This video is a step-by-step tutorial with code as how to create Amazon Bedrock AI agents with boto3 in Python to integrate with Lambda.
Code used: Just use any lambda with it of your choice.
import logging
import boto3
import time
import yaml
import json
import io
from botocore.exceptions import ClientError
def create_agent(bedrock, agent_name, foundation_model, role_arn, instruction):
try:
# Create a low-level client with the service name
response = bedrock.create_agent(
agentName=agent_name,
foundationModel=foundation_model,
agentResourceRoleArn=role_arn,
instruction=instruction,
)
except ClientError as e:
logging.error(f"Couldn't create agent due to: {e}")
raise
else:
return response["agent"]
def create_agent_action_group(bedrock, name, description, agent_id, agent_version, function_arn, api_schema):
try:
response = bedrock.create_agent_action_group(
actionGroupName=name,
description=description,
agentId=agent_id,
agentVersion=agent_version,
actionGroupExecutor={"lambda": function_arn},
apiSchema={"payload": api_schema},
)
agent_action_group = response["agentActionGroup"]
except ClientError as e:
print(f"Error: Couldn't create agent action group. Here's why: {e}")
raise
else:
return agent_action_group
def prepare_agent(bedrock, agent_id):
try:
prepared_agent_details = bedrock.prepare_agent(agentId=agent_id)
except ClientError as e:
print(f"Couldn't prepare agent. {e}")
raise
else:
return prepared_agent_details
def create_agent_alias(bedrock, name, agent_id):
try:
response = bedrock.create_agent_alias(
agentAliasName=name, agentId=agent_id
)
agent_alias = response["agentAlias"]
except ClientError as e:
print(f"Couldn't create agent alias. {e}")
raise
else:
return agent_alias
def main():
# Define your parameters
bedrock = boto3.client(service_name='bedrock-agent',region_name='us-east-1')
agent_name = 'AstroAI'
foundation_model = 'anthropic.claude-v2'
role_arn = 'bedrock role arn'
instruction = 'Your task is to generate unique and insightful daily horoscopes for individuals \\
based on their zodiac sign. Start by analyzing the general characteristics and common \\
themes associated with each zodiac sign. Consider traits, challenges, opportunities, \\
and the emotional and physical wellbeing of individuals under each sign. Use this \\
understanding to create personalized, relevant, and engaging horoscopes that offer \\
guidance, reflection, and encouragement for the day ahead. Ensure the horoscopes \\
are varied and resonate with the unique qualities of each sign, contributing \\
positively to the users day.'
# Call the create_agent function
try:
agent = create_agent(bedrock, agent_name, foundation_model, role_arn, instruction)
agent_id = agent['agentId']
print(f"Agent created successfully: {agent_id}")
except ClientError:
print("Failed to create the agent.")
time.sleep(10)
try:
with open("api_schema.yaml") as file:
api_schema=json.dumps(yaml.safe_load(file))
name="AstroGroup"
description="AI Astrologer"
agent_version="DRAFT"
function_arn="arn:aws:lambda:us-east-1::function:horoscope"
agentgroup = create_agent_action_group(bedrock, name, description, agent_id, agent_version, function_arn, api_schema)
print(agentgroup['actionGroupId'])
except ClientError as e:
print(f"Couldn't create agent action group. Here's why: {e}")
raise
time.sleep(5)
agentprepared = prepare_agent(bedrock, agent_id)
print(agentprepared)
time.sleep(20)
agentalias = create_agent_alias(bedrock, name, agent_id)
print(agentalias['agentAliasId'])
if __name__ == "__main__":
main()