🚀 Redis RAG Workbench

🎯 The ultimate RAG development playground powered by Redis

🔥 Redis RAG Workbench is your go-to development environment for building and experimenting with Retrieval-Augmented Generation (RAG) applications. Drop in a PDF, chat with your documents, and harness the full power of Redis for lightning-fast vector search, intelligent semantic caching, persistent LLM memory, and smart semantic routing.

✨ What makes this special?

• 🚀 One-command setup - Get started in seconds with make setup
• ⚡ Multi-LLM support - OpenAI, Azure OpenAI, Google VertexAI
• 🎯 Redis-powered - Vector search, caching, and memory management
• 🐳 Docker ready - Consistent development across all environments
• 🔧 Developer-first - Hot reload, code formatting, and quality checks built-in

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Table of Contents

• Quick Start
• Prerequisites
• Getting Started
  • Available Commands
  • Development Workflows
  • Environment Configuration
• Using Google VertexAI
• Project Structure
• Connecting to Redis Cloud
• Troubleshooting
• Contributing
• License
• Learn More

Quick Start

Get up and running in 3 commands:

git clone https://github.com/redis-developer/redis-rag-workbench.git
cd redis-rag-workbench
make setup && make dev

Then visit http://localhost:8000 and start chatting with your PDFs! 🎉

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Prerequisites

1. Make sure you have the following tools available:
   • Docker
   • uv
   • make
2. Setup one or more of the following:
   • OpenAI API
     • You will need an API Key
   • Azure OpenAI
     • You will need an API Key
   • Google VertexAI
     • Using Google VertexAI
3. Get a Cohere API key (for optional reranking features)

Getting Started

🌐 Access the workbench at http://localhost:8000 in your web browser.

⏱️ First run may take a few minutes to download model weights from Hugging Face.

Available Commands

Command	Description
make setup	Initial project setup (install deps & create .env)
make install	Install/sync dependencies
make dev	Start development server with hot reload
make serve	Start production server
make format	Format and lint code
make check	Run code quality checks without fixing
make docker	Rebuild and run Docker containers
make docker-up	Start Docker services (without rebuild)
make docker-logs	View Docker application logs
make docker-down	Stop Docker services
make clean	Clean build artifacts and caches

Development Workflows

Local Development:

make setup           # One-time setup
# Edit .env with your API keys
make dev             # Start development server

Docker Development:

make setup           # One-time setup  
# Edit .env with your API keys
make docker          # Build and start containers
make docker-logs     # View logs

Docker Management:

make docker-up       # Start existing containers
make docker-down     # Stop all services
make docker-logs     # Follow application logs

Code Quality:

make format          # Auto-fix formatting issues
make check           # Check code quality without changes

Environment Configuration

The project uses a single .env file for configuration. Copy from the example:

cp .env-example .env

Required variables:

• REDIS_URL - Redis connection (auto-configured for Docker)
• COHERE_API_KEY - For reranking features

At least one LLM provider:

• OPENAI_API_KEY - OpenAI API access
• AZURE_OPENAI_* - Azure OpenAI configuration
• GOOGLE_APPLICATION_CREDENTIALS - Google VertexAI credentials

Using Google VertexAI

The workbench can be used with VertexAI, but requires you to set up your credentials using the gcloud CLI. The easiest way to do this is as follows:

1. Make sure you have a gcloud project setup with the VertexAI API enabled.
2. Install the gcloud CLI
3. Follow the instructions to run the gcloud auth application-default login command
4. Copy the JSON from the generated application_default_credentials.json into your .env file using the GOOGLE_APPLICATION_CREDENTIALS variable
5. Set the GOOGLE_CLOUD_PROJECT_ID environment variable in your .env file to the associated gcloud project you want to use.

Project Structure

• main.py: The entry point of the application
• demos/: Contains workbench demo implementation
• shared_components/: Reusable utilities and components
• static/: Static assets for the web interface

Contributing

🤝 Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Connecting to Redis Cloud

If you don't yet have a database setup in Redis Cloud get started here for free.

To connect to a Redis Cloud database, log into the console and find the following:

1. The public endpoint (looks like redis-#####.c###.us-east-1-#.ec2.redns.redis-cloud.com:#####)
2. Your username (default is the default username, otherwise find the one you setup)
3. Your password (either setup through Data Access Control, or available in the Security section of the database page.

Combine the above values into a connection string and put it in your .env file. It should look something like the following:

REDIS_URL="redis://default:<password>@redis-#####.c###.us-west-2-#.ec2.redns.redis-cloud.com:#####"

📝 Note: When using Docker, the Redis URL is automatically configured to use the internal Docker network. Your .env file can contain either a local Redis URL (redis://localhost:6379) or a Redis Cloud URL - both will work with Docker.

Troubleshooting

Apple Silicon (M1+)

If you find that docker will not work, it's possible you need to add the following line in the docker/Dockerfile (commented out in the Dockerfile for ease-of-use):

RUN apt-get update && apt-get install -y build-essential

Learn More

To learn more about Redis, take a look at the following resources:

• Redis Documentation - learn about Redis products, features, and commands.
• Learn Redis - read tutorials, quick starts, and how-to guides for Redis.
• Redis Demo Center - watch short, technical videos about Redis products and features.