Similarity

Similarity defines the components of Lucene scoring.

Expert: Scoring API.

This is a low-level API, you should only extend this API if you want to implement an information retrieval model. If you are instead looking for a convenient way to alter Lucene's scoring, consider just tweaking the default implementation: BM25Similarity or extend SimilarityBase, which makes it easy to compute a score from index statistics.

Similarity determines how Lucene weights terms, and Lucene interacts with this class at both #indextime and #querytime.

Indexing Time At indexing time, the indexer calls .computeNorm, allowing the Similarity implementation to set a per-document value for the field that will be later accessible via . Lucene makes no assumption about what is in this norm, but it is most useful for encoding length normalization information.

Implementations should carefully consider how the normalization is encoded: while Lucene's default implementation encodes length normalization information with SmallFloat into a single byte, this might not be suitable for all purposes.

Many formulas require the use of average document length, which can be computed via a combination of CollectionStatistics.sumTotalTermFreq and .

Additional scoring factors can be stored in named NumericDocValuesFields and accessed at query-time with org.gnit.lucenekmp.index.LeafReader.getNumericDocValues. However this should not be done in the Similarity but externally, for instance by using FunctionScoreQuery.

Finally, using index-time boosts (either via folding into the normalization byte or via DocValues), is an inefficient way to boost the scores of different fields if the boost will be the same for every document, instead the Similarity can simply take a constant boost parameter C, and PerFieldSimilarityWrapper can return different instances with different boosts depending upon field name.

Query time At query-time, Queries interact with the Similarity via these steps:

1. The .scorer method is called a single time, allowing the implementation to compute any statistics (such as IDF, average document length, etc) across the entire collection. The TermStatistics and CollectionStatistics passed in already contain all of the raw statistics involved, so a Similarity can freely use any combination of statistics without causing any additional I/O. Lucene makes no assumption about what is stored in the returned [ ] object.

2. Then SimScorer.score is called for every matching document to compute its score.

Explanations When IndexSearcher.explain is called, queries consult the Similarity's DocScorer for an explanation of how it computed its score. The query passes in a the document id and an explanation of how the frequency was computed.

See also

Inheritors