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结构化输出

结构化输出使模型遵循作为推理 API 调用的一部分提供的 JSON 架构定义。此方法与旧的 JSON 模式功能形成鲜明对比，该功能保证有效的 JSON，但无法确保严格遵循提供的架构。对函数调用、提取结构化数据以及生成复杂的多步骤工作流使用结构化输出。

入门指南

Python （Microsoft Entra ID）
Python（基于密钥的身份验证）

可以使用 Pydantic 在Python中定义对象架构。根据正在运行的 OpenAI 和 Pydantic 库的版本，可能需要升级到较新版本。这些示例已针对 openai 1.42.0 和 pydantic 2.8.2 进行测试。

pip install openai pydantic azure-identity --upgrade

如果不熟悉使用Microsoft Entra ID进行身份验证，请参阅如何使用身份验证在 Microsoft Foundry Microsoft Entra ID 模型中配置 Azure OpenAI。

from pydantic import BaseModel
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://ai.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

completion = client.beta.chat.completions.parse(
    model="MODEL_DEPLOYMENT_NAME", # replace with the model deployment name of your gpt-4o 2024-08-06 deployment
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ],
    response_format=CalendarEvent,
)

event = completion.choices[0].message.parsed

print(event)
print(completion.model_dump_json(indent=2))

输出

name='Science Fair' date='Friday' participants=['Alice', 'Bob']
{
  "id": "chatcmpl-A1EUP2fAmL4SeB1lVMinwM7I2vcqG",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "{\n  \"name\": \"Science Fair\",\n  \"date\": \"Friday\",\n  \"participants\": [\"Alice\", \"Bob\"]\n}",
        "refusal": null,
        "role": "assistant",
        "function_call": null,
        "tool_calls": [],
        "parsed": {
          "name": "Science Fair",
          "date": "Friday",
          "participants": [
            "Alice",
            "Bob"
          ]
        }
      }
    }
  ],
  "created": 1724857389,
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": "fp_1c2eaec9fe",
  "usage": {
    "completion_tokens": 27,
    "prompt_tokens": 32,
    "total_tokens": 59
  }
}

pip install openai pydantic --upgrade

import os
from pydantic import BaseModel
from openai import OpenAI

client = OpenAI(
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=os.getenv("AZURE_OPENAI_API_KEY")  
)

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

completion = client.beta.chat.completions.parse(
    model="MODEL_DEPLOYMENT_NAME", # replace with the model deployment name of your gpt-4o 2024-08-06 deployment
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ],
    response_format=CalendarEvent,
)

event = completion.choices[0].message.parsed

print(event)
print(completion.model_dump_json(indent=2))

输出

name='Science Fair' date='Friday' participants=['Alice', 'Bob']
{
  "id": "chatcmpl-A1EUP2fAmL4SeB1lVMinwM7I2vcqG",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "{\n  \"name\": \"Science Fair\",\n  \"date\": \"Friday\",\n  \"participants\": [\"Alice\", \"Bob\"]\n}",
        "refusal": null,
        "role": "assistant",
        "function_call": null,
        "tool_calls": [],
        "parsed": {
          "name": "Science Fair",
          "date": "Friday",
          "participants": [
            "Alice",
            "Bob"
          ]
        }
      }
    }
  ],
  "created": 1724857389,
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": "fp_1c2eaec9fe",
  "usage": {
    "completion_tokens": 27,
    "prompt_tokens": 32,
    "total_tokens": 59
  }
}

