Ein end-to-end Beispiel, das zeigt, wie Amazon Bedrock Agents mit einem erstellt und aufgerufen werden AWS SDK - Amazon Bedrock

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Ein end-to-end Beispiel, das zeigt, wie Amazon Bedrock Agents mit einem erstellt und aufgerufen werden AWS SDK

Wie das aussehen kann, sehen Sie am nachfolgenden Beispielcode:

  • Erstellen Sie eine Ausführungsrolle für den Agenten.

  • Erstellen Sie den Agenten und stellen Sie eine DRAFT Version bereit.

  • Erstellen Sie eine Lambda-Funktion, die die Funktionen des Agenten implementiert.

  • Erstellen Sie eine Aktionsgruppe, die den Agenten mit der Lambda-Funktion verbindet.

  • Stellen Sie den vollständig konfigurierten Agenten bereit.

  • Rufen Sie den Agenten mit vom Benutzer bereitgestellten Eingabeaufforderungen auf.

  • Löscht alle erstellten Ressourcen.

Python
SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Das vollständige Beispiel und Informationen zur Einrichtung und Ausführung finden Sie in AWS Repository mit Codebeispielen.

Erstellen Sie einen Agenten und rufen Sie ihn auf.

REGION = "us-east-1"
ROLE_POLICY_NAME = "agent_permissions"


class BedrockAgentScenarioWrapper:
    """Runs a scenario that shows how to get started using Amazon Bedrock Agents."""

    def __init__(
            self, bedrock_agent_client, runtime_client, lambda_client, iam_resource, postfix
    ):
        self.iam_resource = iam_resource
        self.lambda_client = lambda_client
        self.bedrock_agent_runtime_client = runtime_client
        self.postfix = postfix

        self.bedrock_wrapper = BedrockAgentWrapper(bedrock_agent_client)

        self.agent = None
        self.agent_alias = None
        self.agent_role = None
        self.prepared_agent_details = None
        self.lambda_role = None
        self.lambda_function = None

    def run_scenario(self):
        print("=" * 88)
        print("Welcome to the Amazon Bedrock Agents demo.")
        print("=" * 88)

        # Query input from user
        print("Let's start with creating an agent:")
        print("-" * 40)
        name, foundation_model = self._request_name_and_model_from_user()
        print("-" * 40)

        # Create an execution role for the agent
        self.agent_role = self._create_agent_role(foundation_model)

        # Create the agent
        self.agent = self._create_agent(name, foundation_model)

        # Prepare a DRAFT version of the agent
        self.prepared_agent_details = self._prepare_agent()

        # Create the agent's Lambda function
        self.lambda_function = self._create_lambda_function()

        # Configure permissions for the agent to invoke the Lambda function
        self._allow_agent_to_invoke_function()
        self._let_function_accept_invocations_from_agent()

        # Create an action group to connect the agent with the Lambda function
        self._create_agent_action_group()

        # If the agent has been modified or any components have been added, prepare the agent again
        components = [self._get_agent()]
        components += self._get_agent_action_groups()
        components += self._get_agent_knowledge_bases()

        latest_update = max(component["updatedAt"] for component in components)
        if latest_update > self.prepared_agent_details["preparedAt"]:
            self.prepared_agent_details = self._prepare_agent()

        # Create an agent alias
        self.agent_alias = self._create_agent_alias()

        # Test the agent
        self._chat_with_agent(self.agent_alias)

        print("=" * 88)
        print("Thanks for running the demo!\n")

        if q.ask("Do you want to delete the created resources? [y/N] ", q.is_yesno):
            self._delete_resources()
            print("=" * 88)
            print(
                "All demo resources have been deleted. Thanks again for running the demo!"
            )
        else:
            self._list_resources()
            print("=" * 88)
            print("Thanks again for running the demo!")

    def _request_name_and_model_from_user(self):
        existing_agent_names = [
            agent["agentName"] for agent in self.bedrock_wrapper.list_agents()
        ]

        while True:
            name = q.ask("Enter an agent name: ", self.is_valid_agent_name)
            if name.lower() not in [n.lower() for n in existing_agent_names]:
                break
            print(
                f"Agent {name} conflicts with an existing agent. Please use a different name."
            )

        models = ["anthropic.claude-instant-v1", "anthropic.claude-v2"]
        model_id = models[
            q.choose("Which foundation model would you like to use? ", models)
        ]

        return name, model_id

    def _create_agent_role(self, model_id):
        role_name = f"AmazonBedrockExecutionRoleForAgents_{self.postfix}"
        model_arn = f"arn:aws:bedrock:{REGION}::foundation-model/{model_id}*"

        print("Creating an an execution role for the agent...")

