LayoutIR Reader

Overview

LayoutIR Reader uses LayoutIR - a production-grade document ingestion and canonicalization engine with compiler-like architecture. Unlike simple PDF-to-Markdown converters, LayoutIR processes documents through an Intermediate Representation (IR) layer, enabling precise preservation of complex layouts, tables, and multi-column structures.

Why LayoutIR?

LayoutIR stands out for its:

• Deterministic Processing: Hash-based stable IDs ensure reproducible results
• Layout Preservation: Maintains complex multi-column layouts and table structures
• Canonical IR Schema: Typed intermediate representation for reliable downstream processing
• Flexible Chunking: Semantic section-based or fixed-size chunking strategies
• GPU Acceleration: Optional GPU support for faster document processing
• Production-Ready: Designed for enterprise-grade document pipelines

Installation

Basic Installation

pip install llama-index-readers-layoutir

With GPU Support

For GPU acceleration, first install PyTorch with CUDA support:

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu130
pip install llama-index-readers-layoutir

Usage

Basic Usage

Load a PDF document with default settings:

from llama_index.readers.layoutir import LayoutIRReader

reader = LayoutIRReader()
documents = reader.load_data(file_path="document.pdf")

# Each document preserves block structure and metadata
for doc in documents:
    print(f"Block Type: {doc.metadata['block_type']}")
    print(f"Page: {doc.metadata['page_number']}")
    print(f"Content: {doc.text[:100]}...")

With GPU Acceleration

Enable GPU processing for faster performance:

from llama_index.readers.layoutir import LayoutIRReader

reader = LayoutIRReader(use_gpu=True)
documents = reader.load_data(file_path="large_document.pdf")

Custom Chunking Strategy

Use semantic section-based chunking:

from llama_index.readers.layoutir import LayoutIRReader

reader = LayoutIRReader(
    chunk_strategy="semantic",
    max_heading_level=2,  # Split at h1 and h2 headings
)
documents = reader.load_data(file_path="structured_document.pdf")

Processing Multiple Files

Process a batch of documents:

from llama_index.readers.layoutir import LayoutIRReader
from pathlib import Path

reader = LayoutIRReader(use_gpu=True)

file_paths = ["report_2024.pdf", "technical_spec.pdf", "user_manual.pdf"]

documents = reader.load_data(file_path=file_paths)
print(f"Loaded {len(documents)} document blocks from {len(file_paths)} files")

Integration with VectorStoreIndex

Build a searchable index from LayoutIR-processed documents:

from llama_index.readers.layoutir import LayoutIRReader
from llama_index.core import VectorStoreIndex

# Load documents with preserved layout structure
reader = LayoutIRReader(
    use_gpu=True, chunk_strategy="semantic", max_heading_level=2
)
documents = reader.load_data(file_path="company_knowledge_base.pdf")

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query with layout-aware context
query_engine = index.as_query_engine()
response = query_engine.query("What are the key financial metrics in Q4?")
print(response)

With SimpleDirectoryReader

Integrate LayoutIR for PDF processing in directory operations:

from llama_index.core import SimpleDirectoryReader
from llama_index.readers.layoutir import LayoutIRReader

reader = LayoutIRReader(use_gpu=True)

dir_reader = SimpleDirectoryReader(
    input_dir="/path/to/documents",
    file_extractor={".pdf": reader},
)

documents = dir_reader.load_data()
print(f"Processed {len(documents)} blocks")

Advanced Configuration

Full configuration example:

from llama_index.readers.layoutir import LayoutIRReader

reader = LayoutIRReader(
    use_gpu=True,  # Enable GPU acceleration
    chunk_strategy="semantic",  # Use semantic chunking
    max_heading_level=3,  # Split up to h3 level
    model_name="custom_model",  # Optional: specify model
    api_key="your_api_key",  # Optional: for remote processing
)

documents = reader.load_data(
    file_path="complex_layout.pdf",
    extra_info={"department": "research", "year": 2026},
)

# Access rich metadata
for doc in documents:
    print(f"ID: {doc.doc_id}")
    print(f"Type: {doc.metadata['block_type']}")
    print(f"Page: {doc.metadata['page_number']}")
    print(f"Department: {doc.metadata['department']}")

Metadata Structure

Each Document includes the following metadata:

• file_path: Source file path
• file_name: Source file name
• block_type: Type of content block (table, paragraph, heading, etc.)
• block_index: Index of the block in the document
• page_number: Page number where the block appears
• source: Always "layoutir"
• Plus any extra_info passed to load_data()

Requirements

• Python >= 3.12
• llama-index-core >= 0.13.0
• layoutir >= 1.0.3
• Optional: PyTorch with CUDA for GPU acceleration

License

MIT

Forcing CPU Mode

If you encounter CUDA/GPU issues (e.g. cuBLAS version mismatches or missing CUDA drivers), set CUDA_VISIBLE_DEVICES="" before running to force CPU-only processing:

CUDA_VISIBLE_DEVICES="" python your_script.py