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.

LangChainSince 2022-10

The most popular framework for developing applications powered by large language models, including agents and RAG.

Provides components for memory, data retrieval, and tool usage.