semantic_router.encoders.google.GoogleEncoder#

class semantic_router.encoders.google.GoogleEncoder(name: str | None = None, score_threshold: float = 0.75, project_id: str | None = None, location: str | None = None, api_endpoint: str | None = None)#

Bases: BaseEncoder

GoogleEncoder class for generating embeddings using Google’s AI Platform.

Attributes:

client: An instance of the TextEmbeddingModel client. type: The type of the encoder, which is “google”.

__init__(name: str | None = None, score_threshold: float = 0.75, project_id: str | None = None, location: str | None = None, api_endpoint: str | None = None)#

Initializes the GoogleEncoder.

Args:
model_name: The name of the pre-trained model to use for embedding.

If not provided, the default model specified in EncoderDefault will be used.

score_threshold: The threshold for similarity scores. project_id: The Google Cloud project ID.

If not provided, it will be retrieved from the GOOGLE_PROJECT_ID environment variable.

location: The location of the AI Platform resources.

If not provided, it will be retrieved from the GOOGLE_LOCATION environment variable, defaulting to “us-central1”.

api_endpoint: The API endpoint for the AI Platform.

If not provided, it will be retrieved from the GOOGLE_API_ENDPOINT environment variable.

Raises:

ValueError: If the Google Project ID is not provided or if the AI Platform client fails to initialize.

Methods

__init__([name, score_threshold, ...])

Initializes the GoogleEncoder.

acall(docs)

construct([_fields_set])

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.

copy(*[, include, exclude, update, deep])

Duplicate a model, optionally choose which fields to include, exclude and change.

dict(*[, include, exclude, by_alias, ...])

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

from_orm(obj)

json(*[, include, exclude, by_alias, ...])

Generate a JSON representation of the model, include and exclude arguments as per dict().

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

set_score_threshold(v)

update_forward_refs(**localns)

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate(value)

Attributes

client

type

name

score_threshold

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: DictStrAny | None = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns:

new model instance

dict(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → str#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.