FAQ Chatbot

An AI-powered FAQ chatbot built using a RAG (Retrieval-Augmented Generation) approach. The bot scrapes/loads FAQ content, retrieves the most relevant context for a user's question, and generates a grounded answer using the Gemini API.

Features

• Scrapes or loads FAQ data as a knowledge base
• Retrieval-Augmented Generation (RAG) for context-grounded answers
• Streamlit-based chat interface
• Powered by Google's Gemini API

Project Structure

FAQ_CHATBOT/
├── app.py              # Main application logic
├── rag.py               # RAG pipeline (retrieval + generation)
├── scrape.py             # Scrapes/prepares FAQ data
├── streamlit_app.py       # Streamlit UI entry point
├── requirements.txt
└── README.md

Setup

1. Clone the repo

   git clone https://github.com/hazmaali9708/FAQ_CHATBOT.git
   cd FAQ_CHATBOT

2. Create a virtual environment (recommended)

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

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   Create a .env file in the project root and add your Gemini API key and scrape URLs. Use .env.example as a template if you want.

   GEMINI_API_KEY=your_gemini_api_key_here
   SCRAPE_URLS=https://example.com,https://another-example.com
   

Usage

Run the Streamlit app:

streamlit run streamlit_app.py

Or run the console version:

python app.py

Tech Stack

• Python
• Streamlit
• Google Generative AI (Gemini API)
• BeautifulSoup (for scraping FAQ content)

Notes

This project was built as part of an AI/ML internship prototype exploring a lightweight RAG pattern for grounded question answering.