Chatgpt Api（チャットジーピーティーエーピーアイ）

pip install openai

export OPENAI_API_KEY='your-api-key-here'

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "自作 PC の CPU は何を選ぶべき？"}
    ]
)

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

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Pythonでフィボナッチ数列を書いて"}],
    stream=True
)

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

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "指定した都市の天気を取得",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["city"]
        }
    }
}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "東京の天気は？"}],
    tools=tools
)

# tool_calls の処理
if response.choices[0].message.tool_calls:
    for call in response.choices[0].message.tool_calls:
        # 関数を実行
        result = get_weather(**json.loads(call.function.arguments))

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "この画像を説明してください"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image.jpg"}
                }
            ]
        }
    ]
)

from pydantic import BaseModel

class Product(BaseModel):
    name: str
    price: float
    category: str

response = client.beta.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[{"role": "user", "content": "iPhone 15 Pro の商品情報"}],
    response_format=Product
)

product = response.choices[0].message.parsed
print(product.name, product.price)

# ファインチューニング開始
client.fine_tuning.jobs.create(
    training_file="file-abc123",
    model="gpt-4o-mini-2024-07-18"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="自作 PC の選び方"
)
embedding = response.data[0].embedding  # 1536 次元