从 OpenAI 迁移

- client = OpenAI(api_key="sk-...")
+ client = OpenAI(
+   base_url="https://llm.bytespike.ai/v1",
+   api_key="sk-byts-...",
+ )

# 原来:
from openai import OpenAI
client = OpenAI()
r = client.chat.completions.create(
    model="gpt-5-4",
    messages=[{"role": "user", "content": "ping"}],
)

# 现在:
from openai import OpenAI
client = OpenAI(
    base_url="https://llm.bytespike.ai/v1",
    api_key="sk-byts-...",
)
r = client.chat.completions.create(
    model="gpt-5-4",  # 同一个 id；或者换成 gpt-5-5 / claude-sonnet-4-6 / gemini-3-5-flash
    messages=[{"role": "user", "content": "ping"}],
)

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"]
        }
    }
}]

# 同样的调用结构 —— 对 gpt-5、claude、gemini 等都能跑。
r = client.chat.completions.create(
    model="claude-sonnet-4-6",
    messages=[{"role": "user", "content": "weather in Tokyo?"}],
    tools=tools,
)

# OpenAI 的 images.generate 在 ByteSpike 目录上也能用：
r = client.images.generate(
    model="gpt-image-2",       # 或 "nano-banana-pro"、"nano-banana-v2"、……
    prompt="A timelapse of Tokyo at sunset",
    size="1024x1024",
    n=1,
)
print(r.data[0].url)

r = client.responses.create(
    model="gpt-5-5",
    input="Summarize the doc at https://example.com/doc",
    reasoning={"effort": "medium"},
)