Build a RAG-based AI assistant with Quarkus and LangChain

#quarkus #langchain #ai #cloudnative

This tutorial was originally published on IBM Developer.

Enterprise Java developers are familiar with building robust, scalable applications using frameworks like Spring Boot. However, integrating AI capabilities into these applications often involves complex orchestration, high memory usage, and slow startup times.

In this tutorial, you'll discover how Quarkus combined with LangChain4j provides a seamless way to build AI-powered applications that start in milliseconds and consume minimal resources. Quarkus is a Kubernetes-native Java stack tailored for GraalVM and OpenJDK HotSpot. Quarkus offers incredibly fast boot times, low RSS memory consumption, and a fantastic developer experience with features like live coding.

In this tutorial, you'll build a smart document assistant that can ingest PDF documents, create embeddings, and answer questions about the content using retrieval augmented generation (RAG). The application will demonstrate enterprise-grade features like dependency injection, health checks, metrics, and hot reload development, all while integrating cutting-edge AI capabilities.

RAG and why you need it

The retrieval augmented generation (RAG) pattern is a way to extend the knowledge of an LLM used in your applications. While models are pre-trained on large data sets, they have static and general knowledge with a specific knowledge cut-off date. They don't "know" anything beyond that date and also do not contain any company or domain specific data you might want to use in your applications. The RAG pattern allows you to infuse knowledge via the context window of your models and bridges this gab.

There's some steps that need to happen. First, the domain specific knowledge from documents (txt, PDF, and so on) is parsed and tokenized with a so called embedding generation model. The generated vectors are stored in a vector database. When a user queries the LLM, the vector store is searched with a similarity algorithm and relevant content is added as context to the user prompt before it is passed on to the LLM. The LLM then generates the answer based on the foundational knowledge and the additional context which was augumented from the vector search. A high level overview is shown in the following figure.