// rag · netease-youdao/QAnything

QAnything

QAnything es un proyecto de RAG y conocimiento de código abierto de netease-youdao (netease-youdao/QAnything en GitHub). Tiene 14.197 estrellas en GitHub y 1.354 bifurcaciones, se publica bajo la licencia AGPL-3.0 y se actualizó por última vez en GitHub el 23 de septiembre de 2026.

Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.

★ 14,2k estrellas⑂ 1,4k bifurcacionesAGPL-3.0actualizado sep 2026
▦ visual: el README de este proyecto no incluye ninguna captura incrustable,
así que aquí va un marcador. La guía de abajo está completa de todos modos.
// datos del proyecto

Datos del proyecto

Estrellas en GitHub
14.197
Bifurcaciones
1.354
Licencia
AGPL-3.0
Última actualización en GitHub
23 de septiembre de 2026
Tipo de ficha
Guía de instalación completa
Sitio oficial
Official site
// cómo funciona

Cómo funciona

  1. Prepare your machine

    Install Docker and Docker Compose and make sure you have at least 20GB of RAM; no GPU is required for the default setup.

  2. Clone the repository

    Pull the QAnything repo, which contains the per-platform Docker Compose files for one-click startup.

  3. Start the stack

    Run the Docker Compose file for your operating system and wait for the 'qanything backend service is ready' log line (about 30 seconds).

  4. Configure a model and ask

    Open the web UI at http://localhost:8777/qanything/, set your OpenAI-compatible API base and key (or a local Ollama model) in the front-end settings, upload documents and start asking.

// características

Por qué gusta tanto

Two-stage retrieval

Embedding retrieval followed by reranking with BCEmbedding models keeps answer quality stable as data volume grows, instead of degrading like single-stage retrieval.

Many file formats

Supports PDF, Word, PPT, Excel, Markdown, email, TXT, images (with OCR), CSV and web links, with strong parsing of tables, columns and cross-page layouts.

Offline and CPU-friendly

Designed for data security: install and use it with the network unplugged, running by default in a pure CPU environment on Windows, Mac or Linux.

Cross-language Q&A

Ask in Chinese or English regardless of the language of the source files.

Flexible modes

Quick-start mode (upload and ask, or fileless chat), retrieval-only mode, FAQ matching, custom Bots and hybrid BM25-plus-embedding search.

Independent components

PDF parsing, OCR, embedding and rerank services are independent and replaceable, and can be called separately over HTTP.

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// ejecútalo

Ejecútalo tú mismo

  1. Clone the repository

    Pull the QAnything code with its Docker Compose files.

    git clone https://github.com/netease-youdao/QAnything.git
    cd QAnything
  2. Start with Docker Compose

    Launch the stack with the compose file for your platform (Linux shown).

    docker compose -f docker-compose-linux.yaml up
  3. Open the web app

    Once the log says the backend service is ready, open the front end in your browser.

    # Open http://localhost:8777/qanything/ - see README for details
// casos de uso

Qué puedes crear

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// preguntas frecuentes

Preguntas frecuentes

Who maintains QAnything?

QAnything is developed by NetEase Youdao's open-source team and community contributors.

Is it free to use?

The code is AGPL-3.0 licensed; note the README says commercial use should also follow the license of the underlying QwenLM models if you use them.

Does it need a paid API key?

Version 2.0 gets its LLM through an OpenAI-compatible interface: that can be a paid hosted API with your key, or a free local model served by Ollama.

How does QAnything compare with Dify or FastGPT?

Those are broader LLM app platforms; QAnything focuses specifically on local knowledge-base Q&A with strong document parsing and two-stage retrieval, deployable fully offline on CPU.

How do I get started?

Clone the repo, run the Docker Compose file for your OS, open http://localhost:8777/qanything/, set your model endpoint, and upload your first files.

// sigue explorando

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// alternativas

Alternativas en RAG y conocimiento

Los demás proyectos de RAG y conocimiento con más estrellas del catálogo de LibreAI: la lista realista de alternativas si QAnything no es lo que buscas.

// más en rag

Más en RAG y conocimiento

Proyectos de RAG y conocimiento cercanos en el catálogo, los más próximos en estrellas a QAnything: sigue explorando a ambos lados.

Consigue QAnything

Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.