Comment faire une requête d'auto-complétion/des suggestions dans Lucene?

Question

Je suis à la recherche d'un moyen de faire des requêtes d'auto-complétion/des suggestions dans Lucene. J'ai fait un peu de googled autour et joué un peu, mais tous les exemples que j'ai vus semblent installer des filtres dans Solr. Nous n'utilisons pas Solr et ne prévoyons pas d'utiliser Solr dans un futur proche, et Solr ne fait évidemment que contourner Lucene, alors j'imagine qu'il doit y avoir un moyen de le faire! J'ai cherché à utiliser EdgeNGramFilter, et je me rends compte que je devrais exécuter le filtre sur les champs d'index et obtenir les jetons, puis les comparer à la requête entrée ... Je suis juste à la peine de faire le lien entre les deux dans un peu de code, donc l'aide est très appréciée! Pour être clair sur ce que je cherche (je me suis rendu compte que je n'étais pas trop clair, désolé) - Je suis à la recherche d'une solution où lors de la recherche d'un terme, il renverrait une liste de suggestions requêtes. Lorsque vous tapez « entre » dans le champ de recherche, il va revenir avec une liste de requêtes suggérées, comme « Internet », « international », etc.

Answer 1

Sur la base de la réponse de @Alexandre Victoor, j'ai écrit une petite classe basée sur la Lucene Spellchecker dans le paquet contrib (et en utilisant le LuceneDictionary inclus dedans) qui fait exactement ce que je veux.

Cela permet de réindexer à partir d'un index source unique avec un seul champ et fournit des suggestions de termes. Les résultats sont triés par le nombre de documents correspondants avec ce terme dans l'index d'origine, de sorte que les termes les plus populaires apparaissent en premier. Il semble fonctionner assez bien :)

import java.io.IOException; 
import java.io.Reader; 
import java.util.ArrayList; 
import java.util.HashMap; 
import java.util.Iterator; 
import java.util.List; 
import java.util.Map; 

import org.apache.lucene.analysis.Analyzer; 
import org.apache.lucene.analysis.ISOLatin1AccentFilter; 
import org.apache.lucene.analysis.LowerCaseFilter; 
import org.apache.lucene.analysis.StopFilter; 
import org.apache.lucene.analysis.TokenStream; 
import org.apache.lucene.analysis.ngram.EdgeNGramTokenFilter; 
import org.apache.lucene.analysis.ngram.EdgeNGramTokenFilter.Side; 
import org.apache.lucene.analysis.standard.StandardFilter; 
import org.apache.lucene.analysis.standard.StandardTokenizer; 
import org.apache.lucene.document.Document; 
import org.apache.lucene.document.Field; 
import org.apache.lucene.index.CorruptIndexException; 
import org.apache.lucene.index.IndexReader; 
import org.apache.lucene.index.IndexWriter; 
import org.apache.lucene.index.Term; 
import org.apache.lucene.search.IndexSearcher; 
import org.apache.lucene.search.Query; 
import org.apache.lucene.search.ScoreDoc; 
import org.apache.lucene.search.Sort; 
import org.apache.lucene.search.TermQuery; 
import org.apache.lucene.search.TopDocs; 
import org.apache.lucene.search.spell.LuceneDictionary; 
import org.apache.lucene.store.Directory; 
import org.apache.lucene.store.FSDirectory; 

/** 
* Search term auto-completer, works for single terms (so use on the last term 
* of the query). 
* <p> 
* Returns more popular terms first. 
* 
* @author Mat Mannion, M.Mannion@warwick.ac.uk 
*/ 
public final class Autocompleter { 

    private static final String GRAMMED_WORDS_FIELD = "words"; 

    private static final String SOURCE_WORD_FIELD = "sourceWord"; 

    private static final String COUNT_FIELD = "count"; 

    private static final String[] ENGLISH_STOP_WORDS = { 
    "a", "an", "and", "are", "as", "at", "be", "but", "by", 
    "for", "i", "if", "in", "into", "is", 
    "no", "not", "of", "on", "or", "s", "such", 
    "t", "that", "the", "their", "then", "there", "these", 
    "they", "this", "to", "was", "will", "with" 
    }; 

    private final Directory autoCompleteDirectory; 

    private IndexReader autoCompleteReader; 

    private IndexSearcher autoCompleteSearcher; 

    public Autocompleter(String autoCompleteDir) throws IOException { 
     this.autoCompleteDirectory = FSDirectory.getDirectory(autoCompleteDir, 
       null); 

     reOpenReader(); 
    } 

    public List<String> suggestTermsFor(String term) throws IOException { 
     // get the top 5 terms for query 
     Query query = new TermQuery(new Term(GRAMMED_WORDS_FIELD, term)); 
     Sort sort = new Sort(COUNT_FIELD, true); 

