Chunkr - An open-source document processing API launched by Lumina AI
Chunkr is an open-source document processing API from Lumina AI, designed specifically for RAG (Retrieval Augmented Generative) and knowledge base scenarios. Chunkr can convert complex documents (such as PDFs, PPTs, Word documents, images, etc.) into structured...
What is Chunkr?
Chunkr is an open-source document processing API from Lumina AI, designed specifically for RAG (Retrieval Augmented Generation) and knowledge base scenarios. Chunkr can convert complex documents (such as PDFs, PPTs, Word documents, and images) into structured data, supporting intelligent parsing across 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, and Ollama). Users can quickly get started via cloud services or deploy locally using Docker. Chunkr performs exceptionally well in document question answering, enterprise knowledge bases, OCR scenarios, and RAG systems, making it a powerful tool for document processing.
Chunkr's main functions
- Multi-format document parsingIt supports multiple formats such as PDF, PPT, Word, and images, and can convert complex documents into structured data.
- High-precision OCRIt extracts text while preserving the spatial relationships and positional information of the text, and supports OCR with bounding boxes.
- Semantic BlockingAutomatically segments the document into context blocks suitable for RAG and LLM, facilitating subsequent processing.
- Multi-format outputIt supports outputting results in multiple formats, including HTML, Markdown, JSON, and plain text.
- Python SDKProvides a Python SDK for easy integration into Python applications or backend services.
- LLM supportIt supports various local or remote LLMs (such as OpenAI, Claude, Ollam, etc.) and can be flexibly configured.
Chunkr's technical principles
- Visual Language Model (VLM)Chunkr uses a Visual Language Model (VLM) to understand the layout and content of documents. Combining computer vision and natural language processing techniques, VLM can identify elements such as text, images, and tables in a document and 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 analysisChunkr analyzes the layout of a document, identifying the position and structure of elements such as headings, paragraphs, tables, and charts. Based on this layout analysis, it divides the document content into blocks according to their logical structure, generating context blocks suitable for RAG and LLM processing.
- OCR technologyChunkr uses advanced OCR technology to extract text content from documents while preserving the text's location information and spatial relationships. The text and location information extracted by OCR are used in subsequent semantic segmentation and structuring processing.
- Semantic BlockingChunkr uses natural language processing (NLP) technology to perform semantic analysis on the extracted text, dividing the document content into logically independent blocks. Each block contains relevant contextual information, making it suitable for direct use as input for RAG or LLM algorithms.
Chunkr's project address
- Project official websitehttps://chunkr.ai/
- GitHub repositoryhttps://github.com/lumina-ai-inc/chunkr
Chunkr's application scenarios
- Document Q&A SystemIt transforms complex documents into structured data, generates high-quality corpora, and provides accurate contextual information for question-answering systems.
- Enterprise knowledge base constructionQuickly convert internal enterprise documents into structured data, efficiently build knowledge bases, and improve knowledge management efficiency.
- OCR SceneIt provides high-precision OCR and text location information, supporting accurate recognition of complex documents (such as tables and mixed text and images).
- RAG SystemOutput structured data (such as JSON and Markdown) suitable for RAG systems to improve retrieval efficiency and generation quality.
- Intelligent document processingIt uses semantic chunking and LLM support to achieve intelligent processing functions such as document summarization, classification, and automatic annotation.