Complex PDF layouts, tables and scans often throw retrieval off, and answers cannot be traced. RAGFlow is an open-source retrieval-augmented framework focused on parsing: restore documents into structured pieces before recall, so answers trace back to source text. Deployment is heavier than a managed service and needs models and storage. Supported file types and capabilities change by version, so check the project repository.
What it does
- Careful parsing handles tables and multi-column layouts
- Text splitting and indexing are configurable
- Answers include quoted source passages for checking
- Several data sources import in bulk
- Open source, self-hosted and able to use different models
Who it is for
- A batch of complex PDFs must become Q and A material
- Every answer needs to trace back to source text
- Self-built retrieval is unreliable and parsing needs a rethink
- The team must run it in its own environment to control data boundaries
How to get started
- Start with ten of the hardest documents and check parse output
- Decide the splitting method only after parse quality is acceptable
- Build questions with reference answers for comparing recall
