Name: OpenAI GPT For Python Developers
Author: Aymen El Amri
ISBN: 9781836202400

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OpenAI GPT For Python Developers

Aymen El Amri

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OpenAI GPT For Python Developers
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
What’s Next?
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
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 & 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
What’s Next?
Afterword

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OpenAI GPT For Python Developers