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Amazon Titan Text-Modelle
Die Amazon Titan Text-Modelle unterstützen die folgenden Inferenzparameter.
Weitere Informationen zu den technischen Richtlinien für Titan Texteingabeaufforderungen finden Sie unter Richtlinien für die Entwicklung von TitanTexteingabeaufforderungen
Weitere Informationen zu Titan Modellen finden Sie unterTitanAmazon-Modelle.
Anfrage und Antwort
Der Hauptteil der Anfrage wird im body Feld einer InvokeModeloder einer InvokeModelWithResponseStream-Anfrage übergeben.
Codebeispiele
Das folgende Beispiel zeigt, wie Inferenz mit dem Amazon Titan Text Premier-Modell mit dem Python-SDK ausgeführt wird.
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to create a list of action items from a meeting transcript with the Amazon Titan Text model (on demand). """ import json import logging import boto3 from botocore.exceptions import ClientError class ImageError(Exception): "Custom exception for errors returned by Amazon Titan Text models" def __init__(self, message): self.message = message logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_text(model_id, body): """ Generate text using Amazon Titan Text models on demand. Args: model_id (str): The model ID to use. body (str) : The request body to use. Returns: response (json): The response from the model. """ logger.info( "Generating text with Amazon Titan 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()) finish_reason = response_body.get("error") if finish_reason is not None: raise ImageError(f"Text generation error. Error is {finish_reason}") logger.info( "Successfully generated text with Amazon Titan Text model %s", model_id) return response_body def main(): """ Entrypoint for Amazon Titan Text model example. """ try: logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") # You can replace the model_id with any other Titan Text Models # Titan Text Model family model_id is as mentioned below: # amazon.titan-text-premier-v1:0, amazon.titan-text-express-v1, amazon.titan-text-lite-v1 model_id = 'amazon.titan-text-premier-v1:0' prompt = """Meeting transcript: Miguel: Hi Brant, I want to discuss the workstream for our new product launch Brant: Sure Miguel, is there anything in particular you want to discuss? Miguel: Yes, I want to talk about how users enter into the product. Brant: Ok, in that case let me add in Namita. Namita: Hey everyone Brant: Hi Namita, Miguel wants to discuss how users enter into the product. Miguel: its too complicated and we should remove friction. for example, why do I need to fill out additional forms? I also find it difficult to find where to access the product when I first land on the landing page. Brant: I would also add that I think there are too many steps. Namita: Ok, I can work on the landing page to make the product more discoverable but brant can you work on the additonal forms? Brant: Yes but I would need to work with James from another team as he needs to unblock the sign up workflow. Miguel can you document any other concerns so that I can discuss with James only once? Miguel: Sure. From the meeting transcript above, Create a list of action items for each person. """ body = json.dumps({ "inputText": prompt, "textGenerationConfig": { "maxTokenCount": 3072, "stopSequences": [], "temperature": 0.7, "topP": 0.9 } }) response_body = generate_text(model_id, body) print(f"Input token count: {response_body['inputTextTokenCount']}") for result in response_body['results']: print(f"Token count: {result['tokenCount']}") print(f"Output text: {result['outputText']}") print(f"Completion reason: {result['completionReason']}") except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) except ImageError as err: logger.error(err.message) print(err.message) else: print( f"Finished generating text with the Amazon Titan Text Premier model {model_id}.") if __name__ == "__main__": main()
Das folgende Beispiel zeigt, wie Inferenzen mit dem Titan Text G1 - Express Amazon-Modell mit dem Python-SDK ausgeführt werden.
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to create a list of action items from a meeting transcript with the Amazon &titan-text-express; model (on demand). """ import json import logging import boto3 from botocore.exceptions import ClientError class ImageError(Exception): "Custom exception for errors returned by Amazon &titan-text-express; model" def __init__(self, message): self.message = message logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_text(model_id, body): """ Generate text using Amazon &titan-text-express; model on demand. Args: model_id (str): The model ID to use. body (str) : The request body to use. Returns: response (json): The response from the model. """ logger.info( "Generating text with Amazon &titan-text-express; 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()) finish_reason = response_body.get("error") if finish_reason is not None: raise ImageError(f"Text generation error. Error is {finish_reason}") logger.info( "Successfully generated text with Amazon &titan-text-express; model %s", model_id) return response_body def main(): """ Entrypoint for Amazon &titan-text-express; example. """ try: logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = 'amazon.titan-text-express-v1' prompt = """Meeting transcript: Miguel: Hi Brant, I want to discuss the workstream for our new product launch Brant: Sure Miguel, is there anything in particular you want to discuss? Miguel: Yes, I want to talk about how users enter into the product. Brant: Ok, in that case let me add in Namita. Namita: Hey everyone Brant: Hi Namita, Miguel wants to discuss how users enter into the product. Miguel: its too complicated and we should remove friction. for example, why do I need to fill out additional forms? I also find it difficult to find where to access the product when I first land on the landing page. Brant: I would also add that I think there are too many steps. Namita: Ok, I can work on the landing page to make the product more discoverable but brant can you work on the additonal forms? Brant: Yes but I would need to work with James from another team as he needs to unblock the sign up workflow. Miguel can you document any other concerns so that I can discuss with James only once? Miguel: Sure. From the meeting transcript above, Create a list of action items for each person. """ body = json.dumps({ "inputText": prompt, "textGenerationConfig": { "maxTokenCount": 4096, "stopSequences": [], "temperature": 0, "topP": 1 } }) response_body = generate_text(model_id, body) print(f"Input token count: {response_body['inputTextTokenCount']}") for result in response_body['results']: print(f"Token count: {result['tokenCount']}") print(f"Output text: {result['outputText']}") print(f"Completion reason: {result['completionReason']}") except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) except ImageError as err: logger.error(err.message) print(err.message) else: print( f"Finished generating text with the Amazon &titan-text-express; model {model_id}.") if __name__ == "__main__": main()
