azure-search-documents-py

name	azure-search-documents-py
description	Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
license	MIT
metadata	{"author":"Microsoft","version":"1.0.0","package":"azure-search-documents"}

Azure AI Search SDK for Python

Full-text, vector, and hybrid search with AI enrichment capabilities.

Installation

pip install azure-search-documents

Environment Variables

AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net  # Required for all auth methods
AZURE_SEARCH_INDEX_NAME=<your-index-name>  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
AZURE_SEARCH_API_KEY=<your-api-key>  # Only required for the legacy API-key auth path below

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
   • Local dev: DefaultAzureCredential works as-is.
   • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
   • Sync: with <Client>(...) as client:
   • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.search.documents import SearchClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

with SearchClient(
    endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
    index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
    credential=credential,
) as client:
    results = list(client.search(search_text="*", top=5))

Legacy: API Key (existing keyed deployments)

New code should use DefaultAzureCredential above. Use AzureKeyCredential only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout. The same AzureKeyCredential works with SearchIndexClient and SearchIndexerClient for admin operations.

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

with SearchClient(
    endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
    index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
    credential=AzureKeyCredential(os.environ["AZURE_SEARCH_API_KEY"]),
) as client:
    results = list(client.search(search_text="*", top=5))

Client Types

Client	Purpose
SearchClient	Search and document operations
SearchIndexClient	Index management, synonym maps
SearchIndexerClient	Indexers, data sources, skillsets

Create Index with Vector Field

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

fields = [
    SimpleField(name="id", type=SearchFieldDataType.String, key=True),
    SearchableField(name="title", type=SearchFieldDataType.String),
    SearchableField(name="content", type=SearchFieldDataType.String),
    SearchField(
        name="content_vector",
        type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
        searchable=True,
        vector_search_dimensions=1536,
        vector_search_profile_name="my-vector-profile"
    )
]

vector_search = VectorSearch(
    algorithms=[
        HnswAlgorithmConfiguration(name="my-hnsw")
    ],
    profiles=[
        VectorSearchProfile(
            name="my-vector-profile",
            algorithm_configuration_name="my-hnsw"
        )
    ]
)

index = SearchIndex(
    name="my-index",
    fields=fields,
    vector_search=vector_search
)

with SearchIndexClient(endpoint, DefaultAzureCredential()) as index_client:
    index_client.create_or_update_index(index)

Upload Documents

from azure.search.documents import SearchClient

documents = [
    {
        "id": "1",
        "title": "Azure AI Search",
        "content": "Full-text and vector search service",
        "content_vector": [0.1, 0.2, ...]  # 1536 dimensions
    }
]

with SearchClient(endpoint, "my-index", DefaultAzureCredential()) as client:
    result = client.upload_documents(documents)
    print(f"Uploaded {len(result)} documents")

Keyword Search

results = client.search(
    search_text="azure search",
    select=["id", "title", "content"],
    top=10
)

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

Vector Search

from azure.search.documents.models import VectorizedQuery

# Your query embedding (1536 dimensions)
query_vector = get_embedding("semantic search capabilities")

vector_query = VectorizedQuery(
    vector=query_vector,
    k_nearest_neighbors=10,
    fields="content_vector"
)

results = client.search(
    vector_queries=[vector_query],
    select=["id", "title", "content"]
)

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

Hybrid Search (Vector + Keyword)

from azure.search.documents.models import VectorizedQuery

vector_query = VectorizedQuery(
    vector=query_vector,
    k_nearest_neighbors=10,
    fields="content_vector"
)

results = client.search(
    search_text="azure search",
    vector_queries=[vector_query],
    select=["id", "title", "content"],
    top=10
)

Semantic Ranking

from azure.search.documents.models import QueryType

results = client.search(
    search_text="what is azure search",
    query_type=QueryType.SEMANTIC,
    semantic_configuration_name="my-semantic-config",
    select=["id", "title", "content"],
    top=10
)

for result in results:
    print(f"{result['title']}")
    if result.get("@search.captions"):
        print(f"  Caption: {result['@search.captions'][0].text}")

