kreuzberg-dev/kreuzberg

Features

• Document Extraction Engines - Extracts text and metadata from PDFs, Office files, images, and 90+ formats using a Rust core with OCR and LLM support.
• Multi-Format Parsers - Extracting text and structured data from PDFs, Office files, images, and other document formats for downstream processing.
• Text Extraction - Imports the extraction engine into applications across 18 programming languages including Python and Rust.
• AI Agent Servers - Exposes document extraction, embedding, and chunking tools through the Model Context Protocol for direct AI agent integration.
• Document Extraction Tools - Exposing document extraction and processing tools to AI agents through the Model Context Protocol.
• Document Chunking & Embedding - Chunking, embedding, and reranking document content to prepare it for retrieval-augmented generation workflows.
• Document Processors - Chunks extracted text, generates vector embeddings, and reranks results for RAG pipelines.
• Local Embedding Generators - Generates document embeddings locally with ONNX models for semantic search and RAG pipelines.
• ONNX Runtime Inference - All ML tasks including OCR, embeddings, and reranking run through ONNX Runtime.
• MCP Servers - Exposes extraction tools through the Model Context Protocol so AI agents can call them directly.
• Hierarchical Representations - Returns a traversable tree of nodes with heading levels and inline annotations for knowledge graphs.
• Tree Representations - Provides a traversable tree of document nodes with parent-child references for knowledge graph construction.
• Vision-Language Model Backends - Leverages vision language models as an OCR backend and extracts structured JSON from documents using a schema, supporting 146 LLM providers.
• Text Chunks - Splits text into chunks with heading paths for hierarchical context in RAG retrieval.
• Custom OCR Backend Registrations - Recognizes text in images using Tesseract, PaddleOCR, EasyOCR, or VLM backends across 143 vision providers.
• Optical Character Recognition - Processes image data through pluggable OCR backends supporting multiple languages and engines.
• Semantic Chunking - Splits extracted text into sized chunks using recursive, semantic, or Markdown-aware strategies for LLM consumption.
• Configurable Extraction APIs - Exposes document extraction as an HTTP API with configurable host, port, CORS, and upload limits.
• Polyglot Language Bindings - Integrates document extraction into Python, TypeScript, Rust, Go, Java, and 12+ other languages via native bindings.
• Document Text Extractors - Parses PDFs, Office files, images, HTML, email, archives, and academic formats into clean, structured text using a Rust core with SIMD acceleration.
• Custom Format Extractors - Registers custom document extractors for proprietary or unsupported file formats through a plugin system.
• Markup Extraction - Extracts text from HTML, Markdown, XML, and other markup files, converting structured content to plain text.
• Text Extraction - Extracts text and metadata from a file path by detecting its MIME type from the extension and selecting the appropriate parser automatically.
• Multi-Format Extractors - Extracts text and metadata from a file by detecting or validating the MIME type, selecting the appropriate extractor, and running a post-processing pipeline.
• Core Crate Integrations - Integrates the core crate directly into Rust-native applications, CLI tools, API servers, or embedded systems.
• Text Extraction and OCR - Recognizes text from scanned documents and images using multiple OCR backends with quality fallback.
• Presentation Text Extractors - Extracts slide content, metadata, and embedded images from PowerPoint (PPTX) files.
• Multi-Backend OCR Configurators - Selects from multiple OCR engines including Tesseract, PaddleOCR, EasyOCR, and VLM models to balance speed, accuracy, and language support.
• Document Processing Pipelines - Processes thousands of documents with high throughput by calling directly into a compiled Rust core without subprocess or HTTP overhead.
• Typed Element Extraction - Returns typed document elements like titles, paragraphs, and tables with page numbers for RAG pipelines.
• Office Document Parsers - Extracts text from Excel, PowerPoint, and Word files using native Rust parsers, preserving structure like sheets, slides, and formatting.
• Document Metadata Extraction - Pulls document properties like title, author, creation date, and format-specific metadata.
