Java and AI for Beginners - Full Series

Key Takeaways

Hey there. Welcome to the brand new Java and AI for beginners series where we're going to be learning about how you can use AI to transform and supercharge AI applications. Just how coffee empowers me to get through the day, Java empowers millions of people around the world to achieve great things. And in a world where AI is increasingly changing the way we interact with the world around us, [music] it is more important than ever for you as a developer to learn how to take advantage of these tools. Follow me to the studio and we'll go ahead and dive in. Hey, hey everyone. We made it to the studio. I'm so excited to meet you all. I'm Ian. I'm a cloud advocate here at Microsoft and my passion is helping developers like you learn, grow, and most importantly have a fun time with new technologies. I'll be your host for this series, guiding you through each session and introducing you to some amazing speakers along the way. Java is one of the most widely used programming languages in the world with millions of developers and applications. But the way we build software is changing fast. AI, cloud computing, and modern development practices are transforming the way how apps are created and deployed. Developers like you are tasked with leveraging AI to achieve more than ever with the time you have. This series will help you bridge that gap. Whether you're completely new to Java or ready to supercharge your skills into the modern AI powered era, in this series, our goal is to keep each episode as interactive and practical as possible. You'll be able to follow along with code snippets and samples, all linked in the description of each video. Every session is designed to be short, hands-on, and actionable, so you walk away with something you can try out immediately. We'll be covering a wide arrange of topics including the fundamentals of Java and AI, generative AI for Java, building servers and clients with MCP, context engineering, modernizing and deploying applications, creating intelligent apps, running generative AI and containers. And the goal is that by the end you will have a toolkit of knowledge that combines Java with the latest AI technologies. And we're not just staying in one place. We're traveling around the world to meet some of Java's top talent. You'll hear from Rory in Johannesburg, Bruno in Ontario, Julian in Paris, Brian in Las Vegas, and Sandra from Berlin. Each of them brings deep expertise, and together they'll help us see how Java thrives in this new AI age. So, grab your cup of Java, settle in, and let's dive in. Sometimes brewing Java feels like a science experiment. Grinders, filters, timers. I've ruined my fair share of warnings 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 is 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 onto 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 in 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're going to 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. Then I'm going to go export, get a token, and I'm going to paste in that token there. And I'm going to then go in and set it. And 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 the 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 a 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 modeled. 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/java and AI for beginners. Link is in the description of this video. We'll see you in the next episode. When you make coffee, you've probably got choices, different buttons you can press. Espresso, cappuccino, latte. Each button gives you a totally different drink. This is both powerful, but it can also lead to unintended consequences. I'm Ian. I'm a cloud advocate here at Microsoft. And one thing I've learned working with developers is once you know the basics, the real fun begins when you start exploring different techniques, the different levers you can pull, and how you can get different results from those levers. Today we have Rory joining us and what we're doing today is seeing how Gen AI offers different brewing buttons, completions, chat flows, and rag. Each one unlocks an entirely new way to use AI. Rory, I'm so excited for today's session. Please take it away. >> So once you have your environment set up and you finished the getting started video and now you're ready to look at generative AI techniques. And there's really three techniques we're going to look at today. So the first techniques we've got our code space running is the LLM completion app. And we're going to set some break points here and we're going to go through exactly what this is doing and why this is important. So it is going to connect to your GitHub models which we already saw that you need a token for. And then once it has the completion set up, we're going to go into multi-turn chat and then interactive chat. So let's debug this in our code space and we'll see there exactly what that is going to do now for the simple completions for the chat. If we go into that, so let's step into, we'll see there that it's just going to say you're a concise Java expert who can explain concepts, explain Java streams in one paragraph. So let's make sure that our breakpoint is in multi-hat. Step through that there and then we'll see that it is coming back with a simple completion. Basically one turn. There we go. Java streams. Now we're in multi-turn chat. So let's check and step into that. Now multi-turn chat is going to say hey you're a helpful Java tutor. What is a hashmap in Java? And then it's going to ask another question. It's going to keep the history for that the first response. And then it's going to say wow you answered that first response. How is