     TopDocs docs = autoCompleteSearcher.search(query, null, 5, sort); 
     List<String> suggestions = new ArrayList<String>(); 
     for (ScoreDoc doc : docs.scoreDocs) { 
      suggestions.add(autoCompleteReader.document(doc.doc).get(
        SOURCE_WORD_FIELD)); 
     } 

     return suggestions; 
    } 

    @SuppressWarnings("unchecked") 
    public void reIndex(Directory sourceDirectory, String fieldToAutocomplete) 
      throws CorruptIndexException, IOException { 
     // build a dictionary (from the spell package) 
     IndexReader sourceReader = IndexReader.open(sourceDirectory); 

     LuceneDictionary dict = new LuceneDictionary(sourceReader, 
       fieldToAutocomplete); 

     // code from 
     // org.apache.lucene.search.spell.SpellChecker.indexDictionary(
     // Dictionary) 
     IndexReader.unlock(autoCompleteDirectory); 

     // use a custom analyzer so we can do EdgeNGramFiltering 
     IndexWriter writer = new IndexWriter(autoCompleteDirectory, 
     new Analyzer() { 
      public TokenStream tokenStream(String fieldName, 
        Reader reader) { 
       TokenStream result = new StandardTokenizer(reader); 

       result = new StandardFilter(result); 
       result = new LowerCaseFilter(result); 
       result = new ISOLatin1AccentFilter(result); 
       result = new StopFilter(result, 
        ENGLISH_STOP_WORDS); 
       result = new EdgeNGramTokenFilter(
        result, Side.FRONT,1, 20); 

       return result; 
      } 
     }, true); 

     writer.setMergeFactor(300); 
     writer.setMaxBufferedDocs(150); 

     // go through every word, storing the original word (incl. n-grams) 
     // and the number of times it occurs 
     Map<String, Integer> wordsMap = new HashMap<String, Integer>(); 

     Iterator<String> iter = (Iterator<String>) dict.getWordsIterator(); 
     while (iter.hasNext()) { 
      String word = iter.next(); 

      int len = word.length(); 
      if (len < 3) { 
       continue; // too short we bail but "too long" is fine... 
      } 

      if (wordsMap.containsKey(word)) { 
       throw new IllegalStateException(
         "This should never happen in Lucene 2.3.2"); 
       // wordsMap.put(word, wordsMap.get(word) + 1); 
      } else { 
       // use the number of documents this word appears in 
       wordsMap.put(word, sourceReader.docFreq(new Term(
         fieldToAutocomplete, word))); 
      } 
     } 

     for (String word : wordsMap.keySet()) { 
      // ok index the word 
      Document doc = new Document(); 
      doc.add(new Field(SOURCE_WORD_FIELD, word, Field.Store.YES, 
        Field.Index.UN_TOKENIZED)); // orig term 
      doc.add(new Field(GRAMMED_WORDS_FIELD, word, Field.Store.YES, 
        Field.Index.TOKENIZED)); // grammed 
      doc.add(new Field(COUNT_FIELD, 
        Integer.toString(wordsMap.get(word)), Field.Store.NO, 
        Field.Index.UN_TOKENIZED)); // count 

      writer.addDocument(doc); 
     } 

     sourceReader.close(); 

     // close writer 
     writer.optimize(); 
     writer.close(); 

     // re-open our reader 
     reOpenReader(); 
    } 

    private void reOpenReader() throws CorruptIndexException, IOException { 
     if (autoCompleteReader == null) { 
      autoCompleteReader = IndexReader.open(autoCompleteDirectory); 
     } else { 
      autoCompleteReader.reopen(); 
     } 

     autoCompleteSearcher = new IndexSearcher(autoCompleteReader); 
    } 

    public static void main(String[] args) throws Exception { 
     Autocompleter autocomplete = new Autocompleter("/index/autocomplete"); 

     // run this to re-index from the current index, shouldn't need to do 
     // this very often 
     // autocomplete.reIndex(FSDirectory.getDirectory("/index/live", null), 
     // "content"); 

     String term = "steve"; 

     System.out.println(autocomplete.suggestTermsFor(term)); 
     // prints [steve, steven, stevens, stevenson, stevenage] 
    } 

} 

Answer 2

Vous pouvez utiliser la classe PrefixQuery sur un « dictionnaire "index. La classe LuceneDictionary pourrait également vous être utile.

