# 코드 라이브러리
<a name="code-library"></a>

이 섹션에서는 Converse API 또는 InvokeModel API를 사용하는 일반적인 Amazon Nova 작업에 대한 코드 예제를 제공합니다.

## Converse API 예제
<a name="converse-api-examples"></a>

### 기본 요청
<a name="basic-request-converse"></a>

Converse API를 사용하여 Amazon Nova 모델에 기본 텍스트 요청을 전송합니다.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Invoke the model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Write a short story. End the story with 'THE END'."}],
        }
    ],
    system=[{"text": "You are a children's book author."}],  # Optional
    inferenceConfig={  # These parameters are optional
        "maxTokens": 1500,
        "temperature": 0.7,
        "topP": 0.9,
        "stopSequences": ["THE END"],
    },
    additionalModelRequestFields={  # These parameters are optional
        "inferenceConfig": {
            "topK": 50,
        }
    },
)

# Extract the text response
content_list = response["output"]["message"]["content"]
for content in content_list:
    if "text" in content:
        print(content["text"])
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke the model
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Write a short story. End the story with 'THE END'."}],
        }
    ],
    system=[{"text": "You are a children's book author."}],  # Optional
    inferenceConfig={  # These parameters are optional
        "maxTokens": 1500,
        "temperature": 0.7,
        "topP": 0.9,
        "stopSequences": ["THE END"],
    },
    additionalModelRequestFields={  # These parameters are optional
        "inferenceConfig": {
            "topK": 50,
        }
    },
)

# Handle streaming events
for event in response["stream"]:
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]
        if "text" in delta:
            print(delta["text"], end="", flush=True)
```

------

### 임베디드 자산을 사용하는 멀티모달 입력
<a name="multimodal-input-embedded"></a>

요청에 문서, 이미지, 비디오 또는 오디오 데이터를 직접 포함하여 멀티모달 콘텐츠를 처리합니다. 이 예제에서는 이미지 데이터를 사용합니다. 다른 양식의 콘텐츠 구조에 대한 자세한 내용은 [Amazon Bedrock API 설명서의 ContentBlock 세부 정보](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ContentBlock.html)를 참조하세요.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config

# Read a document, image, video, or audio file
with open("sample_image.png", "rb") as image_file:
    binary_data = image_file.read()
    data_format = "png"

# Define message with image
messages = [
    {
        "role": "user",
        "content": [
            {
                "image": {
                    "format": data_format,
                    "source": {
                        "bytes": binary_data  # For Invoke API, encode as Base64 string
                    },
                },
            },
            {"text": "Provide a brief caption for this asset."},
        ],
    }
]

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Invoke model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=messages,
)

# Extract the text response
content_list = response["output"]["message"]["content"]
for content in content_list:
    if "text" in content:
        print(content["text"])
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config

# Read a document, image, video, or audio file
with open("sample_image.png", "rb") as image_file:
    binary_data = image_file.read()
    data_format = "png"

# Define message with image
messages = [
    {
        "role": "user",
        "content": [
            {
                "image": {
                    "format": data_format,
                    "source": {
                        "bytes": binary_data  # For Invoke API, encode as Base64 string
                    },
                },
            },
            {"text": "Provide a brief caption for this asset."},
        ],
    }
]

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke model with streaming
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=messages,
)

# Handle streaming events
for event in response["stream"]:
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]
        if "text" in delta:
            print(delta["text"], end="", flush=True)
```

------

### S3 URI를 사용하는 멀티모달 입력
<a name="multimodal-input-s3"></a>

S3에 저장된 문서, 이미지, 비디오 또는 오디오 파일을 참조하여 멀티모달 콘텐츠를 처리합니다. 이 예제에서는 이미지 참조를 사용합니다. 다른 양식의 콘텐츠 구조에 대한 자세한 내용은 [Amazon Bedrock API 설명서의 ContentBlock 세부 정보](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ContentBlock.html)를 참조하세요.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config

# Define message with image
messages = [
    {
        "role": "user",
        "content": [
            {
                "image": {
                    "format": "png",
                    "source": {
                        "s3Location": {
                            "uri": "s3://path/to/your/asset",
                            # "bucketOwner": "<account_id>" # Optional
                        }
                    },
                },
            },
            {"text": "Provide a brief caption for this asset."},
        ],
    }
]

