Search has changed.

Users no longer search with perfect keywords — they ask questions, describe problems, and expect context-aware answers. Traditional keyword search breaks here.

That’s where Semantic Vector Search comes in.

In this blog, you’ll learn what semantic vector search is, why it matters, and how to build it using Node.js and Azure AI Search — step by step.

What Is Semantic Vector Search?

Traditional search works like this:

“Find documents that contain these exact words”

Semantic search works like this:

“Find documents that mean the same thing”

Instead of matching words, semantic vector search matches intent.

Example

User Query	Traditional Search	Semantic Search
“How do I prepare for interviews?”	Looks for “prepare”, “interviews”	Finds content about mock interviews, resume prep, AI interview coaching
“Fix payment failures”	Matches “payment”	Finds docs about Stripe errors, retries, webhook issues

Why? Because text is converted into vectors (numerical embeddings) that capture meaning.

Why Vector Search Is the Future 🔮

Here’s why modern products are moving fast to vector search:

🔍 Understands natural language
🧠 Context-aware results
🧩 Works across FAQs, docs, resumes, chats
🤖 Powers RAG (Retrieval Augmented Generation) for AI apps
📈 Massive boost in user satisfaction & engagement

If you’re building:

SaaS search
AI chatbots
Resume or job matching
Knowledge bases
LMS or EdTech platforms

👉 Vector search is no longer optional.

Why Use Azure AI Search?

Azure Open AI and Cognitive Search with Private data | by ...

Microsoft Azure offers Azure AI Search, a fully managed, enterprise-grade search engine with native vector search support.

Key Benefits

✅ Built-in vector indexing
✅ Hybrid search (keyword + vector)
✅ Scales to millions of documents
✅ Secure, production-ready
✅ Seamless integration with Azure OpenAI

Perfect for startups and large-scale systems.

High-Level Architecture

What is a Vector Database & How Does it Work? Use Cases + Examples ...

Here’s how semantic vector search works end-to-end:

Data ingestion (docs, resumes, FAQs, blogs)
Generate embeddings (convert text → vectors)
Store vectors in Azure AI Search
User sends a query
Query is embedded
Cosine similarity search
Return most relevant results

Process

Create an Azure AI Search Service

From Azure Portal:

Create Azure AI Search
Choose:
- Pricing tier (Basic or Standard is fine to start)
- Region close to users
Grab:
- Search endpoint
- Admin API key

Define a Vector-Enabled Search Index

Your index needs:

Text fields
Vector field (for embeddings)

Example Index Fields

id (string)
content (searchable text)
contentVector (collection of floats)
metadata (optional)

Azure AI Search supports HNSW under the hood for fast vector similarity.

Generate Embeddings (Node.js)

You’ll typically use OpenAI embeddings via Azure OpenAI.

Install Dependencies

npm install @azure/search-documents openai

Generate Embeddings

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AZURE_OPENAI_KEY,
  baseURL: process.env.AZURE_OPENAI_ENDPOINT
});

const embedding = await client.embeddings.create({
  model: "text-embedding-3-small",
  input: "How to prepare for interviews"
});

const vector = embedding.data[0].embedding;

This vector is what powers semantic understanding.

Upload Documents with Vectors

import { SearchClient, AzureKeyCredential } from "@azure/search-documents";

const searchClient = new SearchClient(
  process.env.SEARCH_ENDPOINT,
  "my-index",
  new AzureKeyCredential(process.env.SEARCH_API_KEY)
);

await searchClient.uploadDocuments([
  {
    id: "1",
    content: "Prepare for interviews using mock interviews and AI feedback",
    contentVector: vector
  }
]);

Now your data is semantically searchable.

Perform Semantic Vector Search

const queryEmbedding = await client.embeddings.create({
  model: "text-embedding-3-small",
  input: "Best way to crack interviews"
});

const results = await searchClient.search("", {
  vectorSearchOptions: {
    queries: [{
      vector: queryEmbedding.data[0].embedding,
      kNearestNeighborsCount: 5,
      fields: ["contentVector"]
    }]
  }
});

for await (const result of results.results) {
  console.log(result.document.content);
}

✨ Even if words don’t match, meaning does.

Hybrid Search: Best of Both Worlds

Azure AI Search allows keyword + vector search together.

Why this matters:

Keyword search = precision
Vector search = relevance

Example Use Case

Filters: location, category, date
Vector: semantic relevance

Perfect for job search, product search, and knowledge bases.

Semantic Vector Search with Node.js & Azure AI Search