Kode Sastra

Photo by Stephanie Cantu on Unsplash Testcontainers simplify integration testing by providing lightweight and disposable containers for various tools and services. These containerized integrations are referred to as modules in the Testcontainers library. Developers can use them in their JUnit tests to start application environments as needed. Ollama is a unified platform for running large language models, such as Llama 3.3, DeepSeek R1, and Mistral Small 3.1 in the local test environment. The Testcontainers library now includes an Ollama module , which allows developers to launch Ollama containers within JUnit tests. This is especially useful for testing code that interacts with language models. In this article, we'll learn how to use the Ollama module on a local laptop with a CPU. Prerequisites In this section, we'll discuss the prerequisites before we can use the Testcontainers library. Container Runtime Our local environment, where we'll run the test cases...

Photo by Jeffrey Hamilton on Unsplash The Spring AI's Text-to-Speech (TTS) API helps convert text documents into audio files. Currently, Spring AI supports integration only with the OpenAI TTS API . Further, TTS has applications in multiple fields, including e-learning, multilingual support, etc. Numerous industries are adopting this technology to improve communication, accessibility, and operational efficiency. Spring AI provides a good abstraction on top of the underlying OpenAI TTS services . Let's learn more. TTS API Key Classes Before we can build a program using Spring AI's TTS API, let's get familiar with a few important components of the library: The Spring AI framework supports auto-configurations for various model client classes, allowing integration with the underlying LLM Service APIs . This functionality is facilitated through special configuration properties, which we will ...

Photo by Elijah Merrell on Unsplash Some large language models (LLMs) can transcribe audio into text. Different businesses are rapidly adopting this technology and reaping productivity benefits. We've seen glimpses of this technology in Zoom, Microsoft Teams, and other collaboration and communication tools, where call transcriptions can be generated automatically. Further, the entertainment industry is rapidly adopting it in Movies, Advertisements, and other fields. Spring AI aims to provide a unified transcription API that integrates with LLM providers like OpenAI and Azure OpenAI . This article will explore how the Transcription API can be used to transcribe an audio file using OpenAI. Transcription API Key Classes We'll start learning some important Spring AI Transcription API classes: We can divide the components into two groups, one specific to the underlying LLM service provider and the other...

Generated by AI Large Language Models (LLMs) such as ChatGPT-4, Claude 2, Llama 2, etc., generate text outputs when invoked with text prompts. However, Image Models such as DALL-E, MidJourney, and Stable Diffusion can generate images from user prompts, which can be either texts or images . All Model providers have their own APIs, and switching between them becomes challenging without a common interface. Luckily, the Spring AI library offers a framework that can help seamlessly integrate with the underlying models. In this article, we'll learn some important components of Spring AI's Image Model API and implement a demo program to generate an image with a prompt. Important Components of Image Model API First, let's look at the important components of the Image Model API that help integrate with the underlying LLM providers such as OpenAI, and Stability AI: Interfaces such as ImageOptions and ImageModel , as well as classes su...

Designed by Freepik Most enterprise applications have to deal with cross-cutting concerns like governance, compliance, auditing, and security. That’s where the interceptor pattern addresses these challenges in most development frameworks. Spring AI Advisors specifically benefit AI applications that interact with Large Language Models (LLMs). These advisors act as interceptors for LLM requests and responses. They can intercept outgoing requests to LLM services, modify them, and then forward them to the LLM. Similarly, they can intercept incoming LLM responses, process them, and then deliver them to the application. The advisors can be useful for numerous use cases: Sanitize and validate data before sending them to LLMs Monitor and control LLM usage costs Auditing and logging LLM requests and responses adhere to governance and compliance requirements as part of the respo...

Photo by Shubham Dhage on Unsplash LLMs generate natural language responses that are easily understood by humans. However, it is difficult for applications to communicate with each other using natural language. Therefore, these responses must be converted into machine-readable formats such as JSON, CSV, XML, or POJOs. Some LLM providers including OpenAI, Google PaLM, and Anthropic(Claude) have specialized LLM models that can understand the output Schema and generate the response accordingly. We'll learn how Spring AI helps integrate with them through its framework. Prerequisites For this article, we'll use OpenAI LLM services. Hence, we'll need an active OpenAI subscription to invoke its APIs. Furthermore, we must import the Maven dependencies to integrate the Spring Boot application with OpenAI: <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-starter-model-openai</artifactId...

Photo by Alex Knight on Unsplash Building AI assistance in existing legacy applications is gaining a lot of momentum. An AI assistant like a chatbot can provide users with a unified experience and enable them to perform functionalities across multiple modules through a single interface. In our article, we'll see how to leverage Spring AI to build an AI assistant. We'll demonstrate how to seamlessly reuse existing application services and functions alongside LLM capabilities. Function Calling Concept An LLM can respond to an application request in multiple ways: LLM responds from its training data LLM looks for the information provided in the prompt to respond to the query LLM has a callback function information in the prompt, that can help get the response Let's try to understand the third option, Spring AI's Function calling ...