OpenAI GPT for Python Developers : The Art and Science of Building AI-Powered Apps with GPT-4, Whisper, Weaviate, and Beyond / Aymen El Amri.

Format:

Book

Author/Creator:

El Amri, Aymen, author.

Language:

English

Subjects (All):

ChatGPT.

Artificial intelligence--Computer programs.

Artificial intelligence.

Natural language processing (Computer science).

Python (Computer program language).

Physical Description:

1 online resource (334 pages)

Edition:

First edition.

Place of Publication:

Paris, France : FAUN, [2024]

System Details:

Mode of access: World Wide Web.

Biography/History:

El Amri Aymen: Aymen El Amri is a polymath software engineer, entrepreneur, and author with extensive experience in various technology fields, including DevOps & Cloud Native, Cloud Architecture, Python, NLP, and more. Aymen has authored multiple books and courses and provided training that has benefited thousands of developers and software engineers. His passion for teaching is evident in his practical approach, which simplifies complex concepts into understandable language and offers real-world examples that resonate with his audience. Aymen is the founder of several projects, including eralabs, FAUN, and MarketToDev, and he has been featured in several reputable publications.

Summary:

“OpenAI GPT for Python Developers” is meticulously crafted to provide Python developers with a deep dive into the mechanics and applications of GPT technology, beginning with a captivating narrative on the evolution of OpenAI and the fundamental workings of GPT models. As readers progress, they will be expertly guided through the essential steps of setting up a development environment tailored for AI innovations, coupled with insightful advice on selecting the most appropriate GPT model to suit specific project needs. The guide progresses into practical tutorials that cover the implementation of chat completions and the art of prompt engineering, providing a solid foundation in harnessing the capabilities of GPT for generating human-like text responses. Practical applications are further expanded with discussions on the creation of autonomous AI-to-AI dialogues, the development of AI avatars, and the strategic use of AI in interactive applications. In addition to technical skills, this book addresses the ethical implications and prospects of AI technologies, encouraging a holistic view of AI development. The guide is enriched with detailed examples, step-by-step tutorials, and comprehensive explanations that illuminate the theoretical aspects and emphasize practical implementation.

Contents:

Intro

Preface

About the Author

The Story of OpenAI and ChatGPT

About This Guide

The Companion Toolkit

Stay Connected

How Does GPT Work?

Setting Up the Development Environment

Notes

Installing Python, pip, and a Virtual Development Environment

Obtain Your OpenAI API Keys

Install the Official Python Bindings

Test our API Keys

Understanding the Available Models and Which One to Use

OpenAI Available Models and Important Considerations

Which Model to Use?

OpenAI Model Series

GPT-4 Series

GPT-3.5 Series

InstructGPT-3 Series

Base GPT-3 Series

Codex Series

Content Filter

DALL-E Series

TTS Series

Whisper Model

Embedding Model

OpenAI Models and Pricing

What's Next?

Using GPT Chat Completions

An Introductory Example

System, User, and Assistant Roles

The System Role

The User Role

The Assistant Role

Few-shot Learning with Chat Completions

Formatting the Output

Controlling the Output's Token Count

Controlling When the Completion Output Stops

Temperature and Hallucination

Sampling with Top_p

Temperature vs Top_p: What's the Difference? Which One Should I Use?

Streaming the API Response

Controlling Repetitiveness: Frequency and Presence Penalties

Frequency vs. Presence Penalty

Controlling the Number of Results from the API

Conclusion

Advanced Examples and Prompt Engineering

What is Prompt Engineering?

Few Shot Learning: A Key Prompt Engineering Technique

Overgeneration and Selection

General Knowledge Prompting (GKP): Generating a Rap Song

Context Stuffing: Is Apple a Fruit or a Company?

Dynamic Max Tokens

Creating an Interactive CLI-Based Assistant

Embedding

What is an Embedding?

Use Cases: From Modern Search Engines to Self-Driving Cars.

Tesla: How Embeddings Are Used in Self-Driving Cars

Kalendar AI: The Power of Embeddings in Sales Outreach

Notion: Enhanced Search Capabilities

DALL·E 2: Text-to-Image Conversion

Understanding Text Embedding

Embeddings for Multiple Inputs

Use case: Semantic Search

Cosine Similarity: A Deeper Look

Semantic Search and OpenAI's Text Embeddings

Behind the Scenes: How Embeddings Work

Advanced Embedding Examples

Predicting Your Preferred Coffee

Creating a "Fuzzier" Search

Predicting News Category: Zero-Shot Classification with Embeddings

Evaluating the Accuracy of a Zero-Shot Classifier

Precision in Zero-Shot Classifier Applications: Examples

Fine-Tuning and Best Practices

Few-Shot Learning

Enhancing Few-Shot Learning

Practical Application of Fine-Tuning

Fine-Tuning Best Practices

Choosing the Model

Validating the Dataset

Token Limit

Dataset Size

Testing and Improving Training (Hyperparameters)

Epochs

Learning Rate Multiplier

Batch Size

Consider Estimated Costs

Dataset Quality

Combining Fine-Tuning with Other Techniques

Experiment and Learn

Use a Validation Set

Test the Model

Analyze the Results

Advanced Fine-Tuning: Mental Health Coach

Dataset Used in the Example

Preparing the Data

Using the Model in Real-World Applications and Challenges

Context &amp

Memory: Making AI More Real

The Problem: No Memory

No Context = Chaos of Randomness and Confusion

History = Context

The Problem with Carrying Over History

Last In First Out (LIFO) Memory

The Problem with Last In, First Out Memory

Selective Context

Using a Vector Database with OpenAI

Introduction

What is a Vector Database?

Example 1: Using Weaviate to Make Our Model More Context-Aware

Example 2: Using Weaviate and OpenAI in Semantic Search.

Example 3: Using Weaviate and OpenAI for Generative Search

Speech Recognition and Translation Using Whisper

What is Whisper?

How to Get Started?

Transcribe and Translate

Using Whisper SDK in Python

Using OpenAI Speech to Text API

Transcription API

Translation API

Improving Whisper Transcription

Cleaning the Audio

Using the Prompt Parameter

Post-Processing the Transcription

Text-to-Speech with OpenAI TTS Models

Autonomous AI-to-AI Discussion Using OpenAI, Weaviate, and AI Avatars

Generating the Audio Files

Using AI Avatar Models

Afterword.

Notes:

Description based upon print version of record.

Combining Fine-Tuning with Other Techniques

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781836202400

1836202407

OCLC:

1435753617