节点 / builtin / ai / aidependencies / embeddings
Google Embeddings
Leverage Google (Gemini) to create embeddings.
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Turn text into vectors with Google’s Gemini embedding models, so a knowledge base can find passages by meaning.
What it does
Section titled “What it does”Google 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 the Gemini API 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 Google. For text that must stay on your device, use Local Embeddings.
When to use it
Section titled “When to use it”- You already have a Google AI Studio key, for example for Chat Google.
- You want cloud embeddings with the same Google account as your chat model.
Inputs and settings
Section titled “Inputs and settings”| 设置项 | 类型 | 作用 |
|---|---|---|
| Model （必填） | Text | The Google embeddings model to use, e.g. text-embedding-004. |
| Authentication （必填） | Credential | Use your Google AI (Gemini) API key as a Bearer token. |
Type the Model id as Google names it, for example text-embedding-004. 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 Google.
In Authentication, save your Google AI (Gemini) API key. Create it in Google AI Studio, at aistudio.google.com/app/apikey. 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. - Google Embeddings in the Local Knowledge’s Embeddings slot, Model
text-embedding-004.
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 Google model. Connect Google 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.
- An “API key not valid” or 401 error. Use a Google AI Studio key (a Vertex AI key does not work here), and check it was not deleted.
Related nodes
Section titled “Related nodes”- Local Knowledge: the knowledge base it plugs into.
- Chat Google: Gemini chat models.
- Local Embeddings: free and on your device.