What problem it solves

Teams building retrieval-augmented AI systems need to extract useful knowledge from complex, heterogeneous documents and ground generated answers in traceable evidence. They also need an orchestrated workflow that can combine document understanding, chunking, retrieval, reasoning, and citations.

Key features

• Deep document understanding — Extract knowledge from unstructured documents with complicated formats and find relevant information within large token contexts.

  Source: README

• Template-based chunking — Chunk documents using intelligent, explainable templates selected from multiple available options. Chunking results can also be visualized for human intervention.

  Source: README

• Agentic retrieval — Perform multi-step retrieval in which the model analyzes a question, decomposes it when needed, retrieves knowledge, and verifies evidence. Retrieval and reasoning depth can be selected according to question complexity.

  Source: README

• Knowledge compilation — Compile document- and dataset-level content into structured artifacts such as Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills. Compilation models, processing rules, and generated artifacts can be configured and managed.

  Source: README

• Heterogeneous data ingestion and orchestration — Ingest office documents, spreadsheets, text files, images, PDFs, scanned copies, structured data, and web pages. The workflow includes configurable language and embedding models, multiple recall, fused re-ranking, and APIs for integration.

  Source: README

Quick start

Copied verbatim from the project's README.

   git clone https://github.com/infiniflow/ragflow.git

   cd ragflow/docker

   docker compose -f docker-compose.yml up -d

Who it may suit

Inference: This may suit developers and teams building document-grounded AI applications, retrieval workflows, or context infrastructure for language models.

Topics to discuss

• Which heterogeneous data sources and document formats should ingestion support next?

• How should retrieval modes be evaluated and selected for different question complexities?

• What knowledge-compilation formats and workflows would be most useful for community projects?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/infiniflow/ragflow#readme

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