Redis plays a crucial role in the LLM & GenAI wave with it's ability to store, retrieve, and search with vector spaces in a low-latency, high-availability setting. With its heritage in enterprise caching, Redis has both the developer community and enterprise-readiness required to deploy quality AI-enabled applications in this demanding marketplace.
OpenAI is shaping the future of next-gen apps through it's release of powerful natural language and computer vision models that are used in a variety of downstream tasks.
This example Streamlit app gives you the tools to get up and running with Redis as a vector database and OpenAI as a LLM provider for embedding creation and text generation. The combination of the two is where the magic lies.
Create your env file:
$ cp .env.template .envfill out values, most importantly, your OPENAI_API_KEY.
Run with docker compose:
$ docker compose upadd -d option to daemonize the processes to the background if you wish.
Navigate to:
http://localhost:8080/
The first time you run the app -- all documents will be downloaded, processed, and stored in Redis. This will take a few minutes to spin up initially. From that point forward, the app should be quicker to load.
Ask the app anything about the 2020 Summer Olympics...