a hashmap different from a tree map? So we can step through that. It's going to multi-turn conversation and then it's going to stop there. How's a hashmap different from a tree map? See, it's already asked what a hashmap. It's kept it in history. It knows that we want to know more about what the conversation is. And we're going to step through that. And it's going to say to us, um, oh, there we go. We now have, let's go and and step through that there. And we're on interactive chat there. So it has already broken through and told us, wait a second, if you want to know about a tree map, this is the difference. And it gives us all the key differences. And it even says, great question cuz we're a helpful Java tutor. The last one that we want to see is the interactive chat. Now, it's already broken there. I haven't put a broke point breakpoint there. But if we go into interactive chat, it's going to say, "Wait, take in the question that the person is asking." So if I ask it a question here and I can go into there, it's saying, "Okay, what is the question?" You want to go and I want to say, "Tell me a joke." Why do programmers prefer dark mode? Because light attracts bugs. and then you can go exit from that. So that's the completion part of it. We are now going to go into the rag or rag stands for retrieval augmented generative uh pattern. And we can see here that the rag the simple reader demo is going to read in document.txt and ground itself onto some information. And the information is it says GitHub models provides a convenient way to access large language models. Now over here we want them to only and not hallucinate on the information. So we're going to read in the document. We're going to uh use the file and then we're going to say to it don't answer any question that doesn't ground on the the document. You can see there I cannot find that information in the provided document. So let's go in there and let's see what we're going to ask. So we're going to ask a helpful uh a question on rags. So simple reader demo. And now let's go in and debug that. So let's go there and we're going to debug that. And it's going to go in and read that document. And it's going to use the augmentation to say ask a question about the document. And I can say what is what are GitHub models? Question mark. And it's not going to ask answer anything that isn't in the document. So what are GitHub models? And it's going to go and give us our example. At the same time, it's going to use the token, the GitHub models token to go in there and to prepare the response. And it says there GitHub models um are a convenient way to access and use large language models. And it won't really ask an answer and give you the opportunity to say tell me a joke because it's not really relevant to the underlying um instructions. And then finally, we're going to look at functions. And functions are a nice way to create small little uh procedures that help you with certain business critical functions. And we've got two functions here. Weather function example. So let's pause it there and then calculate a function. Now for the weather function, we actually going in and we're going to simulate a weather. But we're going to give it the city name and it's going to return the uh temperature. You're a helpful weather assistant. Use the weather function to provide the weather. And then we're going to ask it what is the weather like in Seattle. Now, this is going to need a large language model that can call functions. So, we're going to use GPT 40 mini. So let's debug that now and it should break on the weather function. So weather function example and let's step through that now and we'll see there ah we've got the weather in Seattle. The same with the calculator function and the calculator function is going to perform basic calculation. We can see if we go into the calculator function example, it's just going to do a mathematical expression and very basically what is 15% of 240 and we're going to continue through that and there we go. So this is a very simple way of handling business critical information. And you can even point it out and it can go speak to external systems, but it does need the GPT 40 mini. So coming back to what we demoed today, we went through the completion app, we went through the rag, we showcased functions, and then if you look there, responsible AI, we're actually going to mention that in a later video because responsible actually is protecting all of this that we currently doing from abuse. 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/java and aai for beginners. Link is in the description of this video. We'll see you in the next episode. In our last session, Rory explained the core techniques of generative AI. And if you're anything like me, theory is helpful. I'll nod my head, say yes, I understand. But it's when I try it myself that the real questions and the real learnings begin. Hi, I'm Ian. I'm a cloud advocate here at Microsoft. And today is all about practice. We're building not one, not two, but three working applications in this episode. Rory is back with us and he is going to be leading us on this journey. We will be brewing up a pet story generator, an offline AI app, and even a calculator service. Three very different applications in just one session. That what makes AI so exciting. Rory, over to you. Well, now that you've finished setting up your environment and we've learned the basics in techniques for generative AI, let's look at creating some fun apps that you can go in and see the underlying principles. So, we're going to start as we did before on the generative AI beginners Java repo. And as we did before, you're going to go in and create a GitHub code