OpenAI 响应 API¶

本笔记本演示如何使用 OpenAI Responses 大语言模型。

如果您在 Colab 上打开此 Notebook，可能需要安装 LlamaIndex 🦙。

In [ ]:

%pip install llama-index llama-index-llms-openai

%pip install llama-index llama-index-llms-openai

基本用法¶

In [ ]:

import os

os.environ["OPENAI_API_KEY"] = "..."

import os os.environ["OPENAI_API_KEY"] = "..."

In [ ]:

from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(
    model="gpt-4o-mini",
    # api_key="some key",  # uses OPENAI_API_KEY env var by default
)

from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses( model="gpt-4o-mini", # api_key="some key", # uses OPENAI_API_KEY env var by default )

调用 complete 方法并传入提示词¶

In [ ]:

from llama_index.llms.openai import OpenAI

resp = llm.complete("Paul Graham is ")

from llama_index.llms.openai import OpenAI resp = llm.complete("Paul Graham is ")

In [ ]:

print(resp)

print(resp)

Paul Graham is a prominent computer scientist, entrepreneur, and venture capitalist, best known for co-founding the startup accelerator Y Combinator. He is also recognized for his essays on technology, startups, and programming, which have influenced many in the tech community. Graham has a background in programming languages and artificial intelligence, having earned a Ph.D. from Harvard University. His work has significantly shaped the startup ecosystem, particularly in Silicon Valley. Would you like to know more about a specific aspect of his work or ideas?

使用消息列表调用 chat 方法¶

In [ ]:

from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)

from llama_index.core.llms import ChatMessage messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] resp = llm.chat(messages)

In [ ]:

print(resp)

print(resp)

assistant: Ahoy, matey! Ye can call me Captain Jollybeard, the most colorful pirate to sail the seven seas! What brings ye to me ship today? Arrr!

流式传输¶

使用 stream_complete 端点

In [ ]:

resp = llm.stream_complete("Paul Graham is ")

resp = llm.stream_complete("Paul Graham is ")

In [ ]:

for r in resp:
    print(r.delta, end="")

for r in resp: print(r.delta, end="")

Paul Graham is a prominent computer scientist, entrepreneur, and venture capitalist, best known for co-founding the startup accelerator Y Combinator. He is also recognized for his essays on technology, startups, and programming, which have influenced many in the tech community. Graham has a background in programming languages and artificial intelligence and has authored several influential works, including "Hackers and Painters." His insights on entrepreneurship and innovation have made him a respected figure in Silicon Valley.

使用 stream_chat 端点

In [ ]:

from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)

from llama_index.core.llms import ChatMessage messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] resp = llm.stream_chat(messages)

In [ ]:

for r in resp:
    print(r.delta, end="")

for r in resp: print(r.delta, end="")

Ahoy there! Ye can call me Captain Jollybeard, the most colorful pirate to sail the seven seas! What brings ye to me ship today?

配置参数¶

响应式 API 支持多种选项：

• 设置模型名称
• 生成参数调节（如温度值 temperature、top_p、最大输出令牌数 max_output_tokens）
• 启用内置工具调用功能
• 为 O 系列模型设置推理强度
• 自动追踪历史对话记录
• 以及其他更多功能！

基本参数¶

In [ ]:

from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(
    model="gpt-4o-mini",
    temperature=0.5,  # default is 0.1
    max_output_tokens=100,  # default is None
    top_p=0.95,  # default is 1.0
)

from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses( model="gpt-4o-mini", temperature=0.5, # default is 0.1 max_output_tokens=100, # default is None top_p=0.95, # default is 1.0 )

内置工具调用¶

响应API支持内置工具调用功能，您可在此处了解更多详情此处。

配置该功能后，大语言模型(LLM)将自动调用工具并利用其增强响应内容。

工具以字典列表形式定义，每个字典包含对应工具的配置参数。

以下是使用内置网页搜索工具的示例。

In [ ]:

from llama_index.llms.openai import OpenAIResponses
from llama_index.core.llms import ChatMessage

llm = OpenAIResponses(
    model="gpt-4o-mini",
    built_in_tools=[{"type": "web_search_preview"}],
)

resp = llm.chat(
    [ChatMessage(role="user", content="What is the weather in San Francisco?")]
)
print(resp)
print("========" * 2)
print(resp.additional_kwargs)

from llama_index.llms.openai import OpenAIResponses from llama_index.core.llms import ChatMessage llm = OpenAIResponses( model="gpt-4o-mini", built_in_tools=[{"type": "web_search_preview"}], ) resp = llm.chat( [ChatMessage(role="user", content="What is the weather in San Francisco?")] ) print(resp) print("========" * 2) print(resp.additional_kwargs)

assistant: As of 12:18 AM on Friday, March 28, 2025, in San Francisco, the current weather is partly sunny with a temperature of 61°F (16°C).