调用具有结构化输出的函数

可以通过提供 strict: true单个参数来启用函数调用的结构化输出。

注意

并行函数调用不支持结构化输出。使用结构化输出时，将 parallel_tool_calls 设置为 false。

Python （Microsoft Entra ID）
Python（基于密钥的身份验证）

import openai
from pydantic import BaseModel
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://ai.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

class GetDeliveryDate(BaseModel):
    order_id: str

tools = [openai.pydantic_function_tool(GetDeliveryDate)]

messages = []
messages.append({"role": "system", "content": "You are a helpful customer support assistant. Use the supplied tools to assist the user."})
messages.append({"role": "user", "content": "Hi, can you tell me the delivery date for my order #12345?"}) 

response = client.chat.completions.create(
    model="MODEL_DEPLOYMENT_NAME", # replace with the model deployment name of your gpt-4o 2024-08-06 deployment
    messages=messages,
    tools=tools
)

print(response.choices[0].message.tool_calls[0].function)
print(response.model_dump_json(indent=2))

import os
from pydantic import BaseModel
import openai
from openai import OpenAI

client = OpenAI(
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=os.getenv("AZURE_OPENAI_API_KEY")  
)

class GetDeliveryDate(BaseModel):
    order_id: str

tools = [openai.pydantic_function_tool(GetDeliveryDate)]

messages = []
messages.append({"role": "system", "content": "You are a helpful customer support assistant. Use the supplied tools to assist the user."})
messages.append({"role": "user", "content": "Hi, can you tell me the delivery date for my order #12345?"}) 

response = client.chat.completions.create(
    model="MODEL_DEPLOYMENT_NAME", # replace with the model deployment name of your gpt-4o 2024-08-06 deployment
    messages=messages,
    tools=tools
)

print(response.choices[0].message.tool_calls[0].function)
print(response.model_dump_json(indent=2))

入门指南

将以下包添加到项目：

OpenAI：标准 OpenAI .NET库。
Azure.Identity：跨Azure SDK库提供Microsoft Entra ID令牌身份验证支持。

dotnet add package OpenAI
dotnet add package Azure.Identity

Microsoft Entra ID
API 密钥

如果不熟悉使用Microsoft Entra ID进行身份验证，请参阅如何在具有Microsoft Entra ID身份验证的 Microsoft Foundry 模型中配置 Azure OpenAI。

using Azure.Identity;
using OpenAI;
using OpenAI.Chat;
using System.ClientModel.Primitives;
using System.Text.Json;

#pragma warning disable OPENAI001

BearerTokenPolicy tokenPolicy = new(
    new DefaultAzureCredential(),
    "https://ai.azure.com/.default");

ChatClient client = new(
    model: "gpt-4.1",
    authenticationPolicy: tokenPolicy,
    options: new OpenAIClientOptions()
    {
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);

ChatCompletionOptions options = new()
{
    ResponseFormat = ChatResponseFormat.CreateJsonSchemaFormat(
        jsonSchemaFormatName: "math_reasoning",
        jsonSchema: BinaryData.FromBytes("""
            {
                "type": "object",
                "properties": {
                    "steps": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "explanation": { "type": "string" },
                                "output": { "type": "string" }
                            },
                            "required": ["explanation", "output"],
                            "additionalProperties": false
                        }
                    },
                    "final_answer": { "type": "string" }
                },
                "required": ["steps", "final_answer"],
                "additionalProperties": false
            }
            """u8.ToArray()),
        jsonSchemaIsStrict: true)
};

// Create a list of ChatMessage objects
ChatCompletion completion = client.CompleteChat(
    [
        new UserChatMessage("How can I solve 8x + 7 = -23?")
    ],
    options);

using JsonDocument structuredJson = JsonDocument.Parse(completion.Content[0].Text);

Console.WriteLine($"Final answer: {structuredJson.RootElement.GetProperty("final_answer")}");
Console.WriteLine("Reasoning steps:");

foreach (JsonElement stepElement in structuredJson.RootElement.GetProperty("steps").EnumerateArray())
{
    Console.WriteLine($"  - Explanation: {stepElement.GetProperty("explanation")}");
    Console.WriteLine($"    Output: {stepElement.GetProperty("output")}");
}


using OpenAI;
using OpenAI.Chat;
using System.ClientModel;
using System.Text.Json;

string keyFromEnvironment = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");

ChatClient client = new(
    model: "gpt-4o-mini",
    credential: new ApiKeyCredential(keyFromEnvironment),
    options: new OpenAIClientOptions() { 
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);