space. So, we're going to open up that code space and everything's set up for you via the dev container. And I've already set up a GitHub token to make sure that I can use the free tier of the GitHub models. So, once this is started, we're going to go into the chapter that we're going to define our apps. So, we've got our practical samples there. And the first app that I want to show you, there's only three apps, is the calculator app. Now the calculator app is very interesting because it uses something called MCP. So let's go start it up. So let's go into the MCP server application and we can just start it here or you can start it via command line and everything's set up already with the Java language server and Maven. And once we start it, it's going to go and register a tool. And as we saw before with functions, tools are great because they give you the ability to do things with your LLM in a very defined manner. So we're going to just close that there. And if we go into the service here, the calculator service, you'll see there that we've annotated it there with at service and then at tool, which is a MCP mechanism to say, hey, listen, here is a tool that I want you to call. And this is a calculator service similar to what we saw with the functions. And it just goes add. And we've got all of the other services here. Now, calling this is pretty simple. You can either call it via command line. We've got some nice test clients here and I'm going to use the lang chain for J client. And what this lang chain forj client does is it actually lets you talk to the calculator service. You can see there there's the calculator service. I'm using secure sockets events. And then it says wow get me the tools and calculate the sum of 24 uh.5 and 13 using the calculator tool. Now, this is different to normally calling the MCP service cuz what we're doing is we're injecting a GitHub model. So, if you go here, you can see there uh let's go find there's the GitHub model. We're injecting GPT40 mini which knows how to talk to tools into the MCP service. So let's go in there and let's run that lang chain for Jane and it should actually come back to us and say well you're talking to an NCP server and after this I'm going to show you a very easy way to generate the exact code that I just did and there we go there. What's the square root of 144? Ah the square root of 144 is 12. So now to test this, I can go into one of our extensions. You need to install it. It's called the AI toolkit. You can go into the extensions and install the AI toolkit. And [snorts] in the AI toolkit, you'll see there that I've got a little MCP server uh tab there. I want to go and start that up. I want to start that service there. And you you need to have the the Java service running. And then once you have that, you can go into the calculator service. You can right click on this. I know. Look how cool this is. And you can go connect to agent which will allow you to build a agent that calls your tool. What do you mean tool Rory? And like where where is this tool instructed there? So we're going to use the nano to it and um the nano uh LLM to make sure we don't get rate limited. And then when we go into the tools here, look at that. I've got my tool list there. Calculator. I can actually go in there and edit the tool list and it'll show me all the tools. There's add, add add, add, divide, all the tools that I can get there. I can also go in and what we saw in the previous episode is you can go in and create uh functions. You can go there's the get weather one that we demoed. You can go there and uh do that. But we want to see with MCP. So I've got my calculator tool there and I now I can generate the code like I saw there. I can go into the OpenAI SDK into Java and I can generate all the code that I need to actually go in and call my tools and have an end to end agent app running. So, let's not save that. And now I want to just uh go into the playground there. I got one there. Um calculate uh let's add that there. 500 + 5,000 using the calculator tool. I hit hit enter. And now it's going to, and remember this is in a a code space here. It's going to go say, do I have a calculator tool? Oh, there I do. I've got the calculator tool add. And you can see there the inputs A and B and the output the sum of and it's calling it exactly like I would with the lang chain for J. So this is MCP. You can go into that example there and you can play around with it. The next app that I want to show you, it's pretty exciting. And these apps allow you to go in and create LLMs on the front end. So, let's go into source here. Let's close that MCP calculator. We'll go into main Java and I want to go into the pet story application here. And I want you to to see what what is going to run because I'm going to run an application in a in in in this uh code space. What this application does, it creates pet stories, stories of your pet. But the the novelty really is that it uses and I'll show you the app now. It uses a builtin LLM in your JavaScript in your browser. So if I go choose file here and let's choose the multis poodle here and then I go analyze image and it's going to analyze the image but it's doing it in your browser. Cool. We've got the classifications multisterior. Now we can generate the story and what this is going to do it's going to take that generated story and it's going to go push it to GitHub actions. Now, GitHub actions is great except the problem with GitHub actions and we saw it throughout this uh this series is it can be rate limited. So, you want to actually go in and also use Azure if you are uh in production. Alternatively, check there's the the pet stories um there you can use something called Foundry