RAGFlow

What is RAGFlow?

RAGFlow is an open-source RAG engine designed for deep document understanding. It excels at parsing complex documents like PDFs with tables, figures, charts, and medical notation — making it particularly well-suited for healthcare applications where documents are rarely plain text.

Key Features

• Deep document understanding with layout analysis
• Template-based RAG pipeline configuration
• Open-source under Apache 2.0 license
• Multiple embedding model support (OpenAI, local models)
• PDF parsing with table and figure extraction
• Visual pipeline builder for non-technical users

Healthcare Use Case

RAGFlow is ideal for building medical knowledge bases from clinical guidelines, research papers, drug databases, and institutional protocols. Its advanced PDF parsing handles the complex layouts common in medical documents — multi-column papers, tables of lab values, and embedded figures.

Getting Started

1. Install RAGFlow via Docker: docker-compose up -d
2. Upload medical documents (PDFs, guidelines, protocols)
3. Configure the chunking strategy for medical document types
4. Select an embedding model — local models for privacy-conscious deployment
5. Build a knowledge base and test retrieval quality
6. Connect an LLM and configure the prompt template

Limitations

• Requires Docker infrastructure setup
• Complex configuration for advanced use cases
• Performance depends on embedding model choice