Sam's programming note

Function calling is a powerful mechanism in AI development, allowing developers to specify a set of tasks or functions that an AI model can execute. These functions are clearly defined using a schema that outlines expected inputs, outputs, and any additional parameters the AI needs to perform the desired action effectively. By utilizing function calling, developers can precisely guide the AI’s behavior to ensure its responses are aligned with the application’s specific requirements.

In this installment of our series on building AI applications with Spring AI, we explore the powerful capabilities of vector databases. These specialized databases enhance AI systems by efficiently managing and utilizing embeddings—dense vector representations crucial for processing and understanding large-scale, complex data sets. Read on to learn how vector databases integrate seamlessly with Spring AI to revolutionize data handling in AI applications.

As of February 1, 2024, Sonatype has abandoned the old way of registering via Jira tickets for Maven Central. This article will guide you through the new process of publishing a Jar file to the Maven Central Repository.

Following our initial dive into creating prompts with Spring AI, this article ventures further into enhancing user interactions. We focus on incorporating chat histories and delivering responses in real-time using Server-Sent Events (SSE). This combination not only elevates the user experience by providing instant feedback but also simulates a dynamic conversation flow, akin to real-life interactions.

Welcome to my learning notes on developing AI applications with Spring AI. If the realm of AI has ever piqued your curiosity but you’ve felt daunted about where to begin, these notes are for you. So, let’s embark on this exploration together into the fascinating world of AI development. Feel free to share your thoughts and engage in discussions as we learn and grow together.

Diving into the world of Java development, we often encounter the need to interact with applications directly through the command line. Enter Spring Shell, a project within the larger Spring Framework ecosystem designed to simplify the creation of interactive command-line applications. This beginner-friendly guide aims to introduce you to Spring Shell, helping you unlock its potential to build powerful CLI tools with ease. Whether you’re new to programming or looking to expand your Java toolkit, this journey will set the foundation for your command-line adventures.

In the evolving landscape of Java applications, performance and startup time are paramount, especially in the context of cloud-native environments and microservices architectures. This is where Spring Native and Ahead-of-Time (AOT) compilation come into play, revolutionizing how we build and deploy Spring applications. Let’s dive into these concepts and explore a practical implementation that enhances application performance through efficient runtime hints.

In the dynamic world of software development, efficiency and consistency are key. Developers are continually seeking tools and practices that streamline their workflow, reducing overhead and allowing a greater focus on the creative aspects of coding. Enter the realm of Visual Studio Code’s Dev Containers – a feature revolutionizing the way developers interact with their coding environments.

Dev Containers in VS Code offer a consistent, isolated development environment, ensuring that the “it works on my machine” syndrome is a thing of the past. This innovation brings a fresh approach to the development process, allowing smoother code development, testing, and debugging.

This article is intended for junior developers and focuses on how to integrate Google’s Gemini model using Java and the langchain4j framework. We’ll cover configuration settings, model strategies, and implementation, with a focus on special considerations for using Gemini and a thorough explanation of the provided Java code.

This article dives into the integration of Langchain4j, PostgreSQL, and Liquibase within a Spring Boot environment, tailored specifically for Java developers. Langchain4j, a framework designed for efficient vector data management, when combined with PostgreSQL’s robust database capabilities and Liquibase’s schema version control, creates a powerful ecosystem. This guide will walk you through setting up these integrations, detailing the configurations and code necessary to manage complex vector data. By understanding and implementing these integrations, developers can significantly enhance the performance and scalability of their data-driven applications, paving the way for advanced analytics and machine learning operations.