ChatCompletionOptions options = new()
{
    ResponseFormat = ChatResponseFormat.CreateJsonSchemaFormat(
        jsonSchemaFormatName: "math_reasoning",
        jsonSchema: BinaryData.FromBytes("""
            {
                "type": "object",
                "properties": {
                    "steps": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "explanation": { "type": "string" },
                                "output": { "type": "string" }
                            },
                            "required": ["explanation", "output"],
                            "additionalProperties": false
                        }
                    },
                    "final_answer": { "type": "string" }
                },
                "required": ["steps", "final_answer"],
                "additionalProperties": false
            }
            """u8.ToArray()),
        jsonSchemaIsStrict: true)
};

// Create a list of ChatMessage objects
ChatCompletion completion = client.CompleteChat(
    [
        new UserChatMessage("How can I solve 8x + 7 = -23?")
    ],
    options);

using JsonDocument structuredJson = JsonDocument.Parse(completion.Content[0].Text);

Console.WriteLine($"Final answer: {structuredJson.RootElement.GetProperty("final_answer")}");
Console.WriteLine("Reasoning steps:");

foreach (JsonElement stepElement in structuredJson.RootElement.GetProperty("steps").EnumerateArray())
{
    Console.WriteLine($"  - Explanation: {stepElement.GetProperty("explanation")}");
    Console.WriteLine($"    Output: {stepElement.GetProperty("output")}");
}

入门指南

response_format 设置为 json_schema，已设置 strict: true。

curl -X POST  https://YOUR_RESOURCE_NAME.openai.azure.com/openai/v1/chat/completions \
  -H "api-key: $AZURE_OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
    -d '{
        "model": "YOUR_MODEL_DEPLOYMENT_NAME",
        "messages": [
                {"role": "system", "content": "Extract the event information."},
                {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."}
            ],
            "response_format": {
                "type": "json_schema",
                "json_schema": {
                    "name": "CalendarEventResponse",
                    "strict": true,
                    "schema": {
                        "type": "object",
                        "properties": {
                            "name": {
                              "type": "string"
                            },
                            "date": {
                                "type": "string"
                            },
                            "participants": {
                                "type": "array",
                                "items": {
                                    "type": "string"
                                }
                            }
                        },
                        "required": [
                            "name",
                            "date",
                            "participants"
                        ],
                        "additionalProperties": false
                    }
                }
          }
  }'

输出：

{
  "id": "chatcmpl-A1HKsHAe2hH9MEooYslRn9UmEwsag",
  "object": "chat.completion",
  "created": 1724868330,
  "model": "gpt-4o-2024-08-06",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "{\n  \"name\": \"Science Fair\",\n  \"date\": \"Friday\",\n  \"participants\": [\"Alice\", \"Bob\"]\n}"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 33,
    "completion_tokens": 27,
    "total_tokens": 60
  },
  "system_fingerprint": "fp_1c2eaec9fe"
}

调用具有结构化输出的函数

curl -X POST  https://YOUR_RESOURCE_NAME.openai.azure.com/openai/v1/chat/completions \
  -H "api-key: $AZURE_OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "YOUR_MODEL_DEPLOYMENT_NAME",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function."
    },
    {
      "role": "user",
      "content": "look up all my orders in may of last year that were fulfilled but not delivered on time"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "query",
        "description": "Execute a query.",
        "strict": true,
        "parameters": {
          "type": "object",
          "properties": {
            "table_name": {
              "type": "string",
              "enum": ["orders"]
            },
            "columns": {
              "type": "array",
              "items": {
                "type": "string",
                "enum": [
                  "id",
                  "status",
                  "expected_delivery_date",
                  "delivered_at",
                  "shipped_at",
                  "ordered_at",
                  "canceled_at"
                ]
              }
            },
            "conditions": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "column": {
                    "type": "string"
                  },
                  "operator": {
                    "type": "string",
                    "enum": ["=", ">", "<", ">=", "<=", "!="]
                  },
                  "value": {
                    "anyOf": [
                      {
                        "type": "string"
                      },
                      {
                        "type": "number"
                      },
                      {
                        "type": "object",
                        "properties": {
                          "column_name": {
                            "type": "string"
                          }
                        },
                        "required": ["column_name"],
                        "additionalProperties": false
                      }
                    ]
                  }
                },
                "required": ["column", "operator", "value"],
                "additionalProperties": false
              }
            },
            "order_by": {
              "type": "string",
              "enum": ["asc", "desc"]
            }
          },
          "required": ["table_name", "columns", "conditions", "order_by"],
          "additionalProperties": false
        }
      }
    }
  ]
}'

