How to Build Customized Chatbots Using GPT-4

An overview of GPT

GPT stands for Generative Pre-Trained Transformer, a flagship model released by OpenAI in 2018. It is a language model developed to get text as if it were generated by humans. It has outperformed several other AI language models like Google’s BERT.

It is primarily based on the concept of transformers, which forms the basis of its algorithm. Transformer is a type of neural network architecture that uses a self-attention layer to identify the relationships between different parts of the input, such as words in a sentence.

GPT has several layers of transformers stacked over each other. Each layer takes input from the previous layer, processes it using self-attention and feed-forward layers, and then passes its output to the next layer in the architecture. The output from the final layer is used to get the predicted text.

Based on the previous words, GPT uses this concept to predict the next word in a sentence. This allows the model to learn the patterns and relationships in the language data so that it can generate coherent and contextually appropriate text. Thus, GPT has a variety of applications in text classification, machine translation, and text generation.

Over time, OpenAI released several advanced versions of GPT. Let’s look at the special features of each one of them in brief:

1. GPT-2: This is the next version of GPT. The following are its features:

a. It was trained on a much larger corpus of data with nearly 1.5 billion parameters, enabling the model to study more complex patterns and generate more human-like text.

b. It has a feature to limit the number of predictions which prevents it from generating inappropriate or misleading text.

2. GPT-3: It is more robust and advanced than GPT 2. Some interesting features are:

a. It is trained on 175 billion parameters, making it much larger than GPT-2.

b. OpenAI introduced a new feature called "few-shot learning" and "zero-shot learning" which allows the model to perform well on tasks on which it is not trained. This is achieved through pre-training on very diverse datasets.

c. Another feature called "in-context learning" allows the model to learn from the inputs simultaneously and adjust its answers accordingly.

3. GPT-3.5: This is a more advanced version of GPT-3. It performs all the tasks that GPT-3 does but more accurately. It’s incorporated in ChatGPT in a free version.

4. GPT-4: This latest version is 10 times more advanced than its predecessor. It is trained to solve more complex problems and understands dialects that are extremely hard for any other language model to understand as dialects vary from place to place. It can synthesize stories, poems, essays, etc., and respond to users with some emotion.

Another impressive feature of GPT-4 is that it is capable of analyzing images. It can be used for purposes like generating automated captions and answering questions based on the input image. However, it cannot synthesize images on its own.

Steps to create a GPT-4 chatbot

Let’s look at how we can create our own customized chatbot using GPT-4.

Step 1: Understand the use cases of the chatbot

In order to create an effective chatbot, you need to know the use cases. This will help you create a plan and build it accordingly. You have to identify the target audience and the purpose of the chatbot.

Step 2: Create an account on OpenAI

To access the GPT-4 API, create an account on the official website of OpenAI and request access to the GPT-4 API. Once you receive it, store “OPENAI_API_KEY” securely in a text file.

Step 3: Choose a programming language

For this chatbot, we will be using Python. However, you can use other programming languages like Ruby, and Node.js.

Step 4: Create a virtual environment

Set up a virtual environment by installing the following Python library through the command line:

pip install virtualenv

Enter the project folder and type the command:

virtualenv chatbot_venv

The above command creates a virtual environment named “chatbot_venv”.

To activate the virtual environment, type the command:

chatbot_venv\scripts\activate

The virtual environment will get activated which will allow you to get the list of dependencies through a single command mentioned below:

Pip freeze> requirements.txt

Step 5: Install the required libraries

Install the OpenAI library using this command in the virtual environment:

pip install openai

Step 6: Create an environment file

In this step, you need to create an environment file with a .env extension for storing all the environment variables. You store the OpenAI API key with:

API_KEY=<your_api_key>

You will need to use python-dotenv to access the API key:

# importing all the relevant libraries
import openai
from dotenv import load_dotenv
load_dotenv()
api_key_openai = os.environ.get("API_KEY")
openai.api_key = api_key_openai

Step 7: Build a function to call GPT-4 API

In this step, we will write a function that interacts with the GPT-4 API. It accepts the following parameters:

1. Temperature: A measure of the randomness of a text generated by the model. Its value lies between 0 and 1. Values closer to 1 mean that the output is more random, whereas values close to 0 mean the output is very relatable.
2. Top_p: An alternative to the above method to add randomness to the output.
3. N: Signifies the number of responses generated by the API and provides a choice to the developer to select the best response.
4. Max_tokens: Signifies the length of a response generated by the API, i.e., the number of words present in the text.

def get_response(prompt, model="text-curie-002", max_tokens=150):
    response = openai.Completion.create(
        engine=model, #or "your_chosen_engine",
        prompt=prompt,
        max_tokens=max_tokens,
        n=5,
        stop=None,
        temperature=0.8,
    )  return response.choices[0].text.strip()

We have seen how to call an API to get a response from the GPT-4 model. Now, we will customize our own dataset for a customized chatbot.

Customizing a chatbot with specific knowledge

In this section, we will create a chatbot that is capable of answering domain-specific knowledge based on its use case.

The dataset has to be prepared in JSON with the correct format, as mentioned by OpenAI. For custom training, we will also require the OpenAI fine-tuning API which is only applicable for selective models like GPT-3.

Here’s an example of JSON that has to be prepared for custom training:

[
 {
   "role": "user",
   "content": "How do you define a variable in computer programming?"
 },
 {
   "role": "assistant",
   "content": "It is defined as a state in a programming language that is capable of storing a value."
 },
 {
   "role": "user",
   "content": "How do you create a function in Python?"
 },
 {
   "role": "assistant",
   "content": "’ def’ keyword is used for defining a function in Python."
 }
 // More examples...
]

As a next step, we will upload the above-created dataset on the OpenAI server. Below is the code:

import openai
import json
openai.api_key = "your_openai_api_key"
with open("my_dataset.json", "r") as f:
data = json.load(f)
dataset = openai.Dataset.create(
data=data,
name="my_dataset_name",
description="my_dataset_metadata",
)

Once the dataset is uploaded, the following code will describe how to train the GPT-4 model for a custom dataset.

import openai

openai.api_key = "your_openai_api_key"
fine_tuning = openai.FineTune.create(
model="text-davinci-002",

dataset=dataset["id"],
n_steps=1000,

prompt_tokens=1024,

)
job_id = fine_tuning["id"]

After finishing the fine-tuning stage, you can evaluate the model using the OpenAI API. The ID of the fine-tuned model will be accessible in the fine_tuning_status dictionary, specifically in the 'model' key. You can substitute the 'your_chosen_engine' placeholder in the get_response function example given before with the ID of the fine-tuned model.

Conclusion

We’ve discussed the different types of GPT models available to date and learned to create a customized GPT-4 chatbot using OpenAI’s API. GPT generates human-like text using self-attention layers in transformers. The model has multiple layers that predict the next word in a sentence based on previous words, making it capable of generating coherent and contextually appropriate text.

With each new version, OpenAI has demonstrated the power of AI chatbots. And with GPT-4 and its superior capabilities, including extensive problem-solving, ability to interpret longer blocks of text, and respond more accurately, it remains to be seen what the future holds for language models.