This blog post will integrate the Spring AI framework into a Java application. We’ll use a simple project that includes a ChatService and a ChatController to demonstrate using the Spring AI framework to generate text & image responses and horoscopes based on user input.
As an AI and software professional, you’ve likely heard the buzz around large language models (LLMs) like GPT-3, ChatGPT, and their growing capabilities. These powerful models can handle a wide range of natural language tasks, from text generation to question answering. However, effectively leveraging LLMs in your own applications can be a complex challenge. That’s where LangChain comes in.
Welcome to a step-by-step guide on creating an intelligent chatbot powered by OpenAI using Python and Jupyter Notebooks. In this tutorial, we’ll cover the fundamental concepts and guide you through the process of building a simple yet effective chatbot that leverages the power of OpenAI’s language model.
OpenAI provides us the ability to invoke its features via RESTful APIs. This blog shows how to invoke the API using Spring Boot. There is nothing special here and no OpenAI Java libraries that I use. One can do the same in standard Java (non-spring) or even in more concise code with Nodejs. But here goes a sample with Spring Boot.
So it’s been some time since I wrote a blog. As I was figuring out what my next blog is, how could I ignore ChatGPT, BARD and the AI excitement that it has brought about (or re-awakened among many of us). And then there is GitHub’s Copilot and AWS CodeWhisperer! Code assistants that can make us slightly more efficient developers.