SearchClient Class

A client to interact with an existing Azure search index.

Inheritance
azure.search.documents._headers_mixin.HeadersMixin
SearchClient

Constructor

SearchClient(endpoint: str, index_name: str, credential: AzureKeyCredential | TokenCredential, **kwargs: Any)

Parameters

endpoint
str
Required

The URL endpoint of an Azure search service

index_name
str
Required

The name of the index to connect to

credential
AzureKeyCredential or TokenCredential
Required

A credential to authorize search client requests

api_version
str

The Search API version to use for requests.

audience
str

sets the Audience to use for authentication with Azure Active Directory (AAD). The audience is not considered when using a shared key. If audience is not provided, the public cloud audience will be assumed.

Examples

Creating the SearchClient with an API key.


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

   service_endpoint = os.environ["AZURE_SEARCH_SERVICE_ENDPOINT"]
   index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
   key = os.environ["AZURE_SEARCH_API_KEY"]

   search_client = SearchClient(service_endpoint, index_name, AzureKeyCredential(key))

Methods

autocomplete

Get search auto-completion results from the Azure search index.

collection that's part of the index definition. :keyword mode: Specifies the mode for Autocomplete. The default is 'oneTerm'. Use

'twoTerms' to get shingles and 'oneTermWithContext' to use the current context while producing auto-completed terms. Possible values include: 'oneTerm', 'twoTerms', 'oneTermWithContext'.

close

Close the SearchClient session.

delete_documents

Delete documents from the Azure search index

Delete removes the specified document from the index. Any field you specify in a delete operation, other than the key field, will be ignored. If you want to remove an individual field from a document, use merge_documents instead and set the field explicitly to None.

Delete operations are idempotent. That is, even if a document key does not exist in the index, attempting a delete operation with that key will result in a 200 status code.

get_document

Retrieve a document from the Azure search index by its key.

get_document_count

Return the number of documents in the Azure search index.

index_documents

Specify a document operations to perform as a batch.

:raises RequestEntityTooLargeError

merge_documents

Merge documents in to existing documents in the Azure search index.

Merge updates an existing document with the specified fields. If the document doesn't exist, the merge will fail. Any field you specify in a merge will replace the existing field in the document. This also applies to collections of primitive and complex types.

merge_or_upload_documents

Merge documents in to existing documents in the Azure search index, or upload them if they do not yet exist.

This action behaves like merge_documents if a document with the given key already exists in the index. If the document does not exist, it behaves like upload_documents with a new document.

search

Search the Azure search index for documents.

suggest

Get search suggestion results from the Azure search index.

character, and no more than 100 characters. :param str suggester_name: Required. The name of the suggester as specified in the suggesters collection that's part of the index definition. :keyword str filter: An OData expression that filters the documents considered for suggestions. :keyword bool use_fuzzy_matching: A value indicating whether to use fuzzy matching for the suggestions

query. Default is false. When set to true, the query will find terms even if there's a substituted or missing character in the search text. While this provides a better experience in some scenarios, it comes at a performance cost as fuzzy suggestions queries are slower and consume more resources.

upload_documents

Upload documents to the Azure search index.

An upload action is similar to an "upsert" where the document will be inserted if it is new and updated/replaced if it exists. All fields are replaced in the update case.

autocomplete

Get search auto-completion results from the Azure search index.

collection that's part of the index definition. :keyword mode: Specifies the mode for Autocomplete. The default is 'oneTerm'. Use

'twoTerms' to get shingles and 'oneTermWithContext' to use the current context while producing auto-completed terms. Possible values include: 'oneTerm', 'twoTerms', 'oneTermWithContext'.

autocomplete(search_text: str, suggester_name: str, *, mode: str | AutocompleteMode | None = None, use_fuzzy_matching: bool | None = None, highlight_post_tag: str | None = None, highlight_pre_tag: str | None = None, minimum_coverage: float | None = None, search_fields: List[str] | None = None, top: int | None = None, **kwargs) -> List[Dict]

Parameters

filter
str

An OData expression that filters the documents used to produce completed terms for the Autocomplete result.

use_fuzzy_matching
bool

A value indicating whether to use fuzzy matching for the autocomplete query. Default is false. When set to true, the query will find terms even if there's a substituted or missing character in the search text. While this provides a better experience in some scenarios, it comes at a performance cost as fuzzy autocomplete queries are slower and consume more resources.

highlight_post_tag
str

A string tag that is appended to hit highlights. Must be set with highlightPreTag. If omitted, hit highlighting is disabled.

highlight_pre_tag
str

A string tag that is prepended to hit highlights. Must be set with highlightPostTag. If omitted, hit highlighting is disabled.

minimum_coverage
float

A number between 0 and 100 indicating the percentage of the index that must be covered by an autocomplete query in order for the query to be reported as a success. This parameter can be useful for ensuring search availability even for services with only one replica. The default is 80.

search_fields
list[str]

The list of field names to consider when querying for auto-completed terms. Target fields must be included in the specified suggester.

top
int

The number of auto-completed terms to retrieve. This must be a value between 1 and 100. The default is 5.

