README.md

metadata

title: RAG PDF Assistant
emoji: 📄
colorFrom: blue
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: true

RAG PDF Assistant

A production-ready Retrieval Augmented Generation (RAG) system for interactive PDF analysis and question answering using semantic search and large language models.

🌟 Hosted on HuggingFace Spaces | 📦 Models from HuggingFace Hub | ⚡ Production Ready

Live UI

Live app (running): Open RAG PDF Assistant

Space page: Hugging Face Space Repository

---

🎯 Features

• 📤 PDF Upload - Upload any PDF or use the bundled sample
• 🔍 Smart Retrieval - FAISS-based vector similarity search over PDF content
• 🤖 AI-Powered Answers - FLAN-T5 language model generation (HuggingFace)
• 🌍 Multilingual Support - 100+ languages via multilingual embeddings
• ⚡ Fast Performance - ~0.5-1 second per query after model load
• 🎨 User-Friendly UI - Gradio web interface (no coding needed)
• ✅ Production Ready - Full error handling, model caching, and validation

---

🚀 Quick Start

Option 1: Local Installation

# Clone repository
git clone https://github.com/YOUR_USERNAME/rag_pdf_project.git
cd rag_pdf_project

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

Open your browser to: http://127.0.0.1:7860

Option 2: HuggingFace Spaces (Cloud)

1. Create a new Gradio Space on HuggingFace
2. Upload this repository
3. The app will deploy automatically!

---

📖 Usage

Web UI (Recommended)

1. Load PDF: Click "Use Default PDF" or upload your own
2. Ask Questions: Type your question
3. Get Answers: RAG-powered answers appear instantly

CLI Interface

python main.py

---

🏗️ Architecture

PDF Upload
    ↓
PDF Loader (PyPDF)
    ↓
Text Chunking (300-word chunks)
    ↓
Embedding (HuggingFace: Multilingual-MiniLM, 384-dim)
    ↓
FAISS Vector Index (L2 distance)
    ↓
User Question
    ↓
Query Embedding + Retrieval (Top-3 chunks)
    ↓
Prompt Building
    ↓
HuggingFace FLAN-T5 Generation (200 tokens max)
    ↓
Answer Display

---

📂 Project Structure

rag_pdf_project/
├── app.py                       # Gradio web UI
├── main.py                      # CLI interface
├── requirements.txt             # Dependencies
├── README.md                    # This file
├── .gitignore                   # Git configuration
│
├── configs/
│   └── settings.py              # Model & parameter config
│
├── data/documents/
│   └── 1.pdf                    # Sample PDF
│
├── ingestion/                   # PDF processing
│   ├── build_index.py
│   ├── pdf_loader.py
│   └── chunker.py
│
├── retrieval/                   # Vector search
│   ├── embedder.py
│   ├── retriever.py
│   └── vector_store.py
│
├── generation/                  # Answer generation
│   ├── llm.py
│   └── prompt_builder.py
│
└── pipeline/
    └── rag_pipeline.py

---

🔧 Configuration

Edit configs/settings.py:

# HuggingFace Embedding Model
EMBED_MODEL = 'paraphrase-multilingual-MiniLM-L12-v2'

# HuggingFace Generation Model
GEN_MODEL = 'google/flan-t5-small'

# Chunking
CHUNK_SIZE = 300  # words

# Retrieval
TOP_K = 3  # chunks

# Generation
MAX_NEW_TOKENS = 200  # tokens

---

🤖 HuggingFace Integration

Models Used

Component	Model	Source
Embedding	paraphrase-multilingual-MiniLM-L12-v2	SentenceTransformers
Generation	google/flan-t5-small	Google
Tokenizer	Auto	HuggingFace

Deploy to HuggingFace Spaces

# 1. Login to HuggingFace
huggingface-cli login

# 2. Create Space
huggingface-cli repo create rag-pdf-assistant --type space --space-sdk docker

# 3. Push code
git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/rag-pdf-assistant
git push hf main

---

📊 Performance

Metric	Value
First Query	1.5-2 seconds
Subsequent Queries	0.5-1 second
Model Size	~2GB
Memory Usage	4-6GB

---

🧪 Testing

python app.py
# Click "Use Default PDF"
# Ask: "What is this document about?"

---

📚 Documentation

• QUICK_START.md - Basic usage
• TECHNICAL_DOCS.md - Deep architecture
• EXAMPLES_AND_TESTS.md - Examples & debugging

---

📦 Dependencies

• transformers - HuggingFace T5 models
• sentence-transformers - Multilingual embeddings
• faiss-cpu - Vector search
• pypdf - PDF extraction
• gradio - Web UI
• numpy - Numerical computing

---

🚀 Deployment

Local

python app.py

Docker

FROM python:3.10
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "app.py"]

HuggingFace Spaces ⭐

Just push to HuggingFace!

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📜 License

MIT License

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🙏 Acknowledgments

• HuggingFace - Models and transformers
• Facebook Research - FAISS
• Google - FLAN-T5
• SentenceTransformers - Embeddings
• Gradio - Web interfaces

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Built with ❤️ using HuggingFace models

Made with Transformers • SentenceTransformers • FAISS • Gradio