playlist_flow.py

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

"""
Amazon Bedrock Flow Generator for Music Playlists

This module implements functionality to create, run, and manage an Amazon Bedrock flow
that generates music playlists. The flow consists of three nodes:
- Input node: Accepts genre and number of songs as input
- Prompt node: Uses a foundation model to generate a playlist
- Output node: Returns the generated playlist

The flow takes a music genre and desired number of songs as input and returns
a generated playlist using the specified foundation model.

The script will:
1. Create necessary IAM roles and permissions
2. Create and configure the Bedrock flow
3. Run the flow with user-provided genre and song count
4. Optionally delete the flow and cleanup resources

"""

# snippet-start:[python.example_code.bedrock-agent-runtime.Scenario_GettingStartedBedrockFlows]
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__)

# snippet-start:[python.example_code.bedrock-agent-runtime.create_input_node]
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"
            }
        ]
    }

# snippet-end:[python.example_code.bedrock-agent-runtime.create_input_node]

# snippet-start:[python.example_code.bedrock-agent-runtime.create_prompt_node]
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"
            }
        ]
    }

# snippet-end:[python.example_code.bedrock-agent-runtime.create_prompt_node]

# snippet-start:[python.example_code.bedrock-agent-runtime.create_output_node]
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"
            }
        ]
    }

# snippet-end:[python.example_code.bedrock-agent-runtime.create_output_node]


# snippet-start:[python.example_code.bedrock-agent-runtime.create_playlist_flow]

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

# snippet-end:[python.example_code.bedrock-agent-runtime.create_playlist_flow]


# snippet-start:[python.example_code.bedrock-agent-runtime.create_get_model_arn]
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
# snippet-end:[python.example_code.bedrock-agent-runtime.create_get_model_arn]


# snippet-start:[python.example_code.bedrock-agent-runtime.flow_prepare_version_alias]
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

# snippet-end:[python.example_code.bedrock-agent-runtime.flow_prepare_version_alias]

# snippet-start:[python.example_code.bedrock-agent-runtime.flow_delete_resources]

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)

# snippet-end:[python.example_code.bedrock-agent-runtime.flow_delete_resources]


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()

# snippet-end:[python.example_code.bedrock-agent-runtime.Scenario_GettingStartedBedrockFlows]