Local Knowledge Base Building: Ragflow

💡 What is RAGFlow?

RAGFlow RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine built on deep document understanding.RAGFlow provides a streamlined set of RAG workflows for businesses and individuals of all sizes, combined with large language modeling (LLM) to provide reliable Q&A and evidence-based querying of different types of complex formats of user data. RAGFlow provides a streamlined RAG workflow for organizations and individuals of all sizes.

pure open source

Available out of the box.

Local Knowledge Base Building: Ragflow

OCR function

This helps us unlock a lot of OCR features, whether you want to deal with documents or Excel data, there are corresponding parsing methods.

Local Knowledge Base Building: Ragflow

🔥 Recent Updates

  • 2024-08-02 Support GraphRAG Inspired by graphrag and mind maps.
  • 2024-12-18 Upgraded Deepdoc's document layout analysis model.
  • 2024-12-04 Support for Pagerank scores for knowledge bases.
  • 2024-11-22 Refined the definition and use of variables in Agent.
  • 2024-11-01 Adding keyword extraction and related question generation to parsed chunks to improve recall accuracy.
  • 2024-08-22 Support for conversion from natural language to SQL statements using RAG technology.

🌟 Main Functions

🍭 "Quality in, quality out"

  • on the basis ofDeep Documentation UnderstandingThe ability to extract insights from unstructured data in a variety of complex formats.
  • Really fast needle-in-a-haystack testing in infinite context (token) scenarios.

🍱 Template-based text slicing

  • Not just smart, but also controllable and interpretable.
  • Multiple text templates to choose from

🌱 Justified, minimized hallucinations (hallucination)

  • Visualization of the text slicing process with support for manual adjustment.
  • Justified: answers provide snapshots of key references and support traceability.

🍔 Compatible with various heterogeneous data sources

  • Support rich file types, including Word documents, PPT, excel tables, txt files, images, PDF, photocopies, copies, structured data, web pages and more.

🛀 Hassle free, automated RAG workflows

  • Fully optimized RAG workflows support ecosystems ranging from personal applications to mega-enterprises.
  • Configurations are supported for the Large Language Model LLM as well as the Vector Model.
  • Based on multiplexed recall, fusion reordering.
  • Provides easy-to-use APIs for easy integration into various enterprise systems.

Access Address:https://github.com/infiniflow/ragflow

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