JSON 架构支持和限制

Azure OpenAI 结构化输出支持与 OpenAI 相同的 JSON 架构子集。

支持的类型

字符串
数字
布尔
整数
对象
阵列
枚举
anyOf

注意

根对象不能是 anyOf 类型。

所有字段都必须是必填字段

根据需要包括所有字段或函数参数。在以下示例中，location 和 unit 都出现在 "required": ["location", "unit"] 下。

{
    "name": "get_weather",
    "description": "Fetches the weather in the given location",
    "strict": true,
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The location to get the weather for"
            },
            "unit": {
                "type": "string",
                "description": "The unit to return the temperature in",
                "enum": ["F", "C"]
            }
        },
        "additionalProperties": false,
        "required": ["location", "unit"]
    }
}

如果需要，可以通过使用包含 null 的联合类型来模拟可选参数。在此示例中，此方法由行 "type": ["string", "null"],表示。

{
    "name": "get_weather",
    "description": "Fetches the weather in the given location",
    "strict": true,
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The location to get the weather for"
            },
            "unit": {
                "type": ["string", "null"],
                "description": "The unit to return the temperature in",
                "enum": ["F", "C"]
            }
        },
        "additionalProperties": false,
        "required": [
            "location", "unit"
        ]
    }
}

嵌套深度

架构最多可以有 100 个对象属性，最多可以有 5 个级别的嵌套。

在对象中始终设置 `additionalProperties: false`

此属性控制对象是否可以具有 JSON 架构中未定义的其他键值对。若要使用结构化输出，请将此值设置为 false。

键排序

结构化输出遵循与提供的架构相同的顺序。若要更改输出顺序，请修改作为推理请求的一部分发送的架构的顺序。

不受支持的特定于类型的关键字

类型	不支持的关键字
字符串	最小长度 maxLength（最大长度）模式格式
数字	最低最大 multipleOf
对象	patternProperties 未评估属性属性名称 minProperties maxProperties (最大属性数)
数组	未评估项包含 minContains maxContains minItems maxItems uniqueItems

使用 anyOf 的嵌套架构必须遵循整个 JSON 架构子集

支持 anyOf 的示例架构：

{
    "type": "object",
    "properties": {
        "item": {
            "anyOf": [
                {
                    "type": "object",
                    "description": "The user object to insert into the database",
                    "properties": {
                        "name": {
                            "type": "string",
                            "description": "The name of the user"
                        },
                        "age": {
                            "type": "number",
                            "description": "The age of the user"
                        }
                    },
                    "additionalProperties": false,
                    "required": [
                        "name",
                        "age"
                    ]
                },
                {
                    "type": "object",
                    "description": "The address object to insert into the database",
                    "properties": {
                        "number": {
                            "type": "string",
                            "description": "The number of the address. Eg. for 123 main st, this would be 123"
                        },
                        "street": {
                            "type": "string",
                            "description": "The street name. Eg. for 123 main st, this would be main st"
                        },
                        "city": {
                            "type": "string",
                            "description": "The city of the address"
                        }
                    },
                    "additionalProperties": false,
                    "required": [
                        "number",
                        "street",
                        "city"
                    ]
                }
            ]
        }
    },
    "additionalProperties": false,
    "required": [
        "item"
    ]
}

