Invoking Amazon Bedrock Prompt Flows with Python

When building complex or multi-step generative applications, Amazon Bedrock’s prompt flows provide a robust solution for orchestrating processes that may involve multiple AWS services or conditional logic. This guide demonstrates how to invoke these prompt flows using Python, focusing on integration within AWS Lambda, although the principles apply to any Python-capable execution environment.

Understanding Amazon Bedrock Prompt Flows

Prompt flows in Amazon Bedrock allow developers to orchestrate sophisticated processes that combine multiple services, incorporate conditional routing, and handle various user inputs in one streamlined flow. For example, a customer service application might use prompt flows to dynamically fetch data, process user queries through AI models, and execute actions based on user interactions.

Implementing the Lambda Function

The following Python code illustrates how to set up an AWS Lambda function to invoke a prompt flow within Amazon Bedrock. The example is simplified for clarity and ease of use.

Lambda Function Code

import boto3
import json
import logging

# Configure logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    client = boto3.client('bedrock-agent-runtime', region_name='us-east-1')
    request_body = setup_request_parameters()

    try:
        response = client.invoke_flow(**request_body)
        response_data = extract_data_from_response_stream(response['responseStream'])
        return create_response(200, response_data)
    except Exception as e:
        logger.error(f"Error invoking flow: {str(e)}")
        return create_response(500, {'error': str(e)})

def setup_request_parameters():
    """ Prepare the parameters for the invoke_flow request """
    return {
        "flowAliasIdentifier": 'ExampleAlias',  
        "flowIdentifier": 'ExampleFlowID',            
        "inputs": [{
            "content": {"document": "This is my input data"},
            "nodeName": 'StartNode',                   
            "nodeOutputName": 'ResponseNode'           
        }]
    }

def extract_data_from_response_stream(events):
    """ Process the event stream and extract useful data """
    results = []
    for event in events:
        if 'flowOutputEvent' in event:
            results.append({'type': 'Output', 'data': event['flowOutputEvent']['content']['document']})
        elif 'flowCompletionEvent' in event:
            results.append({'type': 'Completion', 'reason': event['flowCompletionEvent']['completionReason']})
    return results

def create_response(status_code, data):
    """ Format the HTTP response """
    return {
        'statusCode': status_code,
        'body': json.dumps(data)
    }

Detailed Explanation

1. Client Initialization: The boto3.client function initializes a client for the bedrock-agent-runtime. This client is used to call the invoke_flow method, which executes the prompt flow.
2. Request Setup: setup_request_parameters function prepares the necessary parameters to invoke the prompt flow, including identifiers and input data.
3. Response Handling: The invoke_flow method returns a response that includes an event stream (responseStream). The extract_data_from_response_stream function processes this stream to extract meaningful data, such as outputs from different nodes and completion events.
4. Error Management: The code includes error handling to log issues and return an error response if the flow invocation fails.

This example demonstrates how to invoke Amazon Bedrock prompt flows from an AWS Lambda function using Python, making it applicable for both cloud-based and local Python environments. By integrating such flows, developers can leverage the full power of Amazon Bedrock to create sophisticated, AI-driven applications.