LangChain4j

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…

In this blog, you will take a closer look at how you can chat with your documents using LangChain4j and LocalAI. Besides that, you will learn some basics about prompt engineering. Enjoy! Introduction In a previous post, chat with documents using LangChain4j and LocalAI was briefly discussed. In this blog, you will take a closer look at the capabilities of this functionality. You will do so by means of two Wikipedia documents, which will serve as a source for the documents. You will use the discography and list of songs…