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".

37installs

Sourcemicrosoft/agent-skills

Added on2026-02-04

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Azure AI Search SDK for Python

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

Installation

bash

pip install azure-search-documents

Environment Variables

bash

AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net
AZURE_SEARCH_API_KEY=<your-api-key>
AZURE_SEARCH_INDEX_NAME=<your-index-name>

Authentication

API Key

python

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

client = SearchClient(
    endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
    index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
    credential=AzureKeyCredential(os.environ["AZURE_SEARCH_API_KEY"])
)

Entra ID (Recommended)

python

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

client = SearchClient(
    endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
    index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
    credential=DefaultAzureCredential()
)

Client Types

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

Create Index with Vector Field

python

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

index_client = SearchIndexClient(endpoint, AzureKeyCredential(key))

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
)

index_client.create_or_update_index(index)

Upload Documents

python

from azure.search.documents import SearchClient

client = SearchClient(endpoint, "my-index", AzureKeyCredential(key))

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

result = client.upload_documents(documents)
print(f"Uploaded {len(result)} documents")

Keyword Search

python

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

python

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)

python

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

python

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

python

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

Facets

python

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

python

# 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

python

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

indexer_client = SearchIndexerClient(endpoint, AzureKeyCredential(key))

# Create data source
data_source = SearchIndexerDataSourceConnection(
    name="my-datasource",
    type="azureblob",
    connection_string=connection_string,
    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

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

bash

pip install azure-search-documents azure-identity

Environment Variables

bash

AZURE_SEARCH_ENDPOINT=https://<search-service>.search.windows.net
AZURE_SEARCH_INDEX_NAME=<index-name>
# For API key auth (not recommended for production)
AZURE_SEARCH_API_KEY=<api-key>

Authentication

DefaultAzureCredential (preferred):

python

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

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

API Key:

python

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

python

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

python

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

python

# 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

python

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
Prefer
DefaultAzureCredential
over API keys for production
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

python

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

NPX Install

Tags

SKILL.md Content

Azure AI Search SDK for Python

Installation

Environment Variables

Authentication

API Key

Entra ID (Recommended)

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