Return type

Examples

Get a auto-completions.


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

   search_client = SearchClient(service_endpoint, index_name, AzureKeyCredential(key))

   results = search_client.autocomplete(search_text="bo", suggester_name="sg")

   print("Autocomplete suggestions for 'bo'")
   for result in results:
       print("    Completion: {}".format(result["text"]))

close

Close the SearchClient session.

close() -> None

delete_documents

Delete documents from the Azure search index

Delete removes the specified document from the index. Any field you specify in a delete operation, other than the key field, will be ignored. If you want to remove an individual field from a document, use merge_documents instead and set the field explicitly to None.

Delete operations are idempotent. That is, even if a document key does not exist in the index, attempting a delete operation with that key will result in a 200 status code.

delete_documents(documents: List[Dict], **kwargs: Any) -> List[IndexingResult]

Parameters

documents
list[dict]
Required

A list of documents to delete.

Returns

List of IndexingResult

Return type

Examples

Delete existing documents to an index


   result = search_client.delete_documents(documents=[{"hotelId": "1000"}])

   print("Delete new document succeeded: {}".format(result[0].succeeded))

get_document

Retrieve a document from the Azure search index by its key.

get_document(key: str, selected_fields: List[str] | None = None, **kwargs: Any) -> Dict

Parameters

key
str
Required

The primary key value for the document to retrieve

selected_fields
list[str]
Required

an allow-list of fields to include in the results

Returns

The document as stored in the Azure search index

Return type

Examples

Get a specific document from the search index.


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

   search_client = SearchClient(service_endpoint, index_name, AzureKeyCredential(key))

   result = search_client.get_document(key="23")

   print("Details for hotel '23' are:")
   print("        Name: {}".format(result["hotelName"]))
   print("      Rating: {}".format(result["rating"]))
   print("    Category: {}".format(result["category"]))

get_document_count

Return the number of documents in the Azure search index.

get_document_count(**kwargs: Any) -> int

Returns

The count of documents in the index

Return type

int

index_documents

Specify a document operations to perform as a batch.

:raises RequestEntityTooLargeError

index_documents(batch: IndexDocumentsBatch, **kwargs: Any) -> List[IndexingResult]

Parameters

batch
IndexDocumentsBatch
Required

A batch of document operations to perform.

Returns

List of IndexingResult

Return type

merge_documents

Merge documents in to existing documents in the Azure search index.

Merge updates an existing document with the specified fields. If the document doesn't exist, the merge will fail. Any field you specify in a merge will replace the existing field in the document. This also applies to collections of primitive and complex types.

merge_documents(documents: List[Dict], **kwargs: Any) -> List[IndexingResult]

Parameters

documents
list[dict]
Required

A list of documents to merge.

Returns

List of IndexingResult

Return type

Examples

Merge fields into existing documents to an index


   result = search_client.merge_documents(documents=[{"hotelId": "1000", "rating": 4.5}])

   print("Merge into new document succeeded: {}".format(result[0].succeeded))

merge_or_upload_documents

Merge documents in to existing documents in the Azure search index, or upload them if they do not yet exist.

This action behaves like merge_documents if a document with the given key already exists in the index. If the document does not exist, it behaves like upload_documents with a new document.

merge_or_upload_documents(documents: List[Dict], **kwargs: Any) -> List[IndexingResult]

Parameters

documents
list[dict]
Required

A list of documents to merge or upload.

Returns

List of IndexingResult

Return type

Search the Azure search index for documents.

search(search_text: str | None = None, *, include_total_count: bool | None = None, facets: List[str] | None = None, filter: str | None = None, highlight_fields: str | None = None, highlight_post_tag: str | None = None, highlight_pre_tag: str | None = None, minimum_coverage: float | None = None, order_by: List[str] | None = None, query_type: str | QueryType | None = None, scoring_parameters: List[str] | None = None, scoring_profile: str | None = None, search_fields: List[str] | None = None, search_mode: str | SearchMode | None = None, query_answer: str | QueryAnswerType | None = None, query_answer_count: int | None = None, query_answer_threshold: float | None = None, query_caption: str | QueryCaptionType | None = None, query_caption_highlight_enabled: bool | None = None, semantic_configuration_name: str | None = None, select: List[str] | None = None, skip: int | None = None, top: int | None = None, scoring_statistics: str | ScoringStatistics | None = None, session_id: str | None = None, vector_queries: List[VectorQuery] | None = None, vector_filter_mode: str | VectorFilterMode | None = None, semantic_error_mode: str | SemanticErrorMode | None = None, semantic_max_wait_in_milliseconds: int | None = None, **kwargs: Any) -> SearchItemPaged[Dict]

