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Structured Output Parser#

Use the Structured Output Parser node to return fields based on a JSON Schema.

On this page, you'll find the node parameters for the Structured Output Parser 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#

Schema Type: Define the output structure and validation. You have two options to provide the schema:

  1. Generate from JSON Example: Input an example JSON object to automatically generate the schema. The node uses the object property types and names. It ignores the actual values.
  2. Define Below: Manually input the JSON schema. Read the JSON Schema guides and examples for help creating a valid JSON schema.

Templates and examples#

Browse all Structured Output Parser integration templates, or search all templates

Refer to LangChain's structured output parser documentation for more information about the service.

View n8n's Advanced AI documentation.

  • 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.