bigframes.bigquery.ai.embed#

bigframes.bigquery.ai.embed(content: str | Series | Series, *, endpoint: str | None = None, model: str | None = None, task_type: Literal['retrieval_query', 'retrieval_document', 'semantic_similarity', 'classification', 'clustering', 'question_answering', 'fact_verification', 'code_retrieval_query'] | None = None, title: str | None = None, model_params: Mapping[Any, Any] | None = None, connection_id: str | None = None) → Series[source]#

Creates embeddings from text or image data in BigQuery.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> bbq.ai.embed("dog", endpoint="text-embedding-005") 
0    {'result': array([ 1.78243860e-03, -1.10658340...
dtype: struct<result: list<item: double>, status: string>[pyarrow]

>>> s = bpd.Series(['dog'])
>>> bbq.ai.embed(s, endpoint='text-embedding-005') 
0    {'result': array([ 1.78243860e-03, -1.10658340...
dtype: struct<result: list<item: double>, status: string>[pyarrow]

Parameters:

• content (str | Series) A string literal or a Series (either BigFrames series or pandas Series) that provides the text or image to embed.

• endpoint (str, optional) A string value that specifies a supported Vertex AI embedding model endpoint to use. The endpoint value that you specify must include the model version, for example, "text-embedding-005". If you specify this parameter, you cant specify the model parameter.

• model (str, optional) A string value that specifies a built-in embedding model. The only supported value is "embeddinggemma-300m". If you specify this parameter, you cant specify the endpoint, title, model_params, or connection_id parameters.

• task_type (str, optional) A string literal that specifies the intended downstream application to help the model produce better quality embeddings. Accepts "retrieval_query", "retrieval_document", "semantic_similarity", "classification", "clustering", "question_answering", "fact_verification", "code_retrieval_query".

• title (str, optional) A string value that specifies the document title, which the model uses to improve embedding quality. You can only use this parameter if you specify "retrieval_document" for the task_type value.

• model_params (Mapping[Any, Any], optional) A JSON literal that provides additional parameters to the model. For example, {"outputDimensionality": 768} lets you specify the number of dimensions to use when generating embeddings.

• connection_id (str, optional) A STRING value specifying the connection to use to communicate with the model, in the format PROJECT_ID.LOCATION.CONNECTION_ID. For example, myproject.us.myconnection. If not provided, the query uses your end-user credential.

Returns:

A new struct Series with the result data. The struct contains these fields: * result: an ARRAY<FLOAT64> value containing the generated embeddings. * status: a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful.

Return type:

bigframes.series.Series