# Code library
<a name="code-library"></a>

This section provides code examples for common Amazon Nova operations using either the Converse API or the InvokeModel API.

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

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

Send a basic text request to Amazon Nova models using the Converse API.

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

------

### Multimodal input using embedded asset
<a name="multimodal-input-embedded"></a>

Process multimodal content by embedding document, image, video, or audio data directly in the request. This example uses image data. For details on the content structure for other modalities, see the [ContentBlock details in the Amazon Bedrock API documentation.](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)
```

------

### Multimodal input using S3 URI
<a name="multimodal-input-s3"></a>

Process multimodal content by referencing documents, images, videos, or audio files stored in S3. This example uses an image reference. For details on the content structure for other modalities, see the [ContentBlock details in the Amazon Bedrock API documentation.](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)
```

------

### Extended thinking (reasoning)
<a name="extended-thinking-example"></a>

Enable extended thinking for complex problem-solving tasks.

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

------

### Built-in tool: Nova Grounding with citations
<a name="nova-grounding"></a>

Use Nova Grounding to retrieve real-time information from the web with citations.

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

------

### Built-in tool: Code Interpreter
<a name="code-interpreter"></a>

Use the Code Interpreter tool to execute Python code for calculations and data analysis.

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

------

### Tool use
<a name="tool-use"></a>

Define custom tools for the model to use during conversation.

------
#### [ 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 Examples
<a name="invoke-model-api"></a>

The examples below focus on the few key areas where the Invoke API's request and response structures differ slightly from those of the Converse API. In most other ways, the two APIs are compatible, so you should be able to easily adapt the Converse API examples above to work with the InvokeModel API.

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

Send a basic text request to Amazon Nova 2 models using the InvokeModel API.

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

------

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

Use the InvokeModel API with reasoning enabled for complex problem-solving.

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

------