📄 PDF-RAG-AI

An AI-powered PDF Question Answering Assistant built using Streamlit, LangChain, Google Gemini, ChromaDB, and Hugging Face Embeddings.

The application allows users to upload PDF documents, ask natural language questions, retrieve relevant information using Retrieval-Augmented Generation (RAG), generate document summaries, and export chat conversations.

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🚀 Features

• 📄 Upload PDF documents
• 🤖 Ask questions using Google Gemini AI
• 🔍 Semantic search with ChromaDB
• 🧠 Retrieval-Augmented Generation (RAG)
• 📚 Hugging Face Embeddings
• 📑 AI-generated PDF Summary
• 📊 PDF Statistics
• 💬 Chat Interface
• 📄 View Source References
• 💾 Export Chat as TXT
• 📝 Export Chat as Markdown

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🛠️ Tech Stack

Technology	Purpose
Python	Backend
Streamlit	Web Application
LangChain	RAG Pipeline
Google Gemini	Large Language Model
Hugging Face Embeddings	Text Embeddings
ChromaDB	Vector Database
PyMuPDF	PDF Text Extraction

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📂 Project Structure

PDF-RAG-AI/
│
├── app.py
├── requirements.txt
├── README.md
├── .env.example
├── .gitignore
│
├── assets/
│   ├── logo.png
│   └── styles.css
│
├── downloads/
├── uploads/
├── chroma_db/
│
└── utils/
    ├── embedding.py
    ├── export.py
    ├── gemini.py
    ├── pdf_loader.py
    ├── rag.py
    ├── search.py
    ├── statistics.py
    ├── summary.py
    ├── ui.py
    └── vector_store.py

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⚙️ Installation

Clone the repository.

git clone https://github.com/Saikishorep15/PDF-RAG-AI.git

Move into the project folder.

cd PDF-RAG-AI

Create a virtual environment.

python -m venv .venv

Activate the virtual environment.

Windows

.venv\Scripts\activate

Linux / macOS

source .venv/bin/activate

Install the dependencies.

pip install -r requirements.txt

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🔑 Environment Variables

Create a .env file.

GOOGLE_API_KEY=YOUR_GEMINI_API_KEY

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▶️ Run the Application

streamlit run app.py

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📖 How It Works

1. Upload a PDF document.
2. Extract text using PyMuPDF.
3. Split the text into chunks.
4. Generate embeddings using Hugging Face.
5. Store embeddings in ChromaDB.
6. Retrieve the most relevant chunks.
7. Send the context to Google Gemini.
8. Display the generated answer with source references.

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📸 Screenshots

Home Page

Add screenshot here

Chat Interface

Add screenshot here

PDF Statistics

Add screenshot here

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👨‍💻 Author

SaiKishore P

GitHub: https://github.com/Saikishorep15

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⭐ If you like this project

Give this repository a ⭐ on GitHub.

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📄 License

This project is licensed under the MIT License.