Hay más ejemplos de AWS SDK disponibles en el GitHub repositorio de [ejemplos de AWS Doc SDK](https://github.com/awsdocs/aws-doc-sdk-examples).

Las traducciones son generadas a través de traducción automática. En caso de conflicto entre la traducción y la version original de inglés, prevalecerá la version en inglés.

# Ejemplos de Amazon Bedrock Runtime usando SDK para Python (Boto3)
<a name="python_3_bedrock-runtime_code_examples"></a>

Los siguientes ejemplos de código muestran cómo realizar acciones e implementar escenarios comunes mediante Amazon Bedrock Runtime. AWS SDK para Python (Boto3) 

Los *escenarios* son ejemplos de código que muestran cómo llevar a cabo una tarea específica a través de llamadas a varias funciones dentro del servicio o combinado con otros Servicios de AWS.

En cada ejemplo se incluye un enlace al código de origen completo, con instrucciones de configuración y ejecución del código en el contexto.

**Topics**
+ [Escenarios](#scenarios)
+ [Amazon Nova](#amazon_nova)
+ [Amazon Nova Canvas](#amazon_nova_canvas)
+ [Amazon Nova Reel](#amazon_nova_reel)
+ [Amazon Titan Image Generator](#amazon_titan_image_generator)
+ [Amazon Titan Text](#amazon_titan_text)
+ [Incrustaciones de texto de Amazon Titan](#amazon_titan_text_embeddings)
+ [Anthropic Claude](#anthropic_claude)
+ [Cohere Command](#cohere_command)
+ [DeepSeek](#deepseek)
+ [Meta Llama](#meta_llama)
+ [Mistral AI](#mistral_ai)
+ [Stable Diffusion](#stable_diffusion)

## Escenarios
<a name="scenarios"></a>

### Crear una aplicación de sitio de pruebas que interactúe con modelos fundacionales de Amazon Bedrock
<a name="cross_FMPlayground_python_3_topic"></a>

En el siguiente ejemplo de código se muestra cómo crear sitios de pruebas que interactúan con modelos fundacionales de Amazon Bedrock a través de diferentes modalidades.

**SDK para Python (Boto3)**  
 El Python Foundation Model (FM) Playground es una aplicación de ejemplo que Python/FastAPI muestra cómo usar Amazon Bedrock con Python. En este ejemplo se muestra cómo los desarrolladores de Python pueden utilizar Amazon Bedrock para crear aplicaciones habilitadas para IA generativa. Puede probar los modelos fundacionales de Amazon Bedrock e interactuar con ellos mediante los tres sitios de pruebas siguientes:   
+ Un sitio de pruebas de texto.
+ Un sitio de pruebas de chat.
+ Un sitio de pruebas de imágenes.
En el ejemplo también se enumeran y muestran los modelos fundacionales a los que tiene acceso y sus características. Para obtener el código fuente y las instrucciones de implementación, consulte el proyecto en. [GitHub](https://github.com/build-on-aws/python-fm-playground)   

**Servicios utilizados en este ejemplo**
+ Amazon Bedrock Runtime

### Creación e invocación de una petición administrada
<a name="bedrock-agent_GettingStartedWithBedrockPrompts_python_3_topic"></a>

En el siguiente ejemplo de código, se muestra cómo:
+ Crear una petición administrada
+ Crear una versión de la petición
+ Invocar la petición utilizando la versión
+ Limpiar los recursos (opcional).

**SDK para Python (Boto3)**  
 Hay más información al respecto GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples). 
Crear e invocar una petición administrada.  

```
import argparse
import boto3
import logging
import time

# Now import the modules
from prompt import create_prompt, create_prompt_version, delete_prompt
from run_prompt import invoke_prompt

logging.basicConfig(
    level=logging.INFO,
    format='%(levelname)s: %(message)s'
)
logger = logging.getLogger(__name__)



def run_scenario(bedrock_client, bedrock_runtime_client, model_id, cleanup=True):
    """
    Runs the Amazon Bedrock managed prompt scenario.
    
    Args:
        bedrock_client: The Amazon Bedrock Agent client.
        bedrock_runtime_client: The Amazon Bedrock Runtime client.
        model_id (str): The model ID to use for the prompt.
        cleanup (bool): Whether to clean up resources at the end of the scenario.
        
    Returns:
        dict: A dictionary containing the created resources.
    """
    prompt_id = None
    
    try:
        # Step 1: Create a prompt
        print("\n=== Step 1: Creating a prompt ===")
        prompt_name = f"PlaylistGenerator-{int(time.time())}"
        prompt_description = "Playlist generator"
        prompt_template = """
          Make me a {{genre}} playlist consisting of the following number of songs: {{number}}."""
        
        create_response = create_prompt(
            bedrock_client,
            prompt_name,
            prompt_description,
            prompt_template,
            model_id
        )
        
        prompt_id = create_response['id']
        print(f"Created prompt: {prompt_name} with ID: {prompt_id}")
        
        # Create a version of the prompt
        print("\n=== Creating a version of the prompt ===")
        version_response = create_prompt_version(
            bedrock_client,
            prompt_id,
            description="Initial version of the product description generator"
        )
        
        prompt_version_arn = version_response['arn']
        prompt_version = version_response['version']

        print(f"Created prompt version: {prompt_version}")
        print(f"Prompt version ARN: {prompt_version_arn}")
        
        # Step 2: Invoke the prompt directly
        print("\n=== Step 2: Invoking the prompt ===")
        input_variables = {
            "genre": "pop",
            "number": "2",
           }
        
        # Use the ARN from the create_prompt_version response
        result = invoke_prompt(
            bedrock_runtime_client,
            prompt_version_arn,  
            input_variables
        )
        # Display the playlist
        print(f"\n{result}")
    
        
        # Step 3: Clean up resources (optional)
        if cleanup:
            print("\n=== Step 3: Cleaning up resources ===")
            
            # Delete the prompt
            print(f"Deleting prompt {prompt_id}...")
            delete_prompt(bedrock_client, prompt_id)
            
            print("Cleanup complete")
        else:
            print("\n=== Resources were not cleaned up ===")
            print(f"Prompt ID: {prompt_id}")
        
   
        
    except Exception as e:
        logger.exception("Error in scenario: %s", str(e))
        
        # Attempt to clean up if an error occurred and cleanup was requested
        if cleanup and prompt_id:
            try:
                print("\nCleaning up resources after error...")
                
                # Delete the prompt
                try:
                    delete_prompt(bedrock_client, prompt_id)
                    print("Cleanup after error complete")
                except Exception as cleanup_error:
                    logger.error("Error during cleanup: %s", str(cleanup_error))
            except Exception as final_error:
                logger.error("Final error during cleanup: %s", str(final_error))
        
        # Re-raise the original exception
        raise

def main():
    """
    Entry point for the Amazon Bedrock managed prompt scenario.
    """
    parser = argparse.ArgumentParser(
        description="Run the Amazon Bedrock managed prompt scenario."
    )
    parser.add_argument(
        '--region',
        default='us-east-1',
        help="The AWS Region to use."
    )
    parser.add_argument(
        '--model-id',
        default='anthropic.claude-v2',
        help="The model ID to use for the prompt."
    )
    parser.add_argument(
        '--cleanup',
        action='store_true',
        default=True,
        help="Clean up resources at the end of the scenario."
    )
    parser.add_argument(
        '--no-cleanup',
        action='store_false',
        dest='cleanup',
        help="Don't clean up resources at the end of the scenario."
    )
    args = parser.parse_args()

    bedrock_client = boto3.client('bedrock-agent', region_name=args.region)
    bedrock_runtime_client = boto3.client('bedrock-runtime', region_name=args.region)
    
    print("=== Amazon Bedrock Managed Prompt Scenario ===")
    print(f"Region: {args.region}")
    print(f"Model ID: {args.model_id}")
    print(f"Cleanup resources: {args.cleanup}")
    
    try:
        run_scenario(
            bedrock_client,
            bedrock_runtime_client,
            args.model_id,
            args.cleanup
        )
        
    except Exception as e:
        logger.exception("Error running scenario: %s", str(e))
        
if __name__ == "__main__":
    main()
```
+ Para obtener información sobre la API, consulte los siguientes temas en la *Referencia de la API de AWS SDK para Python (Boto3)*.
  + [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse)
  + [CreatePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePrompt)
  + [CreatePromptVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePromptVersion)
  + [DeletePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeletePrompt)

### Orquestación de aplicaciones de IA generativa con Step Functions
<a name="cross_ServerlessPromptChaining_python_3_topic"></a>

En el siguiente ejemplo de código se muestra cómo crear y orquestar aplicaciones de IA generativa mediante Amazon Bedrock y Step Functions.

