GenAI for Java Developers 1: Getting started

Key Takeaways

[Music] Sometimes brewing Java feels like a science experiment. Grinders, filters, timers. I've ruined my fair share of mornings trying to get it right. Hi everyone. I'm I and I'm a cloud advocate here at Microsoft. My job is to help developers learn, experiment, and also have fun with new technologies. And with me, getting started is always the scariest part. However, if we know the right tools, it doesn't have to be. And that's why I'm so excited to have Rory with me here today. Rory's going to be talking about how we can keep it simple. No complicated setups, no intimidating environments. Just like instant coffee, you'll see how easy it is to get going, especially with GitHub code spaces. So, let's go ahead and dive in. So, the first thing you're going to want to start to do is go on to our demo repo, which is generative AI for beginners-java. And it is set up already with a dev container for you to go in and have Java, the necessary tools, and Visual Studio already set up. So, you're going to go in there, you're going to start, and you're going to fork it. And then, once you fork it, let's go into our fork there, you're going to create a code space from that fork. You're going to go to code there to code spaces. And I've already set up a code space there. And we have a very generous free tier that allows you to run the examples end to end at the same time with your free tier of your code space. I need you to go in and create a fine-grained token to be able to call the free tier of GitHub models. So, GitHub models is an online repository of most of the models that Microsoft and our partners want you to test with. You're going to generate a new token. And here, we'll call it uh token test. And then, you're going to set the permissions. So, if we go there, model permissions. And you can generate that token. And then, you're going to take that token that you see there, and you're going to paste it in to your dev container. So, I've started my dev container here. Here's my dev container. And then, I'm going to go export GitHub token, and I'm going to paste in that token there. And then, I'm going to then go in and set it. Now, I've already got a different token set up and everything. So, I'm ready to rock. So, I've got the GitHub token there, and I'm going to open up my code space, and I'm going to go through to the GitHub models example in the setup dev environment folder. And you can just go into that and just hit debug. Now, it's going to debug, and it's going to break on that point of going into the OpenAI client. We're using the OpenAI SDK. And you can see there, it's saying, "I'm going to use the model GPT-4.1 nano. And it's a very low um throttled model. So, you can do all of the examples with GPT-4.1 nano or the other little bit more heavyweight mini model. And we're going to hit that and we want to say, "Well, say hello world." We're going to add a system message just to tell the model, "Hey, listen, what do you want to do? You're a concise assistant." So, let's run that through there. And we're sending the request to GitHub models. It's using the model 4.1 nano. And then, we can see there, it says hello world. Once you're done, you can just close the code space. So, we'll use that a little bit later. And you can close that there. And in future sessions, we're going to go through core generative AI techniques, practical samples with apps, and then also responsible gen AI. So, to summarize the session that we've just done, we used a dev container, we created a GitHub model, we took a fork that is going to run our code space. We created our code space there. We opened up our code space, and then we ran the basic example. Thank you so much, Rory. I appreciate so much the level of detail you went into into your session. But not just that, how fun and entertaining you keep it the entire time. For everybody who joined us for this episode, if you would want to visit resources related to this episode, you can find them at aka.ms/javaandai for beginners. Link is in the description of this video. We'll see you in the next episode.