What it is
RAGFlow is a platform for RAG scenarios where a language model answers using retrieved documents, not only its parameters. It focuses on document parsing, chunking, citations, and heterogeneous data.
It is useful when users need to ask questions over internal documents, PDFs, knowledge bases, or mixed sources and receive answers grounded in specific passages.
What is inside
The repository contains backend services, a web interface, document processing, RAG architecture, data source integrations, self-hosting instructions through Docker Compose, and image edition notes.
A typical flow is to deploy RAGFlow, upload documents, configure chunking and a model, inspect retrieved sources, then use the UI or API for questions.
RAG flow
This snippet shows the core data path: documents become an index, and answers are built from retrieved context.
Documents -> Parsing -> Chunks -> Index
Question -> Retrieval -> LLM answer with citationsStrengths and limits
The strength is product packaging for RAG. Instead of separate scripts, it provides an interface, API, document processing, and visible answer sources.
The limitation is data quality. RAG does not fix bad documents, poor chunking, weak models, or missing evaluation. Enterprise use needs permissions, isolation, observability, and answer testing.