GROQ RAG ChatBot – Chat with Your PDFs

This project implements a Retrieval-Augmented Generation (RAG) chatbot that lets you upload a PDF and interact with their content using GROQ LLM and an in-memory Chroma vector store.

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🚀 Features

• 📄 Upload and process a PDF file
• 🧠 Store document embeddings in Chroma (in-memory)
• 💬 Query with GROQ LLM using RAG
• 🔍 Inspect vector store chunks from the sidebar
• 🛠️ Modular, well-commented code for easy customization

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🛠 Setup Instructions

1. Create a Virtual Environment

Windows

python -m venv .venv
.venv\Scripts\activate

macOS / Linux

python3 -m venv .venv
source .venv/bin/activate

2. Install Dependencies

pip install -r requirements.txt

3. Set up GROQ API Key

To use this RAG chatbot, you'll need a GROQ API key.

Get Your API Key

1. Visit console.groq.com/keys
2. Sign up or log in to your GROQ account
3. Create a new API key
4. Copy the generated API key

Set Environment Variable

Windows (Command Prompt)

set GROQ_API_KEY=your_api_key_here

Windows (PowerShell)

$env:GROQ_API_KEY="your_api_key_here"

macOS / Linux

export GROQ_API_KEY="your_api_key_here"

Alternative: Create a .env file

You can also create a .env file in the project root directory:

GROQ_API_KEY=your_api_key_here

⚠️ Important: Never commit your API key to version control. Add .env to your .gitignore file if using this method.

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▶️ Run the App

After installing dependencies, start the Streamlit app:

streamlit run index.py

The app will open in your default web browser at:

http://localhost:8501

If it doesn’t open automatically, copy and paste the URL from your terminal into your browser.

💡 Build your own custom RAG chatbot effortlessly!