📚 Multi-PDF RAG Streamlit App

A Streamlit-based multi-PDF document Question & Answer system using
Retrieval-Augmented Generation (RAG) powered by Llama-3 via Groq and ChromaDB.

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🚀 Features

• 📄 Upload multiple PDFs
• 🔍 Semantic search using HuggingFace embeddings
• 🧠 Accurate answers using Llama-3.3-70B (Groq)
• 🧩 Vector storage with ChromaDB
• ❓ Ask multiple questions at once
• 🧠 Clean Question → Answer UI
• ⚡ Fast inference via Groq API

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🏗️ Project Structure

├── app.py # Streamlit app
├── rag_utility.py # PDF processing + RAG logic
├── requirements.txt # Dependencies
├── env_template.txt # Environment variable template
├── .gitignore
├── LICENSE
└── README.md

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🧠 How It Works (RAG Pipeline)

1. Upload PDFs using Streamlit
2. PDFs are:
   • Loaded using UnstructuredPDFLoader
   • Split into chunks
   • Converted into embeddings
3. Embeddings are stored in ChromaDB
4. User questions are:
   • Retrieved via similarity search
   • Passed to Llama-3 with context
5. Model returns grounded answers

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⚙️ Setup Instructions

1️⃣ Clone the repository

git clone https://github.com/your-username/multi-pdf-rag-streamlit.git
cd multi-pdf-rag-streamlit

2️⃣ Create a virtual environment (recommended)

conda create -n rag python=3.10
conda activate rag

3️⃣ Install dependencies

pip install -r requirements.txt
(or copy env_template.txt → .env)

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🔐 Environment Variables

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here

You can refer to env_template.txt for guidance.

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▶️ Run the Application

streamlit run app.py

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🧪 Example Usage

1. Upload one or more PDFs
2. Enter questions (one per line), for example:

What is an ecosystem?
What are the types of ecosystems?
Forerunners of Evo-Devo?

3. Click Answer
4. Get structured Question → Answer results

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🧩 Tech Stack

1. Frontend: Streamlit
2. LLM: Llama-3.3-70B (Groq)
3. Embeddings: all-MiniLM-L6-v2
4. Vector Database: ChromaDB
5. Framework: LangChain
6. Language: Python

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🌍 Deployment

This application is ready to deploy on:

1. Streamlit Cloud
2. Docker
3. Any cloud VM (AWS / GCP / Azure)

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📜 License

This project is licensed under the MIT License. You are free to use, modify, and distribute it.

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🙌 Acknowledgements

1. Groq
2. LangChain
3. HuggingFace
4. Streamlit

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