RAGFlow

RAGFlow

An open-source RAG engine focused on deep document understanding and knowledge extraction from complex documents.

Professional overview

RAGFlow focuses on turning complex documents into usable knowledge. It is valuable when documents include PDFs, tables, layouts or formats that simple text extraction handles poorly.

Problem it solves

It connects documents, web pages, databases or private knowledge to models, reducing hallucination risk and making answers more grounded in retrievable sources.

Best-fit developer scenarios

• Document-heavy RAG
• Enterprise knowledge base
• PDF Q&A
• Citation-backed answers

When not to use it

• Chatbots that do not need external knowledge
• Bulk-ingesting uncleaned documents
• Customer-facing launch without retrieval evaluation

Recommended usage workflow

1. Test parsing quality on representative documents 2. Design chunking, indexing, retrieval and reranking 3. Evaluate hit rate, citations and answer quality with real questions 4. Create update, evaluation and rollback procedures before launch

Getting started

1. Start a local RAGFlow server following the official quickstart. 2. Create a dataset and upload representative documents. 3. Review parsing results before building chat applications. 4. Test retrieval and citation quality with real user questions.

Risks and review checklist

• Do not evaluate only the final answer; inspect retrieved evidence
• Parsing, chunking and reranking strongly affect quality
• Frequently changing knowledge bases need versioning and cache strategy