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# SDK for Python (Boto3) を使用した Amazon Bedrock エージェントランタイムの例
<a name="python_3_bedrock-agent-runtime_code_examples"></a>

次のコード例は、Amazon Bedrock エージェントランタイム AWS SDK for Python (Boto3) で を使用してアクションを実行し、一般的なシナリオを実装する方法を示しています。

*基本* は、重要なオペレーションをサービス内で実行する方法を示すコード例です。

*アクション*はより大きなプログラムからのコードの抜粋であり、コンテキスト内で実行する必要があります。アクションは個々のサービス機能を呼び出す方法を示していますが、コンテキスト内のアクションは、関連するシナリオで確認できます。

*シナリオ*は、1 つのサービス内から、または他の AWS のサービスと組み合わせて複数の関数を呼び出し、特定のタスクを実行する方法を示すコード例です。

各例には、完全なソースコードへのリンクが含まれており、そこからコードの設定方法と実行方法に関する手順を確認できます。

**Topics**
+ [基本](#basics)
+ [アクション](#actions)
+ [シナリオ](#scenarios)

## 基本
<a name="basics"></a>

### 基本を学ぶ
<a name="bedrock-agent-runtime_Scenario_ConverseWithFlow_python_3_topic"></a>

次のコード例は、InvokeFlow を使用して、エージェントノードを含む Amazon Bedrock フローと会話する方法を示しています。

詳細については、「[Converse with an Amazon Bedrock flow](https://docs.aws.amazon.com/bedrock/latest/userguide/flows-multi-turn-invocation.html)」を参照してください。

**SDK for Python (Boto3)**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent-runtime#code-examples)での設定と実行の方法を確認してください。

```
"""
Shows how to run an Amazon Bedrock flow with InvokeFlow and handle muli-turn interaction
for a single conversation.
For more information, see https://docs.aws.amazon.com/bedrock/latest/userguide/flows-multi-turn-invocation.html.

"""
import logging
import boto3
import botocore

import botocore.exceptions

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def invoke_flow(client, flow_id, flow_alias_id, input_data, execution_id):
    """
    Invoke an Amazon Bedrock flow and handle the response stream.

    Args:
        client: Boto3 client for Amazon Bedrock agent runtime.
        flow_id: The ID of the flow to invoke.
        flow_alias_id: The alias ID of the flow.
        input_data: Input data for the flow.
        execution_id: Execution ID for continuing a flow. Use the value None on first run.

    Returns:
        Dict containing flow_complete status, input_required info, and execution_id
    """

    response = None
    request_params = None

    if execution_id is None:
        # Don't pass execution ID for first run.
        request_params = {
            "flowIdentifier": flow_id,
            "flowAliasIdentifier": flow_alias_id,
            "inputs": [input_data],
            "enableTrace": True
        }
    else:
        request_params = {
            "flowIdentifier": flow_id,
            "flowAliasIdentifier": flow_alias_id,
            "executionId": execution_id,
            "inputs": [input_data],
            "enableTrace": True
        }

    response = client.invoke_flow(**request_params)

    if "executionId" not in request_params:
        execution_id = response['executionId']

    input_required = None
    flow_status = ""

    # Process the streaming response
    for event in response['responseStream']:

        # Check if flow is complete.
        if 'flowCompletionEvent' in event:
            flow_status = event['flowCompletionEvent']['completionReason']

        # Check if more input us needed from user.
        elif 'flowMultiTurnInputRequestEvent' in event:
            input_required = event

        # Print the model output.
        elif 'flowOutputEvent' in event:
            print(event['flowOutputEvent']['content']['document'])

        # Log trace events.
        elif 'flowTraceEvent' in event:
            logger.info("Flow trace:  %s", event['flowTraceEvent'])

    return {
        "flow_status": flow_status,
        "input_required": input_required,
        "execution_id": execution_id
    }


def converse_with_flow(bedrock_agent_client, flow_id, flow_alias_id):
    """
    Run a conversation with the supplied flow.

    Args:
        bedrock_agent_client: Boto3 client for Amazon Bedrock agent runtime.
        flow_id: The ID of the flow to run.
        flow_alias_id: The alias ID of the flow.

    """

    flow_execution_id = None
    finished = False

    # Get the intial prompt from the user.
    user_input = input("Enter input: ")

    # Use prompt to create input data.
    flow_input_data = {
        "content": {
            "document": user_input
        },
        "nodeName": "FlowInputNode",
        "nodeOutputName": "document"
    }

    try:
        while not finished:
            # Invoke the flow until successfully finished.

            result = invoke_flow(
                bedrock_agent_client, flow_id, flow_alias_id, flow_input_data, flow_execution_id)

            status = result['flow_status']
            flow_execution_id = result['execution_id']
            more_input = result['input_required']
            if status == "INPUT_REQUIRED":
                # The flow needs more information from the user.
                logger.info("The flow %s requires more input", flow_id)
                user_input = input(
                    more_input['flowMultiTurnInputRequestEvent']['content']['document'] + ": ")
                flow_input_data = {
                    "content": {
                        "document": user_input
                    },
                    "nodeName": more_input['flowMultiTurnInputRequestEvent']['nodeName'],
                    "nodeInputName": "agentInputText"

                }
            elif status == "SUCCESS":
                # The flow completed successfully.
                finished = True
                logger.info("The flow %s successfully completed.", flow_id)

    except botocore.exceptions.ClientError as e:
        print(f"Client error: {str(e)}")
        logger.error("Client error: %s", {str(e)})

    except Exception as e:
        print(f"An error occurred: {str(e)}")
        logger.error("An error occurred: %s", {str(e)})
        logger.error("Error type: %s", {type(e)})


def main():
    """
    Main entry point for the script.
    """

    # Replace these with your actual flow ID and flow alias ID.
    FLOW_ID = 'YOUR_FLOW_ID'
    FLOW_ALIAS_ID = 'YOUR_FLOW_ALIAS_ID'

    logger.info("Starting conversation with FLOW: %s ID: %s",
                FLOW_ID, FLOW_ALIAS_ID)

    # Get the Bedrock agent runtime client.
    session = boto3.Session(profile_name='default')
    bedrock_agent_client = session.client('bedrock-agent-runtime')

    # Start the conversation.
    converse_with_flow(bedrock_agent_client, FLOW_ID, FLOW_ALIAS_ID)

    logger.info("Conversation with FLOW: %s ID: %s finished",
                FLOW_ID, FLOW_ALIAS_ID)


if __name__ == "__main__":
    main()
```
+  API の詳細については、AWS SDK for Python (Boto3) API リファレンスの「[InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow)」を参照してください。**

