AI Tutor Chat Application

This Chainlit-based application serves as an AI tutor, leveraging various large language models (LLMs) to provide interactive learning experiences. It includes features for student assessment, knowledge tracking, and adaptive responses.

Features

• Multiple Model Support: Configurable to use different LLMs including OpenAI's GPT-4 and Mistral AI models.
• Student Assessment: Automatically assesses student interactions and updates a student record.
• Knowledge Tracking: Maintains and updates a knowledge base for each student.
• Adaptive Responses: Uses conversation history and student records to provide context-aware responses.
• Streamed Responses: Delivers model responses in real-time as they are generated.
• Configurable System Prompts: Allows enabling/disabling of system prompts and class context.

Prerequisites

• Python 3.7+
• API keys for OpenAI and/or RunPod (depending on chosen model)

Installation and Setup

1. Clone the Repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create a Virtual Environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

3. Install Dependencies:

   pip install -r requirements.txt

Configuration

1. API Keys:

   • Copy the .env.sample file and rename it to .env
   • Replace the placeholder values with your actual API keys and Runpod endpoints
   • Note: Runpod keys are optional

2. Model Selection:

   • Choose the desired model by setting the config_key variable in app.py.

3. System Prompts and Class Context:

   • Adjust the ENABLE_SYSTEM_PROMPT and ENABLE_CLASS_CONTEXT flags as needed.

4. Customize Prompts:

   • Modify the prompt templates in the prompts.py file to suit your educational context.

Running the Application

1. Activate the Virtual Environment (if not already activated):

   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

2. Run the Chainlit App:

   chainlit run app.py -w

3. Open your browser and navigate to the URL displayed in the terminal.

Usage

• Start a conversation with the AI tutor by typing a message.
• The application will process your input, update the student record, and provide a contextual response.
• Student records are automatically updated and stored in student_record.md.

Key Components

• app.py: Main application file containing the Chainlit setup and message handling logic.
• prompts.py: Contains prompt templates for system instructions and assessments.
• student_record.py: Handles reading, writing, and formatting of student records.

Contributing

Contributions are welcome! Please open an issue or submit a pull request with your proposed changes.

License

This project is licensed under the MIT License.