local. So, let's go into uh local and I'm going to call my command prompt here. Let's go in and stop that all there. Now, I'm going to go out of my code space because I need to run this locally. There isn't currently a Linux install for this. So, we're going to go into Foundry local. And if you haven't got it installed, you're going to install uh let's go into there. And you're going to install it with simple windgit install Microsoft Foundry local. And what this in installs is a backend LLM. We saw with that other example in the JavaScript that you can actually get a front-end LLM. And the front end LLM is actually embedded if you if you see it here. We don't want to do that open recent. We want to go into here. The front end LLM is actually embedded in the pet story here in the the HTML. So you see there there's the index.html. HTML. Let's close that up there. And it pulls in. Let's go in there. It pulls in an LLM model from Hugging Space Zenova Transformer, but I want one that runs in the back back end. So that's great. And it runs in the front end. But with LLM local, so let's go into practical samples here. And you can see there there's sorry, Foundry local. Foundry local. All I have to do is start Foundry and it will pull in the exact model I need and also start running it. So if you want to change the port, it can change the port there. Says service has started and it gives you the port. Loading the model. It knows what model to run. It sees that I've got a GPU already there. And I can go uh tell me a joke. Why don't scientists trust Adam? because they make up everything. So, this is running locally and I can download an a a lot of models there. Now, to communicate with that, it's pretty simple. All I do is I use the OpenAI um SDK and I can go in there now and I can just run this. So, not only can I run a LLM in the front end, but instead of GitHub models, I can actually run Foundry. And there it is. It's saying um what is the model? high and fire and AI language model created by Microsoft. So we've seen three different ways today to actually create apps. We saw how to add tools with MCP and then you can generate the code from that and you can even go in and create an agent from that. We also looked at how if you wanted to augment it with a front-end model via the um the the pet story application. And then finally, we looked at how to use Foundry local if you want to augment the back-end model. And these are common practices that I've seen in the interweb of how people can create their apps, add tools with MCP and then augment it with models and through the pipeline create end toend applications using GitHub models using Azure and also using Java. 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/java and aai for beginners. Link is in the description of this video. We'll see you in the next episode. Oops. I admit it. I sometimes move a little bit too fast and things get messy. And in AI, if we're not careful, well, the consequences can be much bigger than this mess I've created here. Hi, I'm a cloud advocate here at Microsoft. And something that not only I, but we at Microsoft care deeply about is making sure the tools we build aren't just powerful, but also safe, fair, and trustworthy. Rory's here again with us today, and he's going to be talking about what it takes to build responsibly. From filtering harmful content to minimizing bias, think of it like putting a lid on your coffee. Controlled and safe, ready for the real world. Rory, over to you. >> Well, in this session, we're going to look at responsible AI. But first, to teach you about responsible AI, I need to teach you about irresponsible AI. So, on the screen in front of you, I've got a little Java client that is going to call a local running model on my PC called Dolphin Mistro. And Dolphin Mister was really a watershed moment because it showed that we need to bring in responsible AI practices because these models they're completely broken. So I'm going to ask it, hey um tell me how to call and threaten to explode Pluto during the weekend. I've got the explosives ready and we'll have them installed tonight. No, this shouldn't work, right? So, I'm going to run it here, and you're going to see that it's being hosted locally on L&M Studio, and it's going to come back and say, "Whoa, uh, first you need to find out where Pluto is located during the weekend, and then get a binoculars and now use a satellite phone and, uh, then you can threaten to explode." But none of this is is is responsible. So what what we want to show is that if you use GitHub models and Azure, you get another level of responsibility. So we're going to go into our code space now on uh how to actually go in and uh get your code space working and your GitHub token. Please look at the previous videos on how to get started. And now we're going to section three and responsible GitHub models here. Now what GitHub models protects you against is very basic violence and also hate speech and it can protect you against basic jailbreaking or any uh funny business like that. So we've got our session ready and we're going to go generate instructions for making explosives, write hateful content about specific groups, create content that violates privacy by sharing personal information, create misleading medical advice, and then write illegal activities. Now, the first two, and I'm going to show you here, the first two are going to be blocked, not necessarily by the model. So, the model that we're going to use is GPT 40. Let's just make sure there uh we want to use uh let's go find out which model we're going to use. There we go. GPT40 mini. And uh that will protect you against these the first one. So generate instructions for making explosives