Retrieve and process key phrases

When working with AI solutions in Microsoft Azure, designing and implementing an effective strategy is crucial. One key aspect is retrieving and processing key phrases related to the solution. In this article, we will explore how to achieve this using Azure services and techniques.

Using Azure Cognitive Services

Azure provides various tools and services that enable us to extract key phrases from text data. One such service is Azure Cognitive Services, specifically the Text Analytics service. This service offers a comprehensive set of natural language processing (NLP) APIs to analyze text and extract valuable insights.

To get started, we need to create an instance of the Text Analytics service in Azure. Once that is done, we can use the Text Analytics API to retrieve key phrases from the text. Here is an example of how to use the API in Python:

python
# Python code to retrieve key phrases using Text Analytics API

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

# Configure Azure Text Analytics settings
key = os.environ[“TEXT_ANALYTICS_KEY”]
endpoint = os.environ[“TEXT_ANALYTICS_ENDPOINT”]

# Create a client object
credential = AzureKeyCredential(key)
client = TextAnalyticsClient(endpoint=endpoint, credential=credential)

# Define the text to analyze
documents = [
“Azure Cognitive Services provide a suite of AI APIs.”,
“Microsoft Azure offers several services for AI solutions.”
]

# Call the API to extract key phrases
response = client.extract_key_phrases(documents)
result = [doc.key_phrases for doc in response]

# Display the extracted key phrases
for phrases in result:
print(phrases)

In this code snippet, we authenticate with the Text Analytics service using the endpoint and key. We define some sample documents to analyze and call the extract_key_phrases method on the client object. The response contains a list of key phrases for each document, which we print to the console.

Using Azure Cognitive Search

Another way to retrieve key phrases is by using Azure Cognitive Search. This service allows us to index and search through structured and unstructured data. By creating an index and defining the necessary fields, we can extract key phrases during the indexing process.

Here is an example of how to define an index in Azure Cognitive Search:

{
“name”: “exam-index”,
“fields”: [
{ “name”: “id”, “type”: “Edm.String”, “key”: true, “searchable”: false },
{ “name”: “content”, “type”: “Edm.String”, “searchable”: true, “filterable”: false, “sortable”: false, “facetable”: false }
],
“analyzers”: [
{
“name”: “exam-analyzer”,
“@odata.type”: “#Microsoft.Azure.Search.CustomAnalyzer”,
“tokenizer”: “standard”,
“tokenFilters”: [“lowercase”, “asciifolding”]
}
]
}

In this example, we define an index called “exam-index” with two fields: “id” and “content”. The “content” field is searchable, and we specify the “exam-analyzer” as the analyzer for text processing.

Once the index is defined, we can upload documents and extract key phrases during the indexing process. Here is an example of how to add a document to the index using the Azure Cognitive Search REST API:

http
POST https://{search-service-name}.search.azure.com/indexes/exam-index/docs/index?api-version=2020-06-30
Content-Type: application/json
api-key: {api-key}

{
“value”: [
{
“@search.action”: “upload”,
“id”: “1”,
“content”: “Azure Cognitive Services provide a suite of AI APIs.”
},
{
“@search.action”: “upload”,
“id”: “2”,
“content”: “Microsoft Azure offers several services for AI solutions.”
}
]
}

In this HTTP request, we specify the index name, document operation, and the content to add. The key phrases will be automatically extracted during the indexing process and stored in the index.

Retrieving Key Phrases

Retrieving the key phrases from Azure Cognitive Search is similar to querying any other search result. You can use the Search API to execute queries and retrieve the key phrases along with the search results.

Retrieving and processing key phrases related to exam Designing and Implementing a Microsoft Azure AI Solution is essential when building AI models. Azure provides powerful services like Cognitive Services and Cognitive Search to assist in this process. By leveraging these services, you can extract key insights and enhance your AI solution’s performance.