These last 24 hours hasn’t been the best of my life, and despite the food poisoning that I’m recovering from, I’ve managed to finish this course that I’ve been working through this last month of June. There are no excuses when the goal is clear.

And I have a gift for you that is a reader from my blog. I’m bringing here the complete suite of repositories that I have developed during the course Spring AI: Beginner to Guru from John Thompson, containing:

  • Chatting with OpenAI with A LOT of Prompt Engineering, system messages and user messages
  • Implementing RAG (Retrieval Augmented Generation, that consists of feeding AI against a specific private set of data to generate the responses considering this data instead of the data on which the LLM is trained, as defined on my own words) technique from JSON and vector databases like Milvus to calculate the best choice of a truck to tow different types of boats, retrieving the boats data from online sources, generating the embeddings on OpenAI and storing it in the vector database.
  • Advanced parameters to save money and reduce the quantity of consumed tokens when generating embeddings to the vector database.
  • Using functions on Spring AI to get realtime response with weather and stock prices, having the response generated on OpenAI
  • Generating different types of image with the AI and even having a photo being analyzed and described textually on what it contains, using OpenAI Vision
  • Generating text to speech using different voices and speeds.

These implementations are fully functional by the time of writing this post with Spring AI 1.0.0 GA released to production and Java 21.

As we can see, I’ve spent MUCH more than the actual 6.5 hours mentioned in the certificate that corresponds to the total of videos in the course, but I’ve learned so much with it that I’ll be writing a post to each of these implemented services, sharing the learnings of each implementation.

Also, feel free to look into the code and run it for yourself. You will only need your own OpenAI API key with credits on it. Each repository contains README instructions on how to run it.

But if you are new to the AI world and Spring AI, I would HEAVILY suggest you to watch my presentation about AI, Java and Spring AI that happened in The Out of the Box Developer where I leverage some foundations about Deep Learning, Machine Learning, LLMs and Generative AI, with a hands-on introduction with live coding, connecting a Spring Boot service to OpenAI with Spring AI. You can find it by clicking here.

These repositories are not forked from the original repository course since I always prefer to create it from scratch, but it contains the logic presented there, having being refactored to work with Spring 1.0.0 where needed instead of the original 0.8.0 and 1.0.0-M5 version from the course code. You can also refer to my source code if you are taking this course as well.

  • spring-ai-rag-expert
    Practical implementation of Spring AI with Java and Milvus Vector Database using RAG (Retrieval Augmented Generation) to implement a truck recommendation system for towing boats, grabbing the boats data directly from Yamaha site.
    Repo Link
  • spring-ai-functions
    Practical implementation of Spring AI with Java implementing functions to retrieve data from external APIs (like weather) and feed OpenAI prompt, providing intelligent, up-to-date real-time data to the user.
    Repo Link
  • spring-ai-image
    Demonstrates the integration of OpenAI’s powerful image generation and vision capabilities using Spring AI and Java.
    Repo Link
  • spring-ai-rag
    Practical implementation of Spring AI with Java and a simple Vector Database to implement Retrieval Augmented Generation, querying from a vector database and using OpenAI to generate responses over this data alone.
    Repo Link
  • spring-ai-audio
    Leverages Spring AI and OpenAI’s text-to-speech capabilities to generate high-quality audio outputs from text inputs. Built with Java and Spring Boot, it provides a seamless way to convert text into natural-sounding speech using OpenAI’s advanced speech synthesis models.
    Repo Link
  • spring-ai-intro
    Practical implementation of Spring AI with Java to integrate the power of OpenAI ChatGPT into microservices, step by step, using the most up-to-date resources from the framework.
    Repo Link

Also, you can find the whole list of repositories pinned on my Github here.

If you liked, subscribe to receive the following lessons that will explain and present in depth the logic implemented on these repositories, so you can learn and create your own implementation.

Subscribe to stay up to date with exclusive content firsthand!

This is the road to Software Engineering powered by AI, the AI Engineering road.

See you on the next post!