LLM API Integration Docs

All /v1 endpoints carry the API key in a request header:

cURL

curl http://www.yziss.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxx" \
  -d '{
    "model": "deepseek-chat",
    "messages": [{"role": "user", "content": "Hello"}],
    "stream": false
  }'

Python (official OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxx",
    base_url="http://www.yziss.com/v1",
)
resp = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Node.js

const resp = await fetch(
  "http://www.yziss.com/v1/chat/completions",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer sk-xxxxxxxx",
    },
    body: JSON.stringify({
      model: "deepseek-chat",
      messages: [{ role: "user", content: "Hello" }],
    }),
  }
);
const data = await resp.json();
console.log(data.choices[0].message.content);

Pass stream=true and the gateway relays the SSE chunks through as they arrive, finishing with data: [DONE] ; upstreams speak the OpenAI, Anthropic and Gemini protocols, and the gateway normalises them all into OpenAI chunk format so the client never has to care about the vendor.

stream = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "write a short poem"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

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