Developing Apps with GPT-4 and ChatGPT - Helion

ebook

Autor: Olivier Caelen, Marie-Alice Blete
ISBN: 9781098152444
stron: 160, Format: ebook
Data wydania: 2023-08-29
Księgarnia: Helion

Cena książki: 219,00 zł

Osoby, które kupiły tę książkę, wybierały także »

This minibook is a comprehensive guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete cover the main features and benefits of GPT-4 and ChatGPT and explain how they work. You'll also get a step-by-step guide for developing applications using the GPT-4 and ChatGPT Python library, including text generation, Q&A, and content summarization tools.

Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must.

You'll learn:

The fundamentals and benefits of ChatGPT and GPT-4 and how they work
How to integrate these models into Python-based applications for NLP tasks
How to develop applications using GPT-4 or ChatGPT APIs in Python for text generation, question answering, and content summarization, among other tasks
Advanced GPT topics including prompt engineering, fine-tuning models for specific tasks, plug-ins, LangChain, and more

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Spis treści

Developing Apps with GPT-4 and ChatGPT eBook -- spis treści

Preface
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. GPT-4 and ChatGPT Essentials
- Introducing Large Language Models
  - Exploring the Foundations of Language Models and NLP
  - Understanding the Transformer Architecture and Its Role in LLMs
  - Demystifying the Tokenization and Prediction Steps in GPT Models
- A Brief History: From GPT-1 to GPT-4
  - GPT-1
  - GPT-2
  - GPT-3
  - From GPT-3 to InstructGPT
  - GPT-3.5, Codex, and ChatGPT
    - GPT-4
- LLM Use Cases and Example Products
  - Be My Eyes
  - Morgan Stanley
  - Khan Academy
  - Duolingo
  - Yabble
  - Waymark
  - Inworld AI
- Beware of AI Hallucinations: Limitations and Considerations
- Optimizing GPT Models with Plug-ins and Fine-Tuning
- Summary
2. A Deep Dive into the GPT-4 and ChatGPT APIs
- Essential Concepts
- Models Available in the OpenAI API
- Trying GPT Models with the OpenAI Playground
- Getting Started: The OpenAI Python Library
  - OpenAI Access and API Key
  - Hello World Example
- Using ChatGPT and GPT-4
  - Input Options for the Chat Completion Endpoint
    - Required input parameters
    - Length of conversations and tokens
    - Additional optional parameters
  - Output Result Format for the Chat Completion Endpoint
  - From Text Completions to Functions
- Using Other Text Completion Models
  - Input Options for the Text Completion Endpoint
    - Main input parameters
    - Length of prompts and tokens
    - Additional optional parameters
  - Output Result Format for the Text Completion Endpoint
- Considerations
  - Pricing and Token Limitations
  - Security and Privacy: Caution!
- Other OpenAI APIs and Functionalities
  - Embeddings
  - Moderation Model
  - Whisper and DALL-E
- Summary (and Cheat Sheet)
3. Building Apps with GPT-4 and ChatGPT
- App Development Overview
  - API Key Management
    - The user provides the API key
    - You provide the API key
  - Security and Data Privacy
- Software Architecture Design Principles
- LLM-Powered App Vulnerabilities
  - Analyzing Inputs and Outputs
  - The Inevitability of Prompt Injection
- Example Projects
  - Project 1: Building a News Generator Solution
  - Project 2: Summarizing YouTube Videos
  - Project 3: Creating an Expert for Zelda BOTW
    - Redis
    - Information retrieval service
    - Intent service
    - Response service
    - Putting it all together
  - Project 4: Voice Control
    - Speech-to-Text with Whisper
    - Assistant with GPT-3.5 Turbo
    - UI with Gradio
    - Demonstration
- Summary
4. Advanced GPT-4 and ChatGPT Techniques
- Prompt Engineering
  - Designing Effective Prompts
    - The context
    - The task
    - The role
  - Thinking Step by Step
  - Implementing Few-Shot Learning
  - Improving Prompt Effectiveness
    - Instruct the model to ask more questions
    - Format the output
    - Repeat the instructions
    - Use negative prompts
    - Add length constraints
- Fine-Tuning
  - Getting Started
    - Adapting GPT base models for domain-specific needs
    - Fine-tuning versus few-shot learning
  - Fine-Tuning with the OpenAI API
    - Preparing your data
    - Making your data available
    - Creating a fine-tuned model
    - Listing fine-tuning jobs
    - Canceling a fine-tuning job
  - Fine-Tuning Applications
    - Legal document analysis
    - Automated code review
    - Financial document summarization
    - Technical document translation
    - News article generation for niche topics
  - Generating and Fine-Tuning Synthetic Data for an Email Marketing Campaign
    - Creating a synthetic dataset
    - Fine-tuning a model with the synthetic dataset
    - Using the fine-tuned model for text completion
  - Cost of Fine-Tuning
- Summary
5. Advancing LLM Capabilities with the LangChain Framework and Plug-ins
- The LangChain Framework
  - Dynamic Prompts
  - Agents and Tools
  - Memory
  - Embeddings
- GPT-4 Plug-ins
  - Overview
  - The API
  - The Plug-in Manifest
  - The OpenAPI Specification
  - Descriptions
- Summary
- Conclusion
Glossary of Key Terms
Index