A local, privacy-first knowledge base with semantic search and question answering using completely free and open-source AI tools.
Memora solves the problem of building a personal knowledge base with AI-powered search and question answering capabilities, all running locally on your machine. No cloud services, no API keys, no data leaving your computer.
Who it's for: Researchers, students, professionals, and anyone who needs to organize, search, and query their documents with AI assistance while maintaining complete privacy.
- Frontend: Next.js 14, React, TypeScript, Tailwind CSS
- Backend: FastAPI, Python 3.12
- Database: MongoDB
- AI/ML: sentence-transformers, FAISS, llama-cpp-python
- LLM: Mistral 7B (GGUF format)
- File Processing: PyMuPDF
- Deployment: Docker, Docker Compose
- Document Upload: Support for PDFs and text files
- Semantic Search: Powered by sentence-transformers for intelligent document retrieval
- AI Question Answering: Local Mistral 7B model for natural language responses
- Privacy First: Everything runs locally - your data never leaves your machine
- Modern UI: Clean interface with dark/light mode support
- Vector Search: FAISS for lightning-fast similarity search
- No External APIs: Completely free and open-source tools
- Python 3.8+
- Node.js 18+
- MongoDB 5.0+
- 8GB+ RAM (recommended)
-
Clone the repository
git clone https://github.com/marvellousz/memora.git cd memora -
Run automated setup
# Linux/macOS ./setup.sh # Windows setup.bat
-
Manual setup (alternative)
# Backend cd backend python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate pip install -r requirements.txt # Frontend cd frontend npm install # Environment files cat > backend/.env << 'EOF' MONGODB_URL=mongodb://localhost:27017 DATABASE_NAME=memora_kb LLM_MODEL_PATH=models/mistral-7b-instruct-v0.1.Q4_K_M.gguf EMBEDDING_MODEL=all-MiniLM-L6-v2 LOG_LEVEL=INFO FAISS_INDEX_PATH=faiss_index.pkl EOF echo "NEXT_PUBLIC_API_URL=http://localhost:8000" > frontend/.env.local
-
Download LLM model (optional)
cd backend/models curl -L -o mistral-7b-instruct-v0.1.Q4_K_M.gguf \ "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf"
# Method 1: Start scripts
./start_app.sh # Linux/macOS
start_app.bat # Windows
# Method 2: Manual (separate terminals)
# Terminal 1 - Backend
cd backend && source venv/bin/activate
python -m uvicorn app.main:app --reload --port 8000
# Terminal 2 - Frontend
cd frontend && npm run dev- Upload Documents: Drag & drop PDFs or enter text manually
- Generate Embeddings: Click "Generate All Embeddings" in System Status
- Ask Questions: Use the "Ask Questions" tab for AI-powered answers
- Manage Documents: View and delete documents in "View Documents"
- Main App: http://localhost:3000
- API Documentation: http://localhost:8000/docs
docker-compose up -d- Build the application
# Backend cd backend source venv/bin/activate python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 # Frontend cd frontend npm run build npm start
Backend (.env):
MONGODB_URL=mongodb://localhost:27017
DATABASE_NAME=memora_kb
LLM_MODEL_PATH=models/mistral-7b-instruct-v0.1.Q4_K_M.gguf
EMBEDDING_MODEL=all-MiniLM-L6-v2
LOG_LEVEL=INFO
FAISS_INDEX_PATH=faiss_index.pklFrontend (.env.local):
NEXT_PUBLIC_API_URL=http://localhost:8000Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Email: pranavmurali024@gmail.com
- GitHub: https://github.com/marvellousz/memora
Built for local, private AI knowledge management