

eb open