ChatOpenAI

本笔记本提供了一个快速概述，帮助您开始使用 OpenAI 聊天模型。有关所有 ChatOpenAI 功能和配置的详细文档，请访问 API 参考。

OpenAI 有几个聊天模型。您可以在 OpenAI 文档中找到有关其最新模型及其成本、上下文窗口和支持的输入类型的信息。

Azure OpenAI

请注意，某些 OpenAI 模型也可以通过 Microsoft Azure 平台访问。要使用 Azure OpenAI 服务，请使用 AzureChatOpenAI 集成。

概述

集成详情

类	包	本地	可序列化	JS 支持	包下载量	包最新版本
ChatOpenAI	langchain-openai	❌	beta	✅

模型功能

工具调用	结构化输出	JSON 模式	图像输入	音频输入	视频输入	令牌级流式传输	原生异步	令牌使用量	Logprobs
✅	✅	✅	✅	✅	❌	✅	✅	✅	✅

设置

要访问 OpenAI 模型，您需要创建一个 OpenAI 帐户，获取一个 API 密钥，并安装 langchain-openai 集成包。

凭证

前往 https://platform.openai.com 注册 OpenAI 并生成 API 密钥。完成后，设置 OPENAI_API_KEY 环境变量

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

如果您想获得模型调用的自动追踪，您还可以通过取消注释下方代码来设置您的 LangSmith API 密钥

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

LangChain OpenAI 集成位于 langchain-openai 包中

%pip install -qU langchain-openai

实例化

现在我们可以实例化我们的模型对象并生成聊天完成

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # api_key="...",  # if you prefer to pass api key in directly instaed of using env vars
    # base_url="...",
    # organization="...",
    # other params...
)

API 参考:ChatOpenAI

调用

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 31, 'total_tokens': 36}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-63219b22-03e3-4561-8cc4-78b7c7c3a3ca-0', usage_metadata={'input_tokens': 31, 'output_tokens': 5, 'total_tokens': 36})

print(ai_msg.content)

J'adore la programmation.

链式调用

我们可以像这样使用提示模板链式调用我们的模型

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API 参考:ChatPromptTemplate

AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-350585e1-16ca-4dad-9460-3d9e7e49aaf1-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})

工具调用

OpenAI 有一个工具调用（我们在这里互换使用“工具调用”和“函数调用”）API，允许您描述工具及其参数，并让模型返回一个 JSON 对象，其中包含要调用的工具和该工具的输入。工具调用对于构建工具使用链和代理，以及更普遍地从模型中获得结构化输出非常有用。

ChatOpenAI.bind_tools()

使用 ChatOpenAI.bind_tools，我们可以轻松地将 Pydantic 类、字典模式、LangChain 工具，甚至函数作为工具传递给模型。在底层，这些被转换为 OpenAI 工具模式，看起来像

{
    "name": "...",
    "description": "...",
    "parameters": {...}  # JSONSchema
}

并在每次模型调用中传递。

from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

llm_with_tools = llm.bind_tools([GetWeather])

ai_msg = llm_with_tools.invoke(
    "what is the weather like in San Francisco",
)
ai_msg

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_o9udf3EVOWiV4Iupktpbpofk', 'function': {'arguments': '{"location":"San Francisco, CA"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 68, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-1617c9b2-dda5-4120-996b-0333ed5992e2-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_o9udf3EVOWiV4Iupktpbpofk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 68, 'output_tokens': 17, 'total_tokens': 85})

`strict=True`

需要 langchain-openai>=0.1.21

截至 2024 年 8 月 6 日，OpenAI 在调用工具时支持 strict 参数，这将强制模型遵守工具参数模式。在此处查看更多信息：https://platform.openai.com/docs/guides/function-calling

注意：如果 strict=True，工具定义也将被验证，并且只接受 JSON 模式的一个子集。至关重要的是，模式不能有可选参数（带有默认值的参数）。在此处阅读有关支持哪些类型的模式的完整文档：https://platform.openai.com/docs/guides/structured-outputs/supported-schemas。

llm_with_tools = llm.bind_tools([GetWeather], strict=True)
ai_msg = llm_with_tools.invoke(
    "what is the weather like in San Francisco",
)
ai_msg

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'function': {'arguments': '{"location":"San Francisco, CA"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 68, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5e3356a9-132d-4623-8e73-dd5a898cf4a6-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'type': 'tool_call'}], usage_metadata={'input_tokens': 68, 'output_tokens': 17, 'total_tokens': 85})