• Multi-Format Extractors - Returns full document text with minimal formatting, per-page breakdowns, and structured tables and image metadata.
• PDF Text Extraction - Extracts text content from PDF files using native Rust parsing, with support for metadata, images, and OCR fallback for scanned documents.
• Semantic Element Extraction - Extracts logical content units with semantic classification, unique identifiers, and position metadata.
• Document Table Extractors - Detects and extracts structured table data from documents, returning cells and markdown representation.
• Extraction Configurations - Controls every extraction stage through a single configuration object loaded from TOML, YAML, or JSON.
• Pipeline Configurations - Controls every stage of the extraction pipeline through a single configuration object.
• Document Extraction Tools - Offers a CLI tool installable via Homebrew or Docker for extracting document content.
• Multi-Format Document Ingestion - Ingests text from 96 formats including PDFs, Office docs, images, email, archives, source code, and niche formats.
• Structured Data Extraction - Applies a JSON schema and an LLM to extracted text to return typed, structured output.
• LLM-to-Structured Data Converters - Applies a JSON schema and an LLM to extracted text to return typed, structured output.
• Byte Array Text Extractors - Extracts text and metadata from a byte array by validating the MIME type, selecting the appropriate extractor, and running a post-processing pipeline.
• Extraction Configurations - Sets extraction options via programmatic config, TOML/YAML/JSON files, or environment variables.
• MIME-Type Based Extractors - Registers a handler for a specific MIME type that extracts text from file paths or raw bytes with priority-based conflict resolution.
• Single File Extraction - Extracts text content from a document file and prints it to standard output.
• Text Chunking Parameters - Provides configurable text chunking with size, overlap, and chunker type for RAG pipelines.
• CLI Tooling - Executes extraction tasks directly from the command line without writing code.
• PDF Asset Extractions - Pulls metadata such as title and author from PDF files during extraction.
• Language Bindings - Provides native packages for Python, TypeScript, Rust, Go, Java, C#, Ruby, PHP, and many other languages.
• REST APIs - Starts an HTTP server that exposes extraction and embedding endpoints for language-agnostic access.
• Document Processing APIs - Starts an HTTP server that exposes endpoints for extraction, batch processing, MIME detection, health checks, and cache management.
• Polyglot FFI Exposures - Exposes a compiled Rust engine's API through FFI to 18+ languages.
• Document Processing - Running a full document extraction and analysis pipeline on private infrastructure with no data leaving the environment.
• Self-Hosted Services - Runs the full extraction pipeline on private infrastructure so data never leaves the environment.
• Model Context Protocol Servers - Exposes extraction tools through the Model Context Protocol for direct AI agent integration.
• Private Data Processing Environments - Processes documents entirely on self-hosted infrastructure with no data leaving the environment.
• File Format Detectors - Identifies file format by extension or MIME type, with fallback to content-based detection and manual override.
• Extraction Pipeline Plugins - Extraction is built as a chain of typed registries for custom extractors and backends.
• Document APIs - Serves a REST API for document extraction, embedding, and chunking on self-hosted infrastructure.
• Integration SDKs - Integrates into any stack using native bindings for Python, TypeScript, Rust, Go, Java, C#, and many other languages.
• Document Translators - Translates extracted text into a target language using any LLM provider with optional Markdown preservation.
• Document Rerankers - Sorts documents by relevance to a query, optionally truncating to top-k results.
• Cross-Encoder Rerankers - Selects pre-configured cross-encoder models for reranking by balancing speed and quality.
• Document Structure Analysis - Builds a hierarchical tree of document elements with headings, tables, and content layers.
• Document Summarization - Generates a prose summary of extracted content using a local TextRank or an LLM-powered abstractive backend.
• Embedding Generators - Generates ultra-fast embeddings using a Rust-native engine with multiple presets.
• GPU Acceleration - Offloads model inference to a GPU by pointing to a GPU-enabled runtime installation.
