"search": "Spacious, air-condition* +\"Ocean view\"",
POST /indexes/hotels/docs/search?api-version= The following example is a search request you might send to Azure Cognitive Search using the REST API. A more realistic example includes parameters, several query terms, perhaps scoped to certain fields, with possibly a filter expression and ordering rules. In simplest form, it's an empty query with no criteria of any kind. Retrieves and scores matching documents based on the contents of the inverted index.Ī search request is a complete specification of what should be returned in a result set. This process can involve transforming, removing, or expanding of query terms.Īn efficient data structure used to store and organize searchable terms extracted from indexed documents. Separate query terms from query operators and create the query structure (a query tree) to be sent to the search engine. The diagram below illustrates the components used to process a search request. Those at the top of the ranked list are returned to the calling application. A result set is then sorted by a relevance score assigned to each individual matching document. The search engine then scans the index to find documents with matching terms and scores each match.
An analysis phase is next, where individual query terms are sometimes broken down and reconstituted into new forms to cast a broader net over what could be considered as a potential match. There are two parsers so that you can choose between speed and complexity.
#Apache lucene similar full#
Architecture overview and diagramĪ full text search query starts with parsing the query text to extract search terms and operators. We selectively expose and extend Lucene functionality to enable the scenarios important to Azure Cognitive Search. Azure Cognitive Search uses Apache Lucene for full text search, but Lucene integration is not exhaustive.
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