Elixir

Extract text, tables, images, and metadata from 88+ file formats including PDF, Office documents, and images. Elixir bindings with native BEAM concurrency, OTP integration, and idiomatic Elixir API.

Installation

Package Installation

Add to your mix.exs dependencies:

def deps do
  [
    kreuzberg: "~> 4.5"
  ]
end

Then run:

mix deps.get

System Requirements

• Elixir 1.12+ and Erlang/OTP 24+ required
• Optional: ONNX Runtime version 1.22.x for embeddings support
• Optional: Tesseract OCR for OCR functionality

Quick Start

Basic Extraction

Extract text, metadata, and structure from any supported document format:

elixir title="Elixir" # Basic document extraction workflow # Load file -> extract -> access results {:ok, result} = Kreuzberg.extract_file("document.pdf") IO.puts("Extracted Content:") IO.puts(result.content) IO.puts("\nMetadata:") IO.puts("Format: #{inspect(result.metadata.format_type)}") IO.puts("Tables found: #{length(result.tables)}")

Common Use Cases

Extract with Custom Configuration

Most use cases benefit from configuration to control extraction behavior:

With OCR (for scanned documents):

elixir title="Elixir" alias Kreuzberg.ExtractionConfig config = %ExtractionConfig{ ocr: %{"enabled" => true, "backend" => "tesseract"} } {:ok, result} = Kreuzberg.extract_file("scanned_document.pdf", nil, config) content = result.content IO.puts("OCR Extracted content:") IO.puts(content) IO.puts("Metadata: #{inspect(result.metadata)}")

Table Extraction

See Table Extraction Guide for detailed examples.

Processing Multiple Files

elixir title="Elixir" file_paths = ["document1.pdf", "document2.pdf", "document3.pdf"] {:ok, results} = Kreuzberg.batch_extract_files(file_paths) Enum.each(results, fn result -> IO.puts("File: #{result.mime_type}") IO.puts("Content length: #{byte_size(result.content)} characters") IO.puts("Tables: #{length(result.tables)}") IO.puts("---") end) IO.puts("Total files processed: #{length(results)}")

Async Processing

For non-blocking document processing:

elixir title="Elixir" # Extract from different file types (PDF, DOCX, etc.) case Kreuzberg.extract_file("document.pdf") do {:ok, result} -> IO.puts("Content: #{result.content}") IO.puts("MIME Type: #{result.metadata.format_type}") IO.puts("Tables: #{length(result.tables)}") {:error, reason} -> IO.puts("Extraction failed: #{inspect(reason)}") end

Next Steps

• Installation Guide - Platform-specific setup
• API Documentation - Complete API reference
• Examples & Guides - Full code examples and usage guides
• Configuration Guide - Advanced configuration options

Features

Supported File Formats (88+)

88+ file formats across 8 major categories with intelligent format detection and comprehensive metadata extraction.

Office Documents

Images (OCR-Enabled)

Web & Data

Email & Archives

Academic & Scientific

Complete Format Reference

Key Capabilities

• Text Extraction - Extract all text content with position and formatting information

• Metadata Extraction - Retrieve document properties, creation date, author, etc.

• Table Extraction - Parse tables with structure and cell content preservation

• Image Extraction - Extract embedded images and render page previews

• OCR Support - Integrate multiple OCR backends for scanned documents

• Async/Await - Non-blocking document processing with concurrent operations

• Plugin System - Extensible post-processing for custom text transformation

• Embeddings - Generate vector embeddings using ONNX Runtime models

• Batch Processing - Efficiently process multiple documents in parallel

• Memory Efficient - Stream large files without loading entirely into memory

• Language Detection - Detect and support multiple languages in documents

• Configuration - Fine-grained control over extraction behavior

Performance Characteristics

OCR Support

Kreuzberg supports multiple OCR backends for extracting text from scanned documents and images:

• Tesseract

• Paddleocr

OCR Configuration Example

elixir title="Elixir" alias Kreuzberg.ExtractionConfig config = %ExtractionConfig{ ocr: %{"enabled" => true, "backend" => "tesseract"} } {:ok, result} = Kreuzberg.extract_file("scanned_document.pdf", nil, config) content = result.content IO.puts("OCR Extracted content:") IO.puts(content) IO.puts("Metadata: #{inspect(result.metadata)}")

Async Support

This binding provides full async/await support for non-blocking document processing:

elixir title="Elixir" # Extract from different file types (PDF, DOCX, etc.) case Kreuzberg.extract_file("document.pdf") do {:ok, result} -> IO.puts("Content: #{result.content}") IO.puts("MIME Type: #{result.metadata.format_type}") IO.puts("Tables: #{length(result.tables)}") {:error, reason} -> IO.puts("Extraction failed: #{inspect(reason)}") end

Plugin System

Kreuzberg supports extensible post-processing plugins for custom text transformation and filtering.

For detailed plugin documentation, visit Plugin System Guide.

Plugin Example

elixir title="Elixir" alias Kreuzberg.Plugin # Word Count Post-Processor Plugin # This post-processor automatically counts words in extracted content # and adds the word count to the metadata. defmodule MyApp.Plugins.WordCountProcessor do @behaviour Kreuzberg.Plugin.PostProcessor require Logger @impl true def name do "WordCountProcessor" end @impl true def processing_stage do :post end @impl true def version do "1.0.0" end @impl true def initialize do :ok end @impl true def shutdown do :ok end @impl true def process(result, _options) do content = result["content"] || "" word_count = content |> String.split(~r/\s+/, trim: true) |> length() # Update metadata with word count metadata = Map.get(result, "metadata", %{}) updated_metadata = Map.put(metadata, "word_count", word_count) {:ok, Map.put(result, "metadata", updated_metadata)} end end # Register the word count post-processor Plugin.register_post_processor(:word_count_processor, MyApp.Plugins.WordCountProcessor) # Example usage result = %{ "content" => "The quick brown fox jumps over the lazy dog. This is a sample document with multiple words.", "metadata" => %{ "source" => "document.pdf", "pages" => 1 } } case MyApp.Plugins.WordCountProcessor.process(result, %{}) do {:ok, processed_result} -> word_count = processed_result["metadata"]["word_count"] IO.puts("Word count added: #{word_count} words") IO.inspect(processed_result, label: "Processed Result") {:error, reason} -> IO.puts("Processing failed: #{reason}") end # List all registered post-processors {:ok, processors} = Plugin.list_post_processors() IO.inspect(processors, label: "Registered Post-Processors")

Embeddings Support

Generate vector embeddings for extracted text using the built-in ONNX Runtime support. Requires ONNX Runtime installation.

Embeddings Guide

Batch Processing

Process multiple documents efficiently:

elixir title="Elixir" file_paths = ["document1.pdf", "document2.pdf", "document3.pdf"] {:ok, results} = Kreuzberg.batch_extract_files(file_paths) Enum.each(results, fn result -> IO.puts("File: #{result.mime_type}") IO.puts("Content length: #{byte_size(result.content)} characters") IO.puts("Tables: #{length(result.tables)}") IO.puts("---") end) IO.puts("Total files processed: #{length(results)}")

Configuration

For advanced configuration options including language detection, table extraction, OCR settings, and more:

Configuration Guide

Documentation

• Official Documentation
• API Reference
• Examples & Guides

Contributing

Contributions are welcome! See Contributing Guide.

License

MIT License - see LICENSE file for details.

Support

• Discord Community: Join our Discord
• GitHub Issues: Report bugs
• Discussions: Ask questions