问题描述
使用Azure Bot Service来部署机器人服务，如何把大模型嵌入其中呢？
比如在对话消息时候，能让大模型来提供回答？
问题解答
其实很简单，在Bot代码中添加大模型的调用就行。
以Python代码为例, 首先是准备好调用LLM的请求代码
## 示例中使用的是Azure OpenAI的模型
import requests # Azure OpenAI 客户端 def get_openai_answer(prompt): api_key = "your api key" endpoint = "your deployment endpoint, like: https://<your azure ai name>.openai.azure.com/openai/deployments/<LLM Name>/chat/completions?api-version=2025-01-01-preview" if not api_key or not endpoint: raise ValueError("请配置 Azure OpenAI 信息") headers = { "Content-Type": "application/json", "api-key": api_key } systemprompt = f"""" 您是一个有趣的聊天智能体，能愉快的和人类聊天""" data = { "messages": [ {"role": "system", "content": systemprompt}, {"role": "user", "content": prompt} ], "max_tokens": 5000, "temperature": 0.7 } response = requests.post(endpoint, headers=headers, json=data) response.raise_for_status() result = response.json() return result["choices"][0]["message"]["content"] # 示例用法 if __name__ == "__main__": prompt = "请介绍一下Azure OpenAI的主要功能。" print(get_openai_answer(prompt))
然后，在 EchoBot 的 on_message_activity 中调用OpenAI接口即可
# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. from botbuilder.core import ActivityHandler, MessageFactory, TurnContext from botbuilder.schema import ChannelAccount from bots.call_openai import get_openai_answer class EchoBot(ActivityHandler): async def on_members_added_activity(self, members_added: [ChannelAccount], turn_context: TurnContext): for member in members_added: if member.id != turn_context.activity.recipient.id: await turn_context.send_activity("Hello and welcome, this is python code.") async def on_message_activity(self, turn_context: TurnContext): #call LLM API to get response llmresponse = get_openai_answer(turn_context.activity.text) return await turn_context.send_activity( MessageFactory.text(f"{llmresponse}") )
测试效果：
[完]
参考资料
当在复杂的环境中面临问题，格物之道需：浊而静之徐清，安以动之徐生。 云中，恰是如此!
