LLM Data Extraction: A Complete Guide to Document Processing Libraries and Tools

Building powerful AI applications with Large Language Models requires one critical foundation: transforming unstructured documents into LLM-ready data. Whether you’re processing PDFs, Word documents, or scanned images, the quality of your document extraction directly impacts your RAG systems, AI agents, and domain-specific applications.

In this guide, I’ll show you the basic implementation of different options you can use to extract data from multiple sources for your AI applications. I use most of these libraries and techniques, so I can assure you these methods are still working.

Quick Reference: Data Extraction Libraries Overview

Open Source Libraries (Free)

PyMuPDF (Fitz) - High-performance PDF processing with granular control
Unstructured.io (Open Source) - Multi-format semantic document partitioning
Docling (IBM) - LLM-optimized document conversion with rich output

Premium/Commercial Services

LlamaParse - AI-powered parsing optimized for RAG workflows
Unstructured.io (Commercial API) - Enhanced models and managed processing

Cloud-Based Services

Azure AI Document Intelligence - Enterprise-grade form and document processing
AWS Textract - Intelligent OCR with advanced layout analysis
Gemini Models - Multimodal parsing and extraction using Google’s service

Why Data Extraction Quality Matters for LLMs

LLMs thrive on well-structured, coherent input. Poor document extraction leads to:

Contextual Loss: Missing spatial relationships and reading order
Hallucinations: Misinterpreted table structures and layout elements
Increased Costs: Bloated, unoptimized token usage
Poor RAG Performance: Irrelevant or broken chunks degrading response quality

The goal is intelligent document partitioning that preserves semantic structure, layout context, and metadata—going far beyond simple OCR.

PyMuPDF (Fitz) - Open Source PDF Powerhouse

PyMuPDF offers high-performance PDF processing with granular control over extraction and manipulation.

📖 Documentation: https://pymupdf.readthedocs.io/

Key Features:

High performance for text extraction and page rendering
Detailed layout information with bounding boxes
OCR integration capabilities
PDF manipulation beyond extraction

Best For: Custom parsing needs, performance-critical applications, local processing

import fitz  # PyMuPDF

def extract_text_pymupdf(pdf_path):
    document = fitz.open(pdf_path)
    text = ""
    for page_num in range(document.page_count):
        page = document.load_page(page_num)
        text += page.get_text("text")
    document.close()
    return text

def extract_with_layout_pymupdf(pdf_path):
    document = fitz.open(pdf_path)
    structured_data = []

    for page_num in range(document.page_count):
        page = document.load_page(page_num)
        blocks = page.get_text("dict")["blocks"]

        for block in blocks:
            if "lines" in block:
                for line in block["lines"]:
                    for span in line["spans"]:
                        structured_data.append({
                            "text": span["text"],
                            "bbox": span["bbox"],
                            "page": page_num + 1,
                            "font": span["font"],
                            "size": span["size"]
                        })

    document.close()
    return structured_data

Unstructured.io - Semantic Document Partitioning

Unstructured.io provides both open-source and commercial APIs designed to prepare unstructured data for LLMs with semantic document understanding.

📖 Documentation: https://unstructured-io.github.io/unstructured/

Key Features:

Multi-format support (PDFs, DOCX, HTML, PPTX, images)
Intelligent semantic element detection
Layout-aware processing for better RAG
LLM framework integration (LangChain, LlamaIndex)

Best For: RAG systems, multi-format processing, semantic chunking

from unstructured.partition.auto import partition
from unstructured.chunking.title import chunk_by_title

def partition_document_unstructured(file_path):
    elements = partition(filename=file_path)

    processed_elements = []
    for element in elements:
        processed_elements.append({
            "type": type(element).__name__,
            "text": element.text,
            "metadata": {
                "page_number": getattr(element.metadata, 'page_number', None),
                "coordinates": getattr(element.metadata, 'coordinates', None),
                "category": getattr(element.metadata, 'category', None)
            }
        })

    return processed_elements

def create_rag_chunks_unstructured(file_path):
    elements = partition(filename=file_path)

    chunks = chunk_by_title(
        elements,
        max_characters=1000,
        combine_text_under_n_chars=200
    )

    return [{"text": str(chunk), "metadata": chunk.metadata} for chunk in chunks]

Docling (IBM) - LLM-Optimized Document Conversion

Docling is IBM’s open-source solution specifically designed to transform documents into LLM-ready formats with rich structural understanding.

