Use for RAGFlow dataset tasks: create, list, inspect, update, or delete datasets; upload, list, update, or delete documents; start or stop parsing; check par...
记忆
RAGFlow Skill
试用Manage everyday RAGFlow datasets, retrieval, chat, and agents.
它能做什么
Operate RAGFlow v0.26.4 deployments through a bundled Node CLI for everyday knowledge-base setup, document ingestion, parsing, retrieval, chat assistants, agents, GraphRAG, connectors, models, and diagnostics. Use when a request explicitly involves a RAGFlow server, dataset, document pipeline, or RAGFlow agent.
技能文档
RAGFlow Skill
Operate common RAGFlow v0.26.4 workflows through node {baseDir}/scripts/ragflow.js [options]. Prefer --json when parsing or chaining results. Prioritize daily operations over exhaustive API coverage.
Requirements
- Set
RAGFLOW_URLandRAGFLOW_API_KEYin the environment or this skill's.env. - Use Node.js to run bundled scripts.
- Run
system-health --jsonafter first-time setup to verify service reachability and dependencies. Uselist-datasets --page-size 1 --jsonto verify API-key authentication.
Security Notes
- Use HTTPS in production. Production deployments should use
https://forRAGFLOW_URLto protect the API key in transit. Local development (http://localhost) is acceptable for testing. - Use a dedicated, rotatable API key for automation. RAGFlow v0.26.4 API keys are tenant-scoped rather than permission-scoped.
- Protect your API key. Never share
RAGFLOW_API_KEYin chat messages or commit it to version control. Use environment variables or the skill's.envfile.
Quick Command Reference
| Scenario | Commands |
|---|---|
| Knowledge base setup | create-dataset, list-datasets, get-dataset, update-dataset, delete-datasets |
| Document ingestion | upload-documents, ingest-documents, list-documents, get-document, update-document, delete-documents, download-document, preview-document, metadata-summary, update-metadata |
| Parsing & chunking | start-parsing, stop-parsing, wait-parsing, list-chunks, get-chunk, add-chunk, update-chunk, delete-chunks, get-document-graph, delete-document-graph |
| Direct retrieval | retrieve |
| Chat assistant | create-chat, list-chats, get-chat, update-chat, patch-chat, delete-chats |
| Chat sessions | create-session, list-sessions, get-session, update-session, delete-sessions, chat, chat-session |
| Agent | create-agent, list-agents, get-agent, update-agent, delete-agents |
| Agent Tags | list-agent-tags, update-agent-tags |
| Agent sessions | create-agent-session, list-agent-sessions, delete-agent-sessions, agent-chat |
| Connector | list-connectors, create-connector, get-connector, update-connector, delete-connector |
| RAPTOR | run-raptor, trace-raptor |
| GraphRAG | get-knowledge-graph, delete-knowledge-graph, run-graphrag, trace-graphrag |
| Embedded website access | list-system-tokens, create-system-token, delete-system-token, embed-code, embed-info, embed-chat, embed-agent-chat |
| Model discovery | list-models, list-added-models, list-default-models, set-default-model |
| Model providers | list-providers, get-provider, add-provider, delete-provider, list-provider-models, list-provider-instances, get-provider-instance, create-provider-instance, delete-provider-instances, verify-provider, list-instance-models, add-instance-model, set-model-status |
| System | system-version, system-health, get-log-levels, set-log-level |
Common Workflows
Full RAG pipeline (upload -> parse -> retrieve)
create-dataset --name "My KB" --chunk-method naiveupload-documents --dataset --files ./doc1.pdf ./doc2.txtstart-parsing --dataset --doc-idswait-parsing --dataset --doc-idsretrieve --question "What is X?" --datasets
Chat assistant with sessions
create-chat --name "Q&A" --datasets --llm-id qwen-turbo@Tongyi-Qianwencreate-session --chatchat-session --chat --session --question "Hello"
Agent workflow
create-agent --title "Assistant" --dsl @agent_dsl.jsoncreate-agent-session --agentagent-chat --agent --session --question "Hello"
agent-chat streams by default. Use --stream false for one final JSON response.
