OpenAI GPT For Python Developers

Book description

"OpenAI GPT for Python Developers" is your comprehensive guide to mastering the integration of OpenAI's GPT models into your Python projects, enhancing applications with various AI capabilities from chat completions to AI avatars.

Key Features

  • Strategies for optimizing and personalizing GPT models for specific applications.
  • Insights into integrating additional OpenAI technologies like Whisper and Weaviate.
  • Strong emphasis on responsible AI development and deployment.

Book Description

“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.

What you will learn

  • Set up the development environment for OpenAI GPT.
  • Understand and choose the right GPT model for your needs.
  • Implement advanced prompt engineering techniques.
  • Explore embedding and advanced embedding examples.
  • Utilize OpenAI's Whisper for speech recognition and translation.
  • Integrate OpenAI TTS models for text-to-speech applications.

Who this book is for

This book is designed for readers at an intermediate to advanced level who have a basic understanding of machine learning concepts and are eager to expand their expertise in AI with a focus on OpenAI's technologies. Ideal for those involved in AI-driven projects, the book assumes familiarity with Python programming and a fundamental grasp of AI principles. It’s especially beneficial for developers aiming to integrate GPT models into applications, AI researchers, and technical professionals involved in AI product development.

Table of contents

  1. Preface
    1. About the Author
  2. The Story of OpenAI and ChatGPT
    1. About This Guide
    2. The Companion Toolkit
    3. Stay Connected
  3. How Does GPT Work?
  4. Setting Up the Development Environment
    1. Notes
    2. Installing Python, pip, and a Virtual Development Environment
    3. Obtain Your OpenAI API Keys
    4. Install the Official Python Bindings
    5. Test our API Keys
  5. Understanding the Available Models and Which One to Use
    1. OpenAI Available Models and Important Considerations
    2. Which Model to Use?
    3. OpenAI Model Series
      1. GPT-4 Series
      2. GPT-3.5 Series
      3. InstructGPT-3 Series
      4. Base GPT-3 Series
      5. Codex Series
      6. Content Filter
      7. DALL-E Series
      8. TTS Series
      9. Whisper Model
      10. Embedding Model
    4. OpenAI Models and Pricing
    5. What’s Next?
  6. Using GPT Chat Completions
    1. An Introductory Example
    2. System, User, and Assistant Roles
      1. The System Role
      2. The User Role
      3. The Assistant Role
    3. Few-shot Learning with Chat Completions
    4. Formatting the Output
    5. Controlling the Output’s Token Count
    6. Controlling When the Completion Output Stops
    7. Temperature and Hallucination
    8. Sampling with Top_p
    9. Temperature vs Top_p: What’s the Difference? Which One Should I Use?
    10. Streaming the API Response
    11. Controlling Repetitiveness: Frequency and Presence Penalties
    12. Frequency vs. Presence Penalty
    13. Controlling the Number of Results from the API
    14. Conclusion
  7. Advanced Examples and Prompt Engineering
    1. What is Prompt Engineering?
    2. Few Shot Learning: A Key Prompt Engineering Technique
    3. Overgeneration and Selection
    4. General Knowledge Prompting (GKP): Generating a Rap Song
    5. Context Stuffing: Is Apple a Fruit or a Company?
    6. Dynamic Max Tokens
    7. Creating an Interactive CLI-Based Assistant
    8. What’s Next?
  8. Embedding
    1. What is an Embedding?
    2. Use Cases: From Modern Search Engines to Self-Driving Cars
      1. Tesla: How Embeddings Are Used in Self-Driving Cars
      2. Kalendar AI: The Power of Embeddings in Sales Outreach
      3. Notion: Enhanced Search Capabilities
      4. DALL·E 2: Text-to-Image Conversion
    3. Understanding Text Embedding
    4. Embeddings for Multiple Inputs
    5. Use case: Semantic Search
    6. Cosine Similarity: A Deeper Look
    7. Semantic Search and OpenAI’s Text Embeddings
    8. Behind the Scenes: How Embeddings Work
  9. Advanced Embedding Examples
    1. Predicting Your Preferred Coffee
    2. Creating a “Fuzzier” Search
    3. Predicting News Category: Zero-Shot Classification with Embeddings
    4. Evaluating the Accuracy of a Zero-Shot Classifier
    5. Precision in Zero-Shot Classifier Applications: Examples
  10. Fine-Tuning and Best Practices
    1. Few-Shot Learning
    2. Enhancing Few-Shot Learning
    3. Practical Application of Fine-Tuning
    4. Fine-Tuning Best Practices
      1. Choosing the Model
      2. Validating the Dataset
      3. Token Limit
      4. Dataset Size
      5. Testing and Improving Training (Hyperparameters)
      6. Epochs
        1. Learning Rate Multiplier
      7. Batch Size
      8. Consider Estimated Costs
      9. Dataset Quality
      10. Combining Fine-Tuning with Other Techniques
      11. Experiment and Learn
      12. Use a Validation Set
      13. Test the Model
      14. Analyze the Results
  11. Advanced Fine-Tuning: Mental Health Coach
    1. Dataset Used in the Example
    2. Preparing the Data
    3. Using the Model in Real-World Applications and Challenges
  12. Context & Memory: Making AI More Real
    1. The Problem: No Memory
    2. No Context = Chaos of Randomness and Confusion
    3. History = Context
    4. The Problem with Carrying Over History
    5. Last In First Out (LIFO) Memory
    6. The Problem with Last In, First Out Memory
    7. Selective Context
  13. Using a Vector Database with OpenAI
    1. Introduction
    2. What is a Vector Database?
    3. Example 1: Using Weaviate to Make Our Model More Context-Aware
    4. Example 2: Using Weaviate and OpenAI in Semantic Search
    5. Example 3: Using Weaviate and OpenAI for Generative Search
  14. Speech Recognition and Translation Using Whisper
    1. What is Whisper?
    2. How to Get Started?
    3. Transcribe and Translate
    4. Using Whisper SDK in Python
    5. Using OpenAI Speech to Text API
      1. Transcription API
      2. Translation API
    6. Improving Whisper Transcription
      1. Cleaning the Audio
      2. Using the Prompt Parameter
      3. Post-Processing the Transcription
  15. Text-to-Speech with OpenAI TTS Models
  16. Autonomous AI-to-AI Discussion Using OpenAI, Weaviate, and AI Avatars
    1. Generating the Audio Files
    2. Using AI Avatar Models
    3. What’s Next?
  17. Afterword

Product information

  • Title: OpenAI GPT For Python Developers
  • Author(s): Aymen El Amri
  • Release date: May 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781836202417