Introduction To Azure Cognitive Search

Question 1

Azure cognitive search:
Searching for information online has never been easier full-stop however it’s still a challenge to find information from documents that aren’t in a search index.
For example Everyday People deal with unstructured typed image-based or handwritten documents. Often people must manually read through these documents to extract and record their insights in order to persist the found data. Now we have Solutions that can automate information extraction. More information at the bottom

Question 2

Knowledge mining:
Knowledge mining is the term used to describe Solutions that involves extracting information from large volumes of often unstructured data.
One of these knowledge mining Solutions is as a cognitive search a cloud search service that has tools for building using managed indexes.
The end exes can be used for internal use only or to enable searchable content on public-facing internet assets.

Answer 2

Importantly as a cognative search can utilise the built-in AI capabilities of azure cognitive services such as image processing content extraction and natural language processing to perform knowledge meaning of documents.
The product ay-ay-ay capabilities make it possible to index previously untouchable documents and to extract and surface insights from large amounts of data quickly.

Question 3

Azure cognitive search:
As a cognitive search provides the infrastructure and tools to create search Solutions that extract data from various structured semi-structured and unstructured documents.

Answer 3

As your cognitive search results can only contain your data which can include text in word or extracted from images or new entities and key phrases detection through text analytics
It’s a platform-as-a-service paas solution.
Microsoft managers the infrastructure and availability allowing your organisation to benefit without the need to purchase or managed dedicated hardware resources.

Question 4

As your cognitive search features:
As you’re a cognitive search exist to complement existing technologies and provides a programmable search engine built on Apache Lucian and open software library.
It’s a highly-available platform offering 99.9% uptime SLA available for cloud and on-premises assets.

Question 5

Azure cognitive search comes with the following features:
Data from any Source Code long as you’re cognitive search access starter from any source provider jsonformat with auto crawling support for selected data sources in Azure.
Full text search and analysis: azure cognitive search offers full text search capabilities supporting both query and full lucene query syntax.
Ai-powered search Caroline azria cognitive such as cognitive AI capabilities built-in for image and text analysis for all content from your content.
Multilingual call on azure cognitive services office domestic and Alice’s 456 languages to intelligently handle phonetic matching or language-specific linguistics. Natural language processors available in Azure cognitive search are also used by being an office. Continued below

Answer 5

Do you have enabled Caroline Adria cognitive search supports jio search filtering based on proximity to a physical location.
Configurable user experience scaling azure cognitive search has several features to improve the user experience including auto complete auto suggest pagni pagination and hit highlighting

Question 6

Identify elements of a cert solution Caroline you line a typical azure cognitive services ocean starts with a data source that contains the data artifacts you want to search.
This could be hierarchy hierarchy of folders and files in Azure storage or text in a database such as as a SQL database to azure cosmos db.

The data format that cognitive search supports is jason.
Regardless of where your daughter originates if you can provide it as a dress and document the search in the search engine can index it.

Answer 6

If your data resides in supported data source you can use an indexer to automate data ingestion including jsonserialization of source data in data formats will stop and index it connects to a data source who realises the data and passes to the first search engine for indexing. Does indexes support change detection which makes data refresh a simple exercise

Question 7

Besides automating data ingestion indexes also support AI in richmond.
You can attach a skill set that applies a sequence of AI skills to enrich the data and making it more suitable for stop a comprehensive set of balls and skills based on cognitive services API can help you derive newfields-for example by recognising entities Intex translating text evaluating sent to mentor predicting appropriate captions for images for stop optionally enriched content can also be sent to a knowledge store which stores output from an AI enrichment pipeline in tables and blobs in as your storage for independent analysis or downstream processing

Answer 7

Whether you write application code that pushes data to an index or use an index that automates data ingestion and adds enrichment-the Fields containing your content are perished in an index which can be searched by client applications.
The Fields are used for searching filtering and sorting to generate a set of results that can be displayed or otherwise used by the client application.

Question 8

Use a skill set to define and enrichment pipeline:
And AI enrichment refers to embedded image and natural language processing in a pipeline that extracts text and information from plant that can’t otherwise be indexed for full text search.

Ai processing is achieved by adding and combining skills and her skill-set. As skillset defines the operations that extract and enrich data to make it searchable. These are skills can either be built in skills such as text translation or optical character recognition OCR or custom skills that you provide.

