🤖 PDF RAG Chatbot with Groq

A simple PDF-based RAG (Retrieval-Augmented Generation) chatbot built with Streamlit, ChromaDB, Sentence Transformers, and Groq API.

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✨ Features

• 📄 Upload PDF files
• 🔍 Extract and process PDF text
• 🧠 Create embeddings using Sentence Transformers
• 🗄️ Store vectors in ChromaDB
• 💬 Ask questions from uploaded PDF
• ⚡ Groq-powered intelligent responses
• 📌 View source chunks used for answers

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📁 Project Structure

RAG Implementation/ │── app.py │── .env │── .env.example │── requirements.txt │── uploads/

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🚀 Installation

1. Clone or Download Project

git clone <your-repo-url>
cd RAG-Implementation

2. Create Virtual Environment

python -m venv venv

3. Activate Virtual Environment

Windows CMD:

venv\Scripts\activate

PowerShell:

venv\Scripts\Activate.ps1

4. Install Dependencies

pip install -r requirements.txt

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⚙️ Environment Setup

Create .env file using .env.example:

GROQ_API_KEY=your_groq_api_key_here
MODEL_NAME=llama-3.1-8b-instant

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▶️ Run Application

streamlit run app.py

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🤖 Groq Model

Recommended:

MODEL_NAME=llama-3.1-8b-instant

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📖 Usage

1. Upload PDF
2. Wait for text extraction + embedding
3. Ask questions
4. Get answers from PDF context

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🛠️ Tech Stack

Component	Technology
UI Framework	Streamlit
Vector Database	ChromaDB
Embeddings	Sentence Transformers
LLM API	Groq API
Orchestration	LangChain
Language	Python

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📝 Notes

• Make sure your Groq API key is valid
• Large PDFs may take more processing time
• Best for text-based PDFs (not scanned images)

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