Wie Abfrage Auto-Vervollständigung / Vorschläge in Lucene zu tun?
https://stackoverflow.com/questions/120180
-
02-07-2019 - |
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Ich bin auf der Suche nach einer Möglichkeit, Abfrage Auto-Vervollständigung / Vorschläge in Lucene zu tun. Ich habe um ein bisschen gegoogelt und um ein bisschen gespielt, aber alle die Beispiele, die ich gesehen habe, scheinen Einrichtung Filter in Solr zu sein. Wir haben keine Solr verwenden und planen nicht mit Solr in der nahen Zukunft zu bewegen, und Solr offensichtlich nur um Lucene Einwickeln sowieso, also ich denke, es muss ein Weg sein, es zu tun!
Ich sah schon in EdgeNGramFilter verwenden, und ich merke, dass ich müsste den Filter auf die Indexfelder laufen und die Token raus und dann vergleichen gegen die eingegebenen Abfrage ... Ich bin zu kämpfen, nur um sicher die Verbindung zwischen den beiden in ein Stück Code, so Hilfe ist sehr willkommen!
klar sein auf das, was ich suche (I merkte, dass ich nicht allzu klar zu sein, sorry) - ich bin auf der Suche nach einer Lösung, wo, wenn für einen Begriff, würde es eine Liste der vorgeschlagenen Abfragen geben . Bei der Eingabe ‚inter‘ in das Suchfeld ein, wird es mit einer Liste der vorgeschlagenen Abfragen zurückkommen, wie ‚Internet‘, ‚international‘, etc.
Lösung
Basierend auf @Alexandre Victoor Antwort, habe ich eine wenig Klasse basierend auf der Lucene Rechtschreibprüfung im contrib-Paket (und mit der LuceneDictionary darin enthalten), das tut genau das, was ich will.
Dies ermöglicht Neuindexierung aus einem Hand-Index mit einem einzigen Feld, und bietet Vorschläge für Bedingungen. Die Ergebnisse werden durch die Anzahl der gefundenen Dokumente mit diesem Begriff in dem ursprünglichen Index, so populäre Begriffe zuerst erscheinen sortiert. Scheint ziemlich gut zu funktionieren:)
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]
}
}
Andere Tipps
Hier ist eine Umschrift von Mat-Implementierung in C # für Lucene.NET, zusammen mit einem Code-Schnipsel für die Verdrahtung eines Textfeldes jQuery Autocomplete-Funktion.
<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;
},
});
... hier ist der ASP.NET MVC-Controller-Code:
//
// 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);
}
... und hier ist Mat Code in 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;
}
}
}
}
mein Code basiert auf Lucene 4.2, können Sie helfen
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();
}
}
}
Zusätzlich zu den oben (sehr geschätzt) Beitrag re: c # Umwandlung, sollten Sie .NET 3.5 verwenden Sie den Code für die EdgeNGramTokenFilter umfassen müssen - oder zumindest ich habe - mit Lucene 2.9.2 - diese Filter wird von der .NET-Version fehlt, soweit ich sagen konnte. Ich musste die .NET-4-Version online im 2.9.3 und Hafen gehen und finden Sie zurück - hoffen, dass dies das Verfahren weniger schmerzhaft macht für jemanden ...
Edit: Bitte beachten Sie auch, dass die Anordnung von Zählung durch die SuggestTermsFor () Funktion aufsteigend sortiert zurückgegeben wird, werden Sie wahrscheinlich wollen, dass es umgekehrt die beliebtestenen Begriffe zuerst in Ihrer Liste zu bekommen
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;
}
}
}
Sie können die Klasse verwenden PrefixQuery auf einem "Wörterbuch" -Index. Die Klasse LuceneDictionary könnte auch hilfreich sein.
Werfen Sie einen Blick auf diesen Artikel unten verlinkt. Es wird erläutert, wie die Funktion implementieren „Meinen Sie?“ in der modernen Suchmaschine zur Verfügung, wie Google. Sie können nicht etwas so Komplexes müssen wie in dem Artikel beschrieben. Doch der Artikel beschreibt, wie das Lucene Zauber-Paket verwenden.
Eine Möglichkeit, einen „Wörterbuch“ Index bauen würde auf einem LuceneDictionary läuft sein.
Hoffe, es hilft
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