Web Article QA Tool

Web Article QA Tool is a user-friendly tool designed for effortless information retrieval. Users can input article URLs and ask questions to receive relevant insights from the given urls.

Features

• Load URLs or upload text files containing URLs to fetch article content.
• Process article content through LangChain's UnstructuredURL Loader
• Construct an embedding vector using open source embeddings and leverage FAISS, a powerful similarity search library, to enable swift and effective retrieval of relevant information
• Interact with the LLM's (Google Gemini) by inputting queries and receiving answers along with source URLs.

Installation

1.Clone this repository to your local machine using:

  git clone https://github.com/parthivshah33/Web-article-QA-Tool.git

2.Navigate to the project directory:

  cd WebArticle QA Bot

3. Install the required dependencies using pip:

  pip install -r requirements.txt

4.Set up your OpenAI API key by creating a .env file in the project root and adding your API

  GEMINI_API_KEY = "your api key"

Usage/Examples

1. Run the Streamlit app by executing:

streamlit run main.py

2.The web app will open in your browser.

• On the sidebar, you can input URLs directly.

• Initiate the data loading and processing by clicking "Process URLs."

• Observe the system as it performs text splitting, generates embedding vectors, and efficiently indexes them using FAISS.

• The embeddings will be stored and indexed using FAISS, enhancing retrieval speed.

• The FAISS index will be saved in a local file path in pickle format for future use.

• One can now ask a question and get the answer based on those news articles

Project Structure

• main.py: The main Streamlit application script.
• requirements.txt: A list of required Python packages for the project.
• vectorStoreDB.pkl: A pickle file to store the FAISS index.
• .env: Configuration file for storing your OpenAI API key.