Question 9

Building skills:
Building skills are based on free trade models from Microsoft which means you can’t train the model using your own training data.
Skills that call the cognitive resources API have a dependency on the services and are billed at the cognitive service pay as you go price when you attach a resourceful stop other skills are made by azure cognitive search all our utility skills that are available at no charge

Question 10

Building skills fall into these categories:
Natural language processing skills with the skills and structured text is mapped as sociable and filter of all fields in an index.
Some examples include:
Keyphrase extraction: uses a pre-trained model to detect important phrases based on term placement linguistic rules proximity to other terms and how unusual the term is within the source tata.

Text translation skill: uses a Preacher and model translate the input text into various languages for normalisation or localisation use cases.

Answer 10

Image processing skills Quran creates tax representations of an image content making it’s searchable using the query capabilities of azure cognitive search.
Some examples include:
Image analysis skill colon uses an image addiction algorithm to identify the content of an image and generate a text description.
Optical character recognition skill Caroline allows you to extract printed or handwritten text from images such as photos of street signs and products as well as from documents-invoices bills financial reports articles and more.

Question 11

Understanding indexes: And other cognitive search index can be thought of as a container of searchable documents. Conceptually you can think of an index as a table and each row in the table represents a document. Tables of columns and the columns can be thought of as equivalent to the fields and a document.
Columns have data types just as Fields do on the documents.

Question 12

Index schema: In Asia cognitive search and index is a persistent collection of Jason documents and other content used to enable search functionality for stop the documents within an index can be thought of as rows in a table each document is a single unit of searchable data in the index.

The index includes a definition of the structure of the data in these documents called its schema.
And example of an index schema with a i extracted Fields key phrases and image tags

Answer 12

Displayed as a document called json new line it has a name with an index and fields
They are curly brackets with that contain the name type Analyser and fields the name type and Eliza and fields each have their own value name is content type is EDM dot string Analyser is standard dot loosen and then filter square brackets this is repeated three times with different values depending on the field The name has the value of content phrases and image tags in each of the three whatever you call them content being first phrase as being sick and image dad’s been that type first sydm string type S is collection idiom string type series collection idiom string
Analyser type 1 standard loosen Analyser type 2 standard loosen Analyser type 3 the same field as just feels with empty []

Question 13

Index attributes:
As the cognitive search needs to know how you would like to search and display the fields in the documents.
You specify that by assigning attributes or behaviours to these fields.
For each field in the document the index stores its name the data type and they supported behaviours for the fields such as is the field searchable or can the field be sorted

Answer 13

The most efficient index is used only the behaviours that are needed for stop
If you forget to set a required behaviour on a field when designing the only way to get that feature is to rebuild the index.

Question 14

Use an indexer to build an index:
In order to index the documents in Azure storage 32 be exported from the original file type to Jason.
In order to export data in any format to Jason and loaded into an index we use it indexer.
New line to create such documents you can either generate Jason documents with application code or you can use Asia indexer to export incoming documents into Jason.

Answer 14

Has your cognitive search lets you create and load Jason documents into an index with two approaches kola
Push method Caroline Jason data is pushed into a search index via either the rest API or the dotnet sdk.
Pushing the actor has the most flexibility as it has no restrictions on the data source type location or frequency of execution.

Full method turn on such services indexes can pull data from Popular as a data sources and if necessary export that data into Jason if it isn’t ready in that format.

Question 15

Useful method to load data with an indexer:
80 cognitive searches indexer is a crawler that extracts searchable text and meta data from an external as a data source and populates a search index using field to field mapping between source data and your index.
Using the index is sometimes referred to as a formal approach because the service pools.in without you having to write any code that adds data to an index.
And index the maps source to their matching fields in the index.

Question 16

Data import monitoring and verification code on new line research services overview page has a dashboard that lets you quickly see the health of the search service.
On the dashboard you can see how many documents are in the search service and how many indexes have been used and how much storage is in use.

When loading new documents into an index the progress can be monitored by clicking on the index is associated index the full stop
The document count will grow as documents are loaded into the index. In some interns instances the portal page can take a few minutes to display up to the document counts. Once the index is ready for querying who can then use the search Explorer to verify the results.
And index is ready when the first document is successfully loaded

Answer 16

Indexes only import new updates documents so it is normal to see zero documents indexed.
Research Explorer can’t perform quick-service to check the contents of an index and ensure that you are getting expected search results for stop having the stool available in the portal and Abel’s you to easily track the index by reviewing the results that are returned as Jason documents.

