Today we will stay in the developer view by using existing endpoints with pre-trained machine models behind the scenes. For this we will focus on our managed services like Azure Cognitive Search and later on Azure Cognitive Services and explore some of the convenient tools to create custom machine learning models like LUIS and Custom Vision. As a developer we can use those REST APIs and enrich our applications without having deep ml expert knowledge. As final task we have prepared a small application which adopts a pre-trained object detection model. This shows the entire lifecycle and brings together best of both worlds.
As Azure Cognitive Search can be enriched by the Azure Cognitive Services with pre-trained machine learning models behind the scenes and it makes data searchable in an intelligent manner this service is included in our AI Developer College as well as in the Azure AI Stack.
Today will be an overview of Azure Cognitive Search, as you will learn:
- Create an Azure Cognitive Search Service in the Portal
- Create and search an index
- Add Cognitive Skills to Cognitive Search
- Use Azure Cognitive Search with AI enrichment to index unstructured content
- Integrate Cognitive Search in a Node JS application
Thus we will cover the following topics in several sections:
| Topic | Section |
|---|---|
| Getting familiar with Cognitive Search | What is Azure Cognitive Search? |
| Create Azure Cognitive Search in the Portal | Create an Azure Cognitive Search Service in the Portal |
| Connect to Azure Cognitive Search | Connect to Azure Cognitive Search |
| Index structured & unstructured content with AI enrichment | Index structured & unstructured content with AI enrichment (e.g. images, audio, etc.) |
| Integrate Azure Search in an Node JS Application | Integrate Azure Search in a Node JS Application |
Azure Cognitive Search is a search-as-a-service cloud solution that gives developers APIs and tools for adding a rich search experience over private, heterogeneous content in web, mobile and enterprise applications. Your code or a tool invokes data ingestion (indexing) to create and load an index. Optionally, you can add cognitive skills to apply Artificial Intelligence processes during indexing. Doing so, you can add new information and structures useful for search and other scenarios.
Regarding your application, your code issues query requests and handles responses from Azure Cognitive Search. The search experience is defined in your client using functionality from Azure Cognitive Search, with query execution over a persisted index that you create, own, and store in your service.
If you are interested in getting to know features of Azure CognitiveSearch and the query syntax we have listed some examples for you in the Details section below. Otherwise you can go ahead and start with the challenge.
| Core Search | Features |
|---|---|
| Free-form text search | Full-text search is a primary use case for most search-based apps. Queries can be formulated using a supported syntax. Simple query syntax provides logical operators, phrase search operators, suffix operators, precedence operators. Lucene query syntax includes all operations in simple syntax, with extensions for fuzzy search, proximity search, term boosting, and regular expressions. |
| Filters and facets | Faceted navigation is enabled through a single query parameter. Azure Cognitive Search returns a faceted navigation structure you can use as the code behind a categories list, for self-directed filtering (for example, to filter catalog items by price-range or brand). Filters can be used to incorporate faceted navigation into your application's UI, enhance query formulation, and filter based on user- or developer-specified criteria. Create filters using the OData syntax. |
Faceted navigation is used for self-directed filtering on query results in a search app, where your application offers UI controls for scoping search to groups of documents (for example, categories or brands), and Azure Cognitive Search provides the data structure to back the experience.
In code, a query that specifies all parts of a valid query, including search expressions, facets, filters, scoring profiles– anything used to formulate a request, can look like the following example:
var sp = new SearchParameters()
{
...
// Add facets
Facets = new[] { "businessTitle" }.ToList()
};This example builds a request that creates facet navigation based on the business title information.
Facets are dynamic and returned on a query. Search responses bring with them the facet categories used to navigate the results.
You can find more details here: Search-Filters-Facets
In the SCM Application, we are using the Lucene query syntax (Lucene Query Syntax Examples).
View the full Azure Cognitive Search Feature list here: Azure Cognitive Search Feature list
Now let's deploy an Azure Search instance as in the following.
