langchain_community.embeddings.bedrock.BedrockEmbeddings

class langchain_community.embeddings.bedrock.BedrockEmbeddings

Bases: BaseModel, Embeddings

Bedrock embedding models.

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 Bedrock service.

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

Bedrock client.

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_url: Optional[str] = None

Needed if you don’t want to default to us-east-1 endpoint

param model_id: str = 'amazon.titan-embed-text-v1'

Id of the model to call, e.g., amazon.titan-embed-text-v1, this is equivalent to the modelId property in the list-foundation-models api

param model_kwargs: Optional[Dict] = None

Keyword arguments to pass to the model.

param region_name: Optional[str] = None

The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable or region specified in ~/.aws/config in case it is not provided here.

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

Asynchronous compute doc embeddings using a Bedrock model.

Parameters

texts – The list of texts to embed

Returns

List of embeddings, one for each text.

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

Asynchronous compute query embeddings using a Bedrock model.

Parameters

text – The text to embed.

Returns

Embeddings for the 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]) → List[List[float]]

Compute doc embeddings using a Bedrock model.

Parameters

texts – The list of texts to embed

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float]

Compute query embeddings using a Bedrock model.

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 BedrockEmbeddings

• Bedrock