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from dotenv import load_dotenv | ||
load_dotenv() | ||
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import boto3 | ||
import json | ||
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from traceloop.sdk import Traceloop | ||
from traceloop.sdk.decorators import task, workflow | ||
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Traceloop.init(app_name="joke_generation_service") | ||
bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-west-2") | ||
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@task(name="gyliu_joke_creation") | ||
def create_joke(): | ||
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express_prompt = "Tell me a joke" | ||
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body = json.dumps({ | ||
"inputText": express_prompt, | ||
"textGenerationConfig":{ | ||
"maxTokenCount":128, | ||
"stopSequences":[], #define phrases that signal the model to conclude text generation. | ||
"temperature":0, #Temperature controls randomness; higher values increase diversity, lower values boost predictability. | ||
"topP":0.9 # Top P is a text generation technique, sampling from the most probable tokens in a distribution. | ||
} | ||
}) | ||
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response = bedrock_runtime.invoke_model( | ||
body=body, | ||
modelId="amazon.titan-text-express-v1", | ||
accept="application/json", | ||
contentType="application/json" | ||
) | ||
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response_body = json.loads(response.get('body').read()) | ||
outputText = response_body.get('results')[0].get('outputText') | ||
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text = outputText[outputText.index('\n')+1:] | ||
about_lambda = text.strip() | ||
return about_lambda | ||
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@workflow(name="gyliu_joke_generator") | ||
def joke_workflow(): | ||
print(create_joke()) | ||
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joke_workflow() |