Amazon Titan 文本嵌入 - Amazon Bedrock
请求和响应代码示例

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Amazon Titan 文本嵌入

Titan Embeddings G1 - Text 不支持使用推理参数。下文详细介绍了请求和响应格式，并提供了一个代码示例。

请求和响应

请求正文在 InvokeModel 请求的 body 字段中传递。

V2 Request

inputText 为必需参数。normalize 和 dimensions 为可选参数。

  • inputText – 输入要转换为嵌入向量的文本。

  • normalize –（可选）标记，用于表示是否对输出嵌入进行规范化。默认值为 true。

  • dimensions –（可选）输出嵌入应具有的维度数。接受以下值：1024（默认）、512、256。

  • embeddingTypes –（可选）接受包含“float”、“binary”或同时包含两者的列表。默认值为 float。

{
    "inputText": string,
    "dimensions": int,
    "normalize": boolean,
    "embeddingTypes": list
}
V2 Response

字段如下所述。

  • embedding – 一个数组，表示您提供的输入的嵌入向量。其类型始终为 float。

  • inputTextTokenCount – 输入中的词元数量。

  • embeddingsByType – 嵌入列表的字典或映射。根据输入的不同会列出“float”、“binary”或同时列出两者。

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

    • 该字段将始终显示。即使您没有在输入中指定 embeddingTypes，也会显示“float”。例如："embeddingsByType": {"float": [float,...]}

{
    "embedding": [float, float, ...],
    "inputTextTokenCount": int,
    "embeddingsByType": {"binary": [int,..], "float": [float,...]}
}
G1 Request

唯一可用的字段是 inputText，您可以在其中输入要转换为嵌入的文本。

{
    "inputText": string
}
G1 Response

响应的 body 包含以下字段。

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

字段如下所述。

  • embedding – 一个数组，表示您提供的输入的嵌入向量。

  • inputTextTokenCount – 输入中的词元数量。

代码示例

以下示例展示了如何调用 Amazon Titan 嵌入模型来生成嵌入。选择与您要使用的模型相对应的选项卡：

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 embeddings 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_embeddings(model_id, body):
    """
    Generate a vector of embeddings for 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 embeddings 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_embeddings(model_id, body)

        print(f"Generated embeddings: {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 embeddings with Amazon Titan Embeddings G1 - Text model {model_id}.")


if __name__ == "__main__":
    main()
Amazon Titan Text Embeddings V2

使用 Titan Text Embeddings V2 时，如果 embeddingTypes 仅包含 binary，则 embedding 字段不会出现在响应中。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate embeddings 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_embeddings(model_id, body):
    """
    Generate a vector of embeddings for 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 embeddings 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_embeddings(model_id, body)

        print(f"Generated embeddings: {response['embeddingByTypes']['binary']}") # returns binary embedding
        # print(f"Generated embeddings: {response['embedding']}") NOTE:"embedding" field is not in "response".
        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 embeddings with Amazon Titan Text Embeddings V2 model {model_id}.")


if __name__ == "__main__":
    main()