**SDK para Python (Boto3)**  
 El escenario de encadenamiento de peticiones de Amazon Bedrock sin servidor muestra cómo se puede utilizar [AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html), [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) y [https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) para crear y orquestar aplicaciones de IA generativa complejas, sin servidor y altamente escalables. Contiene los siguientes ejemplos prácticos:   
+  Escribir un análisis de una novela determinada para un blog de literatura. Este ejemplo ilustra una cadena de peticiones simple y secuencial. 
+  Generar una historia corta sobre un tema determinado. Este ejemplo ilustra cómo la IA puede procesar de forma iterativa una lista de elementos generados previamente. 
+  Crear un itinerario para una salida de fin de semana a un destino determinado. Este ejemplo ilustra cómo paralelizar varias peticiones distintas. 
+  Presentar ideas de películas a un usuario humano que actúe como productor de películas. Este ejemplo ilustra cómo paralelizar la misma petición con diferentes parámetros de inferencia, cómo retroceder a un paso anterior de la cadena y cómo incluir la intervención humana como parte del flujo de trabajo. 
+  Planificar una comida en función de los ingredientes que el usuario tenga a mano. Este ejemplo ilustra cómo las cadenas de peticiones pueden incorporar dos conversaciones distintas de IA, en las que dos personas de IA empiezan a debatir para mejorar el resultado final. 
+  Encuentra y resume el GitHub repositorio de más tendencias de la actualidad. Este ejemplo ilustra cómo encadenar varios agentes de IA que interactúan con agentes externos. APIs 
 Para ver el código fuente completo y las instrucciones de configuración y ejecución, consulta el proyecto completo en [GitHub](https://github.com/aws-samples/amazon-bedrock-serverless-prompt-chaining).   

**Servicios utilizados en este ejemplo**
+ Amazon Bedrock
+ Amazon Bedrock Runtime
+ Agentes de Amazon Bedrock
+ Tiempo de ejecución de agentes de Amazon Bedrock
+ Step Functions

### Uso de la herramienta con la API de Converse
<a name="bedrock-runtime_Scenario_ToolUse_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo crear una interacción típica entre una aplicación, un modelo de IA generativo y herramientas conectadas o cómo APIs mediar en las interacciones entre la IA y el mundo exterior. Se presenta un ejemplo sobre cómo conectar una API meteorológica externa al modelo de IA para que pueda proporcionar información meteorológica en tiempo real en función de las entradas del usuario.

**SDK para Python (Boto3)**  
 Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Es el script de ejecución principal de la demostración. Este script orquesta la conversación entre el usuario, la API de Converse de Amazon Bedrock y una herramienta de previsión meteorológica.  

```
"""
This demo illustrates a tool use scenario using Amazon Bedrock's Converse API and a weather tool.
The script interacts with a foundation model on Amazon Bedrock to provide weather information based on user
input. It uses the Open-Meteo API (https://open-meteo.com) to retrieve current weather data for a given location.
"""

import boto3
import logging
from enum import Enum

import utils.tool_use_print_utils as output
import weather_tool

logging.basicConfig(level=logging.INFO, format="%(message)s")

AWS_REGION = "us-east-1"


# For the most recent list of models supported by the Converse API's tool use functionality, visit:
# https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
class SupportedModels(Enum):
    CLAUDE_OPUS = "anthropic.claude-3-opus-20240229-v1:0"
    CLAUDE_SONNET = "anthropic.claude-3-sonnet-20240229-v1:0"
    CLAUDE_HAIKU = "anthropic.claude-3-haiku-20240307-v1:0"
    COHERE_COMMAND_R = "cohere.command-r-v1:0"
    COHERE_COMMAND_R_PLUS = "cohere.command-r-plus-v1:0"


# Set the model ID, e.g., Claude 3 Haiku.
MODEL_ID = SupportedModels.CLAUDE_HAIKU.value

SYSTEM_PROMPT = """
You are a weather assistant that provides current weather data for user-specified locations using only
the Weather_Tool, which expects latitude and longitude. Infer the coordinates from the location yourself.
If the user provides coordinates, infer the approximate location and refer to it in your response.
To use the tool, you strictly apply the provided tool specification.

- Explain your step-by-step process, and give brief updates before each step.
- Only use the Weather_Tool for data. Never guess or make up information. 
- Repeat the tool use for subsequent requests if necessary.
- If the tool errors, apologize, explain weather is unavailable, and suggest other options.
- Report temperatures in °C (°F) and wind in km/h (mph). Keep weather reports concise. Sparingly use
  emojis where appropriate.
- Only respond to weather queries. Remind off-topic users of your purpose. 
- Never claim to search online, access external data, or use tools besides Weather_Tool.
- Complete the entire process until you have all required data before sending the complete response.
"""

# The maximum number of recursive calls allowed in the tool_use_demo function.
# This helps prevent infinite loops and potential performance issues.
MAX_RECURSIONS = 5


class ToolUseDemo:
    """
    Demonstrates the tool use feature with the Amazon Bedrock Converse API.
    """

    def __init__(self):
        # Prepare the system prompt
        self.system_prompt = [{"text": SYSTEM_PROMPT}]

        # Prepare the tool configuration with the weather tool's specification
        self.tool_config = {"tools": [weather_tool.get_tool_spec()]}

        # Create a Bedrock Runtime client in the specified AWS Region.
        self.bedrockRuntimeClient = boto3.client(
            "bedrock-runtime", region_name=AWS_REGION
        )

    def run(self):
        """
        Starts the conversation with the user and handles the interaction with Bedrock.
        """
        # Print the greeting and a short user guide
        output.header()

        # Start with an emtpy conversation
        conversation = []

        # Get the first user input
        user_input = self._get_user_input()

        while user_input is not None:
            # Create a new message with the user input and append it to the conversation
            message = {"role": "user", "content": [{"text": user_input}]}
            conversation.append(message)

            # Send the conversation to Amazon Bedrock
            bedrock_response = self._send_conversation_to_bedrock(conversation)

            # Recursively handle the model's response until the model has returned
            # its final response or the recursion counter has reached 0
            self._process_model_response(
                bedrock_response, conversation, max_recursion=MAX_RECURSIONS
            )

            # Repeat the loop until the user decides to exit the application
            user_input = self._get_user_input()

        output.footer()

    def _send_conversation_to_bedrock(self, conversation):
        """
        Sends the conversation, the system prompt, and the tool spec to Amazon Bedrock, and returns the response.

        :param conversation: The conversation history including the next message to send.
        :return: The response from Amazon Bedrock.
        """
        output.call_to_bedrock(conversation)

        # Send the conversation, system prompt, and tool configuration, and return the response
        return self.bedrockRuntimeClient.converse(
            modelId=MODEL_ID,
            messages=conversation,
            system=self.system_prompt,
            toolConfig=self.tool_config,
        )

    def _process_model_response(
        self, model_response, conversation, max_recursion=MAX_RECURSIONS
    ):
        """
        Processes the response received via Amazon Bedrock and performs the necessary actions
        based on the stop reason.

        :param model_response: The model's response returned via Amazon Bedrock.
        :param conversation: The conversation history.
        :param max_recursion: The maximum number of recursive calls allowed.
        """

        if max_recursion <= 0:
            # Stop the process, the number of recursive calls could indicate an infinite loop
            logging.warning(
                "Warning: Maximum number of recursions reached. Please try again."
            )
            exit(1)

        # Append the model's response to the ongoing conversation
        message = model_response["output"]["message"]
        conversation.append(message)

        if model_response["stopReason"] == "tool_use":
            # If the stop reason is "tool_use", forward everything to the tool use handler
            self._handle_tool_use(message, conversation, max_recursion)

        if model_response["stopReason"] == "end_turn":
            # If the stop reason is "end_turn", print the model's response text, and finish the process
            output.model_response(message["content"][0]["text"])
            return

    def _handle_tool_use(
        self, model_response, conversation, max_recursion=MAX_RECURSIONS
    ):
        """
        Handles the tool use case by invoking the specified tool and sending the tool's response back to Bedrock.
        The tool response is appended to the conversation, and the conversation is sent back to Amazon Bedrock for further processing.

        :param model_response: The model's response containing the tool use request.
        :param conversation: The conversation history.
        :param max_recursion: The maximum number of recursive calls allowed.
        """

        # Initialize an empty list of tool results
        tool_results = []

        # The model's response can consist of multiple content blocks
        for content_block in model_response["content"]:
            if "text" in content_block:
                # If the content block contains text, print it to the console
                output.model_response(content_block["text"])

            if "toolUse" in content_block:
                # If the content block is a tool use request, forward it to the tool
                tool_response = self._invoke_tool(content_block["toolUse"])

                # Add the tool use ID and the tool's response to the list of results
                tool_results.append(
                    {
                        "toolResult": {
                            "toolUseId": (tool_response["toolUseId"]),
                            "content": [{"json": tool_response["content"]}],
                        }
                    }
                )

        # Embed the tool results in a new user message
        message = {"role": "user", "content": tool_results}

        # Append the new message to the ongoing conversation
        conversation.append(message)

        # Send the conversation to Amazon Bedrock
        response = self._send_conversation_to_bedrock(conversation)

        # Recursively handle the model's response until the model has returned
        # its final response or the recursion counter has reached 0
        self._process_model_response(response, conversation, max_recursion - 1)

    def _invoke_tool(self, payload):
        """
        Invokes the specified tool with the given payload and returns the tool's response.
        If the requested tool does not exist, an error message is returned.

        :param payload: The payload containing the tool name and input data.
        :return: The tool's response or an error message.
        """
        tool_name = payload["name"]

        if tool_name == "Weather_Tool":
            input_data = payload["input"]
            output.tool_use(tool_name, input_data)

            # Invoke the weather tool with the input data provided by
            response = weather_tool.fetch_weather_data(input_data)
        else:
            error_message = (
                f"The requested tool with name '{tool_name}' does not exist."
            )
            response = {"error": "true", "message": error_message}

        return {"toolUseId": payload["toolUseId"], "content": response}

    @staticmethod
    def _get_user_input(prompt="Your weather info request"):
        """
        Prompts the user for input and returns the user's response.
        Returns None if the user enters 'x' to exit.

        :param prompt: The prompt to display to the user.
        :return: The user's input or None if the user chooses to exit.
        """
        output.separator()
        user_input = input(f"{prompt} (x to exit): ")

        if user_input == "":
            prompt = "Please enter your weather info request, e.g. the name of a city"
            return ToolUseDemo._get_user_input(prompt)

        elif user_input.lower() == "x":
            return None

        else:
            return user_input


if __name__ == "__main__":
    tool_use_demo = ToolUseDemo()
    tool_use_demo.run()
```
Es la herramienta de previsión meteorológica que se utiliza en la demostración. Este script define la especificación de la herramienta e implementa la lógica para obtener los datos meteorológicos mediante la API de Open-Meteo.  

```
import requests
from requests.exceptions import RequestException


def get_tool_spec():
    """
    Returns the JSON Schema specification for the Weather tool. The tool specification
    defines the input schema and describes the tool's functionality.
    For more information, see https://json-schema.org/understanding-json-schema/reference.

    :return: The tool specification for the Weather tool.
    """
    return {
        "toolSpec": {
            "name": "Weather_Tool",
            "description": "Get the current weather for a given location, based on its WGS84 coordinates.",
            "inputSchema": {
                "json": {
                    "type": "object",
                    "properties": {
                        "latitude": {
                            "type": "string",
                            "description": "Geographical WGS84 latitude of the location.",
                        },
                        "longitude": {
                            "type": "string",
                            "description": "Geographical WGS84 longitude of the location.",
                        },
                    },
                    "required": ["latitude", "longitude"],
                }
            },
        }
    }


def fetch_weather_data(input_data):
    """
    Fetches weather data for the given latitude and longitude using the Open-Meteo API.
    Returns the weather data or an error message if the request fails.

    :param input_data: The input data containing the latitude and longitude.
    :return: The weather data or an error message.
    """
    endpoint = "https://api.open-meteo.com/v1/forecast"
    latitude = input_data.get("latitude")
    longitude = input_data.get("longitude", "")
    params = {"latitude": latitude, "longitude": longitude, "current_weather": True}

    try:
        response = requests.get(endpoint, params=params)
        weather_data = {"weather_data": response.json()}
        response.raise_for_status()
        return weather_data
    except RequestException as e:
        return e.response.json()
    except Exception as e:
        return {"error": type(e), "message": str(e)}
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

## Amazon Nova
<a name="amazon_nova"></a>

### Converse
<a name="bedrock-runtime_Converse_AmazonNovaText_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Amazon Nova con la API de Converse de Bedrock.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Amazon Nova con la API de Converse de Bedrock.  

