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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.