A very common question I get when presenting and talking about advanced RAG (Retrieval Augmented Generation) techniques, is how to best index and search rich documents like PDF (or web pages), that contain both text and rich elements, like pictures or diagrams.

Another very frequent question that people ask me is about RAG versus long context windows. Indeed, models with long context windows usually have a more global understanding of a document, and each excerpt in its overall context. But of course, you can’t feed all the documents of your users or customers in one single augmented prompt. Also, RAG has other advantages like offering a much lower latency, and is generally cheaper.

In the first article of this Advanced RAG series, I talked about an approach I called sentence window retrieval, where we calculate vector embeddings per sentence, but the chunk of text returned (and added in the context of the LLM) actually contains also surrounding sentences to add more context to that embedded sentence. This tends to give a better vector similarity than the whole surrounding context. It is one of the techniques I’m covering in my talk on advanced RAG techniques.

In a nutshell, the AI agent equation is the following:

AI Agent = LLM + Memory + Planning + Tool Use

AI agents are nothing without tools! And they are actually more than mere Large Language Model calls. They require some memory management to handle the context of the interactions (short term, long term, or contextual information like in the Retrieval Augmented Generation approach. Planning is important (with variations around the Chain-of-Thought prompting approach, and LLM with reasoning or thinking capabilities) for an agent to realize its tasks.

As I’m contributing to ADK (Agent Development Kit) for Java, and LangChain4j (the LLM orchestration framework) I interact with MCP (Model Context Protocol) servers and tools to further expand the capabilities of my LLMs.

Recently, I showed how to vibe-code an MCP server using Micronaut. You know I usually talk about Micronaut, but this time, I wanted to experiment with Quarkus, and in particular with its built-in support for implementing MCP servers.

Getting started with Quarkus’ MCP support

I created a brand new Quarkus project from IntelliJ IDEA, with its Quarkus template, and I added a couple key dependencies for JSON marshalling, but even more important, for the MCP support:

Recently on these pages, I’ve covered ADK (Agent Development Kit) for Java, launched at Google I/O 2025. I showed how to get started writing your first Java agent, and I shared a Github template that you can use to kick start your development.

But you also know that I’m a big fan of, and a contributor to the LangChain4j project, where I’ve worked on the Gemini support, embedding models, GCS document loaders, Imagen generation, etc.

With the unveiling of the Java version of Agent Development Kit (ADK) which lets you build AI agents in Java, I recently covered how to get started developing your first agent.

The installation and quickstart documentation also helps for the first steps, but I realized that it would be handy to provide a template project, to further accelarate your time-to-first-conversation with your Java agents! This led me to play with GitHub’s template project feature, which allows you to create a copy of the template project on your own account or organization. It comes with a ready-made project structure, a configured pom.xml file, and a first Java agent you can customize at will, and run from both the command-line or the ADK Dev UI.

When I talk about Generative AI, whether it’s with developers at conferences or with customers, I often find myself saying the same thing: chatbots are just one way to use Large Language Models (LLMs).

Unfortunately, I see many articles or presentations that just focus on demonstrating LLMs at work within the context of chatbots. I feel guilty of showing the traditional chat interfaces too. But there’s so much more to it!

At Google Cloud Next ‘25, last April, Google released Agent Development Kit (ADK) for Python, a flexible and modular framework for developing and deploying AI agents.

Now at Google I/O, a Java version of ADK has been made available! And I’m glad to have had the chance to participate in its launch, via code samples, documentation, and helping shape the API so it’s idiomatic for Java developers.

In this article, my goal is to give you the basis to get started with the ADK framework, in Java, using the Gemini model, and running your first Java agents locally.

Unlike Quarkus and Spring Boot, Micronaut doesn’t (yet?) provide a module to facilitate the implementation of MCP servers (Model Context Protocol). But being my favorite framework, I decided to see what it takes to build a quick implementation, by vibe coding it, with the help of Gemini!

In a recent article, I explored how to use the MCP reference implementation for Java to implement an MCP server, served as a servlet via Jetty, and to call that server from LangChain4j’s great MCP support. One approach with Micronaut may have been to somehow integrate the servlet I had built via Micronaut’s servlet support, but that didn’t really feel like a genuine and native way to implement a server, so I decided to do it from scratch.

MCP (Model Context Protocol) is making a buzz these days! MCP is a protocol invented last November by Anthropic, integrated in Claude Desktop and in more and more tools and frameworks, to expand LLMs capabilities by giving them access to various external tools and functions.

My colleague Philipp Schmid gave a great introduction to MCP recently, so if you want to learn more about MCP, this is the place for you.

In this article, I’d like to guide you through the implementation of an MCP server, and an MCP client, in Java. As I’m contributing to LangChain4j, I’ll be using LangChain4j’s mcp module for the client.