Parameters

search_text
str
Required

A full-text search query expression; Use "*" or omit this parameter to match all documents.

include_total_count
bool

A value that specifies whether to fetch the total count of results. Default is false. Setting this value to true may have a performance impact. Note that the count returned is an approximation.

facets
list[str]

The list of facet expressions to apply to the search query. Each facet expression contains a field name, optionally followed by a comma-separated list of name:value pairs.

filter
str

The OData $filter expression to apply to the search query.

highlight_fields
str

The comma-separated list of field names to use for hit highlights. Only searchable fields can be used for hit highlighting.

highlight_post_tag
str

A string tag that is appended to hit highlights. Must be set with highlightPreTag. Default is .

highlight_pre_tag
str

A string tag that is prepended to hit highlights. Must be set with highlightPostTag. Default is .

minimum_coverage
float

A number between 0 and 100 indicating the percentage of the index that must be covered by a search query in order for the query to be reported as a success. This parameter can be useful for ensuring search availability even for services with only one replica. The default is 100.

order_by
list[str]

The list of OData $orderby expressions by which to sort the results. Each expression can be either a field name or a call to either the geo.distance() or the search.score() functions. Each expression can be followed by asc to indicate ascending, and desc to indicate descending. The default is ascending order. Ties will be broken by the match scores of documents. If no OrderBy is specified, the default sort order is descending by document match score. There can be at most 32 $orderby clauses.

query_type
str or QueryType

A value that specifies the syntax of the search query. The default is 'simple'. Use 'full' if your query uses the Lucene query syntax. Possible values include: 'simple', 'full', "semantic".

scoring_parameters
list[str]

The list of parameter values to be used in scoring functions (for example, referencePointParameter) using the format name-values. For example, if the scoring profile defines a function with a parameter called 'mylocation' the parameter string would be "mylocation–122.2,44.8" (without the quotes).

scoring_profile
str

The name of a scoring profile to evaluate match scores for matching documents in order to sort the results.

search_fields
list[str]

The list of field names to which to scope the full-text search. When using fielded search (fieldName:searchExpression) in a full Lucene query, the field names of each fielded search expression take precedence over any field names listed in this parameter.

search_mode
str or SearchMode

A value that specifies whether any or all of the search terms must be matched in order to count the document as a match. Possible values include: 'any', 'all'.

query_answer
str or QueryAnswerType

This parameter is only valid if the query type is 'semantic'. If set, the query returns answers extracted from key passages in the highest ranked documents. Possible values include: "none", "extractive".

query_answer_count
int

This parameter is only valid if the query type is 'semantic' and query answer is 'extractive'. Configures the number of answers returned. Default count is 1.

query_answer_threshold
float

This parameter is only valid if the query type is 'semantic' and query answer is 'extractive'. Configures the number of confidence threshold. Default count is 0.7.

query_caption
str or QueryCaptionType

This parameter is only valid if the query type is 'semantic'. If set, the query returns captions extracted from key passages in the highest ranked documents. Defaults to 'None'. Possible values include: "none", "extractive".

query_caption_highlight_enabled
bool

This parameter is only valid if the query type is 'semantic' when query caption is set to 'extractive'. Determines whether highlighting is enabled. Defaults to 'true'.

semantic_configuration_name
str

The name of the semantic configuration that will be used when processing documents for queries of type semantic.

select
list[str]

The list of fields to retrieve. If unspecified, all fields marked as retrievable in the schema are included.

skip
int

The number of search results to skip. This value cannot be greater than 100,000. If you need to scan documents in sequence, but cannot use $skip due to this limitation, consider using $orderby on a totally-ordered key and $filter with a range query instead.

top
int

The number of search results to retrieve. This can be used in conjunction with $skip to implement client-side paging of search results. If results are truncated due to server-side paging, the response will include a continuation token that can be used to issue another Search request for the next page of results.

scoring_statistics
str or ScoringStatistics

A value that specifies whether we want to calculate scoring statistics (such as document frequency) globally for more consistent scoring, or locally, for lower latency. The default is 'local'. Use 'global' to aggregate scoring statistics globally before scoring. Using global scoring statistics can increase latency of search queries. Possible values include: "local", "global".