## Weather for San Francisco, CA:
Current Conditions: Partly sunny, 61°F (16°C)

Daily Forecast:
* Thursday, March 27: Low: 52°F (11°C), High: 61°F (16°C), Description: Periods of rain and drizzle beginning in the late morning; breezy this afternoon
* Friday, March 28: Low: 47°F (8°C), High: 61°F (16°C), Description: A shower in the area in the morning; otherwise, clouds giving way to some sun
* Saturday, March 29: Low: 50°F (10°C), High: 60°F (15°C), Description: Mostly sunny
* Sunday, March 30: Low: 51°F (11°C), High: 59°F (15°C), Description: Cloudy; periods of rain in the morning followed by a shower in spots in the afternoon
* Monday, March 31: Low: 49°F (10°C), High: 58°F (14°C), Description: Cloudy and cool; a couple of showers in the afternoon
* Tuesday, April 01: Low: 53°F (12°C), High: 58°F (14°C), Description: Some sunshine giving way to clouds, breezy and cool; occasional rain in the afternoon
* Wednesday, April 02: Low: 52°F (11°C), High: 56°F (13°C), Description: A couple of showers in the morning; otherwise, cloudy and remaining cool


In March, San Francisco typically experiences daytime temperatures around 61°F (16°C) and nighttime temperatures around 47°F (8°C). The city usually receives about 3.5 inches (89 mm) of rainfall over approximately 11 days during the month. ([weather2visit.com](https://www.weather2visit.com/north-america/united-states/san-francisco-march.htm?utm_source=openai)) 
================
{'built_in_tool_calls': [ResponseFunctionWebSearch(id='ws_67e5eaecce088191ab2edce452ef25420a24041ef7e917b2', status='completed', type='web_search_call')], 'annotations': [AnnotationURLCitation(end_index=1561, start_index=1439, title='San Francisco Weather in March 2025 | United States Averages | Weather-2-Visit', type='url_citation', url='https://www.weather2visit.com/north-america/united-states/san-francisco-march.htm?utm_source=openai')], 'usage': ResponseUsage(input_tokens=327, output_tokens=462, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=789, input_tokens_details={'cached_tokens': 0})}

推理力度¶

对于 O 系列模型，您可以通过设置推理力度来控制模型进行推理的时间量。

更多信息请参阅 OpenAI API 文档。

In [ ]:

from llama_index.llms.openai import OpenAIResponses
from llama_index.core.llms import ChatMessage

llm = OpenAIResponses(
    model="o3-mini",
    reasoning_options={"effort": "high"},
)

resp = llm.chat(
    [ChatMessage(role="user", content="What is the meaning of life?")]
)
print(resp)
print("========" * 2)
print(resp.additional_kwargs)

from llama_index.llms.openai import OpenAIResponses from llama_index.core.llms import ChatMessage llm = OpenAIResponses( model="o3-mini", reasoning_options={"effort": "high"}, ) resp = llm.chat( [ChatMessage(role="user", content="What is the meaning of life?")] ) print(resp) print("========" * 2) print(resp.additional_kwargs)

assistant: The question “What is the meaning of life?” has been asked by philosophers, theologians, scientists, and countless individuals throughout history—and there isn’t one definitive answer that satisfies everyone. Here are a few perspectives that help illustrate why the answer is so open-ended:

1. Philosophical and Existential Views:  
 • Some existentialist thinkers, such as Jean-Paul Sartre and Albert Camus, argue that life doesn’t come with a preordained meaning. Instead, they suggest that it’s up to each individual to create their own purpose through choices, relationships, and personal projects.  
 • Other philosophies, like those in classical philosophy, have emphasized the pursuit of virtue, knowledge, or happiness as key to a meaningful life.

