Azure Cognitive Search client library for Python - Version 11.0.0

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.

The Azure Cognitive Search service is well suited for the following application scenarios:

  • Consolidate varied content types into a single searchable index. To populate an index, you can push JSON documents that contain your content, or if your data is already in Azure, create an indexer to pull in data automatically.
  • Attach skillsets to an indexer to create searchable content from images and large text documents. A skillset leverages AI from Cognitive Services for built-in OCR, entity recognition, key phrase extraction, language detection, text translation, and sentiment analysis. You can also add custom skills to integrate external processing of your content during data ingestion.
  • In a search client application, implement query logic and user experiences similar to commercial web search engines.

Use the Azure.Search.Documents client library to:

  • Submit queries for simple and advanced query forms that include fuzzy search, wildcard search, regular expressions.
  • Implement filtered queries for faceted navigation, geospatial search, or to narrow results based on filter criteria.
  • Create and manage search indexes.
  • Upload and update documents in the search index.
  • Create and manage indexers that pull data from Azure into an index.
  • Create and manage skillsets that add AI enrichment to data ingestion.
  • Create and manage analyzers for advanced text analysis or multi-lingual content.
  • Optimize results through scoring profiles to factor in business logic or freshness.

Source code | Package (PyPI) | API reference documentation | Product documentation | Samples

Getting started

Install the package

Install the Azure Cognitive Search client library for Python - Version 11.0.0 with pip:

pip install azure-search-documents --pre

Prerequisites

To create a new search service, you can use the Azure portal, Azure PowerShell, or the Azure CLI.

az search service create --name <mysearch> --resource-group <mysearch-rg> --sku free --location westus

See choosing a pricing tier for more information about available options.

Authenticate the client

All requests to a search service need an api-key that was generated specifically for your service. The api-key is the sole mechanism for authenticating access to your search service endpoint. You can obtain your api-key from the Azure portal or via the Azure CLI:

az search admin-key show --service-name <mysearch> --resource-group <mysearch-rg>

There are two types of keys used to access your search service: admin (read-write) and query (read-only) keys. Restricting access and operations in client apps is essential to safeguarding the search assets on your service. Always use a query key rather than an admin key for any query originating from a client app.

Note: The example Azure CLI snippet above retrieves an admin key so it's easier to get started exploring APIs, but it should be managed carefully.

We can use the api-key to create a new SearchClient.

import os
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

index_name = "nycjobs";
# Get the service endpoint and API key from the environment
endpoint = os.environ["SEARCH_ENDPOINT"]
key = os.environ["SEARCH_API_KEY"]

# Create a client
credential = AzureKeyCredential(key)
client = SearchClient(endpoint=endpoint,
                      index_name=index_name,
                      credential=credential)

Send your first search request

To get running immediately, we're going to connect to a well known sandbox Search service provided by Microsoft. This means you do not need an Azure subscription or Azure Cognitive Search service to try out this query.

from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

# We'll connect to the Azure Cognitive Search public sandbox and send a
# query to its "nycjobs" index built from a public dataset of available jobs
# in New York.
service_name = "azs-playground"
index_name = "nycjobs"
api_key = "252044BE3886FE4A8E3BAA4F595114BB"

# Create a SearchClient to send queries
endpoint = "https://{}.search.windows.net/".format(service_name)
credential = AzureKeyCredential(api_key)
client = SearchClient(endpoint=endpoint,
                      index_name=index_name,
                      credential=credential)

# Let's get the top 5 jobs related to Microsoft
results = client.search(search_text="Microsoft", top=5)

for result in results:
    # Print out the title and job description
    print("{}\n{}\n)".format(result["business_title"], result["job_description"]))

Key concepts

An Azure Cognitive Search service contains one or more indexes that provide persistent storage of searchable data in the form of JSON documents. (If you're brand new to search, you can make a very rough analogy between indexes and database tables.) The Azure.Search.Documents client library exposes operations on these resources through two main client types.

The Azure.Search.Documents client library (v1) is a brand new offering for Python developers who want to use search technology in their applications. There is an older, fully featured Microsoft.Azure.Search client library (v10) with many similar looking APIs, so please be careful to avoid confusion when exploring online resources.

Examples

The following examples all use a simple Hotel data set that you can import into your own index from the Azure portal. These are just a few of the basics - please check out our Samples for much more.

