📄 Chat with PDF (RAG App)

A Streamlit-based AI application that allows users to upload PDFs and chat with them using Retrieval-Augmented Generation (RAG) powered by LangChain, ChromaDB, HuggingFace embeddings, and Groq LLM.

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🚀 Features

• 📂 Upload PDF files
• 🧠 Ask questions from PDF content
• 🔍 Retrieval-Augmented-Generation (RAG)
• ⚡ Fast embeddings using HuggingFace (all-MiniLM-L6-v2)
• 🤖 LLM responses using Groq (LLaMA 3.1)
• 📚 Page number references in answers
• 🌐 Simple Streamlit UI

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🏗️ Tech Stack

• Frontend: Streamlit
• Backend: Python
• LLM: Groq API (LLaMA 3.1 8B Instant)
• Embeddings: HuggingFace Sentence Transformers
• Vector DB: ChromaDB
• Framework: LangChain
• PDF Processing: PyPDF

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📁 Project Structure

chat_with_pdf/
│── app.py              # Streamlit frontend
│── rag_engine.py       # RAG pipeline (ingestion + QA)
│── requirements.txt    # Dependencies
│── .env                # API keys
│── temp.pdf            # Temporary uploaded file
│── chroma_db/          # Vector database storage

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⚙️ Installation

1️⃣ Clone the repository

git clone https://github.com/your-username/chat-with-pdf.git
cd chat-with-pdf

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2️⃣ Create virtual environment (IMPORTANT)

Use Python 3.10 recommended

python -m venv venv
venv\Scripts\activate   # Windows

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3️⃣ Install dependencies

pip install --upgrade pip
pip install -r requirements.txt

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4️⃣ Setup environment variables

Create a .env file:

GROQ_API_KEY=your_groq_api_key_here

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5️⃣ Run the app

streamlit run app.py

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🧠 How It Works

1. User uploads a PDF
2. PDF is split into chunks
3. Chunks are embedded using HuggingFace embeddings
4. Stored in Chroma vector database
5. User asks a question
6. Relevant chunks are retrieved
7. Groq LLM generates answer using retrieved context

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📌 Example Usage

• “What is this document about?”
• “Summarize page 2”
• “What are the key points?”
• “Explain section 3 in simple terms”

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⚠️ Requirements Notes

• Use Python 3.10 for best compatibility
• Avoid Python 3.11+ due to ChromaDB build issues
• Ensure stable versions of LangChain packages

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🔥 Future Improvements

• 📄 Multi-PDF chat support
• ⚡ Streaming responses (ChatGPT-like typing effect)
• 📌 Highlight answers in PDF
• 💾 Persistent chat memory per file

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👨‍💻 Author

Built by Deeksha Shakyawal For learning RAG and real-world LLM apps.

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