How to Build a Question Answering System with OpenAI GPT-3 and Python

Posted on by Panther

In this tutorial, we will learn how to build a question answering system using OpenAI GPT-3 and Python. OpenAI GPT-3 is a powerful language model that can generate human-like text and answer questions based on the given context.

To build the question answering system, we will use the OpenAI Python library, which provides easy access to the GPT-3 API. We will write a Python script that takes a context and a question as input and outputs the answer using GPT-3.

Here are the steps we will follow in this tutorial:
1. Set up OpenAI GPT-3
2. Understand the GPT-3 API
3. Create the Python script
4. Test the question answering system

Let’s get started!

Step 1: Set up OpenAI GPT-3

Before we can use GPT-3, we need to set up an OpenAI account and obtain an API key. Follow these steps to set up GPT-3:

  1. Go to the OpenAI website (https://openai.com/) and sign up for an account.
  2. Once you have an account, go to the OpenAI API page (https://platform.openai.com/signup) and join the waitlist for GPT-3 access.
  3. Once you have access, create an API key on the API settings page.

Now that we have an API key, we can move on to the next step.

Step 2: Understand the GPT-3 API

The GPT-3 API allows us to interact with the GPT-3 language model. It provides various endpoints for different types of tasks, including question answering. To understand how the API works, let’s look at an example API call:

import openai

openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the world series in 2020?"},
        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
        {"role": "user", "content": "Where was it played?"}
    ]
)

In this example, the model parameter specifies the version of GPT-3 we want to use. The messages parameter is a list of role/content pairs that represent the conversation. Each conversation consists of alternating user and assistant messages.

The API will return a response that contains the assistant’s reply to the last user message. We can extract the answer from the response and use it in our question answering system.

Step 3: Create the Python script

Now that we understand the basics of the GPT-3 API, let’s create a Python script that interacts with the API and builds our question answering system. Follow the steps below to create the script:

  1. Install the OpenAI Python library by running the following command in your command-line interface:
pip install openai
  1. Import the openai module and set up your API key:
import openai

openai.api_key = "YOUR_API_KEY"
  1. Define a function that takes a context and a question as input and returns the answer using GPT-3:
def answer_question(context, question):
    conversation = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": context},
        {"role": "user", "content": question},
        {"role": "assistant", "content": ""}
    ]

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=conversation
    )

    answer = response['choices'][0]['message']['content']
    return answer

In this function, we create a conversation with the given context and question. We pass the conversation to the GPT-3 API and extract the assistant’s reply as the answer.

  1. Test the question answering system by calling the answer_question function with a context and a question:
context = "The Los Angeles Dodgers won the World Series in 2020."
question = "Who won the World Series in 2020?"

answer = answer_question(context, question)
print(answer)

When you run the script, it should output the answer to the question based on the given context.

Step 4: Test the question answering system

Now that our question answering system is ready, let’s test it with different contexts and questions. Here are a few examples:

context = "The capital of France is Paris."
question = "What is the capital of France?"
answer = answer_question(context, question)
print(answer)

This should output “Paris” as the answer.

context = "Python is a programming language."
question = "What is Python?"
answer = answer_question(context, question)
print(answer)

This should output “Python is a programming language.” as the answer.

You can test the question answering system with various contexts and questions to see how it performs.

Conclusion

In this tutorial, we have learned how to build a question answering system using OpenAI GPT-3 and Python. We set up GPT-3, understood the GPT-3 API, created a Python script, and tested the question answering system. OpenAI GPT-3 provides a powerful way to generate human-like text and answer questions based on the given context. You can further enhance the question answering system by expanding the conversation and incorporating more user messages.

Remember to keep your API key secure and avoid exposing it publicly. OpenAI offers detailed documentation on the GPT-3 API, which you can refer to for more advanced usage and options. Have fun experimenting with the GPT-3 language model and building innovative applications!

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