文件输入 | OpenAI API

OpenAI 模型可以接受文件作为 input_file 项。在 Responses API 中，您可以将文件作为 Base64 编码的数据、由 Files API 返回的文件 ID（/v1/files）或外部 URL 发送。

工作原理

input_file 处理方式取决于文件类型：

PDF 文件: 在具有视觉能力的模型上，例如 gpt-4o 及更高版本模型，API 会同时提取文本和页面图像，并将两者一并发送给模型。
非 PDF 文档和文本文件 （例如， .docx, .pptx, .txt, 和代码文件): API 仅提取文本。
电子表格文件 （例如， .xlsx, .csv, .tsv）：API 会运行专用的电子表格增强流程（如下所述）。

当以下相关工具更适合您的任务时，请使用它们：

使用文件搜索用于处理大型文件以进行检索，而不是将其直接作为 input_file.
使用托管 Shell 用于需要进行详细分析的繁重电子表格任务，例如聚合、连接、图表绘制或自定义计算。

非 PDF 图像和图表限制

对于非 PDF 文件，API 不会将嵌入的图像或图表提取到模型上下文中。

要保持图表和图示的精确度，请先将文件转换为 PDF，然后将该 PDF 作为 input_file.

电子表格增强的工作原理

对于类似电子表格的文件（例如 .xlsx, .xls, .csv, .tsv，且 .iif), input_file 使用专用的电子表格增强过程。

API 不会将整个工作表传递给模型，而是解析每个工作表的前 1,000 行，并添加由模型生成的摘要和表头元数据，以便模型能够基于更小、结构化的数据视图进行处理。

接受的文件类型

下表列出了在 input_file。扩展名和 MIME 类型的完整列表将在本页稍后显示。

类别	常见扩展名
PDF 文件	`.pdf`
文本和代码	`.txt`, `.md`, `.json`, `.html`, `.xml`、代码文件
富文本文档	`.doc`, `.docx`, `.rtf`, `.odt`
演示文稿	`.ppt`, `.pptx`
电子表格	`.csv`, `.xls`, `.xlsx`

文件 URL

你可以通过链接外部 URL 来提供文件输入。

使用外部文件 URL

curl

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curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.5",
        "input": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_text",
                        "text": "Analyze the letter and provide a summary of the key points."
                    },
                    {
                        "type": "input_file",
                        "file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf"
                    }
                ]
            }
        ]
    }'

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import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
    model: "gpt-5.5",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_text",
                    text: "Analyze the letter and provide a summary of the key points.",
                },
                {
                    type: "input_file",
                    file_url: "https://www.berkshirehathaway.com/letters/2024ltr.pdf",
                },
            ],
        },
    ],
});

console.log(response.output_text);

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "Analyze the letter and provide a summary of the key points.",
                },
                {
                    "type": "input_file",
                    "file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf",
                },
            ],
        },
    ]
)

print(response.output_text)

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using OpenAI.Files;
using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);

using HttpClient http = new();
using Stream stream = await http.GetStreamAsync("https://www.berkshirehathaway.com/letters/2024ltr.pdf");
OpenAIFileClient files = new(key);
OpenAIFile file = files.UploadFile(stream, "2024ltr.pdf", FileUploadPurpose.UserData);

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("Analyze the letter and provide a summary of the key points."),
        ResponseContentPart.CreateInputFilePart(file.Id),
    ]),
]);

Console.WriteLine(response.GetOutputText());

上传文件

以下示例使用 Files API，然后在向模型发送的请求中引用其文件 ID。

上传文件

curl

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curl https://api.openai.com/v1/files \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -F purpose="user_data" \
    -F file="@draconomicon.pdf"

curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.5",
        "input": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_file",
                        "file_id": "file-6F2ksmvXxt4VdoqmHRw6kL"
                    },
                    {
                        "type": "input_text",
                        "text": "What is the first dragon in the book?"
                    }
                ]
            }
        ]
    }'

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import fs from "fs";
import OpenAI from "openai";
const client = new OpenAI();

const file = await client.files.create({
    file: fs.createReadStream("draconomicon.pdf"),
    purpose: "user_data",
});

const response = await client.responses.create({
    model: "gpt-5.5",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_file",
                    file_id: file.id,
                },
                {
                    type: "input_text",
                    text: "What is the first dragon in the book?",
                },
            ],
        },
    ],
});

console.log(response.output_text);

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from openai import OpenAI
client = OpenAI()

file = client.files.create(
    file=open("draconomicon.pdf", "rb"),
    purpose="user_data"
)

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_file",
                    "file_id": file.id,
                },
                {
                    "type": "input_text",
                    "text": "What is the first dragon in the book?",
                },
            ]
        }
    ]
)

print(response.output_text)

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using OpenAI.Files;
using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.5", apiKey: key);

OpenAIFileClient files = new(key);
OpenAIFile file = files.UploadFile("draconomicon.pdf", FileUploadPurpose.UserData);

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputFilePart(file.Id),
        ResponseContentPart.CreateInputTextPart("What is the first dragon in the book?"),
    ]),
]);

Console.WriteLine(response.GetOutputText());

