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/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the Elastic License
* 2.0 and the Server Side Public License, v 1; you may not use this file except
* in compliance with, at your election, the Elastic License 2.0 or the Server
* Side Public License, v 1.
*/
package org.elasticsearch.search.suggest.phrase;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.MultiTerms;
import org.apache.lucene.index.Terms;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.spell.DirectSpellChecker;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.BytesRefBuilder;
import org.apache.lucene.util.CharsRefBuilder;
import org.elasticsearch.common.lucene.Lucene;
import org.elasticsearch.common.text.Text;
import org.elasticsearch.index.query.AbstractQueryBuilder;
import org.elasticsearch.index.query.ParsedQuery;
import org.elasticsearch.index.query.QueryBuilder;
import org.elasticsearch.index.query.SearchExecutionContext;
import org.elasticsearch.script.TemplateScript;
import org.elasticsearch.search.suggest.Suggest.Suggestion;
import org.elasticsearch.search.suggest.Suggest.Suggestion.Entry;
import org.elasticsearch.search.suggest.Suggest.Suggestion.Entry.Option;
import org.elasticsearch.search.suggest.Suggester;
import org.elasticsearch.search.suggest.SuggestionSearchContext.SuggestionContext;
import org.elasticsearch.search.suggest.phrase.NoisyChannelSpellChecker.Result;
import org.elasticsearch.xcontent.XContentFactory;
import org.elasticsearch.xcontent.XContentParser;
import java.io.CharArrayReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
public final class PhraseSuggester extends Suggester {
private final BytesRef SEPARATOR = new BytesRef(" ");
private static final String SUGGESTION_TEMPLATE_VAR_NAME = "suggestion";
public static final PhraseSuggester INSTANCE = new PhraseSuggester();
private PhraseSuggester() {}
/*
* More Ideas:
* - add ability to find whitespace problems -> we can build a poor mans decompounder with our index based on a automaton?
* - add ability to build different error models maybe based on a confusion matrix?
* - try to combine a token with its subsequent token to find / detect word splits (optional)
* - for this to work we need some way to defined the position length of a candidate
* - phonetic filters could be interesting here too for candidate selection
*/
@Override
public Suggestion> innerExecute(
String name,
PhraseSuggestionContext suggestion,
IndexSearcher searcher,
CharsRefBuilder spare
) throws IOException {
double realWordErrorLikelihood = suggestion.realworldErrorLikelihood();
final PhraseSuggestion response = new PhraseSuggestion(name, suggestion.getSize());
final IndexReader indexReader = searcher.getIndexReader();
List generators = suggestion.generators();
final int numGenerators = generators.size();
final List gens = new ArrayList<>(generators.size());
for (int i = 0; i < numGenerators; i++) {
PhraseSuggestionContext.DirectCandidateGenerator generator = generators.get(i);
DirectSpellChecker directSpellChecker = generator.createDirectSpellChecker();
Terms terms = MultiTerms.getTerms(indexReader, generator.field());
if (terms != null) {
gens.add(
new DirectCandidateGenerator(
directSpellChecker,
generator.field(),
generator.suggestMode(),
indexReader,
realWordErrorLikelihood,
generator.size(),
generator.preFilter(),
generator.postFilter(),
terms
)
);
}
}
final String suggestField = suggestion.getField();
final Terms suggestTerms = MultiTerms.getTerms(indexReader, suggestField);
if (gens.size() > 0 && suggestTerms != null) {
final NoisyChannelSpellChecker checker = new NoisyChannelSpellChecker(
realWordErrorLikelihood,
suggestion.getRequireUnigram(),
suggestion.getTokenLimit()
);
final BytesRef separator = suggestion.separator();
WordScorer wordScorer = suggestion.model()
.newScorer(indexReader, suggestTerms, suggestField, realWordErrorLikelihood, separator);
Result checkerResult;
try (TokenStream stream = tokenStream(suggestion.getAnalyzer(), suggestion.getText(), spare, suggestion.getField())) {
checkerResult = checker.getCorrections(
stream,
new MultiCandidateGeneratorWrapper(suggestion.getShardSize(), gens.toArray(new CandidateGenerator[gens.size()])),
suggestion.maxErrors(),
suggestion.getShardSize(),
wordScorer,
suggestion.confidence(),
suggestion.gramSize()
);
}
PhraseSuggestion.Entry resultEntry = buildResultEntry(suggestion, spare, checkerResult.cutoffScore);
response.addTerm(resultEntry);
final BytesRefBuilder byteSpare = new BytesRefBuilder();
final TemplateScript.Factory scriptFactory = suggestion.getCollateQueryScript();
final boolean collatePrune = (scriptFactory != null) && suggestion.collatePrune();
for (int i = 0; i < checkerResult.corrections.length; i++) {
Correction correction = checkerResult.corrections[i];
spare.copyUTF8Bytes(correction.join(SEPARATOR, byteSpare, null, null));
boolean collateMatch = true;
if (scriptFactory != null) {
// Checks if the template query collateScript yields any documents
// from the index for a correction, collateMatch is updated
final Map vars = suggestion.getCollateScriptParams();
vars.put(SUGGESTION_TEMPLATE_VAR_NAME, spare.toString());
SearchExecutionContext searchExecutionContext = suggestion.getSearchExecutionContext();
final String querySource = scriptFactory.newInstance(vars).execute();
try (
XContentParser parser = XContentFactory.xContent(querySource)
.createParser(searchExecutionContext.getParserConfig(), querySource)
) {
QueryBuilder innerQueryBuilder = AbstractQueryBuilder.parseInnerQueryBuilder(parser);
final ParsedQuery parsedQuery = searchExecutionContext.toQuery(innerQueryBuilder);
collateMatch = Lucene.exists(searcher, parsedQuery.query());
}
}
if (collateMatch == false && collatePrune == false) {
continue;
}
Text phrase = new Text(spare.toString());
Text highlighted = null;
if (suggestion.getPreTag() != null) {
spare.copyUTF8Bytes(correction.join(SEPARATOR, byteSpare, suggestion.getPreTag(), suggestion.getPostTag()));
highlighted = new Text(spare.toString());
}
if (collatePrune) {
resultEntry.addOption(new PhraseSuggestion.Entry.Option(phrase, highlighted, (float) (correction.score), collateMatch));
} else {
resultEntry.addOption(new PhraseSuggestion.Entry.Option(phrase, highlighted, (float) (correction.score)));
}
}
} else {
response.addTerm(buildResultEntry(suggestion, spare, Double.MIN_VALUE));
}
return response;
}
private static TokenStream tokenStream(Analyzer analyzer, BytesRef query, CharsRefBuilder spare, String field) throws IOException {
spare.copyUTF8Bytes(query);
return analyzer.tokenStream(field, new CharArrayReader(spare.chars(), 0, spare.length()));
}
private static PhraseSuggestion.Entry buildResultEntry(SuggestionContext suggestion, CharsRefBuilder spare, double cutoffScore) {
spare.copyUTF8Bytes(suggestion.getText());
return new PhraseSuggestion.Entry(new Text(spare.toString()), 0, spare.length(), cutoffScore);
}
@Override
protected Suggestion> emptySuggestion(
String name,
PhraseSuggestionContext suggestion,
CharsRefBuilder spare
) throws IOException {
PhraseSuggestion phraseSuggestion = new PhraseSuggestion(name, suggestion.getSize());
spare.copyUTF8Bytes(suggestion.getText());
phraseSuggestion.addTerm(new PhraseSuggestion.Entry(new Text(spare.toString()), 0, spare.length()));
return phraseSuggestion;
}
}
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