• Language Detection Tools - Identifies languages present in extracted text using whatlang, supporting 60+ languages.
• Document Chunking Strategies - Determines optimal chunking strategies for documents based on size, format, and content properties.
• Model-Driven Text Extraction - Uses a Vision Language Model to extract Markdown text from image regions.
• Vision-Language Region Extractors - Extracts Markdown text from image regions using a Vision Language Model.
• Model Inference Accelerators - Selects a hardware-specific execution provider to speed up model-based processing tasks.
• Named Entity Recognition - Identifies people, organizations, locations, and other entities in extracted text using ONNX or LLM providers.
• Backend Selectors - Selects between ONNX engine or LLM backend with 143 providers for named entity recognition.
• Heading Level Classifiers - Identifies heading levels (H1-H6) from font size clustering and semantic analysis in PDFs.
• OCR - Uses pure-Rust VLM models like GLM-OCR and Hunyuan-OCR for OCR on complex layouts and low-quality scans.
• OCR Command Line Interfaces - Performs OCR extraction on files directly from the terminal with configurable backends.
• Result Reranking - Sends a query and documents to a POST endpoint and receives scored results in JSON.
• Keyword Extraction - Identifies and ranks keywords from text using a configurable algorithm.
• Automated Video Transcribers - Extracts speech-to-text from audio and video files using Whisper ONNX models, producing a plain-text transcript.
• Per-Page Content Separators - Returns each page's content as a separate array entry in the extraction results.
• Text Classification - Assigns labels to a plain text string using an LLM, without requiring a full extraction result.
• LLM-Based Classifiers - Assigns labels to a single piece of plain text using a configured LLM, without requiring an extraction result.
• Text Embedding Generators - Produces vector embeddings for a list of texts asynchronously, offloading ONNX inference to a blocking thread pool.
• Text Summarization - Scores sentences and returns the top-N in original order to summarize extracted text.
• Configurable Token Reduction - Applies configurable token reduction intensity to extracted text to lower costs when sending to language models.
• Vector Embeddings - Converts text strings into numerical vector embeddings using configurable models for semantic search or RAG pipelines.
• Image Captioning - Generates textual captions for images using a vision language model.
• Custom Extractor Implementations - Provides a trait-based interface for implementing custom document extractors for new formats.
• Browser-Based Text Extractors - Runs text extraction entirely in the browser or edge runtime via a WebAssembly build without server-side dependencies.
• Email Text Extraction - Extracts text content and metadata from EML and MSG email files, including headers and attachments.
• Structural Code Parsers - Parses source code files using tree-sitter to extract structure, imports, symbols, docstrings, and semantic chunks.
• Enrichment Stages - Applying OCR, classification, summarization, translation, and redaction to extracted document content.
• Extraction Confidence Scores - Combines OCR, text coverage, and other signals into a weighted confidence score for extraction results.
• AI-Generated Captions - Generates text captions for extracted images using a vision-language model.
• Per-Page Content Extractors - Returns text, tables, images, and hierarchy blocks for each individual page with byte offsets and bounding box coordinates.
• Document Batch Processors - Processes multiple files concurrently for text extraction with concurrency management.
• Document Furniture Filters - Strips headers, footers, watermarks, and repeating text from extracted document content.
• Image Extractions - Pulls embedded images from documents with configurable DPI, dimension limits, and output format re-encoding.
• Python Plugin Integrations - Integrates Python plugins with the Rust extraction pipeline via PyO3 with zero-copy buffers.
• LLM-Based - Ships a configurable LLM-based page classification enrichment stage for document processing.
• Output Format Rendering - Renders extracted text content as plain text, Markdown, HTML, or Djot markup.
• Token-Efficient Serializations - Outputs extraction results as plain text, JSON, or TOON format optimized for LLM token efficiency.
• Document Classification - Aggregates page-level classifications into a combined label set representing the whole document.
• LLM-Based Document Classifiers - Aggregates page-level classifications across a document's text to produce a combined label set for the whole document.