Jetez un oeil à ce article. Il explique comment implémenter la fonctionnalité "Voulez-vous dire?" disponible dans un moteur de recherche moderne tel que Google. Vous n'avez peut-être pas besoin de quelque chose d'aussi complexe que celui décrit dans l'article. Cependant, l'article explique comment utiliser le paquet de sorts Lucene. Une façon de créer un index "dictionnaire" serait d'itérer sur un LuceneDictionary.

Hope it helps

Answer 3

Voici une translittération de la mise en œuvre de Mat en C# pour Lucene.NET, ainsi que d'un extrait pour le câblage d'une zone de texte en utilisant la fonction de saisie semi-automatique jQuery.

<input id="search-input" name="query" placeholder="Search database." type="text" /> 

... JQuery Autocomplete:

// don't navigate away from the field when pressing tab on a selected item 
$("#search-input").keydown(function (event) { 
    if (event.keyCode === $.ui.keyCode.TAB && $(this).data("autocomplete").menu.active) { 
     event.preventDefault(); 
    } 
}); 

$("#search-input").autocomplete({ 
    source: '@Url.Action("SuggestTerms")', // <-- ASP.NET MVC Razor syntax 
    minLength: 2, 
    delay: 500, 
    focus: function() { 
     // prevent value inserted on focus 
     return false; 
    }, 
    select: function (event, ui) { 
     var terms = this.value.split(/\s+/); 
     terms.pop(); // remove dropdown item 
     terms.push(ui.item.value.trim()); // add completed item 
     this.value = terms.join(" "); 
     return false; 
    }, 
}); 

... voici le code du contrôleur ASP.NET MVC:

// 
    // GET: /MyApp/SuggestTerms?term=something 
    public JsonResult SuggestTerms(string term) 
    { 
     if (string.IsNullOrWhiteSpace(term)) 
      return Json(new string[] {}); 

     term = term.Split().Last(); 

     // Fetch suggestions 
     string[] suggestions = SearchSvc.SuggestTermsFor(term).ToArray(); 

     return Json(suggestions, JsonRequestBehavior.AllowGet); 
    } 

... et voici le code de Mat en C#:

using System; 
using System.Collections.Generic; 
using System.Linq; 
using System.Text; 
using Lucene.Net.Store; 
using Lucene.Net.Index; 
using Lucene.Net.Search; 
using SpellChecker.Net.Search.Spell; 
using Lucene.Net.Analysis; 
using Lucene.Net.Analysis.Standard; 
using Lucene.Net.Analysis.NGram; 
using Lucene.Net.Documents; 

namespace Cipher.Services 
{ 
    /// <summary> 
    /// Search term auto-completer, works for single terms (so use on the last term of the query). 
    /// Returns more popular terms first. 
    /// <br/> 
    /// Author: Mat Mannion, M.Mannion@warwick.ac.uk 
    /// <seealso cref="http://stackoverflow.com/questions/120180/how-to-do-query-auto-completion-suggestions-in-lucene"/> 
    /// </summary> 
    /// 
    public class SearchAutoComplete { 

     public int MaxResults { get; set; } 

     private class AutoCompleteAnalyzer : Analyzer 
     { 
      public override TokenStream TokenStream(string fieldName, System.IO.TextReader reader) 
      { 
       TokenStream result = new StandardTokenizer(kLuceneVersion, reader); 

       result = new StandardFilter(result); 
       result = new LowerCaseFilter(result); 
       result = new ASCIIFoldingFilter(result); 
       result = new StopFilter(false, result, StopFilter.MakeStopSet(kEnglishStopWords)); 
       result = new EdgeNGramTokenFilter(
        result, Lucene.Net.Analysis.NGram.EdgeNGramTokenFilter.DEFAULT_SIDE,1, 20); 

       return result; 
      } 
     } 

     private static readonly Lucene.Net.Util.Version kLuceneVersion = Lucene.Net.Util.Version.LUCENE_29; 

     private static readonly String kGrammedWordsField = "words"; 

     private static readonly String kSourceWordField = "sourceWord"; 

     private static readonly String kCountField = "count"; 