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Invoke model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=messages,
)

# Extract the text response
content_list = response["output"]["message"]["content"]
for content in content_list:
    if "text" in content:
        print(content["text"])
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config

# Define message with image
messages = [
    {
        "role": "user",
        "content": [
            {
                "image": {
                    "format": "png",
                    "source": {
                        "s3Location": {
                            "uri": "s3://path/to/your/asset",
                            # "bucketOwner": "<account_id>" # Optional
                        }
                    },
                },
            },
            {"text": "Provide a brief caption for this asset."},
        ],
    }
]

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke model with streaming
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=messages,
)

# Handle streaming events
for event in response["stream"]:
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]
        if "text" in delta:
            print(delta["text"], end="", flush=True)
```

------

### 확장된 사고 사용(추론)
<a name="extended-thinking-example"></a>

복잡한 문제 해결 태스크에 대해 확장된 사고를 활성화합니다.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Invoke the model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "text": 'How many capital letters appear in the following passage. Your response must include only the number: "Wilfred ordered an anvil from ACME. Shipping was expensive."'
                }
            ],
        }
    ],
    additionalModelRequestFields={
        "reasoningConfig": {
            "type": "enabled",
            "maxReasoningEffort": "low",  # "low" | "medium" | "high"
        }
    },
)

# Extract response content
content_list = response["output"]["message"]["content"]
for content in content_list:
    # Extract the reasoning response
    if "reasoningContent" in content:
        print("\n== Reasoning ==")
        print(content["reasoningContent"]["reasoningText"]["text"])
    # Extract the text response
    if "text" in content:
        print("\n== Text ==")
        print(content["text"])
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke the model
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "text": 'How many capital letters appear in the following passage. Your response must include only the number: "Wilfred ordered an anvil from ACME. Shipping was expensive."'
                }
            ],
        }
    ],
    additionalModelRequestFields={
        "reasoningConfig": {
            "type": "enabled",
            "maxReasoningEffort": "low",  # "low" | "medium" | "high"
        },
    },
)

# Process the streaming response
reasoning_output = ""
text_output = ""
for event in response["stream"]:
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]

        if "reasoningContent" in delta:
            if len(reasoning_output) == 0:
                print("\n\n== Reasoning ==")
            reasoning_text_chunk = delta["reasoningContent"]["text"]
            print(reasoning_text_chunk, end="", flush=True)
            reasoning_output += reasoning_text_chunk

        elif "text" in delta:
            if len(text_output) == 0:
                print("\n\n== Text ==")
            text_chunk = delta["text"]
            print(text_chunk, end="", flush=True)
            text_output += text_chunk
```

------

### 기본 제공 도구: 인용을 사용하는 Nova 그라운딩
<a name="nova-grounding"></a>

Nova 그라운딩을 사용하여 인용을 통해 웹에서 실시간 정보를 검색합니다.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config

# Define the list of tools the model may use
tool_config = {"tools": [{"systemTool": {"name": "nova_grounding"}}]}

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

messages = [
    {
        "role": "user",
        "content": [
            {"text": "What is the latest news about renewable energy sources?"}
        ],
    }
]

# Invoke the model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0", messages=messages, toolConfig=tool_config
)

# Extract the text with interleaved citations
output_with_citations = ""
content_list = response["output"]["message"]["content"]
for content in content_list:
    if "text" in content:
        output_with_citations += content["text"]

    elif "citationsContent" in content:
        citations = content["citationsContent"]["citations"]
        for citation in citations:
            url = citation["location"]["web"]["url"]
            output_with_citations += f"[{url}]"

print(output_with_citations)
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config

# Define the list of tools the model may use
tool_config = {"tools": [{"systemTool": {"name": "nova_grounding"}}]}

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

messages = [
    {
        "role": "user",
        "content": [
            {"text": "What is the latest news about renewable energy sources?"}
        ],
    }
]

# Invoke the model with streaming
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0", messages=messages, toolConfig=tool_config
)

