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ragflow

ragflow

Work with RAGFlow, a full-stack Retrieval-Augmented Generation engine with deployment, backend APIs, SDK/HTTP integration, ingestion/retrieval, document parsing, agent workflows, and frontend integration.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: ragflow description: "Work with RAGFlow, a full-stack Retrieval-Augmented Generation engine with deployment, backend APIs, SDK/HTTP integration, ingestion/retrieval, document parsing, agent workflows, and frontend integration." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

RAGFlow

Use this repo skill when a task is about RAGFlow's source code, deployment, public API, Python SDK, document ingestion, retrieval, DeepDoc parsing, agent canvas/tooling, MCP/memory, or web frontend integration.

Route by Task

User taskRead
Start RAGFlow with Docker/source/Helm, change .env or service_conf.yaml, switch document engines, debug MySQL/Redis/MinIO/document-engine startupdeployment-configuration
Add/debug Quart routes, RESTful versus legacy prefixes, auth/session/API-token behavior, Peewee service logic, system endpoints, chat channelsbackend-api-services
Call RAGFlow from HTTP, Python SDK, OpenAI-compatible clients, streaming chat, retrieval endpoint, API keys, or client examplessdk-http-integration
Work on datasets, documents, chunks, parser_config, task executors, retrieval search, metadata filters, RAPTOR, GraphRAGdataset-ingestion-retrieval
Change DeepDoc parsers, PDF OCR/layout/table behavior, parser backend choices, parser_config parsing options, file-type routingdocument-parsing
Work on agent canvas DSL, components, templates, tools, memory, MCP retrieval, sandbox/code execution, webhooks, debug tracesagent-workflows
Wire or debug React/Vite routes, API constants, services, hooks, React Query cache, dataset/chat/agent UI integration, frontend testsfrontend-integration

First Checks

  1. Identify whether the user is deploying/using RAGFlow or modifying a checkout; deployment and SDK tasks often need no source-code changes.
  2. For code changes, route to the focused sub-skill first, then use sibling cross-links when a workflow spans backend, ingestion, SDK, and frontend.
  3. Keep the process model in mind: the API server and task executors are separate Python process types; a healthy UI does not prove document parsing workers are healthy.
  4. Prefer /api/v1 for newer public REST APIs and reserve /v1 for legacy route families that still exist.
  5. Treat document parsing, parser_config, and retrieval as a chain: file type and parser options affect chunks, embeddings, metadata, retrieval weights, and citation/reference outputs.

Common Entry Points

Cross-Cutting Notes

  • RAGFlow 0.26.1 requires Python >=3.13,<3.14 for the main Python project; the standalone Python SDK is published separately as ragflow-sdk.
  • The backend uses Quart and Peewee; do not assume Flask synchronous handlers or SQLAlchemy models.
  • The frontend uses React/Vite, React Query, service wrappers, and two API prefix families: newer /api/v1 and legacy /v1.
  • DeepDoc parser imports and OCR/layout backends have optional dependencies; missing parser packages should be handled as parser-backend issues, not generic API failures.
  • Source-level inspection found a packaging caveat: a plain editable install can fail if package metadata expects a top-level graphrag package while GraphRAG code is under rag/graphrag. Use repo-supported uv setup or source inspection until packaging is fixed.

References

  • Repo provenance records the source state and evidence paths used to create this skill.
  • Routing metadata is consumed by repo-skills-router during import.
  • Troubleshooting summarizes cross-cutting failure routes and points to the nearest sub-skill.
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