Using Redis VSS as a Retrieval Step in an LLM Chain

This tutorial demonstrates how to create a chain using Relevance AI, Redis VSS, OpenAI GPT, and Cohere Wikipedia embeddings. The purpose is to enable users to ask questions of Wikipedia by utilizing Redis vector search to extract the best article based on their question. To follow along, you need a Redis database that supports JSON document data structures and built-in real-time Search and Query features. After setting up the necessary environment, the tutorial guides users through importing data from Cohere's multilingual Wikipedia embeddings dataset, ingesting each document into Redis using JSON, creating a vector search index in Redis, configuring an OpenAI API key and the Redis connection string, and building a chain with Relevance AI. The final result is a powerful tool that can be deployed as an embeddable application or as an API endpoint, allowing users to query vast swathes of information at lightning speed.

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