azure-search-documents-py
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".
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- azure_search_documents_py
- description
- 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".
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- {"author":"Microsoft","version":"1.0.0","package":"azure-search-documents"}
# 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
1. **Use hybrid search** for best relevance combining vector and keyword
2. **Enable semantic ranking** for natural language queries
3. **Index in batches** of 100-1000 documents for efficiency
4. **Use filters** to narrow results before ranking
5. **Configure vector dimensions** to match your embedding model
6. **Use HNSW algorithm** for large-scale vector search
7. **Create suggesters** at index creation time (cannot add later)
## Reference Files
| File | Contents |
|------|----------|
| [references/vector-search.md](references/vector-search.md) | HNSW configuration, integrated vectorization, multi-vector queries |
| [references/semantic-ranking.md](references/semantic-ranking.md) | Semantic configuration, captions, answers, hybrid patterns |
| [scripts/setup_vector_index.py](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)
在 GitHub 查看这个 SKILL.md 很大，SkillsMP 这里只预览前一段内容。 在 GitHub 查看
