Embeddings Cohere Node#

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

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

Zugangsdaten

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. Wählen Sie aus:
- Embed-English-v2.0(4096 Dimensions)
- Embed-English-Light-v2.0(1024 Dimensions)
- Embed-Multilingual-v2.0(768 Dimensions)

Erfahren Sie mehr über verfügbare Modelle in der Cohere-Modelldokumentation.

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.

Embeddings Cohere Node#

Node-Parameter#

Verwandte Ressourcen#

AI glossary#