## アクション
<a name="actions"></a>

### `InvokeAgent`
<a name="bedrock-agent-runtime_InvokeAgent_python_3_topic"></a>

次のコード例は、`InvokeAgent` を使用する方法を示しています。

**SDK for Python (Boto3)**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent-runtime#code-examples)での設定と実行の方法を確認してください。
エージェントを呼び出します。  

```
    def invoke_agent(self, agent_id, agent_alias_id, session_id, prompt):
        """
        Sends a prompt for the agent to process and respond to.

        :param agent_id: The unique identifier of the agent to use.
        :param agent_alias_id: The alias of the agent to use.
        :param session_id: The unique identifier of the session. Use the same value across requests
                           to continue the same conversation.
        :param prompt: The prompt that you want Claude to complete.
        :return: Inference response from the model.
        """

        try:
            # Note: The execution time depends on the foundation model, complexity of the agent,
            # and the length of the prompt. In some cases, it can take up to a minute or more to
            # generate a response.
            response = self.agents_runtime_client.invoke_agent(
                agentId=agent_id,
                agentAliasId=agent_alias_id,
                sessionId=session_id,
                inputText=prompt,
            )

            completion = ""

            for event in response.get("completion"):
                chunk = event["chunk"]
                completion = completion + chunk["bytes"].decode()

        except ClientError as e:
            logger.error(f"Couldn't invoke agent. {e}")
            raise

        return completion
```
+  API の詳細については、**「AWS SDK for Python (Boto3) API リファレンス」の「[InvokeAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeAgent)」を参照してください。