上传文件

curl

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curl https://api.openai.com/v1/files \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -F purpose="user_data" \
    -F file="@draconomicon.pdf"

curl "https://api.openai.com/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "file",
                        "file": {
                            "file_id": "file-6F2ksmvXxt4VdoqmHRw6kL"
                        }
                    },
                    {
                        "type": "text",
                        "text": "What is the first dragon in the book?"
                    }
                ]
            }
        ]
    }'

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import fs from "fs";
import OpenAI from "openai";
const client = new OpenAI();

const file = await client.files.create({
    file: fs.createReadStream("draconomicon.pdf"),
    purpose: "user_data",
});

const completion = await client.chat.completions.create({
    model: "gpt-5",
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "file",
                    file: {
                        file_id: file.id,
                    }
                },
                {
                    type: "text",
                    text: "What is the first dragon in the book?",
                },
            ],
        },
    ],
});

console.log(completion.choices[0].message.content);

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from openai import OpenAI
client = OpenAI()

file = client.files.create(
    file=open("draconomicon.pdf", "rb"),
    purpose="user_data"
)

completion = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "file",
                    "file": {
                        "file_id": file.id,
                    }
                },
                {
                    "type": "text",
                    "text": "What is the first dragon in the book?",
                },
            ]
        }
    ]
)

print(completion.choices[0].message.content)

Base64 编码的文件

你也可以将文件输入作为 Base64 编码的文件数据发送。

发送 Base64 编码的文件

curl

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curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5",
        "input": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_file",
                        "filename": "draconomicon.pdf",
                        "file_data": "...base64 encoded PDF bytes here..."
                    },
                    {
                        "type": "input_text",
                        "text": "What is the first dragon in the book?"
                    }
                ]
            }
        ]
    }'

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import fs from "fs";
import OpenAI from "openai";
const client = new OpenAI();

const data = fs.readFileSync("draconomicon.pdf");
const base64String = data.toString("base64");

const response = await client.responses.create({
    model: "gpt-5",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_file",
                    filename: "draconomicon.pdf",
                    file_data: `data:application/pdf;base64,${base64String}`,
                },
                {
                    type: "input_text",
                    text: "What is the first dragon in the book?",
                },
            ],
        },
    ],
});

console.log(response.output_text);

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import base64
from openai import OpenAI
client = OpenAI()

with open("draconomicon.pdf", "rb") as f:
    data = f.read()

base64_string = base64.b64encode(data).decode("utf-8")

response = client.responses.create(
    model="gpt-5",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_file",
                    "filename": "draconomicon.pdf",
                    "file_data": f"data:application/pdf;base64,{base64_string}",
                },
                {
                    "type": "input_text",
                    "text": "What is the first dragon in the book?",
                },
            ],
        },
    ]
)

print(response.output_text)

发送 Base64 编码的文件

curl

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curl "https://api.openai.com/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "file",
                        "file": {
                            "filename": "draconomicon.pdf",
                            "file_data": "...base64 encoded bytes here..."
                        }
                    },
                    {
                        "type": "text",
                        "text": "What is the first dragon in the book?"
                    }
                ]
            }
        ]
    }'

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import fs from "fs";
import OpenAI from "openai";
const client = new OpenAI();

const data = fs.readFileSync("draconomicon.pdf");
const base64String = data.toString("base64");

const completion = await client.chat.completions.create({
    model: "gpt-5",
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "file",
                    file: {
                        filename: "draconomicon.pdf",
                        file_data: `data:application/pdf;base64,${base64String}`
                    }
                },
                {
                    type: "text",
                    text: "What is the first dragon in the book?",
                },
            ],
        },
    ],
});

console.log(completion.choices[0].message.content);

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import base64
from openai import OpenAI
client = OpenAI()

with open("draconomicon.pdf", "rb") as f:
    data = f.read()

base64_string = base64.b64encode(data).decode("utf-8")

completion = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "file",
                    "file": {
                        "filename": "draconomicon.pdf",
                        "file_data": f"data:application/pdf;base64,{base64_string}",
                    }
                },
                {
                    "type": "text",
                    "text": "What is the first dragon in the book?",
                }
            ],
        },
    ],
)

print(completion.choices[0].message.content)

使用注意事项

使用文件输入时，请牢记以下限制：

Token 用量： PDF 解析会在上下文中同时包含提取的文本和页面图像，这可能会增加 Token 用量。在大规模部署之前，请查看定价和 Token 影响。了解更多定价.
文件大小限制： 单个请求可以包含多个文件，但每个文件不得超过 50 MB。请求中所有文件的合并大小上限为 50 MB。
支持的模型： 包含文本和页面图像的 PDF 解析需要具备视觉能力的模型，例如 gpt-4o and later models.
文件上传用途： 你可以为计划作为模型输入的文件上传任何支持的用途，但请使用 user_data 针对您计划作为模型输入传递的文件。

