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Structured Output Parser Node#

Verwenden Sie den Structured Output Parser Node, um Felder basierend auf einem JSON-Schema zurückzugeben.

Auf dieser Seite finden Sie die Node-Parameter für den Structured Output Parser Node sowie Links zu weiteren Ressourcen.

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-Parameter#

  • Schema-Typ: Definieren Sie die Ausgabestruktur und -validierung. Sie haben zwei Möglichkeiten, das Schema bereitzustellen:
  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.

Zugehörige Ressourcen#

Weitere Informationen zum Dienst finden Sie in der LangChain-Dokumentation zum Output Parser.

View Localmind Automate's Advanced AI documentation.

Häufige Probleme#

Häufige Fragen oder Probleme und Lösungsvorschläge finden Sie unter Häufige Probleme.

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.