# Process the streaming response with interleaved citations
for event in response["stream"]:
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]

        if "text" in delta:
            print(delta["text"], end="", flush=True)

        elif "citation" in delta:
            url = delta["citation"]["location"]["web"]["url"]
            print(f"[{url}]", end="", flush=True)
```

------

### 기본 제공 도구: 코드 인터프리터
<a name="code-interpreter"></a>

코드 인터프리터 도구를 사용하여 계산 및 데이터 분석을 위해 Python 코드를 실행합니다.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config

# Define the list of tools the model may use
tool_config = {"tools": [{"systemTool": {"name": "nova_code_interpreter"}}]}

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "text": "What is the average of 10, 24, 2, 3, 43, 52, 13, 68, 6, 7, 902, 82?"
            }
        ],
    }
]

# Invoke the model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0", messages=messages, toolConfig=tool_config
)

# Extract the text and the code the was executed
content_list = response["output"]["message"]["content"]
for content in content_list:
    if "text" in content:
        print("\n== Text ==")
        print(content["text"])

    elif "toolUse" in content and content["toolUse"]["name"] == "nova_code_interpreter":
        print("\n== Code Interpreter: input.snippet ==")
        print(content["toolUse"]["input"]["snippet"])
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config
import json

# Define the list of tools the model may use
tool_config = {"tools": [{"systemTool": {"name": "nova_code_interpreter"}}]}

messages = [
    {
        "role": "user",
        "content": [
            {
                "text": "What is the average of 10, 24, 2, 3, 43, 52, 13, 68, 6, 7, 902, 82?"
            }
        ],
    }
]

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke the model with streaming
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0", messages=messages, toolConfig=tool_config
)

# Process the streaming response
current_block_start = None
response_text = ""
for event in response["stream"]:
    if "contentBlockStart" in event:
        current_block_start = event["contentBlockStart"]["start"]

    elif "contentBlockStop" in event:
        current_block_start = None

    elif "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]

        if (
            current_block_start
            and "toolUse" in current_block_start
            and current_block_start["toolUse"]["name"] == "nova_code_interpreter"
        ):
            # This is code interpreter content
            tool_input = json.loads(delta["toolUse"]["input"])
            print("\n== Executed Code Snippet ==")
            print(tool_input["snippet"], end="", flush=True)

        elif "text" in delta:
            # This is text response content
            if len(response_text) == 0:
                print("\n== Text ==")
            text = delta["text"]
            response_text += text
            print(text, end="", flush=True)
```

------

### 도구 사용
<a name="tool-use"></a>

모델이 대화 중에 사용할 사용자 지정 도구를 정의합니다.

------
#### [ Non-streaming ]

```
import boto3
from botocore.config import Config


def get_weather(city):
    # Mock function to simulate weather API
    return {"temperatureF": 48, "conditions": "light rain"}


# Define the toolSpec for the weather tool
weather_tool = {
    "toolSpec": {
        "name": "get_weather",
        "description": "Get the current weather conditions in a given location",
        "inputSchema": {
            "json": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["city"],
            }
        },
    }
}

# Define the list of tools the model may use
tool_config = {"tools": [weather_tool]}

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Start tracking message history
messages = []

messages.append(
    {
        "role": "user",
        "content": [
            {
                "text": "Suggest some activities to do in Seattle based on the current weather."
            }
        ],
    }
)

# Invoke the model
response = bedrock.converse(
    modelId="us.amazon.nova-2-lite-v1:0", messages=messages, toolConfig=tool_config
)

assistant_message = response["output"]["message"]

# Add the assistant response to the message history
messages.append(assistant_message)

content_list = assistant_message["content"]
stop_reason = response["stopReason"]

if stop_reason == "tool_use":
    # Extract the toolUse details
    tool_use = next(
        content["toolUse"] for content in content_list if "toolUse" in content
    )
    tool_name = tool_use["name"]
    tool_use_id = tool_use["toolUseId"]

    if tool_name == "get_weather":
        # Call the tool
        weather = get_weather(tool_use["input"]["city"])

        # Send the result back to the model
        messages.append(
            {
                "role": "user",
                "content": [
                    {
                        "toolResult": {
                            "toolUseId": tool_use_id,
                            "content": [{"json": weather}],
                        }
                    }
                ],
            }
        )

        # Submit the tool result back to the model
        response = bedrock.converse(
            modelId="us.amazon.nova-2-lite-v1:0",
            messages=messages,
            toolConfig=tool_config,
        )

        content_list = response["output"]["message"]["content"]
        for content in content_list:
            # Extract the text response
            if "text" in content:
                print("\n== Text ==")
                print(content["text"])
else:
    # A tool call was not needed
    for content in content_list:
        # Extract the text response
        if "text" in content:
            print("\n== Text ==")
            print(content["text"])
```

