gen_ai_hub.orchestration_v2.models.embeddings module¶

Embeddings Module Configuration Models

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsEncodingFormat(*values)¶

Bases: str, Enum

Encoding format for the embeddings output.

Values:: FLOAT: Returns embeddings as an array of floats. BASE64: Returns embeddings as a base64 encoded string. BINARY: Returns embeddings in binary format.

FLOAT = 'float'¶

BASE64 = 'base64'¶

BINARY = 'binary'¶

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsInputType(*values)¶

Bases: str, Enum

Type hint for the embedding model about the purpose of the text.

Some models use asymmetric embeddings for better search performance.

Values:: TEXT: General purpose text (default). DOCUMENT: Content to be searched/retrieved. QUERY: Short search queries.

TEXT = 'text'¶

DOCUMENT = 'document'¶

QUERY = 'query'¶

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsModelParams(*, dimensions: int | None = None, encoding_format: EmbeddingsEncodingFormat | None = None, normalize: bool | None = None)¶

Bases: ABCBaseModel

Additional parameters for generating embeddings.

Parameters:

dimensions – The number of dimensions for the output embeddings.
encoding_format – The format for the embeddings output (float, base64, or binary).
normalize – Whether to normalize the embeddings.

dimensions: int | None¶

encoding_format: EmbeddingsEncodingFormat | None¶

normalize: bool | None¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsModelDetails(*, name: str, version: str | None = 'latest', params: EmbeddingsModelParams | None = None, timeout: Annotated[int | None, Ge(ge=1), Le(le=600)] = 600, max_retries: Annotated[int | None, Ge(ge=0), Le(le=5)] = 2)¶

Bases: ABCBaseModel

The model and parameters to be used for generating embeddings.

Parameters:

name – Name of the embedding model.
version – Version of the model to be used. Defaults to “latest”.
params – Additional parameters for the model (dimensions, encoding_format, normalize).
timeout – Timeout for the embeddings request in seconds. Ignored for Vertex AI models.
max_retries – Maximum number of retries. Ignored for Vertex AI models.

name: str¶

version: str | None¶

params: EmbeddingsModelParams | None¶

timeout: int | None¶

max_retries: int | None¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsModelConfig(*, model: EmbeddingsModelDetails)¶

Bases: ABCBaseModel

Configuration for the embeddings model.

Parameters:: model – The embedding model details.

model: EmbeddingsModelDetails¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsModuleConfigs(*, embeddings: EmbeddingsModelConfig, masking: MaskingModuleConfig | None = None)¶

Bases: ABCBaseModel

Module configurations for the embeddings endpoint.

Parameters:

embeddings – Required configuration for the embeddings model.
masking – Optional configuration for data masking before embedding.

embeddings: EmbeddingsModelConfig¶

masking: MaskingModuleConfig | None¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsOrchestrationConfig(*, modules: EmbeddingsModuleConfigs)¶

Bases: ABCBaseModel

Configuration for the Embeddings Orchestration endpoint.

Parameters:: modules – The module configurations including embeddings model and optional masking.

modules: EmbeddingsModuleConfigs¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsInput(*, text: str | List[str], type: EmbeddingsInputType | None = None)¶

Bases: ABCBaseModel

Input for the embeddings endpoint.

Parameters:

text – The text to embed. Can be a single string or a list of strings.
type – Optional type hint for the embedding model (text, document, or query).

text: str | List[str]¶

type_: EmbeddingsInputType | None¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsUsage(*, prompt_tokens: int, total_tokens: int)¶

Bases: ABCBaseModel

Token usage information for the embeddings request.

Parameters:

prompt_tokens – The number of tokens used by the prompt.
total_tokens – The total number of tokens used by the request.

prompt_tokens: int¶

total_tokens: int¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingResult(*, object: str = 'embedding', embedding: List[float] | str, index: int)¶

Bases: ABCBaseModel

A single embedding result.

Parameters:

object – The object type, always “embedding”.
embedding – The embedding vector (array of floats) or base64 string.
index – The index of this embedding in the list.

object: str¶

embedding: List[float] | str¶

index: int¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsResponse(*, object: str = 'list', data: List[EmbeddingResult], model: str, usage: EmbeddingsUsage)¶

Bases: ABCBaseModel

The response from the embedding model, following OpenAI specification.

Parameters:

object – The object type, always “list”.
data – The list of embeddings generated by the model.
model – The name of the model used to generate the embeddings.
usage – Token usage information.

object: str¶

data: List[EmbeddingResult]¶

model: str¶

usage: EmbeddingsUsage¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsPostResponse(*, request_id: str, intermediate_results: Dict | None = None, final_result: EmbeddingsResponse)¶

Bases: ABCBaseModel

Response for an embeddings POST request.

Parameters:

request_id – Unique identifier for the request.
intermediate_results – Optional results from intermediate modules (e.g., masking).
final_result – The embeddings response from the model.

request_id: str¶

intermediate_results: Dict | None¶

final_result: EmbeddingsResponse¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.orchestration_v2.models.embeddings.EmbeddingsRequest(*, config: EmbeddingsOrchestrationConfig, input: EmbeddingsInput)¶

Bases: ABCBaseModel

Request body for the embeddings endpoint.

Parameters:

config – The embeddings orchestration configuration.
input – The input text to embed.

config: EmbeddingsOrchestrationConfig¶

input: EmbeddingsInput¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].