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Client-side penggunaan alat
Jika Anda menggunakan Responses API, Chat Completions API, Converse API, atau InvokeModel API untuk mengirim permintaan, maka model tersebut menggunakan pemanggilan alat sisi klien. Ini berarti bahwa dalam kode Anda, Anda memanggil alat atas nama model. Dalam skenario ini, asumsikan implementasi alat adalah API. Alat ini bisa dengan mudah menjadi database, fungsi Lambda, atau perangkat lunak lainnya. Anda memutuskan bagaimana Anda ingin menerapkan alat ini. Anda kemudian melanjutkan percakapan dengan model dengan memberikan pesan dengan hasil dari alat. Terakhir, model menghasilkan respons untuk pesan asli yang menyertakan hasil alat yang Anda kirim ke model.
Mari kita mendefinisikan alat yang akan kita gunakan untuk penggunaan alat. Contoh Python berikut menunjukkan cara menggunakan alat yang mengembalikan lagu paling populer di stasiun radio fiksi.
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")
Menggunakan Responses API untuk perkakas sisi klien
Anda dapat menggunakan fitur pemanggilan Fungsi
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)
Menggunakan Chat Completions API untuk perkakas sisi klien
Anda juga dapat menggunakan Chat Completions API. Berikut adalah kode Python untuk menggunakan Chat Completions:
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)
Untuk detail selengkapnya tentang penggunaan Function Calling on Responses API dan Chat Completions API, lihat Function Calling
Menggunakan Converse API untuk perkakas sisi klien
Anda dapat menggunakan Converse API untuk membiarkan model menggunakan alat dalam percakapan. Contoh Python berikut menunjukkan cara menggunakan alat yang mengembalikan lagu paling populer di stasiun radio fiksi.
# 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()
Menggunakan API Invoke untuk penggunaan alat sisi klien
Dimungkinkan untuk menggunakan alat dengan operasi inferensi dasar (InvokeModelatau InvokeModelWithResponseStream). Untuk menemukan parameter inferensi yang Anda lewatkan di badan permintaan, lihat parameter inferensi untuk model yang ingin Anda gunakan.
