Creating a Formula 1 Chatbot
Use this tutorial to learn how to build a chatbot customized with Formula 1 data.
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Use this tutorial to learn how to build a chatbot customized with Formula 1 data.
Last updated
Was this helpful?
This guide will help you develop a specialized AI agent using Gaia. In this example, we are building a chatbot specializing in Formula 1 data that will respond to inquiries around its history, competitions, and regulations. This guide will show you how to set up a Gaia node, build a knowledge base, and launch a customized LLM chatbot. By the end of this guide, we will have a live chatbot that will answer F1 inquiries.
Gaia is a decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise. It allows individuals and businesses to create AI agents.
Each GaiaNet node provides:
a web-based chatbot UI
an OpenAI compatible API
Before we begin building our F1 chatbot, we must first install a node on your system. A Gaia node can be installed on a variety of devices and operating systems. For more information, check out the
Let’s quickly get a node installed on your system.
Using your terminal, run this script to download the latest version of the GaiaNet node:
After you run the script, you will see the following in your terminal:
Node ID
A command that will make the GaiaNet CLI tool available within the shell
A command that will allow you to initialize the node.
Run the command that will make the CLI tool available, it will begin with source.
Initialize your node by using the gaianet init command. The CLI tool will help you download the models and recover the knowledge embeddings.
After you’ve initialized the node, start it by using the gaianet start command.
Congrats! You’ve installed and started your GaiaNet node! Now that we have completed our initial step, we can start preparing our F1 chatbot.
Our Gaia node is up and running, so now we need to prepare our F1 data. Data preparation takes the following steps:
Aggregating data sources
Creating a .txt file and adding data.
Segmenting the data
For this tutorial, we will be using the following data sources to train our chatbot:
The following example demonstrates the structure of the .txt file and how it should be formatted. If your file is under 768 words, then you do not need line breaks. If your file is over 768 words then you will need line breaks.
Now that the data has been aggregated, it will be converted into a vector database. For this tutorial, we will be using the GaiaNet tool that was developed to make vector collection snapshot files. This tool makes it easy for everyone to create their own knowledge base.
First, you’ll upload the .txt file created earlier into the tool by either dragging the file or uploading it.
In the “snapshot_name” section, provide a name for the snapshot.
Click “Make a RAG,” this process can take a few minutes depending on the size of your file.
After the RAG has been created, a JSON response will be generated and will look something like this:
Congrats, you’ve made your RAG and we will proceed to configuring your node!
So far, we’ve installed a node on our system, built a knowledge base, and made a RAG for our Formula 1 data. We now have to configure our node to use our F1 knowledge base.
Update the configuration changes:
Now that your node is restarted and configured with the Formula 1 data, the fun now begins!
Use the node URL that was generated when your node was first created, to access the agent and test it out.
Update the LLM model, we will use a model with better performance than the default. Run this code to update with :
