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Exemplos de Step Functions usando SDK para Python (Boto3)
Os exemplos de código a seguir mostram como realizar ações e implementar cenários comuns usando o AWS SDK for Python (Boto3) with Step Functions.
As noções básicas são exemplos de código que mostram como realizar as operações essenciais em um serviço.
Ações são trechos de código de programas maiores e devem ser executadas em contexto. Embora as ações mostrem como chamar funções de serviço individuais, é possível ver as ações no contexto em seus cenários relacionados.
Os cenários são exemplos de código que mostram como realizar tarefas específicas chamando várias funções dentro de um serviço ou combinadas com outros Serviços da AWS.
Cada exemplo inclui um link para o código-fonte completo, onde você pode encontrar instruções sobre como configurar e executar o código no contexto.
Conceitos básicos
Os exemplos de código a seguir mostram como começar a usar o Step Functions.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. import boto3 def hello_stepfunctions(stepfunctions_client): """ Use the AWS SDK for Python (Boto3) to create an AWS Step Functions client and list the state machines in your account. This list might be empty if you haven't created any state machines. This example uses the default settings specified in your shared credentials and config files. :param stepfunctions_client: A Boto3 Step Functions Client object. """ print("Hello, Step Functions! Let's list up to 10 of your state machines:") state_machines = stepfunctions_client.list_state_machines(maxResults=10) for sm in state_machines["stateMachines"]: print(f"\t{sm['name']}: {sm['stateMachineArn']}") if __name__ == "__main__": hello_stepfunctions(boto3.client("stepfunctions"))
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Para API obter detalhes, consulte a ListStateMachinesReferência AWS SDK do Python (Boto3). API
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Conceitos básicos
O exemplo de código a seguir mostra como:
Criar uma atividade.
Criar uma máquina de estado a partir de uma definição da Amazon States Language que contenha a atividade criada anteriormente como uma etapa.
Executar a máquina de estado e respondar à atividade com entrada do usuário.
Obtenha o status e a saída finais após a conclusão da execução e, em seguida, limpe os recursos.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. Execute um cenário interativo em um prompt de comando.
class StateMachineScenario: """Runs an interactive scenario that shows how to get started using Step Functions.""" def __init__(self, activity, state_machine, iam_client): """ :param activity: An object that wraps activity actions. :param state_machine: An object that wraps state machine actions. :param iam_client: A Boto3 AWS Identity and Access Management (IAM) client. """ self.activity = activity self.state_machine = state_machine self.iam_client = iam_client self.state_machine_role = None def prerequisites(self, state_machine_role_name): """ Finds or creates an IAM role that can be assumed by Step Functions. A role of this kind is required to create a state machine. The state machine used in this example does not call any additional services, so it needs no additional permissions. :param state_machine_role_name: The name of the role. :return: Data about the role. """ trust_policy = { "Version": "2012-10-17", "Statement": [ { "Sid": "", "Effect": "Allow", "Principal": {"Service": "states.amazonaws.com"}, "Action": "sts:AssumeRole", } ], } try: role = self.iam_client.get_role(RoleName=state_machine_role_name) print(f"Prerequisite IAM role {state_machine_role_name} already exists.") except ClientError as err: if err.response["Error"]["Code"] == "NoSuchEntity": role = None else: logger.error( "Couldn't get prerequisite IAM role %s. Here's why: %s: %s", state_machine_role_name, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise if role is None: try: role = self.iam_client.create_role( RoleName=state_machine_role_name, AssumeRolePolicyDocument=json.dumps(trust_policy), ) except ClientError as err: logger.error( "Couldn't create prerequisite IAM role %s. Here's why: %s: %s", state_machine_role_name, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise self.state_machine_role = role["Role"] def find_or_create_activity(self, activity_name): """ Finds or creates a Step Functions activity. :param activity_name: The name of the activity. :return: The Amazon Resource Name (ARN) of the activity. """ print("First, let's set up an activity and state machine.") activity_arn = self.activity.find(activity_name) if activity_arn is None: activity_arn = self.activity.create(activity_name) print( f"Activity {activity_name} created. Its Amazon Resource Name (ARN) is " f"{activity_arn}." ) else: print(f"Activity {activity_name} already exists.") return activity_arn def find_or_create_state_machine( self, state_machine_name, activity_arn, state_machine_file ): """ Finds or creates a Step Functions state machine. :param state_machine_name: The name of the state machine. :param