The video tutorial demonstrates how to build an AI chat application using the watsonx.AI SDK for JavaScript and Next.js, guiding viewers through setting up a project, creating a chat interface, and implementing messaging functionality with state management. The presenter also covers enhancing user experience with loading states, handling tool calls, and integrating community tools for more complex queries, while encouraging further exploration of the SDK’s capabilities.
In this video, the presenter guides viewers through the process of building an AI chat application using the watsonx.AI SDK for JavaScript and Next.js. The tutorial begins with setting up a new Next.js project using the Next.js CLI, where the presenter creates a boilerplate application named “watsonx chat app.” After bootstrapping the application, the presenter navigates to the project directory and starts the development server to view the initial setup in the browser. The focus is on creating a simple chat interface, which includes a header, an input bar for user questions, and placeholder messages to simulate a chat experience.
Next, the presenter customizes the chat application by removing unnecessary boilerplate code and implementing a basic layout using Tailwind CSS. The layout consists of a header, an input field, and components to display messages. The presenter emphasizes the importance of state management in React, introducing the use of the useState hook to manage the input message and the message history. The input field is set up as a controlled component, allowing it to update the state as the user types. The presenter also prepares to integrate the watsonx.AI SDK by installing it via npm and setting up environment variables required for connecting to the AI models.
The tutorial progresses to the implementation of the messaging functionality. The presenter creates a server-side function to handle sending messages to the large language model (LLM) using the watsonx.AI SDK. This function is designed to format messages appropriately and return responses from the LLM. The presenter explains the significance of defining message types, including roles for user and assistant messages, and how to structure the message history. The integration of the messaging function into the frontend is demonstrated, allowing users to send messages and receive responses from the AI.
As the application evolves, the presenter adds a loading state to enhance user experience, indicating when the application is processing a request. The loading state is managed using another useState hook, which disables the input field while waiting for a response. The presenter also discusses how to handle tool calls, where the LLM can suggest using specific tools for certain tasks, such as mathematical calculations. The tutorial includes setting up a simple tool for adding numbers and explains how to implement logic for executing these tool calls based on the LLM’s responses.
Finally, the presenter introduces the concept of community tools using wxflows, allowing for the integration of more complex mathematical tools without having to define them manually. The tutorial covers the installation of wxflows, importing community tools, and deploying them to an endpoint. The presenter demonstrates how to modify the application to utilize these community tools, showcasing the ability to handle more complex queries. The video concludes by encouraging viewers to explore further capabilities of the watsonx.AI SDK, such as streaming and image-based interactions, while providing access to the complete code in the video description for those interested in building their own AI applications.