client=OpenAI(api_key=os.environ.get("OPENAI_API_KEY"),)response=client.chat.completions.create(model="gpt-4",messages=messages,tools=functions,tool_choice="auto",)responseChatCompletion(id='chatcmpl-9qcEK7rbZMu5p5LNqJejAE1354S3j',choices=[Choice(finish_reason='tool_calls',index=0,logprob...
client=OpenAI()## 我们需要将用户的输入一并传递给OpenAI,以便从中提取函数调用所需要的参数。 messages=[{"role":"user","content":"深圳11月1号天气怎么样"}]### 第一次调用 Chat CompletionsAPI, 提取参数 response=client.chat.completions.create(model="gpt-3.5-turbo-1106",messages=messages,tools=too...
不使用function calling 了解OpenAI 函数调用的最佳方式是举例说明。假设你正在使用 ChatCompletion.create() 方法向 OpenAI 提出一个问题。您的代码看起来是这样的: import os from openai import OpenAI import json os.environ['OPENAI_API_KEY'] = "sk-xxx" client = OpenAI() prompt = ''' Population, Cap...
client=OpenAI classGetDeliveryDate(BaseModel): order_id:str tools=[openai.pydantic_function_tool(GetDeliveryDate)] messages=[] messages.append({"role":"system","content":"Youareahelpfulcustomersupportassistant.Usethesuppliedtoolstoassisttheuser."}) messages.append({"role":"user","content":"Hi,c...
from openai import OpenAI client = OpenAI() tools = [ { "type": "function", "function": { "name": "get_weather", "strict": True, "parameters": { "type": "object", "properties": { "location": {"type": "string"}, "unit": {"type": "string", "enum": ["c", "f"]}, ...
function.arguments) function_response = function_to_call(**function_args) messages.append( { "tool_call_id": tool_call.id, "role": "tool", "name": function_name, "content": function_response, } ) second_response = client.chat....
function_response = function_to_call(**function_args) messages.append( { "tool_call_id": tool_call.id, "role": "tool", "name": function_name, "content": function_response, } ) second_response = client.chat.completions.create(
, school_1_description ] for i, sample in enumerate(descriptions): response = client.chat.completions.create( model = 'gpt-3.5-turbo', messages = [{'role': 'user', 'content': sample}], functions = custom_functions, function_call = 'auto' ) response_message = response.choices[0]....
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","name":function_name,"content":function_response,})second_response=client.chat.co...
You can then complete theRunby submitting the tool output from the function(s) you call. Pass thetool_call_idreferenced in therequired_actionobject above to match output to each function call. Python 1.x REST Python fromopenaiimportAzureOpenAI client = AzureOpenAI( api_key=os.getenv("AZURE_...