activity_arn: The ARN of an activity that is used as a step in the state machine. This ARN is injected into the state machine definition that's used to create the state machine. :param state_machine_file: The path to a file containing the state machine definition. :return: The ARN of the state machine. """ state_machine_arn = self.state_machine.find(state_machine_name) if state_machine_arn is None: with open(state_machine_file) as state_machine_file: state_machine_def = state_machine_file.read().replace( "{{DOC_EXAMPLE_ACTIVITY_ARN}}", activity_arn ) state_machine_arn = self.state_machine.create( state_machine_name, state_machine_def, self.state_machine_role["Arn"], ) print(f"State machine {state_machine_name} created.") else: print(f"State machine {state_machine_name} already exists.") print("-" * 88) print(f"Here's some information about state machine {state_machine_name}:") state_machine_info = self.state_machine.describe(state_machine_arn) for field in ["name", "status", "stateMachineArn", "roleArn"]: print(f"\t{field}: {state_machine_info[field]}") return state_machine_arn def run_state_machine(self, state_machine_arn, activity_arn): """ Run the state machine. The state machine used in this example is a simple chat simulation. It contains an activity step in a loop that is used for user interaction. When the state machine gets to the activity step, it waits for an external application to get task data and submit a response. This function acts as the activity application by getting task input and responding with user input. :param state_machine_arn: The ARN of the state machine. :param activity_arn: The ARN of the activity used as a step in the state machine. :return: The ARN of the run. """ print( f"Let's run the state machine. It's a simplistic, non-AI chat simulator " f"we'll call ChatSFN." ) user_name = q.ask("What should ChatSFN call you? ", q.non_empty) run_input = {"name": user_name} print("Starting state machine...") run_arn = self.state_machine.start(state_machine_arn, json.dumps(run_input)) action = None while action != "done": activity_task = self.activity.get_task(activity_arn) task_input = json.loads(activity_task["input"]) print(f"ChatSFN: {task_input['message']}") action = task_input["actions"][ q.choose("What now? ", task_input["actions"]) ] task_response = {"action": action} self.activity.send_task_success( activity_task["taskToken"], json.dumps(task_response) ) return run_arn def finish_state_machine_run(self, run_arn): """ Wait for the state machine run to finish, then print final status and output. :param run_arn: The ARN of the run to retrieve. """ print(f"Let's get the final output from the state machine:") status = "RUNNING" while status == "RUNNING": run_output = self.state_machine.describe_run(run_arn) status = run_output["status"] if status == "RUNNING": print( "The state machine is still running, let's wait for it to finish." ) wait(1) elif status == "SUCCEEDED": print(f"ChatSFN: {json.loads(run_output['output'])['message']}") else: print(f"Run status: {status}.") def cleanup( self, state_machine_name, state_machine_arn, activity_name, activity_arn, state_machine_role_name, ): """ Clean up resources created by this example. :param state_machine_name: The name of the state machine. :param state_machine_arn: The ARN of the state machine. :param activity_name: The name of the activity. :param activity_arn: The ARN of the activity. :param state_machine_role_name: The name of the role used by the state machine. """ if q.ask( "Do you want to delete the state machine, activity, and role created for this " "example? (y/n) ", q.is_yesno, ): self.state_machine.delete(state_machine_arn) print(f"Deleted state machine {state_machine_name}.") self.activity.delete(activity_arn) print(f"Deleted activity {activity_name}.") self.iam_client.delete_role(RoleName=state_machine_role_name) print(f"Deleted role {state_machine_role_name}.") def run_scenario(self, activity_name, state_machine_name): print("-" * 88) print("Welcome to the AWS Step Functions state machines demo.") print("-" * 88) activity_arn = self.find_or_create_activity(activity_name) state_machine_arn = self.find_or_create_state_machine( state_machine_name, activity_arn, "../../../resources/sample_files/chat_sfn_state_machine.json", ) print("-" * 88) run_arn = self.run_state_machine(state_machine_arn, activity_arn) print("-" * 88) self.finish_state_machine_run(run_arn) print("-" * 88) self.cleanup( state_machine_name, state_machine_arn, activity_name, activity_arn, self.state_machine_role["RoleName"], ) print("-" * 88) print("\nThanks for watching!") print("-" * 88) if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") try: stepfunctions_client = boto3.client("stepfunctions") iam_client = boto3.client("iam") scenario = StateMachineScenario( Activity(stepfunctions_client), StateMachine(stepfunctions_client), iam_client, ) scenario.prerequisites("doc-example-state-machine-chat") scenario.run_scenario("doc-example-activity", "doc-example-state-machine") except Exception: logging.exception("Something went wrong with the demo.")