------
#### [ Streaming ]

```
import boto3
from botocore.config import Config
import json


def get_weather(city):
    # Mock function to simulate weather API
    return {"temperatureF": 48, "conditions": "light rain"}


# Define the toolSpec for the weather tool
weather_tool = {
    "toolSpec": {
        "name": "get_weather",
        "description": "Get the current weather conditions in a given location",
        "inputSchema": {
            "json": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["city"],
            }
        },
    }
}

# Define the list of tools the model may use
tool_config = {"tools": [weather_tool]}

# Create the Bedrock Runtime client, using an extended timeout configuration
# to support long-running requests.
bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Start tracking message history
messages = []

messages.append(
    {
        "role": "user",
        "content": [
            {
                "text": "Suggest some activities to do in Seattle based on the current weather."
            }
        ],
    }
)

# Invoke the model with streaming
response = bedrock.converse_stream(
    modelId="us.amazon.nova-2-lite-v1:0", messages=messages, toolConfig=tool_config
)

# Process the streaming response
assistant_message = {"role": "assistant", "content": []}
current_tool_use = None
stop_reason = None

for event in response["stream"]:
    if "contentBlockStart" in event:
        start = event["contentBlockStart"]["start"]
        if "toolUse" in start:
            current_tool_use = start["toolUse"]
            current_tool_use["input"] = ""

    elif "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]
        if "toolUse" in delta:
            current_tool_use["input"] += delta["toolUse"]["input"]
        elif "text" in delta:
            print(delta["text"], end="", flush=True)

    elif "contentBlockStop" in event:
        if current_tool_use:
            # Parse the accumulated tool input
            current_tool_use["input"] = json.loads(current_tool_use["input"])
            assistant_message["content"].append({"toolUse": current_tool_use})
            current_tool_use = None

    elif "messageStop" in event:
        stop_reason = event["messageStop"]["stopReason"]
        if stop_reason == "end_turn":
            exit

# Add the assistant response to the message history
messages.append(assistant_message)

if stop_reason == "tool_use":
    # Extract the toolUse details
    tool_use = next(
        content["toolUse"]
        for content in assistant_message["content"]
        if "toolUse" in content
    )
    tool_name = tool_use["name"]
    tool_use_id = tool_use["toolUseId"]

    if tool_name == "get_weather":
        # Call the tool
        weather = get_weather(tool_use["input"]["city"])

        # Send the result back to the model
        messages.append(
            {
                "role": "user",
                "content": [
                    {
                        "toolResult": {
                            "toolUseId": tool_use_id,
                            "content": [{"json": weather}],
                        }
                    }
                ],
            }
        )

        # Submit the tool result back to the model with streaming
        response = bedrock.converse_stream(
            modelId="us.amazon.nova-2-lite-v1:0",
            messages=messages,
            toolConfig=tool_config,
        )

        # Handle the final streaming response
        print("\n== Text ==")
        for event in response["stream"]:
            if "contentBlockDelta" in event:
                delta = event["contentBlockDelta"]["delta"]
                if "text" in delta:
                    print(delta["text"], end="", flush=True)
```

------

## InvokeModel API 예제
<a name="invoke-model-api"></a>

아래 예제에서는 Invoke API의 요청 및 응답 구조가 Converse API의 요청 및 응답 구조와 조금 다른 몇 가지 주요 영역에 초점을 맞춥니다. 대부분의 다른 방법에서는 두 API가 호환되므로, InvokeModel API와 함께 작동하도록 위의 Converse API 예제를 쉽게 조정할 수 있습니다.

### 기본 요청
<a name="basic-request-invoke"></a>

InvokeModel API를 사용하여 Amazon Nova 2 모델에 기본 텍스트 요청을 전송합니다.