AIMessage.tool_calls

请注意，AIMessage 有一个 tool_calls 属性。这包含一种标准化的 ToolCall 格式，该格式与模型提供商无关。

ai_msg.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'San Francisco, CA'},
  'id': 'call_jUqhd8wzAIzInTJl72Rla8ht',
  'type': 'tool_call'}]

有关绑定工具和工具调用输出的更多信息，请访问工具调用文档。

Responses API

需要 langchain-openai>=0.3.9

OpenAI 支持 Responses API，该 API 面向构建代理应用程序。它包括一套内置工具，包括网络和文件搜索。它还支持对话状态的管理，允许您继续对话线程，而无需显式传递之前的消息。

如果使用了这些功能之一，ChatOpenAI 将路由到 Responses API。您也可以在实例化 ChatOpenAI 时指定 use_responses_api=True。

内置工具

为 ChatOpenAI 配备内置工具将使其响应基于外部信息，例如文件或网络中的上下文。AIMessage 从模型生成的模型将包括有关内置工具调用的信息。

网络搜索

要触发网络搜索，请将 {"type": "web_search_preview"} 作为另一个工具传递给模型。

提示

您也可以将内置工具作为调用参数传递

llm.invoke("...", tools=[{"type": "web_search_preview"}])

from langchain_openai import ChatOpenAI

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

tool = {"type": "web_search_preview"}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke("What was a positive news story from today?")

API 参考:ChatOpenAI

请注意，响应包括结构化的内容块，其中既包括响应的文本，也包括 OpenAI 注释，其中引用了其来源

response.content

[{'type': 'text',
  'text': 'Today, a heartwarming story emerged from Minnesota, where a group of high school robotics students built a custom motorized wheelchair for a 2-year-old boy named Cillian Jackson. Born with a genetic condition that limited his mobility, Cillian\'s family couldn\'t afford the $20,000 wheelchair he needed. The students at Farmington High School\'s Rogue Robotics team took it upon themselves to modify a Power Wheels toy car into a functional motorized wheelchair for Cillian, complete with a joystick, safety bumpers, and a harness. One team member remarked, "I think we won here more than we do in our competitions. Instead of completing a task, we\'re helping change someone\'s life." ([boredpanda.com](https://www.boredpanda.com/wholesome-global-positive-news/?utm_source=openai))\n\nThis act of kindness highlights the profound impact that community support and innovation can have on individuals facing challenges. ',
  'annotations': [{'end_index': 778,
    'start_index': 682,
    'title': '“Global Positive News”: 40 Posts To Remind Us There’s Good In The World',
    'type': 'url_citation',
    'url': 'https://www.boredpanda.com/wholesome-global-positive-news/?utm_source=openai'}]}]

提示

您可以使用 response.text() 将响应的文本内容恢复为字符串。例如，要流式传输响应文本

for token in llm_with_tools.stream("..."):
    print(token.text(), end="|")

有关更多详细信息，请参阅流式传输指南。

输出消息还将包含来自任何工具调用的信息

response.additional_kwargs

{'tool_outputs': [{'id': 'ws_67d192aeb6cc81918e736ad4a57937570d6f8507990d9d71',
   'status': 'completed',
   'type': 'web_search_call'}]}

文件搜索

要触发文件搜索，请将文件搜索工具作为另一个工具传递给模型。您需要填充 OpenAI 管理的向量存储，并在工具定义中包含向量存储 ID。有关更多详细信息，请参阅 OpenAI 文档。

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

openai_vector_store_ids = [
    "vs_...",  # your IDs here
]

tool = {
    "type": "file_search",
    "vector_store_ids": openai_vector_store_ids,
}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke("What is deep research by OpenAI?")
print(response.text())

Deep Research by OpenAI is a new capability integrated into ChatGPT that allows for the execution of multi-step research tasks independently. It can synthesize extensive amounts of online information and produce comprehensive reports similar to what a research analyst would do, significantly speeding up processes that would typically take hours for a human.

### Key Features:
- **Independent Research**: Users simply provide a prompt, and the model can find, analyze, and synthesize information from hundreds of online sources.
- **Multi-Modal Capabilities**: The model is also able to browse user-uploaded files, plot graphs using Python, and embed visualizations in its outputs.
- **Training**: Deep Research has been trained using reinforcement learning on real-world tasks that require extensive browsing and reasoning.

