The Amazon Titan Text models support the following inference parameters.
For more information on Titan Text prompt engineering guidelines, see Titan Text Prompt Engineering Guidelines
For more information on Titan models, see Overview of Amazon Titan models.
Request and response
The request body is passed in the body field of an InvokeModel or InvokeModelWithResponseStream request.
{
"inputText": string,
"textGenerationConfig": {
"temperature": float,
"topP": float,
"maxTokenCount": int,
"stopSequences": [string]
}
}The following parameters are required:
-
inputText – The prompt to provide the model for generating a response. To generate responses in a conversational style, wrap the prompt by using the following format:
"inputText": "User:<prompt>\nBot:
The textGenerationConfig is optional. You can use it to configure the following inference parameters:
-
temperature – Use a lower value to decrease randomness in responses.
Default Minimum Maximum 0.7 0.0 1.0 -
topP – Use a lower value to ignore less probable options and decrease the diversity of responses.
Default Minimum Maximum 0.9 0.0 1.0 -
maxTokenCount – Specify the maximum number of tokens to generate in the response. Maximum token limits are strictly enforced.
Model Default Minimum Maximum Titan Text Lite 512 0 4,096 Titan Text Express 512 0 8,192 Titan Text Premier 512 0 3,072 -
stopSequences – Specify a character sequence to indicate where the model should stop.
Code examples
The following example shows how to run inference with the Amazon Titan Text Premier model with the Python SDK.
# 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()The following example shows how to run inference with the Amazon Titan Text G1 - Express model with the Python SDK.
# 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()
