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Merge pull request #4042 from santiagxf/satiagxf/js
Fix JS examples
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‎articles/ai-foundry/model-inference/includes/how-to-prerequisites-javascript.md

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@@ -11,14 +11,36 @@ author: santiagxf
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```bash
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npm install @azure-rest/ai-inference
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npm install @azure/core-auth
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npm install @azure/identity
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```
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* Import the following modules:
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If you are using Node.js, you can configure the dependencies in **package.json**:
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```javascript
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import ModelClient, { isUnexpected } from "@azure-rest/ai-inference";
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import { AzureKeyCredential } from "@azure/core-auth";
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import { DefaultAzureCredential } from "@azure/identity";
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import { createRestError } from "@azure-rest/core-client";
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```
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__package.json__
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```json
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{
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"name": "main_app",
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"version": "1.0.0",
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"description": "",
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"main": "app.js",
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"type": "module",
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"dependencies": {
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"@azure-rest/ai-inference": "1.0.0-beta.6",
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"@azure/core-auth": "1.9.0",
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"@azure/core-sse": "2.2.0",
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"@azure/identity": "4.8.0"
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}
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}
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```
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* Import the following:
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```javascript
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import ModelClient from "@azure-rest/ai-inference";
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import { isUnexpected } from "@azure-rest/ai-inference";
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import { createSseStream } from "@azure/core-sse";
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import { AzureKeyCredential } from "@azure/core-auth";
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import { DefaultAzureCredential } from "@azure/identity";
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```

‎articles/ai-foundry/model-inference/includes/use-chat-completions/javascript.md

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@@ -32,29 +32,22 @@ To use chat completion models in your application, you need:
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First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
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```javascript
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import ModelClient from "@azure-rest/ai-inference";
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import { isUnexpected } from "@azure-rest/ai-inference";
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import { AzureKeyCredential } from "@azure/core-auth";
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const client = new ModelClient(
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process.env.AZURE_INFERENCE_ENDPOINT,
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const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL)
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);
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```
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If you've configured the resource with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
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```javascript
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import ModelClient from "@azure-rest/ai-inference";
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import { isUnexpected } from "@azure-rest/ai-inference";
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import { DefaultAzureCredential } from "@azure/identity";
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const clientOptions = { credentials: { "https://cognitiveservices.azure.com" } };
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55-
const client = new ModelClient(
56-
process.env.AZURE_INFERENCE_ENDPOINT,
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const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new DefaultAzureCredential()
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clientOptions,
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);
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```
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@@ -70,6 +63,7 @@ var messages = [
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var response = await client.path("/chat/completions").post({
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body: {
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model: "mistral-large-2407",
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messages: messages,
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}
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});
@@ -122,7 +116,9 @@ var messages = [
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var response = await client.path("/chat/completions").post({
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body: {
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model: "mistral-large-2407",
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messages: messages,
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stream: true,
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}
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}).asNodeStream();
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```
@@ -148,7 +144,7 @@ for await (const event of sses) {
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return;
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}
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for (const choice of (JSON.parse(event.data)).choices) {
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console.log(choice.delta?.content ?? "");
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process.stdout.write(choice.delta?.content ?? "");
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}
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}
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```
@@ -165,6 +161,7 @@ var messages = [
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var response = await client.path("/chat/completions").post({
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body: {
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model: "mistral-large-2407",
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messages: messages,
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presence_penalty: "0.1",
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frequency_penalty: "0.8",
@@ -195,6 +192,7 @@ var messages = [
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var response = await client.path("/chat/completions").post({
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body: {
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model: "mistral-large-2407",
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messages: messages,
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response_format: { type: "json_object" }
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}
@@ -217,6 +215,7 @@ var response = await client.path("/chat/completions").post({
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"extra-params": "pass-through"
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},
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body: {
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model: "mistral-large-2407",
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messages: messages,
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logprobs: true
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}
@@ -280,6 +279,7 @@ Prompt the model to book flights with the help of this function:
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```javascript
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var result = await client.path("/chat/completions").post({
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body: {
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model: "mistral-large-2407",
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messages: messages,
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tools: tools,
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tool_choice: "auto"

‎articles/ai-foundry/model-inference/includes/use-chat-multi-modal/javascript.md

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@@ -35,9 +35,9 @@ To use chat completion models in your application, you need:
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First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
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```javascript
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const client = new ModelClient(
38+
const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new AzureKeyCredential(process.env.AZUREAI_ENDPOINT_KEY)
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new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL)
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);
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```
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@@ -46,9 +46,9 @@ If you've configured the resource with **Microsoft Entra ID** support, you can u
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```javascript
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const clientOptions = { credentials: { "https://cognitiveservices.azure.com" } };
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const client = new ModelClient(
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const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new DefaultAzureCredential(),
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new DefaultAzureCredential()
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clientOptions,
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);
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```