```
# Use the Conversation API to send a text message to Amazon Nova.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Amazon Nova Lite.
model_id = "amazon.nova-lite-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### ConverseStream
<a name="bedrock-runtime_ConverseStream_AmazonNovaText_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Amazon Nova con la API de Converse de Bedrock y procesar el flujo de respuesta en tiempo real.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Amazon Nova con la API de Converse de Bedrock y procese el flujo de respuesta en tiempo real.  

```
# Use the Conversation API to send a text message to Amazon Nova Text
# and print the response stream.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Amazon Nova Lite.
model_id = "amazon.nova-lite-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    streaming_response = client.converse_stream(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the streamed response text in real-time.
    for chunk in streaming_response["stream"]:
        if "contentBlockDelta" in chunk:
            text = chunk["contentBlockDelta"]["delta"]["text"]
            print(text, end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [ConverseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/ConverseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Comprensión de documentos
<a name="bedrock-runtime_DocumentUnderstanding_AmazonNova_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar y procesar un documento con Amazon Nova en Amazon Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Enviar y procesar un documento con Amazon Nova en Amazon Bedrock.  

```
# Send and process a document with Amazon Nova on Amazon Bedrock.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g. Amazon Nova Lite.
model_id = "amazon.nova-lite-v1:0"

# Load the document
with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
    document_bytes = file.read()

# Start a conversation with a user message and the document
conversation = [
    {
        "role": "user",
        "content": [
            {"text": "Briefly compare the models described in this document"},
            {
                "document": {
                    # Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
                    "format": "pdf",
                    "name": "Amazon Nova Service Cards",
                    "source": {"bytes": document_bytes},
                }
            },
        ],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 500, "temperature": 0.3},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

## Amazon Nova Canvas
<a name="amazon_nova_canvas"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModel_AmazonNovaImageGeneration_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo invocar Amazon Nova Canvas en Amazon Bedrock para generar una imagen.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Cree una imagen con Amazon Nova Canvas.  

```
# Use the native inference API to create an image with Amazon Nova Canvas

import base64
import json
import os
import random

import boto3

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID.
model_id = "amazon.nova-canvas-v1:0"

# Define the image generation prompt for the model.
prompt = "A stylized picture of a cute old steampunk robot."

# Generate a random seed between 0 and 858,993,459
seed = random.randint(0, 858993460)

# Format the request payload using the model's native structure.
native_request = {
    "taskType": "TEXT_IMAGE",
    "textToImageParams": {"text": prompt},
    "imageGenerationConfig": {
        "seed": seed,
        "quality": "standard",
        "height": 512,
        "width": 512,
        "numberOfImages": 1,
    },
}

# Convert the native request to JSON.
request = json.dumps(native_request)

# Invoke the model with the request.
response = client.invoke_model(modelId=model_id, body=request)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract the image data.
base64_image_data = model_response["images"][0]

# Save the generated image to a local folder.
i, output_dir = 1, "output"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
while os.path.exists(os.path.join(output_dir, f"nova_canvas_{i}.png")):
    i += 1

image_data = base64.b64decode(base64_image_data)

image_path = os.path.join(output_dir, f"nova_canvas_{i}.png")
with open(image_path, "wb") as file:
    file.write(image_data)

print(f"The generated image has been saved to {image_path}")
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

## Amazon Nova Reel
<a name="amazon_nova_reel"></a>

### T ext-to-video
<a name="bedrock-runtime_Scenario_AmazonNova_TextToVideo_python_3_topic"></a>

En el siguiente ejemplo de código, se muestra cómo utilizar Amazon Nova Reel para generar un video a partir de una petición de texto.

**SDK para Python (Boto3)**  
 Hay más en marcha GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Utilice Amazon Nova Reel para generar un video a partir de una petición de texto.  

```
"""
This example demonstrates how to use Amazon Nova Reel to generate a video from a text prompt.

It shows how to:
- Set up the Amazon Bedrock runtime client
- Configure a text-to-video request
- Submit an asynchronous job for video generation
- Poll for job completion status
- Access the generated video from S3
"""

import random
import time

import boto3

# Replace with your own S3 bucket to store the generated video
# Format: s3://your-bucket-name
OUTPUT_S3_URI = "s3://REPLACE-WITH-YOUR-S3-BUCKET-NAME"


def start_text_to_video_generation_job(bedrock_runtime, prompt, output_s3_uri):
    """
    Starts an asynchronous text-to-video generation job using Amazon Nova Reel.

    :param bedrock_runtime: The Bedrock runtime client
    :param prompt: The text description of the video to generate
    :param output_s3_uri: S3 URI where the generated video will be stored

    :return: The invocation ARN of the async job
    """
    # Specify the model ID for text-to-video generation
    model_id = "amazon.nova-reel-v1:0"

    # Generate a random seed between 0 and 2,147,483,646
    # This helps ensure unique video generation results
    seed = random.randint(0, 2147483646)

    # Configure the video generation request with additional parameters
    model_input = {
        "taskType": "TEXT_VIDEO",
        "textToVideoParams": {"text": prompt},
        "videoGenerationConfig": {
            "fps": 24,
            "durationSeconds": 6,
            "dimension": "1280x720",
            "seed": seed,
        },
    }

    # Specify the S3 location for the output video
    output_config = {"s3OutputDataConfig": {"s3Uri": output_s3_uri}}

    # Invoke the model asynchronously
    response = bedrock_runtime.start_async_invoke(
        modelId=model_id, modelInput=model_input, outputDataConfig=output_config
    )

    invocation_arn = response["invocationArn"]

    return invocation_arn


def query_job_status(bedrock_runtime, invocation_arn):
    """
    Queries the status of an asynchronous video generation job.

