infiniflow/ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
What it solves
RAGFlow addresses the challenge of creating high-fidelity, production-ready AI systems by solving the "garbage in, garbage out" problem in Retrieval-Augmented Generation. It specifically targets the difficulty of extracting precise information from complex, unstructured data formats and reduces LLM hallucinations through grounded citations.
How it works
The system uses a converged context engine and template-based chunking to process diverse data sources including Word, Excel, PDF, images, and web pages. It employs deep document understanding to extract knowledge from complicated formats and supports multiple recall methods paired with fused re-ranking. The platform also allows for agentic workflows, enabling the LLM to use tools like Python/JavaScript code executors to perform tasks.
Who it’s for
- Developers building enterprise-scale AI systems that require high precision.
- Businesses needing to transform complex, heterogeneous data into searchable knowledge bases.
- Users requiring traceable AI answers with visible citations to verify accuracy.
Highlights
- Deep Document Understanding: Extracts knowledge from unstructured data with complex formats.
- Template-based Chunking: Offers intelligent and explainable chunking options.
- Grounded Citations: Provides traceable references and visualization of text chunking to reduce hallucinations.
- Heterogeneous Data Support: Compatible with Word, Slides, Excel, TXT, images, and more.
- Agentic Capabilities: Supports agentic workflows and code execution components.