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Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
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| name | azure-search-documents-py |
| description | Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | cloud-infrastructure |
| category | azure |
| risk | unknown |
| source | community |
| tags | ["skill","cloud-infrastructure","azure","search","documents"] |
Full-text, vector, and hybrid search with AI enrichment capabilities.
pip install azure-search-documents
AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net
AZURE_SEARCH_API_KEY=<your-api-key>
AZURE_SEARCH_INDEX_NAME=<your-index-name>
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"])
)
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 | Purpose |
|---|---|
SearchClient | Search and document operations |
SearchIndexClient | Index management, synonym maps |
SearchIndexerClient | Indexers, data sources, skillsets |
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=, =SearchFieldDataType.String, key=),
SearchableField(name=, =SearchFieldDataType.String),
SearchableField(name=, =SearchFieldDataType.String),
SearchField(
name=,
=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=,
vector_search_dimensions=,
vector_search_profile_name=
)
]
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(name=)
],
profiles=[
VectorSearchProfile(
name=,
algorithm_configuration_name=
)
]
)
index = SearchIndex(
name=,
fields=fields,
vector_search=vector_search
)
index_client.create_or_update_index(index)
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")
results = client.search(
search_text="azure search",
select=["id", "title", "content"],
top=10
)
for result in results:
print(f"{result['title']}: {result['@search.score']}")
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']}")
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
)
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}")
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
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"]
)
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)
| 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 |
Write clean, idiomatic Python code for Azure AI Search using azure-search-documents.
pip install azure-search-documents azure-identity
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>
DefaultAzureCredential (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 | 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 |
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)
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
# 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
)
For LLM-powered Q&A with answer synthesis, see references/agentic-retrieval.md.
Key concepts:
EXTRACTIVE_DATA (raw chunks) or ANSWER_SYNTHESIS (LLM-generated answers)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"])
DefaultAzureCredential over API keys for productionSearchIndexingBufferedSender for batch uploads (handles batching/retries)create_or_update_index for idempotent index creationclose()| 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 |
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}")
This skill is applicable to execute the workflow or actions described in the overview.