genai.text.embedding.embedding_service module#

pydantic model genai.text.embedding.embedding_service.BaseConfig[source]#

Bases: BaseServiceConfig

Config:

extra: str = forbid
validate_assignment: bool = True
validate_default: bool = True

field create_execution_options: CreateExecutionOptions = CreateExecutionOptions(throw_on_error=True, ordered=True, concurrency_limit=None, callback=None)#

pydantic model genai.text.embedding.embedding_service.BaseServices[source]#

Bases: BaseServiceServices

Config:

extra: str = forbid
validate_assignment: bool = True
validate_default: bool = True

field LimitService: type[LimitService] = <class 'genai.text.embedding.limit.limit_service.LimitService'>#

pydantic model genai.text.embedding.embedding_service.CreateExecutionOptions[source]#

Bases: BaseModel

field callback: Callable[[TextEmbeddingCreateResponse], None] | None = None#: Callback which is called everytime the response comes.

field concurrency_limit: int | None = None#

Upper bound for concurrent executions (in case the passed value is higher than the API allows, the API’s limit will be used).

Constraints:

ge = 1

field ordered: bool = True#: Items will be yielded in the order they were passed in, although they may be processed on the server in different order.

field throw_on_error: bool = True#: Flag indicating whether to throw an error if any error occurs during execution (if disabled, ‘None’ may be returned in case of error).

class genai.text.embedding.embedding_service.EmbeddingService[source]#

Bases: BaseService[BaseConfig, BaseServices]

Config#: alias of BaseConfig

Services#: alias of BaseServices

__init__(*, api_client, services=None, config=None)[source]#

Parameters:

api_client (ApiClient) –
services (BaseServices | None) –
config (BaseConfig | dict | None) –

create(*, model_id, inputs, parameters=None, execution_options=None)[source]#

Creates embedding vectors from an input(s).

Parameters:

model_id (str) – The ID of the model.
inputs (str | list[str]) – Text/texts to process. It is recommended not to leave any trailing spaces.
parameters (dict | TextEmbeddingParameters | None) – Parameters for embedding.
execution_options (dict | CreateExecutionOptions | None) – An optional configuration how SDK should work (error handling, limits, callbacks, …)

Return type:

Generator[TextEmbeddingCreateResponse, None, None]

Example:

from genai import Client, Credentials
from genai.text.chat import HumanMessage, TextGenerationParameters

client = Client(credentials=Credentials.from_env())

responses = list(
    client.text.embedding.create(
        model_id="sentence-transformers/all-minilm-l6-v2",
        input="Write a tagline for an alumni association: Together we"
    )
)
print("Output vector", responses[0].results[0])

Yields:

TextEmbeddingCreateResponse object.

Raises:

ApiResponseException – In case of a known API error.
ApiNetworkException – In case of unhandled network error.
ValidationError – In case of provided parameters are invalid.

Parameters:

model_id (str) –
inputs (str | list[str]) –
parameters (dict | TextEmbeddingParameters | None) –
execution_options (dict | CreateExecutionOptions | None) –

Return type:

Generator[TextEmbeddingCreateResponse, None, None]