CS50 Workshop on AI

This workshop is designed to introduce you to the capabilities of OpenAI's APIs, including Chat Completion, Embedding, and Assistant APIs, with hands-on demonstrations and code examples.

Slides for this workshop are available here.

Requirements

• Python 3.x
• OpenAI Python Library (installation guide below)
• OpenAI API Key
• Internet Connection

Installation

Before we dive into the demos, please ensure your environment is set up with the necessary software and libraries:

# Install the OpenAI library and other dependencies
pip3 install -r requirements.txt

Demo 1: Chat Completion API

This demo illustrates how to utilize the Chat Completion API to create an interactive chatbot.

Key Features

• System Message: Sets the context for the AI (e.g., "You are a friendly and supportive teaching assistant for CS50. You are also a cat.")
• User Interaction: Accepts user input to simulate a conversation.
• API Integration: Utilizes the chat.completions.create method to generate responses based on the conversation history.
• Streaming Responses: Demonstrates how to handle long-running completions with streaming.

Demo 2: Text Embeddings and Semantic Search

This demo showcases the use of OpenAI's text embeddings to perform semantic search, enabling the identification of the most relevant information chunk in response to a user query. This technique can significantly enhance the way educational content is queried and retrieved, making it a powerful tool for educators and students alike.

Key Features of Demo 2

• Text Embeddings: Illustrates how to generate and utilize text embeddings using OpenAI's embeddings.create method.
• Semantic Search: Demonstrates how to compute similarity scores between embeddings to find the most relevant content.
• Integration with Chat API: Combines the result of semantic search with the Chat Completion API to generate contextually relevant responses.

Usage Notes

• Pre-computed Embeddings: Before running this demo, ensure you have an embeddings.jsonl file containing pre-computed embeddings for various content chunks relevant to your subject matter.
• Custom Model Selection: You can experiment with different models for embeddings to suit your content and accuracy requirements.

Demo 3: Assistant API with Custom Data and Context

This demo showcases how to create an assistant (with a vector store attached) that can utilize specific data files to provide tailored responses. It is particularly useful for creating specialized assistants for events, courses, or research projects.

Key Features

• Custom Assistant Creation: Guides you through creating an assistant tailored to the needs of answering CS50 or computer science-related questions.
• Data File Utilization: Demonstrates how to upload and associate data files with your assistant to enrich its responses.
• Dynamic Interaction: Engages users in a conversational interface, utilizing the assistant to respond to queries based on the provided data and instructions.

Usage Notes

• Data Preparation: Before running the demo, ensure your FILES_DIR points to the directory containing relevant files you wish to use with your assistant. We have pre-configured the use of lecture transcripts in the example.
• Customization: You can customize the assistant's name, behavior, and capabilities to fit various educational or research contexts.