Kimi K3 API の使い方：キーの取得と最初のリクエスト

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["SEEDROUTER_API_KEY"],
    base_url="https://api.seedrouter.ai/v1",
)

completion = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Explain context caching in one sentence."}],
)
print(completion.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.SEEDROUTER_API_KEY,
  baseURL: "https://api.seedrouter.ai/v1",
});

const completion = await client.chat.completions.create({
  model: "kimi-k3",
  messages: [{ role: "user", content: "Explain context caching in one sentence." }],
});
console.log(completion.choices[0].message.content);

curl https://api.seedrouter.ai/v1/chat/completions \
  -H "Authorization: Bearer $SEEDROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "kimi-k3", "messages": [{"role": "user", "content": "Explain context caching in one sentence."}]}'

completion = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Find the bug: def avg(xs): return sum(xs) / len(xs)"}],
    reasoning_effort="high",
)

stream = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
    stream_options={"include_usage": True},
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

import base64

with open("chart.png", "rb") as f:
    image = base64.b64encode(f.read()).decode()

completion = client.chat.completions.create(
    model="kimi-k3",
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image}"}},
            {"type": "text", "text": "What does this chart show?"},
        ],
    }],
)