TutorAI: Your AI Learning Companion

Project Description

TutorAI is an AI-powered chatbot designed to support your learning journey. By fostering an active learning environment, TutorAI encourages self-discovery through:

• Personalized explanations: Ask questions about various topics and receive clear and concise explanations tailored to your needs.
• Recommended resources: Get directed to relevant resources like YouTube videos that supplement your learning and solidify understanding.
• Guided practice: Practice your newfound knowledge with guidance from TutorAI. This can involve hints, step-by-step breakdowns, and encouraging messages.

Getting Started

Prerequisites:

• An OpenAI API Key (obtainable from OpenAI API)
• Python environment with required libraries (instructions below)

Installation:

1. Clone this repository.

2. Create a virtual environment (recommended) and activate it:

   python -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

Running the application:

• Set your OpenAI API Key as a secret in Streamlit:

  st.secrets["openai_api_key"] = "<YOUR_OPENAI_API_KEY>"

• Run the application from the project directory:

  streamlit run main.py

User Interface

The user interface features a chat-based interaction. Simply type your question or topic in the chat box at the bottom of the page. TutorAI will analyze your input and provide a response that may include:

• A clear explanation of the concept
• Recommended YouTube videos for further learning
• Guidance on how to approach a problem or practice your understanding

The conversation history is displayed on the screen, allowing you to revisit previous interactions.

Implementation Details

This project utilizes several libraries to achieve its functionality:

• Langchain: A framework for building interactive AI systems.
• Streamlit: A web framework for building data apps in Python.
• ChatOpenAI: A Langchain wrapper for OpenAI's GPT-3 language model.
• YouTubeSearchTool: A Langchain tool for searching YouTube videos.
• StreamlitChatMessageHistory: A custom component for managing chat history in Streamlit.

The core logic utilizes a ReAct prompt format guiding the AI model's interaction with the user. This involves analyzing the user's question, suggesting relevant resources, encouraging self-discovery, and offering support when needed. Finally, the model assesses the user's comprehension and recommends further learning paths.

Additional Notes

• This project serves as a foundation for building a more robust AI tutor.
• You can explore customizing the prompt template for more specific subject areas or learning styles.
• Consider integrating additional resources beyond YouTube videos, such as articles or interactive exercises.
• For production deployment, consider security measures for handling user input and API keys.

We hope TutorAI empowers you on your learning journey!