As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

GenerativeService

class google.ai.generativelanguage_v1beta.services.generative_service.GenerativeServiceAsyncClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport, typing.Callable[[...], google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport]]] = 'grpc_asyncio', client_options: typing.Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)[source]

API for using Large Models that generate multimodal content and have additional capabilities beyond text generation.

Instantiates the generative service async client.

Parameters

  • credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.

  • transport (Optional[Union[str,GenerativeServiceTransport,Callable[..., GenerativeServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the GenerativeServiceTransport constructor. If set to None, a transport is chosen automatically.

  • client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

    Custom options for the client.

    1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

    2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

    3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

  • client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises

google.auth.exceptions.MutualTlsChannelError – If mutual TLS transport creation failed for any reason.

property api_endpoint

Return the API endpoint used by the client instance.

Returns

The API endpoint used by the client instance.

Return type

str

async batch_embed_contents(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.BatchEmbedContentsRequest, dict]] = None, *, model: Optional[str] = None, requests: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.generative_service.EmbedContentRequest]] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.BatchEmbedContentsResponse[source]

Generates multiple embedding vectors from the input Content which consists of a batch of strings represented as EmbedContentRequest objects.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_batch_embed_contents():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

    # Initialize request argument(s)
    requests = generativelanguage_v1beta.EmbedContentRequest()
    requests.model = "model_value"

    request = generativelanguage_v1beta.BatchEmbedContentsRequest(
        model="model_value",
        requests=requests,
    )

    # Make the request
    response = await client.batch_embed_contents(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.BatchEmbedContentsRequest, dict]]) – The request object. Batch request to get embeddings from the model for a list of prompts.

  • model (str) –

    Required. The model’s resource name. This serves as an ID for the Model to use.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • requests (MutableSequence[google.ai.generativelanguage_v1beta.types.EmbedContentRequest]) –

    Required. Embed requests for the batch. The model in each of these requests must match the model specified BatchEmbedContentsRequest.model.

    This corresponds to the requests field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

The response to a BatchEmbedContentsRequest.

Return type

google.ai.generativelanguage_v1beta.types.BatchEmbedContentsResponse

static cached_content_path(id: str) → str

Returns a fully-qualified cached_content string.

static common_billing_account_path(billing_account: str) → str

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str

Returns a fully-qualified organization string.

static common_project_path(project: str) → str

Returns a fully-qualified project string.

async count_tokens(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.CountTokensRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.CountTokensResponse[source]

Runs a model’s tokenizer on input Content and returns the token count. Refer to the tokens guide to learn more about tokens.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_count_tokens():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.CountTokensRequest(
        model="model_value",
    )

    # Make the request
    response = await client.count_tokens(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.CountTokensRequest, dict]]) –

    The request object. Counts the number of tokens in the prompt sent to a model.

    Models may tokenize text differently, so each model may return a different token_count.

  • model (str) –

    Required. The model’s resource name. This serves as an ID for the Model to use.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Optional. The input given to the model as a prompt. This field is ignored when generate_content_request is set.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

A response from CountTokens.

It returns the model’s token_count for the prompt.

Return type

google.ai.generativelanguage_v1beta.types.CountTokensResponse

async embed_content(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.EmbedContentRequest, dict]] = None, *, model: Optional[str] = None, content: Optional[google.ai.generativelanguage_v1beta.types.content.Content] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.EmbedContentResponse[source]

Generates a text embedding vector from the input Content using the specified Gemini Embedding model.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_embed_content():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.EmbedContentRequest(
        model="model_value",
    )

    # Make the request
    response = await client.embed_content(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.EmbedContentRequest, dict]]) – The request object. Request containing the Content for the model to embed.

  • model (str) –

    Required. The model’s resource name. This serves as an ID for the Model to use.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • content (google.ai.generativelanguage_v1beta.types.Content) –

    Required. The content to embed. Only the parts.text fields will be counted.

    This corresponds to the content field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

The response to an EmbedContentRequest.

Return type

google.ai.generativelanguage_v1beta.types.EmbedContentResponse

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

GenerativeServiceAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

info.

Parameters

  • info (dict) – The service account private key info.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

GenerativeServiceAsyncClient

classmethod from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

GenerativeServiceAsyncClient

async generate_answer(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.GenerateAnswerRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, safety_settings: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.safety.SafetySetting]] = None, answer_style: Optional[google.ai.generativelanguage_v1beta.types.generative_service.GenerateAnswerRequest.AnswerStyle] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.GenerateAnswerResponse[source]

Generates a grounded answer from the model given an input GenerateAnswerRequest.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_generate_answer():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GenerateAnswerRequest(
        model="model_value",
        answer_style="VERBOSE",
    )

    # Make the request
    response = await client.generate_answer(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest, dict]]) – The request object. Request to generate a grounded answer from the Model.

