QASystem Project

Overview

The QASystem project is a Question-Answering (QA) system that utilizes embeddings and a FAISS index to retrieve and generate answers based on a knowledge base. It can be run as a Flask-ML server, accepting both CLI input and HTTP requests from clients.

Features

• Load Q&A pairs from a specified knowledge file.
• Use a text generation model to generate answers based on user questions.
• Supports batch processing of questions.

Project Structure

QASystem/
├── QA_system.py               # Contains the QASystem class
├── server.py                  # Flask-ML server implementation
├── numpy_qa_store.txt         # Sample knowledge base (Q&A pairs)
├── numpy_questions.txt        # Sample questions file
├── client.py                  # Client to interact with the server
└── README.md                  # Project documentation

Setup

Create a virtual environment:

python -m venv venv

Activate the virtual environment:

On Windows:

venv\Scripts\activate

On macOS/Linux:

source venv/bin/activate

Install the required libraries using pip:

pip install -r requirements.txt

Usage

1. Command Line Interface (CLI)

You can run the QA system directly from the command line using the following command:

python QA_system.py --knowledge knowledge.txt --questions questions.txt --prompt "What is NumPy?"

• --knowledge_path: Path to the file containing Q&A pairs.
• --questions_path: Path to the file containing questions to be answered.
• --prompt: An optional question to ask directly via CLI.

2. Flask-ML

Start the server:

python server.py --knowledge knowledge.txt --questions questions.txt

In a separate terminal window, you can interact with the server using the client.py script. You can specify the inputs to be sent to the server as shown below:

inputs = [
    {"text": "What is NumPy?"}
]

Then run the client via the following:

python client.py