Azure Cognitive Search client library for Python¶
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
Disclaimer¶
Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691
Getting started¶
Install the package¶
Install the Azure Cognitive Search client library for Python with pip:
pip install azure-search-documents
Prerequisites¶
Python 3.7 or later is required to use this package.
You need an Azure subscription and a Azure Cognitive Search service to use this package.
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)
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.
SearchClient
helps with:Searching your indexed documents using rich queries and powerful data shaping
Autocompleting partially typed search terms based on documents in the index
Suggesting the most likely matching text in documents as a user types
Adding, Updating or Deleting Documents documents from an index
SearchIndexClient
allows you to:Most of the
SearchServiceClient
functionality is not yet available in our current preview
SearchIndexerClient
allows you to:
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',
}
search_client = SearchClient(endpoint, index_name, AzureKeyCredential(key))
result = search_client.upload_documents(documents=[DOCUMENT])
print("Upload of new document succeeded: {}".format(result[0].succeeded))
Authenticate in a National Cloud¶
To authenticate in a National Cloud, you will need to make the following additions to your client configuration:
Set the
AuthorityHost
in the credential options or via theAZURE_AUTHORITY_HOST
environment variableSet the
audience
inSearchClient
,SearchIndexClient
, orSearchIndexerClient
# Create a SearchClient that will authenticate through AAD in the China national cloud.
import os
from azure.identity import DefaultAzureCredential, AzureAuthorityHosts
from azure.search.documents import SearchClient
index_name = "hotels"
endpoint = os.environ["SEARCH_ENDPOINT"]
key = os.environ["SEARCH_API_KEY"]
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_CHINA)
search_client = SearchClient(endpoint, index_name, crdential=credential, audience="https://search.azure.cn")
Retrieving a specific document from your 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. 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.
Logging¶
This library uses the standard 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:
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¶
Go further with Azure.Search.Documents and our https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/search/azure-search-documents/samples
Watch a demo or deep dive video
Read more about the Azure Cognitive Search service
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.