In my latest post, I delve into seamlessly integrating Retrieval-Augmented Generation (RAG) with Java code using Langchain4j. Drawing inspiration from RAG over code, I explore Java Parser's potential for robust codebase analysis. The pivotal JavaParsingService and EmbeddingStoreService orchestrate this integration, enabling users to effortlessly load Java projects and glean profound insights. The enhanced controller boasts user-friendly endpoints, fostering dynamic interactions. Witness Retrieval-Augmented Generation breathe life into Java code, from codebase ingestion to insightful querying with models like gpt4all-j, WizardLM, and OpenAI. This narrative unveils the nuanced capabilities of RAG in querying Java codebases.

I embarked on a journey to harness the capabilities of Langchain4j, crafting a powerful AI application in Java using the local language model. Utilizing Spring Boot, Postman, and various Langchain4j components, I explored setting up, implementing a chat service, integrating custom tools, embedding functionality with Chroma, translation, persistence, retrieval, and streaming services. The blog post serves as a comprehensive guide for building personalized AI applications, showcasing the versatility and potential of Langchain4j in Java development.

In this post, I extend the use case from my previous post to demonstrate building a ChatBot for PDF files using LangChain. In the preparation phase, I install Chroma, an open-source embedding database, and ingest a PDF file using PyPDFLoader. I then split the document into chunks and use Chroma's default embeddings. Due to a potential issue, I provide an alternative embedding approach. Next, I load a local LLaMA model, prepare for question-answering, and run queries using RetrievalQAWithSourcesChain. I also touch on running with OpenBLAS for optimization. The guide empowers users to explore personalized question-answering over their PDF documents.