节点与集成
Embeddings Cohere
Use the Embeddings Cohere node to generate embeddings1 for a given text.
On this page, you'll find the node parameters for the Embeddings Cohere node, and links to more resources.
Node parameters
- Model: Select the model to use to generate the embedding. Choose from:
- Embed-English-v2.0(4096 Dimensions)
- Embed-English-Light-v2.0(1024 Dimensions)
- Embed-Multilingual-v2.0(768 Dimensions)
Learn more about available models in Cohere's models documentation.
Templates and examples
Browse Embeddings Cohere node documentation integration templates or search all templates
Related resources
Refer to Langchain's Cohere embeddings documentation for more information about the service.
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。