A RAG system's quality depends more on document reading than on the model. That is where RAGFlow shines: an open-source engine specialized in understanding complex documents, PDFs, tables, figures and even scans, before retrieving information. It is an alternative to paid RAG services.
What is RAGFlow?
With deep parsing (DeepDoc) and chunking with visual inspection, RAGFlow delivers answers anchored in citations, reducing hallucination. It fuses RAG with agent flows and supports techniques like GraphRAG and reranking. It is one of the most-starred AI projects on GitHub.
Key features
- DeepDoc: deep reading of PDFs, tables, figures and scanned documents
- Chunking with visual inspection and cited answers (less hallucination)
- Fuses RAG with agent flows, GraphRAG and reranking
- Self-hostable, with around 80k stars
How Reche uses it
Internal assistants and AI search are only reliable when anchored in the right data. Reche implements real RAG in client products, with attention to data quality and citation, so the AI answers based on what the company knows, not on what it invents.