### Applications:
- Useful for professionals in sectors like finance, science, policy, and engineering, enabling them to obtain accurate and thorough research quickly.
- It can also be beneficial for consumers seeking personalized recommendations on complex purchases.

### Limitations:
Although Deep Research presents significant advancements, it has some limitations, such as the potential to hallucinate facts or struggle with authoritative information. 

Deep Research aims to facilitate access to thorough and documented information, marking a significant step toward the broader goal of developing artificial general intelligence (AGI).

与网络搜索一样，响应将包括带有引用的内容块

response.content[0]["annotations"][:2]

[{'file_id': 'file-3UzgX7jcC8Dt9ZAFzywg5k',
  'index': 346,
  'type': 'file_citation',
  'filename': 'deep_research_blog.pdf'},
 {'file_id': 'file-3UzgX7jcC8Dt9ZAFzywg5k',
  'index': 575,
  'type': 'file_citation',
  'filename': 'deep_research_blog.pdf'}]

它还将包括来自内置工具调用的信息

response.additional_kwargs

{'tool_outputs': [{'id': 'fs_67d196fbb83c8191ba20586175331687089228ce932eceb1',
   'queries': ['What is deep research by OpenAI?'],
   'status': 'completed',
   'type': 'file_search_call'}]}

计算机使用

ChatOpenAI 支持 "computer-use-preview" 模型，这是一个用于内置计算机使用工具的专用模型。要启用，请将计算机使用工具作为另一个工具传递。

目前，计算机使用工具的工具输出存在于 AIMessage.additional_kwargs["tool_outputs"] 中。要回复计算机使用工具调用，请构造一个 ToolMessage，并在其 additional_kwargs 中包含 {"type": "computer_call_output"}。消息的内容将是屏幕截图。下面，我们演示一个简单的示例。

首先，加载两个屏幕截图

import base64


def load_png_as_base64(file_path):
    with open(file_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
        return encoded_string.decode("utf-8")


screenshot_1_base64 = load_png_as_base64(
    "/path/to/screenshot_1.png"
)  # perhaps a screenshot of an application
screenshot_2_base64 = load_png_as_base64(
    "/path/to/screenshot_2.png"
)  # perhaps a screenshot of the Desktop

from langchain_openai import ChatOpenAI

# Initialize model
llm = ChatOpenAI(
    model="computer-use-preview",
    model_kwargs={"truncation": "auto"},
)

# Bind computer-use tool
tool = {
    "type": "computer_use_preview",
    "display_width": 1024,
    "display_height": 768,
    "environment": "browser",
}
llm_with_tools = llm.bind_tools([tool])

# Construct input message
input_message = {
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": (
                "Click the red X to close and reveal my Desktop. "
                "Proceed, no confirmation needed."
            ),
        },
        {
            "type": "input_image",
            "image_url": f"data:image/png;base64,{screenshot_1_base64}",
        },
    ],
}

# Invoke model
response = llm_with_tools.invoke(
    [input_message],
    reasoning={
        "generate_summary": "concise",
    },
)

API 参考:ChatOpenAI

响应将在其 additional_kwargs 中包含对计算机使用工具的调用

response.additional_kwargs

{'reasoning': {'id': 'rs_67ddb381c85081919c46e3e544a161e8051ff325ba1bad35',
  'summary': [{'text': 'Closing Visual Studio Code application',
    'type': 'summary_text'}],
  'type': 'reasoning'},
 'tool_outputs': [{'id': 'cu_67ddb385358c8191bf1a127b71bcf1ea051ff325ba1bad35',
   'action': {'button': 'left', 'type': 'click', 'x': 17, 'y': 38},
   'call_id': 'call_Ae3Ghz8xdqZQ01mosYhXXMho',
   'pending_safety_checks': [],
   'status': 'completed',
   'type': 'computer_call'}]}

接下来，我们使用这些属性构造一个 ToolMessage

它有一个与计算机调用中的 call_id 匹配的 tool_call_id。
它的 additional_kwargs 中包含 {"type": "computer_call_output"}。
其内容是 image_url 或 input_image 输出块（有关格式，请参阅 OpenAI 文档）。

from langchain_core.messages import ToolMessage

tool_call_id = response.additional_kwargs["tool_outputs"][0]["call_id"]

tool_message = ToolMessage(
    content=[
        {
            "type": "input_image",
            "image_url": f"data:image/png;base64,{screenshot_2_base64}",
        }
    ],
    # content=f"data:image/png;base64,{screenshot_2_base64}",  # <-- also acceptable
    tool_call_id=tool_call_id,
    additional_kwargs={"type": "computer_call_output"},
)

API 参考:ToolMessage

我们现在可以使用消息历史记录再次调用模型

messages = [
    input_message,
    response,
    tool_message,
]

response_2 = llm_with_tools.invoke(
    messages,
    reasoning={
        "generate_summary": "concise",
    },
)

response_2.text()

'Done! The Desktop is now visible.'