     private static readonly String[] kEnglishStopWords = { 
      "a", "an", "and", "are", "as", "at", "be", "but", "by", 
      "for", "i", "if", "in", "into", "is", 
      "no", "not", "of", "on", "or", "s", "such", 
      "t", "that", "the", "their", "then", "there", "these", 
      "they", "this", "to", "was", "will", "with" 
     }; 

     private readonly Directory m_directory; 

     private IndexReader m_reader; 

     private IndexSearcher m_searcher; 

     public SearchAutoComplete(string autoCompleteDir) : 
      this(FSDirectory.Open(new System.IO.DirectoryInfo(autoCompleteDir))) 
     { 
     } 

     public SearchAutoComplete(Directory autoCompleteDir, int maxResults = 8) 
     { 
      this.m_directory = autoCompleteDir; 
      MaxResults = maxResults; 

      ReplaceSearcher(); 
     } 

     /// <summary> 
     /// Find terms matching the given partial word that appear in the highest number of documents.</summary> 
     /// <param name="term">A word or part of a word</param> 
     /// <returns>A list of suggested completions</returns> 
     public IEnumerable<String> SuggestTermsFor(string term) 
     { 
      if (m_searcher == null) 
       return new string[] { }; 

      // get the top terms for query 
      Query query = new TermQuery(new Term(kGrammedWordsField, term.ToLower())); 
      Sort sort = new Sort(new SortField(kCountField, SortField.INT)); 

      TopDocs docs = m_searcher.Search(query, null, MaxResults, sort); 
      string[] suggestions = docs.ScoreDocs.Select(doc => 
       m_reader.Document(doc.Doc).Get(kSourceWordField)).ToArray(); 

      return suggestions; 
     } 


     /// <summary> 
     /// Open the index in the given directory and create a new index of word frequency for the 
     /// given index.</summary> 
     /// <param name="sourceDirectory">Directory containing the index to count words in.</param> 
     /// <param name="fieldToAutocomplete">The field in the index that should be analyzed.</param> 
     public void BuildAutoCompleteIndex(Directory sourceDirectory, String fieldToAutocomplete) 
     { 
      // build a dictionary (from the spell package) 
      using (IndexReader sourceReader = IndexReader.Open(sourceDirectory, true)) 
      { 
       LuceneDictionary dict = new LuceneDictionary(sourceReader, fieldToAutocomplete); 

       // code from 
       // org.apache.lucene.search.spell.SpellChecker.indexDictionary(
       // Dictionary) 
       //IndexWriter.Unlock(m_directory); 

       // use a custom analyzer so we can do EdgeNGramFiltering 
       var analyzer = new AutoCompleteAnalyzer(); 
       using (var writer = new IndexWriter(m_directory, analyzer, true, IndexWriter.MaxFieldLength.LIMITED)) 
       { 
        writer.MergeFactor = 300; 
        writer.SetMaxBufferedDocs(150); 

        // go through every word, storing the original word (incl. n-grams) 
        // and the number of times it occurs 
        foreach (string word in dict) 
        { 
         if (word.Length < 3) 
          continue; // too short we bail but "too long" is fine... 

         // ok index the word 
         // use the number of documents this word appears in 
         int freq = sourceReader.DocFreq(new Term(fieldToAutocomplete, word)); 
         var doc = MakeDocument(fieldToAutocomplete, word, freq); 

         writer.AddDocument(doc); 
        } 

        writer.Optimize(); 
       } 

      } 

      // re-open our reader 
      ReplaceSearcher(); 
     } 

     private static Document MakeDocument(String fieldToAutocomplete, string word, int frequency) 
     { 
      var doc = new Document(); 
      doc.Add(new Field(kSourceWordField, word, Field.Store.YES, 
        Field.Index.NOT_ANALYZED)); // orig term 
      doc.Add(new Field(kGrammedWordsField, word, Field.Store.YES, 
        Field.Index.ANALYZED)); // grammed 
      doc.Add(new Field(kCountField, 
        frequency.ToString(), Field.Store.NO, 
        Field.Index.NOT_ANALYZED)); // count 
      return doc; 
     } 

     private void ReplaceSearcher() 
     { 
      if (IndexReader.IndexExists(m_directory)) 
      { 
       if (m_reader == null) 
        m_reader = IndexReader.Open(m_directory, true); 
       else 
        m_reader.Reopen(); 

       m_searcher = new IndexSearcher(m_reader); 
      } 
      else 
      { 
       m_searcher = null; 
      } 
     } 


    } 
} 

Answer 4

En plus de ce qui précède (très apprécié) p Si vous utilisez .NET 3.5, vous devrez inclure le code pour EdgeNGramTokenFilter - ou du moins je l'ai fait - en utilisant Lucene 2.9.2 - ce filtre est absent de la version .NET aussi loin que je pourrait dire. Je devais aller chercher la version .NET 4 en ligne en 2.9.3 et revenir en arrière - espérons que cela rendra la procédure moins pénible pour quelqu'un ...