-
Navigate to the Azure portal and create a new resource group, e.g. adc-azsearch-db-rg
-
First, create a
Cognitive Searchinstance -
Choose a Location - if there are delays switch the azure region to e.g. north europe
-
For our purposes, the
Standard Tieris sufficient -
Leave the other settings as predefined
View the Details of Creating an Azure Search Service in the Portal:
Once provisioned, our service will be reachable via https://xxxxxxx.search.windows.net
Cognitive Search can index data from a variety of sources:
- Azure SQL Database or SQL Server on an Azure virtual machine
- Azure Cosmos DB
- Azure Blob Storage
- Azure Table Storage
- Indexing CSV blobs using the Azure Search Blob indexer
- Indexing JSON blobs with Azure Search Blob indexer
REST calls require the service URL and an access key on every request. A search service is created with both, so if you added Azure Cognitive Search to your subscription, follow these steps to get the necessary information:
-
Navigate to the Azure Cognitive Search service in the Azure portal, and in the Overview page, get the URL (example endpoint
https://xxxxxxx.search.windows.net). -
In Settings > Keys, get an admin key for full rights on the service. There are two interchangeable admin keys, provided for business continuity in case you need to roll one over. You can use either the primary or secondary key on requests for adding, modifying, and deleting objects.
All requests require an api-key on every request sent to your service. Having a valid key establishes trust, on a per request basis, between the application sending the request and the service that handles it.
In this task, start a Jupyter notebook and verify that you can connect to Azure Cognitive Search. You'll do this by requesting a list of indexes from your service.
-
We reuse the
Compute Instance (VM)from yesterday's Azure Machine Learning Service and create a new Notebook. We can click theNewbutton and create a new Notebook of type:Python 3.8 - AzureML. A new browser tab should open up and we can click the nameUntitledand rename it toCognitiveSearch.ipynb. -
In the first cell, load the libraries used for working with JSON and formulating HTTP requests.
import json import requests from pprint import pprint
-
In the second cell, input the request elements that will be constants on every request. Replace the search service name
(YOUR-SEARCH-SERVICE-NAME)and admin API key(YOUR-ADMIN-API-KEY)with valid values.endpoint = 'https://<YOUR-SEARCH-SERVICE-NAME>.search.windows.net/' api_version = '?api-version=2020-06-30' headers = {'Content-Type': 'application/json', 'api-key': '<YOUR-ADMIN-API-KEY>' }
-
In the third cell, formulate the request. This GET request targets the indexes collection of your search service and selects the name property of existing indexes.
url = endpoint + "indexes" + api_version + "&$select=name" response = requests.get(url, headers=headers) index_list = response.json() pprint(index_list)
If you get ConnectionError
"Failed to establish a new connection", verify that the api-key is a primary or secondary admin key, and that all leading and trailing characters (?and/) are in place. -
Run each step. If indexes exist, the response contains a list of index names. In contrast, an empty index collection returns this response:
{
"@odata.context": "https://mydemo.search.windows.net/$metadata#indexes(name)",
"value": []
}Once we have completed the next Create an Index Task it will look like this:
{
"@odata.context": "https://devcollege.search.windows.net/$metadata#indexes(name)",
"value": [{ "name": "hotels-quickstart" }]
}Unless you are using the portal, an index must exist on the service before you can load data. This step uses the Create Index REST API to push an index schema to the service.
Required elements of an index include a name, a fields collection, and a key. The fields collection defines the structure of a document. Each field has a name, type, and attributes that determine how the field is used (for example, whether it is full-text searchable, filterable, or retrievable in search results). Within an index, one of the fields of type Edm.String must be designated as the key for document identity.
Next, we will create an index named "hotels-quickstart", which has the field definitions you see below. It's a subset of a larger Hotels index used in other walkthroughs. We trimmed it in this quickstart for brevity.
