本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
用戶端工具使用
如果您使用回應 API、聊天完成 API、Converse API 或 InvokeModel API 來傳送請求，則模型會使用用戶端工具呼叫。這表示在您的程式碼中，您會代表模型呼叫 工具。在此情境中，假設工具實作是 API。該工具可以輕鬆成為資料庫、Lambda 函式或其他軟體。您可以決定如何實作工具。然後，透過提供具有工具結果的訊息，繼續與模型對話。最後，模型會為原始訊息產生回應，其中包含您傳送至模型的工具結果。
讓我們定義將用於工具的工具。下列 Python 範例示範如何使用工具，傳回虛構廣播台上最熱門的歌曲。
def get_most_popular_song(station_name: str) -> str: stations = { "Radio Free Mars": "Starman – David Bowie", "Neo Tokyo FM": "Plastic Love – Mariya Takeuchi", "Cloud Nine Radio": "Blinding Lights – The Weeknd", } return stations.get(station_name, "Unknown Station – No chart data available")
使用回應 API 進行用戶端工具
您可以使用 OpenAI 提供的函數呼叫
from openai import OpenAI import json client = OpenAI() response = client.responses.create( model="oss-gpt-120b", input="What is the most popular song on Radio Free Mars?", tools=[ { "type": "function", "name": "get_most_popular_song", "description": "Returns the most popular song on a radio station", "parameters": { "type": "object", "properties": { "station_name": { "type": "string", "description": "Name of the radio station" } }, "required": ["station_name"] } } ] ) if response.output and response.output[0].content: tool_call = response.output[0].content[0] args = json.loads(tool_call["arguments"]) result = get_most_popular_song(args["station_name"]) final_response = client.responses.create( model="oss-gpt-120b", input=[ { "role": "tool", "tool_call_id": tool_call["id"], "content": result } ] ) print(final_response.output_text)
使用聊天完成 API 進行用戶端工具
您也可以使用聊天完成 API。以下是使用聊天完成的 Python 程式碼：
from openai import OpenAI import json client = OpenAI() completion = client.chat.completions.create( model="oss-gpt-120b", messages=[{"role": "user", "content": "What is the most popular song on Neo Tokyo FM?"}], tools=[{ "type": "function", "function": { "name": "get_most_popular_song", "description": "Returns the most popular song on a radio station", "parameters": { "type": "object", "properties": { "station_name": {"type": "string", "description": "Name of the radio station"} }, "required": ["station_name"] } } }] ) message = completion.choices[0].message if message.tool_calls: tool_call = message.tool_calls[0] args = json.loads(tool_call.function.arguments) result = get_most_popular_song(args["station_name"]) followup = client.chat.completions.create( model="oss-gpt-120b", messages=[ {"role": "user", "content": "What is the most popular song on Neo Tokyo FM?"}, message, {"role": "tool", "tool_call_id": tool_call.id, "content": result} ] ) print(followup.choices[0].message.content)
如需使用函數呼叫回應 API 和聊天完成 API 的 詳細資訊，請參閱 OpenAI 中的函數呼叫
使用 Converse API 進行用戶端工具
您可以使用 Converse API，讓模型在對話中使用工具。下列 Python 範例示範如何使用工具，傳回虛構廣播台上最熱門的歌曲。
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """Shows how to use tools with the Converse API and the Cohere Command R model.""" import logging import json import boto3 from botocore.exceptions import ClientError class StationNotFoundError(Exception): """Raised when a radio station isn't found.""" pass logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def get_top_song(call_sign): """Returns the most popular song for the requested station. Args: call_sign (str): The call sign for the station for which you want the most popular song. Returns: response (json): The most popular song and artist. """ song = "" artist = "" if call_sign == 'WZPZ': song = "Elemental Hotel" artist = "8 Storey Hike" else: raise StationNotFoundError(f"Station {call_sign} not found.") return song, artist def generate_text(bedrock_client, model_id, tool_config, input_text): """Generates text using the supplied Amazon Bedrock model. If necessary, the function handles tool use requests and sends the result to the model. Args: bedrock_client: The Boto3 Bedrock runtime client. model_id (str): The Amazon Bedrock model ID. tool_config (dict): The tool configuration. input_text (str): The input text. Returns: Nothing. """ logger.info("Generating text with model %s", model_id) # Create the initial message from the user input. messages = [{"role": "user", "content": [{"text": input_text}]}] response = bedrock_client.converse(modelId=model_id, messages=messages, toolConfig=tool_config) output_message = response['output']['message'] messages.append(output_message) stop_reason = response['stopReason'] if stop_reason == 'tool_use': # Tool use requested. Call the tool and send the result to the model. tool_requests = response['output']['message']['content'] for tool_request in tool_requests: if 'toolUse' in tool_request: tool = tool_request['toolUse'] logger.info("Requesting tool %s. Request: %s", tool['name'], tool['toolUseId']) if tool['name'] == 'top_song': tool_result = {} try: song, artist = get_top_song(tool['input']['sign']) tool_result = {"toolUseId": tool['toolUseId'], "content": [{"json": {"song": song, "artist": artist}}]} except StationNotFoundError as err: tool_result = {"toolUseId": tool['toolUseId'], "content": [{"text": err.args[0]}], "status": 'error'} tool_result_message = {"role": "user", "content": [{"toolResult": tool_result}]} messages.append(tool_result_message) # Send the tool result to the model. response = bedrock_client.converse(modelId=model_id, messages=messages, toolConfig=tool_config) output_message = response['output']['message'] # print the final response from the model. for content in output_message['content']: print(json.dumps(content, indent=4)) def main(): """Entrypoint for tool use example.""" logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = "cohere.command-r-v1:0" input_text = "What is the most popular song on WZPZ?" tool_config = { "tools": [ { "toolSpec": { "name": "top_song", "description": "Get the most popular song played on a radio station.", "inputSchema": { "json": { "type": "object", "properties": { "sign": { "type": "string", "description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ, and WKRP." } }, "required": ["sign"] } } } } ] } bedrock_client = boto3.client(service_name='bedrock-runtime') try: print(f"Question: {input_text}") generate_text(bedrock_client, model_id, tool_config, input_text) except ClientError as err: message = err.response['Error']['Message'] logger.error("A client error occurred: %s", message) print(f"A client error occured: {message}") else: print(f"Finished generating text with model {model_id}.") if __name__ == "__main__": main()
將調用 APIs用於用戶端工具
您可以搭配基本推論操作 (InvokeModel 或 InvokeModelWithResponseStream) 使用工具。若要尋找您在請求內文中傳遞的推論參數，請參閱所要使用模型的推論參數。
