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

Features	Demo Video
🔐 Basic Authentication 💬 Chat History Tracking 🧠 Multiple RAG Variations Basic RAG Two additional configurations 👨‍💼 Admin Dashboard 📥 Data Ingestion 📊 Monitoring 👁️ Observability 🔄 RAG Configuration Switching 🗄️ S3 Integration for PDF uploads 🐳 Easy Deployment with Docker / Docker Compose

RAG.SAAS.Fast.mp4

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:

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

🐳 Docker Compose Deployment

Environment Variables

🔑 How to Set up .env

Environment Variables

To properly configure and run RAG-SaaS, you need to set up several environment variables. These are divided into three main sections: Frontend, Backend, and Docker Compose. Here's a detailed explanation of each:

Frontend Environment (./frontend/.env.local)

• NEXT_PUBLIC_SERVER_URL: (Compulsory) The endpoint URL of your FastAPI server.
• NEXT_PUBLIC_CHAT_API: (Compulsory) Derived from NEXT_SERVER_URL, typically set to ${NEXT_PUBLIC_SERVER_URL}/api/chat.

Backend Environment (./backend/.env)

1. Model Configuration:

   • MODEL_PROVIDER: (Compulsory) The AI model provider (e.g., 'openai').
   • MODEL: (Compulsory) The name of the LLM model to use.
   • EMBEDDING_MODEL: (Compulsory) The name of the embedding model.
   • EMBEDDING_DIM: (Compulsory) The dimensionality of the embedding model.

2. OpenAI Configuration:

   • OPENAI_API_KEY: (Compulsory) Your OpenAI API key.

3. Application Settings:

   • CONVERSATION_STARTERS: (Compulsory) A list of starter questions for users.
   • SYSTEM_PROMPT: (Compulsory) The system prompt for the AI model.
   • SYSTEM_CITATION_PROMPT: (Optional) Additional prompt for citation.
   • APP_HOST: (Compulsory) The host address for the backend (default: '0.0.0.0').
   • APP_PORT: (Compulsory) The port for the backend (default: 8000).

4. Database Configuration:

   • MONGODB_URI: (Compulsory) The MongoDB connection URI.
   • MONGODB_NAME: (Compulsory) The MongoDB database name (default: 'RAGSAAS').
   • QDRANT_URL: (Compulsory) The URL for the Qdrant server.
   • QDRANT_COLLECTION: (Compulsory) The Qdrant collection name.
   • QDRANT_API_KEY: (Optional) API key for Qdrant authentication.

5. Authentication:

   • JWT_SECRET_KEY: (Compulsory) Secret key for signing JWT tokens.
   • JWT_REFRESH_SECRET_KEY: (Compulsory) Secret key for signing JWT refresh tokens.
   • ADMIN_EMAIL: (Compulsory) Administrator email for application login.
   • ADMIN_PASSWORD: (Compulsory) Administrator password for application login.

6. AWS S3 Configuration (Optional):

   • AWS_ACCESS_KEY_ID: AWS Access Key ID.
   • AWS_SECRET_ACCESS_KEY: AWS Secret Access Key.
   • AWS_REGION: AWS Region for your services.
   • BUCKET_NAME: The name of the S3 bucket to use.

7. Observability:

   • ARIZE_PHOENIX_ENDPOINT: (Optional) Endpoint for Arize Phoenix observability.

S3 Integration

To enable S3 integration for PDF uploads/Ingestion:

1. Set the following environment variables in your .env file:

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

Docker Compose Env (./env)

  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:

   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:

     docker pull qdrant/qdrant

   • MongoDB:

     docker pull mongo

   • Arize Phoenix:

     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:

   cd backend

   Using Conda:

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

   Using Python's venv:

   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:

   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: adithyaskolavi@gmail.com

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

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