Explain the benefits of using Amazon Bedrock for enterprise applications in 3 bullet points

AmazonBedrockFullAccess

BedrockLabRole

bedrock_text_generation

import boto3
import json

# Initialize Bedrock client
bedrock_runtime = boto3.client(
    service_name='bedrock-runtime',
    region_name='us-east-1'  # Update with your region
)

def lambda_handler(event, context):
    """
    Lambda function to invoke Llama 3.3 70B via Amazon Bedrock
    
    Test Event Format:
    {
        "prompt": "Your prompt here",
        "temperature": 0.7,
        "max_tokens": 512
    }
    """
    
    # Get prompt from event, with a default
    prompt = event.get('prompt', 'Explain the benefits of serverless computing in 3 bullet points')
    temperature = event.get('temperature', 0.7)
    max_tokens = event.get('max_tokens', 512)
    
    try:
        # Prepare the request body for Llama 3.3
        body = json.dumps({
            "prompt": f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
            "max_gen_len": max_tokens,
            "temperature": temperature,
            "top_p": 0.9
        })
        
        # Invoke the model using inference profile
        response = bedrock_runtime.invoke_model(
            body=body,
            modelId="us.meta.llama3-3-70b-instruct-v1:0",
            accept='application/json',
            contentType='application/json'
        )
        
        # Parse the response
        response_body = json.loads(response.get('body').read())
        generated_text = response_body['generation']
        
        # Return successful response
        return {
            'statusCode': 200,
            'body': json.dumps({
                'prompt': prompt,
                'generated_text': generated_text,
                'model': 'Llama 3.3 70B Instruct',
                'parameters': {
                    'temperature': temperature,
                    'max_tokens': max_tokens
                }
            })
        }
        
    except Exception as e:
        # Return error response
        return {
            'statusCode': 500,
            'body': json.dumps({
                'error': str(e),
                'message': 'Failed to invoke Bedrock model'
            })
        }

{
  "prompt": "What are the top 3 benefits of using Amazon Bedrock?",
  "temperature": 0.7,
  "max_tokens": 300
}

{
  "prompt": "Write a Python function to reverse a string",
  "temperature": 0.5,
  "max_tokens": 200
}

{
  "prompt": "Write a haiku about cloud computing",
  "temperature": 0.9,
  "max_tokens": 100
}

cat << 'EOF' > bedrock_llama_generation.py
import boto3
import json

# Initialize Bedrock client
bedrock_runtime = boto3.client(
    service_name='bedrock-runtime',
    region_name='us-east-1'  # Update with your region
)

def generate_text(prompt):
    # Prepare the request body for Llama 3.3
    body = json.dumps({
        "prompt": f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
        "max_gen_len": 512,
        "temperature": 0.7,
        "top_p": 0.9
    })
    
    # Invoke the model using inference profile
    response = bedrock_runtime.invoke_model(
        body=body,
        modelId="us.meta.llama3-3-70b-instruct-v1:0",  # Using inference profile ID
        accept='application/json',
        contentType='application/json'
    )
    
    # Parse the response
    response_body = json.loads(response.get('body').read())
    return response_body['generation']

# Test the function
if __name__ == "__main__":
    prompt = "Write a brief product description for an eco-friendly water bottle"
    result = generate_text(prompt)
    print("Generated Text:")
    print(result)
EOF

python3 bedrock_llama_generation.py