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I modelli Amazon Titan Text supportano i seguenti parametri di inferenza.
Per ulteriori informazioni su Titan Linee guida per la progettazione dei prompt di testo, vedere Titan Linee guida per la progettazione di Text Prompt.
Per ulteriori informazioni su Titan modelli, vediPanoramica di Amazon Titan modelli.
Argomenti
Richiesta e risposta
Il corpo della richiesta viene passato nel body campo di una InvokeModelWithResponseStreamrichiesta InvokeModelor.
{
"inputText": string,
"textGenerationConfig": {
"temperature": float,
"topP": float,
"maxTokenCount": int,
"stopSequences": [string]
}
}I parametri seguenti sono obbligatori:
-
inputText— La richiesta di fornire il modello per generare una risposta. Per generare risposte in stile colloquiale, racchiudi il prompt utilizzando il seguente formato:
"inputText": "User:<prompt>\nBot:
È textGenerationConfig facoltativo. È possibile utilizzarlo per configurare i seguenti parametri di inferenza:
-
temperatura: utilizza un valore più basso per ridurre la casualità nelle risposte.
Predefinita Minimo Massimo 0.7 0,0 1 -
TopP: utilizza un valore più basso per ignorare le opzioni meno probabili e ridurre la diversità delle risposte.
Predefinita Minimo Massimo 0.9 0,0 1 -
maxTokenCount— Specificare il numero massimo di token da generare nella risposta. I limiti massimi di token vengono applicati rigorosamente.
Modello Predefinita Minimo Massimo Titan Text Lite 512 0 4,096 Titan Text Express 512 0 8,192 Titan Text Premier 512 0 3.072 -
stopSequences— Specificate una sequenza di caratteri per indicare dove il modello deve fermarsi.
Esempi di codice
L'esempio seguente mostra come eseguire l'inferenza con Amazon Titan Text Premier model con PythonSDK.
# 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()L'esempio seguente mostra come eseguire l'inferenza con Amazon Titan Text G1 - Express modello con PythonSDK.
# 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()
