Skip to content

Workflow Retriever node#

Use the Workflow Retriever node to retrieve data from an n8n workflow for use in a Retrieval QA Chain or another Retriever node.

On this page, you'll find the node parameters for the Workflow Retriever node, and links to more resources.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Node parameters#

Source#

Tell n8n which workflow to call. You can choose either:

  • Database and enter a workflow ID.
  • Parameter and copy in a complete workflow JSON.

Workflow values#

Set values to pass to the workflow you're calling.

These values appear in the output data of the trigger node in the workflow you call. You can access these values in expressions in the workflow. For example, if you have:

  • Workflow Values with a Name of myCustomValue
  • A workflow with an Execute Workflow Trigger node as its trigger

The expression to access the value of myCustomValue is {{ $('Execute Workflow Trigger').item.json.myCustomValue }}.

Templates and examples#

Browse Workflow Retriever integration templates, or search all templates

Refer to LangChain's general retriever documentation for more information about the service.

View n8n's Advanced AI documentation.

AI glossary#

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.