  • model (str) –

    Required. The name of the Model to use for generating the grounded response.

    Format: model=models/{model}.

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Required. The content of the current conversation with the Model. For single-turn queries, this is a single question to answer. For multi-turn queries, this is a repeated field that contains conversation history and the last Content in the list containing the question.

    Note: GenerateAnswer only supports queries in English.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • safety_settings (MutableSequence[google.ai.generativelanguage_v1beta.types.SafetySetting]) –

    Optional. A list of unique SafetySetting instances for blocking unsafe content.

    This will be enforced on the GenerateAnswerRequest.contents and GenerateAnswerResponse.candidate. There should not be more than one setting for each SafetyCategory type. The API will block any contents and responses that fail to meet the thresholds set by these settings. This list overrides the default settings for each SafetyCategory specified in the safety_settings. If there is no SafetySetting for a given SafetyCategory provided in the list, the API will use the default safety setting for that category. Harm categories HARM_CATEGORY_HATE_SPEECH, HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT, HARM_CATEGORY_HARASSMENT are supported. Refer to the guide for detailed information on available safety settings. Also refer to the Safety guidance to learn how to incorporate safety considerations in your AI applications.

    This corresponds to the safety_settings field on the request instance; if request is provided, this should not be set.

  • answer_style (google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest.AnswerStyle) –

    Required. Style in which answers should be returned.

    This corresponds to the answer_style field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from the model for a grounded answer.

Return type

google.ai.generativelanguage_v1beta.types.GenerateAnswerResponse

async generate_content(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentResponse[source]

Generates a model response given an input GenerateContentRequest. Refer to the text generation guide for detailed usage information. Input capabilities differ between models, including tuned models. Refer to the model guide and tuning guide for details.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_generate_content():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GenerateContentRequest(
        model="model_value",
    )

    # Make the request
    response = await client.generate_content(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]]) – The request object. Request to generate a completion from the model.

  • model (str) –

    Required. The name of the Model to use for generating the completion.

    Format: name=models/{model}.

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Required. The content of the current conversation with the model.

    For single-turn queries, this is a single instance. For multi-turn queries like chat, this is a repeated field that contains the conversation history and the latest request.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from the model supporting multiple candidate responses.

Safety ratings and content filtering are reported for both prompt in GenerateContentResponse.prompt_feedback and for each candidate in finish_reason and in safety_ratings. The API: - Returns either all requested candidates or none of them - Returns no candidates at all only if there was something wrong with the prompt (check prompt_feedback) - Reports feedback on each candidate in finish_reason and safety_ratings.

Return type

google.ai.generativelanguage_v1beta.types.GenerateContentResponse

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[google.api_core.client_options.ClientOptions] = None)[source]

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns

returns the API endpoint and the

client cert source to use.

Return type

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

classmethod get_transport_class(label: Optional[str] = None) → Type[google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport]

Returns an appropriate transport class.

Parameters

label – The name of the desired transport. If none is provided, then the first transport in the registry is used.

Returns

The transport class to use.

static model_path(model: str) → str

Returns a fully-qualified model string.

static parse_cached_content_path(path: str) → Dict[str, str]

Parses a cached_content path into its component segments.

static parse_common_billing_account_path(path: str) → Dict[str, str]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]

Parse a project path into its component segments.

static parse_model_path(path: str) → Dict[str, str]

Parses a model path into its component segments.

stream_generate_content(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Awaitable[AsyncIterable[google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentResponse]][source]

Generates a streamed response from the model given an input GenerateContentRequest.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_stream_generate_content():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GenerateContentRequest(
        model="model_value",
    )

    # Make the request
    stream = await client.stream_generate_content(request=request)

    # Handle the response
    async for response in stream:
        print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]]) – The request object. Request to generate a completion from the model.

  • model (str) –

    Required. The name of the Model to use for generating the completion.

    Format: name=models/{model}.

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Required. The content of the current conversation with the model.

    For single-turn queries, this is a single instance. For multi-turn queries like chat, this is a repeated field that contains the conversation history and the latest request.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from the model supporting multiple candidate responses.

Safety ratings and content filtering are reported for both prompt in GenerateContentResponse.prompt_feedback and for each candidate in finish_reason and in safety_ratings. The API: - Returns either all requested candidates or none of them - Returns no candidates at all only if there was something wrong with the prompt (check prompt_feedback) - Reports feedback on each candidate in finish_reason and safety_ratings.

Return type

AsyncIterable[google.ai.generativelanguage_v1beta.types.GenerateContentResponse]

property transport: google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport

Returns the transport used by the client instance.

Returns

The transport used by the client instance.

Return type

GenerativeServiceTransport

property universe_domain: str

Return the universe domain used by the client instance.

Returns

The universe domain used

by the client instance.

