Import .miscellaneous. This filter converts characters not in the (7-byte) ASCII range to the ASCII characters that they resemble most closely for example, it converts é to e (and © to (c), etc).Ī custom analyzer is easy to implement this is what ours looks like in java (the matchVersion and stopwords variables are fields from its Analyzer and StopwordAnalyzerBase superclasses, and the TokenStreamComponents is an inner class of Analyzer): Beyond the tokenizing filters that the StandardAnalyzer includes, the EnglishAnalyzer also includes the EnglishPossesiveFilter (for stripping 's from words) and the PorterStemFilter (for chopping off common word suffixes, like removing ming from stemming, etc).īecause some of our text includes non-English names with letters not in the English alphabet (like é in liberté), and we know our users are going to want to search for those names using just English-alphabet letters, we implemented our own analyzer that included the ASCIIFoldingFilter on top of the filters in the regular EnglishAnalyzer. Lucene's StandardAnalyzer does a good job generally of tokenizing text into individual words (aka "terms"), and it skips English "stopwords" (like the, a, etc) by default - but if you have only English text, you can get better results by using the EnglishAnalyzer. Use a SpellChecker for auto-complete suggestions.
Highlight results with a PostingsHighlighter.Use a SearcherManager for multi-threaded searching.
#Apache lucene phrase search example java plus#
Here are a few tricks we used for our content (which is English-only, jargon-heavy, and contains many terms used only by a few documents), plus some more basic techniques that just took us a while to figure out: While Lucene works amazingly well right out of the box, to get "Google-like" relevancy for your results, you usually need to devise a custom strategy for indexing and querying the particular content your application has. Questions explained agreeable preferred strangers too him her son.Just spent the last week tuning our search engine using the latest version of Lucene (4.3.1). Highlighter = new UnifiedHighlighter(searcher, analyzer) įragments = highlighter.highlight("contents", query, hits) Query = new WildcardQuery(new Term("contents", "prefer?d")) String fragments = highlighter.highlight("contents", query, hits) UnifiedHighlighter highlighter = new UnifiedHighlighter(searcher, analyzer) ("Search terms found in :: " + hits.totalHits + " files") TopDocs hits = arch(query, 10, Sort.INDEXORDER) Query query = new WildcardQuery(new Term("contents", "prefer*")) IndexSearcher searcher = new IndexSearcher(reader) Īnalyzer analyzer = new StandardAnalyzer()
IndexReader reader = DirectoryReader.open(dir) Index reader - an interface for accessing a point-in-time view of a lucene index Public static void main(String args) throws Exceptionĭirectory dir = FSDirectory.open(Paths.get(INDEX_DIR)) In addition to having query language interfaces, some graph databases are. Private static final String INDEX_DIR = "indexedFiles" A graph database (GDB) is a database that uses graph structures for semantic queries with. This contains the lucene indexed documents If you want to learn more about creating lucene indexes with text files, follow linked article. In this example, I am reusing the indexes created in previous lucene example.
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