Google em invoker

Defines a module to interact with Google embedding models.

Authors

Henry Wicaksono (henry.wicaksono@gdplabs.id)

References

[1] https://googleapis.github.io/python-genai

`GoogleEMInvoker(model_name, api_key=None, credentials_path=None, project_id=None, location='us-central1', model_kwargs=None, default_hyperparameters=None, retry_config=None)`

Bases: BaseEMInvoker

An embedding model invoker to interact with Google embedding models.

Attributes:

Name	Type	Description
`model_id`	`str`	The model ID of the embedding model.
`model_provider`	`str`	The provider of the embedding model.
`model_name`	`str`	The name of the embedding model.
`client_params`	`dict[str, Any]`	The Google client instance init parameters.
`default_hyperparameters`	`dict[str, Any]`	Default hyperparameters for invoking the embedding model.
`retry_config`	`RetryConfig \| None`	The retry configuration for the language model.

Initialization

The GoogleEMInvoker can use either Google Gen AI or Google Vertex AI.

Google Gen AI is recommended for quick prototyping and development. It requires a Gemini API key for authentication.

Usage example:

em_invoker = GoogleEMInvoker(
    model_name="text-embedding-004",
    api_key="your_api_key"
)

Google Vertex AI is recommended to build production-ready applications. It requires a service account JSON file for authentication.

Usage example:

em_invoker = GoogleEMInvoker(
    model_name="text-embedding-004",
    credentials_path="path/to/service_account.json"
)

If neither api_key nor credentials_path is provided, Google Gen AI will be used by default. The GOOGLE_API_KEY environment variable will be used for authentication.

Input types

The GoogleEMInvoker only supports text inputs.

Output format

The GoogleEMInvoker can embed either: 1. A single content. 1. A single content is a single text. 2. The output will be a Vector, representing the embedding of the content.

# Example 1: Embedding a text content. python text = "This is a text" result = await em_invoker.invoke(text)

The above examples will return a Vector with a size of (embedding_size,).

A list of contents.
A list of contents is a list of texts.
The output will be a list[Vector], where each element is a Vector representing the embedding of each single content.

# Example: Embedding a list of contents. python text1 = "This is a text" text2 = "This is another text" text3 = "This is yet another text" result = await em_invoker.invoke([text1, text2, text3])

The above examples will return a list[Vector] with a size of (3, embedding_size).

Retry and timeout

The GoogleEMInvoker supports retry and timeout configuration. By default, the max retries is set to 0 and the timeout is set to 30.0 seconds. They can be customized by providing a custom RetryConfig object to the retry_config parameter.

Retry config examples:

retry_config = RetryConfig(max_retries=0, timeout=0.0)  # No retry, no timeout
retry_config = RetryConfig(max_retries=0, timeout=10.0)  # No retry, 10.0 seconds timeout
retry_config = RetryConfig(max_retries=5, timeout=0.0)  # 5 max retries, no timeout
retry_config = RetryConfig(max_retries=5, timeout=10.0)  # 5 max retries, 10.0 seconds timeout

Usage example:

em_invoker = GoogleEMInvoker(..., retry_config=retry_config)

Initializes a new instance of the GoogleEMInvoker class.

Parameters:

Name	Type	Description	Default
`model_name`	`str`	The name of the model to use.	required
`api_key`	`str \| None`	Required for Google Gen AI authentication. Cannot be used together with `credentials_path`. Defaults to None.	`None`
`credentials_path`	`str \| None`	Required for Google Vertex AI authentication. Path to the service account credentials JSON file. Cannot be used together with `api_key`. Defaults to None.	`None`
`project_id`	`str \| None`	The Google Cloud project ID for Vertex AI. Only used when authenticating with `credentials_path`. Defaults to None, in which case it will be loaded from the credentials file.	`None`
`location`	`str`	The location of the Google Cloud project for Vertex AI. Only used when authenticating with `credentials_path`. Defaults to "us-central1".	`'us-central1'`
`model_kwargs`	`dict[str, Any] \| None`	Additional keyword arguments for the Google client. Defaults to None.	`None`
`default_hyperparameters`	`dict[str, Any] \| None`	Default hyperparameters for invoking the model. Defaults to None.	`None`
`retry_config`	`RetryConfig \| None`	The retry configuration for the embedding model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout is used.	`None`

Note

If neither api_key nor credentials_path is provided, Google Gen AI will be used by default. The GOOGLE_API_KEY environment variable will be used for authentication.