定义得到支持

支持的示例：

{
    "type": "object",
    "properties": {
        "steps": {
            "type": "array",
            "items": {
                "$ref": "#/$defs/step"
            }
        },
        "final_answer": {
            "type": "string"
        }
    },
    "$defs": {
        "step": {
            "type": "object",
            "properties": {
                "explanation": {
                    "type": "string"
                },
                "output": {
                    "type": "string"
                }
            },
            "required": [
                "explanation",
                "output"
            ],
            "additionalProperties": false
        }
    },
    "required": [
        "steps",
        "final_answer"
    ],
    "additionalProperties": false
}

支持递归架构

使用 # 的根递归示例：

{
        "name": "ui",
        "description": "Dynamically generated UI",
        "strict": true,
        "schema": {
            "type": "object",
            "properties": {
                "type": {
                    "type": "string",
                    "description": "The type of the UI component",
                    "enum": ["div", "button", "header", "section", "field", "form"]
                },
                "label": {
                    "type": "string",
                    "description": "The label of the UI component, used for buttons or form fields"
                },
                "children": {
                    "type": "array",
                    "description": "Nested UI components",
                    "items": {
                        "$ref": "#"
                    }
                },
                "attributes": {
                    "type": "array",
                    "description": "Arbitrary attributes for the UI component, suitable for any element",
                    "items": {
                        "type": "object",
                        "properties": {
                            "name": {
                                "type": "string",
                                "description": "The name of the attribute, for example onClick or className"
                            },
                            "value": {
                                "type": "string",
                                "description": "The value of the attribute"
                            }
                        },
                      "additionalProperties": false,
                      "required": ["name", "value"]
                    }
                }
            },
            "required": ["type", "label", "children", "attributes"],
            "additionalProperties": false
        }
    }

显式递归的示例：

{
    "type": "object",
    "properties": {
        "linked_list": {
            "$ref": "#/$defs/linked_list_node"
        }
    },
    "$defs": {
        "linked_list_node": {
            "type": "object",
            "properties": {
                "value": {
                    "type": "number"
                },
                "next": {
                    "anyOf": [
                        {
                            "$ref": "#/$defs/linked_list_node"
                        },
                        {
                            "type": "null"
                        }
                    ]
                }
            },
            "additionalProperties": false,
            "required": [
                "next",
                "value"
            ]
        }
    },
    "additionalProperties": false,
    "required": [
        "linked_list"
    ]
}

注意

目前，结构化输出不支持：

自带数据方案。
助手或 Foundry 智能体服务。
gpt-4o-audio-preview 和 gpt-4o-mini-audio-preview 版本： 2024-12-17.

支持的模型

gpt-5.1-codex 版本： 2025-11-13
gpt-5.1-codex mini 版本： 2025-11-13
gpt-5.1 版本： 2025-11-13
gpt-5.1-chat 版本： 2025-11-13
gpt-5-pro 版本 2025-10-06
gpt-5-codex 版本 2025-09-11
gpt-5 版本 2025-08-07
gpt-5-mini 版本 2025-08-07
gpt-5-nano 版本 2025-08-07
codex-mini 版本 2025-05-16
o3-pro 版本 2025-06-10
o3-mini 版本 2025-01-31
o1 版本： 2024-12-17
gpt-4o-mini 版本： 2024-07-18
gpt-4o 版本： 2024-08-06
gpt-4o 版本： 2024-11-20
gpt-4.1 版本 2025-04-14
gpt-4.1-nano 版本 2025-04-14
gpt-4.1-mini 版本： 2025-04-14
o4-mini 版本： 2025-04-16
o3 版本： 2025-04-16

API 支持

API 版本 2024-08-01-preview 是支持结构化输出的第一个版本。最新的预览 API 和最新的 GA API v1也支持结构化输出。

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Last updated on 2026-05-14

结构化输出

入门指南

输出

调用具有结构化输出的函数

入门指南

入门指南

调用具有结构化输出的函数

JSON 架构支持和限制

支持的类型

所有字段都必须是必填字段

嵌套深度

在对象中始终设置 additionalProperties: false

键排序

不受支持的特定于类型的关键字

使用 anyOf 的嵌套架构必须遵循整个 JSON 架构子集

定义得到支持

支持递归架构

支持的模型

API 支持

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其他资源

在对象中始终设置 `additionalProperties: false`