    :param bedrock_runtime: The Bedrock runtime client
    :param invocation_arn: The ARN of the async invocation to check

    :return: The runtime response containing the job status and details
    """
    return bedrock_runtime.get_async_invoke(invocationArn=invocation_arn)


def main():
    """
    Main function that demonstrates the complete workflow for generating
    a video from a text prompt using Amazon Nova Reel.
    """
    # Create a Bedrock Runtime client
    # Note: Credentials will be loaded from the environment or AWS CLI config
    bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

    # Configure the text prompt and output location
    prompt = "Closeup of a cute old steampunk robot. Camera zoom in."

    # Verify the S3 URI has been set to a valid bucket
    if "REPLACE-WITH-YOUR-S3-BUCKET-NAME" in OUTPUT_S3_URI:
        print("ERROR: You must replace the OUTPUT_S3_URI with your own S3 bucket URI")
        return

    print("Submitting video generation job...")
    invocation_arn = start_text_to_video_generation_job(
        bedrock_runtime, prompt, OUTPUT_S3_URI
    )
    print(f"Job started with invocation ARN: {invocation_arn}")

    # Poll for job completion
    while True:
        print("\nPolling job status...")
        job = query_job_status(bedrock_runtime, invocation_arn)
        status = job["status"]

        if status == "Completed":
            bucket_uri = job["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"]
            print(f"\nSuccess! The video is available at: {bucket_uri}/output.mp4")
            break
        elif status == "Failed":
            print(
                f"\nVideo generation failed: {job.get('failureMessage', 'Unknown error')}"
            )
            break
        else:
            print("In progress. Waiting 15 seconds...")
            time.sleep(15)


if __name__ == "__main__":
    main()
```
+ Para obtener información sobre la API, consulte los siguientes temas en la *Referencia de la API de AWS SDK para Python (Boto3)*.
  + [GetAsyncInvoke](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/GetAsyncInvoke)
  + [StartAsyncInvoke](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/StartAsyncInvoke)

## Amazon Titan Image Generator
<a name="amazon_titan_image_generator"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModel_TitanImageGenerator_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo invocar Amazon Titan Image en Amazon Bedrock para generar una imagen.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Cree una imagen con Amazon Titan Image Generator.  

```
# Use the native inference API to create an image with Amazon Titan Image Generator

import base64
import boto3
import json
import os
import random

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Titan Image Generator G1.
model_id = "amazon.titan-image-generator-v2:0"

# Define the image generation prompt for the model.
prompt = "A stylized picture of a cute old steampunk robot."

# Generate a random seed.
seed = random.randint(0, 2147483647)

# Format the request payload using the model's native structure.
native_request = {
    "taskType": "TEXT_IMAGE",
    "textToImageParams": {"text": prompt},
    "imageGenerationConfig": {
        "numberOfImages": 1,
        "quality": "standard",
        "cfgScale": 8.0,
        "height": 512,
        "width": 512,
        "seed": seed,
    },
}

# Convert the native request to JSON.
request = json.dumps(native_request)

# Invoke the model with the request.
response = client.invoke_model(modelId=model_id, body=request)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract the image data.
base64_image_data = model_response["images"][0]

# Save the generated image to a local folder.
i, output_dir = 1, "output"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
while os.path.exists(os.path.join(output_dir, f"titan_{i}.png")):
    i += 1

image_data = base64.b64decode(base64_image_data)

image_path = os.path.join(output_dir, f"titan_{i}.png")
with open(image_path, "wb") as file:
    file.write(image_data)

print(f"The generated image has been saved to {image_path}")
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

## Amazon Titan Text
<a name="amazon_titan_text"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModel_TitanText_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Amazon Titan Text con la API Invoke Model.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Use la API de Invoke Model para enviar un mensaje de texto.  

```
# Use the native inference API to send a text message to Amazon Titan Text.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Titan Text Premier.
model_id = "amazon.titan-text-premier-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "inputText": prompt,
    "textGenerationConfig": {
        "maxTokenCount": 512,
        "temperature": 0.5,
    },
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["results"][0]["outputText"]
print(response_text)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

## Incrustaciones de texto de Amazon Titan
<a name="amazon_titan_text_embeddings"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModelWithResponseStream_TitanTextEmbeddings_python_3_topic"></a>

En el siguiente ejemplo de código, se muestra cómo:
+ Comience a crear su primera incrustación.
+ Para crear incrustaciones, configure el número de dimensiones y la normalización (solo en la versión 2).

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Cree su primera incrustación con Amazon Titan Text Embeddings.  

```
# Generate and print an embedding with Amazon Titan Text Embeddings V2.

import boto3
import json

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Titan Text Embeddings V2.
model_id = "amazon.titan-embed-text-v2:0"

# The text to convert to an embedding.
input_text = "Please recommend books with a theme similar to the movie 'Inception'."

# Create the request for the model.
native_request = {"inputText": input_text}

# Convert the native request to JSON.
request = json.dumps(native_request)

# Invoke the model with the request.
response = client.invoke_model(modelId=model_id, body=request)

# Decode the model's native response body.
model_response = json.loads(response["body"].read())

# Extract and print the generated embedding and the input text token count.
embedding = model_response["embedding"]
input_token_count = model_response["inputTextTokenCount"]

print("\nYour input:")
print(input_text)
print(f"Number of input tokens: {input_token_count}")
print(f"Size of the generated embedding: {len(embedding)}")
print("Embedding:")
print(embedding)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

## Anthropic Claude
<a name="anthropic_claude"></a>

### Converse
<a name="bedrock-runtime_Converse_AnthropicClaude_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Amazon Claude con la API de Converse de Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Anthropic Claude mediante la API de Converse de Bedrock.  

```
# Use the Conversation API to send a text message to Anthropic Claude.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Claude 3 Haiku.
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### ConverseStream
<a name="bedrock-runtime_ConverseStream_AnthropicClaude_python_3_topic"></a>

El siguiente ejemplos de código muestra cómo enviar un mensaje de texto a Anthropic Claude con la API de Converse de Bedrock y procesar el flujo de respuesta en tiempo real.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Anthropic Claude con la API de Converse de Bedrock y procese el flujo de respuesta en tiempo real.  

```
# Use the Conversation API to send a text message to Anthropic Claude
# and print the response stream.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Claude 3 Haiku.
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    streaming_response = client.converse_stream(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the streamed response text in real-time.
    for chunk in streaming_response["stream"]:
        if "contentBlockDelta" in chunk:
            text = chunk["contentBlockDelta"]["delta"]["text"]
            print(text, end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [ConverseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/ConverseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Comprensión de documentos
<a name="bedrock-runtime_DocumentUnderstanding_AnthropicClaude_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar y procesar un documento con Anthropic Claude en Amazon Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Enviar y procesar un documento con Anthropic Claude en Amazon Bedrock.  

```
# Send and process a document with Anthropic Claude on Amazon Bedrock.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g. Claude 3 Haiku.
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

# Load the document
with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
    document_bytes = file.read()

# Start a conversation with a user message and the document
conversation = [
    {
        "role": "user",
        "content": [
            {"text": "Briefly compare the models described in this document"},
            {
                "document": {
                    # Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
                    "format": "pdf",
                    "name": "Amazon Nova Service Cards",
                    "source": {"bytes": document_bytes},
                }
            },
        ],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 500, "temperature": 0.3},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### InvokeModel
<a name="bedrock-runtime_InvokeModel_AnthropicClaude_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Anthropic Claude con la API de Invoke Model.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Use la API de Invoke Model para enviar un mensaje de texto.  

```
# Use the native inference API to send a text message to Anthropic Claude.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Claude 3 Haiku.
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 512,
    "temperature": 0.5,
    "messages": [
        {
            "role": "user",
            "content": [{"type": "text", "text": prompt}],
        }
    ],
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["content"][0]["text"]
print(response_text)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

### InvokeModelWithResponseStream
<a name="bedrock-runtime_InvokeModelWithResponseStream_AnthropicClaude_python_3_topic"></a>

El siguiente ejemplos de código muestran cómo enviar un mensaje de texto a los modelos de Anthropic Claude mediante la API de Invoke Model e imprimir el flujo de respuesta.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Utilice la API de Invoke Model para enviar un mensaje de texto y procesar el flujo de respuesta en tiempo real.  

```
# Use the native inference API to send a text message to Anthropic Claude
# and print the response stream.

import boto3
import json

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Claude 3 Haiku.
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 512,
    "temperature": 0.5,
    "messages": [
        {
            "role": "user",
            "content": [{"type": "text", "text": prompt}],
        }
    ],
}

# Convert the native request to JSON.
request = json.dumps(native_request)

# Invoke the model with the request.
streaming_response = client.invoke_model_with_response_stream(
    modelId=model_id, body=request
)

# Extract and print the response text in real-time.
for event in streaming_response["body"]:
    chunk = json.loads(event["chunk"]["bytes"])
    if chunk["type"] == "content_block_delta":
        print(chunk["delta"].get("text", ""), end="")
```
+  Para obtener más información sobre la API, consulta [InvokeModelWithResponseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModelWithResponseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Escenario: uso de la herramienta con la API de Converse
<a name="bedrock-runtime_Scenario_ToolUseDemo_AnthropicClaude_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo crear una interacción típica entre una aplicación, un modelo de IA generativo y las herramientas conectadas o cómo APIs mediar en las interacciones entre la IA y el mundo exterior. Se presenta un ejemplo sobre cómo conectar una API meteorológica externa al modelo de IA para que pueda proporcionar información meteorológica en tiempo real en función de las entradas del usuario.

