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Automated Question Answering System

Live Link: https://assignment-llm.streamlit.app/

Introduction

Welcome to the Automated Question Answering System project! This project leverages advanced natural language processing (NLP) and machine learning techniques to create a system capable of processing large textual datasets, analyzing them, and generating coherent responses to user queries.

Features

  • Data Extraction: Load and read text documents from specified directories.
  • Data Preprocessing: Clean and chunk text data for processing.
  • Embedding Generation: Generate embeddings using the meta-llama/Meta-Llama-3-8B-Instruct model.
  • Index Building: Use FAISS to build an index for efficient similarity search.
  • Query Handling: Retrieve relevant document chunks and generate answers using Hugging Face models.
  • Deployment: Deploy the application using Streamlit for an interactive user experience.

Requirements

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/automated-question-answering-system.git
cd automated-question-answering-system

  2. Create a virtual environment:

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

  3. Install the required packages:

    pip install -r requirements.txt

  4. Set up Hugging Face API Token: Store your Hugging Face API token as an environment variable:

    export HUGGINGFACEHUB_API_TOKEN=your_huggingface_api_token  # On Windows use `set HUGGINGFACEHUB_API_TOKEN=your_huggingface_api_token`

Usage

  1. Prepare Data:

    • Place your text files in the data/milestone_papers_text and data/lecture_notes directories.

  2. Run the Streamlit Application:

    streamlit run src/app.py

  3. Interact with the Application:

    • Enter your query in the text box and get responses based on the provided data.

File Structure

automated-question-answering-system/
│
├── data/
│   ├── milestone_papers_text/
│   └── lecture_notes/
│
├── src/
│   ├── app.py
│   ├── faiss_module.py
│   ├── process_text.py
│
├── requirements.txt
└── README.md

Contributing

We welcome contributions to enhance the functionality and performance of this project. Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch for your feature or bugfix.
  3. Commit your changes and push them to your branch.
  4. Open a pull request with a detailed description of your changes.

Acknowledgements