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Amazon Titan Embeddings テキスト
Titan Embeddings G1 - Text は推論パラメータの使用をサポートしていません。以下のセクションでは、リクエストとレスポンスの形式の詳細とコード例を示します。
リクエストとレスポンス
リクエスト本文は、InvokeModelリクエストの body
フィールドに渡されます。
- V2 Request
-
T inputText TThe パラメータは必須です。正規化パラメータとディメンションパラメータはオプションです。
-
inputText – 埋め込みに変換するテキストを入力します。
-
normalize - 出力埋め込みを正規化するかどうかを示すフラグ。デフォルトは true です。
-
dimensions - 出力埋め込みに含めるディメンションの数。次の値を使用できます。1024 (デフォルト)、512、256。
{ "inputText": string, "dimensions": int, "normalize": boolean }
-
- V2 Response
-
フィールドについて以下に説明します。
-
embedding – 指定した入力の埋め込みベクトルを表す配列。
-
input TextTokenCount – 入力内のトークンの数。
{ "embedding": [float, float, ...], "inputTextTokenCount": int }
-
- G1 Request
-
使用可能なフィールドは のみです。このフィールドには
inputText
、埋め込みに変換するテキストを含めることができます。{ "inputText": string }
- G1 Response
-
レスポンス
body
の には、次のフィールドが含まれます。{ "embedding": [float, float, ...], "inputTextTokenCount": int }
フィールドについて以下に説明します。
-
embedding – 指定した入力の埋め込みベクトルを表す配列。
-
input TextTokenCount – 入力内のトークンの数。
-
サンプルのコード
次の例は、Amazon Titan Embeddings モデルを呼び出して埋め込みを生成する方法を示しています。使用しているモデルに対応するタブを選択します。
- 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
-
# 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, "dimensions": 512, "normalize": True }) 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 Text Embeddings V2 model {model_id}.") if __name__ == "__main__": main() </programlisting> <para><emphasis role="bold">Configure your accuracy-cost tradeoff as you go</emphasis></para> <para>While normalization is available via API customers can also reduce the embedding dimension after generating the embeddings allowing them to tradeoff between accuracy and cost as their need evolve. This empower customers to generate 1024-dim index embeddings, store them in low-cost storage options such as S3 and load their 1024, 512 or 256 dimension version in their favorite vector DB as they go. </para> <para>To reduce a given embedding from 1024 to 256 dimensions you can use the following example logic:</para> <programlisting language="json">import numpy as np from numpy import linalg def normalize_embedding(embedding: np.Array): ''' Args: embedding: Unnormlized 1D/2D numpy array - 1D: (emb_dim) - 2D: (batch_size, emb_dim) Return: np.array: Normalized 1D/2D numpy array ''' return embedding/linalg.norm(embedding, dim=-1, keep_dim=True) def reduce_emb_dim(embedding: np.Array, target_dim:int, normalize:bool=True) -> np.Array: ''' Args: embedding: Unnormlized 1D/2D numpy array. Expected shape: - 1D: (emb_dim) - 2D: (batch_size, emb_dim) target_dim: target dimension to reduce the embedding to Return: np.array: Normalized 1D numpy array ''' smaller_embedding = embedding[..., :target_dim] if normalize: smaller_embedding = normalize_embedding(smaller_embedding) return smaller_embedding if __name__ == '__main__': embedding = # bedrock client call reduced_embedding = # bedrock client call with dim=256 post_reduction_embeddings = reduce_emb_dim(np.array(embeddings), dim=256) print(linalg.norm(np.array(reduced_embedding) - post_reduction_embeddings))
Amazon Titan Image Generator G1 モデル
Amazon Titan Multimodal Embeddings G1