Edit: S'il vous plaît noter également que le tableau renvoyé par la fonction SuggestTermsFor() est Triés par ordre croissant, vous aurez probablement envie de revenir pour obtenir les termes les plus populaires d'abord dans votre liste

using System.IO; 
using System.Collections; 
using Lucene.Net.Analysis; 
using Lucene.Net.Analysis.Tokenattributes; 
using Lucene.Net.Util; 

namespace Lucene.Net.Analysis.NGram 
{ 

/** 
* Tokenizes the given token into n-grams of given size(s). 
* <p> 
* This {@link TokenFilter} create n-grams from the beginning edge or ending edge of a input token. 
* </p> 
*/ 
public class EdgeNGramTokenFilter : TokenFilter 
{ 
    public static Side DEFAULT_SIDE = Side.FRONT; 
    public static int DEFAULT_MAX_GRAM_SIZE = 1; 
    public static int DEFAULT_MIN_GRAM_SIZE = 1; 

    // Replace this with an enum when the Java 1.5 upgrade is made, the impl will be simplified 
    /** Specifies which side of the input the n-gram should be generated from */ 
    public class Side 
    { 
     private string label; 

     /** Get the n-gram from the front of the input */ 
     public static Side FRONT = new Side("front"); 

     /** Get the n-gram from the end of the input */ 
     public static Side BACK = new Side("back"); 

     // Private ctor 
     private Side(string label) { this.label = label; } 

     public string getLabel() { return label; } 

     // Get the appropriate Side from a string 
     public static Side getSide(string sideName) 
     { 
      if (FRONT.getLabel().Equals(sideName)) 
      { 
       return FRONT; 
      } 
      else if (BACK.getLabel().Equals(sideName)) 
      { 
       return BACK; 
      } 
      return null; 
     } 
    } 

    private int minGram; 
    private int maxGram; 
    private Side side; 
    private char[] curTermBuffer; 
    private int curTermLength; 
    private int curGramSize; 
    private int tokStart; 

    private TermAttribute termAtt; 
    private OffsetAttribute offsetAtt; 

    protected EdgeNGramTokenFilter(TokenStream input) : base(input) 
    { 
     this.termAtt = (TermAttribute)AddAttribute(typeof(TermAttribute)); 
     this.offsetAtt = (OffsetAttribute)AddAttribute(typeof(OffsetAttribute)); 
    } 

    /** 
    * Creates EdgeNGramTokenFilter that can generate n-grams in the sizes of the given range 
    * 
    * @param input {@link TokenStream} holding the input to be tokenized 
    * @param side the {@link Side} from which to chop off an n-gram 
    * @param minGram the smallest n-gram to generate 
    * @param maxGram the largest n-gram to generate 
    */ 
    public EdgeNGramTokenFilter(TokenStream input, Side side, int minGram, int maxGram) 
     : base(input) 
    { 

     if (side == null) 
     { 
      throw new System.ArgumentException("sideLabel must be either front or back"); 
     } 

     if (minGram < 1) 
     { 
      throw new System.ArgumentException("minGram must be greater than zero"); 
     } 

     if (minGram > maxGram) 
     { 
      throw new System.ArgumentException("minGram must not be greater than maxGram"); 
     } 

     this.minGram = minGram; 
     this.maxGram = maxGram; 
     this.side = side; 
     this.termAtt = (TermAttribute)AddAttribute(typeof(TermAttribute)); 
     this.offsetAtt = (OffsetAttribute)AddAttribute(typeof(OffsetAttribute)); 
    } 

    /** 
    * Creates EdgeNGramTokenFilter that can generate n-grams in the sizes of the given range 
    * 
    * @param input {@link TokenStream} holding the input to be tokenized 
    * @param sideLabel the name of the {@link Side} from which to chop off an n-gram 
    * @param minGram the smallest n-gram to generate 
    * @param maxGram the largest n-gram to generate 
    */ 
    public EdgeNGramTokenFilter(TokenStream input, string sideLabel, int minGram, int maxGram) 
     : this(input, Side.getSide(sideLabel), minGram, maxGram) 
    { 

