You are currently browsing legacy 4.0 version of documentation. Click here to switch to the newest 4.2 version.

Language

Article For

Session: Querying: How to Work with Suggestions

The Suggestion feature is available through query extension methods. It gives you the ability to find word similarities using string distance algorithms.

Syntax

ISuggestionQuery<T> SuggestUsing<T>(SuggestionBase suggestion);

ISuggestionQuery<T> SuggestUsing<T>(Action<ISuggestionBuilder<T>> builder);

Parameters
suggestion	`SuggestionBase`	Defines the type of suggestion that should be executed
builder	`Action<ISuggestionBuilder<T>>`	Builder with a fluent API that constructs a `SuggestionBase` instance

Builder

ISuggestionOperations<T> ByField(string fieldName, string term);

ISuggestionOperations<T> ByField(string fieldName, string[] terms);

ISuggestionOperations<T> ByField(Expression<Func<T, object>> path, string term);

ISuggestionOperations<T> ByField(Expression<Func<T, object>> path, string[] terms);

ISuggestionOperations<T> WithOptions(SuggestionOptions options);

Parameters
fieldName	string	Points to the index field that should be used for operation
path	`Expression<Func<T, object>>`	Points to the index field that should be used for operation
term	string	Term that will be used as a basis of the suggestions
terms	string[]	Terms that will be used as a basis of the suggestions
options	`SuggestionOptions`	Non-default options that should be used for operation

Options

public int PageSize { get; set; } = 15;

public StringDistanceTypes? Distance { get; set; } = StringDistanceTypes.Levenshtein;

public float? Accuracy { get; set; } = 0.5f;

public SuggestionSortMode SortMode { get; set; } = SuggestionSortMode.Popularity;

Options
PageSize	int	Maximum number of suggestions that will be returned
Distance	`StringDistanceTypes`	String distance algorithm to use (`None`, `Levenshtein`, `JaroWinkler`, `NGram`)
Accuracy	float?	Suggestion accuracy
SortMode	`SuggestionSortMode`	Indicates in what order the results should be returned (`None`, `Popularity`)

Example I

Dictionary<string, SuggestionResult> suggestions = session
    .Query<Employee, Employees_ByFullName>()
    .SuggestUsing(builder => builder
        .ByField("FullName", "johne")
        .WithOptions(new SuggestionOptions
        {
            Accuracy = 0.4f,
            PageSize = 5,
            Distance = StringDistanceTypes.JaroWinkler,
            SortMode = SuggestionSortMode.Popularity
        }))
    .Execute();

Dictionary<string, SuggestionResult> suggestions = await asyncSession
    .Query<Employee, Employees_ByFullName>()
    .SuggestUsing(builder => builder
        .ByField("FullName", "johne")
        .WithOptions(new SuggestionOptions
        {
            Accuracy = 0.4f,
            PageSize = 5,
            Distance = StringDistanceTypes.JaroWinkler,
            SortMode = SuggestionSortMode.Popularity
        }))
    .ExecuteAsync();

from index 'Employees/ByFullName' 
select suggest('FullName', 'johne', '{ "Accuracy" : 0.4, "PageSize" : 5, "Distance" : "JaroWinkler", "SortMode" : "Popularity" }')

Example II

Dictionary<string, SuggestionResult> suggestions = session
    .Query<Employee, Employees_ByFullName>()
    .SuggestUsing(new SuggestionWithTerm("FullName") { Term = "johne" })
    .Execute();

Dictionary<string, SuggestionResult> suggestions = await asyncSession
    .Query<Employee, Employees_ByFullName>()
    .SuggestUsing(new SuggestionWithTerm("FullName") { Term = "johne" })
    .ExecuteAsync();

from index 'Employees/ByFullName' 
select suggest('FullName', 'johne')