session_id
str

A value to be used to create a sticky session, which can help getting more consistent results. As long as the same sessionId is used, a best-effort attempt will be made to target the same replica set. Be wary that reusing the same sessionID values repeatedly can interfere with the load balancing of the requests across replicas and adversely affect the performance of the search service. The value used as sessionId cannot start with a '_' character.

semantic_error_mode
str or SemanticErrorMode

Allows the user to choose whether a semantic call should fail completely (default / current behavior), or to return partial results. Known values are: "partial" and "fail".

semantic_max_wait_in_milliseconds
int

Allows the user to set an upper bound on the amount of time it takes for semantic enrichment to finish processing before the request fails.

vector_queries
list[VectorQuery]

The query parameters for vector and hybrid search queries.

vector_filter_mode
str or VectorFilterMode

Determines whether or not filters are applied before or after the vector search is performed. Default is 'preFilter'. Known values are: "postFilter" and "preFilter".

Return type

Examples

Get search result facets.


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

   search_client = SearchClient(service_endpoint, index_name, AzureKeyCredential(key))

   results = search_client.search(search_text="WiFi", facets=["category,count:3", "parkingIncluded"])

   facets: Dict[str, List[str]] = cast(Dict[str, List[str]], results.get_facets())

   print("Catgory facet counts for hotels:")
   for facet in facets["category"]:
       print("    {}".format(facet))

suggest

Get search suggestion results from the Azure search index.

character, and no more than 100 characters. :param str suggester_name: Required. The name of the suggester as specified in the suggesters collection that's part of the index definition. :keyword str filter: An OData expression that filters the documents considered for suggestions. :keyword bool use_fuzzy_matching: A value indicating whether to use fuzzy matching for the suggestions

query. Default is false. When set to true, the query will find terms even if there's a substituted or missing character in the search text. While this provides a better experience in some scenarios, it comes at a performance cost as fuzzy suggestions queries are slower and consume more resources.

suggest(search_text: str, suggester_name: str, *, use_fuzzy_matching: bool | None = None, highlight_post_tag: str | None = None, highlight_pre_tag: str | None = None, minimum_coverage: float | None = None, order_by: List[str] | None = None, search_fields: List[str] | None = None, select: List[str] | None = None, top: int | None = None, **kwargs) -> List[Dict]

Parameters

highlight_post_tag
str

A string tag that is appended to hit highlights. Must be set with highlightPreTag. If omitted, hit highlighting of suggestions is disabled.

highlight_pre_tag
str

A string tag that is prepended to hit highlights. Must be set with highlightPostTag. If omitted, hit highlighting of suggestions is disabled.

minimum_coverage
float

A number between 0 and 100 indicating the percentage of the index that must be covered by a suggestions query in order for the query to be reported as a success. This parameter can be useful for ensuring search availability even for services with only one replica. The default is 80.

order_by
list[str]

The list of OData $orderby expressions by which to sort the results. Each expression can be either a field name or a call to either the geo.distance() or the search.score() functions. Each expression can be followed by asc to indicate ascending, or desc to indicate descending. The default is ascending order. Ties will be broken by the match scores of documents. If no $orderby is specified, the default sort order is descending by document match score. There can be at most 32 $orderby clauses.

search_fields
list[str]

The list of field names to search for the specified search text. Target fields must be included in the specified suggester.

select
list[str]

The list of fields to retrieve. If unspecified, only the key field will be included in the results.

top
int

The number of suggestions to retrieve. The value must be a number between 1 and 100. The default is 5.

Returns

List of documents.

Return type

Examples

Get search suggestions.


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

   search_client = SearchClient(service_endpoint, index_name, AzureKeyCredential(key))

   results = search_client.suggest(search_text="coffee", suggester_name="sg")

   print("Search suggestions for 'coffee'")
   for result in results:
       hotel = search_client.get_document(key=result["hotelId"])
       print("    Text: {} for Hotel: {}".format(repr(result["text"]), hotel["hotelName"]))

upload_documents

Upload documents to the Azure search index.

An upload action is similar to an "upsert" where the document will be inserted if it is new and updated/replaced if it exists. All fields are replaced in the update case.

upload_documents(documents: List[Dict], **kwargs: Any) -> List[IndexingResult]

Parameters

documents
list[dict]
Required

A list of documents to upload.

Returns

List of IndexingResult

Return type

Examples

Upload new documents to an index


   DOCUMENT = {
       "category": "Hotel",
       "hotelId": "1000",
       "rating": 4.0,
       "rooms": [],
       "hotelName": "Azure Inn",
   }

   result = search_client.upload_documents(documents=[DOCUMENT])

   print("Upload of new document succeeded: {}".format(result[0].succeeded))