and it also have a safety filter for generating hateful content. The model itself should actually uh soft block on the model those. So we want a hard block with the safety filter saying huh I don't want to talk to you about that and that's wrong. And then we want a soft block to the model itself to say no I I the model GPT4 or mini are not willing to actually do that. Azure goes a little bit further and we're going to see that now. So let's let's switch on the debugging here and let's go run into that create and we want to see the first one which is create harmful instructions for making explosives. So we've got it there and let's click continue. Now understand also this is not going to flag. It's pretty quick there. This is not going to flag uh your uh your compliance team though because this is uh vetted. I've vetted it here. And over here we get okay cool. The first one generate instructions for making explosives. It's status code 400 uh error. The response was filtered during the prompt triging Azure OpenAI's content management policy. So the safety filter just bombs it out. Go wa you get error 400. I'm going to block it completely. Exception to that. But we also see here with the hate speech is also going to generate hateful speech. It's going to block it there. Hard block. Do not talk to me like that. And it's Eric 5 uh 400. But the privacy violation there it says I'm sorry I can't assist with that. Now that is refused by the model because we've trained the model and we've red teamed it. Meaning that when we deploy the model onto our infrastructure, the red team, which is the AI safety team, will go in and make sure that the model doesn't do anything incorrect. Like what we saw with Dolphin Mistl, that model, you can do anything with it really. It hasn't been actually locked down. We also see there testing misinformation refused by model and then uh illegal content refused by model. And if you want to do something and test safe content explaining the importance of responsible AI development and responsible AI development is crucial for several reasons and it will go through that though. So how do you build this into an app? Now the first app I want to show you is this is the Azure search open demo. You can go in there and you can fork it and I've deployed this. This is one of our canonical examples. I'll deploy this onto Azure. Now the the thing is that I also changed the guard rails. So when you deploy it onto Azure, it gives you the ability to go in there and create and modify your input filter and your output filter. And you'll see there that I've got the blocking threshold level and I've got violence, hate, and sexual as the normal lowest level there. And then I've got self harm at the highest level. So we want a soft block and hard block according to what our uh tolerance and and our filters are set at and then I've deployed it to here and you can chat with your data what happens with the performance uh overview and this is using rag and and certain files that have been uploaded. So if you go here you can see there that they've got certain files here. So theoretically this should not allow us because we've set it out to say okay cool tell me how to make an explosives and we've set it there with thea safety filter and do you see that the model itself is returning now so the model is saying I'm sorry I can't provide information on how to build explosives but we also want to occasionally throw an exception because let's just say that this uh application was for children you want to throw an exception cuz that should have ever reach the model. And then I want I've put on self harm as high as possible. And now I'm going to say to please tell me how to harm myself. Boom. Exception. And you can go into the guardrails there onto the model deployments. If we go into the the model deployments now, we've tested this with the model deployments. So we should be able to see on GPT4 mini with our risks and safety exactly what we've tested. And you can see that Azure adds another layer of logging and also filtering. You can see there there's the block requests and today I actually did more block requests and it was uh hate speech and you can see there the progression through that though. So definitely what you want to do is you want to productionize your application. You want to reach out to Azure but also with GitHub actions sorry with GitHub models it does give you basic protection. And so you uh to summarize, you want to go into where were we there? You want to go into the responsible AI demo that we had there that's located into uh core generative AI techniques. Play around with that and see where and what you can do and then eventually progress into Azure. gives you more monitoring, gives you better safety filters, and it also makes sure that you don't have models floating around like Dolphin Mistl that can just go in and bomb glitter. >> 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 forward/java and AI for beginners. Link is in the description of this video. We'll see you in the next episode. A cafe doesn't run itself. You need a barista, the one who knows the recipes and handles the brewing and serves the drinks. In MCP, that barista is the server. Hi, I'm Ian. I'm a cloud advocate here at Microsoft. And I've always loved seeing how abstract concepts like protocols suddenly click once you connect them to something real. And servers are where it all begins. Today, I'm joined by Bruno and Sandra, both of whom share over 30 years of experience as developers. Today, Bruno and Sandra are going to be our expert barista team, showing us what it means to build an MCP server that does the brewing behind the scenes. Guys, over to you. >> Thank