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 ·
| Entity Passport | |
| Registry ID | gh-tool--adithya-s-k--rag-saas |
| Provider | github |
Cite this tool
Academic & Research Attribution
@misc{gh_tool__adithya_s_k__rag_saas,
author = {Adithya S K},
title = {Rag Saas Tool},
year = {2026},
howpublished = {\url{https://github.com/adithya-s-k/RAG-SaaS}},
note = {Accessed via Free2AITools Knowledge Fortress}
} 🔬Technical Deep Dive
Full Specifications [+]▾
Quick Commands
git clone https://github.com/adithya-s-k/RAG-SaaS npm install rag-saas ⚖️ Nexus Index V16.5
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📋 Specs
- Language
- TypeScript
- License
- Open Source
- Version
- 1.0.0
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🔗 View Source Code ↗Technical Documentation
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 |
|---|---|
|
|
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:
bash
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)
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
.envfile:
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
frontenddirectory and install the required dependencies. Then, run the development server:bashcd frontend npm install npm run devSet 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:
bashdocker pull qdrant/qdrantMongoDB:
bashdocker pull mongoArize Phoenix:
bashdocker pull arizephoenix/phoenix
Hosted Options:
- Qdrant Cloud: Qdrant Cloud
- MongoDB Atlas: MongoDB Atlas
- Arize Phoenix: Arize Phoenix
Start the FastAPI Server:
Navigate to the
backenddirectory and set up the Python environment. You can use either Conda or Python's built-invenv:bashcd backendUsing Conda:
bashconda create -n ragsaas-venv python=3.11 conda activate ragsaas-venvUsing Python's
venv:bashpython -m venv ragsaas-venv \ragsaas-venv\Scripts\activate # On Windows source ragsaas-venv/bin/activate # On macOS/LinuxInstall the required dependencies and run the server:
bashpip 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.
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🚀 Quick Start
git clone https://github.com/adithya-s-k/RAG-SaaS.git
cd RAG-SaaS
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🆔 Identity & Source
- id
- gh-tool--adithya-s-k--rag-saas
- source
- github
- author
- Adithya S K
- tags
- ai-saasarize-phoenixllamaindexmongodbqdrantragsaassaas-boilerplatetypescript
⚙️ Technical Specs
- architecture
- null
- params billions
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- pipeline tag
- feature-extraction
📊 Engagement & Metrics
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- github stars
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