节点 / builtin / ai / aidependencies / embeddings
Cohere Embeddings
Leverage Cohere to create embeddings.
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Turn text into vectors with Cohere’s embedding models, so a knowledge base can find passages by meaning.
What it does
Section titled “What it does”Cohere Embeddings plugs into the Embeddings slot of a Local Knowledge node. It does not run on its own and has no output. When the knowledge base is filled or searched, the text is sent to Cohere with your key and comes back as vectors (lists of numbers that capture meaning).
A knowledge base remembers the embedding model it was built with and always uses it. So this node decides the model when a new, empty knowledge base is first filled. After that, connect the same node to search it.
Your passages and questions are sent to Cohere. For text that must stay on your device, use Local Embeddings.
When to use it
Section titled “When to use it”- Documents in many languages: Cohere has multilingual embedding models.
- You already use Cohere and want its embeddings in AWFlow.
Inputs and settings
Section titled “Inputs and settings”| 设置项 | 类型 | 作用 |
|---|---|---|
| Model （必填） | Text | The Cohere embeddings model to use, e.g. embed-english-v3.0. |
| Authentication （必填） | Credential | Use your Cohere API key as a Bearer token. |
Type the Model id as Cohere names it, for example embed-english-v3.0, or embed-multilingual-v3.0 for other languages. Don’t change it on a knowledge base that already has content.
Outputs
Section titled “Outputs”This node has no output port. It plugs into a slot on another node instead of passing items along.
- Embeddings slot of Local Knowledge
The vectors go to the knowledge base, not to the next node.
Dependencies and credentials
Section titled “Dependencies and credentials”- Authentication (connection): required. See Connect Cohere.
In Authentication, save your Cohere API key. Create it on the API keys page of the Cohere dashboard. See Create a credential.
Example workflow
Section titled “Example workflow”- A Context Menu trigger on the page.
- Get All Text with Output field name
pageText. - Indexer with Input
{{ $input.pageText }}, a Recursive Character Text Splitter in its Text Splitter slot, and a Local Knowledge pointing to a new knowledge base in its Knowledge slot. - Cohere Embeddings in the Local Knowledge’s Embeddings slot, Model
embed-multilingual-v3.0.
Common issues
Section titled “Common issues”- “Model parameter is required and cannot be empty”. Fill in Model.
- “… was built with … embeddings. Connect that embeddings node to use it.” The knowledge base was built with this Cohere model. Connect Cohere Embeddings with the same Model to its Local Knowledge.
- A “Model mismatch” warning in the run log. The knowledge base was built with another model, so this node was ignored. Disconnect it, or re-embed the knowledge base from its settings.
- A 401 error. The key is wrong or revoked. Create a new key and update the credential.
Related nodes
Section titled “Related nodes”- Local Knowledge: the knowledge base it plugs into.
- Voyage Embeddings, OpenAI Embeddings: other cloud embedding models.
- Local Embeddings: free and on your device.