azure-search-documents-py

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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".

npx skills add microsoft/skills --skill azure-search-documents-py

Documentation

SKILL.mdView source

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:

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.

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"]
)

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

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.
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.
Use hybrid search for best relevance combining vector and keyword
Enable semantic ranking for natural language queries
Index in batches of 100-1000 documents for efficiency
Use filters to narrow results before ranking
Configure vector dimensions to match your embedding model
Use HNSW algorithm for large-scale vector search
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

Use environment variables for endpoints, keys, and deployment names
Use DefaultAzureCredential for code that runs locally (instead of API keys). Use a specific token credential for code that runs in Azure.
Use SearchIndexingBufferedSender for batch uploads (handles batching/retries)
Always define semantic configuration for agentic retrieval indexes
Use create_or_update_index for idempotent index creation
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}")

npx skills add microsoft/skills --skill azure-search-documents-py

Documentation

SKILL.md

npx skills add microsoft/skills --skill azure-search-documents-py

Documentation

SKILL.mdView source

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:

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.

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)

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"]
)

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

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.
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.
Use hybrid search for best relevance combining vector and keyword
Enable semantic ranking for natural language queries
Index in batches of 100-1000 documents for efficiency
Use filters to narrow results before ranking
Configure vector dimensions to match your embedding model
Use HNSW algorithm for large-scale vector search
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

Use environment variables for endpoints, keys, and deployment names
Use DefaultAzureCredential for code that runs locally (instead of API keys). Use a specific token credential for code that runs in Azure.
Use SearchIndexingBufferedSender for batch uploads (handles batching/retries)
Always define semantic configuration for agentic retrieval indexes
Use create_or_update_index for idempotent index creation
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}")

azure-search-documents-py

Documentation

Documentation

azure-search-documents-py

Documentation

Azure AI Search SDK for Python

Installation

Environment Variables

Authentication & Lifecycle

Legacy: API Key (existing keyed deployments)

Client Types

Create Index with Vector Field

Upload Documents

Keyword Search

Vector Search

Hybrid Search (Vector + Keyword)

Semantic Ranking

Filters

Facets

Autocomplete & Suggest

Indexer with Skillset

Best Practices

Reference Files

Additional Azure AI Search Patterns

Azure AI Search Python SDK

Installation

Environment Variables

Authentication

Client Selection

Index Creation Pattern

Document Operations

Search Patterns

Agentic Retrieval (Knowledge Bases)

Async Pattern

Best Practices

Field Types Reference

Error Handling

Documentation

Documentation

Azure AI Search SDK for Python

Installation

Environment Variables

Authentication & Lifecycle

Legacy: API Key (existing keyed deployments)

Client Types

Create Index with Vector Field

Upload Documents

Keyword Search

Vector Search

Hybrid Search (Vector + Keyword)

Semantic Ranking

Filters

Facets

Autocomplete & Suggest

Indexer with Skillset

Best Practices

Reference Files

Additional Azure AI Search Patterns

Azure AI Search Python SDK

Installation

Environment Variables

Authentication

Client Selection

Index Creation Pattern

Document Operations

Search Patterns

Agentic Retrieval (Knowledge Bases)

Async Pattern

Best Practices

Field Types Reference

Error Handling