KI-Inferenz im großen Maßstab: Zugriff auf 300+ Modelle mit einer API

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.LLMBASE_API_KEY,
  baseURL: 'https://api.llmbase.ai/v1'
});

const chat = await openai.chat.completions.create({
  model: "deepseek-r1",
  messages: [{ role: "user", content: "Hello!" }],
});

import openai

openai.api_key = "LLMBASE_API_KEY"
openai.base_url = "https://api.llmbase.ai/v1"

response = openai.ChatCompletion.create(
  model="deepseek-r1",
  messages=[{"role": "user", "content": "Hello!"}]
)

var client = new OpenAIClient("LLMBASE_API_KEY", new OpenAIAuthenticationOptions
{
  BaseUrl = "https://api.llmbase.ai/v1"
});
var chat = await openAi.ChatEndpoint.GetCompletionAsync(new ChatRequest
{
  Model = "deepseek-r1",
  Messages = new List<ChatMessage>
  {
    ChatMessage.FromUser("Hello!")
  }
});

OpenAiService service = new OpenAiService(
    new OpenAiApi("https://api.llmbase.ai/v1"),
    "LLMBASE_API_KEY"
);

ChatCompletionRequest request = ChatCompletionRequest.builder()
    .model("deepseek-r1")
    .messages(List.of(new ChatMessage("user", "Hello!")))
    .build();

ChatCompletionResult result = service.createChatCompletion(request);

curl https://api.llmbase.ai/v1/chat/completions \
  -H "Authorization: Bearer LLMBASE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-r1",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'