### `InvokeFlow`
<a name="bedrock-agent-runtime_InvokeFlow_python_3_topic"></a>

次のコード例は、`InvokeFlow` を使用する方法を示しています。

**SDK for Python (Boto3)**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent-runtime#code-examples)での設定と実行の方法を確認してください。
フローを呼び出します。  

```
    def invoke_flow(self, flow_id, flow_alias_id, input_data, execution_id):
        """
        Invoke an Amazon Bedrock flow and handle the response stream.

        Args:
            param flow_id: The ID of the flow to invoke.
            param flow_alias_id: The alias ID of the flow.
            param input_data: Input data for the flow.
            param execution_id: Execution ID for continuing a flow. Use the value None on first run.

        Return: Response from the flow.
        """
        try:
      
            request_params = None

            if execution_id is None:
                # Don't pass execution ID for first run.
                request_params = {
                    "flowIdentifier": flow_id,
                    "flowAliasIdentifier": flow_alias_id,
                    "inputs": input_data,
                    "enableTrace": True
                }
            else:
                request_params = {
                    "flowIdentifier": flow_id,
                    "flowAliasIdentifier": flow_alias_id,
                    "executionId": execution_id,
                    "inputs": input_data,
                    "enableTrace": True
                }

            response = self.agents_runtime_client.invoke_flow(**request_params)

            if "executionId" not in request_params:
                execution_id = response['executionId']

            result = ""

            # Get the streaming response
            for event in response['responseStream']:
                result = result + str(event) + '\n'
            print(result)

        except ClientError as e:
            logger.error("Couldn't invoke flow %s.", {e})
            raise

        return result
```
+  API の詳細については、AWS SDK for Python (Boto3) API リファレンスの「[InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow)」を参照してください。**

## シナリオ
<a name="scenarios"></a>

### フローを作成して呼び出す
<a name="bedrock-agent_GettingStartedWithBedrockFlows_python_3_topic"></a>

次のコード例は、以下の操作方法を示しています。
+ フローの実行ロールを作成します。
+ フローを作成します。
+ 完全に設定されたフローをデプロイします。
+ ユーザー指定のプロンプトでフローを呼び出します。
+ すべての作成されたリソースを削除します。

