Lucene TermVector用法:相关搜索功能及提高高亮显示性能

public class TermVectorTest {

	Analyzer analyzer = new SimpleAnalyzer();
	Directory ramDir = new RAMDirectory();

	public void createRamIndex() throws CorruptIndexException, LockObtainFailedException, IOException{

		IndexWriter writer = new IndexWriter(ramDir,analyzer,IndexWriter.MaxFieldLength.LIMITED);

		Document doc1 = new Document();
		doc1.add(new Field("title","java",Store.YES,Index.ANALYZED));
		doc1.add(new Field("author","callan",Store.YES,Index.ANALYZED));
		doc1.add(new Field("subject","java一门编程语言,用java的人很多，编程语言也不少，但是java最流行",Store.YES,Index.ANALYZED,TermVector.WITH_POSITIONS_OFFSETS));

		Document doc2 = new Document();
		doc2.add(new Field("title","english",Store.YES,Index.ANALYZED));
		doc2.add(new Field("author","wcq",Store.YES,Index.ANALYZED));
		doc2.add(new Field("subject","英语用的人很多",Store.YES,Index.ANALYZED,TermVector.WITH_POSITIONS_OFFSETS));

		Document doc3 = new Document();
		doc3.add(new Field("title","asp",Store.YES,Index.ANALYZED));
		doc3.add(new Field("author","ca",Store.YES,Index.ANALYZED));
		doc3.add(new Field("subject","英语用的人很多",Store.YES,Index.ANALYZED,TermVector.WITH_POSITIONS_OFFSETS));

		writer.addDocument(doc1);
		writer.addDocument(doc2);
		writer.addDocument(doc3);

		writer.optimize();
		writer.close();
	}

	public void search() throws CorruptIndexException, IOException{
		IndexReader reader = IndexReader.open(ramDir);
		IndexSearcher searcher = new IndexSearcher(reader);
		Term term = new Term("title","java");	//在title里查询java词条
		TermQuery query = new TermQuery(term);
		Hits hits = searcher.search(query);
		for (int i = 0; i < hits.length(); i++)
		{
			Document doc = hits.doc(i);
			System.out.println(doc.get("title"));
			System.out.println(doc.get("subject"));
			System.out.println("moreLike search: ");

			morelikeSearch(reader,hits.id(i));
		}
	}

	private void morelikeSearch(IndexReader reader,int id) throws IOException
	{
		//根据这个document的id获取这个field的Term Vector 信息，就是这个field分词之后在这个field里的频率、位置、等信息
		TermFreqVector vector = reader.getTermFreqVector(id, "subject");

		BooleanQuery query = new BooleanQuery();  

		for (int i = 0; i < vector.size(); i++)
		{
			 TermQuery tq = new TermQuery(new Term("subject",   
		                vector.getTerms()[i]));   //获取每个term保存的Token

		         query.add(tq, BooleanClause.Occur.SHOULD);   

		}

		IndexSearcher searcher = new IndexSearcher(ramDir);   

	    Hits hits = searcher.search(query);   

	    //显示代码，略


	}

//Lucene使用TermVector提高高亮显示性能
	public void highterLightSearch() throws CorruptIndexException, IOException{
		IndexReader reader = IndexReader.open(ramDir);   

        IndexSearcher searcher = new IndexSearcher(reader);   

        TermQuery query = new TermQuery(new Term("subject","java"));   

        Hits hits = searcher.search(query);   

        //高亮显示设置   
        SimpleHTMLFormatter simpleHTMLFormatter = new SimpleHTMLFormatter("<font color='red'>","</font>");

        Highlighter highlighter =new Highlighter(simpleHTMLFormatter,new QueryScorer(query));   

         // 这个100是指定关键字字符串的context的长度，你可以自己设定，因为不可能返回整篇正文内容   
        highlighter.setTextFragmenter(new SimpleFragmenter(100));   

        for(int i = 0; i < hits.length(); i++){   

            Document doc = hits.doc(i);   

            TermPositionVector termFreqVector = (TermPositionVector)reader.getTermFreqVector(hits.id(i), "subject");   

            TermFreqVector vector = reader.getTermFreqVector(hits.id(i), "subject");
            TokenStream tokenStream = TokenSources.getTokenStream(termFreqVector);   

            String result = highlighter.getBestFragment(tokenStream, doc.get("subject"));   

            System.out.println(doc.get("title"));   

            System.out.println(result);   

        }   


	}

	public static void main(String[] args) throws CorruptIndexException, IOException
	{
		TermVectorTest  t = new TermVectorTest();
		t.createRamIndex();
		t.search();
	}

}