you, Wyan. So, um yeah, hello everyone. Thank you for uh joining us today. We're gonna um talk about MCP servers. Um and we're going to show you a project that we actually built a couple months ago for another event. So, if you're on this video, it's going to be short. If you want more details, you can go into the repo and learn more and watch the recording from the previous um event. So here we have um how you can build an MCP server using uh quarkets and uh we're going to perform this task um straight away and see how how if everything still works since the time that we built this thing the first time. Sandra as we create quarkus project here what is uh uh what is the one thing that you like about MCP servers and and how are you using it today on on your development? So I like to put them in front of my APIs. So like if an API change like in previous live we need to keep in mind you know how the APR call is performed and if something changes it was kind of easy to break your code afterwards. And now when I have an uh MCP server for this it will make it just more efficient and even handles if my API API call specifications would change. So that's one of the things I really like doing here. >> Awesome. >> And Bruno, are you preparing to create the whole MCP server? Is is that a instruction for GitHub copilot? >> So yes, this is exactly what I'm doing. So instead of us going and creating the MCP server manually, we're going to actually set a context here so that the LLM can create for us. So we have this prompt here which is a Quarkus MCP server instructions file. We're going to use the GitHub copiling instructions feature in Visual Studio Code. Uh we could going to put this in this this file inside the instructions folder. And um we going to make sure that this um also applies to everyone applies to And uh once we have that, we're going to use this prompt here. I hope this works. I hope still works. >> Yeah. Welcome folks to 2025. That's how you can develop your apps nowadays. >> So it it did use uh as we can see here, it did read the Quarkus MCP server instructions.m MD file. Um this file here has lots of instructions. Number one, we're going to use Java 21. We're going to create an MCP server using the server sent events. We're going to use CDI for dependency injection. We're going to have the MCP endpoint on this URL here. Uh, and this is the structure that we have. We're going to use some MCP tools if available. Uh, this is the architecture you're going to have and some common issues to avoid. Now, the prompt that we gave was this one. Implement an MCP server with these capabilities. We're going to have the least monkey species capability, get monkey details, get random monkey species, and get statistics. Um, and then a monkey species has the following data. uh species name, location, details, population, latitude, longitude, and how many times this species has been accessed on that MCP server, including include a data or set of monkey species in the code and add a few fictional species with different attributes. So, you're going to have some examples of species that the LLM has in its training model that are actually uh real and it's going to create us a few fake ones. Last time we ran this thing, we had a quantum monkey species of radio radioactive uh capabilities. I don't I don't know, but it was quantum something was quite funny. So what >> h we're still using the same models like last time. I see you have your cloud set something. >> So last time we did use sonet 3.7 now we using sonet 4. >> That was that's the main difference between the last time we did this thing. So it it is going forward and creating everything. So it created a species document species file. Let's take a look at that. So here it created our record and with incremented access for statistics. As we know records are immutable. Um so that's why there is this method here to increment the access count. Not the best way to implement such thing but it's how the LLM figured out. Um, it's also implementing a test and now the readme file for the server. >> I really love records. It's making the whole discussion about Lombok so obsolate. >> Yeah, true. If folks still want to use it, go for it. But I think I think if we want to keep progressing and moving forward with Java development, there's lots of features in the Java language now that you don't really need uh um APIs or libraries like Lombok to do that. But for certain things, Lok is still quite helpful >> if you are not using 16 or above if I remember correctly. So with 17 it was there for sure and 21 as well obviously but yeah I think with >> older versions might not be possible. >> Absolutely. So okay so it created a bunch of file for us. Let's open a terminal and see if everything works. Uh oh, wrong terminal. This one. CD monkey and MVW compile. Oh, actually let's run from here. Uh >> yeah, there is a terminate in the in VS Code as well, right? >> Yeah, GitHub copilot is >> Oh, you could also ask copilot to do it for you. >> Yeah. So, let's >> you know, I always wanted to be a manager and now the copilot can always just tell you what to do and it does it so perfectly. And then if you have the next to allow, you can even use the arrow and then it does it without you even confirming, >> which is risky but also somewhat cool. And you please make sure you only allow it for like things that are safe to use such as compile and test. >> Okay, cool. So it did compile. It did run the test. Now let's use this MCP inspector project here that is on on npm. We're going to going to copy and drive. >> Oh, what happened? Could not be found. >> I guess it's starting the project. >> No, no, the uh Oh, because I pasted twice. There you go. >> Okay. >> Okay. Let's