Workflow Retriever Node

Verwenden Sie den Workflow Retriever Node, um Daten aus einem Localmind Automate-Workflow abzurufen und in einer Retrieval QA Chain oder einem anderen Retriever Node zu verwenden.

Auf dieser Seite finden Sie die Node-Parameter für den Workflow Retriever 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

Quelle

Sagen Sie Localmind Automate, welchen Workflow aufgerufen werden soll. Sie können entweder:

• Datenbank auswählen und eine Workflow-ID eingeben.
• Parameter auswählen und ein vollständiges Workflow-JSON kopieren.

Workflow-Werte

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 Sub-workflow Trigger node as its trigger

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

Verwandte Ressourcen

Weitere Informationen zum Dienst finden Sie in der allgemeinen Retriever-Dokumentation von LangChain.

View Localmind Automate'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.