An open source RAG engine built based on deep document understanding

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RAGFlow can provide a streamlined RAG workflow for enterprises and individuals of all sizes, combined with the Big Language Model (LLM) to provide reliable questions and answers and well-founded citations for users of various complex format data.

RAGFflow is an excellent open source project from infiniflow, with its core being a set of RAG engine based on Deep Document Understanding(Retrieval‑Augmented Generation). Simply put, it can help you import documents in multiple complex formats (PDF, Word, Excel, PPT, scanned images, etc.) into the system, intelligently segment and encode them into vectors, and then use them with the Big Language Model (LLM) for Q & A, generate high-quality answers with citations.

Main content of the project

1.& nbsp;Deep document understanding

  • Use the self-developed DeepDoc model to identify document structure-such as tables, titles, and paragraph positions.
  • The templated chunk strategy at the natural language inference-level divides the generated response into “structured” blocks to make the generated response more accurate.

2.& nbsp;Comprehensive data compatibility

  • Support Word, PDF, PPT, Excel, Markdown, structured tables, scanned images and other popular documents and formats.

3.& nbsp;Simple and automated RAG workflow

  • Document import → automatic chunk + vector embedding + vector retrieval (with ElasticSearch or Infinity) → docking with LLM to generate answers.
  • Support the configuration of multiple recalls, multiple rounds of reordering (embedding/keyword/multi recall), and can interactively view and correct chunks through UI to prevent Hallucination

4.& nbsp;Multimodal code execution capabilities

  • Supports image recognition (OCR) tasks, which converts image content into text and then performs RAG.
  • Built-in code executor, which can run Python/JS code in a sandbox environment, suitable for understanding script fragments in complex documents

5.& nbsp;Highly cited, reliable and verifiable

  • When the system interface or API returns an answer, it will attach the reference location extracted by chunk to make the answer more reliable and traceable.

System architecture and operating principles

  1. Document upload and chunk analysis
    Use the DeepDoc model to structurally parse the document, intelligently split and generate corresponding embedding vectors based on the “chunk template”.
  2. vector index storage
    Elasticsearch uses default to store full-text text and vectors; optional “Infinity” is used as the backend engine
  3. LLM access
    The front-end deploys local or cloud LLM through APIs or Olama/LocalAI to complete generative Q & A.
  4. Recall + rearrangement + generation
    Multiple recall mechanism (embedding + keyword + phrase matching, etc.) → Rerank → Match LLM to generate answers and attach snippet quotes from the original document.
  5. Agent multimodal
    Support image text recognition, multi-language query, and even generate code to run, with strong scalability

丨ˇ Why is it worth paying attention?

  • Advanced chunk mechanism: Compared to traditional splitting by character length (such as LangChain), RAGFlow is smarter-it can resolve semantic boundaries such as tables/headers.
  • enterprise features: Support a large number of documents, configuration recall policies, visual chunk adjustments, code sandboxes, etc., suitable for large-scale scenarios.
  • Open source + license friendly: Apache‑2.0, suitable for commercial deployment.

Start suggestions

  1. Quick Trial: Official online demo (demo.ragflow.io)git clone…docker-compose up -dDefault relies on x86, 4-core CPU, 16GB RAM, 50GB disk

🚧Current challenges

  • According to user feedback, DeepDoc’s chunk performs slightly less well with specific complex document structures (such as legal documents) and may require manual fine-tuning or combination with other tools (such as LangChain).
  • During enterprise deployments, security vulnerabilities have been reported many times (PDF ReDos, IDOR, XSS, etc.), and attention needs to be paid to timely patching

Summary

RAGFlow is an enterprise-level solution for processing complex documents → vector retrieval + LLM answers in a one-stop manner. Its advantages lie in structured chunk, visual tuning, integration of OCR and code sandbox, etc. However, at the same time, in terms of deployment security and adaptation to extremely complex documents, it is still necessary to introduce appropriate tools or optimize parameters.

Github：https://github.com/infiniflow/ragflow

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