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Series
In this series I am going to explore LangChain4J for building AI applications using Java/SpringBoot.
In this blog post, we'll walk through creating your first LangChain4j application using Spring Boot. We'll build a simple interactive command-line application that demonstrates the basic setup and usage of LangChain4j in a Java environment. What is L...
In our previous post, we built a simple command-line chat with LangChain4j. It worked, but there was one big limitation: the model had no memory. Every question was answered in isolation, as if the co
In previous posts we got LangChain4j up and running and gave our chatbot memory. But chatting only goes so far — what if you want to search through a pile of documents using meaning rather than keywor
In the last few posts we gave our chatbot a memory and the ability to search documents by meaning. Now it's time to combine the two. A large language model knows a lot, but it doesn't know your docume
In the last post we built a RAG pipeline that answers questions from indexed documents. But the pipeline is only as good as its input: real-world data lives in PDFs, mixed file types, and messy format
So far the demo's LLM calls have been single-shot: the chat model answers a question in one step. But many real tasks need more than that — a task may require arithmetic, a lookup in a knowledge base,