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# `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 normalize: bool = False

    Whether the embeddings should be normalized to unit vectors

  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 (List[str]) – The list of texts to embed

    Returns

      List of embeddings, one for each text.

    Return type

      List[List[float]]

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

    Asynchronous compute query embeddings using a Bedrock model.

    Parameters

      text (str) – The text to embed.

    Returns

      Embeddings for the text.

    Return type

      List[float]

  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

    Parameters

      - _fields_set (Optional[SetStr]) –

      - values (Any) –

    Return type

      Model

  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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

      - update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

      - self (Model) –

    Returns

      new model instance

    Return type

      Model

  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.

    Parameters

      - include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - by_alias (bool) –

      - skip_defaults (Optional[bool]) –

      - exclude_unset (bool) –

      - exclude_defaults (bool) –

      - exclude_none (bool) –

    Return type

      DictStrAny

  embed_documents(texts: List[str]) → List[List[float]]

    Compute doc embeddings using a Bedrock model.

    Parameters

      texts (List[str]) – The list of texts to embed

    Returns

      List of embeddings, one for each text.

    Return type

      List[List[float]]

  embed_query(text: str) → List[float]

    Compute query embeddings using a Bedrock model.

    Parameters

      text (str) – The text to embed.

    Returns

      Embeddings for the text.

    Return type

      List[float]

  classmethod from_orm(obj: Any) → Model
    Parameters

      obj (Any) –

    Return type

      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().

    Parameters

      - include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - by_alias (bool) –

      - skip_defaults (Optional[bool]) –

      - exclude_unset (bool) –

      - exclude_defaults (bool) –

      - exclude_none (bool) –

      - encoder (Optional[Callable[[Any], Any]]) –

      - models_as_dict (bool) –

      - dumps_kwargs (Any) –

    Return type

      unicode

  classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
    Parameters

      - path (Union[str, Path]) –

      - content_type (unicode) –

      - encoding (unicode) –

      - proto (Protocol) –

      - allow_pickle (bool) –

    Return type

      Model

  classmethod parse_obj(obj: Any) → Model
    Parameters

      obj (Any) –

    Return type

      Model

  classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
    Parameters

      - b (Union[str, bytes]) –

      - content_type (unicode) –

      - encoding (unicode) –

      - proto (Protocol) –

      - allow_pickle (bool) –

    Return type

      Model

  classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
    Parameters

      - by_alias (bool) –

      - ref_template (unicode) –

    Return type

      DictStrAny

  classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
    Parameters

      - by_alias (bool) –

      - ref_template (unicode) –

      - dumps_kwargs (Any) –

    Return type

      unicode

  classmethod update_forward_refs(**localns: Any) → None

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

    Parameters

      localns (Any) –

    Return type

      None

  classmethod validate(value: Any) → Model
    Parameters

      value (Any) –

    Return type

      Model

# Examples using BedrockEmbeddings

- AWS

- async embed query