2. Religious and Spiritual Perspectives:  
 • Many religious traditions offer meanings tied to their spiritual beliefs. In these views, life might be seen as a journey of growth, moral development, or fulfilling a divine plan—whether that means living according to God’s commandments, achieving enlightenment, or learning lessons through reincarnation.  
 • Spiritual traditions often encourage adherents to find meaning in love, compassion, and service to others.

3. Scientific and Naturalistic Approaches:  
 • From a biological standpoint, one might say that the purpose of life is simply to survive and reproduce—the basic mechanisms that drive natural selection and evolution.  
 • Some argue that while biology defines life’s mechanics, human consciousness allows us to transcend mere survival and imbue our existence with personal and cultural meaning.

4. Personal and Cultural Interpretations:  
 • For many, meaning is found in everyday connections: love, creativity, learning, and contributing to the community or society at large.  
 • Different cultures and individuals may prioritize various values (such as achievement, compassion, or exploration), which means the “meaning” can vary widely based on one’s upbringing, experiences, and personal reflections.

5. A Humorous Take:  
 • Popular culture, notably in Douglas Adams’ The Hitchhiker’s Guide to the Galaxy, famously suggests that the answer to the ultimate question of life is “42”—a playful reminder that the real significance often lies in the journey of questioning itself rather than arriving at a single answer.

In the end, the meaning of life might be considered less of an absolute truth and more of an invitation to explore, question, and define what matters most to you personally. Whether through relationships, creative pursuits, intellectual challenges, or spiritual practices, many believe we have the power to create our own meaning in a vast and complex universe.
================
{'built_in_tool_calls': [], 'reasoning': ResponseReasoningItem(id='rs_683e2dde0e308198a72c5e7f2e9bf52a0dd2faa5908183b8', summary=[], type='reasoning', encrypted_content=None, status=None), 'annotations': [], 'usage': ResponseUsage(input_tokens=13, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=841, output_tokens_details=OutputTokensDetails(reasoning_tokens=320), total_tokens=854)}

图像支持¶

OpenAI 的众多模型已支持在聊天消息输入中处理图像内容。

通过聊天消息的内容区块功能，您可以轻松地将文本和图像组合到同一个大语言模型提示中。

In [ ]:

!wget https://cdn.pixabay.com/photo/2016/07/07/16/46/dice-1502706_640.jpg -O image.png

!wget https://cdn.pixabay.com/photo/2016/07/07/16/46/dice-1502706_640.jpg -O image.png

In [ ]:

from llama_index.core.llms import ChatMessage, TextBlock, ImageBlock
from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(model="gpt-4o")

messages = [
    ChatMessage(
        role="user",
        blocks=[
            ImageBlock(path="image.png"),
            TextBlock(text="Describe the image in a few sentences."),
        ],
    )
]

resp = llm.chat(messages)
print(resp.message.content)

from llama_index.core.llms import ChatMessage, TextBlock, ImageBlock from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses(model="gpt-4o") messages = [ ChatMessage( role="user", blocks=[ ImageBlock(path="image.png"), TextBlock(text="Describe the image in a few sentences."), ], ) ] resp = llm.chat(messages) print(resp.message.content)

The image shows three white dice with black dots, captured in mid-air above a checkered surface. The dice are in various orientations, displaying different numbers of dots. The background is dark, with a subtle light illuminating the dice, creating a dramatic effect. The checkered surface resembles a chess or checkerboard.

使用函数/工具调用功能¶

OpenAI 模型原生支持函数调用功能。这一特性与 LlamaIndex 的工具抽象层无缝集成，允许您将任意 Python 函数接入大语言模型。

在以下示例中，我们定义了一个用于生成 Song 对象的函数。

In [ ]:

from pydantic import BaseModel
from llama_index.core.tools import FunctionTool


class Song(BaseModel):
    """A song with name and artist"""

    name: str
    artist: str


def generate_song(name: str, artist: str) -> Song:
    """Generates a song with provided name and artist."""
    return Song(name=name, artist=artist)


tool = FunctionTool.from_defaults(fn=generate_song)

