Train Custom AI Chatbot with Chatgpt API and GPT Index

Table of Contents

1. Introduction
2. Setting Up Custom Data
3. Requirements
4. Constructing the Index
5. Charging the Chatbot
6. Launching the UI
7. Asking for Information
8. Describing Students
9. Asking About Branch and Performance
10. Conclusion

📚 Introduction

Welcome back to Learn with Puja! In this video, we will discuss how to train our own data with the Charge GPT API and Create a custom knowledge base for our chatbot. The chatbot will be built using Charge GPT, the APA Lang chain, and the GPT index.

Setting Up Custom Data

To start, let's create a CSV file with some student information. The file will include the student ID, name, branch, score, phone number, and address. We will save this file in a "docs" folder.

Requirements

Before we can start coding, we need to ensure we have the necessary requirements. These include an operating system (Windows, Linux, or Mac), a CPU or GPU machine, and Python installed on our system. We also need to install the OpenAI GPT index and the Lantern library for external data connection.

Constructing the Index

Once we have all the requirements in place, we can proceed with coding the custom chatbot. We will import the necessary libraries, including the GPT index and the GPT simple Vector index. Then, we will construct the index by providing the directory path, max input size, number of outputs, and other parameters.

Charging the Chatbot

After constructing the index, we will load the data and convert it into vectors. We will then save the vectors to a disk in JSON format. Next, we will load the index and Interact with it using user input. We will prompt the user to enter their text, and the chatbot will provide a response Based on the training data.

Launching the UI

To make the chatbot accessible, we can launch a user interface (UI). The UI will provide a public URL where users can test the chatbot by entering their text and receiving responses. We will run the UI on a local URL, which will be displayed as an HTTP address.

Asking for Information

Once the chatbot is up and running, we can ask it questions about the student information. For example, we can ask for information about a specific student, such as Puja. The chatbot will provide the student's details, including their ID, branch, score, phone number, and address.

Describing Students

We can also ask the chatbot to describe a student in more Detail. For instance, if we ask for a description of Puja, the chatbot will provide a brief summary of Puja's profile, including her Roll number, branch of study, score in exams, and hometown.

Asking About Branch and Performance

Additionally, we can Inquire about a student's branch of study and their performance in exams. For example, we can ask about the branch of Bobby and how well he is performing in exams. The chatbot will respond with Bobby's branch (civil) and his exam score (89%).

Conclusion

In this video, we learned how to train a custom chatbot using our own data. We covered the steps to set up the custom data, install the necessary requirements, construct the index, and charge the chatbot. We also explored how to launch the chatbot's user interface and ask questions to retrieve student information. The chatbot provided accurate descriptions of students and their performance. Stay tuned for more videos on different topics. Thank You for watching, and don't forget to subscribe to our Channel!

FAQ

Q: Can I use different file formats instead of CSV for the student information? A: Yes, you can use different file formats like Excel or JSON. However, you would need to modify the code to parse and load the data accordingly.

Q: How can I train the chatbot with additional data? A: To train the chatbot with additional data, you can append the new data to the existing CSV file and rerun the code to update the index. The chatbot will then be able to provide responses based on the updated data.

Q: Can the chatbot handle large amounts of data? A: Yes, the chatbot is designed to handle large amounts of data. The GPT index allows for efficient storage and retrieval of information, making it suitable for scaling the chatbot's capabilities.

Q: Can I customize the chatbot's responses? A: Yes, you can customize the chatbot's responses by modifying the training data or by incorporating additional logic into the code. However, it is important to ensure that the responses align with the context and purpose of the chatbot.

Q: Is it possible to integrate the chatbot with other applications? A: Yes, it is possible to integrate the chatbot with other applications by using APIs or webhooks. This would allow the chatbot to communicate with other systems and provide its responses in a seamless manner.