capo vs AWS SDK for Python v4 vs boto3¶

from capo_bedrock_runtime import AsyncBedrockRuntimeClient

async with AsyncBedrockRuntimeClient() as bedrock:
    response = await bedrock.converse(
        "amazon.nova-lite-v1:0",
        messages=[{"role": "user", "content": [{"text": "What's the weather in Yerevan?"}]}],
        system=[{"text": "You are a weather assistant. Always use the tool."}],
        inference_config={"max_tokens": 512, "temperature": 0.2, "stop_sequences": ["</answer>"]},
        tool_config={
            "tools": [
                {
                    "toolSpec": {
                        "name": "get_weather",
                        "description": "Current weather for a city",
                        "input_schema": {
                            "json": {
                                "type": "object",
                                "properties": {"city": {"type": "string"}},
                                "required": ["city"],
                            }
                        },
                    }
                }
            ],
            "tool_choice": {"tool": {"name": "get_weather"}},
        },
    )
    print(response["stop_reason"])

from capo_bedrock_runtime import AsyncBedrockRuntimeClient
from capo_bedrock_runtime.types.content_block import ContentBlockTextMember
from capo_bedrock_runtime.types.inference_configuration import InferenceConfiguration
from capo_bedrock_runtime.types.message import Message
from capo_bedrock_runtime.types.specific_tool_choice import SpecificToolChoice
from capo_bedrock_runtime.types.system_content_block import SystemContentBlockTextMember
from capo_bedrock_runtime.types.tool import ToolToolSpecMember
from capo_bedrock_runtime.types.tool_choice import ToolChoiceToolMember
from capo_bedrock_runtime.types.tool_configuration import ToolConfiguration
from capo_bedrock_runtime.types.tool_input_schema import ToolInputSchemaJsonMember
from capo_bedrock_runtime.types.tool_specification import ToolSpecification

async with AsyncBedrockRuntimeClient() as bedrock:
    response = await bedrock.converse(
        "amazon.nova-lite-v1:0",
        messages=[
            Message(role="user", content=[ContentBlockTextMember(text="What's the weather in Yerevan?")])
        ],
        system=[SystemContentBlockTextMember(text="You are a weather assistant. Always use the tool.")],
        inference_config=InferenceConfiguration(max_tokens=512, temperature=0.2, stop_sequences=["</answer>"]),
        tool_config=ToolConfiguration(
            tools=[
                ToolToolSpecMember(
                    toolSpec=ToolSpecification(
                        name="get_weather",
                        description="Current weather for a city",
                        input_schema=ToolInputSchemaJsonMember(
                            json={
                                "type": "object",
                                "properties": {"city": {"type": "string"}},
                                "required": ["city"],
                            }
                        ),
                    )
                )
            ],
            tool_choice=ToolChoiceToolMember(tool=SpecificToolChoice(name="get_weather")),
        ),
    )
    print(response["stop_reason"])

from aws_sdk_bedrock_runtime.client import AsyncBedrockRuntimeClient
from aws_sdk_bedrock_runtime.models import (
    ContentBlockText,
    ConverseInput,
    InferenceConfiguration,
    Message,
    SpecificToolChoice,
    SystemContentBlockText,
    ToolChoiceTool,
    ToolConfiguration,
    ToolInputSchemaJson,
    ToolSpecification,
    ToolToolSpec,
)
from smithy_core.documents import Document

async with AsyncBedrockRuntimeClient() as bedrock:
    response = await bedrock.converse(
        ConverseInput(
            model_id="amazon.nova-lite-v1:0",
            messages=[
                Message(role="user", content=[ContentBlockText(value="What's the weather in Yerevan?")])
            ],
            system=[SystemContentBlockText(value="You are a weather assistant. Always use the tool.")],
            inference_config=InferenceConfiguration(
                max_tokens=512, temperature=0.2, stop_sequences=["</answer>"]
            ),
            tool_config=ToolConfiguration(
                tools=[
                    ToolToolSpec(
                        value=ToolSpecification(
                            name="get_weather",
                            description="Current weather for a city",
                            input_schema=ToolInputSchemaJson(
                                value=Document(
                                    {
                                        "type": "object",
                                        "properties": {"city": {"type": "string"}},
                                        "required": ["city"],
                                    }
                                )
                            ),
                        )
                    )
                ],
                tool_choice=ToolChoiceTool(value=SpecificToolChoice(name="get_weather")),
            ),
        )
    )
    print(response.stop_reason)