Ci sono altri AWS SDK esempi disponibili nel repository AWS Doc SDK Examples
Le traduzioni sono generate tramite traduzione automatica. In caso di conflitto tra il contenuto di una traduzione e la versione originale in Inglese, quest'ultima prevarrà.
Una demo sull'uso degli strumenti che illustra come connettere i modelli di intelligenza artificiale su Amazon Bedrock con uno strumento personalizzato o API
Il seguente esempio di codice mostra come creare un'interazione tipica tra un'applicazione, un modello di intelligenza artificiale generativa e strumenti connessi o come APIs mediare le interazioni tra l'IA e il mondo esterno. Utilizza l'esempio del collegamento di un meteo esterno API al modello di intelligenza artificiale in modo che possa fornire informazioni meteorologiche in tempo reale basate sull'input dell'utente.
- Python
-
- SDKper Python (Boto3)
-
Nota
C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS
. Lo script di esecuzione principale della demo. Questo script orchestra la conversazione tra l'utente, Amazon Bedrock Converse e uno strumento API meteorologico.
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ 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()
Lo strumento meteorologico utilizzato dalla demo. Questo script definisce le specifiche dello strumento e implementa la logica per recuperare i dati meteorologici utilizzando Open-Meteo. API
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 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)}
-
Per API i dettagli, consulta Converse in AWS SDKfor Python (APIBoto3) Reference.
-