**SDK para Python (Boto3)**  
 Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Es el script de ejecución principal de la demostración. Este script orquesta la conversación entre el usuario, la API de Converse de Amazon Bedrock y una herramienta de previsión meteorológica.  

```
"""
This demo illustrates a tool use scenario using Amazon Bedrock's Converse API and a weather tool.
The script interacts with a foundation model on Amazon Bedrock to provide weather information based on user
input. It uses the Open-Meteo API (https://open-meteo.com) to retrieve current weather data for a given location.
"""

import boto3
import logging
from enum import Enum

import utils.tool_use_print_utils as output
import weather_tool

logging.basicConfig(level=logging.INFO, format="%(message)s")

AWS_REGION = "us-east-1"


# For the most recent list of models supported by the Converse API's tool use functionality, visit:
# https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
class SupportedModels(Enum):
    CLAUDE_OPUS = "anthropic.claude-3-opus-20240229-v1:0"
    CLAUDE_SONNET = "anthropic.claude-3-sonnet-20240229-v1:0"
    CLAUDE_HAIKU = "anthropic.claude-3-haiku-20240307-v1:0"
    COHERE_COMMAND_R = "cohere.command-r-v1:0"
    COHERE_COMMAND_R_PLUS = "cohere.command-r-plus-v1:0"


# Set the model ID, e.g., Claude 3 Haiku.
MODEL_ID = SupportedModels.CLAUDE_HAIKU.value

SYSTEM_PROMPT = """
You are a weather assistant that provides current weather data for user-specified locations using only
the Weather_Tool, which expects latitude and longitude. Infer the coordinates from the location yourself.
If the user provides coordinates, infer the approximate location and refer to it in your response.
To use the tool, you strictly apply the provided tool specification.

- Explain your step-by-step process, and give brief updates before each step.
- Only use the Weather_Tool for data. Never guess or make up information. 
- Repeat the tool use for subsequent requests if necessary.
- If the tool errors, apologize, explain weather is unavailable, and suggest other options.
- Report temperatures in °C (°F) and wind in km/h (mph). Keep weather reports concise. Sparingly use
  emojis where appropriate.
- Only respond to weather queries. Remind off-topic users of your purpose. 
- Never claim to search online, access external data, or use tools besides Weather_Tool.
- Complete the entire process until you have all required data before sending the complete response.
"""

# The maximum number of recursive calls allowed in the tool_use_demo function.
# This helps prevent infinite loops and potential performance issues.
MAX_RECURSIONS = 5


class ToolUseDemo:
    """
    Demonstrates the tool use feature with the Amazon Bedrock Converse API.
    """

    def __init__(self):
        # Prepare the system prompt
        self.system_prompt = [{"text": SYSTEM_PROMPT}]

        # Prepare the tool configuration with the weather tool's specification
        self.tool_config = {"tools": [weather_tool.get_tool_spec()]}

        # Create a Bedrock Runtime client in the specified AWS Region.
        self.bedrockRuntimeClient = boto3.client(
            "bedrock-runtime", region_name=AWS_REGION
        )

    def run(self):
        """
        Starts the conversation with the user and handles the interaction with Bedrock.
        """
        # Print the greeting and a short user guide
        output.header()

        # Start with an emtpy conversation
        conversation = []

        # Get the first user input
        user_input = self._get_user_input()

        while user_input is not None:
            # Create a new message with the user input and append it to the conversation
            message = {"role": "user", "content": [{"text": user_input}]}
            conversation.append(message)

            # Send the conversation to Amazon Bedrock
            bedrock_response = self._send_conversation_to_bedrock(conversation)

            # Recursively handle the model's response until the model has returned
            # its final response or the recursion counter has reached 0
            self._process_model_response(
                bedrock_response, conversation, max_recursion=MAX_RECURSIONS
            )

            # Repeat the loop until the user decides to exit the application
            user_input = self._get_user_input()

        output.footer()

    def _send_conversation_to_bedrock(self, conversation):
        """
        Sends the conversation, the system prompt, and the tool spec to Amazon Bedrock, and returns the response.

        :param conversation: The conversation history including the next message to send.
        :return: The response from Amazon Bedrock.
        """
        output.call_to_bedrock(conversation)

        # Send the conversation, system prompt, and tool configuration, and return the response
        return self.bedrockRuntimeClient.converse(
            modelId=MODEL_ID,
            messages=conversation,
            system=self.system_prompt,
            toolConfig=self.tool_config,
        )

    def _process_model_response(
        self, model_response, conversation, max_recursion=MAX_RECURSIONS
    ):
        """
        Processes the response received via Amazon Bedrock and performs the necessary actions
        based on the stop reason.

        :param model_response: The model's response returned via Amazon Bedrock.
        :param conversation: The conversation history.
        :param max_recursion: The maximum number of recursive calls allowed.
        """

        if max_recursion <= 0:
            # Stop the process, the number of recursive calls could indicate an infinite loop
            logging.warning(
                "Warning: Maximum number of recursions reached. Please try again."
            )
            exit(1)

        # Append the model's response to the ongoing conversation
        message = model_response["output"]["message"]
        conversation.append(message)

        if model_response["stopReason"] == "tool_use":
            # If the stop reason is "tool_use", forward everything to the tool use handler
            self._handle_tool_use(message, conversation, max_recursion)

        if model_response["stopReason"] == "end_turn":
            # If the stop reason is "end_turn", print the model's response text, and finish the process
            output.model_response(message["content"][0]["text"])
            return

    def _handle_tool_use(
        self, model_response, conversation, max_recursion=MAX_RECURSIONS
    ):
        """
        Handles the tool use case by invoking the specified tool and sending the tool's response back to Bedrock.
        The tool response is appended to the conversation, and the conversation is sent back to Amazon Bedrock for further processing.

        :param model_response: The model's response containing the tool use request.
        :param conversation: The conversation history.
        :param max_recursion: The maximum number of recursive calls allowed.
        """

        # Initialize an empty list of tool results
        tool_results = []

        # The model's response can consist of multiple content blocks
        for content_block in model_response["content"]:
            if "text" in content_block:
                # If the content block contains text, print it to the console
                output.model_response(content_block["text"])

            if "toolUse" in content_block:
                # If the content block is a tool use request, forward it to the tool
                tool_response = self._invoke_tool(content_block["toolUse"])

                # Add the tool use ID and the tool's response to the list of results
                tool_results.append(
                    {
                        "toolResult": {
                            "toolUseId": (tool_response["toolUseId"]),
                            "content": [{"json": tool_response["content"]}],
                        }
                    }
                )

        # Embed the tool results in a new user message
        message = {"role": "user", "content": tool_results}

        # Append the new message to the ongoing conversation
        conversation.append(message)

        # Send the conversation to Amazon Bedrock
        response = self._send_conversation_to_bedrock(conversation)

        # Recursively handle the model's response until the model has returned
        # its final response or the recursion counter has reached 0
        self._process_model_response(response, conversation, max_recursion - 1)

    def _invoke_tool(self, payload):
        """
        Invokes the specified tool with the given payload and returns the tool's response.
        If the requested tool does not exist, an error message is returned.

        :param payload: The payload containing the tool name and input data.
        :return: The tool's response or an error message.
        """
        tool_name = payload["name"]

        if tool_name == "Weather_Tool":
            input_data = payload["input"]
            output.tool_use(tool_name, input_data)

            # Invoke the weather tool with the input data provided by
            response = weather_tool.fetch_weather_data(input_data)
        else:
            error_message = (
                f"The requested tool with name '{tool_name}' does not exist."
            )
            response = {"error": "true", "message": error_message}

        return {"toolUseId": payload["toolUseId"], "content": response}

    @staticmethod
    def _get_user_input(prompt="Your weather info request"):
        """
        Prompts the user for input and returns the user's response.
        Returns None if the user enters 'x' to exit.

        :param prompt: The prompt to display to the user.
        :return: The user's input or None if the user chooses to exit.
        """
        output.separator()
        user_input = input(f"{prompt} (x to exit): ")

        if user_input == "":
            prompt = "Please enter your weather info request, e.g. the name of a city"
            return ToolUseDemo._get_user_input(prompt)

        elif user_input.lower() == "x":
            return None

        else:
            return user_input


if __name__ == "__main__":
    tool_use_demo = ToolUseDemo()
    tool_use_demo.run()
```
Es la herramienta de previsión meteorológica que se utiliza en la demostración. Este script define la especificación de la herramienta e implementa la lógica para obtener los datos meteorológicos mediante la API de Open-Meteo.  

```
import requests
from requests.exceptions import RequestException


def get_tool_spec():
    """
    Returns the JSON Schema specification for the Weather tool. The tool specification
    defines the input schema and describes the tool's functionality.
    For more information, see https://json-schema.org/understanding-json-schema/reference.

    :return: The tool specification for the Weather tool.
    """
    return {
        "toolSpec": {
            "name": "Weather_Tool",
            "description": "Get the current weather for a given location, based on its WGS84 coordinates.",
            "inputSchema": {
                "json": {
                    "type": "object",
                    "properties": {
                        "latitude": {
                            "type": "string",
                            "description": "Geographical WGS84 latitude of the location.",
                        },
                        "longitude": {
                            "type": "string",
                            "description": "Geographical WGS84 longitude of the location.",
                        },
                    },
                    "required": ["latitude", "longitude"],
                }
            },
        }
    }


def fetch_weather_data(input_data):
    """
    Fetches weather data for the given latitude and longitude using the Open-Meteo API.
    Returns the weather data or an error message if the request fails.

    :param input_data: The input data containing the latitude and longitude.
    :return: The weather data or an error message.
    """
    endpoint = "https://api.open-meteo.com/v1/forecast"
    latitude = input_data.get("latitude")
    longitude = input_data.get("longitude", "")
    params = {"latitude": latitude, "longitude": longitude, "current_weather": True}

    try:
        response = requests.get(endpoint, params=params)
        weather_data = {"weather_data": response.json()}
        response.raise_for_status()
        return weather_data
    except RequestException as e:
        return e.response.json()
    except Exception as e:
        return {"error": type(e), "message": str(e)}
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

## Cohere Command
<a name="cohere_command"></a>

### Converse
<a name="bedrock-runtime_Converse_CohereCommand_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Cohere Command con la API de Converse de Bedrock.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Cohere Command mediante la API de Converse de Bedrock.  

