Azure Cognitive Search Updates and New Features

Azure Cognitive Search continues to evolve with powerful AI capabilities. Let’s explore the latest features that make building intelligent search applications easier than ever.

What’s New in Cognitive Search

Recent updates include:

• Semantic search for natural language understanding
• Vector search preview for similarity matching
• Enhanced debug sessions for skillset development
• Improved knowledge store capabilities

Creating a Search Service

# Create search service
az search service create \
    --name mysearchservice \
    --resource-group myResourceGroup \
    --location eastus \
    --sku standard \
    --partition-count 1 \
    --replica-count 1

Basic Index Configuration

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

# Connect to search service
endpoint = "https://mysearchservice.search.windows.net"
credential = AzureKeyCredential("your-admin-key")

index_client = SearchIndexClient(endpoint=endpoint, credential=credential)

# Define index schema
fields = [
    SimpleField(name="id", type=SearchFieldDataType.String, key=True),
    SearchableField(name="title", type=SearchFieldDataType.String,
                    analyzer_name="en.microsoft"),
    SearchableField(name="content", type=SearchFieldDataType.String,
                    analyzer_name="en.microsoft"),
    SearchableField(name="category", type=SearchFieldDataType.String,
                    filterable=True, facetable=True),
    SimpleField(name="price", type=SearchFieldDataType.Double,
                filterable=True, sortable=True, facetable=True),
    SimpleField(name="rating", type=SearchFieldDataType.Double,
                filterable=True, sortable=True),
    SimpleField(name="lastUpdated", type=SearchFieldDataType.DateTimeOffset,
                filterable=True, sortable=True),
    ComplexField(name="address", fields=[
        SearchableField(name="street", type=SearchFieldDataType.String),
        SearchableField(name="city", type=SearchFieldDataType.String,
                        filterable=True, facetable=True),
        SimpleField(name="zipCode", type=SearchFieldDataType.String, filterable=True)
    ])
]

# Create index
index = SearchIndex(
    name="products-index",
    fields=fields,
    cors_options=CorsOptions(allowed_origins=["*"])
)

result = index_client.create_or_update_index(index)
print(f"Index created: {result.name}")

Indexing Documents

from azure.search.documents import SearchClient
import json

search_client = SearchClient(
    endpoint=endpoint,
    index_name="products-index",
    credential=credential
)

# Prepare documents
documents = [
    {
        "id": "1",
        "title": "Azure Machine Learning Guide",
        "content": "Comprehensive guide to building ML solutions on Azure...",
        "category": "Technology",
        "price": 49.99,
        "rating": 4.5,
        "lastUpdated": "2022-08-22T00:00:00Z",
        "address": {
            "street": "123 Tech St",
            "city": "Seattle",
            "zipCode": "98101"
        }
    },
    {
        "id": "2",
        "title": "Data Science Fundamentals",
        "content": "Learn the basics of data science and analytics...",
        "category": "Technology",
        "price": 39.99,
        "rating": 4.8,
        "lastUpdated": "2022-08-21T00:00:00Z",
        "address": {
            "street": "456 Data Ave",
            "city": "San Francisco",
            "zipCode": "94102"
        }
    }
]

# Upload documents
result = search_client.upload_documents(documents=documents)
print(f"Uploaded {len(result)} documents")

# Merge or upload (upsert)
result = search_client.merge_or_upload_documents(documents=documents)

Basic Search Queries

# Simple search
results = search_client.search(search_text="machine learning")

for result in results:
    print(f"{result['title']} (Score: {result['@search.score']:.2f})")

# Search with options
from azure.search.documents.models import QueryType

results = search_client.search(
    search_text="azure data",
    query_type=QueryType.SIMPLE,
    select=["id", "title", "category", "price"],
    filter="price lt 50",
    order_by=["rating desc"],
    top=10,
    include_total_count=True
)

print(f"Total results: {results.get_count()}")
for result in results:
    print(f"{result['title']}: ${result['price']}")

Faceted Search

# Search with facets
results = search_client.search(
    search_text="*",
    facets=["category,count:10", "address/city,count:5", "price,values:0|25|50|100"]
)

# Access facets
for facet in results.get_facets()["category"]:
    print(f"{facet['value']}: {facet['count']}")

print("\nCities:")
for facet in results.get_facets()["address/city"]:
    print(f"{facet['value']}: {facet['count']}")

Autocomplete and Suggestions

from azure.search.documents.indexes.models import (
    SearchIndex,
    Suggester
)

# Add suggester to index
index = SearchIndex(
    name="products-index",
    fields=fields,
    suggesters=[
        Suggester(name="sg", source_fields=["title", "category"])
    ]
)

index_client.create_or_update_index(index)

# Get suggestions
suggestions = search_client.suggest(
    search_text="mach",
    suggester_name="sg",
    select=["title"],
    top=5
)

for suggestion in suggestions:
    print(suggestion["title"])

# Autocomplete
completions = search_client.autocomplete(
    search_text="mach",
    suggester_name="sg",
    mode="twoTerms"
)

for completion in completions:
    print(completion["text"])

Scoring Profiles

from azure.search.documents.indexes.models import (
    ScoringProfile,
    TextWeights,
    FreshnessScoringFunction,
    FreshnessScoringParameters,
    ScoringFunctionInterpolation
)

# Define scoring profile
scoring_profile = ScoringProfile(
    name="boostRecent",
    text_weights=TextWeights(weights={"title": 2.0, "content": 1.0}),
    functions=[
        FreshnessScoringFunction(
            field_name="lastUpdated",
            boost=5,
            parameters=FreshnessScoringParameters(boosting_duration="P7D"),
            interpolation=ScoringFunctionInterpolation.LINEAR
        )
    ]
)

# Add to index
index.scoring_profiles = [scoring_profile]
index.default_scoring_profile = "boostRecent"

index_client.create_or_update_index(index)

# Search with scoring profile
results = search_client.search(
    search_text="azure",
    scoring_profile="boostRecent"
)

Azure Cognitive Search provides enterprise-grade search capabilities with AI enrichment options.