azure-ai-search-python

Write clean, idiomatic Python code for Azure AI Search using azure-search-documents.

Authentication Patterns

Microsoft Entra ID (preferred):

from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient

credential = DefaultAzureCredential()
client = SearchClient(endpoint, index_name, credential)

API Key:

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

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

Client Selection

| Client | Purpose | |--------|---------| | SearchClient | Query indexes, upload/update/delete documents | | SearchIndexClient | Create/manage indexes, knowledge sources, knowledge bases | | SearchIndexerClient | Manage indexers, skillsets, data sources | | KnowledgeBaseRetrievalClient | Agentic retrieval with LLM-powered Q&A |

Index Creation Pattern

from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    SearchIndex, SearchField, VectorSearch, VectorSearchProfile,
    HnswAlgorithmConfiguration, AzureOpenAIVectorizer,
    AzureOpenAIVectorizerParameters, SemanticSearch,
    SemanticConfiguration, SemanticPrioritizedFields, SemanticField
)

index = SearchIndex(
    name=index_name,
    fields=[
        SearchField(name="id", type="Edm.String", key=True),
        SearchField(name="content", type="Edm.String", searchable=True),
        SearchField(name="embedding", type="Collection(Edm.Single)",
                   vector_search_dimensions=3072,
                   vector_search_profile_name="vector-profile"),
    ],
    vector_search=VectorSearch(
        profiles=[VectorSearchProfile(
            name="vector-profile",
            algorithm_configuration_name="hnsw-algo",
            vectorizer_name="openai-vectorizer"
        )],
        algorithms=[HnswAlgorithmConfiguration(name="hnsw-algo")],
        vectorizers=[AzureOpenAIVectorizer(
            vectorizer_name="openai-vectorizer",
            parameters=AzureOpenAIVectorizerParameters(
                resource_url=aoai_endpoint,
                deployment_name=embedding_deployment,
                model_name=embedding_model
            )
        )]
    ),
    semantic_search=SemanticSearch(
        default_configuration_name="semantic-config",
        configurations=[SemanticConfiguration(
            name="semantic-config",
            prioritized_fields=SemanticPrioritizedFields(
                content_fields=[SemanticField(field_name="content")]
            )
        )]
    )
)

index_client = SearchIndexClient(endpoint, credential)
index_client.create_or_update_index(index)

Document Operations

from azure.search.documents import SearchIndexingBufferedSender

# Batch upload with automatic batching
with SearchIndexingBufferedSender(endpoint, index_name, credential) as sender:
    sender.upload_documents(documents)

# Direct operations via SearchClient
search_client = SearchClient(endpoint, index_name, credential)
search_client.upload_documents(documents)      # Add new
search_client.merge_documents(documents)       # Update existing
search_client.merge_or_upload_documents(documents)  # Upsert
search_client.delete_documents(documents)      # Remove

Search Patterns

# Basic search
results = search_client.search(search_text="query")

# Vector search
from azure.search.documents.models import VectorizedQuery

results = search_client.search(
    search_text=None,
    vector_queries=[VectorizedQuery(
        vector=embedding,
        k_nearest_neighbors=5,
        fields="embedding"
    )]
)

# Hybrid search (vector + keyword)
results = search_client.search(
    search_text="query",
    vector_queries=[VectorizedQuery(vector=embedding, k_nearest_neighbors=5, fields="embedding")],
    query_type="semantic",
    semantic_configuration_name="semantic-config"
)

# With filters
results = search_client.search(
    search_text="query",
    filter="category eq 'technology'",
    select=["id", "title", "content"],
    top=10
)

Agentic Retrieval (Knowledge Bases)

For LLM-powered Q&A with answer synthesis, see references/agentic-retrieval.md.

Key concepts:

• Knowledge Source: Points to a search index
• Knowledge Base: Wraps knowledge sources + LLM for query planning and synthesis
• Output modes: EXTRACTIVE_DATA (raw chunks) or ANSWER_SYNTHESIS (LLM-generated answers)

Async Pattern

from azure.search.documents.aio import SearchClient

async with SearchClient(endpoint, index_name, credential) as client:
    results = await client.search(search_text="query")
    async for result in results:
        print(result["title"])

Best Practices

1. Use environment variables for endpoints, keys, and deployment names
2. Prefer DefaultAzureCredential over API keys for production
3. Use SearchIndexingBufferedSender for batch uploads (handles batching/retries)
4. Always define semantic configuration for agentic retrieval indexes
5. Use create_or_update_index for idempotent index creation
6. Close clients with context managers or explicit close()

Field Types Reference

| EDM Type | Python | Notes | |----------|--------|-------| | Edm.String | str | Searchable text | | Edm.Int32 | int | Integer | | Edm.Int64 | int | Long integer | | Edm.Double | float | Floating point | | Edm.Boolean | bool | True/False | | Edm.DateTimeOffset | datetime | ISO 8601 | | Collection(Edm.Single) | List[float] | Vector embeddings | | Collection(Edm.String) | List[str] | String arrays |

Error Handling

from azure.core.exceptions import (
    HttpResponseError,
    ResourceNotFoundError,
    ResourceExistsError
)

try:
    result = search_client.get_document(key="123")
except ResourceNotFoundError:
    print("Document not found")
except HttpResponseError as e:
    print(f"Search error: {e.message}")