pyseekdb.utils.embedding_functions.GoogleVertexEmbeddingFunction

class pyseekdb.utils.embedding_functions.GoogleVertexEmbeddingFunction(model_name: str = 'textembedding-gecko', project_id: str = 'cloud-large-language-models', region: str = 'us-central1', api_key_env: str | None = 'GOOGLE_VERTEX_API_KEY')[source]

Bases: EmbeddingFunction[str | list[str]]

A convenient embedding function for Google Vertex AI embedding models.

For more information about Google Vertex AI models, see https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api

Example

pip install pyseekdb google-cloud-aiplatform

__init__(model_name: str = 'textembedding-gecko', project_id: str = 'cloud-large-language-models', region: str = 'us-central1', api_key_env: str | None = 'GOOGLE_VERTEX_API_KEY')[source]

Initialize the GoogleVertexEmbeddingFunction.

Parameters:

api_key_env (str, optional) – Environment variable name that contains your API key for the Google Vertex AI API. Defaults to “GOOGLE_VERTEX_API_KEY”.
model_name (str, optional) – The name of the model to use for text embeddings. Defaults to “textembedding-gecko”.
project_id (str, optional) – The Google Cloud project ID. Defaults to “cloud-large-language-models”.
region (str, optional) – The Google Cloud region. Defaults to “us-central1”.

Methods

`__init__`([model_name, project_id, region, ...])	Initialize the GoogleVertexEmbeddingFunction.
`build_from_config`(config)
`get_config`()	Get the configuration dictionary for the embedding function.
`name`()
`support_persistence`(embedding_function)	Check if the embedding function supports persistence.

get_config() → dict[str, Any][source]

Get the configuration dictionary for the embedding function.

This method should return a dictionary that contains all the information needed to restore the embedding function after restart.

Returns:: Dictionary containing the embedding function’s configuration. Note: The ‘name’ field is not included as it’s handled by the upper layer for routing.