from pydantic import BaseModel from llama_index.core.tools import FunctionTool class Song(BaseModel): """A song with name and artist""" name: str artist: str def generate_song(name: str, artist: str) -> Song: """Generates a song with provided name and artist.""" return Song(name=name, artist=artist) tool = FunctionTool.from_defaults(fn=generate_song)

strict 参数用于指示 OpenAI 在生成工具调用/结构化输出时是否使用约束采样。这意味着生成的工具调用模式将始终包含预期字段。

由于该参数似乎会增加延迟，默认值为 false。

In [ ]:

from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(model="gpt-4o-mini", strict=True)
response = llm.predict_and_call(
    [tool],
    "Write a random song for me",
    # strict=True  # can also be set at the function level to override the class
)
print(str(response))

from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses(model="gpt-4o-mini", strict=True) response = llm.predict_and_call( [tool], "Write a random song for me", # strict=True # can also be set at the function level to override the class ) print(str(response))

name='Chasing Stars' artist='Luna Sky'

我们还可以进行多函数调用。

In [ ]:

llm = OpenAIResponses(model="gpt-4o-mini")
response = llm.predict_and_call(
    [tool],
    "Generate five songs from the Beatles",
    allow_parallel_tool_calls=True,
)
for s in response.sources:
    print(f"Name: {s.tool_name}, Input: {s.raw_input}, Output: {str(s)}")

llm = OpenAIResponses(model="gpt-4o-mini") response = llm.predict_and_call( [tool], "Generate five songs from the Beatles", allow_parallel_tool_calls=True, ) for s in response.sources: print(f"Name: {s.tool_name}, Input: {s.raw_input}, Output: {str(s)}")

Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Hey Jude', 'artist': 'The Beatles'}}, Output: name='Hey Jude' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Let It Be', 'artist': 'The Beatles'}}, Output: name='Let It Be' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Come Together', 'artist': 'The Beatles'}}, Output: name='Come Together' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Yesterday', 'artist': 'The Beatles'}}, Output: name='Yesterday' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Twist and Shout', 'artist': 'The Beatles'}}, Output: name='Twist and Shout' artist='The Beatles'

手动工具调用¶

若需控制工具的调用方式，您也可以将工具调用与工具选择拆分为独立步骤。

首先，让我们选择工具。

In [ ]:

from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(model="gpt-4o-mini")

chat_history = [ChatMessage(role="user", content="Write a random song for me")]

resp = llm.chat_with_tools([tool], chat_history=chat_history)

from llama_index.core.llms import ChatMessage from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses(model="gpt-4o-mini") chat_history = [ChatMessage(role="user", content="Write a random song for me")] resp = llm.chat_with_tools([tool], chat_history=chat_history)

现在，让我们调用大语言模型选择的工具（如果有的话）。

如果存在工具调用，我们应该将结果发送给大语言模型以生成最终响应（或者触发另一个工具调用！）。

In [ ]:

tools_by_name = {t.metadata.name: t for t in [tool]}
tool_calls = llm.get_tool_calls_from_response(
    resp, error_on_no_tool_call=False
)

while tool_calls:
    # add the LLM's response to the chat history
    chat_history.append(resp.message)

    for tool_call in tool_calls:
        tool_name = tool_call.tool_name
        tool_kwargs = tool_call.tool_kwargs

        print(f"Calling {tool_name} with {tool_kwargs}")
        tool_output = tool(**tool_kwargs)
        chat_history.append(
            ChatMessage(
                role="tool",
                content=str(tool_output),
                # most LLMs like OpenAI need to know the tool call id
                additional_kwargs={"call_id": tool_call.tool_id},
            )
        )

        resp = llm.chat_with_tools([tool], chat_history=chat_history)
        tool_calls = llm.get_tool_calls_from_response(
            resp, error_on_no_tool_call=False
        )

tools_by_name = {t.metadata.name: t for t in [tool]} tool_calls = llm.get_tool_calls_from_response( resp, error_on_no_tool_call=False ) while tool_calls: # add the LLM's response to the chat history chat_history.append(resp.message) for tool_call in tool_calls: tool_name = tool_call.tool_name tool_kwargs = tool_call.tool_kwargs print(f"Calling {tool_name} with {tool_kwargs}") tool_output = tool(**tool_kwargs) chat_history.append( ChatMessage( role="tool", content=str(tool_output), # most LLMs like OpenAI need to know the tool call id additional_kwargs={"call_id": tool_call.tool_id}, ) ) resp = llm.chat_with_tools([tool], chat_history=chat_history) tool_calls = llm.get_tool_calls_from_response( resp, error_on_no_tool_call=False )

Calling generate_song with {'name': 'Chasing Stars', 'artist': 'Luna Sky'}

现在，我们应该能得到最终响应了！

In [ ]:

print(resp.message.content)

print(resp.message.content)

Here's a song for you titled **"Chasing Stars"** by **Luna Sky**!