        try:
            role = self.iam_resource.create_role(
                RoleName=role_name,
                AssumeRolePolicyDocument=json.dumps(
                    {
                        "Version": "2012-10-17",
                        "Statement": [
                            {
                                "Effect": "Allow",
                                "Principal": {"Service": "bedrock.amazonaws.com"},
                                "Action": "sts:AssumeRole",
                            }
                        ],
                    }
                ),
            )

            role.Policy(ROLE_POLICY_NAME).put(
                PolicyDocument=json.dumps(
                    {
                        "Version": "2012-10-17",
                        "Statement": [
                            {
                                "Effect": "Allow",
                                "Action": "bedrock:InvokeModel",
                                "Resource": model_arn,
                            }
                        ],
                    }
                )
            )
        except ClientError as e:
            logger.error(f"Couldn't create role {role_name}. Here's why: {e}")
            raise

        return role

    def _create_agent(self, name, model_id):
        print("Creating the agent...")

        instruction = """
            You are a friendly chat bot. You have access to a function called that returns
            information about the current date and time. When responding with date or time,
            please make sure to add the timezone UTC.
            """
        agent = self.bedrock_wrapper.create_agent(
            agent_name=name,
            foundation_model=model_id,
            instruction=instruction,
            role_arn=self.agent_role.arn,
        )
        self._wait_for_agent_status(agent["agentId"], "NOT_PREPARED")

        return agent

    def _prepare_agent(self):
        print("Preparing the agent...")

        agent_id = self.agent["agentId"]
        prepared_agent_details = self.bedrock_wrapper.prepare_agent(agent_id)
        self._wait_for_agent_status(agent_id, "PREPARED")

        return prepared_agent_details

    def _create_lambda_function(self):
        print("Creating the Lambda function...")

        function_name = f"AmazonBedrockExampleFunction_{self.postfix}"

        self.lambda_role = self._create_lambda_role()

        try:
            deployment_package = self._create_deployment_package(function_name)

            lambda_function = self.lambda_client.create_function(
                FunctionName=function_name,
                Description="Lambda function for Amazon Bedrock example",
                Runtime="python3.11",
                Role=self.lambda_role.arn,
                Handler=f"{function_name}.lambda_handler",
                Code={"ZipFile": deployment_package},
                Publish=True,
            )

            waiter = self.lambda_client.get_waiter("function_active_v2")
            waiter.wait(FunctionName=function_name)

        except ClientError as e:
            logger.error(
                f"Couldn't create Lambda function {function_name}. Here's why: {e}"
            )
            raise

        return lambda_function

    def _create_lambda_role(self):
        print("Creating an execution role for the Lambda function...")

        role_name = f"AmazonBedrockExecutionRoleForLambda_{self.postfix}"

        try:
            role = self.iam_resource.create_role(
                RoleName=role_name,
                AssumeRolePolicyDocument=json.dumps(
                    {
                        "Version": "2012-10-17",
                        "Statement": [
                            {
                                "Effect": "Allow",
                                "Principal": {"Service": "lambda.amazonaws.com"},
                                "Action": "sts:AssumeRole",
                            }
                        ],
                    }
                ),
            )
            role.attach_policy(
                PolicyArn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
            )
            print(f"Created role {role_name}")
        except ClientError as e:
            logger.error(f"Couldn't create role {role_name}. Here's why: {e}")
            raise

        print("Waiting for the execution role to be fully propagated...")
        wait(10)

        return role

    def _allow_agent_to_invoke_function(self):
        policy = self.iam_resource.RolePolicy(
            self.agent_role.role_name, ROLE_POLICY_NAME
        )
        doc = policy.policy_document
        doc["Statement"].append(
            {
                "Effect": "Allow",
                "Action": "lambda:InvokeFunction",
                "Resource": self.lambda_function["FunctionArn"],
            }
        )
        self.agent_role.Policy(ROLE_POLICY_NAME).put(PolicyDocument=json.dumps(doc))

    def _let_function_accept_invocations_from_agent(self):
        try:
            self.lambda_client.add_permission(
                FunctionName=self.lambda_function["FunctionName"],
                SourceArn=self.agent["agentArn"],
                StatementId="BedrockAccess",
                Action="lambda:InvokeFunction",
                Principal="bedrock.amazonaws.com",
            )
        except ClientError as e:
            logger.error(
                f"Couldn't grant Bedrock permission to invoke the Lambda function. Here's why: {e}"
            )
            raise

    def _create_agent_action_group(self):
        print("Creating an action group for the agent...")

        try:
            with open("./scenario_resources/api_schema.yaml") as file:
                self.bedrock_wrapper.create_agent_action_group(
                    name="current_date_and_time",
                    description="Gets the current date and time.",
                    agent_id=self.agent["agentId"],
                    agent_version=self.prepared_agent_details["agentVersion"],
                    function_arn=self.lambda_function["FunctionArn"],
                    api_schema=json.dumps(yaml.safe_load(file)),
                )
        except ClientError as e:
            logger.error(f"Couldn't create agent action group. Here's why: {e}")
            raise

    def _get_agent(self):
        return self.bedrock_wrapper.get_agent(self.agent["agentId"])

    def _get_agent_action_groups(self):
        return self.bedrock_wrapper.list_agent_action_groups(
            self.agent["agentId"], self.prepared_agent_details["agentVersion"]
        )

    def _get_agent_knowledge_bases(self):
        return self.bedrock_wrapper.list_agent_knowledge_bases(
            self.agent["agentId"], self.prepared_agent_details["agentVersion"]
        )

    def _create_agent_alias(self):
        print("Creating an agent alias...")