-
In the next cell, paste the following example into a cell to provide the schema.
index_schema = { "name": "hotels-quickstart", "fields": [ {"name": "HotelId", "type": "Edm.String", "key": "true", "filterable": "true"}, {"name": "HotelName", "type": "Edm.String", "searchable": "true", "filterable": "false", "sortable": "true", "facetable": "false"}, {"name": "Description", "type": "Edm.String", "searchable": "true", "filterable": "false", "sortable": "false", "facetable": "false", "analyzer": "en.lucene"}, {"name": "Description_fr", "type": "Edm.String", "searchable": "true", "filterable": "false", "sortable": "false", "facetable": "false", "analyzer": "fr.lucene"}, {"name": "Category", "type": "Edm.String", "searchable": "true", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "Tags", "type": "Collection(Edm.String)", "searchable": "true", "filterable": "true", "sortable": "false", "facetable": "true"}, {"name": "ParkingIncluded", "type": "Edm.Boolean", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "LastRenovationDate", "type": "Edm.DateTimeOffset", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "Rating", "type": "Edm.Double", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "Address", "type": "Edm.ComplexType", "fields": [ {"name": "StreetAddress", "type": "Edm.String", "filterable": "false", "sortable": "false", "facetable": "false", "searchable": "true"}, {"name": "City", "type": "Edm.String", "searchable": "true", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "StateProvince", "type": "Edm.String", "searchable": "true", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "PostalCode", "type": "Edm.String", "searchable": "true", "filterable": "true", "sortable": "true", "facetable": "true"}, {"name": "Country", "type": "Edm.String", "searchable": "true", "filterable": "true", "sortable": "true", "facetable": "true"} ] } ] }
-
In another cell, formulate the request. This POST request targets the indexes collection of your search service and creates an index based on the index schema you provided in the previous cell. Check that the indentation is correct as shown below.
url = endpoint + "indexes" + api_version response = requests.post(url, headers=headers, json=index_schema) index = response.json() pprint(index)
-
Run each step.
The response includes the JSON representation of the schema.
- Another way to verify index creation is to check the Indexes list in the portal on the overview page under indexes.
To push documents, use an HTTP POST request to your index's URL endpoint. The REST API enables you to Add, Update, or Delete Documents. Documents originate from HotelsData on GitHub.
-
In a new cell, provide four documents that conform to the index schema. Specify an upload action for each document.
documents = { "value": [ { "@search.action": "upload", "HotelId": "1", "HotelName": "Secret Point Motel", "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Time's Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.", "Description_fr": "L'hôtel est idéalement situé sur la principale artère commerciale de la ville en plein cœur de New York. A quelques minutes se trouve la place du temps et le centre historique de la ville, ainsi que d'autres lieux d'intérêt qui font de New York l'une des villes les plus attractives et cosmopolites de l'Amérique.", "Category": "Boutique", "Tags": [ "pool", "air conditioning", "concierge" ], "ParkingIncluded": "false", "LastRenovationDate": "1970-01-18T00:00:00Z", "Rating": 3.60, "Address": { "StreetAddress": "677 5th Ave", "City": "New York", "StateProvince": "NY", "PostalCode": "10022", "Country": "USA" } }, { "@search.action": "upload", "HotelId": "2", "HotelName": "Twin Dome Motel", "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.", "Description_fr": "L'hôtel est situé dans une place du XIXe siècle, qui a été agrandie et rénovée aux plus hautes normes architecturales pour créer un hôtel moderne, fonctionnel et de première classe dans lequel l'art et les éléments historiques uniques coexistent avec le confort le plus moderne.", "Category": "Boutique", "Tags": [ "pool", "free wifi", "concierge" ], "ParkingIncluded": "false", "LastRenovationDate": "1979-02-18T00:00:00Z", "Rating": 3.60, "Address": { "StreetAddress": "140 University Town Center Dr", "City": "Sarasota", "StateProvince": "FL", "PostalCode": "34243", "Country": "USA" } }, { "@search.action": "upload", "HotelId": "3", "HotelName": "Triple Landscape Hotel", "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.", "Description_fr": "L'hôtel est situé dans une place du XIXe siècle, qui a été agrandie et rénovée aux plus hautes normes architecturales pour créer un hôtel moderne, fonctionnel et de première classe dans lequel l'art et les éléments historiques uniques coexistent avec le confort le plus moderne.", "Category": "Resort and Spa", "Tags": [ "air conditioning", "bar", "continental breakfast" ], "ParkingIncluded": "true", "LastRenovationDate": "2015-09-20T00:00:00Z", "Rating": 4.80, "Address": { "StreetAddress": "3393 Peachtree Rd", "City": "Atlanta", "StateProvince": "GA", "PostalCode": "30326", "Country": "USA" } }, { "@search.action": "upload", "HotelId": "4", "HotelName": "Sublime Cliff Hotel", "Description": "Sublime Cliff Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 1800 palace.", "Description_fr": "Le sublime Cliff Hotel est situé au coeur du centre historique de sublime dans un quartier extrêmement animé et vivant, à courte distance de marche des sites et monuments de la ville et est entouré par l'extraordinaire beauté des églises, des bâtiments, des commerces et Monuments. Sublime Cliff fait partie d'un Palace 1800 restauré avec amour.", "Category": "Boutique", "Tags": [ "concierge", "view", "24-hour front desk service" ], "ParkingIncluded": "true", "LastRenovationDate": "1960-02-06T00:00:00Z", "Rating": 4.60, "Address": { "StreetAddress": "7400 San Pedro Ave", "City": "San Antonio", "StateProvince": "TX", "PostalCode": "78216", "Country": "USA" } } ] }
-
In another cell, formulate the request. This POST request targets the docs collection of the hotels-quickstart index and pushes the documents provided in the previous step.
