GitHub

RAGFlow Project Description

What is the project about?

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that leverages deep document understanding and Large Language Models (LLMs) to provide accurate and well-cited question-answering capabilities.

What problem does it solve?

It addresses the challenge of extracting knowledge from unstructured data with complex formats and providing truthful answers based on reliable sources, reducing hallucinations often associated with LLMs. It allows to find the "needle in the haystack" with unlimited tokens.

What are the features of the project?

  • Deep Document Understanding: Extracts knowledge from various unstructured data formats using advanced document layout analysis.
  • Template-Based Chunking: Offers intelligent and explainable chunking with multiple template options.
  • Grounded Citations: Provides traceable citations and visualizes text chunking to reduce hallucinations and ensure answer accuracy.
  • Heterogeneous Data Sources: Supports a wide range of data sources, including Word documents, PDFs, Excel spreadsheets, images, web pages, and more.
  • Automated RAG Workflow: Streamlines the RAG process with configurable LLMs, embedding models, multiple recall strategies, and fused re-ranking.
  • Intuitive APIs: Offers easy integration with existing business systems.
  • Knowledge Graph Extraction: Optimized extraction and application with various configuration options.
  • Keyword Extraction: Adds keyword extraction and related question generation to improve retrieval accuracy.
  • Text to SQL: Support text to SQL statements through RAG.

What are the technologies used in the project?

  • Docker and Docker Compose
  • Large Language Models (LLMs)
  • Embedding Models
  • Elasticsearch (default) or Infinity (optional) for document storage and retrieval.
  • Python
  • Frontend technologies (not specified, but likely JavaScript-based given the npm commands)
  • MinIO, Redis, and MySQL

What are the benefits of the project?

  • Improved Accuracy: Provides more accurate and reliable answers by grounding them in retrieved evidence.
  • Reduced Hallucinations: Minimizes the generation of false or misleading information.
  • Scalability: Designed to handle large-scale data and complex queries.
  • Flexibility: Supports various data formats and allows customization of LLMs and embedding models.
  • Ease of Use: Offers a streamlined workflow and intuitive APIs for easy integration.
  • Explainability: Provides transparency through traceable citations and visualization of the chunking process.

What are the use cases of the project?

  • Question-answering systems for businesses and organizations.
  • Knowledge base creation and management.
  • Research and development in the field of natural language processing.
  • Content generation and summarization.
  • Any application requiring accurate and well-cited information retrieval from complex documents.
  • Text to SQL.
ragflow screenshot