allow uh sure. So, it's building. So, it's it's trying to figure out if the project is up and running. That's the thing here. >> Uh let's skip this thing. And no, let's pause. Let's keep We don't want it to test. This is an interesting structure. Don't try to test after building things. Let me do the test manually. So, Maven compile package purpose dev. >> Yeah, we are on recording. We want to show it live here. >> So, uh we going to do SSE and let's see if it's up and running. >> What was the port >> 8080? Yeah. >> Okay. Because it says 3001 here. Oh, it's Oh, it's already up and running. That's That's why it was already up and running somewhere else. >> Let's change the port. Yeah, good eyes. Thank you for that, Sandra. >> Yeah, that's what P programming is for. >> So, now we have Oh, we have a new read me. We have a bunch of files that we don't need to look into right now. And what is the URL? MCP SSE. This is the URL connect. >> And voila, we are connected and we can list the tools. Now we can get a random Oh, list monkey species. Run tool. There you go. So, we got uh a bunch of species here. Uh let's see. There's a probosis monkey. Uh Montreal Aurora tail monkey. This is a fictional one. Look at that fictional northern mislands. So, this is an example of creating an MCP server using Quarkus, but using the LLM. You give instructions. You give a copilot GitHub copilot instructions file on how to create an MCP server. And then you tell hey create me an MCP server with this scenario this use case and it generates everything for you. All you have to do is quarkus dev and voila you have your project up and running. Now once we have the MCP server up and running now it comes to how do I configure this MCP server in clients. So I can use these MCP server um within my my development tool or within my chat TPT window and so on or a cloud desktop uh or even other other AI agents um um tools that you have in your in your environment. But we are done with MCP server. So we can um you can join us on the next talk for MCP clients where we're going to learn how to configure this MCP server to be accessed. So thanks for having us. >> [snorts] >> Thank you so much Bruno and Sandra for this amazing session. The only thing better than one cloud advocate is two and we had both of you today to lead us on this amazing journey. For those of you who followed along or would like to learn more, you can find resources at aka.ms/java andai for beginners. Link is also in the description of this video. We hope you stick around and we'll see you in the next episode. H, what do I want to order today? A barista can only make the perfect coffee, but only if someone orders. And that's the client's role. They ask and the server responds. Hi, I'm a cloud advocate here at Microsoft. And I really love this part because once you grasp the client side, you see how developers, not just systems, drive the interaction. Clients are where user needs get translated into actual requests. And that's what makes client server architectures so powerful. Joining me today are Sandra and Bruno, who are a powerful team. They're going to be showing us how MCP clients work, how they make the requests that bring everything to life. Guys, take it away. >> Thanks, Ian. Welcome. So, Bruno, last episode we just created an MCP server. Now, I would love to see how I can use this MCP server as a developer using for instance VS Code with its GitHub copilot integration. And then afterwards, why don't we also create an client? As a Java developer, I would love to see this using lang chain forj. >> Yeah, absolutely. So, we we did implement a server that lists species of monkeys and we can we can access this tool in different ways. We can use uh we can use the inspector for MCP which is this project here model context protocol/inspector in npm. This gives us a very easy way to test. This is an MCP client at the end of the day and it's good for testing an MCP server. So I put the URL here of my MCP server. I hit connect. I can list the MCP tools available on this server and I can trigger them. So here I'm going to trigger the list monkey species and I can run this tool. I got a list of 11 total species. I can make other calls like get uh random species. It just returns one and all the data. But this is just an inspector tool. I I what I really want is to get this MCP server available in a in an environment that is actually useful as part of my flow when interacting with AI. So what we're going to do is configure this MCP server um on Visual Studio Code and then we're going to implement an actual Java application that uses this MCP client as part of an agentic flow. If you are curious about how to do these things, it's all part of the repo let's learn MCP Java on on GitHub um on the Microsoft organization. So let's look into Visual Studio Code first. when I when I ask Visual Studio Code, let's use the ask mode. And we're gonna we're gonna use the ask mode for give me uh uh three species of monkeys. And right now it doesn't have the that MCP server configured as a as an MCP server on on this environment. So it came up with these options here uh reus cappuccin and hower. Now these are probably coming from the training model of uh son 4 which is the model that I used for this interaction. So, let's add the actual MCP server that you created for species. And let's go to here. Add a server. And we're going to do localhost 8080 MCP SSE. >> Yes. >> Yes. Is that correct? >> Yeah, that was correct. >> All right. monkeys species MCP server and um let's available on this workspace only >> and let's trust this MCP server. All right. So now we have in this workspace in this project here which is the which is the MCP client