function_name = response_message["function_call"]["name"] function_to_call = available_functions[function_name] function_args = json.loads(response_message["function_call"]["arguments"]) function_response = function_to_call( location=function_args.get("location"), unit=function_args.get("unit...
function_to_call = available_functions[function_name] function_args = json.loads(tool_call.function.arguments) function_response = function_to_call( location=function_args.get("location"), unit=function_args.get("unit"), ) messages.append( { "tool_call_id": tool_call.id, "role": "tool"...
chat.completionsMessage(content='Hithere!Icanhelpwiththat.CanyoupleaseprovideyourorderID?',role='assistant',function_call=None,tool_calls=None) 在助手使用场景中,你通常会希望向用户展示这个回应,并让他们对其进行回复,在这种情况下,你将再次调用API(将助手的最新回应和用户的回应都追加到消息中)。 假设我们...
const functionName = message.function_call.name const functionCall = functionCalls[functionName] const functionArguments = JSON.parse(message.function_call.arguments) const functionResponse = await functionCall(functionArguments) messages.push({ role: 'function', name: functionName, content: functionResp...
OpenAI 新的Function Calling O网页链接 的一种用法:让它返回格式化的JSON数据。以前GPT 3.5让它稳定的输出JSON格式是有点麻烦的,首先是Prompt比较长,可能还要Few-Shot,现在有了Function Calling,就可以要求它在返回结果之前去调用指定的Function,将结果作为参数传给Function,那么它就能返回一个标准的JSON格式,然后你...
{"id": "chatcmpl-7TQuwzJpQAY470saQM2RPfxwF6DDE","object": "chat.completion","created": 1687249338,"model": "gpt-3.5-turbo-0613","choices": [ {"index": ,"message": {"role": "assistant","content": null,"function_call": {"name": "send_email","arguments": "{\n \"to...
function_call="auto",# auto is default, but we'll be explicit) response_message = response["choices"][0]["message"] """ 输出: <OpenAIObject at 0x7a90d69f1620> JSON: { "role": "assistant", "content": null, "function_call": { ...
Function calling: const { google } = require('googleapis') const chatUseOpenAi = async (req, res) => { let { model = 'gpt-3.5-turbo-16k-0613', messages = [ { role: 'user', content: '你好', }, ], apiKey = 'sk-xxx', ...
"role": "assistant", "content": null, "function_call": { "name": "get_student_score", "arguments": "{\n\"name\": \"Lucy\"\n}" } } Lucy 拿着对应参数 name='Lucy',去执行相应函数 get_student_score 获得 json 结果,再次调用 chat 函数完成自然语言的回复 ...
function_to_call = available_functions[function_name] function_args = json.loads(tool_call.function.arguments) function_response = function_to_call(**function_args) messages.append( { "tool_call_id": tool_call.id, "role": "tool",