Azure AI Search Advanced Features: Beyond Basic Indexing

Azure AI Search offers powerful features beyond basic search. Let’s explore advanced capabilities.

Advanced Azure AI Search

from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    SearchIndex,
    SearchField,
    SearchFieldDataType,
    VectorSearch,
    HnswAlgorithmConfiguration,
    VectorSearchProfile,
    SemanticConfiguration,
    SemanticField,
    SemanticPrioritizedFields,
    SearchableField,
    SimpleField
)

class AdvancedAzureSearch:
    def __init__(self, endpoint: str, key: str):
        self.index_client = SearchIndexClient(endpoint, AzureKeyCredential(key))
        self.search_client = None

    def create_advanced_index(self, index_name: str) -> SearchIndex:
        """Create index with vector, semantic, and hybrid search."""
        fields = [
            SimpleField(name="id", type=SearchFieldDataType.String, key=True),
            SearchableField(name="content", type=SearchFieldDataType.String,
                           analyzer_name="en.microsoft"),
            SearchableField(name="title", type=SearchFieldDataType.String),
            SearchField(
                name="contentVector",
                type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
                searchable=True,
                vector_search_dimensions=1536,
                vector_search_profile_name="vector-profile"
            ),
            SimpleField(name="category", type=SearchFieldDataType.String, filterable=True),
            SimpleField(name="date", type=SearchFieldDataType.DateTimeOffset,
                       filterable=True, sortable=True)
        ]

        vector_search = VectorSearch(
            algorithms=[
                HnswAlgorithmConfiguration(
                    name="hnsw-config",
                    parameters={"m": 4, "efConstruction": 400, "efSearch": 500}
                )
            ],
            profiles=[
                VectorSearchProfile(
                    name="vector-profile",
                    algorithm_configuration_name="hnsw-config"
                )
            ]
        )

        semantic_config = SemanticConfiguration(
            name="semantic-config",
            prioritized_fields=SemanticPrioritizedFields(
                title_field=SemanticField(field_name="title"),
                content_fields=[SemanticField(field_name="content")]
            )
        )

        index = SearchIndex(
            name=index_name,
            fields=fields,
            vector_search=vector_search,
            semantic_configurations=[semantic_config]
        )

        return self.index_client.create_or_update_index(index)

    async def hybrid_search(
        self,
        query: str,
        query_vector: list[float],
        filters: str = None,
        top: int = 10
    ) -> list:
        """Perform hybrid search combining vector, keyword, and semantic."""
        results = self.search_client.search(
            search_text=query,
            vector_queries=[{
                "vector": query_vector,
                "k": top,
                "fields": "contentVector"
            }],
            filter=filters,
            query_type="semantic",
            semantic_configuration_name="semantic-config",
            top=top,
            select=["id", "title", "content", "category"]
        )

        return [
            {
                "id": r["id"],
                "title": r["title"],
                "content": r["content"],
                "score": r["@search.score"],
                "reranker_score": r.get("@search.reranker_score")
            }
            for r in results
        ]

    async def search_with_captions(self, query: str) -> list:
        """Search with AI-generated captions and answers."""
        results = self.search_client.search(
            search_text=query,
            query_type="semantic",
            semantic_configuration_name="semantic-config",
            query_caption="extractive",
            query_answer="extractive"
        )

        return {
            "answers": [a.text for a in results.get_answers()],
            "results": [{
                "content": r["content"],
                "captions": [c.text for c in r.get("@search.captions", [])]
            } for r in results]
        }

Azure AI Search combines the best of vector, keyword, and semantic search in one service.