Quickstart

Get up and running with AI Foundation Services in minutes. This guide walks you through installing the SDK, setting up authentication, and making your first API call.

Step 1: Install the OpenAI Package

AI Foundation Services uses an OpenAI-compatible API, so you can use the official OpenAI SDKs.

Python

pip install openai

Node.js

npm install openai

Step 2: Get an API Key

Free Trial Key

Get started immediately with a free trial key:

1. Visit the API Key Portal
2. Create an account and generate your API key
3. Your trial key gives you access to all available models

Production Key

For production workloads, purchase via the T-Cloud Marketplace.

Step 3: Set Environment Variables

macOS / Linux

export OPENAI_API_KEY="your_api_key_here"
export OPENAI_BASE_URL="https://llm-server.llmhub.t-systems.net/v2"

Windows (PowerShell)

[System.Environment]::SetEnvironmentVariable('OPENAI_API_KEY', 'your_api_key_here', 'User')
[System.Environment]::SetEnvironmentVariable('OPENAI_BASE_URL', 'https://llm-server.llmhub.t-systems.net/v2', 'User')

Open a new PowerShell window for the variables to take effect. ($env:VAR = '...' only sets a variable for the current session.)

Windows (CMD)

setx OPENAI_API_KEY "your_api_key_here"
setx OPENAI_BASE_URL "https://llm-server.llmhub.t-systems.net/v2"

Step 4: Make Your First API Call

curl

curl -X POST "$OPENAI_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Llama-3.3-70B-Instruct",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is quantum computing in simple terms?"}
    ],
    "temperature": 0.5,
    "max_tokens": 150
  }'

Python

from openai import OpenAI

client = OpenAI()  # Reads OPENAI_API_KEY and OPENAI_BASE_URL from env

response = client.chat.completions.create(
    model="Llama-3.3-70B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is quantum computing in simple terms?"},
    ],
    temperature=0.5,
    max_tokens=150,
)

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

Node.js

import OpenAI from "openai";

const client = new OpenAI(); // Reads OPENAI_API_KEY and OPENAI_BASE_URL from env

const response = await client.chat.completions.create({
  model: "Llama-3.3-70B-Instruct",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What is quantum computing in simple terms?" },
  ],
  temperature: 0.5,
  max_tokens: 150,
});

console.log(response.choices[0].message.content);

More Examples

Create Embeddings

from openai import OpenAI

client = OpenAI()

texts = ["The quick brown fox jumps over the lazy dog", "Data science is fun!"]
result = client.embeddings.create(input=texts, model="jina-embeddings-v2-base-de")

print(f"Embedding dimension: {len(result.data[0].embedding)}")
print(f"Token usage: {result.usage}")

Vision / Multimodal

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="Qwen3-VL-30B-A3B-Instruct-FP8",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://images.unsplash.com/photo-1546069901-ba9599a7e63c?w=400"
                    },
                },
            ],
        }
    ],
    max_tokens=300,
)

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

Next Steps

• Authentication — API key management and best practices
• Available Models — Browse all supported models
• Chat Completions Guide — Detailed guide with streaming, parameters, and more
• LangChain Integration — Use AIFS with LangChain for RAG
• LlamaIndex Integration — Use AIFS with LlamaIndex for RAG