Amazon Titan Text Embeddings

Titan Embeddings G1 - Text doesn't support the use of inference parameters. The following sections detail the request and response formats and provides a code example.

Request and response

The request body is passed in the body field of an InvokeModel request.

V2 Request

The inputText parameter is required. The normalize and dimensions parameters are optional.

• inputText – Enter text to convert to an embedding.

• normalize – (optional) Flag indicating whether or not to normalize the output embedding. Defaults to true.

• dimensions – (optional) The number of dimensions the output embedding should have. The following values are accepted: 1024 (default), 512, 256.

• embeddingTypes – (optional) Accepts a list containing "float", "binary", or both. Defaults to float.

{
    "inputText": string,
    "dimensions": int,
    "normalize": boolean,
    "embeddingTypes": list
}

V2 Response

The fields are described below.

• embedding – An array that represents the embedding vector of the input you provided. This will always be type float.

• inputTextTokenCount – The number of tokens in the input.

• embeddingsByType – A dictionary or map of the embedding list. Depends on the input, lists "float", "binary", or both.

  • Example: "embeddingsByType": {"binary": [int,..], "float": [float,...]}

  • This field will always appear. Even if you don't specify embeddingTypes in your input, there will still be “float”. Example: "embeddingsByType": {"float": [float,...]}

{
    "embedding": [float, float, ...],
    "inputTextTokenCount": int,
    "embeddingsByType": {"binary": [int,..], "float": [float,...]}
}

G1 Request

The only available field is inputText, in which you can include text to convert into an embedding.

{
    "inputText": string
}

G1 Response

The body of the response contains the following fields.

{
    "embedding": [float, float, ...],
    "inputTextTokenCount": int
}

The fields are described below.

• embedding – An array that represents the embedding vector of the input you provided.

• inputTextTokenCount – The number of tokens in the input.

Example code

The following examples show how to call the Amazon Titan Embeddings models to generate embedding. Select the tab that corresponds to the model you're using:

Amazon Titan Embeddings G1 - Text

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate an embedding with the Amazon Titan Embeddings G1 - Text model (on demand).
"""

import json
import logging
import boto3


from botocore.exceptions import ClientError


logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_embedding(model_id, body):
    """
    Generate an embedding with the vector representation of a text input using Amazon Titan Embeddings G1 - Text on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (JSON): The embedding created by the model and the number of input tokens.
    """

    logger.info("Generating an embedding with Amazon Titan Embeddings G1 - Text model %s", model_id)

    bedrock = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )

    response_body = json.loads(response.get('body').read())

    return response_body


def main():
    """
    Entrypoint for Amazon Titan Embeddings G1 - Text example.
    """

    logging.basicConfig(level=logging.INFO,
                        format="%(levelname)s: %(message)s")

    model_id = "amazon.titan-embed-text-v1"
    input_text = "What are the different services that you offer?"


    # Create request body.
    body = json.dumps({
        "inputText": input_text,
    })


    try:

        response = generate_embedding(model_id, body)

        print(f"Generated an embedding: {response['embedding']}")
        print(f"Input Token count:  {response['inputTextTokenCount']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))

    else:
        print(f"Finished generating an embedding with Amazon Titan Embeddings G1 - Text model {model_id}.")


if __name__ == "__main__":
    main()

Amazon Titan Text Embeddings V2

When using Titan Text Embeddings V2, the embedding field is not in the response if the embeddingTypes only contains binary.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate an embedding with the Amazon Titan Text Embeddings V2 Model
"""

import json
import logging
import boto3


from botocore.exceptions import ClientError


logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_embedding(model_id, body):
    """
    Generate an embedding with the vector representation of a text input using Amazon Titan Text Embeddings G1 on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (JSON): The embedding created by the model and the number of input tokens.
    """

    logger.info("Generating an embedding with Amazon Titan Text Embeddings V2 model %s", model_id)

    bedrock = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )

    response_body = json.loads(response.get('body').read())

    return response_body


def main():
    """
    Entrypoint for Amazon Titan Embeddings V2 - Text example.
    """

    logging.basicConfig(level=logging.INFO,
                        format="%(levelname)s: %(message)s")

    model_id = "amazon.titan-embed-text-v2:0"
    input_text = "What are the different services that you offer?"


    # Create request body.
    body = json.dumps({
        "inputText": input_text,
        "embeddingTypes": ["binary"]
    })


    try:

        response = generate_embedding(model_id, body)

        print(f"Generated an embedding: {response['embeddingsByType']['binary']}") # returns binary embedding
        print(f"Input Token count:  {response['inputTextTokenCount']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))

    else:
        print(f"Finished generating an embedding with Amazon Titan Text Embeddings V2 model {model_id}.")


if __name__ == "__main__":
    main()