RAGFlow — Deep Document Understanding RAG Engine

What it is

RAGFlow is an open-source retrieval-augmented generation engine that specializes in deep document understanding. It parses complex PDFs, tables, images, and structured documents with higher fidelity than generic text extractors. The engine powers agent-based Q&A with inline citations.

The project targets teams building knowledge bases, document search systems, or AI assistants that need accurate answers grounded in technical documentation, research papers, or enterprise documents.

How it saves time or tokens

Generic RAG pipelines struggle with tables, multi-column layouts, and embedded images. RAGFlow's document understanding layer extracts structured content correctly, reducing hallucinations caused by garbled input. Better parsing means fewer tokens wasted on noise and more accurate retrieval.

How to use

1. Deploy RAGFlow using Docker: docker compose up -d.
2. Upload your documents through the web UI or API.
3. Query your knowledge base via the built-in chat interface or REST API.

Example

# Deploy RAGFlow with Docker
git clone https://github.com/infiniflow/ragflow.git
cd ragflow
docker compose -f docker-compose.yml up -d

# Access the web UI at http://localhost:9380
# Upload PDFs, Word docs, or Excel files
# Ask questions and get answers with citations

# API usage:
curl -X POST http://localhost:9380/api/v1/chat \
  -H 'Content-Type: application/json' \
  -d '{"question": "What are the key findings?", "kb_id": "your-kb-id"}'

Related on TokRepo

• AI Tools for RAG -- compare RAG frameworks and engines
• AI Tools for Documents -- document processing and understanding tools

Common pitfalls

• RAGFlow requires significant resources for document parsing. Allocate at least 8GB RAM and 4 CPU cores for the Docker deployment.
• Document parsing quality depends on the parser configuration. Test with representative samples before bulk uploading.
• The web UI is functional but basic. For production use, integrate via the REST API and build your own frontend.