使用 SDK for Python (Boto3)的 Amazon Bedrock 代理程式執行時期範例

下列程式碼範例示範如何使用 適用於 Python (Boto3) 的 AWS SDK 搭配 Amazon Bedrock Agents 執行期來執行動作和實作常見案例。

基本概念是程式碼範例，這些範例說明如何在服務內執行基本操作。

Actions 是大型程式的程式碼摘錄，必須在內容中執行。雖然動作會告訴您如何呼叫個別服務函數，但您可以在其相關情境中查看內容中的動作。

案例是向您展示如何呼叫服務中的多個函數或與其他 AWS 服務組合來完成特定任務的程式碼範例。

每個範例均包含完整原始碼的連結，您可在連結中找到如何設定和執行內容中程式碼的相關指示。

基本概念

了解基本概念

下列程式碼範例示範如何使用 InvokeFlow，與包含代理程式節點的 Amazon Bedrock 流程展開對話。

如需詳細資訊，請參閱與 Amazon Bedrock 流程展開對話。

適用於 Python 的 SDK (Boto3)

注意

GitHub 上提供更多範例。尋找完整範例，並了解如何在 AWS 程式碼範例儲存庫中設定和執行。

"""
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()

• 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》中的 InvokeFlow。

動作

InvokeAgent

以下程式碼範例顯示如何使用 InvokeAgent。

適用於 Python 的 SDK (Boto3)

注意

GitHub 上提供更多範例。尋找完整範例，並了解如何在 AWS 程式碼範例儲存庫中設定和執行。

調用代理程式。

    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

• 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》中的 InvokeAgent。

InvokeFlow

以下程式碼範例顯示如何使用 InvokeFlow。

適用於 Python 的 SDK (Boto3)

注意

GitHub 上提供更多範例。尋找完整範例，並了解如何在 AWS 程式碼範例儲存庫中設定和執行。

調用流程。

    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

• 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》中的 InvokeFlow。

案例

建立和調用流程

以下程式碼範例顯示做法：

• 為流程建立執行角色。

• 建立流程。

• 部署完整設定的流程。

• 利用使用者提供的提示調用流程。

• 刪除所有建立的資源。

適用於 Python 的 SDK (Boto3)

注意

GitHub 上提供更多範例。尋找完整範例，並了解如何在 AWS 程式碼範例儲存庫中設定和執行。

根據使用者指定的類型和歌曲數量，產生音樂播放清單。

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

• 如需 API 詳細資訊，請參閱《適用於 Python (Boto3) 的AWS SDK API 參考》中的下列主題。

  • CreateFlow

  • CreateFlowAlias

  • CreateFlowVersion

  • DeleteFlow

  • DeleteFlowAlias

  • DeleteFlowVersion

  • GetFlow

  • GetFlowAlias

  • GetFlowVersion

  • InvokeFlow

  • PrepareFlow

使用 Step Functions 協調生成式 AI 應用程式

下列程式碼範例示範如何使用 Amazon Bedrock 和 Step Functions，建置和協調生成式 AI 應用程式。

適用於 Python 的 SDK (Boto3)

Amazon Bedrock Serverless 提示鏈接案例展示，AWS Step Functions、Amazon Bedrock 和 https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html 如何用於建置和協調複雜、無伺服器和可高度擴展的生成式 AI 應用程式。該案例包含下列工作範例：

• 針對文獻部落格撰寫指定小說的分析。此範例描述簡易、循序的提示鏈。

• 產生有關指定主題的簡短故事。此範例描述 AI 如何反覆處理先前產生的項目清單。

• 建立前往指定目的地的週末假期行程。此範例描述如何平行處理多個不同的提示。

• 向擔任電影製片的人類使用者推銷電影創意。此範例描述如何使用不同的推論參數平行處理相同的提示、如何回溯到鏈接的上一個步驟，以及如何將人工輸入包含在工作流程中。

• 根據使用者手上的配料來規劃用餐。此範例描述提示鏈如何整合兩個不同的 AI 對話，其中兩個 AI 角色互相爭論以改善最終結果。

• 尋找並總結目前最熱門的 GitHub 儲存庫。此範例說明鏈接多個與外部 API 互動的 AI 代理程式。

如需完整的原始碼，以及有關如何設定及執行的指示，請參閱 GitHub 上的完整專案。

此範例中使用的服務

• Amazon Bedrock

• Amazon Bedrock 執行時期

• Amazon Bedrock 代理程式

• Amazon Bedrock 代理程式執行時期

• 步驟函數