RAGFlow
Open-source RAG engine with deep OCR and chunking for PDFs and scanned documents - 40,000+ GitHub stars, supports tables, figures, and knowledge graph extraction.
RAGFlow is an open-source RAG engine built by InfiniFlow that prioritizes deep document understanding with built-in OCR, table detection, figure extraction, and structured chunking for PDFs, Word files, Excel spreadsheets, and HTML documents. The project reached 40,000 GitHub stars within months of its 2024 launch, driven by its ability to handle real-world enterprise documents where other RAG frameworks produce poor retrieval from table rows and embedded figures. RAGFlow runs locally with Docker Compose and provides a full web interface for uploading documents, inspecting parsed chunks visually, managing knowledge bases, and chatting with document collections without writing Python. The platform supports Elasticsearch, InfiniFlow Infinity, and external vector databases including Qdrant and Weaviate, with chunking strategies including naive, Q&A-oriented, and knowledge-graph-based chunk types to match different retrieval patterns.
Key Features
- Deep document OCR extracts text from scanned PDFs, embedded tables, and mixed-layout documents that standard PDF parsers fail to handle cleanly
- Visual chunk inspector shows parsed document chunks overlaid on the original document, letting users verify extraction quality before indexing into the vector database
- Multiple chunking strategies including naive, Q&A-optimized, and knowledge-graph chunking let teams select the right retrieval pattern for each document type
- Knowledge graph extraction builds entity and relationship graphs from documents to enable graph-augmented retrieval alongside standard vector search
- Full web interface for document management, knowledge base creation, and chatbot configuration requires no Python or coding to run a complete RAG application
- Docker Compose deployment runs the full stack locally or on a server without Kubernetes, including Elasticsearch, the chunking pipeline, and the chat interface
- REST API exposes all knowledge base management and retrieval operations to integrate RAGFlow document parsing into existing applications
Use Cases
- Developers building enterprise document chatbots who need reliable parsing of complex PDF reports, financial tables, and mixed-media documents without pre-processing scripts
- IT teams deploying a self-hosted knowledge base for internal documentation where data cannot leave the company network and cloud RAG services are not permitted
- Researchers building academic paper Q&A systems where proper table and figure extraction is critical for retrieving quantitative results from research documents
- Product teams prototyping RAG applications using the RAGFlow web interface without writing code, then integrating via API once the chunking configuration is validated
Pros
- Visual chunk inspector lets non-engineers verify document parsing quality without reading code - unique among open-source RAG frameworks for transparency into extraction
- Deep OCR and table detection handle complex enterprise documents that fail in LangChain and LlamaIndex default loaders without custom pre-processing pipelines
- 40,000+ GitHub stars with active contributions mean production issues and document format edge cases are being fixed continuously with frequent releases
Cons
- Docker Compose deployment requires at least 16GB RAM for the full stack - teams running on shared development servers or minimal cloud instances will hit memory limits
- Knowledge graph extraction adds significant processing time per document compared to naive chunking - large document libraries take hours to fully index with graph mode
- Web interface and API are newer than established RAG frameworks - complex multi-source retrieval patterns require more custom work than LangChain or LlamaIndex offer
RAGFlow Alternatives
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LlamaIndex
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LangChain
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Haystack
Open-source NLP framework by deepset for building production RAG pipelines and LLM applications with 18k+ GitHub stars and a modular pipeline design.
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