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As a Java Developer Advocate at JetBrains, I frequently demonstrate IntelliJ IDEA features through videos and articles. To do this, I usually create small demo applications focused on specific features. While this works well initially, the number of such applications grows quickly. Soon, it becomes difficult to remember which app was built for what purpose, turning it into a maintenance headache. Some of the newer features in IntelliJ IDEA, like Spring Debugger and Workspaces, require more complex applications to showcase their capabilities effectively.

ChatGPT and other Generative AI tools took the world by a storm. People are using these AI tools for various purposes such as to explore a topic, to seek answers to their questions or to get help in coding, etc. ChatGPT and Google’s Gemini are very popular AI tools that are available for free with some usage limitations. Coding without help from Google, StackOverflow and blogs is challenging. But what if you had to code without internet connectivity?

In this article, we will explore the following: Introduction to Embedding Models. Loading data using DocumentReaders. Storing embeddings in VectorStores. Implementing RAG (Retrieval-Augmented Generation), a.k.a. Prompt Stuffing. Sample Code Repository You can find the sample code for this article in the GitHub repository Large Language Models(LLMs) like OpenAI, Azure Open AI, Google Vertex, etc are trained on large datasets. But those models are not trained on your private data, so they may not be able to answer questions specific to your domain.

In this article, we will explore the following: Introduction to Spring AI. Interacting with Open AI using Spring AI. Using PromptTemplates. Using OutputConverters. Sample Code Repository You can find the sample code for this article in the GitHub repository Introduction to Open AI and Spring AI ChatGPT took the world by storm when it was released by OpenAI. It was the first time that a language model was able to generate human-like responses to prompts.

In this article, we will explore the following: Understand the need for Retrieval-Augmented Generation (RAG). Understand EmbeddingModel, EmbeddingStore, DocumentLoaders, EmbeddingStoreIngestor. Working with different EmbeddingModels and EmbeddingStores. Ingesting data into EmbeddingStore. Querying LLMs with data from EmbeddingStore. Sample Code Repository You can find the sample code for this article in the GitHub repository LangChain4j Tutorial Series You can check out the other articles in this series: Part 1: Getting Started with Generative AI using Java, LangChain4j, OpenAI and Ollama Part 2: Generative AI Conversations using LangChain4j ChatMemory Part 3: LangChain4j AiServices Tutorial Part 4: LangChain4j Retrieval-Augmented Generation (RAG) Tutorial Understand the need for Retrieval-Augmented Generation (RAG) In the previous articles, we have seen how to ask questions and get responses from the AI models.

In this article, we will explore the following: Using LangChain4j AiServices to interact with LLMs. How to ask questions and map responses to different formats? Summarizing the given text in different formats. Analyzing the sentiment of the given text. Sample Code Repository You can find the sample code for this article in the GitHub repository In the previous article, we have seen how to have a conversation using LangChain4j ChatMemory and ConversationalChain.

In this article, we will explore the following: How to use LangChain4j ChatMemory and ConversationalChain to implement conversation style interaction? How to ask questions using PromptTemplate? In the previous article, we have seen how to interact with OpenAI using Java and LangChain4j. LangChain4j Tutorial Series You can check out the other articles in this series: Part 1: Getting Started with Generative AI using Java, LangChain4j, OpenAI and Ollama Part 2: Generative AI Conversations using LangChain4j ChatMemory Part 3: LangChain4j AiServices Tutorial Part 4: LangChain4j Retrieval-Augmented Generation (RAG) Tutorial Sample Code Repository

In this article, we will explore the following: Brief introduction to Generative AI? How to interact with Open AI APIs using Java? How to use LangChain4j to interact with OpenAI? How to run a LLM model locally using Ollama? Working with Ollama using LangChain4j and Testcontainers. LangChain4j Tutorial Series You can check out the other articles in this series: Part 1: Getting Started with Generative AI using Java, LangChain4j, OpenAI and Ollama Part 2: Generative AI Conversations using LangChain4j ChatMemory Part 3: LangChain4j AiServices Tutorial Part 4: LangChain4j Retrieval-Augmented Generation (RAG) Tutorial Introduction to Generative AI Unless you are living under a rock, you might have heard about Generative AI.

I’m a happy Lombok library user. I found this library a decade ago, and I’ve been using it ever since. I don’t remember facing any major problems with it so far. However, I see few people that I greatly admire and learn from, often complain that Lombok is very bad, and you should avoid it. Obviously, I am curious to know what am I missing? I know a couple of scenarios where using Lombok brings some challenges.

TLDR; I am happy to announce that I am starting the SpringBoot - The Missing Guide video series on my SivaLabs YouTube Channel. This SpringBoot - The Missing Guide video series is for those who need help understanding SpringBoot’s magic. In this series, I will explain the core concepts used in Spring and SpringBoot with practical examples. Also, I will give a deep-dive explanation of SpringBoot’s AutoConfiguration and how to customize the default configuration.