**SDK for Python (Boto3)**  
 GitHub には、その他のリソースもあります。用例一覧を検索し、[AWS コード例リポジトリ](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)での設定と実行の方法を確認してください。
ユーザーが指定したジャンルと曲数に基づいて音楽プレイリストを生成します。  

```
from datetime import datetime
import logging
import boto3

from botocore.exceptions import ClientError

from roles import create_flow_role, delete_flow_role, update_role_policy
from flow import create_flow, prepare_flow, delete_flow
from run_flow import run_playlist_flow
from flow_version import create_flow_version, delete_flow_version
from flow_alias import create_flow_alias, delete_flow_alias

logging.basicConfig(
    level=logging.INFO
)
logger = logging.getLogger(__name__)

def create_input_node(name):
    """
    Creates an input node configuration for an Amazon Bedrock flow.

    The input node serves as the entry point for the flow and defines
    the initial document structure that will be passed to subsequent nodes.

    Args:
        name (str): The name of the input node.

    Returns:
        dict: The input node configuration.

    """
    return {
        "type": "Input",
        "name": name,
        "outputs": [
            {
                "name": "document",
                "type": "Object"
            }
        ]
    }


def create_prompt_node(name, model_id):
    """
    Creates a prompt node configuration for a Bedrock flow that generates music playlists.

    The prompt node defines an inline prompt template that creates a music playlist based on
    a specified genre and number of songs. The prompt uses two variables that are mapped from
    the input JSON object:
    - {{genre}}: The genre of music to create a playlist for
    - {{number}}: The number of songs to include in the playlist

    Args:
        name (str): The name of the prompt node.
        model_id (str): The identifier of the foundation model to use for the prompt.

    Returns:
        dict: The prompt node.

    """

    return {
        "type": "Prompt",
        "name": name,
        "configuration": {
            "prompt": {
                "sourceConfiguration": {
                    "inline": {
                        "modelId": model_id,
                        "templateType": "TEXT",
                        "inferenceConfiguration": {
                            "text": {
                                "temperature": 0.8
                            }
                        },
                        "templateConfiguration": {
                            "text": {
                                "text": "Make me a {{genre}} playlist consisting of the following number of songs: {{number}}."
                            }
                        }
                    }
                }
            }
        },
        "inputs": [
            {
                "name": "genre",
                "type": "String",
                "expression": "$.data.genre"
            },
            {
                "name": "number",
                "type": "Number",
                "expression": "$.data.number"
            }
        ],
        "outputs": [
            {
                "name": "modelCompletion",
                "type": "String"
            }
        ]
    }


def create_output_node(name):
    """
    Creates an output node configuration for a Bedrock flow.

    The output node validates that the output from the last node is a string
    and returns it unmodified. The input name must be "document".

    Args:
        name (str): The name of the output node.

    Returns:
        dict: The output node configuration containing the output node:

    """

    return {
        "type": "Output",
        "name": name,
        "inputs": [
            {
                "name": "document",
                "type": "String",
                "expression": "$.data"
            }
        ]
    }




def create_playlist_flow(client, flow_name, flow_description, role_arn, prompt_model_id):
    """
    Creates the playlist generator flow.
    Args:
        client: bedrock agent boto3 client.
        role_arn (str): Name for the new IAM role.
        prompt_model_id (str): The id of the model to use in the prompt node.
    Returns:
        dict: The response from the create_flow operation.
    """

    input_node = create_input_node("FlowInput")
    prompt_node = create_prompt_node("MakePlaylist", prompt_model_id)
    output_node = create_output_node("FlowOutput")

    # Create connections between the nodes
    connections = []

    #  First, create connections between the output of the flow 
    # input node and each input of the prompt node.
    for prompt_node_input in prompt_node["inputs"]:
        connections.append(
            {
                "name": "_".join([input_node["name"], prompt_node["name"],
                                   prompt_node_input["name"]]),
                "source": input_node["name"],
                "target": prompt_node["name"],
                "type": "Data",
                "configuration": {
                    "data": {
                        "sourceOutput": input_node["outputs"][0]["name"],
                        "targetInput": prompt_node_input["name"]
                    }
                }
            }
        )

    # Then, create a connection between the output of the prompt node and the input of the flow output node
    connections.append(
        {
            "name": "_".join([prompt_node["name"], output_node["name"]]),
            "source": prompt_node["name"],
            "target": output_node["name"],
            "type": "Data",
            "configuration": {
                "data": {
                    "sourceOutput": prompt_node["outputs"][0]["name"],
                    "targetInput": output_node["inputs"][0]["name"]
                }
            }
        }
    )

    flow_def = {
        "nodes": [input_node, prompt_node, output_node],
        "connections": connections
    }

    # Create the flow.

    response = create_flow(
        client, flow_name, flow_description, role_arn, flow_def)

    return response



def get_model_arn(client, model_id):
    """
    Gets the Amazon Resource Name (ARN) for a model.
    Args:
        client (str): Amazon Bedrock boto3 client.
        model_id (str): The id of the model.
    Returns:
        str: The ARN of the model.
    """

    try:
        # Call GetFoundationModelDetails operation
        response = client.get_foundation_model(modelIdentifier=model_id)

        # Extract model ARN from the response
