Open-source RAG engine with deep document understanding, chunk visualization, and citation tracking for enterprise knowledge bases.
An open-source system for building AI that answers questions from your documents β with deep understanding of complex document formats.
RAGFlow is an open-source Retrieval-Augmented Generation engine designed for enterprise-grade document understanding and question answering. What sets RAGFlow apart from simpler RAG solutions is its focus on deep document parsing β it doesn't just split text into chunks, it understands document structure including tables, figures, headers, and hierarchical layouts.
The platform provides a visual chunking interface where users can see exactly how documents were parsed and manually adjust chunk boundaries when needed. This transparency is rare in RAG tooling and critical for enterprise deployments where accuracy matters more than speed. Every answer includes citations linking back to specific source chunks, enabling verification and building user trust.
RAGFlow supports multiple document formats including PDF, Word, Excel, PowerPoint, and web pages. Its table understanding is particularly strong β it can parse complex tables and maintain row/column relationships during retrieval, a common failure point for simpler RAG systems. The platform also handles images within documents using OCR and vision models.
The architecture is modular: you can swap embedding models, LLM providers, and vector stores. It ships with support for Elasticsearch, Infinity, and other backends. The system includes conversation management with multi-turn context tracking, making it suitable for building conversational knowledge assistants.
RAGFlow runs as a Docker-based service with a web UI for document management, knowledge base configuration, and chat interface. It supports multi-tenancy, making it viable for SaaS deployments. The API layer enables integration with custom applications and agent frameworks.
For organizations that need production-grade RAG with full control over their data pipeline, RAGFlow offers a compelling alternative to managed services like Azure AI Search or Pinecone's assistant features. Its document understanding capabilities, visual debugging tools, and citation tracking make it particularly well-suited for regulated industries, legal tech, healthcare, and financial services where answer provenance is non-negotiable.
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Parses PDFs, Word docs, and more with structure-aware chunking that preserves tables, headers, figures, and hierarchical relationships.
Use Case:
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.
Use Case:
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.
Use Case:
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.
Use Case:
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.
Use Case:
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.
Use Case:
Deploying a shared RAG platform across departments with isolated data access controls.
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View Pricing Options βEnterprise knowledge management
Regulated industry document QA
Legal and compliance research
Financial document analysis
We believe in transparent reviews. Here's what RAGFlow doesn't handle well:
RAGFlow uses specialized table detection and parsing that preserves row/column structure. Tables are indexed as structured data rather than flattened text, enabling accurate retrieval of tabular information.
Yes, RAGFlow supports OpenAI, Azure OpenAI, local models via Ollama, and any OpenAI-compatible API endpoint.
RAGFlow supports Elasticsearch and Infinity as vector backends, with the architecture designed for pluggable storage.
Yes, RAGFlow is designed for production with multi-tenancy, API access, conversation management, and citation tracking. Several enterprises use it in regulated industries.
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