Rag Saas

RAG SaaS

Ship RAG solutions quickly⚡

A end to end SaaS Solution for Retrieval-Augmented Generation (RAG)
and Agentic based applications.

Features · Tech Stack · Getting Started · Deployment · Roadmap

Tech Stack

• 🦙 LlamaIndex: For building and orchestrating RAG pipelines
• 📦 MongoDB: Used as both a normal database and a vector database
• ⚡ FastAPI: Backend API framework
• ⚛️ Next.js: Frontend framework
• 🔍 Qdrant: Vector database for efficient similarity search
• 👁️ Arize Phoenix: Observability Platform to monitor/evaluate your RAG system

🌟 Why RAG-SaaS?

Setting up reliable RAG systems can be time-consuming and complex. RAG-SaaS allows developers to focus on fine-tuning and developing their RAG pipeline rather than worrying about packaging it into a usable application. Built on top of create-llama by LlamaIndex, RAG-SaaS provides a solid foundation for your RAG-based projects.

🚀 Getting Started

1. Clone the repository:
   bash Copy

   git clone https://github.com/adithya-s-k/RAG-SaaS.git
   cd RAG-SaaS

🐳 Docker Compose Deployment

Environment Variables

AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=bucket_region
BUCKET_NAME=your_bucket_name

  backend:
    build:
      context: ./backend
      dockerfile: Dockerfile
    image: ragsaas/backend:latest
    container_name: backend
    ports:
      - '8000:8000'
    environment:
      # MongoDB Configuration
      MONGODB_NAME: RAGSAAS
      MONGODB_URI: mongodb://admin:password@mongodb:27017/
      # Qdrant Configuration
      QDRANT_COLLECTION: default
      QDRANT_URL: http://qdrant:6333
      # QDRANT_API_KEY:
      # OPENAI_API_KEY is compulsory
      OPENAI_API_KEY:
      # Backend Application Configuration
      MODEL_PROVIDER: openai
      MODEL: gpt-4o-mini
      EMBEDDING_MODEL: text-embedding-3-small
      EMBEDDING_DIM: 1536
      FILESERVER_URL_PREFIX: http://backend:8000/api/files
      SYSTEM_PROMPT: 'You are a helpful assistant who helps users with their questions.'
      APP_HOST: 0.0.0.0
      APP_PORT: 8000
      JWT_SECRET_KEY:
      JWT_REFRESH_SECRET_KEY:
      ARIZE_PHOENIX_ENDPOINT: http://arizephoenix:4317

For Docker Compose deployment, use:

docker compose up -d

Pull down the containers

docker compose down

Development Mode

To run the project in development mode, follow these steps:

1. Start the Next.js Frontend:

   Navigate to the frontend directory and install the required dependencies. Then, run the development server:

   bash Copy

   cd frontend
   npm install
   npm run dev

2. Set Up the Vector Database (Qdrant), Database (MongoDB), and Observability Platform (Arize Phoenix):

   You can either self-host these services using Docker or use hosted solutions.

   Self-Hosted Options:

   • Qdrant:

     bash Copy

     docker pull qdrant/qdrant

   • MongoDB:

     bash Copy

     docker pull mongo

   • Arize Phoenix:

     bash Copy

     docker pull arizephoenix/phoenix

   Hosted Options:

   • Qdrant Cloud: Qdrant Cloud
   • MongoDB Atlas: MongoDB Atlas
   • Arize Phoenix: Arize Phoenix

3. Start the FastAPI Server:

   Navigate to the backend directory and set up the Python environment. You can use either Conda or Python's built-in venv:

   bash Copy

   cd backend

   Using Conda:

   bash Copy

   conda create -n ragsaas-venv python=3.11
   conda activate ragsaas-venv

   Using Python's venv:

   bash Copy

   python -m venv ragsaas-venv
   \ragsaas-venv\Scripts\activate  # On Windows
   source ragsaas-venv/bin/activate  # On macOS/Linux

   Install the required dependencies and run the server:

   bash Copy

   pip install -e .
   python main.py

Roadmap

• add support to store ingested data in AWS S3
• Add Docker compose for each set up
• Implement Observability
• Improve authentication system
• Integrate OmniParse API for efficient Data ingestion
• Provide more control to Admin over RAG configuration
• Implement Advanced and Agentic RAG

👥 Contributing

We welcome contributions to RAG-SaaS! Please see our CONTRIBUTING.md for more details on how to get started.

📄 Licensing

This project is available under a dual license:

• Apache License 2.0 for students, developers, and individuals
• GNU General Public License v3.0 for companies and commercial use

See the LICENSING.md file for more details.

🙏 Acknowledgements

This project is built on the following frameworks, technologies and tools:

• LlamaIndex for the create-llama tool and RAG orchestration
• FastAPI
• Next.js
• MongoDB
• Qdrant
• Arize Phoenix

Contact & Support

Bug Reports

If you encounter any issues or bugs, please report them in the Issues tab of our GitHub repository.

Commercial Use & Custom Solutions

For inquiries regarding:

• Commercial licensing
• Custom modifications
• Managed deployment
• Specialized integrations

Please contact: [email protected]

We're here to help tailor RAG-SaaS to your specific needs and ensure you get the most out of our solution.

Star History

🚀 Quick Start

git clone https://github.com/adithya-s-k/RAG-SaaS.git
   cd RAG-SaaS