Querying

Let's start by importing our namespaces.

import os
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

We'll then create a SearchClient to access our hotels search index.

index_name = "hotels"
# Get the service endpoint and API key from the environment
endpoint = os.environ["SEARCH_ENDPOINT"]
key = os.environ["SEARCH_API_KEY"]

# Create a client
credential = AzureKeyCredential(key)
client = SearchClient(endpoint=endpoint,
                      index_name=index_name,
                      credential=credential)

Let's search for a "luxury" hotel.

results = client.search(search_text="luxury")

for result in results:
    print("{}: {})".format(result["hotelId"], result["hotelName"]))

Creating an index

You can use the SearchIndexClient to create a search index. Fields can be defined using convenient SimpleField, SearchableField, or ComplexField models. Indexes can also define suggesters, lexical analyzers, and more.

import os
from azure.core.credentials import AzureKeyCredential
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import ( 
    ComplexField, 
    CorsOptions, 
    SearchIndex, 
    ScoringProfile, 
    SearchFieldDataType, 
    SimpleField, 
    SearchableField 
)

endpoint = os.environ["SEARCH_ENDPOINT"]
key = os.environ["SEARCH_API_KEY"]

# Create a service client
client = SearchIndexClient(endpoint, AzureKeyCredential(key))

# Create the index
name = "hotels"
fields = [
        SimpleField(name="hotelId", type=SearchFieldDataType.String, key=True),
        SimpleField(name="baseRate", type=SearchFieldDataType.Double),
        SearchableField(name="description", type=SearchFieldDataType.String),
        ComplexField(name="address", fields=[
            SimpleField(name="streetAddress", type=SearchFieldDataType.String),
            SimpleField(name="city", type=SearchFieldDataType.String),
        ])
    ]
cors_options = CorsOptions(allowed_origins=["*"], max_age_in_seconds=60)
scoring_profiles = []

index = SearchIndex(
    name=name,
    fields=fields,
    scoring_profiles=scoring_profiles,
    cors_options=cors_options)

result = client.create_index(index)

Adding documents to your index

You can Upload, Merge, MergeOrUpload, and Delete multiple documents from an index in a single batched request. There are a few special rules for merging to be aware of.

import os
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

index_name = "hotels"
endpoint = os.environ["SEARCH_ENDPOINT"]
key = os.environ["SEARCH_API_KEY"]

DOCUMENT = {
    'Category': 'Hotel',
    'HotelId': '1000',
    'Rating': 4.0,
    'Rooms': [],
    'HotelName': 'Azure Inn',
}

result = client.upload_documents(documents=[DOCUMENT])

print("Upload of new document succeeded: {}".format(result[0].succeeded))

Retrieve a specific document from an index

In addition to querying for documents using keywords and optional filters, you can retrieve a specific document from your index if you already know the key. You could get the key from a query, for example, and want to show more information about it or navigate your customer to that document.

import os
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

index_name = "hotels"
endpoint = os.environ["SEARCH_ENDPOINT"]
key = os.environ["SEARCH_API_KEY"]

client = SearchClient(endpoint, index_name, AzureKeyCredential(key))

result = client.get_document(key="1")

print("Details for hotel '1' are:")
print("        Name: {}".format(result["HotelName"]))
print("      Rating: {}".format(result["Rating"]))
print("    Category: {}".format(result["Category"]))

Async APIs

This library includes a complete async API supported on Python 3.5+. To use it, you must first install an async transport, such as aiohttp. See azure-core documentation for more information.

from azure.core.credentials import AzureKeyCredential
from azure.search.documents.aio import SearchClient

client = SearchClient(endpoint, index_name, AzureKeyCredential(api_key))

async with client:
  results = await client.search(search_text="hotel")
  async for result in results:
    print("{}: {})".format(result["hotelId"], result["hotelName"]))
...


## Troubleshooting

### General

The Azure Cognitive Search client will raise exceptions defined in [Azure Core][azure_core].

### Logging

This library uses the standard [logging][python_logging] library for logging.
Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO
level.

Detailed DEBUG level logging, including request/response bodies and unredacted
headers, can be enabled on a client with the `logging_enable` keyword argument:
```python
import sys
import logging
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

# Create a logger for the 'azure' SDK
logger = logging.getLogger('azure')
logger.setLevel(logging.DEBUG)

# Configure a console output
handler = logging.StreamHandler(stream=sys.stdout)
logger.addHandler(handler)

# This client will log detailed information about its HTTP sessions, at DEBUG level
client = SearchClient("<service endpoint>", "<index_name>", AzureKeyCredential("<api key>"), logging_enable=True)

Similarly, logging_enable can enable detailed logging for a single operation, even when it isn't enabled for the client:

result =  client.search(search_text="spa", logging_enable=True)

Next steps

Contributing

See our Search CONTRIBUTING.md for details on building, testing, and contributing to this library.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Impressions

Impressions