What is Azure AI Search

Azure AI Search is a cloud-based search-as-a-service solution that allows you to integrate sophisticated search capabilities into your applications, including:

Full-text search
Semantic ranking
Vector-based similarity
Hybrid search (text + vector)

It supports unstructured and structured content from documents, databases, websites, and more.

💡 Two Phases of Working with Azure AI Search

There are two key phases when working with Azure AI Search:

1. Ingestion (Define & Populate Indexes)

This phase involves both defining the structure of the index and populating it with data.

🔧 Index Definition

Before you can search anything, you must define how your data will be stored and retrieved:

Define index fields (types, attributes like searchable, filterable, sortable, facetable, etc.)
Configure analyzers for full-text search
Add semantic configuration for semantic search capabilities
Add vector configuration for vector search (e.g., field dimensions, vector profiles)
Enable scoring profiles, synonyms, and suggesters as needed

This step defines the “search contract” — how your data can be queried later.

📥 Index Population

Once the schema is defined, you populate the index with documents:

Push: Upload data using REST API or SDK
Pull: Use indexers (e.g., Azure Blob, SQL DB, ADLS Gen2)
Supports batch or incremental updates

2. Retrieval (Search & Rank Results)

In this phase, you query the indexed data:

Send search requests (keyword, semantic, vector, or hybrid)
Use filters, scoring, semantic captions, and highlights
Receive structured search results

❓ Why REST API Instead of SDK?

While Azure provides an official Python SDK (azure-search-documents), it:

Wraps the REST API, offering limited abstraction
Lags behind the REST API in supporting newer features (e.g., hybrid search and latest vector capabilities)
Lacks clear documentation for advanced use cases