使用 OpenAI API 新增的工具 ～ 远程 MCP、图像生成、代码解释器 ～

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model='gpt-4.1-mini',
    input='用markdown格式总结系统要求和使用步骤，大约200字。 GitHub Repository: openai/codex',
    # 指定 MCP 工具
    tools=[{
        'type': 'mcp',
        'server_label': 'deepwiki',
        'server_url': 'https://mcp.deepwiki.com/mcp',
        # 如果无需审批执行
        # 'require_approval': 'never',
    }],
)

while any(entity.type == 'mcp_approval_request' for entity in response.output):
    # 审批过程
    approval_inputs = []
    for entity in response.output:
        if entity.type == 'mcp_approval_request':
            print((
                '*' * 10 + ' 执行工具 ' + '*' * 10 + '\n'
                f'请求ID: {entity.id}\n'
                f'工具: {entity.name}\n'
                f'参数: {entity.arguments}\n'
                f'标签: {entity.server_label}\n'
            ))
            approval_inputs.append({
                'type': 'mcp_approval_response',
                'approval_request_id': entity.id,
                'approve': True
            })
    # 审批 & 执行 MCP 工具
    response = client.responses.create(
        model='gpt-4.1-mini',
        previous_response_id=response.id, # 保持上下文
        tools=[{
            'type': 'mcp',
            'server_label': 'deepwiki',
            'server_url': 'https://mcp.deepwiki.com/mcp',
        }],
        input=approval_inputs
    )

print('*' * 10 + ' 最终执行结果 ' + '*' * 10)
print(response.output_text)

from openai import OpenAI
import base64

client = OpenAI()

response = client.responses.create(
    model='gpt-4.1-mini',
    input=(
        '请为豆藏开发者活动"Mamezou Tech Fest 2025"制作宣传横幅。\n'
        '- 明亮愉快的氛围\n'
        '- 豆子角色们聚集在一起的场景\n'
        '- 包含活动名称和日期"2025年7月20日"\n'
        '- 动漫风格，色彩丰富的配色\n'    ),
    # 指定图像生成工具
    tools=[{
        'type': 'image_generation',
        'size': '1024x1536',
        'quality': 'medium',
        'output_format': 'webp',
        'model': 'gpt-image-1', # Responses API 当前仅支持此模型（DALL-E 不可用）
    }],
    tool_choice={'type': 'image_generation'}
)

images = [
    output.result for output in response.output
    if output.type == 'image_generation_call'
]

if images:
    with open('banner.webp', 'wb') as f:
        f.write(base64.b64decode(images[0]))

from openai import OpenAI
import base64

client = OpenAI()

# 原始图像
with open("mameka.png", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

response = client.responses.create(
    model='gpt-4.1-mini',
    input=[{
        'role': 'user',
        'content': [{
            'type': 'input_text',
            'text': '请编辑成笑着唱歌的样子'
        },
        # 待编辑图像
        {
            'type': 'input_image',
            'image_url': 'data:image/png;base64,' + base64_image
        }],
    }],
    tools=[{'type': 'image_generation'}],
    tool_choice={'type': 'image_generation'}
)

images = [
    output.result for output in response.output
    if output.type == 'image_generation_call'
]

if images:
    with open('singer-mameka.webp', 'wb') as f:
        f.write(base64.b64decode(images[0]))

from openai import OpenAI

client = OpenAI()

# 分析目标 CSV 文件
with open('sales.csv', 'rb') as data_file:
    file_response = client.files.create(
        file=data_file,
        purpose='user_data'
    )
# 上传日文字体
# https://fonts.google.com/share?selection.family=Noto+Sans+JP:wght@100..900
with open('NotoSansJP-Regular.ttf', 'rb') as font_file:
    font_response = client.files.create(
        file=font_file,
        purpose='user_data'
    )

# 创建容器
container = client.containers.create(
    name='sales_data', file_ids=[file_response.id, font_response.id]
)

response = client.responses.create(
    model='gpt-4.1-mini',
    input=[{
        'role': 'user',
        'content': [{
            'type': 'input_text',
            'text': '请创建按类别的销售额构成比（饼图）。请应用Noto Sans JP字体。'
        }]
    }],
    # 指定代码解释器工具
    tools=[{
        'type': 'code_interpreter',
        'container': container.id,
    }],
    tool_choice={'type': 'code_interpreter'}
)

annotations = [
    annotation for annotation in response.output[-1].content[0].annotations
    if annotation.type == 'container_file_citation'
]

if annotations:
    output_response = client.containers.files.content.with_raw_response.retrieve(
        container_id=annotations[0].container_id,
        file_id=annotations[0].file_id,
    )
    if output_response.status_code == 200:
        with open('sales_summary.png', 'wb') as f:
            f.write(output_response.content)
    else:
        print(
            f'Error retrieving file: {output_response.status_code} - {output_response.text}')

订单ID,订单日期,商品名称,类别,数量,单价（日元）
1001,2025-05-01,苹果,水果,10,120
1002,2025-05-01,香蕉,水果,8,90
1003,2025-05-02,哈密瓜,水果,1,800
1004,2025-05-02,卷心菜,蔬菜,5,150
1005,2025-05-03,牛奶,饮料,3,180
1006,2025-05-04,番茄,蔬菜,6,130
1007,2025-05-04,橙子,水果,7,100
1008,2025-05-05,酸奶,乳制品,4,200
1009,2025-05-06,胡萝卜,蔬菜,10,80
1010,2025-05-06,咖啡,饮料,2,250