Easy RAG: the easiest way to get started with Retrieval Augmented Generation

The Quarkus LangChain4j project provides a separate extension named quarkus-langchain4j-easy-rag which provides an extremely easy way to get a RAG pipeline up and running. After adding this extension to your application, all that is needed to ingest documents into an embedding store is to add a dependency that provides an embedding model, and provide a single configuration property, quarkus.langchain4j.easy-rag.path, which denotes a path in the local filesystem where your documents are stored. On startup, Quarkus will automatically scan all files in the directory and ingest them into an in-memory embedding store.

Apache Tika, a library for parsing various file formats, is used under the hood, so your documents can be in any of its supported formats (plain text, PDF, DOCX, HTML, etc), including images with text, which will be parsed using OCR (OCR requires to have the Tesseract library installed in your system - see https://cwiki.apache.org/confluence/display/TIKA/TikaOCR).

You also don’t even need to add a persistent embedding store - the quarkus-langchain4j-easy-rag extension will automatically register a simple in-memory store for you if no other store is detected. You also don’t have to provide an implementation of RetrievalAugmentor, a basic default instance will be generated for you and wired to the in-memory store.

You have to add an extension that provides an embedding model. For that, you can choose from the plethora of extensions like quarkus-langchain4j-openai, quarkus-langchain4j-ollama, or import an in-process embedding model - these have the advantage of not having to send data over the wire.