ragflow
ragflow
https://github.com/infiniflow/ragflow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
Demo
Try our demo at https://demo.ragflow.io.
Key Features
"Quality in, quality out"
- Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
- Finds "needle in a data haystack" of literally unlimited tokens.
- Intelligent and explainable.
- Plenty of template options to choose from.
- Visualization of text chunking to allow human intervention.
- Quick view of the key references and traceable citations to support grounded answers.
- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
- Streamlined RAG orchestration catered to both personal and large businesses.
- Configurable LLMs as well as embedding models.
- Multiple recall paired with fused re-ranking.
- Intuitive APIs for seamless integration with business.
System Architecture

Configure a knowledge base
https://ragflow.io/docs/dev/configure_knowledge_base
Deploy a local LLM
https://ragflow.io/docs/dev/deploy_local_llm
Introduction to Agentic RAG
https://ragflow.io/docs/dev/agentic_rag_introduction
RAGFlow offers RESTful APIs for you to integrate its capabilities into third-party applications.
https://ragflow.io/docs/dev/api
https://github.com/infiniflow/ragflow/blob/main/docs/references/api.md
Ollama Docker 镜像指南(支持GPU)
https://ollama.qianniu.city/doc/Ollama%20Docker%20%E9%95%9C%E5%83%8F%E6%8C%87%E5%8D%97.html

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