---

name: azure-search-documents-py description: Azure AI Search Python SDK。用于向量搜索、混合搜索、语义排序、索引和技能集。 risk: unknown source: community date_added: '2026-02-27'

Azure AI Search Python SDK

全文搜索、向量搜索和混合搜索，具备AI增强能力。

安装

pip install azure-search-documents

环境变量

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

认证

API密钥

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（推荐）

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()
)

客户端类型

| 客户端 | 用途 | |--------|------| | SearchClient | 搜索和文档操作 | | SearchIndexClient | 索引管理、同义词映射 | | SearchIndexerClient | 索引器、数据源、技能集 |

创建带向量字段的索引

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)

上传文档

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")

关键词搜索

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

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

向量搜索

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']}")

混合搜索（向量 + 关键词）

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
)

语义排序

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}")

过滤器

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

分面

results = client.search(
    search_text="*",
    facets=["category", "rating,values:1|2|3|4|5"]
)