已接受文件类型的完整列表

类别	扩展名	MIME 类型
PDF 文件	PDF 文件 (`.pdf`)	`application/pdf`
电子表格	Excel 表格 (`.xla`, `.xlb`, `.xlc`, `.xlm`, `.xls`, `.xlsx`, `.xlt`, `.xlw`)	`application/vnd.openxmlformats-officedocument.spreadsheetml.sheet`, `application/vnd.ms-excel`
电子表格	CSV / TSV / IIF (`.csv`, `.tsv`, `.iif`)、Google Sheets	`text/csv`, `application/csv`, `text/tsv`, `text/x-iif`, `application/x-iif`, `application/vnd.google-apps.spreadsheet`
富文本文档	Word/ODT/RTF 文档 (`.doc`, `.docx`, `.dot`, `.odt`, `.rtf`）、Pages、Google Docs	`application/vnd.openxmlformats-officedocument.wordprocessingml.document`, `application/msword`, `application/rtf`, `text/rtf`, `application/vnd.oasis.opendocument.text`, `application/vnd.apple.pages`, `application/vnd.google-apps.document`, `application/vnd.apple.iwork`
演示文稿	PowerPoint 幻灯片 (`.pot`, `.ppa`, `.pps`, `.ppt`, `.pptx`, `.pwz`, `.wiz`）、Keynote、Google Slides	`application/vnd.openxmlformats-officedocument.presentationml.presentation`, `application/vnd.ms-powerpoint`, `application/vnd.apple.keynote`, `application/vnd.google-apps.presentation`, `application/vnd.apple.iwork`
文本和代码	文本/代码格式 (`.asm`, `.bat`, `.c`, `.cc`, `.conf`, `.cpp`, `.css`, `.cxx`, `.def`, `.dic`, `.eml`, `.h`, `.hh`, `.htm`, `.html`, `.ics`, `.ifb`, `.in`, `.js`, `.json`, `.ksh`, `.list`, `.log`, `.markdown`, `.md`, `.mht`, `.mhtml`, `.mime`, `.mjs`, `.nws`, `.pl`, `.py`, `.rst`, `.s`, `.sql`, `.srt`, `.text`, `.txt`, `.vcf`, `.vtt`, `.xml`)	`application/javascript`, `application/typescript`, `text/xml`, `text/x-shellscript`, `text/x-rst`, `text/x-makefile`, `text/x-lisp`, `text/x-asm`, `text/vbscript`, `text/css`, `message/rfc822`, `application/x-sql`, `application/x-scala`, `application/x-rust`, `application/x-powershell`, `text/x-diff`, `text/x-patch`, `application/x-patch`, `text/plain`, `text/markdown`, `text/x-java`, `text/x-script.python`, `text/x-python`, `text/x-c`, `text/x-c++`, `text/x-golang`, `text/html`, `text/x-php`, `application/x-php`, `application/x-httpd-php`, `application/x-httpd-php-source`, `text/x-ruby`, `text/x-sh`, `text/x-bash`, `application/x-bash`, `text/x-zsh`, `text/x-tex`, `text/x-csharp`, `application/json`, `text/x-typescript`, `text/javascript`, `text/x-go`, `text/x-rust`, `text/x-scala`, `text/x-kotlin`, `text/x-swift`, `text/x-lua`, `text/x-r`, `text/x-R`, `text/x-julia`, `text/x-perl`, `text/x-objectivec`, `text/x-objectivec++`, `text/x-erlang`, `text/x-elixir`, `text/x-haskell`, `text/x-clojure`, `text/x-groovy`, `text/x-dart`, `text/x-awk`, `application/x-awk`, `text/jsx`, `text/tsx`, `text/x-handlebars`, `text/x-mustache`, `text/x-ejs`, `text/x-jinja2`, `text/x-liquid`, `text/x-erb`, `text/x-twig`, `text/x-pug`, `text/x-jade`, `text/x-tmpl`, `text/x-cmake`, `text/x-dockerfile`, `text/x-gradle`, `text/x-ini`, `text/x-properties`, `text/x-protobuf`, `application/x-protobuf`, `text/x-sql`, `text/x-sass`, `text/x-scss`, `text/x-less`, `text/x-hcl`, `text/x-terraform`, `application/x-terraform`, `text/x-toml`, `application/x-toml`, `application/graphql`, `application/x-graphql`, `text/x-graphql`, `application/x-ndjson`, `application/json5`, `application/x-json5`, `text/x-yaml`, `application/toml`, `application/x-yaml`, `application/yaml`, `text/x-astro`, `text/srt`, `application/x-subrip`, `text/x-subrip`, `text/vtt`, `text/x-vcard`, `text/calendar`

后续步骤

接下来，您可能想要探索以下资源之一：

在 Playground 中体验文件输入

使用 Playground 通过文件输入来开发和迭代提示词。

完整 API 参考

查看 API 参考文档以获取更多选项。

对大型语料库使用文件搜索

当您需要可扩展的搜索，而不是在单个上下文窗口中发送整个文件时，请对分块文件使用检索功能。

使用 Hosted Shell 进行深度电子表格分析

使用 Hosted Shell 处理高级电子表格工作流，例如连接、聚合和图表绘制。

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项目

活动

工作原理

非 PDF 图像和图表限制

电子表格增强的工作原理

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上传文件

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使用注意事项

已接受文件类型的完整列表

后续步骤