Embeddings AWS Bedrock Node

Verwenden Sie den Embeddings AWS Bedrock Node, um Embeddings für einen gegebenen Text zu generieren.

Auf dieser Seite finden Sie die Node-Parameter für den Embeddings AWS Bedrock Node und Links zu weiteren Ressourcen.

Anmeldeinformationen

Sie finden Authentifizierungsinformationen für diesen Node hier.

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

• Model: Wählen Sie das Modell aus, das zum Generieren des Embeddings verwendet werden soll.

Erfahren Sie mehr über verfügbare Modelle in der Amazon Bedrock-Dokumentation.

Verwandte Ressourcen

Weitere Informationen zu AWS Bedrock finden Sie in der AWS Bedrock Embeddings-Dokumentation von LangChain und der AWS Bedrock-Dokumentation.

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.