Histronaut - History Tutor Chatbot

This project implements a Retrieval-Augmented Generation (RAG) chatbot focused on answering questions about a Grade 11 History textbook. It uses Google's Gemini AI for language understanding and generation, and FAISS for efficient information retrieval from the textbook content. The project intentionally uses minimal dependencies (Flask, FAISS, Gemini, PyPDF) for simplicity, speed, and maintainability.

Prerequisites

• Python: Version 3.10 or higher recommended.
• pip: Python package installer.
• Google AI API Key: You need an API key from Google AI Studio (or Google Cloud Vertex AI) for the Gemini models.
• Git: (Optional) If cloning the repository.

Setup

1. Clone the Repository (if applicable):

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

2. Install Dependencies: It's recommended to use a virtual environment:

   python -m venv venv
   # On Windows
   .\venv\Scripts\activate
   # On macOS/Linux
   source venv/bin/activate
   
   pip install -r requirements.txt

   (Note: The requirements.txt file lists the necessary dependencies: flask, google-generativeai, faiss-cpu, pypdf, python-dotenv, numpy)

3. Configure API Key:

   • Create a file named .env in the root project directory.
   • Add your Google AI API key to the .env file:

     GOOGLE_API_KEY=YOUR_API_KEY_HERE
     

   • The config.py file likely loads this key.

4. Process the PDF (if not already done): The project needs to process the grade-11-history-text-book.pdf into a vector store. There might be a script for this, or it might happen automatically on the first run. Check main.py, embed_store.py, or pdf_chunker.py for clues. If a specific script exists (e.g., python embed_store.py), run it:

   python faiss_store.py

   This will create the faiss_index.index and faiss_metadata.pkl files (or similar).

Running the Application

There seem to be multiple ways to interact with the chatbot:

1. Web Interface (Recommended):

   • Run the Flask web server:

     python web.py

   • Open your web browser and navigate to the address provided (usually http://127.0.0.1:5000 or similar).

2. Command-Line Interface:

   • Run the CLI script:

     python cli_chat.py

   • Interact with the bot directly in your terminal. Type 'exit' or 'quit' to end the session.

Project Overview

Read the Project Overview