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Descargar Zip Descargando... 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"]
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
AZURE_SEARCH_API_KEY=<your-api-key>
AZURE_SEARCH_INDEX_NAME=<your-index-name>
Authentication
API Key
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)
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 SearchClientSearch and document operations SearchIndexClientIndex management, synonym maps SearchIndexerClientIndexers, 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
)
index_client = SearchIndexClient(endpoint, AzureKeyCredential(key))
fields = [
SimpleField(name="id"
type
True
"title"
type
"content"
type
"content_vector"
type
True
1536
"my-vector-profile"
"my-hnsw"
"my-vector-profile"
"my-hnsw"
"my-index"
Upload Documents 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 , ...]
}
]
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
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
)
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
results = client.autocomplete(
search_text="sea" ,
suggester_name="my-suggester" ,
mode="twoTerms"
)
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
)
indexer_client = SearchIndexerClient(endpoint, AzureKeyCredential(key))
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)
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)
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 keywordEnable semantic ranking for natural language queriesIndex in batches of 100-1000 documents for efficiencyUse filters to narrow results before rankingConfigure vector dimensions to match your embedding modelUse HNSW algorithm for large-scale vector searchCreate 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
AZURE_SEARCH_INDEX_NAME=<index-name>
AZURE_SEARCH_API_KEY=<api-key>
Authentication DefaultAzureCredential (preferred) :
from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient
credential = DefaultAzureCredential()
client = SearchClient(endpoint, index_name, credential)
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
client = SearchClient(endpoint, index_name, AzureKeyCredential(api_key))
Client Selection Client Purpose SearchClientQuery indexes, upload/update/delete documents SearchIndexClientCreate/manage indexes, knowledge sources, knowledge bases SearchIndexerClientManage indexers, skillsets, data sources KnowledgeBaseRetrievalClientAgentic 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
with SearchIndexingBufferedSender(endpoint, index_name, credential) as sender:
sender.upload_documents(documents)
search_client = SearchClient(endpoint, index_name, credential)
search_client.upload_documents(documents)
search_client.merge_documents(documents)
search_client.merge_or_upload_documents(documents)
search_client.delete_documents(documents)
Search Patterns
results = search_client.search(search_text="query" )
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"
)]
)
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"
)
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.
Knowledge Source : Points to a search indexKnowledge Base : Wraps knowledge sources + LLM for query planning and synthesisOutput 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 namesPrefer DefaultAzureCredential over API keys for productionUse SearchIndexingBufferedSender for batch uploads (handles batching/retries)Always define semantic configuration for agentic retrieval indexesUse create_or_update_index for idempotent index creationClose clients with context managers or explicit close()
Field Types Reference EDM Type Python Notes Edm.Stringstr Searchable text Edm.Int32int Integer Edm.Int64int Long integer Edm.Doublefloat Floating point Edm.Booleanbool True/False Edm.DateTimeOffsetdatetime 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} " )
When to Use This skill is applicable to execute the workflow or actions described in the overview.
Connections
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