prompt_templating.py

from aws_cdk import (
    Stack,
    aws_bedrock as bedrock,
    aws_stepfunctions as sfn,
    aws_stepfunctions_tasks as tasks,
)
from constructs import Construct


class PromptTemplating(Stack):
    def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
        super().__init__(scope, construct_id, **kwargs)

        get_summary = tasks.BedrockInvokeModel(
            self,
            "Generate Book Summary",
            # Choose the model to invoke
            model=bedrock.FoundationModel.from_foundation_model_id(
                self,
                "Model",
                bedrock.FoundationModelIdentifier.ANTHROPIC_CLAUDE_3_HAIKU_20240307_V1_0,
            ),
            # Provide the input to the model, including the templated prompt and inference properties
            body=sfn.TaskInput.from_object(
                {
                    "anthropic_version": "bedrock-2023-05-31",
                    "messages": [
                        {
                            "role": "user",
                            "content": [
                                {
                                    "type": "text",
                                    # The prompt is templated with the novel name as variable input.
                                    # The input to the Step Functions execution could be:
                                    # "Pride and Prejudice"
                                    "text": sfn.JsonPath.format(
                                        "Write a 1-2 sentence summary for the book {}.",
                                        sfn.JsonPath.string_at("$$.Execution.Input"),
                                    ),
                                }
                            ],
                        }
                    ],
                    "max_tokens": 250,
                    "temperature": 1,
                }
            ),
            # Extract the final response from the model as the result of the Step Functions execution
            output_path="$.Body.content[0].text",
        )

        sfn.StateMachine(
            self,
            "PromptTemplatingExample",
            state_machine_name="Techniques-PromptTemplating",
            definition_body=sfn.DefinitionBody.from_chainable(get_summary),
        )