Azure.Search.Documents.Models Namespace

The result of Autocomplete requests.

The result of Autocomplete query.

Contains debugging information that can be used to further explore your search results.

Contains debugging information that can be used to further explore your search results.

A single bucket of a facet query result. Reports the number of documents with a field value falling within a particular range or having a particular value or interval.

TThe query parameters to configure hybrid search behaviors.

Provides data for ActionCompleted event.

Provides data for ActionAdded and ActionSent events.

Provides data for ActionCompleted event.

Creates IndexDocumentsAction<T> instances to add to an IndexDocumentsBatch<T> and submit via IndexDocuments<T>(IndexDocumentsBatch<T>, IndexDocumentsOptions, CancellationToken).

Represents an index action that operates on a document.

Creates IndexDocumentsBatch<T> instances to update search indexes via IndexDocuments<T>(IndexDocumentsBatch<T>, IndexDocumentsOptions, CancellationToken).

Contains a batch of document write actions to send to a search index via IndexDocuments<T>(IndexDocumentsBatch<T>, IndexDocumentsOptions, CancellationToken).

Response containing the status of operations for all documents in the indexing request.

Status of an indexing operation for a single document.

Configuration for how semantic search returns answers to the search.

An answer is a text passage extracted from the contents of the most relevant documents that matched the query. Answers are extracted from the top search results. Answer candidates are scored and the top answers are selected.

Configuration for how semantic search captions search results.

Captions are the most representative passages from the document relatively to the search query. They are often used as document summary. Captions are only returned for queries of type semantic.

The raw concatenated strings that were sent to the semantic enrichment process.

Description of fields that were sent to the semantic enrichment process, as well as how they were used.

The breakdown of subscores between the text and vector query components of the search query for this document. Each vector query is shown as a separate object in the same order they were received.

Configuration for how semantic search returns query rewrite to the search.

Contains debugging information specific to query rewrites.

Contains debugging information specific to query rewrites.

A single bucket of a range facet query result that reports the number of documents with a field value falling within a particular range.

The DocumentKeysOrIds.

The SkillNames.

Represents an untyped document returned from a search or document lookup. It can be accessed as either a dynamic object or a dictionary.

Model factory for models.

Contains a document found by a search query, plus associated metadata.

Response containing search results from an index.

A page of SearchResult<T>s returned from GetResultsAsync()'s AsPages(String, Nullable<Int32>) method.

The results of the vector query will filter based on the '@search.score' value. Note this is the @search.score returned as part of the search response. The threshold direction will be chosen for higher @search.score.

A result containing a document found by a suggestion query, plus associated metadata.

The SemanticDebugInfo.

Options for performing Semantic Search.

Semantic search result.

Semantic search results from an index.

A single vector field result. Both @search.score and vector similarity values are returned. Vector similarity is related to @search.score by an equation.

Response containing suggestion query results from an index.

The BM25 or Classic score for the text portion of the query.

A single bucket of a simple or interval facet query result that reports the number of documents with a field falling within a particular interval or having a specific value.

The query parameters to use for vector search when a base 64 encoded binary of an image that needs to be vectorized is provided.

The query parameters to use for vector search when an url that represents an image value that needs to be vectorized is provided.

The query parameters to use for vector search when a text value that needs to be vectorized is provided.

The query parameters to use for vector search when a raw vector value is provided.

The query parameters for vector and hybrid search queries. Please note VectorQuery is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include VectorizableImageBinaryQuery, VectorizableImageUrlQuery, VectorizableTextQuery and VectorizedQuery.

The VectorsDebugInfo.

Options for performing Vector Search.

The results of the vector query will be filtered based on the vector similarity metric. Note this is the canonical definition of similarity metric, not the 'distance' version. The threshold direction (larger or smaller) will be chosen automatically according to the metric used by the field.

The threshold used for vector queries. Please note VectorThreshold is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include SearchScoreThreshold and VectorSimilarityThreshold.

Determines whether the count and facets should includes all documents that matched the search query, or only the documents that are retrieved within the 'maxTextRecallSize' window. The default value is 'countAllResults'.

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. The number of answers returned can be configured by appending the pipe character | followed by the count-<number of answers> option after the answers parameter value, such as extractive|count-3. Default count is 1. The confidence threshold can be configured by appending the pipe character | followed by the threshold-<confidence threshold> option after the answers parameter value, such as extractive|threshold-0.9. Default threshold is 0.7. The maximum character length of answers can be configured by appending the pipe character '|' followed by the 'count-<number of maximum character length>', such as 'extractive|maxcharlength-600'.

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. When Captions is set to extractive, highlighting is enabled by default, and can be configured by appending the pipe character | followed by the highlight-<true/false> option, such as extractive|highlight-true. Defaults to None. The maximum character length of captions can be configured by appending the pipe character '|' followed by the 'count-<number of maximum character length>', such as 'extractive|maxcharlength-600'.

Enables a debugging tool that can be used to further explore your search results. You can enable multiple debug modes simultaneously by separating them with a | character, for example: semantic|queryRewrites.

The language of the query.

This parameter is only valid if the query type is semantic. When QueryRewrites is set to generative, the query terms are sent to a generate model which will produce 10 (default) rewrites to help increase the recall of the request. The requested count can be configured by appending the pipe character | followed by the count-<number of rewrites> option, such as generative|count-3. Defaults to None.

Improve search recall by spell-correcting individual search query terms.

Allows the user to choose whether a semantic call should fail completely, or to return partial results.

Reason that a partial response was returned for a semantic ranking request.

The way the field was used for the semantic enrichment process.

Type of query rewrite that was used for this request.

Type of partial response that was returned for a semantic ranking request.

Determines whether or not filters are applied before or after the vector search is performed.

Specifies the mode for Autocomplete. The default is 'oneTerm'. Use 'twoTerms' to get shingles and 'oneTermWithContext' to use the current context in producing autocomplete terms.

Specifies the type of a facet query result.

The operation to perform on a document in an indexing batch.

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.

Specifies whether any or all of the search terms must be matched in order to count the document as a match.

Specifies the syntax of the search query. The default is "simple". Use "full" if your query uses the Lucene query syntax.