Open Source Ecosystems

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

Clone the repository:

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

🐳 Docker Compose Deployment

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)

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.
OpenAI Configuration:
- OPENAI_API_KEY: (Compulsory) Your OpenAI API key.
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).
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.
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.
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.
Observability:
- ARIZE_PHOENIX_ENDPOINT: (Optional) Endpoint for Arize Phoenix observability.

S3 Integration

To enable S3 integration for PDF uploads/Ingestion:

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:

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
```
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

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: [email protected]

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

RAG-SaaS

Tech Stack

🌟 Why RAG-SaaS?

🚀 Getting Started

🐳 Docker Compose Deployment

Environment Variables

Environment Variables

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

Backend Environment (./backend/.env)

S3 Integration

Docker Compose Env (./env)

Development Mode

Roadmap

👥 Contributing

📄 Licensing

🙏 Acknowledgements

Contact & Support

Bug Reports

Commercial Use & Custom Solutions

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