org.apache.lucene.search (Lucene 10.5.0 core API)
Description: declaration: package: org.apache.lucene.search

# Package org.apache.lucene.search

package org.apache.lucene.search
Code to search indices.

## Table Of Contents

1. Search Basics
2. The Query Classes
3. Scoring: Introduction
4. Scoring: Basics
5. Changing the Scoring
6. Appendix: Search Algorithm

## Search Basics

Lucene offers a wide variety of `Query` implementations, most of which are in this package or the queries module. These implementations can be combined in a wide variety of ways to provide complex querying capabilities along with information about where matches took place in the document collection. The Query Classes section below highlights some of the more important Query classes. For details on implementing your own Query class, see Custom Queries -- Expert Level below.

Make sure to look at `Query` factory methods on `IndexableField`s that you feed into the index writer, they are convenient to use and sometimes more efficient than a naively constructed `Query`. See `LongField.newRangeQuery(String, long, long)` for instance.

To perform a search, applications usually call `IndexSearcher.search(Query,int)`.

Once a Query has been created and submitted to the `IndexSearcher`, the scoring process begins. After some infrastructure setup, control finally passes to the `Weight` implementation and its `Scorer` or `BulkScorer` instances. See the Algorithm section for more notes on the process.

## Query Classes

### `TermQuery`

Of the various implementations of `Query`, the `TermQuery` is the easiest to understand and the most often used in applications. A `TermQuery` matches all the documents that contain the specified `Term`, which is a word that occurs in a certain `Field`. Thus, a `TermQuery` identifies and scores all `Document`s that have a `Field` with the specified string in it. Constructing a `TermQuery` is as simple as:

     TermQuery tq = new TermQuery(new Term("fieldName", "term"));

In this example, the `Query` identifies all `Document`s that have the `Field` named `"fieldName"` containing the word `"term"`.

### `BooleanQuery`

Things start to get interesting when one combines multiple `TermQuery` instances into a `BooleanQuery`. A `BooleanQuery` contains multiple `BooleanClause`s, where each clause contains a sub-query (`Query` instance) and an operator (from `BooleanClause.Occur`) describing how that sub-query is combined with the other clauses:

1. `SHOULD` — Use this operator when a clause can occur in the result set, but is not required. If a query is made up of all SHOULD clauses, then every document in the result set matches at least one of these clauses.

2. `MUST` — Use this operator when a clause is required to occur in the result set and should contribute to the score. Every document in the result set will match all such clauses.

3. `FILTER` — Use this operator when a clause is required to occur in the result set but should not contribute to the score. Every document in the result set will match all such clauses.

4. `MUST NOT` — Use this operator when a clause must not occur in the result set. No document in the result set will match any such clauses.

Boolean queries are constructed by adding two or more `BooleanClause` instances. If too many clauses are added, a `TooManyClauses` exception will be thrown during searching. This most often occurs when a `Query` is rewritten into a `BooleanQuery` with many `TermQuery` clauses, for example by `WildcardQuery`. The default setting for the maximum number of clauses is 1024, but this can be changed via the static method `IndexSearcher.setMaxClauseCount(int)`.

### Phrases

Another common search is to find documents containing certain phrases. This is handled in different ways:

1. `PhraseQuery` — Matches a sequence of `Term`s. `PhraseQuery` uses a slop factor to determine how many positions may occur between any two terms in the phrase and still be considered a match. The slop is 0 by default, meaning the phrase must match exactly.

2. `MultiPhraseQuery` — A more general form of PhraseQuery that accepts multiple Terms for a position in the phrase. For example, this can be used to perform phrase queries that also incorporate synonyms.

3. Interval queries in the Queries module

### `PointRangeQuery`

The `PointRangeQuery` matches all documents that occur in a numeric range. For PointRangeQuery to work, you must index the values using a one of the numeric fields (`IntPoint`, `LongPoint`, `FloatPoint`, or `DoublePoint`).

### `PrefixQuery`, `WildcardQuery`, `RegexpQuery`

While the `PrefixQuery` has a different implementation, it is essentially a special case of the `WildcardQuery`. The `PrefixQuery` allows an application to identify all documents with terms that begin with a certain string. The `WildcardQuery` generalizes this by allowing for the use of `*` (matches 0 or more characters) and `?` (matches exactly one character) wildcards. Note that the `WildcardQuery` can be quite slow. Also note that `WildcardQuery` should not start with `*` and `?`, as these are extremely slow. Some QueryParsers may not allow this by default, but provide a `setAllowLeadingWildcard` method to remove that protection. The `RegexpQuery` is even more general than WildcardQuery, allowing an application to identify all documents with terms that match a regular expression pattern.

