Quickstart: Create an Azure Search index in Python using Jupyter notebooks

Build a Jupyter notebook that creates, loads, and queries an Azure Search index using Python and the Azure Search REST APIs. This article explains how to build a notebook step by step. Alternatively, you can download and run a finished Jupyter Python notebook.

If you don't have an Azure subscription, create a free account before you begin.


The following services and tools are required for this quickstart.

Get a key and URL

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 Search to your subscription, follow these steps to get the necessary information:

  1. Sign in to the Azure portal, and in your search service Overview page, get the URL. An example endpoint might look like https://mydemo.search.windows.net.

  2. 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.

Get an HTTP endpoint and access key

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 Search. You'll do this by requesting a list of indexes from your service. On Windows with Anaconda3, you can use Anaconda Navigator to launch a notebook.

  1. Create a new Python3 notebook.

  2. In the first cell, load the libraries used for working with JSON and formulating HTTP requests.

    import json
    import requests
    from pprint import pprint
  3. 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=2019-05-06'
    headers = {'Content-Type': 'application/json',
            'api-key': '<YOUR-ADMIN-API-KEY>' }

    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.

  4. 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()
  5. Run each step. If indexes exist, the response contains a list of index names. In the screenshot below, the service already has an azureblob-index and a realestate-us-sample index.

    Python script in Jupyter notebook with HTTP requests to Azure Search

    In contrast, an empty index collection returns this response: {'@odata.context': 'https://mydemo.search.windows.net/$metadata#indexes(name)', 'value': []}

1 - Create an index

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.

This index is named "hotels-quickstart" and 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.

  1. 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"}
  2. In another cell, formulate the request. This PUT request targets the indexes collection of your search service and creates an index based on the index schema you provided in the previous cell.

    url = endpoint + "indexes" + api_version
    response  = requests.post(url, headers=headers, json=index_schema)
    index = response.json()
  3. Run each step.

    The response includes the JSON representation of the schema. The following screenshot is showing just a portion of the response.

    Request to create an index


Another way to verify index creation is to check the Indexes list in the portal.

2 - Load documents

To push documents, use an HTTP POST request to your index's URL endpoint. The REST API is Add, Update, or Delete Documents. Documents originate from HotelsData on GitHub.

  1. 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"
  2. 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()
  3. Run each step to push the documents to an index in your search service. Results should look similar to the following example.

    Send documents to an index

3 - Search an index

This step shows you how to query an index using the Search Documents REST API.

  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 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.

    searchstring = '&search=*&$count=true'
  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.

    searchstring = '&search=hotels wifi&$count=true&$select=HotelId,HotelName'
  3. 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.

    url = endpoint + "indexes/hotels-quickstart/docs" + api_version + searchstring
    response  = requests.get(url, headers=headers, json=searchstring)
    query = response.json()
  4. Run each step. Results should look similar to the following output.

    Search an index

  5. Try a few other query examples to get a feel for the syntax. You can replace the searchstring with the following examples and then rerun the search request.

    Apply a filter:

    searchstring = '&search=*&$filter=Rating gt 4&$select=HotelId,HotelName,Description,Rating'

    Take the top two results:

    searchstring = '&search=boutique&$top=2&$select=HotelId,HotelName,Description,Category'

    Order by a specific field:

    searchstring = '&search=pool&$orderby=Address/City&$select=HotelId, HotelName, Address/City, Address/StateProvince, Tags'

Clean up

When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.

You can find and manage resources in the portal, using the All resources or Resource groups link in the left-navigation pane.

If you are using a free service, remember that you are limited to three indexes, indexers, and data sources. You can delete individual items in the portal to stay under the limit.

Next steps

As a simplification, this quickstart uses an abbreviated version of the Hotels index. You can create the full version to try out more interesting queries. To get the full version and all 50 documents, run the Import data wizard, selecting hotels-sample from the built-in sample data sources.