        model_arn = response['modelDetails']['modelArn']

        return model_arn

    except ClientError as e:
        logger.exception("Client error getting model ARN: %s", {str(e)})
        raise

    except Exception as e:
        logger.exception("Unexpected error getting model ARN: %s", {str(e)})
        raise


def prepare_flow_version_and_alias(bedrock_agent_client,
                                   flow_id):
    """
    Prepares the flow and then creates a flow version and flow alias.
    Args:
        bedrock_agent_client: Amazon Bedrock Agent boto3 client.
        flowd_id (str): The ID of the flow that you want to prepare.
    Returns: The flow_version and flow_alias. 

    """

    status = prepare_flow(bedrock_agent_client, flow_id)

    flow_version = None
    flow_alias = None

    if status == 'Prepared':

        # Create the flow version and alias.
        flow_version = create_flow_version(bedrock_agent_client,
                                           flow_id,
                                           f"flow version for flow {flow_id}.")

        flow_alias = create_flow_alias(bedrock_agent_client,
                                       flow_id,
                                       flow_version,
                                       "latest",
                                       f"Alias for flow {flow_id}, version {flow_version}")

    return flow_version, flow_alias



def delete_role_resources(bedrock_agent_client,
                          iam_client,
                          role_name,
                          flow_id,
                          flow_version,
                          flow_alias):
    """
    Deletes the flow, flow alias, flow version, and IAM roles.
    Args:
        bedrock_agent_client: Amazon Bedrock Agent boto3 client.
        iam_client: Amazon IAM boto3 client.
        role_name (str): The name of the IAM role.
        flow_id (str): The id of the flow.
        flow_version (str): The version of the flow.
        flow_alias (str): The alias of the flow.
    """

    if flow_id is not None:
        if flow_alias is not None:
            delete_flow_alias(bedrock_agent_client, flow_id, flow_alias)
        if flow_version is not None:
            delete_flow_version(bedrock_agent_client,
                        flow_id, flow_version)
        delete_flow(bedrock_agent_client, flow_id)
    
    if role_name is not None:
        delete_flow_role(iam_client, role_name)



def main():
    """
    Creates, runs, and optionally deletes a Bedrock flow for generating music playlists.

    Note:
        Requires valid AWS credentials in the default profile
    """

    delete_choice = "y"
    try:

        # Get various boto3 clients.
        session = boto3.Session(profile_name='default')
        bedrock_agent_runtime_client = session.client('bedrock-agent-runtime')
        bedrock_agent_client = session.client('bedrock-agent')
        bedrock_client = session.client('bedrock')
        iam_client = session.client('iam')
        
        role_name = None
        flow_id = None
        flow_version = None
        flow_alias = None

        #Change the model as needed.
        prompt_model_id = "amazon.nova-pro-v1:0"

        # Base the flow name on the current date and time
        current_time = datetime.now()
        timestamp = current_time.strftime("%Y-%m-%d-%H-%M-%S")
        flow_name = f"FlowPlayList_{timestamp}"
        flow_description = "A flow to generate a music playlist."

        # Create a role for the flow.
        role_name = f"BedrockFlowRole-{flow_name}"
        role = create_flow_role(iam_client, role_name)
        role_arn = role['Arn']

        # Create the flow.
        response = create_playlist_flow(
            bedrock_agent_client, flow_name, flow_description, role_arn, prompt_model_id)
        flow_id = response.get('id')

        if flow_id:
            # Update accessible resources in the role.
            model_arn = get_model_arn(bedrock_client, prompt_model_id)
            update_role_policy(iam_client, role_name, [
                               response.get('arn'), model_arn])

            # Prepare the flow and flow version.
            flow_version, flow_alias = prepare_flow_version_and_alias(
                bedrock_agent_client, flow_id)

            # Run the flow.
            if flow_version and flow_alias:
                run_playlist_flow(bedrock_agent_runtime_client,
                                  flow_id, flow_alias)

                delete_choice = input("Delete flow? y or n : ").lower()


            else:
                print("Couldn't run. Deleting flow and role.")
                delete_flow(bedrock_agent_client, flow_id)
                delete_flow_role(iam_client, role_name)
        else:
            print("Couldn't create flow.")


    except Exception as e:
        print(f"Fatal error: {str(e)}")
    
    finally:
        if delete_choice == 'y':
                delete_role_resources(bedrock_agent_client,
                                          iam_client,
                                          role_name,
                                          flow_id,
                                          flow_version,
                                          flow_alias)
        else:
            print("Flow not deleted. ")
            print(f"\tFlow ID: {flow_id}")
            print(f"\tFlow version: {flow_version}")
            print(f"\tFlow alias: {flow_alias}")
            print(f"\tRole ARN: {role_arn}")
       
        print("Done!")
 
if __name__ == "__main__":
    main()


def invoke_flow(client, flow_id, flow_alias_id, input_data):
    """
    Invoke an Amazon Bedrock flow and handle the response stream.

    Args:
        client: Boto3 client for Amazon Bedrock agent runtime.
        flow_id: The ID of the flow to invoke.
        flow_alias_id: The alias ID of the flow.
        input_data: Input data for the flow.

    Returns:
        Dict containing flow status and flow output.
    """

    response = None
    request_params = None

    request_params = {
            "flowIdentifier": flow_id,
            "flowAliasIdentifier": flow_alias_id,
            "inputs": [input_data],
            "enableTrace": True
        }


    response = client.invoke_flow(**request_params)

    flow_status = ""
    output= ""

    # Process the streaming response
    for event in response['responseStream']:

        # Check if flow is complete.
        if 'flowCompletionEvent' in event:
            flow_status = event['flowCompletionEvent']['completionReason']

        # Save the model output.
        elif 'flowOutputEvent' in event:
            output = event['flowOutputEvent']['content']['document']
            logger.info("Output : %s", output)

        # Log trace events.
        elif 'flowTraceEvent' in event:
            logger.info("Flow trace:  %s", event['flowTraceEvent'])
    
    return {
        "flow_status": flow_status,
        "output": output

    }




def run_playlist_flow(bedrock_agent_client, flow_id, flow_alias_id):
    """
    Runs the playlist generator flow.

    Args:
        bedrock_agent_client: Boto3 client for Amazon Bedrock agent runtime.
        flow_id: The ID of the flow to run.
        flow_alias_id: The alias ID of the flow.

    """


    print ("Welcome to the playlist generator flow.")
    # Get the initial prompt from the user.
    genre = input("Enter genre: ")
    number_of_songs = int(input("Enter number of songs: "))


    # Use prompt to create input data for the input node.
    flow_input_data = {
        "content": {
            "document": {
                "genre" : genre,
                "number" : number_of_songs
            }
        },
        "nodeName": "FlowInput",
        "nodeOutputName": "document"
    }

    try:

        result = invoke_flow(
                bedrock_agent_client, flow_id, flow_alias_id, flow_input_data)

        status = result['flow_status']
  
        if status == "SUCCESS":
                # The flow completed successfully.
                logger.info("The flow %s successfully completed.", flow_id)
                print(result['output'])
        else:
            logger.warning("Flow status: %s",status)

    except ClientError as e:
        print(f"Client error: {str(e)}")
        logger.error("Client error: %s", {str(e)})
        raise

    except Exception as e:
        logger.error("An error occurred: %s", {str(e)})
        logger.error("Error type: %s", {type(e)})
        raise



def create_flow_role(client, role_name):
    """
    Creates an IAM role for Amazon Bedrock with permissions to run a flow.
    
    Args:
        role_name (str): Name for the new IAM role.
    Returns:
        str: The role Amazon Resource Name.
    """

    
    # Trust relationship policy - allows Amazon Bedrock service to assume this role.
    trust_policy = {
        "Version":"2012-10-17",		 	 	 
        "Statement": [{
            "Effect": "Allow",
            "Principal": {
                "Service": "bedrock.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }]
    }
    
    # Basic inline policy for for running a flow.

    resources = "*"

    bedrock_policy = {
        "Version":"2012-10-17",		 	 	 
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "bedrock:InvokeModel",
                    "bedrock:Retrieve",
                    "bedrock:RetrieveAndGenerate"
                ],
                # Using * as placeholder - Later you update with specific ARNs.
                "Resource": resources
            }
        ]
    }


    
    try:
        # Create the IAM role with trust policy
        logging.info("Creating role: %s",role_name)
        role = client.create_role(
            RoleName=role_name,
            AssumeRolePolicyDocument=json.dumps(trust_policy),
            Description="Role for Amazon Bedrock operations"
        )
        
        # Attach inline policy to the role
        print("Attaching inline policy")
        client.put_role_policy(
            RoleName=role_name,
            PolicyName=f"{role_name}-policy",
            PolicyDocument=json.dumps(bedrock_policy)
        )
        
        logging.info("Create Role ARN: %s", role['Role']['Arn'])
        return role['Role']
        
    except ClientError as e:
        logging.warning("Error creating role: %s", str(e))
        raise
    except Exception as e:
        logging.warning("Unexpected error: %s", str(e))
        raise


def update_role_policy(client, role_name, resource_arns):
    """
    Updates an IAM role's inline policy with specific resource ARNs.
    
    Args:
        role_name (str): Name of the existing role.
        resource_arns (list): List of resource ARNs to allow access to.
    """

    
    updated_policy = {
        "Version":"2012-10-17",		 	 	 
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "bedrock:GetFlow",
                    "bedrock:InvokeModel",
                    "bedrock:Retrieve",
                    "bedrock:RetrieveAndGenerate"
                ],
                "Resource": resource_arns
            }
        ]
    }
    
    try:
        client.put_role_policy(
            RoleName=role_name,
            PolicyName=f"{role_name}-policy",
            PolicyDocument=json.dumps(updated_policy)
        )
        logging.info("Updated policy for role: %s",role_name)
        
    except ClientError as e:
        logging.warning("Error updating role policy: %s", str(e))
        raise


def delete_flow_role(client, role_name):
    """
    Deletes an IAM role.

    Args:
        role_name (str): Name of the role to delete.
    """



    try:
        # Detach and delete inline policies
        policies = client.list_role_policies(RoleName=role_name)['PolicyNames']
        for policy_name in policies:
            client.delete_role_policy(RoleName=role_name, PolicyName=policy_name)

        # Delete the role
        client.delete_role(RoleName=role_name)
        logging.info("Deleted role: %s", role_name)


    except ClientError as e:
        logging.info("Error Deleting role: %s", str(e))
        raise
```
+ API の詳細については、『*AWS SDK for Python (Boto3) API リファレンス*』の以下のトピックを参照してください。
  + [CreateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlow)
  + [CreateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowAlias)
  + [CreateFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowVersion)
  + [DeleteFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlow)
  + [DeleteFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowAlias)
  + [DeleteFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowVersion)
  + [GetFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlow)
  + [GetFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowAlias)
  + [GetFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowVersion)
  + [InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow)
  + [PrepareFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareFlow)