Question 17

Making changes to an index score line
You have to drop and recreate indexes if you need to make sure and just to feel definitions.
Adding new Fields is supported with all existing documents having null values. Will find it faster using a code based approach to literature designs as working in the fourth requires the index to be deleted recreated and the schema details to be manually filled out.

Answer 17

An approach to updating an index without affecting your users is to create a new index under a different name. You can use the same index and data source after importing data you can switch your app to use the new index

Question 18

Persist in Ridge starter in a knowledge store:
And knowledge store is persistent storage of enrichment content. The purpose of a knowledge store is to store the data generated from a iron-rich mint in a container.
For example-you may want to save the results of an AI skill set that generates captions from an image or images.

Answer 18

Recall that skill sets move a document through a sequence of enrichment that invoke transformation such as recognising entities or translating text. The outcome can be a search index or projections in a known knowledge store. The two outputs search index and knowledge store are mutually exclusive products of the same pipeline; derived from the same inputs but resulting in output that is structured stored and used in different applications.

Question 19

While the focus of an azure cognitive services solution is usually to create a searchable index you can also take advantage of its data extraction and enrichment capabilities to persist and Richard data in a knowledge store for further analysis or processing.
And now the store can contain one or more of three types of projection of the extracted data:

Answer 19

Table predictions are used to structure the extracted data in a relational schema for querying and visualisation.
Object projections are Jason documents that represent each data entity.
File projections are used to store extracted images in JPEG format.

Question 20

Create an index in the Azure portal:
Before using an indexer to create an index you’ll first need to make your data available in a supported data source.
Supported data sources include:

Answer 20

Cosmos DB (SQL api)
Azure sql (database managed instance and SQL server on an azure VM plus)
Azure storage (blob storage table storage adls gen2)

Question 21

Using the other portals import data was at carolina
Once your daughter is in an area data source you can begin using azure cognitive search.
Contained within the other equipment of such service in Azure portal is the important data visit Which automates processes in the Azure portal to create various objects needed for the search engine.
You can see it in Action when creating any of the following objects using the Azure portal:

Answer 21

Datasource Cole haan purses connection information to source data including credentials full-stop a data source object is used exclusively with indexes.
Index scale on physical data structure used for full-text search and other queries.
Indexer coil on a configuration object specifying a data source target index and operational AI skill-set optional schedule and optional configuration configuration settings for error handling and bass live in 64 encoding.
Skill-set girl on a complete set of instructions for manipulating is transforming and shaping content including analysing and extracting information from image files. Except for very simple and limited structures it includes a reference to a cognitive services resource that provides enrichment
Knowledge store: stores output from Nain Richmond pipeline and tables and blogs and azure storage for independent analysis or downstream processing.

Question 22

To use a drill cognitive search will need an extra cognitive search resource will stop you can create a resource in the Azure portal. Once the resources created you can manage components of your service from The Resource overview page in the portal. You can build a research indexes using the Azure portal programmatically with the rest API or software development kits (sdks).

Question 23

Query data in an Israel cognitive search index:
Index and query designer closely linked. After we build the index we can perform queries will stop a crucial component to understand is that the scheme of the index disturbance what queries can be answered.

As recognitive search queries can be submitted as an HTTP or rest API request with the response coming back as jason.
Queries can specify what Fields are searched and returned how search results are shaved and how the results should be filtered unsorted full-stop a query that does not specify the field to search will execute against all the searchable Fields within the index.

Answer 23

Azure cognitive services supports two types of syntax:
Simple and for Lucy in. Simple syntax covers all of the common query scenario as well for Lucy in is useful for advanced scenarios.

Question 24

Simple query requests kalan
A query request is a list all words search terms and or query operator simple or full of what you would like to see Returned in a resultset. Ie coffee (- busy + Wi-Fi)

Answer 24

This query is trying to find context about coffee excluding busy and including Wi-Fi breaking the query into components it’s made up of search terms ie coffee in brackets plus two vertebrae phrases busy and Wi-Fi and operators minus and plus and brackets.
The search terms can be matched in the search index in any order or location in the content. The two phrases will only match with exactly what is specified so wife I would not be a match. Finally a query can contain a number of operators.
In this example the minus operator tells the search engine that these phrases should not be in the results for stop the parenthesis group terms together and set their presidents.