Book Image

Modern Generative AI with ChatGPT and OpenAI Models

By : Valentina Alto
4.9 (8)
Book Image

Modern Generative AI with ChatGPT and OpenAI Models

4.9 (8)
By: Valentina Alto

Overview of this book

Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You’ll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you’ll explore use cases where ChatGPT can boost productivity and enhance creativity. You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You’ll also discover enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you’ll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models’ APIs in your own projects.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Generative AI and GPT Models
4
Part 2: ChatGPT in Action
11
Part 3: OpenAI for Enterprises

Exploring semantic search

Semantic search is a cutting-edge search technology that has revolutionized the way people find information online. In the world of enterprise, it has become a vital tool for businesses that need to search through vast amounts of data quickly and accurately. The semantic search engine uses NLP techniques to understand the meaning of the search query and the content being searched. This technology goes beyond traditional keyword-based search engines by using ML algorithms to understand the context of the search query, resulting in more accurate and relevant results.

A key component of semantic search is the use of embedding, which is the process of representing words or phrases as numerical vectors. These vectors are generated by a neural network that analyzes the context of each word or phrase in a given text corpus. By converting words into vectors, it becomes easier to measure the semantic similarity between words and phrases, which is crucial for accurate...