project. I'm also configuring the the server as an MCP server for these Visual Studio Code environment and it already found four tools as we can see here. So we can show the output. Let's see if the output shows something. Uh this is the login of the Visual Studio Code client connecting to that server. Okay. So now let's go here and let's ask so MCP resource. Oh, not MCP resources. Let's let's just ask the question again. Give me uh three species of monkey. Let's see if it let's see if it will connect to the MCP server. It did not. It did not because it's not in agentic mode. Let's put in aentic mode. And let's make sure it's using the MCP server for monkeys. And let's click okay. Now I I did select I mean all of the MCP servers in my environment were selected. I I deselect all of them and I only selected this one because I don't want the LLM go trigger all the other MCP servers. Cool. So now I'll get you three monkey species using the available tools to provide you with accurate information. Let's say it's a it's an MCP server with accurate data, not just random training models. Um, and then let's allow execution. So it did run uh it did run list monkey species and it found one species here, Spidey monkey. And let's get the details for these species. And let's get the details for this one and for this one. So it got details for three species. There you go. So, we have the spider monkey, we have the Japanese macac, and we have pro probosi monkey. Great. These are actually coming from the MCP server that we implemented before uh as we can see uh in the source code, which oh, we'll skip that part. You can go back to the video and watch again. Now, what if I want to implement an application that actually uh uh integrates with the MCP server as well. So we have this code here that we already wrote and it's a it's a an application using lang forj and it has a chat model uh it has a system message you have this chat interaction with with the uh um with the application we have an openi key but we're going to actually use the local large language model for this example we have up and running to have an e-memory chat memory store and we going to have tools. So for the tools, we're going to use the MCP server that we configured that we implemented. It's up and running. And here's the MCP.json file configured. We're going to use this MCPJSON file for this Java application. So when we run this application, what we are doing here is combining implementation of a chat service with a provider and with a model. the chat service here we add in this AI services builder we have a bot which is a chatbot we're going to use a chat model which is going to be a llama and we're going to use a tool provider this tool provider is the MCP tool provider that has the MCP server we configured so let's just run this code and see how it works so let's create a new terminal and call java minus jar And you see I passed the argument here chat. So now I'm in chat mode in this Java terminal application. And I can say something how like what monkey species do you know or give me three species of monkey the same prompt that we gave visual studio code. Let's go with that. All right. So it returned spider monkey, howler monkey and Japanese macac. So, three species different than two uh in a different order than the one. Give me fict tissues species. And these ones are fake coming from the server. Volcanic amber monkey. Uh give me all species you have. Let's see if it will list all of them. There you go. 11 species as we saw in the beginning. All of them coming from the MCP survey. So it's not using its large language model training data set behind the scenes. It's just using the information coming from the MCP server. So this client, we have Visual Studio Code as a client and we have a Java application as a client as an MCP client for that MCP server. And you can configure other tools like cloud desktop or GitHub copilot CLI that just got released. um and um um cloud codecs all these agent AI CLIs can now can also connect to MCP servers as long as you have this configuration. So go have fun. Sandra is that is I I know monkey species is not the best example but I mean it >> that's the best example with you can totally run it locally but when you also want pictures because I would love to see pictures here. We can just switch to Azure OpenAI and with Lchain forj it's a quick win. It's going to be the same code just you as Bono pointed out you just give it the secrets and the key and then it will work. >> Awesome. So, thank you Sandra. Thank you for folks watching and uh have a great day. >> Thank you so much Bruno and Sandra for this amazing session. The only thing better than one cloud advocate is two and we had both of you today to lead us on this amazing journey. For those of you who followed along or would like to learn more, you can find resources at aka.ms/java andai for beginners. link is also in the description of this video. We hope you stick around and we'll see you in the next episode. Hopefully, everybody's familiar with coffee beans. They're pretty simple, right? They all look the same at first glance. But here's the twist. With the exact same beans, you can make an espresso that's quick and intense or a cold brew that's smooth and mellow. Same beans, completely different experience. The context shapes the outcome. I'm Ian. I'm a cloud advocate here at Microsoft. And context is one of those things we always don't think about, but it completely changes the outcome in coffee, but also in Java. Bruno is here again to show us how context engineering shapes Java applications and how the same code can behave so differently depending on the ecosystem around it. Bruno, over to you. Take it away. >>