Defina uma classe que envolva ações de máquina de estado.
class StateMachine: """Encapsulates Step Functions state machine actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def create(self, name, definition, role_arn): """ Creates a state machine with the specific definition. The state machine assumes the provided role before it starts a run. :param name: The name to give the state machine. :param definition: The Amazon States Language definition of the steps in the the state machine. :param role_arn: The Amazon Resource Name (ARN) of the role that is assumed by Step Functions when the state machine is run. :return: The ARN of the newly created state machine. """ try: response = self.stepfunctions_client.create_state_machine( name=name, definition=definition, roleArn=role_arn ) except ClientError as err: logger.error( "Couldn't create state machine %s. Here's why: %s: %s", name, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response["stateMachineArn"] def find(self, name): """ Find a state machine by name. This requires listing the state machines until one is found with a matching name. :param name: The name of the state machine to search for. :return: The ARN of the state machine if found; otherwise, None. """ try: paginator = self.stepfunctions_client.get_paginator("list_state_machines") for page in paginator.paginate(): for state_machine in page.get("stateMachines", []): if state_machine["name"] == name: return state_machine["stateMachineArn"] except ClientError as err: logger.error( "Couldn't list state machines. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise def describe(self, state_machine_arn): """ Get data about a state machine. :param state_machine_arn: The ARN of the state machine to look up. :return: The retrieved state machine data. """ try: response = self.stepfunctions_client.describe_state_machine( stateMachineArn=state_machine_arn ) except ClientError as err: logger.error( "Couldn't describe state machine %s. Here's why: %s: %s", state_machine_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response def start(self, state_machine_arn, run_input): """ Start a run of a state machine with a specified input. A run is also known as an "execution" in Step Functions. :param state_machine_arn: The ARN of the state machine to run. :param run_input: The input to the state machine, in JSON format. :return: The ARN of the run. This can be used to get information about the run, including its current status and final output. """ try: response = self.stepfunctions_client.start_execution( stateMachineArn=state_machine_arn, input=run_input ) except ClientError as err: logger.error( "Couldn't start state machine %s. Here's why: %s: %s", state_machine_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response["executionArn"] def describe_run(self, run_arn): """ Get data about a state machine run, such as its current status or final output. :param run_arn: The ARN of the run to look up. :return: The retrieved run data. """ try: response = self.stepfunctions_client.describe_execution( executionArn=run_arn ) except ClientError as err: logger.error( "Couldn't describe run %s. Here's why: %s: %s", run_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response def delete(self, state_machine_arn): """ Delete a state machine and all of its run data. :param state_machine_arn: The ARN of the state machine to delete. """ try: response = self.stepfunctions_client.delete_state_machine( stateMachineArn=state_machine_arn ) except ClientError as err: logger.error( "Couldn't delete state machine %s. Here's why: %s: %s", state_machine_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
Defina uma classe que envolva ações de atividade.
