LangChain4j Community Resources
This repository is a space to find and share resources (articles, videos, more elaborate examples, etc.) using the LangChain4j library. The idea is to allow the community to learn and inspire each other.
Here you find all sorts of samples so you can get some inspiration to build application based on these examples or to use them for demo's. Please read the usage conditions at the end of this page, and check the license of the project in question before using the examples, and credit the creator. For the official LangChain4j examples, tutorials and documentation, see more information.
We welcome all types of more elaborate examples, such as
- interesting use cases
- elaborate examples with specific providers, frameworks or set-ups
- experimental programs that push the limits of what is possible with LLMs and AI integration.
| Title | Short Description | Contributor | Usage and Extension allowed | Usage for Demos allowed |
|---|---|---|---|---|
| Customer Assistant in Spring Boot | Car rental service customer assistant with memory, access to terms of use, and tools to intervene on bookings. Powered by GPT-4. | Dmytro Liubarskyi | ✅ | ✅ |
| Customer Assistant in Quarkus | Car rental service customer assistant with memory, access to terms of use, and tools to intervene on bookings. Powered by GPT-4. Including simple frontend. | Georgios Andrianakis | ✅ | ✅ |
| Feedback Analyser | Example splitting and categorizing feedback with LLM + dashboard and ChatBot to explore the recieved feedback. Quarkus example with as many plain java parts as possible for demo purpose. Includes ChatBot, RAG, metadat filtering, persistence to SQLite database, frontend with form data post, chatbot and dashboard. | Lize Raes and Vincent Peres | ✅ | ✅ |
| RAG Genie | LLM RAG prototype to test and evaluate your embeddings, chunk splitting strategies using Q&A and evaluations. | Stephan Janssen | ✅ | ✅ |
| LLM Tree-of-Thought | Explores the implementation of the Tree of Thought (ToT) approach (metacognition) with LLMs. More info here. | Cyril Sadovsky | ? | ? |
| Devoxx Genie IntelliJ Assistant | Using LangChain4j to build an AI Coding Assistant in IntelliJ that supports local models. Includes code for IntelliJ Plugins. | Devoxx | ✅ | ✅ |
| Serverless Book Management App (Google Next 24) | Using LangChain4j to build a big serverless book / library manaegement app leveraging Google models and services. | Dan Dobrin and Yanni Peng | ? | ? |
| Build your own ChatGPT in Quarkus (DevoxxFR) | Workshop to build a chatbot trained with your own pdf's, using Quarkus, LangChain4J and a website to test our chatbot. | Yohan Lasorsa, Anontio Goncalves, Julien Dubois, Sandra Ahlgrimm | ✅ | ✅ |
| The Petclinic Sample in SpringBoot and Langchain4j | A chatrobot built with Langchain4j for Spring petclinic, a demo with prompt, memory, RAG, retrieval augmentor and interaction with native functions | Zhiyong Li | ✅ | ✅ |
| The Quarkus Superheroes Sample applcation | The official Quarkus sample application showcasing integration with OpenAI and Azure OpenAI | Eric Deandrea | ✅ | ✅ |
| langgraph4j library | A library for building stateful, multi-actor applications with LLMs, built for work jointly with langchain4j. It is a porting of original langgraph from langChain ai project in Java eco-system. | Bartolomeo Sorrentino | ✅ | ✅ |
| Add you example here | Short description of your app and the interesting parts | Your Name(s) | ✅ | ✅ |
Here you find all sorts resources (articles, tutorials, videos, etc.) sorted by date on LangChain4j.
