import org.codelibs.fess.suggest.Suggester;
import org.opensearch.client.Client;

// Initialize with your OpenSearch client
String suggestId = "my-suggest-index";
Suggester suggester = Suggester.builder().build(client, suggestId);

import org.codelibs.fess.suggest.entity.SuggestItem;

// Create suggestion item with text, readings, and metadata
String[][] readings = new String[2][];
readings[0] = new String[]{"kensaku", "engine"};
readings[1] = new String[]{"search", "injin"};

String[] tags = new String[]{"technology", "search"};
String[] roles = new String[]{"admin", "user"};

SuggestItem item = new SuggestItem(
    new String[]{"Search Engine", "検索エンジン"}, // text variations
    readings,                                      // pronunciation readings
    1,                                            // boost score
    tags,                                         // categorization tags
    roles,                                        // access roles
    SuggestItem.Kind.DOCUMENT                     // suggestion type
);

suggester.indexer().index(item);

import org.codelibs.fess.suggest.request.suggest.SuggestResponse;

// Synchronous suggestion request
SuggestResponse response = suggester.suggest()
    .setQuery("sea")                    // user input
    .setSize(10)                        // max suggestions
    .execute()
    .getResponse();

// Process suggestions
response.getItems().forEach(item -> {
    System.out.println("Suggestion: " + item.getText()[0]);
    System.out.println("Score: " + item.getScore());
});

suggester.suggest()
    .setQuery("sea")
    .execute()
    .done(response -> {
        // Handle successful response
        response.getItems().forEach(item -> 
            System.out.println("Async suggestion: " + item.getText()[0])
        );
    })
    .error(throwable -> {
        // Handle error
        System.err.println("Error: " + throwable.getMessage());
    });

import org.codelibs.fess.suggest.index.contents.document.ESSourceReader;

// Index suggestions from existing Elasticsearch documents
DocumentReader reader = new ESSourceReader(
    client,
    suggester.settings(),
    "content-index",        // source index
    "document"             // document type
);

suggester.indexer()
    .indexFromDocument(reader, 2, 100)  // threads=2, batch=100
    .getResponse();

import org.codelibs.fess.suggest.index.contents.querylog.QueryLog;

// Add suggestions from search query logs
QueryLog queryLog = new QueryLog("user search query", "user123");
suggester.indexer().indexFromQueryLog(queryLog);

import org.codelibs.fess.suggest.request.popularwords.PopularWordsResponse;

PopularWordsResponse popularWords = suggester.popularWords()
    .setSize(20)                    // top 20 words
    .setQuery("tech*")              // filter pattern
    .execute()
    .getResponse();

popularWords.getItems().forEach(item -> {
    System.out.println("Popular: " + item.getText() + 
                      " (freq: " + item.getDocFreq() + ")");
});

import org.codelibs.fess.suggest.settings.SuggestSettings;
import org.codelibs.fess.suggest.converter.ReadingConverterChain;
import org.codelibs.fess.suggest.normalizer.ICUNormalizer;

// Configure custom settings
SuggestSettings settings = SuggestSettings.builder()
    .analyzer(analyzers -> {
        // Configure custom analyzers
        analyzers.addAnalyzer("custom", customAnalyzerSettings);
    })
    .build();

Suggester customSuggester = Suggester.builder()
    .settings(settings)
    .readingConverter(new ReadingConverterChain())
    .normalizer(new ICUNormalizer())
    .build(client, "custom-suggest");

import org.codelibs.fess.suggest.settings.SuggestSettingsBuilder;

SuggestSettings settings = SuggestSettingsBuilder.builder()
    .arraySettings(arraySettings -> {
        arraySettings.setDefaultBadWords(new String[]{"spam", "inappropriate"});
    })
    .analyzerSettings(analyzerSettings -> {
        analyzerSettings.setReadingAnalyzer("kuromoji_reading");
        analyzerSettings.setNormalizeAnalyzer("keyword");
    })
    .badWordSettings(badWordSettings -> {
        badWordSettings.setList(new String[]{"blocked", "terms"});
    })
    .elevateWordSettings(elevateWordSettings -> {
        elevateWordSettings.setElevateWords(Collections.singletonList(
            new ElevateWord("priority", 2.0f, Collections.emptyList())
        ));
    })
    .build();

SuggestResponse response = suggester.suggest()
    .setQuery("search term")
    .setSize(10)                           // max results
    .setTags(new String[]{"category"})      // filter by tags
    .setRoles(new String[]{"user"})         // filter by roles  
    .setLanguages(new String[]{"en", "ja"}) // language preference
    .setFields(new String[]{"title"})       // search specific fields
    .execute()
    .getResponse();

// Product search suggestions
SuggestItem product = new SuggestItem(
    new String[]{"iPhone 15 Pro", "Apple iPhone 15 Pro"},
    new String[][]{{"iphone"}, {"apple", "phone"}},
    1.5f,                               // boost popular products
    new String[]{"electronics", "mobile"},
    new String[]{"customer"},
    SuggestItem.Kind.DOCUMENT
);
suggester.indexer().index(product);

// Article/blog suggestions
DocumentReader reader = new ESSourceReader(
    client, 
    suggester.settings(), 
    "articles-index", 
    "article"
);
suggester.indexer().indexFromDocument(reader, 4, 50);

// Track user queries for analytics
QueryLog userQuery = new QueryLog("machine learning tutorials", "user456");
suggester.indexer().indexFromQueryLog(userQuery);

// Get trending searches
PopularWordsResponse trending = suggester.popularWords()
    .setSize(10)
    .execute()
    .getResponse();

// Add to your logging configuration
logger.debug.org.codelibs.fess.suggest=DEBUG