Return type

str

class google.ai.generativelanguage_v1beta.services.generative_service.GenerativeServiceClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport, typing.Callable[[...], google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport]]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)[source]

API for using Large Models that generate multimodal content and have additional capabilities beyond text generation.

Instantiates the generative service client.

Parameters

  • credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.

  • transport (Optional[Union[str,GenerativeServiceTransport,Callable[..., GenerativeServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the GenerativeServiceTransport constructor. If set to None, a transport is chosen automatically.

  • client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

    Custom options for the client.

    1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

    2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

    3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

  • client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)[source]

Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

property api_endpoint

Return the API endpoint used by the client instance.

Returns

The API endpoint used by the client instance.

Return type

str

batch_embed_contents(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.BatchEmbedContentsRequest, dict]] = None, *, model: Optional[str] = None, requests: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.generative_service.EmbedContentRequest]] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.BatchEmbedContentsResponse[source]

Generates multiple embedding vectors from the input Content which consists of a batch of strings represented as EmbedContentRequest objects.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_batch_embed_contents():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceClient()

    # Initialize request argument(s)
    requests = generativelanguage_v1beta.EmbedContentRequest()
    requests.model = "model_value"

    request = generativelanguage_v1beta.BatchEmbedContentsRequest(
        model="model_value",
        requests=requests,
    )

    # Make the request
    response = client.batch_embed_contents(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.BatchEmbedContentsRequest, dict]) – The request object. Batch request to get embeddings from the model for a list of prompts.

  • model (str) –

    Required. The model’s resource name. This serves as an ID for the Model to use.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • requests (MutableSequence[google.ai.generativelanguage_v1beta.types.EmbedContentRequest]) –

    Required. Embed requests for the batch. The model in each of these requests must match the model specified BatchEmbedContentsRequest.model.

    This corresponds to the requests field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

The response to a BatchEmbedContentsRequest.

Return type

google.ai.generativelanguage_v1beta.types.BatchEmbedContentsResponse

static cached_content_path(id: str) → str[source]

Returns a fully-qualified cached_content string.

static common_billing_account_path(billing_account: str) → str[source]

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]

Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]

Returns a fully-qualified project string.

count_tokens(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.CountTokensRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.CountTokensResponse[source]

Runs a model’s tokenizer on input Content and returns the token count. Refer to the tokens guide to learn more about tokens.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_count_tokens():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.CountTokensRequest(
        model="model_value",
    )

    # Make the request
    response = client.count_tokens(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.CountTokensRequest, dict]) –

    The request object. Counts the number of tokens in the prompt sent to a model.

    Models may tokenize text differently, so each model may return a different token_count.

  • model (str) –

    Required. The model’s resource name. This serves as an ID for the Model to use.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Optional. The input given to the model as a prompt. This field is ignored when generate_content_request is set.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

A response from CountTokens.

It returns the model’s token_count for the prompt.

Return type

google.ai.generativelanguage_v1beta.types.CountTokensResponse

embed_content(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.EmbedContentRequest, dict]] = None, *, model: Optional[str] = None, content: Optional[google.ai.generativelanguage_v1beta.types.content.Content] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.EmbedContentResponse[source]

Generates a text embedding vector from the input Content using the specified Gemini Embedding model.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_embed_content():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.EmbedContentRequest(
        model="model_value",
    )

    # Make the request
    response = client.embed_content(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.EmbedContentRequest, dict]) – The request object. Request containing the Content for the model to embed.

  • model (str) –

    Required. The model’s resource name. This serves as an ID for the Model to use.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • content (google.ai.generativelanguage_v1beta.types.Content) –

    Required. The content to embed. Only the parts.text fields will be counted.

    This corresponds to the content field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

The response to an EmbedContentRequest.

Return type

google.ai.generativelanguage_v1beta.types.EmbedContentResponse

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

GenerativeServiceClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

info.

Parameters

  • info (dict) – The service account private key info.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

GenerativeServiceClient

classmethod from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

GenerativeServiceClient

generate_answer(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.GenerateAnswerRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, safety_settings: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.safety.SafetySetting]] = None, answer_style: Optional[google.ai.generativelanguage_v1beta.types.generative_service.GenerateAnswerRequest.AnswerStyle] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.GenerateAnswerResponse[source]

Generates a grounded answer from the model given an input GenerateAnswerRequest.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_generate_answer():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GenerateAnswerRequest(
        model="model_value",
        answer_style="VERBOSE",
    )

    # Make the request
    response = client.generate_answer(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest, dict]) – The request object. Request to generate a grounded answer from the Model.

  • model (str) –

    Required. The name of the Model to use for generating the grounded response.

    Format: model=models/{model}.

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Required. The content of the current conversation with the Model. For single-turn queries, this is a single question to answer. For multi-turn queries, this is a repeated field that contains conversation history and the last Content in the list containing the question.

