Azure AI API is complicated :) But is works.
Azure OpenAI is "legacy" only for OpenAI API
Azure AI Foundry is "generic", that also includes OpenAI
Azure AI Services, used to be "Cognitive Services", is "native" Microsoft's non-LLM AI services
Azure AI Foundry Docs
openai/openai-node: Official JavaScript / TypeScript library for the OpenAI API @GitHub
JavaScript
baseURL: "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"
import { OpenAI } from "openai";
const endpoint = process.env.AZURE_OPENAI_ENDPOINT
const apiKey = process.env.AZURE_OPENAI_API_KEY
const baseURL = `${endpoint}/openai/v1/`
const model = 'gpt-4.1' // model deployment name
const client = new OpenAI({
baseURL,
apiKey,
maxRetries: 0, // default is 2; Configure the default for all requests
});
// == responses ===
const response1 = await client.responses.create({
model,
instructions: 'You are a helpful AI agent',
input: 'how tall is triglav?',
});
console.log(response1.output_text);
// == streaming ===
const stream = await client.responses.create({
model,
input: 'Provide a brief history of the attention is all you need paper.',
stream: true,
});
for await (const event of stream) {
if (event.type === 'response.output_text.delta' && event.delta) {
process.stdout.write(event.delta);
}
}
// == chat ==
const response2 = await client.chat.completions.create({
messages: [
{ role:"system", content: "You are a helpful assistant." },
{ role:"user", content: "I am going to Paris, what should I see?" }
],
max_completion_tokens: 13107,
temperature: 1,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
model,
});
if (response2?.error !== undefined && response2.status !== "200") {
throw response2.error;
}
console.log(response2.choices[0].message.content);
// == retries, configure per-request:
const response3 = await client.chat.completions.create(
{ messages: [{ role: 'user', content: 'How can I get the name of the current day in Node.js?' }], model },
{ maxRetries: 5 }
);
if (response3?.error !== undefined && response3.status !== "200") {
console.error(response3.status, response3.error);
} else {
console.log(response3.choices[0].message.content);
}
// == tools, use MCP server
const response4 = await client.responses.create({
model,
tools: [
{
type: "mcp",
server_label: "microsoft_learn",
server_description: "Microsoft Learn MCP server for searching and fetching Microsoft documentation.",
server_url: "https://learn.microsoft.com/api/mcp",
require_approval: "never",
},
],
input: "Search for information about Azure Functions",
});
console.log(response4.output_text);