除了传回整个序列之外，我们还可以使用 previous_response_id

previous_response_id = response.response_metadata["id"]

response_2 = llm_with_tools.invoke(
    [tool_message],
    previous_response_id=previous_response_id,
    reasoning={
        "generate_summary": "concise",
    },
)

response_2.text()

'The Visual Studio Code terminal has been closed and your desktop is now visible.'

管理对话状态

Responses API 支持对话状态的管理。

手动管理状态

您可以手动管理状态，也可以像使用其他聊天模型一样使用 LangGraph

from langchain_openai import ChatOpenAI

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

tool = {"type": "web_search_preview"}
llm_with_tools = llm.bind_tools([tool])

first_query = "What was a positive news story from today?"
messages = [{"role": "user", "content": first_query}]

response = llm_with_tools.invoke(messages)
response_text = response.text()
print(f"{response_text[:100]}... {response_text[-100:]}")

API 参考:ChatOpenAI

As of March 12, 2025, here are some positive news stories that highlight recent uplifting events:

*...  exemplify positive developments in health, environmental sustainability, and community well-being.

second_query = (
    "Repeat my question back to me, as well as the last sentence of your answer."
)

messages.extend(
    [
        response,
        {"role": "user", "content": second_query},
    ]
)
second_response = llm_with_tools.invoke(messages)
print(second_response.text())

Your question was: "What was a positive news story from today?"

The last sentence of my answer was: "These stories exemplify positive developments in health, environmental sustainability, and community well-being."

提示

您可以使用 LangGraph 在各种后端（包括内存和 Postgres）中为您管理对话线程。请参阅本教程以开始使用。

传递 `previous_response_id`

使用 Responses API 时，LangChain 消息将在其元数据中包含一个 "id" 字段。将此 ID 传递给后续调用将继续对话。请注意，从计费角度来看，这与手动传递消息是等效的。

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-mini",
    use_responses_api=True,
)
response = llm.invoke("Hi, I'm Bob.")
print(response.text())

API 参考:ChatOpenAI

Hi Bob! How can I assist you today?

second_response = llm.invoke(
    "What is my name?",
    previous_response_id=response.response_metadata["id"],
)
print(second_response.text())

Your name is Bob. How can I help you today, Bob?

微调

您可以通过传入相应的 modelName 参数来调用微调的 OpenAI 模型。

这通常采用 ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID} 的形式。例如

fine_tuned_model = ChatOpenAI(
    temperature=0, model_name="ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR"
)

fine_tuned_model.invoke(messages)

AIMessage(content="J'adore la programmation.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0f39b30e-c56e-4f3b-af99-5c948c984146-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})

多模态输入

OpenAI 有支持多模态输入的模型。您可以将图像或音频传递给这些模型。有关如何在 LangChain 中执行此操作的更多信息，请访问多模态输入文档。

您可以在 OpenAI 的文档中查看支持不同模态的模型列表。

在本文档撰写时，您将使用的主要 OpenAI 模型将是

图像输入：gpt-4o, gpt-4o-mini
音频输入：gpt-4o-audio-preview

有关传递图像输入的示例，请参阅多模态输入操作指南。

以下是将音频输入传递到 gpt-4o-audio-preview 的示例

import base64

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-audio-preview",
    temperature=0,
)

with open(
    "../../../../libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav",
    "rb",
) as f:
    # b64 encode it
    audio = f.read()
    audio_b64 = base64.b64encode(audio).decode()


output_message = llm.invoke(
    [
        (
            "human",
            [
                {"type": "text", "text": "Transcribe the following:"},
                # the audio clip says "I'm sorry, but I can't create..."
                {
                    "type": "input_audio",
                    "input_audio": {"data": audio_b64, "format": "wav"},
                },
            ],
        ),
    ]
)
output_message.content

API 参考:ChatOpenAI

"I'm sorry, but I can't create audio content that involves yelling. Is there anything else I can help you with?"