    Note: GenerateAnswer only supports queries in English.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • safety_settings (MutableSequence[google.ai.generativelanguage_v1beta.types.SafetySetting]) –

    Optional. A list of unique SafetySetting instances for blocking unsafe content.

    This will be enforced on the GenerateAnswerRequest.contents and GenerateAnswerResponse.candidate. There should not be more than one setting for each SafetyCategory type. The API will block any contents and responses that fail to meet the thresholds set by these settings. This list overrides the default settings for each SafetyCategory specified in the safety_settings. If there is no SafetySetting for a given SafetyCategory provided in the list, the API will use the default safety setting for that category. Harm categories HARM_CATEGORY_HATE_SPEECH, HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT, HARM_CATEGORY_HARASSMENT are supported. Refer to the guide for detailed information on available safety settings. Also refer to the Safety guidance to learn how to incorporate safety considerations in your AI applications.

    This corresponds to the safety_settings field on the request instance; if request is provided, this should not be set.

  • answer_style (google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest.AnswerStyle) –

    Required. Style in which answers should be returned.

    This corresponds to the answer_style field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from the model for a grounded answer.

Return type

google.ai.generativelanguage_v1beta.types.GenerateAnswerResponse

generate_content(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentResponse[source]

Generates a model response given an input GenerateContentRequest. Refer to the text generation guide for detailed usage information. Input capabilities differ between models, including tuned models. Refer to the model guide and tuning guide for details.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_generate_content():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GenerateContentRequest(
        model="model_value",
    )

    # Make the request
    response = client.generate_content(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]) – The request object. Request to generate a completion from the model.

  • model (str) –

    Required. The name of the Model to use for generating the completion.

    Format: name=models/{model}.

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Required. The content of the current conversation with the model.

    For single-turn queries, this is a single instance. For multi-turn queries like chat, this is a repeated field that contains the conversation history and the latest request.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from the model supporting multiple candidate responses.

Safety ratings and content filtering are reported for both prompt in GenerateContentResponse.prompt_feedback and for each candidate in finish_reason and in safety_ratings. The API: - Returns either all requested candidates or none of them - Returns no candidates at all only if there was something wrong with the prompt (check prompt_feedback) - Reports feedback on each candidate in finish_reason and safety_ratings.

Return type

google.ai.generativelanguage_v1beta.types.GenerateContentResponse

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[google.api_core.client_options.ClientOptions] = None)[source]

Deprecated. Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns

returns the API endpoint and the

client cert source to use.

Return type

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

static model_path(model: str) → str[source]

Returns a fully-qualified model string.

static parse_cached_content_path(path: str) → Dict[str, str][source]

Parses a cached_content path into its component segments.

static parse_common_billing_account_path(path: str) → Dict[str, str][source]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str][source]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str][source]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str][source]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str][source]

Parse a project path into its component segments.

static parse_model_path(path: str) → Dict[str, str][source]

Parses a model path into its component segments.

stream_generate_content(request: Optional[Union[google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentRequest, dict]] = None, *, model: Optional[str] = None, contents: Optional[MutableSequence[google.ai.generativelanguage_v1beta.types.content.Content]] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Iterable[google.ai.generativelanguage_v1beta.types.generative_service.GenerateContentResponse][source]

Generates a streamed response from the model given an input GenerateContentRequest.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_stream_generate_content():
    # Create a client
    client = generativelanguage_v1beta.GenerativeServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GenerateContentRequest(
        model="model_value",
    )

    # Make the request
    stream = client.stream_generate_content(request=request)

    # Handle the response
    for response in stream:
        print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]) – The request object. Request to generate a completion from the model.

  • model (str) –

    Required. The name of the Model to use for generating the completion.

    Format: name=models/{model}.

    This corresponds to the model field on the request instance; if request is provided, this should not be set.

  • contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]) –

    Required. The content of the current conversation with the model.

    For single-turn queries, this is a single instance. For multi-turn queries like chat, this is a repeated field that contains the conversation history and the latest request.

    This corresponds to the contents field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from the model supporting multiple candidate responses.

Safety ratings and content filtering are reported for both prompt in GenerateContentResponse.prompt_feedback and for each candidate in finish_reason and in safety_ratings. The API: - Returns either all requested candidates or none of them - Returns no candidates at all only if there was something wrong with the prompt (check prompt_feedback) - Reports feedback on each candidate in finish_reason and safety_ratings.

Return type

Iterable[google.ai.generativelanguage_v1beta.types.GenerateContentResponse]

property transport: google.ai.generativelanguage_v1beta.services.generative_service.transports.base.GenerativeServiceTransport

Returns the transport used by the client instance.

Returns

The transport used by the client

instance.

Return type

GenerativeServiceTransport

property universe_domain: str

Return the universe domain used by the client instance.

Returns

The universe domain used by the client instance.

Return type

str