adding search applications app

08-building-search-applications/js-githubmodels/app.js

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+import ModelClient from "@azure-rest/ai-inference";
+import { isUnexpected } from "@azure-rest/ai-inference";
+import { AzureKeyCredential } from "@azure/core-auth";
+import { brotliDecompress } from "zlib";
+
+const token = process.env["GITHUB_TOKEN"];
+const endpoint = "https://models.inference.ai.azure.com";
+
+/* By using the Azure AI Inference SDK, you can easily experiment with different models
+   by modifying the value of `modelName` in the code below. For this code sample
+   you need an embedding model. The following embedding models are
+   available in the GitHub Models service:
+
+   Cohere: Cohere-embed-v3-english, Cohere-embed-v3-multilingual
+   Azure OpenAI: text-embedding-3-small, text-embedding-3-large */
+const modelName = "text-embedding-3-small";
+
+function cosineSimilarity(vector1, vector2) {
+    if (vector1.length !== vector2.length) {
+        throw new Error("Vector dimensions must match for cosine similarity calculation.");
+    }
+
+    const dotProduct = vector1.reduce((acc, val, index) => acc + val * vector2[index], 0);
+    const magnitude1 = Math.sqrt(vector1.reduce((acc, val) => acc + val ** 2, 0));
+    const magnitude2 = Math.sqrt(vector2.reduce((acc, val) => acc + val ** 2, 0));
+
+    if (magnitude1 === 0 || magnitude2 === 0) {
+        throw new Error("Magnitude of a vector must be non-zero for cosine similarity calculation.");
+    }
+
+    return dotProduct / (magnitude1 * magnitude2);
+}
+
+
+
+export async function main() {
+    let carEmbedding, vehicleEmbedding, birdEmbedding
+
+    const client = new ModelClient(endpoint, new AzureKeyCredential(token));
+
+    const response = await client.path("/embeddings").post({
+        body: {
+            input: ["Car", "Vehicle", "Bird"],
+            model: modelName
+        }
+    });
+
+    if (isUnexpected(response)) {
+        throw response.body.error;
+    }
+
+    for (const item of response.body.data) {
+        const { embedding, index } = item;  // Destructure item for cleaner code
+        const length = embedding.length;
+
+        switch (index) {
+            case 0:
+                carEmbedding = embedding;
+                break;
+            case 1:
+                vehicleEmbedding = embedding;
+                break;
+            case 2:
+                birdEmbedding = embedding;
+                break;
+        }
+
+        console.log(
+            `data[${item.index}]: length=${length}, ` +
+            `[${item.embedding[0]}, ${item.embedding[1]}, ` +
+            `..., ${item.embedding[length - 2]}, ${item.embedding[length - 1]}]`);
+
+
+    }
+
+    console.log(response.body.usage);
+    console.log(carEmbedding)
+    const scoreCarWithVehicle = cosineSimilarity(carEmbedding, vehicleEmbedding);
+    console.log("Comparing - Car vs Vehicle...: ", scoreCarWithVehicle.toFixed(7));
+
+
+    const scoreCarWithBird = cosineSimilarity(carEmbedding, birdEmbedding);
+    console.log("Comparing - Car vs Bird...: ", scoreCarWithBird.toFixed(7));
+
+}
+
+main().catch((err) => {
+    console.error("The sample encountered an error:", err);
+});

08-building-search-applications/js-githubmodels/package.json

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+{
+  "type": "module",
+  "dependencies": {
+    "@azure-rest/ai-inference": "latest",
+    "@azure/core-auth": "latest",
+    "@azure/core-sse": "latest"
+  }
+}