### Step Functions を使用して生成 AI アプリケーションをオーケストレーションする
<a name="cross_ServerlessPromptChaining_python_3_topic"></a>

次のコード例は、Amazon Bedrock と Step Functions を使用して生成 AI アプリケーションを構築およびオーケストレーションする方法を示しています。

**SDK for Python (Boto3)**  
 Amazon Bedrock Serverless のプロンプトチェイニングシナリオは、[AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html)、[Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)、および [https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) を使用して、複雑でサーバーレス、高度にスケーラブルな生成 AI アプリケーションを構築およびオーケストレーションする方法を示しています。これには、次の実際の例が含まれています。  
+  文学ブログの特定の小説の分析を行う。この例では、プロンプトのシンプルでシーケンシャルなチェーンを示しています。
+  特定のトピックに関する短いストーリーを生成する。この例では、AI が以前に生成した項目のリストを繰り返し処理する方法を示しています。
+  特定の目的地への週末の旅程を作成する。この例では、複数の個別のプロンプトを並列化する方法を示しています。
+  映画のプロデューサーに映画のアイデアを提案する。この例では、異なる推論パラメータを使用して同じプロンプトを並列化する方法、チェーン内の前のステップにバックトラックする方法、ワークフローの一部として人間の入力を含める方法を示しています。
+  ユーザーの手元にある材料に基づいて料理を計画する。この例では、プロンプトチェーンが 2 つの異なる AI 会話を組み込んで、2 つの AI ペルソナが相互に議論を行い、最終的な結果を改善する方法を示しています。
+  当日中で最も人気のある GitHub リポジトリを検索して要約する。この例では、外部 API とやり取りする複数の AI エージェントをチェーンさせる方法を示しています。
 完全なソースコードと設定および実行の手順については、[GitHub](https://github.com/aws-samples/amazon-bedrock-serverless-prompt-chaining) で完全なプロジェクトを参照してください。  

**この例で使用されているサービス**
+ Amazon Bedrock
+ Amazon Bedrock ランタイム
+ Amazon Bedrock エージェント
+ Amazon Bedrock エージェントランタイム
+ ステップ関数