How should Multi-Question RAG Workflow be evaluated before use?

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Multi-Question RAG Workflow

This instructional workflow demonstrates how to build a multi‑step pipeline in Dify. It accepts a body of text, extracts all standalone questions using a parameter‑extraction node, loops through each question, retrieves relevant knowledge from a dataset, and uses an agent to draft an answer with citations. Finally, the answers are formatted into a structured reply. The template is intended for educational purposes to help new users understand loops, iterations, knowledge retrieval and agent nodes. Prepare a knowledge base/dataset in Dify and note its dataset ID; ensure retrieval is enabled. Configure the Parameter Extractor nodes with your preferred LLM provider (e.g., OpenAI) and the prompt to extract questions. In the retrieval nodes, set the dataset ID and choose the desired retrieval parameters (e.g., search type and top‑k). Provide an LLM provider for the agent that generates answers and final formatting.

workflowsupportknowledgecohereopenai

View on Dify

Rank

Safety

Updated

Oct 9, 2026

Source

Dify

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Difyvendor · observed May 11, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/snapshot"

Documentation

Dify

557 characters of source documentation, loaded on request.

Machine-readable data

The same record, as JSON, for agents and crawlers.

{
  "facts": [
    {
      "factKey": "vendor",
      "label": "Vendor",
      "value": "Dify",
      "category": "vendor",
      "href": "https://marketplace.dify.ai/api/v1/templates/d2d2ae92-d95b-4d55-b343-be411c3147d3",
      "sourceUrl": "https://marketplace.dify.ai/api/v1/templates/d2d2ae92-d95b-4d55-b343-be411c3147d3",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-05-11T06:21:02.527Z",
      "isPublic": true,
      "metadata": {}
    },
    {
      "factKey": "handshake_status",
      "label": "Handshake status",
      "value": "UNKNOWN",
      "category": "security",
      "href": "https://www.xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/dify-langgenius-multi-question-rag-workflow/trust",
      "sourceType": "trust",
      "confidence": "medium",
      "observedAt": null,
      "isPublic": true,
      "metadata": {}
    }
  ],
  "events": []
}

Record generated Oct 9, 2026.