langchain.embeddings.bedrock.BedrockEmbeddings

class langchain.embeddings.bedrock.BedrockEmbeddings(*, client: Any = None, region_name: Optional[str] = None, credentials_profile_name: Optional[str] = None, model_id: str = 'amazon.titan-e1t-medium', model_kwargs: Optional[Dict] = None, endpoint_url: Optional[str] = None)

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-e1t-medium'

Id of the model to call, e.g., amazon.titan-e1t-medium, this is equivalent to the modelId property in the list-foundation-models api

param model_kwargs: Optional[Dict] = None

Key word 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.

embed_documents(texts: List[str], chunk_size: int = 1) → List[List[float]]

Compute doc embeddings using a Bedrock model.

Parameters

• texts – The list of texts to embed.

• chunk_size – Bedrock currently only allows single string inputs, so chunk size is always 1. This input is here only for compatibility with the embeddings interface.

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.

validator validate_environment  »  all fields

Validate that AWS credentials to and python package exists in environment.

model Config

Bases: object

Configuration for this pydantic object.

extra = 'forbid'

Examples using BedrockEmbeddings

• Bedrock Embeddings

• Bedrock