Create AI-Infused Java Apps with LangChain4j

What if you could connect an LLM to your database with a simple Java annotation? Learn to build powerful, autonomous AI agents, entirely in Java.

#1about 2 minutes

Navigating the complex AI landscape for Java developers

The overwhelming Python-centric AI ecosystem doesn't require Java developers to switch languages, as powerful Java-native tools exist for AI integration.

#2about 2 minutes

Understanding LangChain4j for Java AI applications

LangChain4j, inspired by Python's LangChain, provides a Java-native framework for integrating AI models, with Quarkus offering simplified integration features.

#3about 5 minutes

Getting started with prompting and structured output

Begin by adding dependencies and using annotations like @AiService to define prompts, parameterize questions, and automatically map model responses to Java objects.

#4about 2 minutes

Implementing stateful conversations with chat memory

LangChain4j provides out-of-the-box chat memory to maintain conversational context, enabling follow-up questions and parallel conversations using a memory ID.

#5about 3 minutes

Connecting AI models to external Java services

Use function calling, also known as tools, to allow the AI model to invoke your existing Java methods and services by describing them with the @Tool annotation.

#6about 4 minutes

Building autonomous agents with the MCP protocol

The Multi-tool Calling Protocol (MCP) enables an AI model to autonomously decide which external tools to call in sequence to fulfill a user's request within a Java environment.

#7about 4 minutes

Implementing guardrails to secure AI interactions

Protect against misuse like prompt injection by using input and output guardrails to sanitize requests and responses, ensuring the model behaves as intended.

#8about 2 minutes

Adding custom knowledge with retrieval-augmented generation

Use Retrieval-Augmented Generation (RAG) to supplement the model's knowledge with your own documents by loading them into a vector store for relevant context retrieval.

#9about 5 minutes

Demo of an AI assistant using LangChain4j and Quarkus

A demonstration of a car rental chatbot showcases how to integrate a database, an external weather service via MCP, and custom documents via RAG to create a comprehensive AI assistant.