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AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function. You can do so by using awscli and also add your session_token: 
aws configure
aws configure set aws_session_token "<your_session_token>"
to ensure that the credentials are set up correctly, you can run the following command: 
aws sts get-caller-identity
Supported Embedding modelIDs are:
  • amazon.titan-embed-text-v1
  • cohere.embed-english-v3
  • cohere.embed-multilingual-v3
Supported parameters (to be passed in create method) are:
ParameterTypeDefault ValueDescription
namestr”amazon.titan-embed-text-v1”The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3
regionstr”us-east-1”Optional name of the AWS Region in which the service should be called (e.g., “us-east-1”).
profile_namestrNoneOptional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used.
assumed_rolestrNoneOptional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used.
role_session_namestr”lancedb-embeddings”Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a “lancedb-embeddings” name will be used.
runtimeboolTrueOptional choice of getting different client to perform operations with the Amazon Bedrock service.
max_retriesint7Optional number of retries to perform when a request fails.
Usage Example: