README.md

metadata

license: apache-2.0
title: Multi-Model-Rag
sdk: streamlit
emoji: 📚
colorFrom: gray
colorTo: indigo

📄 Multi-Modal RAG PDF Chatbot

A Streamlit application that allows you to upload a PDF, ask questions about its content, and get accurate responses using a Multi-Modal Retrieval-Augmented Generation (RAG) pipeline powered by Groq's Gemma-2 9B model.

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🚀 Features

• 📁 Upload any PDF
• 🔍 Intelligent chunking and embedding
• 🧠 Ask natural language questions about your PDF
• ⚡ Powered by FAISS + HuggingFace + Groq LLM
• 🧠 Caches session so PDF isn't reprocessed on every query

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🛠️ Installation (with venv)

1. Clone the repo:

git clone https://github.com/Warishayat/Multimodel-Rag-Application01.git
cd Multimodal-Rag-Application01

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Set up your .env file:

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here

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📦 Project Structure

📁 Multimodal-Rag-Application01
├── main.py                 # Streamlit frontend
├── pdfparsing.py          # PDF parser using pymupdf4llm
├── Datapreprocessing.py   # Chunking & text cleaning
├── vectorstore.py         # Embedding & FAISS logic
├── .env                   # API keys
├── requirements.txt       # Python dependencies
└── README.md              # You're here!

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▶️ Run the App

streamlit run main.py

Then open http://localhost:8501 in your browser.

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🧪 Example Queries

After uploading a PDF, try asking:

• "What is the summary of section 3?"
• "List all benchmarks mentioned."
• "How is this model different from others?"

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💡 Tips

• PDF is processed only once per session using st.session_state.
• Uses RecursiveCharacterTextSplitter for effective chunking.
• Embedding with HuggingFaceEmbeddings.

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📋 Requirements

Make sure your requirements.txt includes at least:

streamlit
python-dotenv
langchain
langchain-community
langchain-groq
faiss-cpu
pymupdf4llm

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📬 Credits

Built with ❤️ by Waris Hayat Abbasi.

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