节点与集成
Embeddings AWS Bedrock
Use the Embeddings AWS Bedrock node to generate embeddings1 for a given text.
On this page, you'll find the node parameters for the Embeddings AWS Bedrock node, and links to more resources.
Node parameters
- Authentication: Select the authentication method:
- AWS (IAM): Use an IAM access key. Select an AWS credential.
- AWS (Assume Role): Temporarily assume an IAM role. Select an AWS (Assume Role) credential.
- Model: Select the model to use to generate the embedding. If the dropdown is empty, your IAM role may not have the
bedrock:ListFoundationModelspermission. Switch the field to Expression mode and enter the model ID directly.
Learn more about available models in the Amazon Bedrock documentation.
Templates and examples
Browse Embeddings AWS Bedrock node documentation integration templates or search all templates
Related resources
Refer to LangChains's AWS Bedrock embeddings documentation and the AWS Bedrock documentation for more information about AWS Bedrock.
View n8n's Advanced AI documentation.
Footnotes
- Back
Embeddings are numerical representations of data using vectors. They're used by AI to interpret complex data and relationships by mapping values across many dimensions. Vector databases, or vector stores, are databases designed to store and access embeddings.
官方原文和授权
本页来自 N8N 英文官方网站固定快照，并转换成 xueai 静态页面。内容以 N8N 持续更新的官方页面为准。