Bedrock API Part 2

• InvokeModel — a low-level, model-specific API that gives full control over the request body.
• Converse — a higher-level, standardized, message-based API designed for chat and multi-turn interactions.

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InvokeModel

import json

response = client.invoke_model(
    modelId="model-id",
    body=json.dumps(body)
)

• Supply modelId (the model’s programmatic identifier).
• The body is a JSON string (commonly produced by json.dumps() from a Python dict).
• Payload structure and supported fields are model-specific.

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Converse

response = client.converse(
    modelId="us.meta.llama3-2-3b-instruct-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"type": "text", "text": "Explain Bedrock in 2 sentences."}]
        }
    ]
)

• Use messages with roles ("user", "system", "assistant").
• Content items typically include a type (such as "text") and the payload text.
• Converse is ideal for conversational flows, multi-turn sessions, or instruction-style prompts.
• The unified format improves portability between models and cloud vendors.

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When to use which

• Use InvokeModel when you need model-specific parameters or nonstandard inputs.
• Use Converse for chat, multi-turn interactions, or when you want a portable, standardized message format.

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Full example: InvokeModel with boto3 (Python)

import json
import boto3

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

prompt = "Explain Amazon Bedrock in one sentence."

# Model-specific request payload
body = {
    "prompt": prompt,
    "max_tokens": 100
}

# Invoke the model (replace modelId with the target model)
response = client.invoke_model(
    modelId="amazon.nova-micro-v1:0",
    body=json.dumps(body)
)

# The SDK returns a StreamingBody under response["body"]
# Read the stream, decode to text, then parse JSON
result = json.loads(response["body"].read().decode("utf-8"))
print(result)

• Imports json and boto3.
• Constructs a Bedrock Runtime client for us-east-1.
• Builds a model-specific body containing prompt and max_tokens.
• Calls invoke_model() with modelId and the serialized body.
• Reads and decodes the streaming response body and deserializes the JSON result.

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Best practices and tips

• Prefer Converse for conversational agents and when you want portability across models or cloud vendors.
• Use InvokeModel when needing special model features (image inputs, advanced sampling parameters, token controls, or vendor/model-specific flags).
• Always read and decode streaming responses before parsing JSON.
• Verify the required/requested fields for the target model in its documentation—field names and structures vary per model.

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Summary

• InvokeModel: low-level, model-specific JSON payloads; use for full control and nonstandard inputs.
• Converse: high-level, standardized messages format; use for conversational flows and cross-model portability.

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Links and references

• Amazon Bedrock overview: https://docs.aws.amazon.com/bedrock/latest/ug/what-is-amazon-bedrock.html
• boto3 documentation: https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
• AWS SDKs and tools: https://aws.amazon.com/tools/