In this blog, you will learn how to implement Retrieval Augmented Generation (RAG) using Weaviate, LangChain4j, and LocalAI. This implementation allows you to ask questions about your documents using natural language. Enjoy!
1. Introduction
In the previous post, Weaviate was used as a vector database in order to perform a semantic search. The source documents used are two Wikipedia documents. The discography and list of songs recorded by Bruce Springsteen are the documents used. The interesting part of these documents is that they contain facts and are mainly in a table format. Parts of these documents are converted to Markdown in order to have a better representation. The Markdown files are embedded in Collections in Weaviate. The result was amazing: all questions asked, resulted in the correct answer to the question. That is, the correct segment was returned. You still needed to extract the answer yourself, but this was quite easy.