Developing a Gen AI Application Using IBM Granite Code

speaker1

Welcome, everyone, to today's podcast! We're diving into the exciting world of developing a generative AI application using IBM Granite Code. I'm your host, and joining me is my co-host. Are you ready to explore how we can build a meeting summarizer using Python, Flask, and the IBM Granite Code model?

speaker2

Absolutely! I'm so excited to be here. So, what exactly is IBM Granite Code, and why is it such a game-changer for developers?

speaker1

Great question! IBM Granite Code is an open-source AI model designed to assist developers in their day-to-day tasks. It can run locally on your machine, which means you don't have to worry about data privacy or API costs. It's incredibly versatile and can help with everything from code completion to generating test cases and even documenting your code. Let's start by setting up our development environment.

speaker2

That sounds amazing! Can you walk us through the prerequisites for setting up the environment? I'm sure a lot of developers will want to follow along.

speaker1

Of course! To get started, you'll need to follow the steps in Gabe Goodhart’s tutorial, which will guide you through configuring your environment using the `granite-code:8b` model for auto-completion and the `granite-code:20b` model for everything else. You'll need to install Continue, an open-source AI code assistant IDE plugin, and Ollama, which will run the AI models locally. Once you have everything set up, we can dive into designing and building our API.

speaker2

Okay, I have everything set up. Now, how do we design the API for our meeting summarizer?

speaker1

Great! The first step is to think about the structure of your API. We'll use a RESTful design to manage meeting transcripts. Here’s a basic outline: we have endpoints for creating, listing, retrieving, updating, and deleting transcripts. We'll also need to handle the summarization process. I'll start by asking the Granite Code model to critique my proposed API design and then generate the initial scaffolding using Python and Flask. Let me show you how that works.

speaker2

Hmm, that sounds like a lot of steps. Can you give me a quick example of what the API endpoints might look like?

speaker1

Sure! Here’s a simplified version: the `/transcripts/` endpoint will handle creating and listing transcripts, while the `/transcripts/:transcript_id` endpoint will handle retrieving, updating, and deleting specific transcripts. The `POST` method will create a new transcript, `GET` will list all transcripts, `DELETE` will remove a transcript, and `PUT` will update a transcript and generate a new summary. I’ll ask the Granite Code model to generate the initial code for these endpoints.

speaker2

That makes sense. Now, how do we implement the core functionality, like actually generating the summary of a meeting transcript?

speaker1

To do that, we’ll need to integrate with the Ollama API, which we’re already running locally. I’ll write a method called `summarize()` that takes a file path as input, makes a call to the Ollama API, and returns the summary. I’ll use the Continue plugin to generate this method, and then I’ll test it to make sure it works. Let’s see how that goes.

speaker2

Okay, I’m following along. How do we generate test cases to ensure everything is working as expected?

speaker1

Good question! We’ll use the Granite Code model to generate pytest cases for our `create_transcript()` function. The model will create a test case that uploads a transcript, generates a summary, and checks if the summary is stored correctly. I’ll show you how to generate and run these tests using the Continue plugin. It’s a great way to catch any issues early in the development process.

speaker2

That’s really helpful. What about iterating and refining the code? How does the code assistant help with that?

speaker1

The code assistant is incredibly useful for handling boilerplate tasks like setting up data persistence in Redis and generating a simple HTML/JavaScript UI. It can also help with code completion, suggesting logging messages, and even drafting documentation. For example, when I was adding logging to a Python function, the code assistant auto-suggested appropriate messages. Let me show you a few examples of how it helps with troubleshooting and refining the code.

speaker2

Wow, that’s really impressive! How about documenting the code? Can the code assistant help with that too?

speaker1

Absolutely! I asked the Granite model to generate a README for the application, and it gave me a solid draft to build on. After some minor tweaks, I had a well-documented project. The model even auto-formatted the content into Markdown. It’s a huge time-saver, and it ensures that your project is easy to understand and maintain.

speaker2

That’s fantastic! So, what are the key takeaways from using IBM Granite Code as a local AI assistant?

speaker1

The key takeaways are that IBM Granite Code can be run anywhere, but it’s especially useful when kept local for data privacy and offline work. It’s fully open source, both in terms of the model and the data it’s trained on. The productivity boost is real, from generating boilerplate code to handling more tedious tasks like debugging and documentation. Best of all, it’s free to use. If you’re a developer curious about local AI assistants, I highly recommend giving it a try.

speaker2

Thanks for sharing all of this! For those who want to learn more, what are the next steps and where can they find further reading?

speaker1

To get started, check out more articles and tutorials on IBM Developer. You can also view the completed sample Meeting Summarizer project on GitHub. For a more in-depth look at the Granite models, including Granite Code, visit the IBM Developer site. And if you’re interested in trying out watsonx, IBM’s collaborative AI and data platform, you can sign up for a free trial. It’s a great way to explore generative AI and build powerful applications with ease.