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概要

LemonData は OpenAI SDK と完全に互換性があります。ベース URL を変更するだけで、300 以上のモデルにアクセスできます。

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インストール

pip install openai

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設定

curl https://api.lemondata.cc/v1/chat/completions \
  -H "Authorization: Bearer sk-your-lemondata-key" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello!"}]}'

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チャットコンプリーション

OpenAI API とまったく同じように動作します： 
response = client.chat.completions.create(
    model="gpt-4o",  # または任意の LemonData モデル
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    temperature=0.7,
    max_tokens=1000
)

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

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ストリーミング

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

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関数呼び出し / ツール

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }]
)

# モデルが関数を呼び出したいか確認
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

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Vision

response = client.chat.completions.create(
    model="gpt-4o",  # または claude-sonnet-4-5
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
    }]
)

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画像生成

response = client.images.generate(
    model="dall-e-3",
    prompt="A white siamese cat",
    size="1024x1024",
    quality="standard",
    n=1
)

print(response.data[0].url)

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エンベディング

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello world"
)

print(response.data[0].embedding[:5])  # 最初の 5 次元

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オーディオ - テキスト読み上げ

response = client.audio.speech.create(
    model="tts-1",
    voice="alloy",
    input="Hello, welcome to LemonData!"
)

response.stream_to_file("output.mp3")

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オーディオ - 文字起こし

with open("audio.mp3", "rb") as audio_file:
    response = client.audio.transcriptions.create(
        model="whisper-1",
        file=audio_file
    )

print(response.text)

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異なるモデルの使用

LemonData の主な利点は、複数のプロバイダーにアクセスできることです： 
# OpenAI
response = client.chat.completions.create(model="gpt-4o", messages=messages)

# Anthropic Claude
response = client.chat.completions.create(model="claude-sonnet-4-5", messages=messages)

# Google Gemini
response = client.chat.completions.create(model="gemini-2.5-flash", messages=messages)

# DeepSeek
response = client.chat.completions.create(model="deepseek-r1", messages=messages)

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エラーハンドリング

from openai import APIError, RateLimitError, AuthenticationError

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}]
    )
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded, please wait")
except APIError as e:
    print(f"API error: {e.status_code} - {e.message}")