```
# Use the Conversation API to send a text message to Cohere Command.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Command R.
model_id = "cohere.command-r-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### ConverseStream
<a name="bedrock-runtime_ConverseStream_CohereCommand_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Cohere Command con la API de Converse de Bedrock y procesar el flujo de respuesta en tiempo real.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Cohere Command con la API de Converse de Bedrock y procese el flujo de respuesta en tiempo real.  

```
# Use the Conversation API to send a text message to Cohere Command
# and print the response stream.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Command R.
model_id = "cohere.command-r-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    streaming_response = client.converse_stream(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the streamed response text in real-time.
    for chunk in streaming_response["stream"]:
        if "contentBlockDelta" in chunk:
            text = chunk["contentBlockDelta"]["delta"]["text"]
            print(text, end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [ConverseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/ConverseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Comprensión de documentos
<a name="bedrock-runtime_DocumentUnderstanding_CohereCommand_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar y procesar un documento con modelos de Cohere Command en Amazon Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Enviar y procesar un documento con los modelos de Cohere Command en Amazon Bedrock.  

```
# Send and process a document with Cohere Command models on Amazon Bedrock.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g. Command R+.
model_id = "cohere.command-r-plus-v1:0"

# Load the document
with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
    document_bytes = file.read()

# Start a conversation with a user message and the document
conversation = [
    {
        "role": "user",
        "content": [
            {"text": "Briefly compare the models described in this document"},
            {
                "document": {
                    # Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
                    "format": "pdf",
                    "name": "Amazon Nova Service Cards",
                    "source": {"bytes": document_bytes},
                }
            },
        ],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 500, "temperature": 0.3},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### InvokeModel: Comando R y R\$1
<a name="bedrock-runtime_InvokeModel_CohereCommandR_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Cohere Command R y R\$1 con la API Invoke Model.

**SDK para Python (Boto3)**  
 Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Use la API de Invoke Model para enviar un mensaje de texto.  

```
# Use the native inference API to send a text message to Cohere Command R and R+.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Command R.
model_id = "cohere.command-r-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "message": prompt,
    "max_tokens": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["text"]
print(response_text)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

### InvokeModelWithResponseStream: Comando R y R\$1
<a name="bedrock-runtime_InvokeModelWithResponseStream_CohereCommandR_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Cohere Command mediante la API Invoke Model con un flujo de respuesta.

**SDK para Python (Boto3)**  
 Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Utilice la API de Invoke Model para enviar un mensaje de texto y procesar el flujo de respuesta en tiempo real.  

```
# Use the native inference API to send a text message to Cohere Command R and R+
# and print the response stream.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Command R.
model_id = "cohere.command-r-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "message": prompt,
    "max_tokens": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    streaming_response = client.invoke_model_with_response_stream(
        modelId=model_id, body=request
    )

    # Extract and print the response text in real-time.
    for event in streaming_response["body"]:
        chunk = json.loads(event["chunk"]["bytes"])
        if "generations" in chunk:
            print(chunk["generations"][0]["text"], end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Escenario: uso de la herramienta con la API de Converse
<a name="bedrock-runtime_Scenario_ToolUseDemo_CohereCommand_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo crear una interacción típica entre una aplicación, un modelo de IA generativo y las herramientas conectadas o cómo APIs mediar en las interacciones entre la IA y el mundo exterior. Se presenta un ejemplo sobre cómo conectar una API meteorológica externa al modelo de IA para que pueda proporcionar información meteorológica en tiempo real en función de las entradas del usuario.

