RAGFlow
Open-source RAG engine based on deep document understanding & OCR
About RAGFlow
RAGFlow is an open-source enterprise RAG (Retrieval-Augmented Generation) engine based on deep document understanding and multimodal document parsing. Unlike naive RAG systems that slice documents into arbitrary character chunks—scrambling complex tables, footnotes, and multi-column layouts—RAGFlow preserves the original semantic structure of complex documents. Powered by DeepDoc computer vision models, RAGFlow extracts clean text, recognizes complex tables across multiple pages, identifies corporate hierarchies, and visually highlights exact source citations with bounding boxes inside PDF viewers. With self-hosted Docker deployment, low-code workflow orchestration, and native support for local and cloud LLMs, RAGFlow is widely adopted by enterprise organizations requiring zero hallucination in legal, financial, and technical document analysis.
RAGFlow distinguishes itself through template-based document chunking algorithms tailored to specific document types, including scientific papers, financial quarterly reports, legal contracts, manuals, and Excel sheets. This eliminates context fragmentation and ensures answers cite complete data tables. The retrieval engine combines hybrid vector embedding search with full-text keyword ranking (BM25) and cross-encoder re-ranking models, consistently achieving higher retrieval precision on benchmark evaluations. All search results feature verifiable ground-truth citations, allowing users to hover over AI answers and inspect the exact highlight coordinates on the original PDF document page.
Deploy RAGFlow via Docker Compose (`docker compose up -d`) on local or cloud servers.
Upload complex documents in PDF, DOCX, XLSX, PPTX, or image formats.
Select a parsing template tailored to your document type (financial, legal, academic).
RAGFlow extracts structured content, tables, and visual chunks into the hybrid vector index.
Query the knowledge base via the web chat UI or REST API with verifiable PDF bounding-box citations.
Capabilities & Features
Common Use Cases
enterprise-rag
document-qa
table-extraction
knowledge-base
Frequently Asked Questions
How does RAGFlow prevent hallucinations in enterprise document Q&A?
RAGFlow uses deep vision models to understand exact table and paragraph layout, pairs hybrid vector/keyword search with re-rankers, and provides visual bounding-box highlights on the original PDF for every generated answer.
Can RAGFlow be self-hosted on private on-premise servers?
Yes, RAGFlow is fully open-source and provides one-command Docker Compose deployment for air-gapped enterprise environments.
What document formats does RAGFlow support?
RAGFlow supports PDF, DOCX, PPTX, XLSX, CSV, TXT, HTML, Markdown, and scanned document images.
Free Plan
Open-source Docker edition is 100% free with unlimited local document indexing; Cloud free tier includes 50MB storage.
Paid Plan
Cloud Pro tier starts at $20/month for 5GB vector storage, multi-tenant team workspaces, and prioritized GPU OCR rendering.
Pros & Cons
Deep document understanding that parses complex multi-page tables accurately
Verifiable ground-truth citations with visual PDF bounding-box highlights
Hybrid retrieval combining dense vector search, BM25 keyword matching, and re-ranking
Complete self-hosting via Docker with zero cloud data transmission
Pre-configured parsing templates for financial reports, legal contracts, and manuals
Docker deployment requires at least 16GB RAM and dedicated CPU/GPU resources
Initial document parsing is slower than naive character splitters due to OCR models
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