RAG & sistem pengetahuan

onyx

onyx-dot-app/onyx

A self-hostable team AI platform combining knowledge connectors, retrieval, agents, and research workflows.

★ 32KBintang
⑂ 4,4KFork
434Isu terbuka
PythonBahasa
Lisensi
Q90Skor editorial

Ringkasan

Onyx packages the interface, knowledge ingestion, retrieval, and agent actions into an application. Standard deployment includes the knowledge index and connector workers. Lite is a simpler chat-and-agent setup and should not be used to estimate full-stack RAG resources.

Fitur utama

  • Connectors
  • Hybrid retrieval
  • Agent actions
  • Research and artifacts
  • Two deployment modes

Persyaratan, instalasi, dan mulai cepat

Use the official deployment overview. Choose Standard for knowledge indexing, configure one model and one small data source, and finish indexing before evaluation.

Penggunaan

Test known-answer, unanswerable, and permission-isolation questions. Check citations, synchronization lag, and document deletion before expanding connectors.

How it works
Connectors synchronize documents, workers index them, and retrieval supplies context to a model. Agents can invoke actions and MCP tools.

Audience and requirements
Teams operating an internal knowledge platform. Containers, a model service, and index/worker/storage resources for Standard.

Practical use cases
Internal knowledge access; team research; specialized agents.

Limitations and selection
Standard requires several services. Community and Enterprise features differ. Self-hosting the app does not make external model calls local.

Related projects and selection
labring/FastGPT:Comparison: FastGPT emphasizes visual workflows; evaluate Onyx for team access and connector synchronization.

open-webui/open-webui:Comparison: evaluate Open WebUI for chat access, and full stacks for synchronized knowledge.

Source review
Editorial analysis of upstream sources, without runtime or benchmark testing. Proposed workflows are editorial suggestions.

Kompatibilitas model dan kasus penggunaan

Supports multiple hosted and self-hosted model providers; Standard also uses indexing-related model services.

Catatan lisensi dan risiko

Community Edition uses MIT. Enterprise features have separate availability and terms; check the component and edition.

Editorial source review 2026-09-09T05:00:00.950Z. README and live repository page verified; current stars/forks from GitHub HTML. Last-push metadata retained from 2026-09-05 discovery snapshot. No runtime benchmark. Integration proposals are editorial analysis.

Rilis dan pemeliharaan

Reviewed 2026-09-09. Counters come from repository pages; features are based on upstream documentation. See Releases in the source links. Editorial analysis of upstream sources, without runtime or benchmark testing. Proposed workflows are editorial suggestions.

milvus

milvus-io/milvus

★ 45,7KGo

LanceDB

lancedb/lancedb

★ 11,1KRust

EvalScope

modelscope/evalscope

★ 3,2KPython

llm-app

pathwaycom/llm-app

★ 58,9KJupyter Notebook

graphrag

microsoft/graphrag

★ 35,9KPython

FastGPT

labring/FastGPT

★ 29,6KTypeScript