RAGFlow
Open-source RAG engine with deep document understanding
RAGFlow is profiled here as a RAG Framework tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
RAGFlow is an open-source retrieval-augmented generation engine from InfiniFlow that centers on parsing complex documents well before retrieval ever runs. Its document understanding layer extracts text, tables, and layout from PDFs and office files with high fidelity, then chunks the result so answers cite accurate source content. RAGFlow pairs this pipeline with agent templates and a visual workflow builder, giving teams a path from raw files to a production answer service, with a hosted cloud option for those who prefer not to self-host.
Key Capabilities:
Deep document parsing that preserves tables, layout, and reading order
Template-based chunking tuned to different document types
Retrieval combining full-text and vector search over Elasticsearch or Infinity
A visual workflow builder for assembling RAG and agent pipelines
Grounded citations that trace answers back to source passages
Connectors that ingest from files, web pages, and chat channels
Alternative tools
- Mixedbread
Embedding and reranking models with a hosted API
- LanceDB
Embedded multimodal vector database on the Lance format
- Milvus
Open-source vector database built for billion-scale search
- Sentence Transformers
Python framework for dense text and image embeddings
- R2R
Production retrieval system with ingestion and an API
- Mem0
Long-term memory layer for AI agents and assistants