🧠 Document and Website Chat Application powered by RAG

This project is a lightweight Retrieval-Augmented Generation (RAG) chat interface built with Streamlit for the frontend and Python for the backend. It integrates with the Groq API to provide intelligent responses to user queries and also extraction relevant Documents from the Knowledge Base.

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🚀 Features

• Streamlit-based interactive chat UI
• Backend RAG pipeline with local document retrieval
• Can upload .pdf files, .txt files and Website URLs as data sources for RAG
• Can dynamically upload additional sources during an ongoing conversation
• Groq API integration for high-performance LLM responses
• Plotly support for future interactive visualizations

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📦 Tech Stack

• Python 3.10+
• LangChain
• Streamlit
• Plotly
• Groq API

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🛠️ Setup Instructions

Clone the repository

git clone https://github.com/viraj200524/Document-Website-Chat.git

Then navigate to Document-Website-Chat

cd Document-Website-Chat

1. Create and Activate Virtual Environment

Linux/macOS:

python3 -m venv venv
source venv/bin/activate

Windows:

python -m venv venv
venv\Scripts\activate

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🔑 Set Up Groq API Key

🧾 How to Get a Groq API Key:

Go to https://console.groq.com/keys

Log in with your account or sign up if you don’t have one.

Click on Create API Key

Copy the generated key.

🔐 Add to .env file: Create a .env file in the root directory (if not already present) and paste the key like this:

GROQ_API_KEY="<YOUR API KEY>"

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Install all requirements:

Run the following command to Install all requirements:

pip install -r requirements.txt

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Run the application:

streamlit run app.py