**SDK para Python (Boto3)**  
 Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Es el script de ejecución principal de la demostración. Este script orquesta la conversación entre el usuario, la API de Converse de Amazon Bedrock y una herramienta de previsión meteorológica.  

```
"""
This demo illustrates a tool use scenario using Amazon Bedrock's Converse API and a weather tool.
The script interacts with a foundation model on Amazon Bedrock to provide weather information based on user
input. It uses the Open-Meteo API (https://open-meteo.com) to retrieve current weather data for a given location.
"""

import boto3
import logging
from enum import Enum

import utils.tool_use_print_utils as output
import weather_tool

logging.basicConfig(level=logging.INFO, format="%(message)s")

AWS_REGION = "us-east-1"


# For the most recent list of models supported by the Converse API's tool use functionality, visit:
# https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
class SupportedModels(Enum):
    CLAUDE_OPUS = "anthropic.claude-3-opus-20240229-v1:0"
    CLAUDE_SONNET = "anthropic.claude-3-sonnet-20240229-v1:0"
    CLAUDE_HAIKU = "anthropic.claude-3-haiku-20240307-v1:0"
    COHERE_COMMAND_R = "cohere.command-r-v1:0"
    COHERE_COMMAND_R_PLUS = "cohere.command-r-plus-v1:0"


# Set the model ID, e.g., Claude 3 Haiku.
MODEL_ID = SupportedModels.CLAUDE_HAIKU.value

SYSTEM_PROMPT = """
You are a weather assistant that provides current weather data for user-specified locations using only
the Weather_Tool, which expects latitude and longitude. Infer the coordinates from the location yourself.
If the user provides coordinates, infer the approximate location and refer to it in your response.
To use the tool, you strictly apply the provided tool specification.

- Explain your step-by-step process, and give brief updates before each step.
- Only use the Weather_Tool for data. Never guess or make up information. 
- Repeat the tool use for subsequent requests if necessary.
- If the tool errors, apologize, explain weather is unavailable, and suggest other options.
- Report temperatures in °C (°F) and wind in km/h (mph). Keep weather reports concise. Sparingly use
  emojis where appropriate.
- Only respond to weather queries. Remind off-topic users of your purpose. 
- Never claim to search online, access external data, or use tools besides Weather_Tool.
- Complete the entire process until you have all required data before sending the complete response.
"""

# The maximum number of recursive calls allowed in the tool_use_demo function.
# This helps prevent infinite loops and potential performance issues.
MAX_RECURSIONS = 5


class ToolUseDemo:
    """
    Demonstrates the tool use feature with the Amazon Bedrock Converse API.
    """

    def __init__(self):
        # Prepare the system prompt
        self.system_prompt = [{"text": SYSTEM_PROMPT}]

        # Prepare the tool configuration with the weather tool's specification
        self.tool_config = {"tools": [weather_tool.get_tool_spec()]}

        # Create a Bedrock Runtime client in the specified AWS Region.
        self.bedrockRuntimeClient = boto3.client(
            "bedrock-runtime", region_name=AWS_REGION
        )

    def run(self):
        """
        Starts the conversation with the user and handles the interaction with Bedrock.
        """
        # Print the greeting and a short user guide
        output.header()

        # Start with an emtpy conversation
        conversation = []

        # Get the first user input
        user_input = self._get_user_input()

        while user_input is not None:
            # Create a new message with the user input and append it to the conversation
            message = {"role": "user", "content": [{"text": user_input}]}
            conversation.append(message)

            # Send the conversation to Amazon Bedrock
            bedrock_response = self._send_conversation_to_bedrock(conversation)

            # Recursively handle the model's response until the model has returned
            # its final response or the recursion counter has reached 0
            self._process_model_response(
                bedrock_response, conversation, max_recursion=MAX_RECURSIONS
            )

            # Repeat the loop until the user decides to exit the application
            user_input = self._get_user_input()

        output.footer()

    def _send_conversation_to_bedrock(self, conversation):
        """
        Sends the conversation, the system prompt, and the tool spec to Amazon Bedrock, and returns the response.

        :param conversation: The conversation history including the next message to send.
        :return: The response from Amazon Bedrock.
        """
        output.call_to_bedrock(conversation)

        # Send the conversation, system prompt, and tool configuration, and return the response
        return self.bedrockRuntimeClient.converse(
            modelId=MODEL_ID,
            messages=conversation,
            system=self.system_prompt,
            toolConfig=self.tool_config,
        )

    def _process_model_response(
        self, model_response, conversation, max_recursion=MAX_RECURSIONS
    ):
        """
        Processes the response received via Amazon Bedrock and performs the necessary actions
        based on the stop reason.

        :param model_response: The model's response returned via Amazon Bedrock.
        :param conversation: The conversation history.
        :param max_recursion: The maximum number of recursive calls allowed.
        """

        if max_recursion <= 0:
            # Stop the process, the number of recursive calls could indicate an infinite loop
            logging.warning(
                "Warning: Maximum number of recursions reached. Please try again."
            )
            exit(1)

        # Append the model's response to the ongoing conversation
        message = model_response["output"]["message"]
        conversation.append(message)

        if model_response["stopReason"] == "tool_use":
            # If the stop reason is "tool_use", forward everything to the tool use handler
            self._handle_tool_use(message, conversation, max_recursion)

        if model_response["stopReason"] == "end_turn":
            # If the stop reason is "end_turn", print the model's response text, and finish the process
            output.model_response(message["content"][0]["text"])
            return

    def _handle_tool_use(
        self, model_response, conversation, max_recursion=MAX_RECURSIONS
    ):
        """
        Handles the tool use case by invoking the specified tool and sending the tool's response back to Bedrock.
        The tool response is appended to the conversation, and the conversation is sent back to Amazon Bedrock for further processing.

        :param model_response: The model's response containing the tool use request.
        :param conversation: The conversation history.
        :param max_recursion: The maximum number of recursive calls allowed.
        """

        # Initialize an empty list of tool results
        tool_results = []

        # The model's response can consist of multiple content blocks
        for content_block in model_response["content"]:
            if "text" in content_block:
                # If the content block contains text, print it to the console
                output.model_response(content_block["text"])

            if "toolUse" in content_block:
                # If the content block is a tool use request, forward it to the tool
                tool_response = self._invoke_tool(content_block["toolUse"])

                # Add the tool use ID and the tool's response to the list of results
                tool_results.append(
                    {
                        "toolResult": {
                            "toolUseId": (tool_response["toolUseId"]),
                            "content": [{"json": tool_response["content"]}],
                        }
                    }
                )

        # Embed the tool results in a new user message
        message = {"role": "user", "content": tool_results}

        # Append the new message to the ongoing conversation
        conversation.append(message)

        # Send the conversation to Amazon Bedrock
        response = self._send_conversation_to_bedrock(conversation)

        # Recursively handle the model's response until the model has returned
        # its final response or the recursion counter has reached 0
        self._process_model_response(response, conversation, max_recursion - 1)

    def _invoke_tool(self, payload):
        """
        Invokes the specified tool with the given payload and returns the tool's response.
        If the requested tool does not exist, an error message is returned.

        :param payload: The payload containing the tool name and input data.
        :return: The tool's response or an error message.
        """
        tool_name = payload["name"]

        if tool_name == "Weather_Tool":
            input_data = payload["input"]
            output.tool_use(tool_name, input_data)

            # Invoke the weather tool with the input data provided by
            response = weather_tool.fetch_weather_data(input_data)
        else:
            error_message = (
                f"The requested tool with name '{tool_name}' does not exist."
            )
            response = {"error": "true", "message": error_message}

        return {"toolUseId": payload["toolUseId"], "content": response}

    @staticmethod
    def _get_user_input(prompt="Your weather info request"):
        """
        Prompts the user for input and returns the user's response.
        Returns None if the user enters 'x' to exit.

        :param prompt: The prompt to display to the user.
        :return: The user's input or None if the user chooses to exit.
        """
        output.separator()
        user_input = input(f"{prompt} (x to exit): ")

        if user_input == "":
            prompt = "Please enter your weather info request, e.g. the name of a city"
            return ToolUseDemo._get_user_input(prompt)

        elif user_input.lower() == "x":
            return None

        else:
            return user_input


if __name__ == "__main__":
    tool_use_demo = ToolUseDemo()
    tool_use_demo.run()
```
Es la herramienta de previsión meteorológica que se utiliza en la demostración. Este script define la especificación de la herramienta e implementa la lógica para obtener los datos meteorológicos mediante la API de Open-Meteo.  

```
import requests
from requests.exceptions import RequestException


def get_tool_spec():
    """
    Returns the JSON Schema specification for the Weather tool. The tool specification
    defines the input schema and describes the tool's functionality.
    For more information, see https://json-schema.org/understanding-json-schema/reference.

    :return: The tool specification for the Weather tool.
    """
    return {
        "toolSpec": {
            "name": "Weather_Tool",
            "description": "Get the current weather for a given location, based on its WGS84 coordinates.",
            "inputSchema": {
                "json": {
                    "type": "object",
                    "properties": {
                        "latitude": {
                            "type": "string",
                            "description": "Geographical WGS84 latitude of the location.",
                        },
                        "longitude": {
                            "type": "string",
                            "description": "Geographical WGS84 longitude of the location.",
                        },
                    },
                    "required": ["latitude", "longitude"],
                }
            },
        }
    }


def fetch_weather_data(input_data):
    """
    Fetches weather data for the given latitude and longitude using the Open-Meteo API.
    Returns the weather data or an error message if the request fails.

    :param input_data: The input data containing the latitude and longitude.
    :return: The weather data or an error message.
    """
    endpoint = "https://api.open-meteo.com/v1/forecast"
    latitude = input_data.get("latitude")
    longitude = input_data.get("longitude", "")
    params = {"latitude": latitude, "longitude": longitude, "current_weather": True}

    try:
        response = requests.get(endpoint, params=params)
        weather_data = {"weather_data": response.json()}
        response.raise_for_status()
        return weather_data
    except RequestException as e:
        return e.response.json()
    except Exception as e:
        return {"error": type(e), "message": str(e)}
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

## DeepSeek
<a name="deepseek"></a>

### Comprensión de documentos
<a name="bedrock-runtime_DocumentUnderstanding_DeepSeek_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar y procesar un documento DeepSeek en Amazon Bedrock.

**SDK para Python (Boto3)**  
 Hay más información sobre. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envía y procesa un documento con DeepSeek Amazon Bedrock.  

```
# Send and process a document with DeepSeek on Amazon Bedrock.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g. DeepSeek-R1
model_id = "us.deepseek.r1-v1:0"

# Load the document
with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
    document_bytes = file.read()

# Start a conversation with a user message and the document
conversation = [
    {
        "role": "user",
        "content": [
            {"text": "Briefly compare the models described in this document"},
            {
                "document": {
                    # Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
                    "format": "pdf",
                    "name": "Amazon Nova Service Cards",
                    "source": {"bytes": document_bytes},
                }
            },
        ],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 2000, "temperature": 0.3},
    )

    # Extract and print the reasoning and response text.
    reasoning, response_text = "", ""
    for item in response["output"]["message"]["content"]:
        for key, value in item.items():
            if key == "reasoningContent":
                reasoning = value["reasoningText"]["text"]
            elif key == "text":
                response_text = value

    print(f"\nReasoning:\n{reasoning}")
    print(f"\nResponse:\n{response_text}")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

## Meta Llama
<a name="meta_llama"></a>

### Converse
<a name="bedrock-runtime_Converse_MetaLlama_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Meta Llama con la API de Converse de Bedrock.

**SDK para Python (Boto3)**  
 Hay más información sobre. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Meta Llama mediante la API de Converse de Bedrock.  

```
# Use the Conversation API to send a text message to Meta Llama.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Llama 3 8b Instruct.
model_id = "meta.llama3-8b-instruct-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### ConverseStream
<a name="bedrock-runtime_ConverseStream_MetaLlama_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Meta Llama con la API de Converse de Bedrock y procesar el flujo de respuesta en tiempo real.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Meta Llama con la API de Converse de Bedrock y procese el flujo de respuesta en tiempo real.  