class Activity: """Encapsulates Step Function activity actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def create(self, name): """ Create an activity. :param name: The name of the activity to create. :return: The Amazon Resource Name (ARN) of the newly created activity. """ try: response = self.stepfunctions_client.create_activity(name=name) except ClientError as err: logger.error( "Couldn't create activity %s. Here's why: %s: %s", name, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response["activityArn"] def find(self, name): """ Find an activity by name. This requires listing activities until one is found with a matching name. :param name: The name of the activity to search for. :return: If found, the ARN of the activity; otherwise, None. """ try: paginator = self.stepfunctions_client.get_paginator("list_activities") for page in paginator.paginate(): for activity in page.get("activities", []): if activity["name"] == name: return activity["activityArn"] except ClientError as err: logger.error( "Couldn't list activities. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise def get_task(self, activity_arn): """ Gets task data for an activity. When a state machine is waiting for the specified activity, a response is returned with data from the state machine. When a state machine is not waiting, this call blocks for 60 seconds. :param activity_arn: The ARN of the activity to get task data for. :return: The task data for the activity. """ try: response = self.stepfunctions_client.get_activity_task( activityArn=activity_arn ) except ClientError as err: logger.error( "Couldn't get a task for activity %s. Here's why: %s: %s", activity_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response def send_task_success(self, task_token, task_response): """ Sends a success response to a waiting activity step. A state machine with an activity step waits for the activity to get task data and then respond with either success or failure before it resumes processing. :param task_token: The token associated with the task. This is included in the response to the get_activity_task action and must be sent without modification. :param task_response: The response data from the activity. This data is received and processed by the state machine. """ try: self.stepfunctions_client.send_task_success( taskToken=task_token, output=task_response ) except ClientError as err: logger.error( "Couldn't send task success. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise def delete(self, activity_arn): """ Delete an activity. :param activity_arn: The ARN of the activity to delete. """ try: response = self.stepfunctions_client.delete_activity( activityArn=activity_arn ) except ClientError as err: logger.error( "Couldn't delete activity %s. Here's why: %s: %s", activity_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
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Para API obter detalhes, consulte os tópicos a seguir em AWS SDKfor Python (Boto3) Reference. API
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Ações
O código de exemplo a seguir mostra como usar CreateActivity
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class Activity: """Encapsulates Step Function activity actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def create(self, name): """ Create an activity. :param name: The name of the activity to create. :return: The Amazon Resource Name (ARN) of the newly created activity. """ try: response = self.stepfunctions_client.create_activity(name=name) except ClientError as err: logger.error( "Couldn't create activity %s. Here's why: %s: %s", name, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response["activityArn"]
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Para API obter detalhes, consulte a CreateActivityReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar CreateStateMachine
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class StateMachine: """Encapsulates Step Functions state machine actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def create(self, name, definition, role_arn): """ Creates a state machine with the specific definition. The state machine assumes the provided role before it starts a run. :param name: The name to give the state machine. :param definition: The Amazon States Language definition of the steps in the the state machine. :param role_arn: The Amazon Resource Name (ARN) of the role that is assumed by Step Functions when the state machine is run. :return: The ARN of the newly created state machine. """ try: response = self.stepfunctions_client.create_state_machine( name=name, definition=definition, roleArn=role_arn ) except ClientError as err: logger.error( "Couldn't create state machine %s. Here's why: %s: %s", name, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response["stateMachineArn"]
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Para API obter detalhes, consulte a CreateStateMachineReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar DeleteActivity
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class Activity: """Encapsulates Step Function activity actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def delete(self, activity_arn): """ Delete an activity. :param activity_arn: The ARN of the activity to delete. """ try: response = self.stepfunctions_client.delete_activity( activityArn=activity_arn ) except ClientError as err: logger.error( "Couldn't delete activity %s. Here's why: %s: %s", activity_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
