Azure Cognitive Search Introduction

What is Azure Cognitive Search?

Azure Cognitive Search, formerly known as "Azure Search," is a robust cloud-based search service designed to empower developers with the necessary infrastructure, APIs, and resources to create a powerful search experience within their web, mobile, and enterprise applications. This service allows you to efficiently search and retrieve information from diverse and private data sources, making it a valuable tool for enhancing your applications.

When you create a search service, you'll work with the following capabilities:

• A search engine for full text and vector search over a search index containing user-owned content

• Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation

• Rich query syntax for vector queries, text search, fuzzy search, autocomplete, geo-search and more

• Programmability through REST APIs and client libraries in Azure SDKs

• Azure integration at the data layer, machine learning layer, and AI (Azure AI services)

How can Azure Cognitive Search integrate with other Azure services?

Answer: Azure Cognitive Search can integrate with other Azure services through indexers and skillsets.

What is the role of indexers in Azure Cognitive Search integration?

Answer: Indexers are responsible for automating data ingestion and retrieval from Azure data sources.

How do skillsets contribute to Azure Cognitive Search integration with Azure services?

Answer: Skillsets enable the incorporation of consumable AI from Azure AI services, including image and natural language processing. They can also work with custom AI that you create in Azure Machine Learning or wrap inside Azure Functions.

On the search service itself, the two primary workloads are indexing and querying.

Indexing:

Let's break down the process of indexing and AI enrichment step by step in simple terms:

Step 1: What is Indexing?

• Indexing is like getting all your stuff organized so you can easily find what you need. Imagine you have a messy room, and you want to find your toys. You need to put your toys in separate boxes and label them so you know which box has what. That's what indexing does for your data.

Step 2: What Does Indexing Do Internally?

• Inside your search service, when you want to search for something, like a word or a picture, the service breaks it down into smaller parts called tokens. These tokens are like the building blocks of your search.

Step 3: What Can Be Indexed?

• Cognitive Search can only understand a specific type of document format called JSON. It's like a specific language for your data. So, to make your data searchable, you need to convert it into this JSON format.

Step 4: How to Index Data?

• You can put your data into this JSON format by creating JSON documents. Think of them like special information cards for your data. You can either make these cards yourself or use a tool called an indexer to automatically make them from your data.

Step 5: What Is AI Enrichment?

• Now, here's where things get even smarter. If you have pictures or a lot of messy text that's hard to understand, AI enrichment can help. It's like having a super helper who can look at your pictures and messy text and figure out what's in them.

Step 6: How Does AI Enrichment Work?

• This super helper can do many things, like reading text in pictures, translating text from one language to another, and even understanding what's in non-text files (like images or videos) by analyzing their content. It makes your data even more useful and smart.

So, in simple terms, indexing is about organizing your data in a way that makes it easy to search, and AI enrichment makes your data even smarter by understanding what's in it, even if it's messy or in different languages.

Querying:

Let's break down the process of querying and semantic search step by step in simple terms:

Step 1: Indexing

1. First, you need to populate an index with searchable text. An index is like a big database of information that you want to search through.

Step 2: Querying

1. When you want to search for something, your app (like a website or mobile app) sends a request to a search service. This request contains the words or phrases you're looking for.

2. The search service then looks through the index and finds the information that matches your request.

3. The search service sends back the results to your app, which displays them to you. This is when you see the search results on your screen.

Step 3: Semantic Search

1. Semantic search is like a smart upgrade to regular searching. It goes beyond just finding matching words.

2. With semantic search, the system tries to understand the meaning behind the words. It figures out what you're really looking for and ranks the results accordingly.

3. So, instead of just showing any matches, semantic search promotes the most relevant results to the top. It's like having a helpful assistant who knows what you want, even if you don't use the exact words.

In simple terms, querying is the process of searching for information in a database, and semantic search makes this process smarter by understanding the meaning behind your words and showing you the most relevant results.