RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
Key Features
🍭 "Quality in, quality out"
Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
Finds "needle in a data haystack" of literally unlimited tokens.
🍱 Template-based chunking
Intelligent and explainable.
Plenty of template options to choose from.
🌱 Grounded citations with reduced hallucinations
Visualization of text chunking to allow human intervention.
Quick view of the key references and traceable citations to support grounded answers.
🍔 Compatibility with heterogeneous data sources
Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
🛀 Automated and effortless RAG workflow
Streamlined RAG orchestration catered to both personal and large businesses.
Configurable LLMs as well as embedding models.
Multiple recall paired with fused re-ranking.
Intuitive APIs for seamless integration with business.
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