帮我写一个基于lucene8.11版本的自评分搜索系统

好的，下面是一个基于Lucene 8.11版本的自评分搜索系统的实现流程：

1. 创建索引

首先，需要创建一个索引，并将需要搜索的文档添加到索引中。可以使用Lucene提供的IndexWriter类来实现。

IndexWriterConfig config = new IndexWriterConfig(new StandardAnalyzer());
IndexWriter writer = new IndexWriter(FSDirectory.open(indexDir), config);

Document doc1 = new Document();
doc1.add(new StringField("id", "1", Field.Store.YES));
doc1.add(new TextField("title", "Lucene in Action", Field.Store.YES));
doc1.add(new TextField("content", "Lucene is a full-text search library in Java.", Field.Store.YES));
writer.addDocument(doc1);

Document doc2 = new Document();
doc2.add(new StringField("id", "2", Field.Store.YES));
doc2.add(new TextField("title", "Java Programming", Field.Store.YES));
doc2.add(new TextField("content", "Java is a popular programming language.", Field.Store.YES));
writer.addDocument(doc2);

writer.close();

2. 搜索文档

使用Lucene提供的IndexSearcher类进行文档搜索，可以使用QueryParser类将用户输入的搜索关键字解析成Query对象。

IndexReader reader = DirectoryReader.open(FSDirectory.open(indexDir));
IndexSearcher searcher = new IndexSearcher(reader);

QueryParser parser = new QueryParser("content", new StandardAnalyzer());
Query query = parser.parse("Java");

TopDocs topDocs = searcher.search(query, 10);
ScoreDoc[] scoreDocs = topDocs.scoreDocs;
for (ScoreDoc scoreDoc : scoreDocs) {
    Document doc = searcher.doc(scoreDoc.doc);
    System.out.println(doc.get("title"));
    System.out.println(doc.get("content"));
    System.out.println(scoreDoc.score);
}

reader.close();

3. 自定义评分算法

可以通过实现自定义的Similarity类来实现自定义评分算法。在Similarity类中，需要实现两个方法：`computeNorm(FieldInvertState state)`和`computeWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)`。

- `computeNorm(FieldInvertState state)`方法用于计算文档的归一化因子，影响文档的评分。可以根据需要实现自定义的归一化因子计算逻辑。

- `computeWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)`方法用于计算查询的权重，影响文档的评分。可以根据需要实现自定义的查询权重计算逻辑。

public class CustomSimilarity extends Similarity {

    @Override
    public long computeNorm(FieldInvertState state) {
        // 自定义归一化因子计算逻辑
        return state.getLength();
    }

    @Override
    public SimWeight computeWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) {
        // 自定义查询权重计算逻辑
        return new CustomSimWeight(boost, collectionStats, termStats);
    }

    @Override
    public SimScorer simScorer(SimWeight weight, LeafReaderContext context) throws IOException {
        // 自定义评分器实现
        return new CustomSimScorer(weight, context);
    }
}

public class CustomSimWeight extends SimWeight {

    public CustomSimWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) {
        super(boost, collectionStats, termStats);
    }

    @Override
    public float getValueForNormalization() {
        return 1.0f;
    }

    @Override
    public void normalize(float queryNorm, float boost) {
        // 不进行归一化
    }
}

public class CustomSimScorer extends SimScorer {

    public CustomSimScorer(SimWeight weight, LeafReaderContext context) throws IOException {
        super(weight, context);
    }

    @Override
    public float score(int doc, float freq) throws IOException {
        // 自定义评分逻辑
        return freq;
    }

    @Override
    public float computeSlopFactor(int distance) {
        return 1.0f;
    }

    @Override
    public float computePayloadFactor(int doc, int start, int end, BytesRef payload) {
        return 1.0f;
    }
}

4. 使用自定义评分算法进行搜索

将自定义的Similarity类注册到IndexSearcher中，即可使用自定义的评分算法进行搜索。

IndexReader reader = DirectoryReader.open(FSDirectory.open(indexDir));
IndexSearcher searcher = new IndexSearcher(reader);
searcher.setSimilarity(new CustomSimilarity());

QueryParser parser = new QueryParser("content", new StandardAnalyzer());
Query query = parser.parse("Java");

TopDocs topDocs = searcher.search(query, 10);
ScoreDoc[] scoreDocs = topDocs.scoreDocs;
for (ScoreDoc scoreDoc : scoreDocs) {
    Document doc = searcher.doc(scoreDoc.doc);
    System.out.println(doc.get("title"));
    System.out.println(doc.get("content"));
    System.out.println(scoreDoc.score);
}

reader.close();

以上是一个基于Lucene 8.11版本的自评分搜索系统的实现流程。需要注意的是，具体实现中可能还需要根据实际需求进行一些调整和优化。