        agent_alias_name = "test_agent_alias"
        agent_alias = self.bedrock_wrapper.create_agent_alias(
            agent_alias_name, self.agent["agentId"]
        )

        self._wait_for_agent_status(self.agent["agentId"], "PREPARED")

        return agent_alias

    def _wait_for_agent_status(self, agent_id, status):
        while self.bedrock_wrapper.get_agent(agent_id)["agentStatus"] != status:
            wait(2)

    def _chat_with_agent(self, agent_alias):
        print("-" * 88)
        print("The agent is ready to chat.")
        print("Try asking for the date or time. Type 'exit' to quit.")

        # Create a unique session ID for the conversation
        session_id = uuid.uuid4().hex

        while True:
            prompt = q.ask("Prompt: ", q.non_empty)

            if prompt == "exit":
                break

            response = asyncio.run(self._invoke_agent(agent_alias, prompt, session_id))

            print(f"Agent: {response}")

    async def _invoke_agent(self, agent_alias, prompt, session_id):
        response = self.bedrock_agent_runtime_client.invoke_agent(
            agentId=self.agent["agentId"],
            agentAliasId=agent_alias["agentAliasId"],
            sessionId=session_id,
            inputText=prompt,
        )

        completion = ""

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

        return completion

    def _delete_resources(self):
        if self.agent:
            agent_id = self.agent["agentId"]

            if self.agent_alias:
                agent_alias_id = self.agent_alias["agentAliasId"]
                print("Deleting agent alias...")
                self.bedrock_wrapper.delete_agent_alias(agent_id, agent_alias_id)

            print("Deleting agent...")
            agent_status = self.bedrock_wrapper.delete_agent(agent_id)["agentStatus"]
            while agent_status == "DELETING":
                wait(5)
                try:
                    agent_status = self.bedrock_wrapper.get_agent(
                        agent_id, log_error=False
                    )["agentStatus"]
                except ClientError as err:
                    if err.response["Error"]["Code"] == "ResourceNotFoundException":
                        agent_status = "DELETED"

        if self.lambda_function:
            name = self.lambda_function["FunctionName"]
            print(f"Deleting function '{name}'...")
            self.lambda_client.delete_function(FunctionName=name)

        if self.agent_role:
            print(f"Deleting role '{self.agent_role.role_name}'...")
            self.agent_role.Policy(ROLE_POLICY_NAME).delete()
            self.agent_role.delete()

        if self.lambda_role:
            print(f"Deleting role '{self.lambda_role.role_name}'...")
            for policy in self.lambda_role.attached_policies.all():
                policy.detach_role(RoleName=self.lambda_role.role_name)
            self.lambda_role.delete()

    def _list_resources(self):
        print("-" * 40)
        print(f"Here is the list of created resources in '{REGION}'.")
        print("Make sure you delete them once you're done to avoid unnecessary costs.")
        if self.agent:
            print(f"Bedrock Agent:   {self.agent['agentName']}")
        if self.lambda_function:
            print(f"Lambda function: {self.lambda_function['FunctionName']}")
        if self.agent_role:
            print(f"IAM role:        {self.agent_role.role_name}")
        if self.lambda_role:
            print(f"IAM role:        {self.lambda_role.role_name}")

    @staticmethod
    def is_valid_agent_name(answer):
        valid_regex = r"^[a-zA-Z0-9_-]{1,100}$"
        return (
            answer
            if answer and len(answer) <= 100 and re.match(valid_regex, answer)
            else None,
            "I need a name for the agent, please. Valid characters are a-z, A-Z, 0-9, _ (underscore) and - (hyphen).",
        )

    @staticmethod
    def _create_deployment_package(function_name):
        buffer = io.BytesIO()
        with zipfile.ZipFile(buffer, "w") as zipped:
            zipped.write(
                "./scenario_resources/lambda_function.py", f"{function_name}.py"
            )
        buffer.seek(0)
        return buffer.read()


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    postfix = "".join(
        random.choice(string.ascii_lowercase + "0123456789") for _ in range(8)
    )
    scenario = BedrockAgentScenarioWrapper(
        bedrock_agent_client=boto3.client(
            service_name="bedrock-agent", region_name=REGION
        ),
        runtime_client=boto3.client(
            service_name="bedrock-agent-runtime", region_name=REGION
        ),
        lambda_client=boto3.client(service_name="lambda", region_name=REGION),
        iam_resource=boto3.resource("iam"),
        postfix=postfix,
    )
    try:
        scenario.run_scenario()
    except Exception as e:
        logging.exception(f"Something went wrong with the demo. Here's what: {e}")

Für eine vollständige Liste von AWS SDKEntwicklerhandbücher und Codebeispiele finden Sie unterNutzung dieses Dienstes mit einem AWS SDK. Dieses Thema enthält auch Informationen zu den ersten Schritten und Details zu früheren SDK Versionen.