Agent tags workflow
list-agent-tags --agentupdate-agent-tags --agent --tags "Tag1,Tag2"
Connector workflow
create-connector --dataset --config @connector.jsonlist-connectors --datasetget-connector --id
Model provider workflow (v0.26.4)
list-providers --availableto see configurable providersadd-provider --name- Set
RAGFLOW_PROVIDER_API_KEY, then runcreate-provider-instance --name --instance(credentials live on an instance; a provider can have several) add-instance-model --name --instance --model-name --model-type chatset-default-model --model-type chat --model-provider --model-instance --model-name
Use verify-provider --name with RAGFLOW_PROVIDER_API_KEY set, or pass --api-key-file , to test a key without persisting an instance.
RAPTOR workflow
run-raptor --datasettrace-raptor --dataset
GraphRAG workflow
run-graphrag --datasettrace-graphrag --datasetget-knowledge-graph --dataset
Embedded website access
embed-code --chat --type fullscreenorembed-code --agent --type widgetembed-info --chatorembed-info --agentembed-chat --chat --question "Hello"orembed-agent-chat --agent --question "Hello"
embed-chat automatically creates the embedded chatbot session when --session is omitted. RAGFlow's shared-site route only creates a session and returns the prologue on the first no-session request, so the CLI bootstraps session_id first and then sends the real question.
Workflow Decision Guide
The first step in any RAGFlow operation is resolving the target resource ID. After that, choose the right path:
- Authoring or debugging a custom agent DSL? -> Read references/AGENT_GUIDE.md - it is a self-contained guide to the current RAGFlow agent DSL schema and includes minimal examples.
- Need CLI syntax or option details? -> Read references/COMMANDS.md - it's organized by workflow scenario with full option tables.
- Editing client code or checking request/response shapes? -> Read references/API.md - it has examples for supported
RagflowClientworkflows. - A command failed? -> Read references/TROUBLESHOOTING.md - common errors with causes and fixes.
- Formatting output for the user? -> Read references/REFERENCE.md - consistent response templates and status labels.
Key Constraints
- Confirm destructive scope. Confirm the exact target before any
delete-*command or beforeupdate-metadatadeletes metadata or selects every document. Skip confirmation only when removing temporary resources created in the same requested workflow. - Choose the ingestion path first. For built-in chunking, upload documents, adjust their parser configuration when needed, then run
start-parsing. For ingestion-pipeline datasets, useingest-documentsinstead. - Preserve source filenames. When an attachment is stored under a temporary or task-generated path, upload it as
--files =so RAGFlow retains the user-facing name. - Resolve complete, stable inputs. Discover resource IDs with the corresponding
list-*orget-*command, and paginate beyond RAGFlow's 100-item list limit. Use@identifiers fromlist-modelsfor--embedding-modeland--llm-id; treat numeric model row IDs as display data only. - Preserve session-history intent. Let
chat-sessionappend the latest user message by default. Use--pass-all-historyonly when replacing stored history, and use--legacyonly for a caller that requires cumulative legacy streaming. - Protect operational secrets. Keep
RAGFLOW_API_KEY, provider keys, system tokens, beta values, and embed URLs containingauth=out of user-facing output. Supply provider credentials throughRAGFLOW_PROVIDER_API_KEYor--api-key-file; reveal secret material only when the user explicitly requests copy-paste output. - Use the correct public embed origin. Pass
--originwhen the browser-facing RAGFlow URL differs fromRAGFLOW_URL. Let the CLI reuse or create a beta token and bootstrap the embedded chat session. - Start Agent DSL work from the guide. Read references/AGENT_GUIDE.md before authoring or debugging agents, and adapt its minimal examples instead of reconstructing the canvas schema from memory.
Output Format
Use raw --json internally, then summarize the operational result. Preserve the server's parsing labels (UNSTART, RUNNING, CANCEL, DONE, FAIL) and similarity scores. Redact API keys, system tokens, beta values, and auth= query values unless the user explicitly requests copy-paste secret material. Read references/REFERENCE.md only when a result needs a domain-specific response template.
相关技能
区分检索失败与生成失败,先定位再动手修 RAG 流水线。
Agent skill recommender. Input a user need, task description, or existing skill list; output best matching skills, install rationale, duplicate/merge candida...
Discover, install, update, or create the right skill when a workflow gap appears. Use when a task is repetitive, a role lacks a reliable procedure, an existi...
把消息转发到任意 OpenAI 兼容的 AI 代理,并跨调用维持多轮会话。
Use when an AI Agent (Claude Code, Codex, OpenClaw, or similar) needs to operate an llm-wiki knowledge base: ingest source files into Markdown wiki pages, answer questions from wiki/index.md and linked pages, run agent-bridge status/lint/link/relink/merge/query/index tasks, preserve provenance and t