Filters

results = client.search(
    search_text="*",
    filter="category eq 'Technology' and rating gt 4",
    order_by=["rating desc"],
    select=["id", "title", "category", "rating"]
)

Facets

results = client.search(
    search_text="*",
    facets=["category,count:10", "rating"],
    top=0  # Only get facets, no documents
)

for facet_name, facet_values in results.get_facets().items():
    print(f"{facet_name}:")
    for facet in facet_values:
        print(f"  {facet['value']}: {facet['count']}")

Autocomplete & Suggest

# Autocomplete
results = client.autocomplete(
    search_text="sea",
    suggester_name="my-suggester",
    mode="twoTerms"
)

# Suggest
results = client.suggest(
    search_text="sea",
    suggester_name="my-suggester",
    select=["title"]
)

Indexer with Skillset

from azure.search.documents.indexes import SearchIndexerClient
from azure.search.documents.indexes.models import (
    SearchIndexer,
    SearchIndexerDataSourceConnection,
    SearchIndexerSkillset,
    EntityRecognitionSkill,
    InputFieldMappingEntry,
    OutputFieldMappingEntry
)

with SearchIndexerClient(endpoint, DefaultAzureCredential()) as indexer_client:
    # Use managed identity (search service must have RBAC role on the storage account). Avoid storage connection strings with embedded keys.
    data_source = SearchIndexerDataSourceConnection(
        name="my-datasource",
        type="azureblob",
        connection_string="ResourceId=/subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.Storage/storageAccounts/<acct>",
        container={"name": "documents"}
    )
    indexer_client.create_or_update_data_source_connection(data_source)

    # Create skillset
    skillset = SearchIndexerSkillset(
        name="my-skillset",
        skills=[
            EntityRecognitionSkill(
                inputs=[InputFieldMappingEntry(name="text", source="/document/content")],
                outputs=[OutputFieldMappingEntry(name="organizations", target_name="organizations")]
            )
        ]
    )
    indexer_client.create_or_update_skillset(skillset)

    # Create indexer
    indexer = SearchIndexer(
        name="my-indexer",
        data_source_name="my-datasource",
        target_index_name="my-index",
        skillset_name="my-skillset"
    )
    indexer_client.create_or_update_indexer(indexer)

Best Practices

1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
3. Use hybrid search for best relevance combining vector and keyword
4. Enable semantic ranking for natural language queries
5. Index in batches of 100-1000 documents for efficiency
6. Use filters to narrow results before ranking
7. Configure vector dimensions to match your embedding model
8. Use HNSW algorithm for large-scale vector search
9. Create suggesters at index creation time (cannot add later)

Reference Files

File	Contents
references/vector-search.md	HNSW configuration, integrated vectorization, multi-vector queries
references/semantic-ranking.md	Semantic configuration, captions, answers, hybrid patterns
scripts/setup_vector_index.py	CLI script to create vector-enabled search index

---

Additional Azure AI Search Patterns

Azure AI Search Python SDK

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

Installation

pip install azure-search-documents azure-identity

Environment Variables

AZURE_SEARCH_ENDPOINT=https://<search-service>.search.windows.net  # Required for all auth methods
AZURE_SEARCH_INDEX_NAME=<index-name>  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication

import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.search.documents import SearchClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

with SearchClient(
    endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
    index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
    credential=credential,
) as client:
    results = list(client.search(search_text="*", top=5))

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")]
            )
        )]
    )
)

with SearchIndexClient(endpoint, credential) as index_client:
    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
with SearchClient(endpoint, index_name, credential) as search_client:
    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. Use DefaultAzureCredential for code that runs locally (instead of API keys). Use a specific token credential for code that runs in Azure.
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}")