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: 21 days ago
JSON representation
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 (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-24T15:35:57.000Z (2 months ago)
- Last Synced: 2025-04-24T16:48:31.632Z (2 months ago)
- Language: Python
- Homepage:
- Size: 1.31 MB
- Stars: 11
- Watchers: 4
- Forks: 9
- Open Issues: 1
-
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
RAG workbench
[](https://opensource.org/licenses/MIT)
🛠️ **Redis RAG workbench** is a development playground for exploring Retrieval-Augmented Generation (RAG) techniques with Redis. Upload a PDF and begin building a RAG app to chat with the document, taking full advantage of Redis features like **vector search**, **semantic caching**, **LLM memory**, and **semantic routing**.
- [Prerequisites](#prerequisites)
- [Getting started](#getting-started)
- [Run in docker](#run-in-docker)
- [Run locally outside docker](#run-locally-outside-docker)
- [Other useful scripts](#other-useful-scripts)
- [Using Google VertexAI](#using-google-vertexai)
- [Project Structure](#project-structure)
- [Contributing](#contributing)
- [License](#license)
- [Connecting to Redis Cloud](#connecting-to-redis-cloud)
- [Troubleshooting](#troubleshooting)
- [Apple Silicon (M1+)](#apple-silicon-m1)
- [Learn more](#learn-more)
## 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
Clone the repository:
```bash
git clone https://github.com/redis-developer/redis-rag-workbench.git
cd redis-rag-workbench
```
Copy and edit the `.env` file:
```bash
cp .env.example .env
```
Your `.env` file should contain your Redis connection string as well as necessary API keys based on which platform(s) you want to use.
Your `.env.docker` file will look similar to `.env`, but should use the appropriate docker internal URLs. Here is
an example:
```bash
REDIS_URL="redis://redis:6379"
```
### Run in docker
To spin up docker containers:
```bash
make docker
```
> This will start the server, and you can access the workbench by navigating to `http://localhost:8000` in your web browser.
> The first time the application runs, it will have to download model weights from huggingface and may take a few minutes.
### Run locally outside docker
To run the server outside of docker you need to first create a `.venv`:
```bash
make install && source .venv/bin/activate
```
From then on, you can run a dev server:
```bash
make dev
```
Or a production server:
```bash
make serve
```
### Other useful scripts
Get help for all `make` commands:
```bash
make
```
Formatting code:
```bash
make format
```
Cleaning up files:
```bash
make clean
```
## 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` and `.env.docker` accordingly. It should
look something like the following:
```bash
REDIS_URL="redis://default:@redis-#####.c###.us-west-2-#.ec2.redns.redis-cloud.com:#####"
```
## Troubleshooting
### Apple Silicon (M1+)
If you find that `docker` will not work, it's possible you need to add the following line in the 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.