    } 

    public override bool IncrementToken() 
    { 
     while (true) 
     { 
      if (curTermBuffer == null) 
      { 
       if (!input.IncrementToken()) 
       { 
        return false; 
       } 
       else 
       { 
        curTermBuffer = (char[])termAtt.TermBuffer().Clone(); 
        curTermLength = termAtt.TermLength(); 
        curGramSize = minGram; 
        tokStart = offsetAtt.StartOffset(); 
       } 
      } 
      if (curGramSize <= maxGram) 
      { 
       if (!(curGramSize > curTermLength   // if the remaining input is too short, we can't generate any n-grams 
        || curGramSize > maxGram)) 
       {  // if we have hit the end of our n-gram size range, quit 
        // grab gramSize chars from front or back 
        int start = side == Side.FRONT ? 0 : curTermLength - curGramSize; 
        int end = start + curGramSize; 
        ClearAttributes(); 
        offsetAtt.SetOffset(tokStart + start, tokStart + end); 
        termAtt.SetTermBuffer(curTermBuffer, start, curGramSize); 
        curGramSize++; 
        return true; 
       } 
      } 
      curTermBuffer = null; 
     } 
    } 

    public override Token Next(Token reusableToken) 
    { 
     return base.Next(reusableToken); 
    } 
    public override Token Next() 
    { 
     return base.Next(); 
    } 
    public override void Reset() 
    { 
     base.Reset(); 
     curTermBuffer = null; 
    } 
} 
} 

Answer 5

mon code basé sur Lucene 4.2, peut vous aider

import java.io.File; 
import java.io.IOException; 

import org.apache.lucene.analysis.miscellaneous.PerFieldAnalyzerWrapper; 
import org.apache.lucene.index.DirectoryReader; 
import org.apache.lucene.index.IndexWriterConfig; 
import org.apache.lucene.index.IndexWriterConfig.OpenMode; 
import org.apache.lucene.search.spell.Dictionary; 
import org.apache.lucene.search.spell.LuceneDictionary; 
import org.apache.lucene.search.spell.PlainTextDictionary; 
import org.apache.lucene.search.spell.SpellChecker; 
import org.apache.lucene.store.Directory; 
import org.apache.lucene.store.FSDirectory; 
import org.apache.lucene.store.IOContext; 
import org.apache.lucene.store.RAMDirectory; 
import org.apache.lucene.util.Version; 
import org.wltea4pinyin.analyzer.lucene.IKAnalyzer4PinYin; 


/** 
* 
* 
* @author <a href="mailto:liu.gang@renren-inc.com"></a> 
* @version 2013-11-25上午11:13:59 
*/ 
public class LuceneSpellCheckerDemoService { 

private static final String INDEX_FILE = "/Users/r/Documents/jar/luke/youtui/index"; 
private static final String INDEX_FILE_SPELL = "/Users/r/Documents/jar/luke/spell"; 

private static final String INDEX_FIELD = "app_name_quanpin"; 

public static void main(String args[]) { 

    try { 
     // 
     PerFieldAnalyzerWrapper wrapper = new PerFieldAnalyzerWrapper(new IKAnalyzer4PinYin(
       true)); 

     // read index conf 
     IndexWriterConfig conf = new IndexWriterConfig(Version.LUCENE_42, wrapper); 
     conf.setOpenMode(OpenMode.CREATE_OR_APPEND); 

     // read dictionary 
     Directory directory = FSDirectory.open(new File(INDEX_FILE)); 
     RAMDirectory ramDir = new RAMDirectory(directory, IOContext.READ); 
     DirectoryReader indexReader = DirectoryReader.open(ramDir); 

     Dictionary dic = new LuceneDictionary(indexReader, INDEX_FIELD); 


     SpellChecker sc = new SpellChecker(FSDirectory.open(new File(INDEX_FILE_SPELL))); 
     //sc.indexDictionary(new PlainTextDictionary(new File("myfile.txt")), conf, false); 
     sc.indexDictionary(dic, conf, true); 
     String[] strs = sc.suggestSimilar("zhsiwusdazhanjiangshi", 10); 
     for (int i = 0; i < strs.length; i++) { 
      System.out.println(strs[i]); 
     } 
     sc.close(); 
    } catch (IOException e) { 
     e.printStackTrace(); 
    } 
} 


}