• Tree-Sitter Parsers - Identifies code structures from 306 programming languages via tree-sitter grammars.
• Embedding Generation - Creates vector embeddings from document content using built-in ONNX models.
• Hardware Acceleration - Selects execution provider (CPU, CoreML, CUDA, TensorRT) for ONNX Runtime model inference.
• Parallel Batch Processing - Processes multiple files faster than sequential extraction using a work-stealing scheduler.
• Document Batch Processors - Processes multiple file paths concurrently for text extraction in a single batch operation.
• Structured Data File Extractors - Extracts text from JSON, YAML, TOML, CSV, and TSV files, preserving field names and tabular structure.
• Text Vectorizers - Converts text into numerical embedding vectors using a configured ONNX model.
• Archive Extraction Utilities - Extracts text content from files inside ZIP, TAR, 7-Zip, and Gzip archives without manual decompression.
• Custom Plugin Registrations - Ships a trait-based plugin system for registering custom extractors, OCR backends, and validators.
• Document Extraction Post-Processors - Modifies extraction output through ordered post-processing stages for cleanup, analysis, redaction, or reformatting without failing on errors.
• Multi-Language Code Parsers - Parses functions, classes, imports, and symbols from code files across 306 programming languages for structured analysis.
• Academic Format Text Extractors - Extracts text from LaTeX, EPUB, BibTeX, and Jupyter Notebooks among other academic formats.
• Environment Variable Configurations - Sets runtime behavior via environment variables that override config files and defaults.
• Document Extraction Containers - Mounts host directories and runs extraction commands inside a Docker container.
• Custom Post-Processors - Registers custom post-processors that transform extraction results in early, middle, or late stages.
• PDF to Image Rendering - Renders individual PDF pages as PNG images at configurable DPI for thumbnails, vision model input, or custom OCR pipelines.
• Multi-Channel File Uploads - Uploads files through an API, SDK, CLI, or Docker for processing, supporting over 90 formats.
• Email Content Parsing - Extracts headers, body content, and attachments from .eml and .msg email files.
• Structured Result Deliveries - Returns a structured JSON response with full document structure and supports webhook delivery for asynchronous workflows.
• Document Processing Schedulers - Distributes document extraction tasks across CPU cores using a work-stealing scheduler.
• Document Furniture Filters - Strips headers, footers, watermarks, and repeating text from extracted document content.
• Document Content Redaction - Rewrites textual fields in extraction results by removing or masking patterns and NER-detected entities.
• Batch Document Processing - Processes multiple documents concurrently with 2-5x throughput gains via parallelization.
• Extraction Result Translators - Translates extracted document content into target languages using an LLM.
• Plugin Extenders - Allows loading custom document extractors, OCR backends, and validators through typed registries.
• Pipeline Plugin Registrations - Adds user-defined plugins to a typed registry for automatic dispatch during extraction.
• LLM-Based Page Classifiers - Assigns labels to each page of an extraction result using an LLM, appending classifications and usage data to the result.
• Extraction Validation Frameworks - Inspects extraction results against custom criteria and rejects them if they fail to meet requirements.
• Document Extraction Timeouts - Abort an extraction that exceeds a configured time limit and surface the timeout as a distinct error.
• Extraction Quality Validators - Inspects extraction results against custom rules and halts the pipeline if the output fails to meet defined quality requirements.
• Document Batch Processors - Processes multiple byte arrays concurrently for text extraction with concurrency management.
• MIME Type Mappings - Identifies MIME type of a file using its extension and optionally its content.
• Document Processing Runtimes - Enables fully local document processing in browsers via WebAssembly.
• RAG Frameworks - Polyglot library for extracting text and metadata from diverse document formats.
• Data Ingestion Pipelines - Polyglot library for document intelligence and extraction.
• Document and File Processing - Extracts content from various document types using a Rust core.
• Productivity and Collaboration - Extracts text, tables, and metadata from diverse document formats.

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