Book Image

Exploring GPT-3

By : Steve Tingiris
Book Image

Exploring GPT-3

By: Steve Tingiris

Overview of this book

Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text that is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API. If you want to get hands-on with leveraging artificial intelligence for natural language processing (NLP) tasks, this easy-to-follow book will help you get started. Beginning with a high-level introduction to NLP and GPT-3, the book takes you through practical examples that show how to leverage the OpenAI API and GPT-3 for text generation, classification, and semantic search. You'll explore the capabilities of the OpenAI API and GPT-3 and find out which NLP use cases GPT-3 is best suited for. You’ll also learn how to use the API and optimize requests for the best possible results. With examples focusing on the OpenAI Playground and easy-to-follow JavaScript and Python code samples, the book illustrates the possible applications of GPT-3 in production. By the end of this book, you'll understand the best use cases for GPT-3 and how to integrate the OpenAI API in your applications for a wide array of NLP tasks.

Related Content you might be interested in

Current Title:

Small book image
Exploring GPT-3
Steve Tingiris
Aug, 2021 | 296 pages
Small book image
GPT-3
Sandra Kublik | Shubham Saboo
Feb, 2023 | 150 pages
Small book image
OpenAI API Cookbook
Henry Habib
Mar, 2024 | 192 pages
Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Understanding GPT-3 and the OpenAI API
Section 1: Understanding GPT-3 and the OpenAI API
Free Chapter
2
Chapter 1: Introducing GPT-3 and the OpenAI API
Chapter 1: Introducing GPT-3 and the OpenAI API
Technical requirements
Introduction to GPT-3
Democratizing NLP
Understanding prompts, completions, and tokens
Introducing Davinci, Babbage, Curie, and Ada
Understanding GPT-3 risks
Summary
3
Chapter 2: GPT-3 Applications and Use Cases
Chapter 2: GPT-3 Applications and Use Cases
Technical requirements
Understanding general GPT-3 use cases
Introducing the Playground
Handling text generation and classification tasks
Understanding semantic search
Summary
4
Section 2: Getting Started with GPT-3
Section 2: Getting Started with GPT-3
5
Chapter 3: Working with the OpenAI Playground
Chapter 3: Working with the OpenAI Playground
Technical requirements
Exploring the OpenAI developer console
Diving deeper into the Playground
Working with presets
Summary
6
Chapter 4: Working with the OpenAI API
Chapter 4: Working with the OpenAI API
Technical requirements
Understanding APIs
Getting familiar with HTTP
Reviewing the OpenAI API endpoints
Introducing CURL and Postman
Understanding API authentication
Making an authenticated request to the OpenAI API
Introducing JSON
Using the Completions endpoint
Using the Semantic Search endpoint
Summary
7
Chapter 5: Calling the OpenAI API in Code
Chapter 5: Calling the OpenAI API in Code
Technical requirements
Choosing your programming language
Introducing replit
Using the OpenAI API with Node.js/JavaScript
Using the OpenAI API in Python
Using other programming languages
Summary
8
Section 3: Using the OpenAI API
Section 3: Using the OpenAI API
9
Chapter 6: Content Filtering
Chapter 6: Content Filtering
Technical requirements
Preventing inappropriate and offensive results
Understanding content filtering
Testing the content filtering process
Filtering content with JavaScript
Filtering content with Python
Summary
10
Chapter 7: Generating and Transforming Text
Chapter 7: Generating and Transforming Text
Technical requirements
Using the examples
Generating content and lists
Translating and transforming text
Extracting text
Creating chatbots
Summary
11
Chapter 8: Classifying and Categorizing Text
Chapter 8: Classifying and Categorizing Text
Technical requirements
Understanding text classification
Introducing the classifications endpoint
Implementing sentiment analysis
Assigning an ESRB rating to text
Classifying text by language
Classifying text from keywords
Summary
12
Chapter 9: Building a GPT-3-Powered Question-Answering App
Chapter 9: Building a GPT-3-Powered Question-Answering App
Technical requirements
Introducing GPT Answers
Introducing the Answers endpoint
Setting up and testing Express
Creating the API endpoint for GPT Answers
Creating the GPT Answers user interface
Integrating the Answers endpoint
Generating relevant and factual answers
Using files with the Answers endpoint
Summary
13
Chapter 10: Going Live with OpenAI-Powered Apps
Chapter 10: Going Live with OpenAI-Powered Apps
Technical requirements
Going live
Understanding use case guidelines
Addressing potential approval issues
Completing the pre-launch review request
Summary
Why subscribe?
14
Other Books You May Enjoy
Other Books You May Enjoy
Packt is searching for authors like you
Leave a review - let other readers know what you think

Classifying text from keywords

Another common text classification task is to classify documents based on keywords. To do this, we can use GPT3 to create a list of keywords that will be related to the content of the document. However, GPT3 doesn't just extract keywords from the document. It determines keywords that are relevant based on the document content. Let's try an example.

In this example, we will use the completions endpoint to classify a document based on relevant keywords.

Node.js/JavaScript example

To create the keywords classifier example in Node.js/JavaScript, follow these steps:

  1. Log in to replit.com and open your exploring-gpt3-node REPL.
  2. Create a new file – chapter08/keywords-classifier.js.
  3. Add the following code to the beginning of the keywords-classifier.js file:
    const axios = require('axios');
const apiKey = process.env.OPENAI_API_KEY;
const client = axios.create({
  headers: { &apos...
Previous Section
End of Section 8
Next Section