OpenAI SDK

pip install openai

npm install openai

from openai import OpenAI
import os

client = OpenAI(max_retries=0, 
    base_url="https://api.ailoot.ai/v1",
    api_key=os.environ["AILOOT_API_KEY"],
)

resp = client.chat.completions.create(
    model="claude-sonnet-5-5",
    messages=[{"role": "user", "content": "Dviem sakiniais paaiškink, kas yra API."}],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({ maxRetries: 0,
  baseURL: "https://api.ailoot.ai/v1",
  apiKey: process.env.AILOOT_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "claude-sonnet-5-5",
  messages: [{ role: "user", content: "Dviem sakiniais paaiškink, kas yra API." }],
});
console.log(resp.choices[0].message.content);

resp = client.responses.create(
    model="gpt-6.1-sol",
    instructions="Atsakyk trumpai ir konkrečiai.",
    input="Kuo TCP skiriasi nuo UDP?",
)
print(resp.output_text)

const resp = await client.responses.create({
  model: "gpt-6.1-sol",
  instructions: "Atsakyk trumpai ir konkrečiai.",
  input: "Kuo TCP skiriasi nuo UDP?",
});
console.log(resp.output_text);

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

import json

messages = [{"role": "user", "content": "Koks oras Kazanėje?"}]
# Išjunkite automatinius pakartotinius bandymus: pasibaigus laukimo laikui, pirmiausia naudojimo istorijoje patikrinkite Request ID.
client = client.with_options(max_retries=0)
for turn in range(4):
    resp = client.chat.completions.create(
        model="claude-sonnet-5-5", messages=messages, tools=tools,
    )
    message = resp.choices[0].message
    messages.append(message.model_dump(exclude_none=True))
    if not message.tool_calls:
        print(message.content)
        break
    for call in message.tool_calls:
        if call.function.name != "get_weather":
            raise ValueError("Unexpected function")
        arguments = json.loads(call.function.arguments)
        # Pakeiskite savo orų funkcija; šiame pavyzdyje pateikti duomenys skirti tik demonstravimui.
        result = {"city": arguments["city"], "temperature_c": 20, "demo": True}
        messages.append({"role": "tool", "tool_call_id": call.id,
                         "content": json.dumps(result, ensure_ascii=False)})
else:
    raise RuntimeError("Tool loop limit reached")

emb = client.embeddings.create(
    model="nvidia/nemotron-3-embed-1b",
    input=["Pirmasis dokumentas", "Antrasis dokumentas"],
)
print(len(emb.data), len(emb.data[0].embedding))

resp = client.chat.completions.create(
    model="gpt-6.1-sol",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Kas pavaizduota šiame paveikslėlyje?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},
        ],
    }],
)

import openai

try:
    resp = client.chat.completions.create(model="claude-sonnet-5-5", messages=[{"role": "user", "content": "Labas"}])
except openai.APIStatusError as e:
    print(e.status_code, e.response.json()["error"]["code"])
    print(e.response.headers.get("x-request-id"))

try {
  await client.chat.completions.create({ model: "claude-sonnet-5-5", messages: [{ role: "user", content: "Labas" }] });
} catch (err) {
  if (err instanceof OpenAI.APIError) {
    console.log(err.status, err.code, err.headers?.["x-request-id"]);
  }
}