Method●Since v0.0

embed

Embed a list of strings.

embed(
  self,
  texts: list[str],
  embeddings_task_type: EmbeddingTaskTypes | None = None,
  dimensions: int | None = None,
  title: str | None = None
) -> list[list[float]]

Parameters

Name	Type	Description
`texts`*	`list[str]`	The list of strings to embed.
`embeddings_task_type`	`EmbeddingTaskTypes \| None`	Default:`None` Optional embeddings task type, one of the following: `RETRIEVAL_QUERY` - Text is a query in a search/retrieval setting `RETRIEVAL_DOCUMENT` - Text is a document in a search/retrieval setting `SEMANTIC_SIMILARITY` - Embeddings will be used for Semantic Textual Similarity (STS). `CLASSIFICATION` - Embeddings will be used for classification. `CLUSTERING` - Embeddings will be used for clustering. `CODE_RETRIEVAL_QUERY` - Embeddings will be used for code retrieval for Java and Python. The following are only supported on preview models: `QUESTION_ANSWERING`, `FACT_VERIFICATION`.
`dimensions`	`int \| None`	Default:`None` Output embeddings dimensions. Only supported on preview models. If not provided, uses the default dimensions specified in the constructor.
`title`	`str \| None`	Default:`None` Title for the text. Only applicable when `TaskType` is `RETRIEVAL_DOCUMENT`.

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embed

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embed

Parameters