url = endpoint + "indexes/hotels-quickstart/docs/index" + api_version response = requests.post(url, headers=headers, json=documents) index_content = response.json() pprint(index_content)
-
Run each step to push the documents to an index in your search service. Results should look similar to the following example.
-
You can now see in the portal under overview and indexes that the document count has increased to 4.
This step shows you how to query an index using the Search Documents REST API.
-
Option 1: In a cell, provide a query expression that executes an empty search (search=*), returning an unranked list (search score = 1.0) of arbitrary documents. By default, Azure Cognitive Search returns 50 matches at a time. As structured, this query returns an entire document structure and values. Add $count=true to get a count of all documents in the results.
search_string_option1 = '&search=*&$count=true'
-
Option 2: In a new cell, provide the following example to search on the terms "hotels" and "wifi". Add $select to specify which fields to include in the search results.
search_string_option2 = '&search=hotels wifi&$count=true&$select=HotelId,HotelName'
-
In another cell, formulate a request. This GET request targets the docs collection of the hotels-quickstart index, and attaches the query you specified in the previous step, please choose the first Option and afterwards the second Option to execute the different queries:
search_string_option<fill In: 1 or 2>url = endpoint + "indexes/hotels-quickstart/docs" + api_version + search_string_option2 response = requests.get(url, headers=headers) query = response.json() pprint(query)
-
Run each step. Results should look similar to the following output.
If you are interested in querying with different searchstrings we have listed some samples in the Details section for you:
-
Try a few other query examples to get a feel for the syntax. You can replace the
searchstringwith the following examples and then rerun the search request.Apply a filter:
search_string_option3 = '&search=*&$filter=Rating gt 4&$select=HotelId,HotelName,Description,Rating'
Take the top two results:
search_string_option4 = '&search=boutique&$top=2&$select=HotelId,HotelName,Description,Category'
Order by a specific field:search_string_option5 = '&search=pool&$orderby=Address/City&$select=HotelId, HotelName, Address/City, Address/StateProvince, Tags'
Now that you have created and searched an index using REST API calls, we will now do the same using the Azure Portal User Interface. Moreover, you will query unstructured data such as images using AI enrichments.
🚩 Goal: Index an unstructured and structured data set with Cognitive Search
- Add another index to the Cognitive Search instance, but this time enable Cognitive Skills
- Index an existing data set coming from
Azure Blob Storage(data set can be downloaded here)
Cognitive Search can out of the box index structured data like PDFs, PowerPoints, etc., as long as the documents are easily machine readable (=text). Moreover, Azure Cognitive Search allows us to also index unstructured data such as images and audio content using AI enrichment. More precisely, it adds capabilities for data extraction, natural language processing (NLP), and image processing to the Azure Cognitive Search indexing pipeline (for more see here). In Azure Cognitive Search, a skillset is responsible for the pipeline of the data and consists of multiple skills. Some skills have been pre-included, but it is also possible for us to write our own skills or use third-party products.
Please reuse the Azure Cognitive Search instance which you initially created.
Here we'll upload our data to Blob Storage and let Cognitive Search index it from there. Hence, we need to create a new Storage Account and create a new Blob container, where we'll upload our dataset to. We can do this completely through the Azure Portal (as described below), use Storage Explorer or use the API/CLI.
