infiniflow/ragflow

What it solves

RAGFlow addresses the challenge of transforming complex, unstructured data into high-fidelity AI systems. It solves the problem of "needle in a haystack" retrieval from massive datasets and reduces hallucinations by providing grounded citations and traceable references for LLM responses.

How it works

It functions as a Retrieval-Augmented Generation (RAG) engine that combines a converged context engine with agentic capabilities. The system uses deep document understanding to extract knowledge from unstructured data with complex formats, employs template-based chunking for explainable data processing, and supports multiple recall methods paired with fused re-ranking to ensure precise retrieval.

Who it’s for

It is designed for developers and enterprises of any scale who need to build production-ready AI systems based on their own complex data sources, including Word, Slides, Excel, TXT, images, and web pages.

Highlights

• Deep Document Understanding: Extracts knowledge from unstructured data with complicated formats.
• Agentic Workflow: Supports agentic workflows, MCP, and includes a Python/JavaScript code executor component.
• Grounded Citations: Provides visualization of text chunking and traceable citations to reduce hallucinations.
• Heterogeneous Data Support: Compatible with various formats including scanned copies, structured data, and multi-modal models for images within PDFs or DOCX files.
• Flexible Infrastructure: Supports configurable LLMs, embedding models, and a choice between Elasticsearch and Infinity as the document engine.