PDFStract Documentation

Welcome to PDFStract — the data preparation layer for RAG. Extract. Chunk. Embed.

PDFStract is a comprehensive tool for converting PDFs into vector-ready content for AI applications.

One unified API. Switch between 10+ extraction libraries, 10+ chunking methods, and multiple embedding providers with a single parameter change. Focus on your RAG outcomes, not library dependencies.

What is PDFStract?

PDFStract handles the complete data preparation pipeline for RAG:

• Extract: Convert PDFs using 10+ libraries (Marker, Docling, PyMuPDF4LLM, Unstructured, PaddleOCR, and more)
• Chunk: Split text into optimal segments using 10+ methods (Token, Semantic, Recursive, Code-aware, and more)
• Embed: Generate vector embeddings with multiple providers (OpenAI, Sentence Transformers, Ollama, and more)
• Multiple Interfaces: Use as Python library, CLI tool, or Web UI

Key Features

🚀 10+ PDF Conversion Libraries: Choose the best tool for your documents
✂️ 10+ Chunking Methods: From simple token-based to advanced semantic chunking
📱 Modern Web UI: Beautiful React interface with real-time preview
💻 Full CLI Support: Batch processing and automation
🐳 Docker Ready: One-command deployment
⚡ High Performance: Parallel processing and optimized workflows

Quick Example

from pdfstract import PDFStract

pdfstract = PDFStract()

# 1. Convert PDF
text = pdfstract.convert('document.pdf', library='docling')

# 2. Chunk for RAG
chunks = pdfstract.chunk(text, chunker='semantic', chunk_size=512)

# 3. Generate embeddings
vectors = pdfstract.embed_texts([c['text'] for c in chunks['chunks']], model='auto')

print(f"Ready for vector DB: {len(vectors)} embeddings")

Or use the all-in-one pipeline:

# Complete RAG pipeline in one call
result = pdfstract.convert_chunk_embed(
    'document.pdf',
    library='marker',
    chunker='semantic',
    embedding='sentence-transformers'
)
# Each chunk now has an embedding attached!

Installation Tiers

Choose the right tier for your needs:

# Base - Fast extractors (pymupdf4llm, markitdown)
pip install pdfstract

# Standard - Adds OCR (pytesseract, unstructured)
pip install pdfstract[standard]

# Advanced - ML-powered (marker, docling, paddleocr, deepseek)
pip install pdfstract[advanced]

# All - All converters combined
pip install pdfstract[all]

Tier	Libraries	Best For
Base	pymupdf4llm, markitdown	Fast extraction, simple PDFs
Standard	pytesseract, unstructured	OCR support, structured docs
Advanced	marker, docling, paddleocr, deepseek	Best quality, ML-powered
All	All libraries combined	Complete RAG pipeline

Get Started

Ready to dive in? Here are some great starting points:

• Quick Start Guide - Get up and running in 5 minutes
• Installation - Install PDFStract in your environment

Core Features

• Extract - PDF conversion with 10+ libraries
• Chunk - Text splitting with 10+ methods
• Embed - Vector embeddings with multiple providers

Interfaces

• Python API - Integrate into your applications
• CLI Guide - Use from the command line
• Web UI - Try the visual interface

Architecture

PDFStract follows a modular architecture:

PDF Input → Converter Library → Raw Text → Chunker → Structured Chunks → Your RAG Pipeline

Each step is configurable, allowing you to choose the best tools for your specific use case.

Community

• GitHub: aksarav/pdfstract
• Issues: Report bugs or request features
• Discussions: Share use cases and get help

Let's get started! 🚀