Master RAGFlow with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make RAGFlow powerful for ai memory & search workflows.
Parses PDFs, Word docs, and more with structure-aware chunking that preserves tables, headers, figures, and hierarchical relationships.
Processing financial reports where table data and section context must be preserved for accurate retrieval.
Web UI showing exactly how each document was chunked, with the ability to manually adjust boundaries and verify parsing quality.
Quality-checking document parsing before deploying a knowledge base to production users.
Every generated answer includes links to specific source chunks, enabling users to verify claims against original documents.
Building a compliance knowledge assistant where every answer must be traceable to source policy documents.
Maintains conversation context across multiple exchanges, enabling follow-up questions and clarification without losing thread.
Creating a customer-facing knowledge assistant that handles complex multi-step inquiries.
Specialized parsing for complex tables that maintains row/column relationships during indexing and retrieval.
Querying data from annual reports, spec sheets, or compliance matrices embedded in PDF documents.
Built-in tenant isolation enabling multiple teams or clients to have separate knowledge bases within one deployment.
Deploying a shared RAG platform across departments with isolated data access controls.
Yes. The GitHub repository lists RAGFlow under the Apache-2.0 license. The product also offers a hosted cloud service with Free, Starter, Pro, and Enterprise tiers.
RAGFlow states support for Word documents, slides, spreadsheets, text files, images, scanned copies, structured data, web pages, and other heterogeneous sources. Its website also describes a built-in ingestion pipeline for cleansing and processing multi-format data.
No. The website describes high-precision hybrid search that combines vector search, BM25, custom scoring, and advanced reranking. The README also mentions multiple recall paired with fused reranking.
Yes. The README provides Docker Compose and source-development instructions. Documented self-hosting prerequisites include at least 4 CPU cores, 16 GB RAM, 50 GB disk, Docker 24.0.0 or later, Docker Compose v2.26.1 or later, and Python 3.13.
Yes. RAGFlow describes unified AI agent orchestration with RAG, tools, MCPs, visual workflows, web search, chat, models, retrieval, and datasets. Recent listed updates include agentic workflow and MCP, agent memory, and a Python/JavaScript code executor component.
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Tutorial updated March 2026