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Berget AI Embeddings Model

Use a Berget AI embedding model with Vector Store, Question and Answer Chain, or other LangChain nodes

Berget AI Embeddings Model

AI-generated

Summary

Provide embeddings for text data using a selected Berget AI embedding model, designed specifically to be used as part of a Vector Store or QA Chain in a workflow.

Inputs

  • Model (required) — Select the Berget AI embedding model to use, dynamically fetched from the Berget API at runtime.
  • Options — Optional parameters to customize embedding requests, including batch size (number of documents per API call), whether to strip newlines from input text, request timeout in milliseconds, and dimensions (currently disabled due to backend issues).

Output shape

a single embedding response object compatible with LangChain nodes or other vector store workflows

The operation does not execute standalone and must be connected downstream to a Vector Store or QA Chain node. The dimensions option is currently disabled due to the Berget API rejecting it; default behavior uses the model's native embedding dimensions.

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