See the upload to the Storage Account below:
Let's create a Storage Account in the Azure Portal:
Fill in a unique name and choose the region west europe:
Then create a Container with the Public Access Level Private (See Screenshot below): The Public Access Level specifies whether data in the container may be accessed publicly. By default, container data is private to the account owner. Use 'Blob' to allow public read access for blobs. Use 'Container' to allow public read and list access to the entire container.
Now we can upload the unzipped data to the created Storage Account:
⚠ Caution: Please be aware to first unzip the folder and upload the unzipped content of the folder.
You can conveniently select and upload all files at once:
Once we have uploaded the files, we can go into our Cognitive Search instance and go to Import Data:
Next, we need to define the Data Source:
To choose a Connection string click on Choose an existing connection and then choose your storage account.
Next, we need to define the skillset. In our case, we'll enable all features:
We might not want to make our content field retrievable, as it does not necessarily provide a lot of value - however, we want to keep it searchable, so that Azure Search can do its job. Since we have the original files in Blob and the location stored in metadata_storage_path, we can always just retrieve the original file.
Azure Cognitive Search automatically looks at the Blob container and will now extract the content and the metadata from all the files. Let's give our Index a better name:
Lastly, we need to give our Indexer a name and also set the schedule. In our case, we'll only run it once, but in a real world scenario, we might want to keep it running to index new, incoming data:
Once we finished the next two tabs, Azure Cognitive Search will start indexing our data (this will take a bit longer, as it needs to run image recognition, Object Character Recognition Cognitive Services, etc. on the files). After that, our Indexer will have indexed all the files and we should be able to query them.
Azure Cognitive Search now indexed all our files via the indexer into the index. Ideally, we would use the REST API of Cognitive Search to perform sophisticated queries, but for training purpose this time, we can use the Azure Search Explorer:
Querying data in Azure Search can get quite sophisticated, but for our example here, we can just put in a simple query. Let's try some search queries:
"Pin to Dashboard"--> returnscreate-search-service.png(text was recognized via OCR): It also detects key phrases, locations, personal identifiable information etc."Los Angeles"--> returnsMSFT_cloud_architecture_contoso.pdf(location was recognized via OCR in image)
Using double-quotes "..." will search for the whole string, rather than each substring. If we want to make a search term mandatory, we need to prefix a +. Try out some more queries if you like.
If we want to figure out the original file, we can look at: metadata_storage_path. Since it is base64-encoded, we need to decode it, either via command line or by using e.g., www.base64decode.org.
Good, so looks like our skillset worked. Please note that ideally we'd query through the API and directly specify the relevant fields, e.g., location:Los Angeles in the second example:
As we have now seen how to use the Azure Cognitive Search service we still want to combine the ml expert view with the developer view. Thus we will show a quick example how to integrate Azure Cognitive Search in a simple Node.js application.
Now let's jump into code. We will create a Node.js application that creates, loads, and queries an Azure Cognitive Search index. This article demonstrates how to create the application step-by-step.
Begin by opening the Cloud Shell (next to the search field) in the Browser - a Bash console, Powershell console or other environment in which you've installed Node.js:
-
Create a development directory, giving it the name
adv-search:mkdir adv-search cd adv-search git clone https://github.com/Azure-Samples/azure-search-javascript-samples.git cd azure-search-javascript-samples/quickstart/REST code .
-
Insert your search service data in the file azure_search_config.json:
{ "serviceName": "[SEARCH_SERVICE_NAME]", "adminKey": "[SEARCH_SERVICE_ADMIN_KEY]", "queryKey": "[SEARCH_SERVICE_QUERY_KEY]", "indexName": "hotels-quickstart" }Replace the
[SEARCH_SERVICE_NAME]value with the name of your search service. Replace[SEARCH_SERVICE_ADMIN_KEY]and[SEARCH_SERVICE_QUERY_KEY]with the key values you recorded earlier.
If your endpoint URL were https://mydemo.search.windows.net, your service name would be mydemo.
