OpenAI responses系列教程:
OpenAI responses使用教程(一) ------导论
OpenAI responses使用教程(二) ------Responses create API介绍
OpenAI responses使用教程(三) ------Responses create python SDK 介绍
前面提到过，OpenAI SDK的代码，并非由手写代码完成，是使用AI根据OpenAI的API介绍自动生成的，仅有少部分需要人工参与，因此SDK的同步周期非常短，可以始终与API文档保持一致，同时也需要开发者在开发新项目时或者学习时，先更新SDK到最新版本。
目录
[四、最简单function tool用法](#四、最简单function tool用法)
[五、完整的function tool使用举例：](#五、完整的function tool使用举例：)
一、最简单文字用法
python SDK的使用相当精简且统一，我们以官方最简单的示例开始：
python
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-4.1",
input="Tell me a three sentence bedtime story about a unicorn."
)
print(response)与之前常用的chat.completion API相比，主要是使用input替换掉了原来的参数。上一篇介绍过，input其实相当相当复杂，它不仅支持直接使用字符串，还支持字典。以下是python sdk的代码部分，我们可以从代码部分来直观感受下:
python
@overload
def create(
self,
*,
stream: Literal[True],
background: Optional[bool] | Omit = omit,
conversation: Optional[response_create_params.Conversation] | Omit = omit,
include: Optional[List[ResponseIncludable]] | Omit = omit,
input: Union[str, ResponseInputParam] | Omit = omit,
instructions: Optional[str] | Omit = omit,
max_output_tokens: Optional[int] | Omit = omit,
max_tool_calls: Optional[int] | Omit = omit,
metadata: Optional[Metadata] | Omit = omit,
model: ResponsesModel | Omit = omit,
parallel_tool_calls: Optional[bool] | Omit = omit,
previous_response_id: Optional[str] | Omit = omit,
prompt: Optional[ResponsePromptParam] | Omit = omit,
prompt_cache_key: str | Omit = omit,
reasoning: Optional[Reasoning] | Omit = omit,
safety_identifier: str | Omit = omit,
service_tier: Optional[Literal["auto", "default", "flex", "scale", "priority"]] | Omit = omit,
store: Optional[bool] | Omit = omit,
stream_options: Optional[response_create_params.StreamOptions] | Omit = omit,
temperature: Optional[float] | Omit = omit,
text: ResponseTextConfigParam | Omit = omit,
tool_choice: response_create_params.ToolChoice | Omit = omit,
tools: Iterable[ToolParam] | Omit = omit,
top_logprobs: Optional[int] | Omit = omit,
top_p: Optional[float] | Omit = omit,
truncation: Optional[Literal["auto", "disabled"]] | Omit = omit,
user: str | Omit = omit,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = not_given,
) -> Stream[ResponseStreamEvent]:
注意，这里的'overload', 这表示不仅仅这一套签名，目前sdk代码中有四种签名，所以create的真是参数系统会更复杂。好在大多数情况，我们根本不需要全都使用，只需要使用常用的几个即可。
二、最简单图片用法
官方示例：
python
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-4.1",
input=[
{
"role": "user",
"content": [
{ "type": "input_text", "text": "what is in this file?" },
{
"type": "input_file",
"file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf"
}
]
}
]
)
print(response)三、最简单文件用法
官方示例：
python
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-4.1",
tools=[{
"type": "file_search",
"vector_store_ids": ["vs_1234567890"],
"max_num_results": 20
}],
input="What are the attributes of an ancient brown dragon?",
)
print(response)四、最简单function tool用法
python
from openai import OpenAI
client = OpenAI()
tools = [
{
"type": "function",
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location", "unit"],
}
}
]
response = client.responses.create(
model="gpt-4.1",
tools=tools,
input="What is the weather like in Boston today?",
tool_choice="auto"
)
print(response)这样，response中包含了所有的返回信息，但实际使用中，我们需要的不仅是把工具告诉大模型，还要跟大模型交互，接下来的例子，在官方示例的基础上改造，完成与实际项目中一致的需求。
