Cohere Embeddings
Cohere Embeddings
Section titled “Cohere Embeddings”Leverage Cohere to create embeddings.
What it does
Section titled “What it does”Converts text into vector embeddings using a Cohere embeddings model. Cohere exposes an OpenAI-compatible API, so this node runs the OpenAI embeddings client against Cohere’s base URL.
When to use it
Section titled “When to use it”Connect this to indexer or RAG nodes when you need to create embeddings for semantic search and retrieval using Cohere.
Inputs and settings
Section titled “Inputs and settings”| Setting | Notes |
|---|---|
| Model | The Cohere embeddings model to use, e.g. embed-english-v3.0. Placeholder: embed-english-v3.0. |
| Authentication | Credential field. Use your Cohere API key as a Bearer token. |
Outputs
Section titled “Outputs”Returns an embeddings dependency for vector stores and retrieval workflows.
Dependencies and credentials
Section titled “Dependencies and credentials”- Requires an Authentication credential (Cohere API key), sent as a Bearer token.
- The node throws an error if Model is empty.
Example workflow
Section titled “Example workflow”Connect Cohere Embeddings to an AI agent or dependency input that accepts this dependency type, set the Model to a Cohere embeddings model such as embed-english-v3.0, attach your Cohere API key credential, then run the agent with data from previous nodes.
Troubleshooting
Section titled “Troubleshooting”- Check that the Model field is set — the node throws an error if it is empty.
- Verify the Cohere API key credential is valid.
- If it calls an external service, verify credentials, permissions, and rate limits.