RAG Engine SaaS (Gemini)

Production-oriented RAG project:

• Backend (FastAPI + SQLite): upload files, chunk + index, semantic retrieval (Gemini embeddings when available), chat Q&A with citations
• Frontend (React + Mantine): session-based workspace, drag/drop upload, chat, and source cards

UI Preview

Upload + library:

Chat + citations:

Architecture

• Ingestion: extract -> chunk -> (optional) embed -> store
• Retrieval: cosine similarity over stored embeddings; BM25 lexical fallback if embeddings unavailable
• Generation: Gemini gets question + top-k sources and returns a grounded answer

Quick Start

1) Backend

cd backend
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
# set GEMINI_API_KEY
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

2) Frontend

cd frontend
npm install
npm run dev

Then open the frontend dev URL and keep backend URL as http://localhost:8000.

Docs

• Backend details: backend/README.md
• Frontend details: frontend/README.md
• Development: docs/development.md
• API: docs/api.md
• Architecture: docs/architecture.md
• Troubleshooting: docs/troubleshooting.md
• Roadmap: docs/roadmap.md

Contributing / Security

• Contributing: CONTRIBUTING.md
• Code of Conduct: CODE_OF_CONDUCT.md
• Security: SECURITY.md