------
#### [ Non-streaming ]

```
import json

import boto3
from botocore.config import Config

# Configure the request
request_body = {
    "messages": [
        {
            "role": "user",
            "content": [{"text": "Write a short story. End the story with 'THE END'."}],
        }
    ],
    "system": [{"text": "You are a children's book author."}],  # Optional
    "inferenceConfig": {  # These parameters are optional
        "maxTokens": 1500,
        "temperature": 0.7,
        "topP": 0.9,
        "topK": 50,
        "stopSequences": ["THE END"],
    },
}

bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Invoke the model
response = bedrock.invoke_model(
    modelId="us.amazon.nova-2-lite-v1:0", body=json.dumps(request_body)
)
response_body = json.loads(response["body"].read())

# Extract the text response
content_list = response_body["output"]["message"]["content"]
for content in content_list:
    if "text" in content:
        print(content["text"])
```

------
#### [ Streaming ]

```
import json

import boto3
from botocore.config import Config

# Configure the request
request_body = {
    "messages": [
        {
            "role": "user",
            "content": [{"text": "Write a short story. End the story with 'THE END'."}],
        }
    ],
    "system": [{"text": "You are a children's book author."}],  # Optional
    "inferenceConfig": {  # These parameters are optional
        "maxTokens": 1500,
        "temperature": 0.7,
        "topP": 0.9,
        "topK": 50,
        "stopSequences": ["THE END"],
    },
}

bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke the model with streaming
response = bedrock.invoke_model_with_response_stream(
    modelId="us.amazon.nova-2-lite-v1:0", body=json.dumps(request_body)
)

# Process the streaming response
for event in response["body"]:
    chunk = json.loads(event["chunk"]["bytes"])
    if "contentBlockDelta" in chunk:
        delta = chunk["contentBlockDelta"]["delta"]
        if "text" in delta:
            print(delta["text"], end="", flush=True)
```

------

### 추론을 사용하는 InvokeModel API
<a name="invoke-model-reasoning"></a>

복잡한 문제 해결을 위한 추론이 활성화된 상태로 InvokeModel API를 사용합니다.

------
#### [ Non-streaming ]

```
import json

import boto3
from botocore.config import Config

# Configure the request
request_body = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "text": 'How many capital letters appear in the following passage. Your response must include only the number: "Wilfred ordered an anvil from ACME. Shipping was expensive."'
                }
            ],
        }
    ],
    "reasoningConfig": {
        "type": "enabled",
        "maxReasoningEffort": "low",  # "low" | "medium" | "high"
    },
}

bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(read_timeout=3600),
)

# Invoke the model
response = bedrock.invoke_model(
    modelId="us.amazon.nova-2-lite-v1:0", body=json.dumps(request_body)
)
response_body = json.loads(response["body"].read())

# Extract response content
content_list = response_body["output"]["message"]["content"]
for content in content_list:
    # Extract the reasoning response
    if "reasoningContent" in content:
        print("\n== Reasoning ==")
        print(content["reasoningContent"]["reasoningText"]["text"])
    # Extract the text response
    if "text" in content:
        print("\n== Text ==")
        print(content["text"])
```

------
#### [ Streaming ]

```
import json

import boto3
from botocore.config import Config

# Configure the request
request_body = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "text": 'How many capital letters appear in the following passage. Your response must include only the number: "Wilfred ordered an anvil from ACME. Shipping was expensive."'
                }
            ],
        }
    ],
    "reasoningConfig": {
        "type": "enabled",
        "maxReasoningEffort": "low",  # "low" | "medium" | "high"
    },
}

bedrock = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
    config=Config(connect_timeout=3600, read_timeout=3600),
)

# Invoke the model with streaming
response = bedrock.invoke_model_with_response_stream(
    modelId="us.amazon.nova-2-lite-v1:0", body=json.dumps(request_body)
)

# Process the streaming response
for event in response["body"]:
    chunk = json.loads(event["chunk"]["bytes"])

    if "contentBlockDelta" in chunk:
        delta = chunk["contentBlockDelta"]["delta"]

        # Extract the reasoning response
        if "reasoningContent" in delta:
            print("\n== Reasoning ==")
            print(delta["reasoningContent"]["reasoningText"]["text"], end="", flush=True)

        # Extract the text response
        if "text" in delta:
            print("\n== Text ==")
            print(delta["text"], end="", flush=True)
```

------