name
(str
): The name of the pre-trained model to use for embedding.
If not provided, the default model specified in EncoderDefault will
be used.input_type
(str
): The type of input to use for the embedding.
If not provided, the default input type specified in EncoderDefault will
be used.score_threshold
(float
): The threshold for similarity scores.access_key_id
(str
): The AWS access key id for an IAM principle.
If not provided, it will be retrieved from the access_key_id
environment variable.secret_access_key
(str
): The secret access key for an IAM principle.
If not provided, it will be retrieved from the AWS_SECRET_KEY
environment variable.str
0: The session token for an IAM principle.
If not provided, it will be retrieved from the AWS_SESSION_TOKEN
environment variable.str
1 (str
): The location of the Bedrock resources.
If not provided, it will be retrieved from the AWS_REGION
environment variable, defaulting to “us-west-1”str
3: If the Bedrock Platform client fails to initialize.docs
(list[str]
): A list of strings representing the documents to embed.model_kwargs
(dict
): A dictionary of model-specific inference parameters.ValueError
: If the Bedrock Platform client is not initialized or if the
API call fails.list[list[float]]
: A list of lists, where each inner list contains the embedding values for a
document.
strings
(list
): A list of strings to be chunked.max_chunk_size
(int
): The maximum size of each chunk. Default is 20.list[list[str]]
: A list of lists, where each inner list contains a chunk of strings.
var_name
(str
): The name of the environment variable.provided_value
(Optional[str]
): The provided value to use if not None.default
(Optional[str]
): The default value if the environment variable is not set.ValueError
: If no value is provided and the environment variable is not set.str
: The value of the environment variable or the provided/default value.