What is Chunkr
Chunkr is an open source document processing API by Lumina AI, designed specifically for RAG (Retrieval-Augmented Generation) and knowledge base scenarios. Chunkr can convert complex documents (such as PDF, PPT, Word, images, etc.) into structured data, supporting intelligent parsing of multiple formats. Chunkr's core features include high-precision OCR, semantic chunking, multi-format output (HTML, Markdown, JSON, plain text), and seamless integration with various LLMs (such as OpenAI, Claude, Ollama, etc.). Users can quickly get started through the cloud service or deploy locally with Docker. Chunkr performs excellently in document Q&A, enterprise knowledge bases, OCR scenarios, and RAG systems, making it a powerful tool for document processing.
Chunkr's Main Features
- Multi-format document parsing: Supports multiple formats such as PDF, PPT, Word, images, etc., and can convert complex documents into structured data.
- High-precision OCR: Extracts text while preserving the spatial relationships and positional information of the text, supporting OCR with bounding boxes.
- Semantic chunking: Automatically splits documents into context chunks suitable for RAG and LLMs, facilitating subsequent processing.
- Multi-format output: Supports outputting results in multiple formats such as HTML, Markdown, JSON, and plain text.
- Python SDK: Provides a Python SDK for easy integration into Python applications or backend services.
- LLM support: Supports various local or remote LLMs (such as OpenAI, Claude, Ollama, etc.), with flexible configuration.
Chunkr's Technical Principles
- Vision-Language Model (VLM): Chunkr uses a Vision-Language Model (VLM) to understand document layout and content. VLM combines computer vision and natural language processing technologies to recognize elements such as text, images, and tables in documents, and to understand spatial relationships. Based on VLM, Chunkr can achieve high-precision OCR and semantic chunking, ensuring accurate extraction and reasonable segmentation of document content.
- Document layout analysis: Chunkr analyzes document layout to identify the positions and structures of elements such as headings, paragraphs, tables, and charts in documents. Based on layout analysis, document content is chunked according to logical structure, generating context chunks suitable for RAG and LLM processing.
- OCR technology: Chunkr uses advanced OCR technology to extract text content from documents while preserving the positional information and spatial relationships of the text. The text and positional information extracted by OCR are used in subsequent semantic chunking and structured processing.
- Semantic chunking: Chunkr performs semantic analysis on the extracted text based on natural language processing technology, splitting document content into logically independent chunks. Each chunk contains relevant context information, suitable for direct use as input to RAG or LLMs.
Chunkr's Project Address
- Project official website：https://chunkr.ai/
- GitHub repository：https://github.com/lumina-ai-inc/chunkr
Chunkr's Application Scenarios
- Document Q&A system: Converts complex documents into structured data, generates high-quality corpora, and provides accurate context information for Q&A systems.
- Enterprise knowledge base construction: Quickly converts internal enterprise document materials into structured data, efficiently builds knowledge bases, and improves knowledge management efficiency.
- OCR scenarios: Provides high-precision OCR and text positional information, supporting accurate recognition of complex documents (such as tables, mixed text and images).
- RAG systems: Output structured data suitable for RAG systems (such as JSON, Markdown) to improve retrieval efficiency and generation quality.
- Intelligent document processing: With semantic chunking and LLM support, implement intelligent processing functions such as document summarization, classification, and automatic tagging.