我们先来看以上简单场景下的response返回内容，以下是真是请求响应的内容:
python
Response(
id='resp_abcdc38752e835a6006911a1c8ef4481a3805065a6a620fa35',
created_at=1762763208.0,
error=None,
incomplete_details=None,
instructions=None,
metadata={},
model='gpt-4.1-2025-04-14',
object='response',
output=[
ResponseFunctionToolCall(
arguments='{"location":"Boston, MA","unit":"celsius"}',
call_id='call_ABCDpNPI4FBEy8B8jxaLfAPC',
name='get_current_weather',
type='function_call',
id='fc_abcdc38752e835a6006911a1cb148481a3b4c0ad9d6d9423b3',
status='completed')
],
parallel_tool_calls=True,
temperature=1.0,
tool_choice='auto',
tools=[
FunctionTool(
name='get_current_weather',
parameters={
'type': 'object',
'properties': {
'location': {
'type': 'string',
'description': 'The city and state, e.g. San Francisco, CA'},
'unit': {
'type': 'string',
'enum': [
'celsius', 'fahrenheit'
]
}
},
'required': [
'location',
'unit'
],
'additionalProperties': False
},
strict=True,
type='function',
description='Get the current weather in a given location')
],
top_p=1.0,
background=False,
conversation=None,
max_output_tokens=None,
max_tool_calls=None,
previous_response_id=None,
prompt=None,
prompt_cache_key=None,
reasoning=Reasoning(
effort=None, '
generate_summary=None,
summary=None
),
safety_identifier=None,
service_tier='default',
status='completed',
text=ResponseTextConfig(
format=ResponseFormatText(
type='text'
),
verbosity='medium'
), top_logprobs=0,
truncation='disabled',
usage=ResponseUsage(
input_tokens=75,
input_tokens_details=InputTokensDetails(cached_tokens=0),
output_tokens=23,
output_tokens_details=OutputTokensDetails(reasoning_tokens=0),
total_tokens=98
),
user=None,
billing={'payer': 'developer'},
prompt_cache_retention=None,
store=True
)从上述内容来看，response返回了get_current_weather所需要的参数，因此我们接下来的工作，就是从response中提取参数，并调用get_current_weather函数。
下面是一个更完整的示例。
五、完整的function tool使用举例
python
from openai import OpenAI
import json
client = OpenAI()
# 工具定义
tools = [
{
"type": "function",
"name": "get_horoscope",
"description": "Get today's horoscope for an astrological sign.",
"parameters": {
"type": "object",
"properties": {
"sign": {
"type": "string",
"description": "An astrological sign like Taurus or Aquarius",
},
},
"required": ["sign"],
},
},
]
def get_horoscope(sign):
return f"{sign}: Next Tuesday you will befriend a baby otter."
# 构造输入内容
input_list = [
{"role": "user", "content": "What is my horoscope? I am an Aquarius."}
]
# 2. Prompt the model with tools defined
response = client.responses.create(
model="gpt-5",
tools=tools,
input=input_list,
)
# 关键点，需要把输出内容附加到输入中再次回灌
input_list += response.output
for item in response.output:
if item.type == "function_call":
if item.name == "get_horoscope":
# 3. Execute the function logic for get_horoscope
horoscope = get_horoscope(json.loads(item.arguments))
# 4. Provide function call results to the model
input_list.append({
"type": "function_call_output",
"call_id": item.call_id,
"output": json.dumps({
"horoscope": horoscope
})
})
print("Final input:")
print(input_list)
response = client.responses.create(
model="gpt-5",
instructions="Respond only with a horoscope generated by a tool.",
tools=tools,
input=input_list,
)
# 最终响应
print("Final output:")
print(response.model_dump_json(indent=2))
print("\n" + response.output_text)