Bedrock Playground and API Demonstrations

import boto3

# Create the Bedrock Runtime client (adjust region as needed)
inference_client = boto3.client("bedrock-runtime", region_name="us-east-1")

# A structured prompt improves consistency and reduces iteration time.
user_prompt = """
context:
you are helping a developer learn Amazon Bedrock generative AI service.
the developer already knows python and has some experience with the AWS SDK for Python.

role:
act as a clear technical instructor.

instruction:
explain the difference between the Bedrock Runtime SDK client and the Bedrock SDK client and when to use each one.

output format:
return the answer as a short paragraph of up to a maximum of 4 sentences.
"""

# Unified messages format (suitable for high-level "converse" interfaces)
messages = [
    {
        "role": "user",
        "content": [
            {"text": user_prompt}
        ]
    }
]

# Choose the foundation model you want to use
model_id = "meta.llama3-8b-instruct-v1:0"

# Call the Converse API
response = inference_client.converse(modelId=model_id, messages=messages)

# Extract the generated text from the response safely
text = ""
try:
    text = response["output"]["message"]["content"][0]["text"]
except Exception:
    # Fallback: stringify the whole response for debugging
    text = str(response)

print(f"{model_id}:\n{text}\n")