skill-for-ragflow

name skill-for-ragflow description Operate RAGFlow v0.27.0 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. metadata {"openclaw":{"requires":{"bins":"[Truncated]","env":"[Truncated]"},"primaryEnv":"RAGFLOW_API_KEY","homepage":"https://github.com/LunarCache/ragflow-skill"}} RAGFlow Skill Operate common RAGFlow v0.27.0 workflows through node {baseDir}/scripts/ragflow.js <command> [options] . Prefer --json when parsing or chaining results. Prioritize daily operations over exhaustive API coverage. Requirements Set RAGFLOW_URL and RAGFLOW_API_KEY in the environment or this skill's .env . Use Node.js to run bundled scripts. Run system-health --json after first-time setup to verify service reachability and dependencies. Use list-datasets --page-size 1 --json to verify API-key authentication. Security Notes Use HTTPS in production. Production deployments should use https:// for RAGFLOW_URL to protect the API key in transit. Local development ( http://localhost ) is acceptable for testing. Use a dedicated, rotatable API key for automation. RAGFlow v0.27.0 API keys are tenant-scoped rather than permission-scoped. Protect your API key. Never share RAGFLOW_API_KEY in chat messages or commit it to version control. Use environment variables or the skill's .env file. 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 naive upload-documents --dataset <id> --files ./doc1.pdf ./doc2.txt start-parsing --dataset <id> --doc-ids <doc_id1> <doc_id2> wait-parsing --dataset <id> --doc-ids <doc_id1> <doc_id2> retrieve --question "What is X?" --datasets <id> Chat assistant with sessions create-chat --name "Q&A" --datasets <id> --llm-id qwen-turbo@Tongyi-Qianwen create-session --chat <chat_id> chat-session --chat <chat_id> --session <session_id> --question "Hello" Agent workflow create-agent --title "Assistant" --dsl @agent_dsl.json create-agent-session --agent <agent_id> agent-chat --agent <agent_id> --session <session_id> --question "Hello" agent-chat streams by default. Use --stream false for one final JSON response. Agent tags workflow list-agent-tags --agent <agent_id> update-agent-tags --agent <agent_id> --tags "Tag1,Tag2" Connector workflow create-connector --config @connector.json list-connectors get-connector --id <id> Model provider workflow (v0.27.0) list-providers --available to see configurable providers add-provider --name <provider> Set RAGFLOW_PROVIDER_API_KEY , then run create-provider-instance --name <provider> --instance <name> (credentials live on an instance; a provider can have several) add-instance-model --name <provider> --instance <name> --model-name <model> --model-type chat set-default-model --model-type chat --model-provider <provider> --model-instance <name> --model-name <model> Use verify-provider --name <provider> with RAGFLOW_PROVIDER_API_KEY set, or pass --api-key-file <path> , to test a key without persisting an instance. RAPTOR workflow run-raptor --dataset <id> trace-raptor --dataset <id> GraphRAG workflow run-graphrag --dataset <id> trace-graphrag --dataset <id> get-knowledge-graph --dataset <id> Embedded website access embed-code --chat <chat_id> --type fullscreen or embed-code --agent <agent_id> --type widget embed-info --chat <chat_id> or embed-info --agent <agent_id> embed-chat --chat <chat_id> --question "Hello" or embed-agent-chat --agent <agent_id> --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 RagflowClient workflows. 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 before update-metadata deletes 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, use ingest-documents instead. Preserve source filenames. When an attachment is stored under a temporary or task-generated path, upload it as --files <original-name>=<path> so RAGFlow retains the user-facing name. Resolve complete, stable inputs. Discover resource IDs with the corresponding list-* or get-* command, and paginate beyond RAGFlow's 100-item list limit. Use <model>@<provider> identifiers from list-models for --embedding-model and --llm-id ; treat numeric model row IDs as display data only. Preserve session-history intent. Let chat-session append the latest user message by default. Use --pass-all-history only when replacing stored history, and use --legacy only for a caller that requires cumulative legacy streaming. Protect operational secrets. Keep RAGFLOW_API_KEY , provider keys, system tokens, beta values, and embed URLs containing auth= out of user-facing output. Supply provider credentials through RAGFLOW_PROVIDER_API_KEY or --api-key-file ; reveal secret material only when the user explicitly requests copy-paste output. Use the correct public embed origin. Pass --origin when the browser-facing RAGFlow URL differs from RAGFLOW_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.