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In some examples, the method includes receiving an unstructured search query, parsing the unstructured search query into a plurality of structured search attributes using a search term attribute dictionary, performing a search of a structured database based upon the plurality of structured search ...
The following examples show how to use operators when you create a search string. Expand table Filter for items that meet this criteria:Enter the following string: Assigned to Peter and not Active. A:Peter -S=Active In which the Activity field wasn't Development. - Activity=Development Resol...
In both of the examples above, the artificial intelligence (AI) investment is in one part of the search process or one type of AI. This is like painting in a single color — so for example, while blue is a very popular color with a lot of versatility, if you only paint with blue,...
The following examples demonstrate the capabilities of Semantic Search. At the same time these examples demonstrate the three Transact-SQL rowset functions that you use to query the semantic indexes and retrieve the results as structured data. ...
Semantic search capabilities in Azure Cognitive Search Below are the features enabled by semantic search in Azure Cognitive Search. Before our public preview release, we tested many of these features on our own products, and the examples below show the dramatic improvements in results we achieved by...
// 创建一个测试类,继承自BaseTestnamespaceExamples; publicclassExample09_FunctionTypes:BaseTest{// 使用Xunit的Fact特性来标识这是个测试方法[Fact]publicasyncTaskRunAsync(){// 输出测试方法名this.WriteLine("=== Method Function types ==="); // 创建并配置Semantic...
Examples of such similarity functions include Jaccard and cosine functions. Let V t s be the set of all vectors created by the S T sub-sequences. The coefficient is calculated as c o e f ( S T ) = m a x ( s i m i l a r i t y ( v → q , x → ) ) | v → q ...
Examples >>> from gensim.test.utils import common_corpus, common_dictionary, get_tmpfile >>> from gensim.models import LsiModel >>> >>> model = LsiModel(common_corpus[:3], id2word=common_dictionary) # train model >>> vector = model[common_corpus[4]] # apply model to BoW document ...