https://github.com/redis-developer/redis-rag-workbench
Interactive RAG workbench to demonstrate Redis features and enhancements for improving accuracy, speed, cost, and reliability of LLM applications.
https://github.com/redis-developer/redis-rag-workbench
Last synced: 29 days ago
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Interactive RAG workbench to demonstrate Redis features and enhancements for improving accuracy, speed, cost, and reliability of LLM applications.
- Host: GitHub
- URL: https://github.com/redis-developer/redis-rag-workbench
- Owner: redis-developer
- License: mit
- Created: 2024-06-20T20:34:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-06T13:25:34.000Z (about 1 month ago)
- Last Synced: 2025-10-06T15:23:35.342Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 1.47 MB
- Stars: 23
- Watchers: 4
- Forks: 11
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- redis-ai-resources - Redis RAG Workbench - based chatbot over a user-uploaded PDF. Toggle different settings and configurations to improve chatbot performance and quality. Utilizes RedisVL, LangChain, RAGAs, and more. | (Demos)
README
🚀 Redis RAG Workbench
[](https://opensource.org/licenses/MIT)
**🎯 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
---
## Table of Contents
- [Quick Start](#quick-start)
- [Prerequisites](#prerequisites)
- [Getting Started](#getting-started)
- [Available Commands](#available-commands)
- [Development Workflows](#development-workflows)
- [Environment Configuration](#environment-configuration)
- [Using Google VertexAI](#using-google-vertexai)
- [Project Structure](#project-structure)
- [Connecting to Redis Cloud](#connecting-to-redis-cloud)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [License](#license)
- [Learn More](#learn-more)
## Quick Start
**Get up and running in 3 commands:**
```bash
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! 🎉
---
## Prerequisites
1. Make sure you have the following tools available:
- [Docker](https://www.docker.com/products/docker-desktop/)
- [uv](https://docs.astral.sh/uv/)
- [make](https://www.make.com/en)
2. Setup one or more of the following:
- [OpenAI API](https://platform.openai.com/)
- You will need an API Key
- [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service)
- You will need an API Key
- [Google VertexAI](https://cloud.google.com/vertex-ai?hl=en)
- [Using Google VertexAI](#using-google-vertexai)
3. Get a [Cohere API key](https://cohere.com/) (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:**
```bash
make setup # One-time setup
# Edit .env with your API keys
make dev # Start development server
```
**Docker Development:**
```bash
make setup # One-time setup
# Edit .env with your API keys
make docker # Build and start containers
make docker-logs # View logs
```
**Docker Management:**
```bash
make docker-up # Start existing containers
make docker-down # Stop all services
make docker-logs # Follow application logs
```
**Code Quality:**
```bash
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:
```bash
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](https://cloud.google.com/vertex-ai/docs/start/cloud-environment) with the VertexAI API enabled.
2. Install the [gcloud CLI](https://cloud.google.com/sdk/docs/install)
3. Follow the instructions to run the [`gcloud auth application-default login`](https://cloud.google.com/docs/authentication/application-default-credentials#personal) 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](https://redis.io/try-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:#####`)
1. Your `username` (`default` is the default username, otherwise find the one you setup)
1. 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:
```bash
REDIS_URL="redis://default:@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):
```dockerfile
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](https://redis.io/docs/latest/) - learn about Redis products, features, and commands.
- [Learn Redis](https://redis.io/learn/) - read tutorials, quick starts, and how-to guides for Redis.
- [Redis Demo Center](https://redis.io/demo-center/) - watch short, technical videos about Redis products and features.