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Para API obter detalhes, consulte a DeleteActivityReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar DeleteStateMachine
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class StateMachine: """Encapsulates Step Functions state machine actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def delete(self, state_machine_arn): """ Delete a state machine and all of its run data. :param state_machine_arn: The ARN of the state machine to delete. """ try: response = self.stepfunctions_client.delete_state_machine( stateMachineArn=state_machine_arn ) except ClientError as err: logger.error( "Couldn't delete state machine %s. Here's why: %s: %s", state_machine_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
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Para API obter detalhes, consulte a DeleteStateMachineReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar DescribeExecution
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. def describe_run(self, run_arn): """ Get data about a state machine run, such as its current status or final output. :param run_arn: The ARN of the run to look up. :return: The retrieved run data. """ try: response = self.stepfunctions_client.describe_execution( executionArn=run_arn ) except ClientError as err: logger.error( "Couldn't describe run %s. Here's why: %s: %s", run_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
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Para API obter detalhes, consulte a DescribeExecutionReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar DescribeStateMachine
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class StateMachine: """Encapsulates Step Functions state machine actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def describe(self, state_machine_arn): """ Get data about a state machine. :param state_machine_arn: The ARN of the state machine to look up. :return: The retrieved state machine data. """ try: response = self.stepfunctions_client.describe_state_machine( stateMachineArn=state_machine_arn ) except ClientError as err: logger.error( "Couldn't describe state machine %s. Here's why: %s: %s", state_machine_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
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Para API obter detalhes, consulte a DescribeStateMachineReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar GetActivityTask
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class Activity: """Encapsulates Step Function activity actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def get_task(self, activity_arn): """ Gets task data for an activity. When a state machine is waiting for the specified activity, a response is returned with data from the state machine. When a state machine is not waiting, this call blocks for 60 seconds. :param activity_arn: The ARN of the activity to get task data for. :return: The task data for the activity. """ try: response = self.stepfunctions_client.get_activity_task( activityArn=activity_arn ) except ClientError as err: logger.error( "Couldn't get a task for activity %s. Here's why: %s: %s", activity_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response
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Para API obter detalhes, consulte a GetActivityTaskReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar ListActivities
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class Activity: """Encapsulates Step Function activity actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def find(self, name): """ Find an activity by name. This requires listing activities until one is found with a matching name. :param name: The name of the activity to search for. :return: If found, the ARN of the activity; otherwise, None. """ try: paginator = self.stepfunctions_client.get_paginator("list_activities") for page in paginator.paginate(): for activity in page.get("activities", []): if activity["name"] == name: return activity["activityArn"] except ClientError as err: logger.error( "Couldn't list activities. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise
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Para API obter detalhes, consulte a ListActivitiesReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar ListStateMachines
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. Encontre uma máquina de estado pelo nome pesquisando a lista de máquinas de estado da conta.
class StateMachine: """Encapsulates Step Functions state machine actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def find(self, name): """ Find a state machine by name. This requires listing the state machines until one is found with a matching name. :param name: The name of the state machine to search for. :return: The ARN of the state machine if found; otherwise, None. """ try: paginator = self.stepfunctions_client.get_paginator("list_state_machines") for page in paginator.paginate(): for state_machine in page.get("stateMachines", []): if state_machine["name"] == name: return state_machine["stateMachineArn"] except ClientError as err: logger.error( "Couldn't list state machines. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise
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Para API obter detalhes, consulte a ListStateMachinesReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar SendTaskSuccess
.
- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class Activity: """Encapsulates Step Function activity actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def send_task_success(self, task_token, task_response): """ Sends a success response to a waiting activity step. A state machine with an activity step waits for the activity to get task data and then respond with either success or failure before it resumes processing. :param task_token: The token associated with the task. This is included in the response to the get_activity_task action and must be sent without modification. :param task_response: The response data from the activity. This data is received and processed by the state machine. """ try: self.stepfunctions_client.send_task_success( taskToken=task_token, output=task_response ) except ClientError as err: logger.error( "Couldn't send task success. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise
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Para API obter detalhes, consulte a SendTaskSuccessReferência AWS SDK do Python (Boto3). API
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O código de exemplo a seguir mostra como usar StartExecution
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- SDKpara Python (Boto3)
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nota
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS
. class StateMachine: """Encapsulates Step Functions state machine actions.""" def __init__(self, stepfunctions_client): """ :param stepfunctions_client: A Boto3 Step Functions client. """ self.stepfunctions_client = stepfunctions_client def start(self, state_machine_arn, run_input): """ Start a run of a state machine with a specified input. A run is also known as an "execution" in Step Functions. :param state_machine_arn: The ARN of the state machine to run. :param run_input: The input to the state machine, in JSON format. :return: The ARN of the run. This can be used to get information about the run, including its current status and final output. """ try: response = self.stepfunctions_client.start_execution( stateMachineArn=state_machine_arn, input=run_input ) except ClientError as err: logger.error( "Couldn't start state machine %s. Here's why: %s: %s", state_machine_arn, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise else: return response["executionArn"]
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Para API obter detalhes, consulte a StartExecutionReferência AWS SDK do Python (Boto3). API
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Cenários
O exemplo de código a seguir mostra como criar um aplicativo de AWS Step Functions mensagens que recupera registros de mensagens de uma tabela de banco de dados.
- SDKpara Python (Boto3)
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Mostra como usar o AWS SDK for Python (Boto3) with AWS Step Functions para criar um aplicativo de mensagens que recupera registros de mensagens de uma tabela do Amazon DynamoDB e os envia com o Amazon Simple Queue Service (Amazon). SQS A máquina de estado se integra a uma AWS Lambda função para verificar o banco de dados em busca de mensagens não enviadas.
Crie uma máquina de estado que recupere e atualize registros de mensagens de uma tabela do Amazon DynamoDB.
Atualize a definição da máquina de estado para também enviar mensagens para o Amazon Simple Queue Service (AmazonSQS).
Inicie e interrompa execuções da máquina de estado.
Conecte-se ao Lambda, ao DynamoDB e à SQS Amazon a partir de uma máquina de estado usando integrações de serviços.
Para obter o código-fonte completo e instruções sobre como configurar e executar, veja o exemplo completo em GitHub
. Serviços usados neste exemplo
DynamoDB
Lambda
Amazon SQS
Step Functions
O exemplo de código a seguir mostra como criar e orquestrar aplicativos generativos de IA com o Amazon Bedrock e o Step Functions.
- SDKpara Python (Boto3)
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O cenário Amazon Bedrock Serverless Prompt Chaining demonstra como AWS Step Functionso Amazon Bedrock pode ser usado para criar https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html e orquestrar aplicativos de IA generativos complexos, sem servidor e altamente escaláveis. Ele contém os seguintes exemplos de trabalho:
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Escreva uma análise de um determinado romance para um blog de literatura. Este exemplo ilustra uma cadeia de solicitações simples e sequencial.
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Gere uma história curta sobre um determinado tópico. Este exemplo ilustra como a IA pode processar iterativamente uma lista de itens que ela gerou anteriormente.
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Crie um itinerário para férias de fim de semana em um determinado destino. Este exemplo ilustra como paralelizar vários prompts distintos.
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Apresente ideias de filmes para um usuário humano atuando como produtor de filmes. Este exemplo ilustra como paralelizar o mesmo prompt com diferentes parâmetros de inferência, como voltar a uma etapa anterior na cadeia e como incluir a entrada humana como parte do fluxo de trabalho.
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Planeje uma refeição com base nos ingredientes que o usuário tem em mãos. Este exemplo ilustra como cadeias imediatas podem incorporar duas conversas distintas de IA, com duas personas de IA participando de um debate entre si para melhorar o resultado final.
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Encontre e resuma o repositório mais popular GitHub da atualidade. Este exemplo ilustra o encadeamento de vários agentes de IA que interagem com agentes externos. APIs
Para obter o código-fonte completo e as instruções de configuração e execução, consulte o projeto completo em GitHub
. Serviços utilizados neste exemplo
Amazon Bedrock
Amazon Bedrock Runtime
Agentes Amazon Bedrock
Tempo de execução do Amazon Bedrock Agents
Step Functions
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