| Date | Type | Title | Short Description | Author |
|---|---|---|---|---|
| 2024/06/03 | Article | Let's make Gemini Groovy! | The happy users of Gemini Advanced, the powerful AI web assistant powered by the Gemini model, can execute some Python code, thanks to a built-in Python interpreter. So, for math, logic, calculation questions, the assistant can let Gemini invent a Python script, and execute it, to let users get a more accurate answer to their queries. | Guillaume Laforge |
| 2024/05/28 | Article | Grounding Gemini with Web Search results in LangChain4j | The latest release of LangChain4j (version 0.31) added the capability of grounding large language models with results from web searches. There’s an integration with Google Custom Search Engine, and also Tavily. | Guillaume Laforge |
| 2024/05/03 | Article | Gemini, Google's Large Language Model, for Java Developers | As a follow-up to my talk on generative AI for Java developers, I’ve developed a new presentation that focuses more on the Gemini large multimodal model by Google. | Guillaume Laforge |
| 2024/04 | Video | LangChain4J - use the power of LLMs in Java! | An introduction to LangChain4J : what it is, why use it, with 7 short demos showing its main use cases in Java. We'll use Azure OpenAI and MistralAI, Dalle-3, GPT-4 and Mistral 7B, learn how to use embeddings and vector databases, and how to use the RAG pattern. | Julien Dubois |
| 2024/04/03 | Article | Calling Gemma with Ollama, TestContainers, and LangChain4j | Lately, for my Generative AI powered Java apps, I’ve used the Gemini multimodal large language model from Google. But there’s also Gemma, its little sister model. | Guillaume Laforge |
| 2024/03/27 | Article | Gemini codelab for Java developers using LangChain4j | No need to be a Python developer to do Generative AI! If you’re a Java developer, you can take advantage of LangChain4j to implement some advanced LLM integrations in your Java applications. And if you’re interested in using Gemini, one of the best models available, I invite you to have a look at the following “codelab” that I worked on. | Guillaume Laforge |
| 2024/03/03 | Video | GenAI with Quarkus, Langchain4j and Ollama ! | Let's see how we can see up an dev environment , all running locally using cool technologies like Quarkus, langchain4j and Ollama | Sebastien Blanc |
| 2024/03 | Video | Java Meets AI: How to build LLM-Powered Applications with LangChain4j | Do you want to build applications powered by Large Language Models (LLMs) using Java and Spring Boot? | Lize Raes |
| 2024/03 | Video | The Definitive Guide to Tool Support in LangChain4J | LangChain4J lets you add classes and methods that an AI model can invoke automatically. That allows you to supply functions that do well what the AI model does badly. | Tales from the jar side |
| 2024/03 | Video | Unleashing AI in Java: A Guide to Semantic Kernel, LangChain4j, and Spring AI by Marcus Hellberg | Are you a Java developer curious about building AI apps, but feel restricted by the prevalence of Python and JavaScript tools? This talk is for you. We'll explore Java AI libraries like Semantic Kernel, LangChain4j, and Spring AI, demystifying how to get started with AI engineering right within your favorite language. | Marcus Hellberg |
| 2024/02/01 | Article | Image generation with Imagen and LangChain4j | This week LangChain4j, the LLM orchestration framework for Java developers, released version 0.26.1, which contains my first significant contribution to the open source project: support for the Imagen image generation model. | Guillaume Laforge |
| 2023/12 | Video | How to build a retrieval-augmented generation (RAG) AI system in Java (Spring Boot + LangChain4j) | Learn how to build an AI powered application that knows your business context and is able to interact with your Java code. Recorded at Vaadin Create 2023. | vaadinofficial |
| 2023/11/13 | Article | Generative AI in practice: Concrete LLM use cases in Java, with the PaLM API | Large Language Models, available through easy to use APIs, bring powerful machine learning tools in the hands of developers. Although Python is usually seen as the lingua franca of everything ML, with LLM APIs and LLM orchestration frameworks, complex tasks become easier to implement for enterprise developers. | Guillaume Laforge |
For users: Please check the README of the example and make sure you use it only as allowed by the permissions
For contributors: If you have an inspiring application that you want to share with the community, you're very welcome to add it here, either by committing the code or by adding a link in the table below. Please make sure your project compiles and runs correctly. Please provide a README explaining what your example does, how to run it, and which of the following permissions you grant:
- permission to get inspired and make a similar app
- permission to use the example for demos, while crediting you
- permission to use and extend the application