In Settings > Keys, get the primary admin key for full rights on the service.There are two interchangeable admin keys, provided for business continuity in case you need to roll one over. You can use either the primary or secondary key on requests for adding, modifying, and deleting objects.
⚠ Get the query key as well. It's a best practice to issue query requests with read-only access. Use the key underneath the Manage query keys (e.g. C4DD...).
Find the file hotels_quickstart_index.json. This file defines how Azure Cognitive Search works with the documents you'll be loading in the next step. Each field will be identified by a name and has a specified type. Each field also has a series of index attributes that specify whether Azure Cognitive Search can search, filter, sort, and facet upon the field.
Most of the fields are simple data types, but some, like
AddressTypeare complex types that allow you to create rich data structures in your index. You can read more about supported data types and index attributes.
Have a look at the hotels_quickstart_index.json and get familiar with its setup/content.
{
"name": "hotels-quickstart",
"fields": [
{
"name": "HotelId",
"type": "Edm.String",
"key": true,
"filterable": true
},
{
"name": "HotelName",
"type": "Edm.String",
"searchable": true,
"filterable": false,
"sortable": true,
"facetable": false
},
{
"name": "Description",
"type": "Edm.String",
"searchable": true,
"filterable": false,
"sortable": false,
"facetable": false,
"analyzer": "en.lucene"
},
{
"name": "Description_fr",
"type": "Edm.String",
"searchable": true,
"filterable": false,
"sortable": false,
"facetable": false,
"analyzer": "fr.lucene"
},
{
"name": "Category",
"type": "Edm.String",
"searchable": true,
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "Tags",
"type": "Collection(Edm.String)",
"searchable": true,
"filterable": true,
"sortable": false,
"facetable": true
},
{
"name": "ParkingIncluded",
"type": "Edm.Boolean",
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "LastRenovationDate",
"type": "Edm.DateTimeOffset",
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "Rating",
"type": "Edm.Double",
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "Address",
"type": "Edm.ComplexType",
"fields": [
{
"name": "StreetAddress",
"type": "Edm.String",
"filterable": false,
"sortable": false,
"facetable": false,
"searchable": true
},
{
"name": "City",
"type": "Edm.String",
"searchable": true,
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "StateProvince",
"type": "Edm.String",
"searchable": true,
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "PostalCode",
"type": "Edm.String",
"searchable": true,
"filterable": true,
"sortable": true,
"facetable": true
},
{
"name": "Country",
"type": "Edm.String",
"searchable": true,
"filterable": true,
"sortable": true,
"facetable": true
}
]
}
],
"suggesters": [
{
"name": "sg",
"searchMode": "analyzingInfixMatching",
"sourceFields": ["HotelName"]
}
]
}Now, have a look at the file AzureSearchClient.js and take your time to understand what happens here. You will find methods to check, if an index exists, to create and delete an index as well as methods to add data to the index (postDataAsync) and - of course - query data (queryAsync).
Use the Cloud Shell in the Azure Portal window for the following commands.
- Navigate
cdto the source code folderadv-search/azure-search-javascript-samples/quickstart/REST. - Install the packages for the sample with
npm install. This command will download the packages upon which the code depends. - Run
node index.jsand examine the results
You should see a series of messages describing the actions being taken by the program. Have a look at the OData response from Azure Search.
If you are interested in how you can use everything via the REST API and show some fun demos we have added a section with some necessary links in the Details section below.
As we've have been lazy and did some parts through the portal - obviously not the way we want to work in the real world. Especially data ingestion and search should (and most likely needs) to be performed through the API. Luckily, the API is pretty easy to use (even using curl for it is easy):
For sake of time today we won't be able to go into more detail here, but feel free to have a look at it.
- We created an Azure Cognitive Search Service in the Portal
- Added Cognitive Skills to Azure Cognitive Search
- Used Azure Cognitive Search with cognitive skills to index unstructured and structured content
- Integrated Azure Search in a Node JS application
As we used Cognitive Services behind the scenes of Azure Cognitive Search and as we now want to continue the path of combining the ml expert view and the developer view we will jump into our Cognitive Services challenge next:
⚡ Let's go to AI Developer College Day2 - Cognitive Services!
Remove the sample resource group.
$ az group delete -n adc-azsearch-db-rg