gen_ai_hub.proxy.native.google_genai.clients module

class gen_ai_hub.proxy.native.google_genai.clients.AICoreDynamicTransport(proxy_client: BaseProxyClient, **deployment_selector_kwargs)

Bases: BaseTransport

Synchronous transport that dynamically resolves deployment URLs per request.

__init__(proxy_client: BaseProxyClient, **deployment_selector_kwargs)

Transport constructor.

Parameters:

proxy_client (BaseProxyClient) – The proxy client used to select deployments.

get_selector_kwargs()

Get the deployment selector kwargs.

Returns:

The deployment selector kwargs.

Return type:

dict

handle_request(request: Request) → Response

Handles the request by rewriting it to route through the appropriate AI Core deployment.

Parameters:

request (httpx.Request) – The original HTTPX request.

Returns:

The HTTPX response from the AI Core deployment.

Return type:

httpx.Response

close()

Closes the inner transport.

class gen_ai_hub.proxy.native.google_genai.clients.AsyncAICoreDynamicTransport(proxy_client: BaseProxyClient, **deployment_selector_kwargs)

Bases: AsyncBaseTransport

Asynchronous transport that dynamically resolves deployment URLs per request.

__init__(proxy_client: BaseProxyClient, **deployment_selector_kwargs)

Transport constructor.

Parameters:

proxy_client (BaseProxyClient) – The proxy client used to select deployments.

get_selector_kwargs()

get the deployment selector kwargs.

Returns:

The deployment selector kwargs.

Return type:

dict

async handle_async_request(request: Request) → Response

Handles the request by rewriting it to route through the appropriate AI Core deployment.

Parameters:

request (httpx.Request) – The original HTTPX request.

Returns:

The HTTPX response from the AI Core deployment.

Return type:

httpx.Response

async aclose()

Closes the inner transport.

class gen_ai_hub.proxy.native.google_genai.clients.Models(api_client_: BaseApiClient)

Bases: Models

Class that extends the original Google Models class for patch embed_content method.

embed_content(*, model: str, contents: Content | str | Image | File | Part | list[str | Image | File | Part] | list[Content | str | Image | File | Part | list[str | Image | File | Part]] | ContentDict | FileDict | PartDict | list[str | Image | File | FileDict | Part | PartDict] | list[Content | ContentDict | str | Image | File | FileDict | Part | PartDict | list[str | Image | File | FileDict | Part | PartDict]], config: EmbedContentConfig | EmbedContentConfigDict | None = None) → EmbedContentResponse

Need for add model version for “gemini-embedding” model.

class gen_ai_hub.proxy.native.google_genai.clients.Client(vertexai: bool = True, project: str = 'placeholder', location: str = 'placeholder', deployment_id: str = '', config_id: str = '', config_name: str = '', proxy_client: BaseProxyClient = None, timeout: int = None, **kwargs)

Bases: Client

Unified, native-feeling client that dynamically routes requests through SAP AI Core deployments based on the requested identifiers e.g. model name.

__init__(vertexai: bool = True, project: str = 'placeholder', location: str = 'placeholder', deployment_id: str = '', config_id: str = '', config_name: str = '', proxy_client: BaseProxyClient = None, timeout: int = None, **kwargs)

Initializes the Client.

Parameters:

  • vertexai (bool, optional) – to indicate Vertex AI usage, defaults to True

  • project (str, optional) – the GCP project, defaults to “placeholder”

  • location (str, optional) – the GCP location, defaults to “placeholder”

  • deployment_id (str, optional) – the deployment identifier, defaults to “”

  • config_id (str, optional) – the configuration identifier, defaults to “”

  • config_name (str, optional) – the configuration name, defaults to “”

  • proxy_client (BaseProxyClient, optional) – the proxy client to use, defaults to None

  • timeout (int, optional) – the request timeout, defaults to None