### Chasing Stars

**Verse 1**  
In the midnight glow, we wander free,  
With dreams like fireflies, lighting up the sea.  
Whispers of the night, calling out our names,  
Together we’ll ignite, this wild, untamed flame.

**Chorus**  
We’re chasing stars, through the endless night,  
With every heartbeat, we’ll take flight.  
Hand in hand, we’ll break the dark,  
In this cosmic dance, we’ll leave our mark.

**Verse 2**  
Underneath the moon, secrets softly shared,  
Every glance a promise, every touch a dare.  
The universe is ours, let the journey start,  
With every step we take, we’re painting art.

**Chorus**  
We’re chasing stars, through the endless night,  
With every heartbeat, we’ll take flight.  
Hand in hand, we’ll break the dark,  
In this cosmic dance, we’ll leave our mark.

**Bridge**  
And when the dawn arrives, we’ll still be here,  
With the echoes of our laughter, crystal clear.  
No matter where we go, no matter how far,  
Forever in our hearts, we’ll chase those stars.

**Chorus**  
We’re chasing stars, through the endless night,  
With every heartbeat, we’ll take flight.  
Hand in hand, we’ll break the dark,  
In this cosmic dance, we’ll leave our mark.

**Outro**  
So let’s chase the stars, let’s light the way,  
In this beautiful journey, we’ll never stray.  
With dreams as our compass, love as our guide,  
Together we’ll soar, side by side.

Feel free to let me know if you'd like any changes or another song!

结构化预测¶

函数调用的一个重要应用场景是提取结构化对象。LlamaIndex 提供了一套直观的接口，可将任何大型语言模型转化为结构化语言模型——只需定义目标 Pydantic 类（支持嵌套结构），在给定提示词的情况下，系统便能提取出所需的对象。

In [ ]:

from llama_index.llms.openai import OpenAIResponses
from llama_index.core.prompts import PromptTemplate
from pydantic import BaseModel
from typing import List


class MenuItem(BaseModel):
    """A menu item in a restaurant."""

    course_name: str
    is_vegetarian: bool


class Restaurant(BaseModel):
    """A restaurant with name, city, and cuisine."""

    name: str
    city: str
    cuisine: str
    menu_items: List[MenuItem]


llm = OpenAIResponses(model="gpt-4o-mini")
prompt_tmpl = PromptTemplate(
    "Generate a restaurant in a given city {city_name}"
)
# Option 1: Use `as_structured_llm`
restaurant_obj = (
    llm.as_structured_llm(Restaurant)
    .complete(prompt_tmpl.format(city_name="Dallas"))
    .raw
)
# Option 2: Use `structured_predict`
# restaurant_obj = llm.structured_predict(Restaurant, prompt_tmpl, city_name="Miami")

from llama_index.llms.openai import OpenAIResponses from llama_index.core.prompts import PromptTemplate from pydantic import BaseModel from typing import List class MenuItem(BaseModel): """A menu item in a restaurant.""" course_name: str is_vegetarian: bool class Restaurant(BaseModel): """A restaurant with name, city, and cuisine.""" name: str city: str cuisine: str menu_items: List[MenuItem] llm = OpenAIResponses(model="gpt-4o-mini") prompt_tmpl = PromptTemplate( "Generate a restaurant in a given city {city_name}" ) # Option 1: Use `as_structured_llm` restaurant_obj = ( llm.as_structured_llm(Restaurant) .complete(prompt_tmpl.format(city_name="Dallas")) .raw ) # Option 2: Use `structured_predict` # restaurant_obj = llm.structured_predict(Restaurant, prompt_tmpl, city_name="Miami")

In [ ]:

restaurant_obj

restaurant_obj

Out[ ]:

Restaurant(name='Tex-Mex Delight', city='Dallas', cuisine='Tex-Mex', menu_items=[MenuItem(course_name='Tacos', is_vegetarian=False), MenuItem(course_name='Vegetarian Enchiladas', is_vegetarian=True), MenuItem(course_name='Fajitas', is_vegetarian=False), MenuItem(course_name='Chips and Salsa', is_vegetarian=True), MenuItem(course_name='Queso Dip', is_vegetarian=True)])

异步处理¶

In [ ]:

from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(model="gpt-4o")

from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses(model="gpt-4o")

In [ ]:

resp = await llm.acomplete("Paul Graham is ")

resp = await llm.acomplete("Paul Graham is ")

In [ ]:

print(resp)

print(resp)

Paul Graham is a British-American entrepreneur, venture capitalist, and essayist. He is best known for co-founding Viaweb, one of the first web-based applications, which was later sold to Yahoo and became Yahoo Store. Graham is also a co-founder of Y Combinator, a highly influential startup accelerator that has funded and supported numerous successful startups, including Dropbox, Airbnb, and Reddit. In addition to his work in technology and startups, Graham is known for his insightful essays on topics such as programming, entrepreneurship, and the philosophy of work.

In [ ]:

resp = await llm.astream_complete("Paul Graham is ")

resp = await llm.astream_complete("Paul Graham is ")

In [ ]:

async for delta in resp:
    print(delta.delta, end="")

async for delta in resp: print(delta.delta, end="")

Paul Graham is a British-American entrepreneur, venture capitalist, and essayist. He is best known for co-founding Viaweb, one of the first web-based applications, which was later sold to Yahoo and became Yahoo Store. Graham is also a co-founder of Y Combinator, a highly influential startup accelerator that has funded and supported numerous successful startups, including Dropbox, Airbnb, and Reddit. In addition to his work in technology and startups, Graham is known for his insightful essays on topics related to entrepreneurship, technology, and society.

异步函数调用同样受支持。

In [ ]:

llm = OpenAIResponses(model="gpt-4o-mini")
response = await llm.apredict_and_call([tool], "Generate a random song")
print(str(response))

llm = OpenAIResponses(model="gpt-4o-mini") response = await llm.apredict_and_call([tool], "Generate a random song") print(str(response))

name='Chasing Stars' artist='Luna Sky'

额外关键字参数¶

如果存在构造函数中未包含的额外关键字参数（kwargs），您可以通过 additional_kwargs 在单个实例级别进行设置。

这些参数将被传递至每次调用大语言模型（LLM）的过程中。

In [ ]:

from llama_index.llms.openai import OpenAIResponses

llm = OpenAIResponses(
    model="gpt-4o-mini", additional_kwargs={"user": "your_user_id"}
)
resp = llm.complete("Paul Graham is ")
print(resp)

from llama_index.llms.openai import OpenAIResponses llm = OpenAIResponses( model="gpt-4o-mini", additional_kwargs={"user": "your_user_id"} ) resp = llm.complete("Paul Graham is ") print(resp)

图像生成¶

您可以通过内置工具调用图像生成功能，传入参数 {'type': 'image_generation'}。如需启用流式传输，可使用 {'type': 'image_generation', 'partial_images': 2}：

In [ ]:

import base64
from llama_index.llms.openai import OpenAIResponses
from llama_index.core.llms import ChatMessage, ImageBlock, TextBlock

# run without streaming
llm = OpenAIResponses(
    model="gpt-4.1-mini", built_in_tools=[{"type": "image_generation"}]
)
messages = [
    ChatMessage.from_str(
        content="A llama dancing with a cat in a meadow", role="user"
    )
]
response = llm.chat(
    messages
)  # response = await llm.achat(messages) for an async implementation
for block in response.message.blocks:
    if isinstance(block, ImageBlock):
        with open("llama_and_cat_dancing.png", "wb") as f:
            f.write(bas64.b64decode(block.image))
    elif isinstance(block, TextBlock):
        print(block.text)

