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Testo di Amazon Titan Embeddings
Titan Embeddings G1 - Textnon supporta l'uso di parametri di inferenza. Le sezioni seguenti descrivono in dettaglio i formati di richiesta e risposta e forniscono un esempio di codice.
Richiesta e risposta
Il corpo della richiesta viene passato nel body
campo di una InvokeModelrichiesta.
- V2 Request
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Il parametro InputText è obbligatorio. I parametri normalize e dimensions sono opzionali.
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InputText: immettete il testo da convertire in incorporamenti.
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normalize - flag che indica se normalizzare o meno gli incorporamenti di output. Il valore predefinito è true.
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dimensioni - Il numero di dimensioni che dovrebbero avere gli incorporamenti di output. Sono accettati i seguenti valori: 1024 (impostazione predefinita), 512, 256.
{ "inputText": string, "dimensions": int, "normalize": boolean }
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- V2 Response
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I campi sono descritti di seguito.
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incorporamento: un array che rappresenta il vettore di incorporamento dell'input fornito.
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input TextTokenCount — Il numero di token nell'input.
{ "embedding": [float, float, ...], "inputTextTokenCount": int }
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- G1 Request
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L'unico campo disponibile è
inputText
, in cui è possibile includere testo da convertire in incorporamenti.{ "inputText": string }
- G1 Response
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Il campo
body
della risposta contiene i seguenti campi.{ "embedding": [float, float, ...], "inputTextTokenCount": int }
I campi sono descritti di seguito.
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incorporamento: un array che rappresenta il vettore di incorporamento dell'input fornito.
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TextTokenNumero di input: il numero di token nell'input.
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Codice di esempio
Gli esempi seguenti mostrano come chiamare il modello Amazon Titan Embeddings per generare incorporamenti. Seleziona la scheda corrispondente al modello che stai utilizzando:
- Amazon Titan Embeddings G1 - Text
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# 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
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# 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))