# Biblioteca de códigos
<a name="code-library"></a>

Esta seção fornece exemplos de código para operações comuns do Amazon Nova usando a API Converse ou a API InvokeModel.

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

### Solicitação básica
<a name="basic-request-converse"></a>

Envie uma solicitação de texto básica para modelos do Amazon Nova usando a API Converse.

------
#### [ 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)
```

------

### Entrada multimodal usando ativo incorporado
<a name="multimodal-input-embedded"></a>

Processe conteúdo multimodal incorporando dados de documentos, imagens, vídeos ou áudios diretamente na solicitação. Este exemplo usa dados de imagem. Para obter detalhes sobre a estrutura de conteúdo para outras modalidades, consulte os [detalhes do ContentBlock na documentação da API do Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ContentBlock.html).

------
#### [ 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)
```

------

### Entrada multimodal usando o URI do S3
<a name="multimodal-input-s3"></a>

Processe conteúdo multimodal referenciando documentos, imagens, vídeos ou arquivos de áudio armazenados no S3. Este exemplo usa uma referência de imagem. Para obter detalhes sobre a estrutura de conteúdo para outras modalidades, consulte os [detalhes do ContentBlock na documentação da API do Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ContentBlock.html).

------
#### [ 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)
```

------

### Pensamento estendido (raciocínio)
<a name="extended-thinking-example"></a>

Habilite o pensamento estendido para tarefas complexas de resolução de problemas.

------
#### [ 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
```

------

### Ferramenta integrada: Ancoragem do Nova com citações
<a name="nova-grounding"></a>

Use a Ancoragem do Nova para recuperar informações em tempo real da web com citações.

------
#### [ 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)
```

------

### Ferramenta integrada: Interpretador de Código
<a name="code-interpreter"></a>

Use a ferramenta Interpretador de Código para executar código Python para cálculos e análise de dados.

------
#### [ 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)
```

------

### Uso de ferramentas
<a name="tool-use"></a>

Defina ferramentas personalizadas para o modelo usar durante a conversa.

------
#### [ 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)
```

------

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

Os exemplos abaixo focam as poucas áreas-chave em que as estruturas de solicitação e resposta da API Invoke diferem levemente das da API Converse. Em quase todos os outros aspectos, as duas APIs são compatíveis, então você poderá adaptar facilmente os exemplos da API Converse acima para trabalhar com a API InvokeModel.

### Solicitação básica
<a name="basic-request-invoke"></a>

Envie uma solicitação de texto básica para os modelos do Amazon Nova 2 usando a API InvokeModel.

------
#### [ 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)
```

------

### API InvokeModel com raciocínio
<a name="invoke-model-reasoning"></a>

Use a API InvokeModel com o raciocínio habilitado para a resolução de problemas complexos.

------
#### [ 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)
```

------