# run with streaming
llm_stream = OpenAIResponses(
    model="gpt-4.1-mini",
    built_in_tools=[{"type": "image_generation", "partial_images": 2}],
)
response = llm_stream.stream_chat(
    messages
)  # response = await llm_stream.asteam_chat(messages) for an async implementation
for event in response:
    for block in event.message.blocks:
        if isinstance(block, ImageBlock):
            # block.detail contains the ID of the image
            with open(f"llama_and_cat_dancing_{block.detail}.png", "wb") as f:
                f.write(bas64.b64decode(block.image))
        elif isinstance(block, TextBlock):
            print(block.text)

import base64 from llama_index.llms.openai import OpenAIResponses from llama_index.core.llms import ChatMessage, ImageBlock, TextBlock # run without streaming llm = OpenAIResponses( model="gpt-4.1-mini", built_in_tools=[{"type": "image_generation"}] ) messages = [ ChatMessage.from_str( content="A llama dancing with a cat in a meadow", role="user" ) ] response = llm.chat( messages ) # response = await llm.achat(messages) for an async implementation for block in response.message.blocks: if isinstance(block, ImageBlock): with open("llama_and_cat_dancing.png", "wb") as f: f.write(bas64.b64decode(block.image)) elif isinstance(block, TextBlock): print(block.text) # run with streaming llm_stream = OpenAIResponses( model="gpt-4.1-mini", built_in_tools=[{"type": "image_generation", "partial_images": 2}], ) response = llm_stream.stream_chat( messages ) # response = await llm_stream.asteam_chat(messages) for an async implementation for event in response: for block in event.message.blocks: if isinstance(block, ImageBlock): # block.detail contains the ID of the image with open(f"llama_and_cat_dancing_{block.detail}.png", "wb") as f: f.write(bas64.b64decode(block.image)) elif isinstance(block, TextBlock): print(block.text)

MCP 远程调用¶

您可以通过 OpenAI 响应 API 调用任意 远程 MCP，只需将 MCP 的具体参数作为内置工具传递给大语言模型即可。

In [ ]:

from llama_index.llms.openai import OpenAIResponses
from llama_index.core.llms import ChatMessage

llm = OpenAIResponses(
    model="gpt-4.1",
    built_in_tools=[
        {
            "type": "mcp",
            "server_label": "deepwiki",
            "server_url": "https://mcp.deepwiki.com/mcp",
            "require_approval": "never",
        }
    ],
)
messages = [
    ChatMessage.from_str(
        content="What transport protocols are supported in the 2025-03-26 version of the MCP spec?",
        role="user",
    )
]
response = llm.chat(messages)
# see the textual output
print(response.message.content)
# see the MCP tool call
print(response.raw.output[0])

from llama_index.llms.openai import OpenAIResponses from llama_index.core.llms import ChatMessage llm = OpenAIResponses( model="gpt-4.1", built_in_tools=[ { "type": "mcp", "server_label": "deepwiki", "server_url": "https://mcp.deepwiki.com/mcp", "require_approval": "never", } ], ) messages = [ ChatMessage.from_str( content="What transport protocols are supported in the 2025-03-26 version of the MCP spec?", role="user", ) ] response = llm.chat(messages) # see the textual output print(response.message.content) # see the MCP tool call print(response.raw.output[0])

代码解释器¶

您只需将 "type": "code_interpreter", "container": { "type": "auto" } 设置为内置工具，即可使用代码解释器。

In [ ]:

from llama_index.llms.openai import OpenAIResponses
from llama_index.core.llms import ChatMessage

llm = OpenAIResponses(
    model="gpt-4.1",
    built_in_tools=[
        {
            "type": "code_interpreter",
            "container": {"type": "auto"},
        }
    ],
)
messages = messages = [
    ChatMessage.from_str(
        content="I need to solve the equation 3x + 11 = 14. Can you help me?",
        role="user",
    )
]
response = llm.chat(messages)
# see the textual output
print(response.message.content)
# see the MCP tool call
print(response.raw.output[0])

from llama_index.llms.openai import OpenAIResponses from llama_index.core.llms import ChatMessage llm = OpenAIResponses( model="gpt-4.1", built_in_tools=[ { "type": "code_interpreter", "container": {"type": "auto"}, } ], ) messages = messages = [ ChatMessage.from_str( content="I need to solve the equation 3x + 11 = 14. Can you help me?", role="user", ) ] response = llm.chat(messages) # see the textual output print(response.message.content) # see the MCP tool call print(response.raw.output[0])