BedrockEmbeddings#

class langchain_aws.embeddings.bedrock.BedrockEmbeddings[source]#

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 config: Any = None#: An optional botocore.config.Config instance to pass to the client.

param credentials_profile_name: str | None = 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: str | None = 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: Dict | None = None#: Keyword arguments to pass to the model.

param normalize: bool = False#: Whether the embeddings should be normalized to unit vectors

param region_name: str | None = 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]][source]#

Asynchronous compute doc embeddings using a Bedrock model.

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

Asynchronous compute query embeddings using a Bedrock model.

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

Compute doc embeddings using a Bedrock model.

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

Compute query embeddings using a Bedrock model.

Examples using BedrockEmbeddings#