Lesson 1: Introduction to OpenAI API and AI Agents

1. Introduction to AI Agents

What are AI agents?

AI agents are programs designed to interact with their environment, make decisions, and perform tasks autonomously. They often use APIs to retrieve or send data.

Examples of AI agents in the real world:

• Chatbots for customer support
• Personal assistants like Siri or Alexa
• Agents for automating tasks like email sorting or scheduling meetings

Key Concepts:

• Autonomy: Agents can perform tasks without constant user input
• Interactivity: They interact with humans or other systems (like APIs)
• Decision-Making: They can make decisions based on data or user inputs

2. Introduction to OpenAI API

What is OpenAI API?

OpenAI API provides powerful models that can understand and generate human language. It allows developers to build apps that leverage natural language processing (NLP), text generation, and more.

Key Features:

• Text completion: Generate text based on a given prompt
• Conversational agents: Create bots that can engage in human-like conversations
• Code generation: Generate code snippets based on instructions

API Use Cases:

• Writing assistants (e.g., Grammarly-like apps)
• Personalized recommendations
• Automated customer service agents

How to Use OpenAI API:

Make an API call using Python (or another language), send a prompt, and receive a response.

3. Prerequisites

• Basic knowledge of Python (variables, functions)
• An OpenAI account and API key (signup at OpenAI API)

4. Setting Up the Environment

Installing Python and pip:

Ensure you have Python installed on your machine, along with pip to install libraries.

Installing OpenAI Python Library:

pip install openai

Setting Up the API Key:

Create a .env file in the root of your project and add your OpenAI API key:

OPENAI_API_KEY=sk-proj-1111

Installing dotenv:

pip install python-dotenv

Importing the API Key:

from dotenv import load_dotenv
load_dotenv()

Models we can call:

• GPT-4o Our high-intelligence flagship model for complex, multi-step tasks
• GPT-4o mini Our affordable and intelligent small model for fast, lightweight tasks
• o1-preview and o1-mini Language models trained with reinforcement learning to perform complex reasoning.
• GPT-4 Turbo and GPT-4 The previous set of high-intelligence models
• GPT-3.5 Turbo A fast, inexpensive model for simple tasks
• DALL·E A model that can generate and edit images given a natural language prompt
• TTS A set of models that can convert text into natural sounding spoken audio
• Whisper A model that can convert audio into text
• Embeddings A set of models that can convert text into a numerical form
• Moderation A fine-tuned model that can detect whether text may be sensitive or unsafe
• GPT base A set of models without instruction following that can understand as well as generate natural language or code

03 - simple chatbot.py

• This is a simple chatbot that uses the OpenAI API to generate responses to user prompts.
• It uses the gpt-4o-mini model.
• It uses the conversation_history to keep track of the chat history.
• It uses the client.chat.completions.create method to generate responses.

At the most basic level, we are sending a prompt to the model and receiving a response in a while loop until the user types 'exit'.

04 - simple agent.py

• This is a simple agent that uses the OpenAI API to generate responses to user prompts.
• We create a class called SimpleAgent that has a generate_response method that takes a prompt and returns a response.
• We give it a personality and a system prompt.
• It uses the gpt-4o-mini model.
• It uses the conversation_history to keep track of the chat history.
• It uses the client.chat.completions.create method to generate responses.

This is a very simple agent, but it shows the power of the OpenAI API.

05 - TTS (text-to-speech)

• This is a simple TTS app that uses the OpenAI API to convert text to speech.
• It uses the tts-1 model.
• It uses the client.audio.speech.create method to generate speech.
• It uses the speech_file_path to save the speech to a file.
• It uses the response.stream_to_file method to save the speech to a file.

Documents:

• models