langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings¶

class langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings[source]¶

Bases: BaseModel, Embeddings

Custom Sagemaker Inference Endpoints.

To use, you must supply the endpoint name from your deployed Sagemaker model & the region where it is deployed.

To authenticate, the AWS client uses the following methods to automatically load credentials: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

If a specific credential profile should be used, you must pass the name of the profile from the ~/.aws/credentials file that is to be used.

Make sure the credentials / roles used have the required policies to access the Sagemaker endpoint. See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param client: Any = None¶
param content_handler: EmbeddingsContentHandler [Required]¶

The content handler class that provides an input and output transform functions to handle formats between LLM and the endpoint.

param credentials_profile_name: Optional[str] = None¶

The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which has either access keys or role information specified. If not specified, the default credential profile or, if on an EC2 instance, credentials from IMDS will be used. See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

param endpoint_kwargs: Optional[Dict] = None¶

Optional attributes passed to the invoke_endpoint function. See `boto3`_. docs for more info. .. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>

param endpoint_name: str = ''¶

The name of the endpoint from the deployed Sagemaker model. Must be unique within an AWS Region.

param model_kwargs: Optional[Dict] = None¶

Keyword arguments to pass to the model.

param region_name: str = ''¶

The aws region where the Sagemaker model is deployed, eg. us-west-2.

async aembed_documents(texts: List[str]) → List[List[float]]¶

Asynchronous Embed search docs.

async aembed_query(text: str) → List[float]¶

Asynchronous Embed query text.

classmethod construct(_fields_set: Optional[SetStr] = 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: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = 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: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = 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.

embed_documents(texts: List[str], chunk_size: int = 64) → List[List[float]][source]¶

Compute doc embeddings using a SageMaker Inference Endpoint.

Parameters

  • texts – The list of texts to embed.

  • chunk_size – The chunk size defines how many input texts will be grouped together as request. If None, will use the chunk size specified by the class.

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float][source]¶

Compute query embeddings using a SageMaker inference endpoint.

Parameters

text – The text to embed.

Returns

Embeddings for the text.

classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶

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 parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶

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

classmethod validate(value: Any) → Model¶

Examples using SagemakerEndpointEmbeddings¶