📖 Documentation: https://github.com/docling-project/docling

Key Features:

Multi-format support (PDFs, DOCX, PPTX, XLSX, HTML, images)
Advanced PDF understanding with intelligent layout parsing
Unified Markdown or JSON output preserving context
Local execution for privacy-sensitive environments

Best For: Local AI applications, rich document understanding, Markdown output

from docling.document_converter import DocumentConverter

def convert_document_docling(file_path):
    converter = DocumentConverter()
    result = converter.convert(file_path)

    return {
        "markdown": result.document.export_to_markdown(),
        "json": result.document.export_to_json(),
        "metadata": {
            "page_count": len(result.document.pages),
            "tables": len([item for item in result.document.texts if item.label == "table"]),
            "figures": len([item for item in result.document.texts if item.label == "figure"])
        }
    }

def extract_tables_docling(file_path):
    converter = DocumentConverter()
    result = converter.convert(file_path)

    tables = []
    for item in result.document.texts:
        if item.label == "table":
            tables.append({
                "content": item.text,
                "page": item.prov[0].page_no if item.prov else None,
                "bbox": item.prov[0].bbox if item.prov else None
            })

    return tables

LlamaParse - Freemium AI-Powered Parsing

LlamaParse is LlamaIndex’s proprietary document parsing service, specifically optimized for RAG workflows with state-of-the-art AI models.

📖 Documentation: LlamaIndex LlamaParse Guide

Key Features:

AI-powered parsing with advanced multi-modal models
RAG-optimized output formatting
Natural language parsing instructions
Multi-format support with generous free tier (1000 pages)

Best For: Complex documents with tables/charts, high-accuracy requirements, RAG systems

from llama_parse import LlamaParse
import asyncio

async def parse_document_llamaparse(file_path, custom_instruction=None):
    parser = LlamaParse(
        result_type="markdown",
        parsing_instruction=custom_instruction or "Extract all text, tables, and charts accurately with proper formatting."
    )

    documents = await parser.aparse(file_path)
    return documents[0].text if documents else ""

async def parse_financial_document(file_path):
    parser = LlamaParse(
        result_type="json",
        parsing_instruction="""
        Focus on extracting financial data, tables, and key metrics.
        Preserve all numerical data and their associated labels.
        Identify income statements, balance sheets, and cash flow statements.
        """
    )

    documents = await parser.aparse(file_path)
    return documents[0] if documents else None

Cloud Services: Azure AI & AWS Textract

Azure AI Document Intelligence

Microsoft’s enterprise-grade document processing service offers pre-built models for invoices, receipts, and identity documents, plus custom model training capabilities. Best for enterprise applications requiring compliance (GDPR, HIPAA, SOC2).

AWS Textract

Amazon’s fully managed OCR service provides intelligent text extraction, handwriting recognition, and advanced layout understanding. Includes specialized APIs for expenses and identity documents. Ideal for AWS-centric environments and scalable OCR workloads.

Both services offer excellent accuracy for complex documents but require cloud deployment and have pay-per-page pricing models.

Library Comparison: Quick Decision Guide

Feature	PyMuPDF	Unstructured	Docling	LlamaParse	Cloud Services
Cost	Free	Free/Paid	Free	Credit-based	Pay-per-page
Deployment	Local	Local	Local	Cloud API	Cloud API
Best For	Speed & control	Semantic chunking	Rich output	RAG optimization	Enterprise/OCR
Table Extraction	Manual	Good	Good	Excellent	Excellent
Multi-format	PDF focus	Excellent	Excellent	Excellent	Good

Best Practices for LLM Data Extraction

1. Choose Based on Your Use Case

High-volume, simple PDFs: PyMuPDF for speed and cost
Multi-format RAG systems: Unstructured.io or Docling
Complex documents with tables: LlamaParse
Enterprise compliance: Azure AI or AWS Textract

2. Optimize for LLM Consumption

Preserve semantic structure and reading order
Extract metadata (page numbers, sections, document titles)
Implement intelligent chunking strategies
Maintain table relationships and formatting

3. Implementation Strategy

Many production systems use hybrid approaches:

def smart_document_processor(file_path):
    file_type = get_file_type(file_path)

    if file_type == "pdf" and is_simple_text_pdf(file_path):
        return extract_text_pymupdf(file_path)
    elif has_complex_tables(file_path):
        return await parse_document_llamaparse(file_path)
    else:
        return partition_document_unstructured(file_path)

Conclusion

The success of your LLM applications depends heavily on quality document extraction. Start with open-source solutions like PyMuPDF or Unstructured.io for prototyping, then scale to specialized services like LlamaParse or cloud platforms based on your specific needs.

Key takeaways:

Budget-conscious projects: Start with open-source solutions
RAG systems: Prioritize semantic understanding
Enterprise applications: Invest in cloud services for reliability
Complex documents: Consider AI-powered parsing services

As an AI engineering consultant, I help organizations implement robust document processing pipelines that transform unstructured data into intelligent, LLM-ready formats. The right extraction strategy can significantly improve accuracy, reduce hallucinations, and enhance user experiences.

Ready to optimize your document processing for LLM applications? Let’s discuss how we can build the perfect extraction pipeline for your specific needs and constraints.

Contact me at [email protected] to explore how we can maximize your LLM application performance through intelligent document processing.