预测输出

信息

需要 langchain-openai>=0.2.6

一些 OpenAI 模型（例如其 gpt-4o 和 gpt-4o-mini 系列）支持预测输出，这允许您提前传递 LLM 预期输出的已知部分，以减少延迟。这对于编辑文本或代码等情况非常有用，在这些情况下，模型的输出只有一小部分会发生更改。

这是一个例子

code = """
/// <summary>
/// Represents a user with a first name, last name, and username.
/// </summary>
public class User
{
    /// <summary>
    /// Gets or sets the user's first name.
    /// </summary>
    public string FirstName { get; set; }

    /// <summary>
    /// Gets or sets the user's last name.
    /// </summary>
    public string LastName { get; set; }

    /// <summary>
    /// Gets or sets the user's username.
    /// </summary>
    public string Username { get; set; }
}
"""

llm = ChatOpenAI(model="gpt-4o")
query = (
    "Replace the Username property with an Email property. "
    "Respond only with code, and with no markdown formatting."
)
response = llm.invoke(
    [{"role": "user", "content": query}, {"role": "user", "content": code}],
    prediction={"type": "content", "content": code},
)
print(response.content)
print(response.response_metadata)

/// <summary>
/// Represents a user with a first name, last name, and email.
/// </summary>
public class User
{
    /// <summary>
    /// Gets or sets the user's first name.
    /// </summary>
    public string FirstName { get; set; }

    /// <summary>
    /// Gets or sets the user's last name.
    /// </summary>
    public string LastName { get; set; }

    /// <summary>
    /// Gets or sets the user's email.
    /// </summary>
    public string Email { get; set; }
}
{'token_usage': {'completion_tokens': 226, 'prompt_tokens': 166, 'total_tokens': 392, 'completion_tokens_details': {'accepted_prediction_tokens': 49, 'audio_tokens': None, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 107}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_45cf54deae', 'finish_reason': 'stop', 'logprobs': None}

请注意，目前的预测功能会作为额外的 tokens 收费，并可能会增加您的用量和成本，以换取更低的延迟。

音频生成 (预览版)

信息

需要 langchain-openai>=0.2.3

OpenAI 推出了新的音频生成功能，允许您将音频输入和输出与 gpt-4o-audio-preview 模型一起使用。

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-audio-preview",
    temperature=0,
    model_kwargs={
        "modalities": ["text", "audio"],
        "audio": {"voice": "alloy", "format": "wav"},
    },
)

output_message = llm.invoke(
    [
        ("human", "Are you made by OpenAI? Just answer yes or no"),
    ]
)

API 参考:ChatOpenAI

output_message.additional_kwargs['audio'] 将包含一个类似如下的字典

{
    'data': '<audio data b64-encoded',
    'expires_at': 1729268602,
    'id': 'audio_67127d6a44348190af62c1530ef0955a',
    'transcript': 'Yes.'
}

格式将是 model_kwargs['audio']['format'] 中传入的格式。

我们也可以在 openai expires_at 到期之前，将包含音频数据的此消息作为消息历史记录的一部分传递回模型。

注意

输出音频存储在 AIMessage.additional_kwargs 中的 audio 键下，但输入内容块使用 HumanMessage.content 列表中的 input_audio 类型和键进行类型化。

有关更多信息，请参阅 OpenAI 的音频文档。

history = [
    ("human", "Are you made by OpenAI? Just answer yes or no"),
    output_message,
    ("human", "And what is your name? Just give your name."),
]
second_output_message = llm.invoke(history)

API 参考

有关所有 ChatOpenAI 功能和配置的详细文档，请访问 API 参考：https://python.langchain.ac.cn/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html

聊天模型概念指南
聊天模型操作指南

概述​

集成详情​

模型功能​

设置​

凭证​

安装​

实例化​

调用​

链式调用​

工具调用​

ChatOpenAI.bind_tools()​

strict=True​

AIMessage.tool_calls​

Responses API​

内置工具​

网络搜索​

文件搜索​

计算机使用​

管理对话状态​

手动管理状态​

传递 previous_response_id​

微调​

多模态输入​

预测输出​

音频生成 (预览版)​

API 参考​

相关内容​

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概述

集成详情

模型功能

设置

凭证

安装

实例化

调用

链式调用

工具调用

ChatOpenAI.bind_tools()

`strict=True`

AIMessage.tool_calls

Responses API

内置工具

网络搜索

文件搜索

计算机使用

管理对话状态

手动管理状态

传递 `previous_response_id`

微调

多模态输入

预测输出

音频生成 (预览版)

API 参考

相关内容