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.
Table of Contents (15 chapters)
1
Section 1: Understanding GPT-3 and the OpenAI API
4
Section 2: Getting Started with GPT-3
8
Section 3: Using the OpenAI API

Understanding semantic search

A semantic search matches a search term or query words with semantically similar documents containing any amount of text. A simple keyword search might just look for words in the query that match words in the documents. However, a semantic search goes way beyond that. It looks at the meaning of words and ranks the documents with the highest-ranking document representing the document that is most semantically similar to the query. For example, suppose we have the query an animal with wings and five one-word documents: dog, cat, snake, rabbit, eagle. A semantic search would rank each of the five documents and assign the highest rank to the document containing the word eagle because it is most semantically similar to the query.

Every time you query Google, you're using semantic search and like Google, GPT 3 can also search over documents. However, rather than searching documents on the web, the documents are provided as part of the request to the...