Google lm invoker

Defines a module to interact with Google language models.

GoogleLMInvoker(model_name, api_key=None, credentials_path=None, project_id=None, location='us-central1', model_kwargs=None, default_hyperparameters=None, tools=None, response_schema=None, output_analytics=False, retry_config=None, thinking=None, data_stores=None, auto_upload=True, input_transformer=InputTransformerType.IDENTITY, output_transformer=OutputTransformerType.IDENTITY)

Bases: BaseLMInvoker

A language model invoker to interact with Google language models.

Examples:

lm_invoker = GoogleLMInvoker(model_name="gemini-2.5-flash")
result = await lm_invoker.invoke("Hi there!")

Authentication

The GoogleLMInvoker 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:

lm_invoker = GoogleLMInvoker(
    model_name="gemini-2.5-flash",
    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:

lm_invoker = GoogleLMInvoker(
    model_name="gemini-2.5-flash",
    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.

Supported features

1. Basic invocation
2. Streaming output
3. Message roles
4. Multimodal input a. Text b. Audio c. Document d. Image e. Video
5. Structured output
6. Tool calling
7. Native tools a. Code interpreter b. Data store c. Image generation d. Web search
8. Thinking
9. Output analytics
10. Retry and timeout
11. Extra capabilities a. Input transformer b. Output transformer c. Batch invocation d. File management e. Data store management

For full documentation and examples, please refer to the LM Invoker tutorial: https://gdplabs.gitbook.io/sdk/gen-ai-sdk/tutorials/inference/lm-invoker

Attributes:

Name	Type	Description
model_id	str	The model ID of the language model.
model_provider	str	The provider of the language model.
model_name	str	The name of the language model.
client_params	dict[str, Any]	The Google client instance init parameters.
default_config	dict[str, Any]	Default config for invoking the model.
tools	list[Tool]	The list of tools provided to the model to enable tool calling.
response_schema	ResponseSchema | None	The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary.
output_analytics	bool	Whether to output the invocation analytics.
retry_config	RetryConfig | None	The retry configuration for the language model.
thinking	ThinkingConfig	The thinking configuration for the language model.
image_generation	bool	Whether to generate image. Only allowed for image generation models.
data_stores	list[AttachmentStore]	The data stores to retrieve internal knowledge from.
auto_upload	bool	Whether to automatically upload attachments to files API if the inputs total size exceeds the threshold of 20MB.
input_transformer	InputTransformerType	The type of input transformer to use.
output_transformer	OutputTransformerType	The type of output transformer to use.

Initializes a new instance of the GoogleLMInvoker 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 Vertex AI client.	None
default_hyperparameters	dict[str, Any] | None	Default hyperparameters for invoking the model. Defaults to None.	None
tools	list[LMTool] | None	Tools provided to the model to enable tool calling. Defaults to None.	None
response_schema	ResponseSchema | None	The schema of the response. If provided, the model will output a structured response as defined by the schema. Supports both Pydantic BaseModel and JSON schema dictionary. Defaults to None.	None
output_analytics	bool	Whether to output the invocation analytics. Defaults to False.	False
retry_config	RetryConfig | None	The retry configuration for the language model. Defaults to None, in which case a default config with no retry and 30.0 seconds timeout will be used.	None
thinking	bool | ThinkingConfig | None	A boolean or ThinkingConfig object to configure thinking. Defaults to None.	None
data_stores	list[AttachmentStore] | None	The data stores to retrieve internal knowledge from. Defaults to None, in which case no data stores will be used.	None
auto_upload	bool	Whether to automatically upload attachments to files API if the inputs total size exceeds the threshold of 20MB. Defaults to True.	True
input_transformer	InputTransformerType	The type of input transformer to use. Defaults to InputTransformerType.IDENTITY, which returns the input without transformation.	IDENTITY
output_transformer	OutputTransformerType	The type of output transformer to use. Defaults to OutputTransformerType.IDENTITY, which returns the output without transformation.	IDENTITY

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.

batch cached property

The batch operations for the language model.

Returns:

Name	Type	Description
GoogleBatchOperations	GoogleBatchOperations	The batch operations for the language model.

data_store cached property

The data store operations for the language model.

Returns:

Name	Type	Description
GoogleDataStoreOperations	GoogleDataStoreOperations	The data store operations for the language model.

file cached property

The file operations for the language model.

Returns:

Name	Type	Description
GoogleFileOperations	GoogleFileOperations	The file operations for the language model.

set_data_stores(data_stores)

Sets the data stores for the Google language model.

This method sets the data stores for the Google language model. Any existing data stores will be replaced.

Parameters:

Name	Type	Description	Default
data_stores	list[AttachmentStore]	The list of data stores to be used.	required

URLPattern

Defines specific Google related URL patterns.