RAGFlow

RAGFlowSince 2024-02

RAGFlow is an open-source enterprise RAG (Retrieval-Augmented Generation) engine based on deep document understanding and multimodal document parsing. Unlike naive RAG systems that slice documents into arbitrary character chunks—scrambling complex tables, footnotes, and multi-column layouts—RAGFlow preserves the original semantic structure of complex documents. Powered by DeepDoc computer vision models, RAGFlow extracts clean text, recognizes complex tables across multiple pages, identifies corporate hierarchies, and visually highlights exact source citations with bounding boxes inside PDF viewers. With self-hosted Docker deployment, low-code workflow orchestration, and native support for local and cloud LLMs, RAGFlow is widely adopted by enterprise organizations requiring zero hallucination in legal, financial, and technical document analysis.

RAGFlow distinguishes itself through template-based document chunking algorithms tailored to specific document types, including scientific papers, financial quarterly reports, legal contracts, manuals, and Excel sheets. This eliminates context fragmentation and ensures answers cite complete data tables. The retrieval engine combines hybrid vector embedding search with full-text keyword ranking (BM25) and cross-encoder re-ranking models, consistently achieving higher retrieval precision on benchmark evaluations. All search results feature verifiable ground-truth citations, allowing users to hover over AI answers and inspect the exact highlight coordinates on the original PDF document page.

UnstructuredSince 2023-01

Unstructured is the leading enterprise ETL (Extract, Transform, Load) platform engineered to prepare messy, unstructured business documents for Retrieval-Augmented Generation (RAG) and LLM fine-tuning. Over 80% of enterprise data lives in complex formats like scanned PDFs, PowerPoint decks, Word files, HTML tables, and email threads that break standard text scrapers. Unstructured utilizes specialized computer vision and vision-language models to segment documents into structural semantic elements (titles, paragraphs, headers, embedded tables, and image captions) while preserving exact spatial and hierarchical context. Available as an open-source Python library and a high-throughput serverless cloud API, Unstructured integrates directly with LangChain, LlamaIndex, and major vector databases to power mission-critical enterprise knowledge retrieval.

Unstructured processes more than 25 document formats through a multi-stage layout detection pipeline. Its vision models detect complex multi-column layouts, rotated text, complex mathematical notation, and embedded charts. For tabular data, Unstructured extracts full HTML and Markdown table structures, ensuring that financial balance sheets and technical specifications retain exact row-and-column relationships when embedded into vector stores. Unstructured includes automated semantic chunking strategies that respect document boundaries (chunk_by_title), preventing context fragmentation and maximizing retrieval accuracy in downstream RAG applications.