```
# Use the Conversation API to send a text message to Meta Llama
# and print the response stream.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Llama 3 8b Instruct.
model_id = "meta.llama3-8b-instruct-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    streaming_response = client.converse_stream(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the streamed response text in real-time.
    for chunk in streaming_response["stream"]:
        if "contentBlockDelta" in chunk:
            text = chunk["contentBlockDelta"]["delta"]["text"]
            print(text, end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [ConverseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/ConverseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Comprensión de documentos
<a name="bedrock-runtime_DocumentUnderstanding_MetaLlama_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar y procesar un documento con Llama en Amazon Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Enviar y procesar un documento con Llama en Amazon Bedrock.  

```
# Send and process a document with Llama on Amazon Bedrock.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g. Llama 3.1 8B Instruct.
model_id = "us.meta.llama3-1-8b-instruct-v1:0"

# Load the document
with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
    document_bytes = file.read()

# Start a conversation with a user message and the document
conversation = [
    {
        "role": "user",
        "content": [
            {"text": "Briefly compare the models described in this document"},
            {
                "document": {
                    # Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
                    "format": "pdf",
                    "name": "Amazon Nova Service Cards",
                    "source": {"bytes": document_bytes},
                }
            },
        ],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 500, "temperature": 0.3},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### InvokeModel
<a name="bedrock-runtime_InvokeModel_MetaLlama3_python_3_topic"></a>

El siguiente ejemplos de código muestra cómo enviar un mensaje de texto a Meta Llama con la API de Invoke Model.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Use la API de Invoke Model para enviar un mensaje de texto.  

```
# Use the native inference API to send a text message to Meta Llama 3.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-west-2")

# Set the model ID, e.g., Llama 3 70b Instruct.
model_id = "meta.llama3-70b-instruct-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Embed the prompt in Llama 3's instruction format.
formatted_prompt = f"""
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
{prompt}
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""

# Format the request payload using the model's native structure.
native_request = {
    "prompt": formatted_prompt,
    "max_gen_len": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["generation"]
print(response_text)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

### InvokeModelWithResponseStream
<a name="bedrock-runtime_InvokeModelWithResponseStream_MetaLlama3_python_3_topic"></a>

El siguiente ejemplos de código muestra cómo enviar un mensaje de texto a Meta Llama mediante la API de Invoke Model e imprimir el flujo de respuesta.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Utilice la API de Invoke Model para enviar un mensaje de texto y procesar el flujo de respuesta en tiempo real.  

```
# Use the native inference API to send a text message to Meta Llama 3
# and print the response stream.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-west-2")

# Set the model ID, e.g., Llama 3 70b Instruct.
model_id = "meta.llama3-70b-instruct-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Embed the prompt in Llama 3's instruction format.
formatted_prompt = f"""
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
{prompt}
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""

# Format the request payload using the model's native structure.
native_request = {
    "prompt": formatted_prompt,
    "max_gen_len": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    streaming_response = client.invoke_model_with_response_stream(
        modelId=model_id, body=request
    )

    # Extract and print the response text in real-time.
    for event in streaming_response["body"]:
        chunk = json.loads(event["chunk"]["bytes"])
        if "generation" in chunk:
            print(chunk["generation"], end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [InvokeModelWithResponseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModelWithResponseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

## Mistral AI
<a name="mistral_ai"></a>

### Converse
<a name="bedrock-runtime_Converse_Mistral_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Mistral con la API de Converse de Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Mistral mediante la API de Converse de Bedrock.  

```
# Use the Conversation API to send a text message to Mistral.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Mistral Large.
model_id = "mistral.mistral-large-2402-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### ConverseStream
<a name="bedrock-runtime_ConverseStream_Mistral_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a Mistral con la API de Converse de Bedrock y procesar el flujo de respuesta en tiempo real.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Envíe un mensaje de texto a Mistral mediante la API de Converse de Bedrock y procese el flujo de respuesta en tiempo real.  

```
# Use the Conversation API to send a text message to Mistral
# and print the response stream.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Mistral Large.
model_id = "mistral.mistral-large-2402-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    streaming_response = client.converse_stream(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the streamed response text in real-time.
    for chunk in streaming_response["stream"]:
        if "contentBlockDelta" in chunk:
            text = chunk["contentBlockDelta"]["delta"]["text"]
            print(text, end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [ConverseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/ConverseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

### Comprensión de documentos
<a name="bedrock-runtime_DocumentUnderstanding_Mistral_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar y procesar un documento con modelos de Mistral en Amazon Bedrock.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Enviar y procesar un documento con modelos de Mistral en Amazon Bedrock.  

```
# Send and process a document with Mistral models on Amazon Bedrock.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Mistral Large.
model_id = "mistral.mistral-large-2402-v1:0"

# Load the document
with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
    document_bytes = file.read()

# Start a conversation with a user message and the document
conversation = [
    {
        "role": "user",
        "content": [
            {"text": "Briefly compare the models described in this document"},
            {
                "document": {
                    # Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
                    "format": "pdf",
                    "name": "Amazon Nova Service Cards",
                    "source": {"bytes": document_bytes},
                }
            },
        ],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 500, "temperature": 0.3},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulte [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) en la *Referencia de la API de AWS SDK para Python (Boto3)*. 

### InvokeModel
<a name="bedrock-runtime_InvokeModel_MistralAi_python_3_topic"></a>

Los siguientes ejemplos de código muestran cómo enviar un mensaje de texto a los modelos Mistral con la API de Invoke Model.

**SDK para Python (Boto3)**  
 Hay más información GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Use la API de Invoke Model para enviar un mensaje de texto.  

```
# Use the native inference API to send a text message to Mistral.

import boto3
import json
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Mistral Large.
model_id = "mistral.mistral-large-2402-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Embed the prompt in Mistral's instruction format.
formatted_prompt = f"<s>[INST] {prompt} [/INST]"

# Format the request payload using the model's native structure.
native_request = {
    "prompt": formatted_prompt,
    "max_tokens": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["outputs"][0]["text"]
print(response_text)
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*). 

### InvokeModelWithResponseStream
<a name="bedrock-runtime_InvokeModelWithResponseStream_MistralAi_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo enviar un mensaje de texto a los modelos de Mistral AI mediante la API Invoke Model e imprimir el flujo de respuesta.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Utilice la API de Invoke Model para enviar un mensaje de texto y procesar el flujo de respuesta en tiempo real.  

```
# Use the native inference API to send a text message to Mistral
# and print the response stream.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Mistral Large.
model_id = "mistral.mistral-large-2402-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Embed the prompt in Mistral's instruction format.
formatted_prompt = f"<s>[INST] {prompt} [/INST]"

# Format the request payload using the model's native structure.
native_request = {
    "prompt": formatted_prompt,
    "max_tokens": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    streaming_response = client.invoke_model_with_response_stream(
        modelId=model_id, body=request
    )

    # Extract and print the response text in real-time.
    for event in streaming_response["body"]:
        chunk = json.loads(event["chunk"]["bytes"])
        if "outputs" in chunk:
            print(chunk["outputs"][0].get("text"), end="")

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}''. Reason: {e}")
    exit(1)
```
+  Para obtener más información sobre la API, consulta [InvokeModelWithResponseStream](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModelWithResponseStream)la *AWS Referencia de API de SDK for Python (Boto3*). 

## Stable Diffusion
<a name="stable_diffusion"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModel_StableDiffusion_python_3_topic"></a>

El siguiente ejemplo de código muestra cómo invocar el modelo Stability.ai Stable Diffusion XL en Amazon Bedrock para generar una imagen.

**SDK para Python (Boto3)**  
 Hay más información al respecto. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el [Repositorio de ejemplos de código de AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-runtime#code-examples). 
Cree una imagen con Stable Diffusion.  

```
# Use the native inference API to create an image with Stability.ai Stable Diffusion

import base64
import boto3
import json
import os
import random

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Stable Diffusion XL 1.
model_id = "stability.stable-diffusion-xl-v1"

# Define the image generation prompt for the model.
prompt = "A stylized picture of a cute old steampunk robot."

# Generate a random seed.
seed = random.randint(0, 4294967295)

# Format the request payload using the model's native structure.
native_request = {
    "text_prompts": [{"text": prompt}],
    "style_preset": "photographic",
    "seed": seed,
    "cfg_scale": 10,
    "steps": 30,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

# Invoke the model with the request.
response = client.invoke_model(modelId=model_id, body=request)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract the image data.
base64_image_data = model_response["artifacts"][0]["base64"]

# Save the generated image to a local folder.
i, output_dir = 1, "output"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
while os.path.exists(os.path.join(output_dir, f"stability_{i}.png")):
    i += 1

image_data = base64.b64decode(base64_image_data)

image_path = os.path.join(output_dir, f"stability_{i}.png")
with open(image_path, "wb") as file:
    file.write(image_data)

print(f"The generated image has been saved to {image_path}")
```
+  Para obtener más información sobre la API, consulta [InvokeModel](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/InvokeModel)la *AWS Referencia de API de SDK for Python (Boto3*).