‎articles/ai-foundry/model-inference/includes/use-chat-reasoning/javascript.md

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@@ -30,31 +30,20 @@ To complete this tutorial, you need:
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First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
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```javascript
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import ModelClient from "@azure-rest/ai-inference";
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import { isUnexpected } from "@azure-rest/ai-inference";
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import { AzureKeyCredential } from "@azure/core-auth";
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const client = new ModelClient(
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process.env.AZURE_INFERENCE_ENDPOINT,
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const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL)
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);
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```
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> [!TIP]
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> Verify that you have deployed the model to Azure AI Services resource with the Azure AI model inference API. `Deepseek-R1` is also available as Serverless API Endpoints. However, those endpoints don't take the parameter `model` as explained in this tutorial. You can verify that by going to [Azure AI Foundry portal]() > Models + endpoints, and verify that the model is listed under the section **Azure AI Services**.
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If you have configured the resource to with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
39+
If you've configured the resource with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
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```javascript
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import ModelClient from "@azure-rest/ai-inference";
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import { isUnexpected } from "@azure-rest/ai-inference";
51-
import { DefaultAzureCredential } from "@azure/identity";
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const clientOptions = { credentials: { "https://cognitiveservices.azure.com" } };
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55-
const client = new ModelClient(
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const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new DefaultAzureCredential(),
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new DefaultAzureCredential()
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clientOptions,
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);
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```

‎articles/ai-foundry/model-inference/includes/use-embeddings/javascript.md

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@@ -32,31 +32,22 @@ To use embedding models in your application, you need:
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First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
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```javascript
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import ModelClient from "@azure-rest/ai-inference";
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import { isUnexpected } from "@azure-rest/ai-inference";
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import { AzureKeyCredential } from "@azure/core-auth";
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const client = new ModelClient(
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process.env.AZURE_INFERENCE_ENDPOINT,
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new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL),
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"text-embedding-3-small"
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const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL)
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);
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```
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48-
If you have configured the resource to with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
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42+
If you've configured the resource with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
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```javascript
52-
import ModelClient from "@azure-rest/ai-inference";
53-
import { isUnexpected } from "@azure-rest/ai-inference";
54-
import { DefaultAzureCredential } from "@azure/identity";
55-
56-
const client = new ModelClient(
57-
process.env.AZURE_INFERENCE_ENDPOINT,
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new DefaultAzureCredential(),
59-
"text-embedding-3-small"
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const clientOptions = { credentials: { "https://cognitiveservices.azure.com" } };
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47+
const client = ModelClient(
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"https://<resource>.services.ai.azure.com/models",
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new DefaultAzureCredential()
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clientOptions,
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);
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```
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@@ -67,6 +58,7 @@ Create an embedding request to see the output of the model.
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```javascript
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var response = await client.path("/embeddings").post({
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body: {
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model: "text-embedding-3-small",
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input: ["The ultimate answer to the question of life"],
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}
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});
@@ -94,6 +86,7 @@ It can be useful to compute embeddings in input batches. The parameter `inputs`
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```javascript
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var response = await client.path("/embeddings").post({
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body: {
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model: "text-embedding-3-small",
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input: [
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"The ultimate answer to the question of life",
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"The largest planet in our solar system is Jupiter",
@@ -126,6 +119,7 @@ You can specify the number of dimensions for the embeddings. The following examp
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```javascript
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var response = await client.path("/embeddings").post({
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body: {
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model: "text-embedding-3-small",
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input: ["The ultimate answer to the question of life"],
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dimensions: 1024,
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}
@@ -142,6 +136,7 @@ The following example shows how to create embeddings that are used to create an
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```javascript
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var response = await client.path("/embeddings").post({
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body: {
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model: "text-embedding-3-small",
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input: ["The answer to the ultimate question of life, the universe, and everything is 42"],
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input_type: "document",
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}
@@ -154,6 +149,7 @@ When you work on a query to retrieve such a document, you can use the following
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```javascript
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var response = await client.path("/embeddings").post({
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body: {
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model: "text-embedding-3-small",
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input: ["What's the ultimate meaning of life?"],
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input_type: "query",
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}

‎articles/ai-foundry/model-inference/includes/use-image-embeddings/javascript.md

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@@ -34,28 +34,22 @@ To use embedding models in your application, you need:
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First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.
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3837
```javascript
39-
import ModelClient from "@azure-rest/ai-inference";
40-
import { isUnexpected } from "@azure-rest/ai-inference";
41-
import { AzureKeyCredential } from "@azure/core-auth";
42-
43-
const client = new ModelClient(
44-
process.env.AZURE_INFERENCE_ENDPOINT,
38+
const client = ModelClient(
39+
"https://<resource>.services.ai.azure.com/models",
4540
new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL)
4641
);
4742
```
4843

49-
If you configured the resource to with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
44+
If you've configured the resource with **Microsoft Entra ID** support, you can use the following code snippet to create a client.
5045

5146
```javascript
52-
import ModelClient from "@azure-rest/ai-inference";
53-
import { isUnexpected } from "@azure-rest/ai-inference";
54-
import { DefaultAzureCredential } from "@azure/identity";
47+
const clientOptions = { credentials: { "https://cognitiveservices.azure.com" } };
5548

56-
const client = new ModelClient(
57-
process.env.AZURE_INFERENCE_ENDPOINT,
49+
const client = ModelClient(
50+
"https://<resource>.services.ai.azure.com/models",
5851
new DefaultAzureCredential()
52+
clientOptions,
5953
);
6054
```
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