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Spring Boot is the most popular framework in the Java world to build enterprise applications. Also, Spring Boot is the most sought-after skill to get hired as a Java developer. Here is my recommended approach to learn Spring Boot. 1. Prerequisites: What you should already know If you are completely new to Java, then directly jumping on to Spring Boot is NOT recommended. First, learning Core Java and get familiar with Java ecosystem.

Nowadays, we are using a wide range of technologies and tools for building modern software systems. As software developers, we need to keep upskilling ourselves to be able to build the software efficiently. One question that often pops up is, should I become a Generalist or a Specialist? Personally, I highly recommend to be a generalist at the beginning of your career so that you can get some experience in building software end-to-end.

Typically, in Spring Boot + Thyemleaf applications, we use thymeleaf-layout-dialect to define the common layout of the web pages and it works fine. But when we compile the Spring Boot application to GraalVM native image, it is failing due to this error. I tried many suggestions mentioned in the above issue, but none of them worked for me. Then Oliver Drotbohm suggested me Flexible layouts approach to create layouts support natively provided by Thymeleaf itself.

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.

In the software development world, trends come and go, and often we go through the same cycle again and again. It seems 2024 is the year of “Framework vs Libraries” debate. I mean this debate is not new, but it is getting louder again. For example, most of the Go community prefers to use libraries instead of a framework. The Java community is divided into two groups, one prefers to use Spring Boot or Quarkus or Micronaut, and the other prefers to use libraries.