### `FuzzyQuery`

A `FuzzyQuery` matches documents that contain terms similar to the specified term. Similarity is determined using Levenshtein distance. This type of query can be useful when accounting for spelling variations in the collection.

## Scoring — Introduction

Lucene scoring is the heart of why we all love Lucene. It is blazingly fast and it hides almost all of the complexity from the user. In a nutshell, it works. At least, that is, until it doesn't work, or doesn't work as one would expect it to work. Then we are left digging into Lucene internals or asking for help on `java-user@lucene.apache.org` to figure out why a document with five of our query terms scores lower than a different document with only one of the query terms.

While this document won't answer your specific scoring issues, it will, hopefully, point you to the places that can help you figure out the what and why of Lucene scoring.

Lucene scoring supports a number of pluggable information retrieval models, including:

- Vector Space Model (VSM)
- Probabilistic Models such as Okapi BM25 and DFR
- Language models

These models can be plugged in via the `Similarity API`, and offer extension hooks and parameters for tuning. In general, Lucene first finds the documents that need to be scored based on boolean logic in the Query specification, and then ranks this subset of matching documents via the retrieval model. For some valuable references on VSM and IR in general refer to Lucene Wiki IR references.

The rest of this document will cover Scoring basics and explain how to change your `Similarity`. Next, it will cover ways you can customize the lucene internals in Custom Queries -- Expert Level, which gives details on implementing your own `Query` class and related functionality. Finally, we will finish up with some reference material in the Appendix.

## Scoring — Basics

Scoring is very much dependent on the way documents are indexed, so it is important to understand indexing. (see Lucene overview before continuing on with this section) Be sure to use the useful `IndexSearcher.explain(Query, doc)` to understand how the score for a certain matching document was computed.

Generally, the Query determines which documents match (a binary decision), while the Similarity determines how to assign scores to the matching documents.

### Fields and Documents

In Lucene, the objects we are scoring are `Document`s. A Document is a collection of `Field`s. Each Field has `semantics` about how it is created and stored (`tokenized`, `stored`, etc). It is important to note that Lucene scoring works on Fields and then combines the results to return Documents. This is important because two Documents with the exact same content, but one having the content in two Fields and the other in one Field may return different scores for the same query due to length normalization.

### Score Boosting

Lucene allows influencing the score contribution of various parts of the query by wrapping with `BoostQuery`.

## Changing Scoring — Similarity

### Changing the scoring formula

Changing `Similarity` is an easy way to influence scoring, this is done at index-time with `IndexWriterConfig.setSimilarity(Similarity)` and at query-time with `IndexSearcher.setSimilarity(Similarity)`. Be sure to use search-time similarities that encode the length normalization factor the same way as the similarity that you used at index time. All Lucene built-in similarities use the default encoding so they are compatible, but if you use a custom similarity that changes the encoding of the length normalization factor, you are on your own: Lucene makes no effort to ensure that the index-time and the search-time similarities are compatible.

You can influence scoring by configuring a different built-in Similarity implementation, or by tweaking its parameters, subclassing it to override behavior. Some implementations also offer a modular API which you can extend by plugging in a different component (e.g. term frequency normalizer).

Finally, you can extend the low level `Similarity` directly to implement a new retrieval model.

See the `org.apache.lucene.search.similarities` package documentation for information on the built-in available scoring models and extending or changing Similarity.

### Scoring multiple fields

In the real world, documents often have multiple fields with different degrees of relevance. A robust way of scoring across multiple fields is called BM25F, which is implemented via `CombinedFieldQuery`. It scores documents with multiple fields as if their content had been indexed in a single combined field. It supports configuring per-field boosts where the value of the boost is interpreted as the number of times that the content of the field exists in the virtual combined field.

Here is an example that constructs a query on "apache OR lucene" on fields "title" with a boost of 10, and "body" with a boost of 1:

     BooleanQuery.Builder builder = new BooleanQuery.Builder();
     for (String term : new String[] { "apache", "lucene" }) {
       Query query = new CombinedFieldQuery(term)
             .addField("title", 10f)
             .addField("body", 1f)
             .build();
       builder.add(query, Occur.SHOULD);
     }
     Query query = builder.build();

### Integrating field values into the score

While similarities help score a document relatively to a query, it is also common for documents to hold features that measure the quality of a match. Such features are best integrated into the score by indexing a `FeatureField` with the document at index-time, and then combining the similarity score and the feature score using a linear combination. For instance the below query matches the same documents as `originalQuery` and computes scores as `similarityScore + 0.7 * featureScore`:

     Query originalQuery = new BooleanQuery.Builder()
         .add(new TermQuery(new Term("body", "apache")), Occur.SHOULD)
         .add(new TermQuery(new Term("body", "lucene")), Occur.SHOULD)
         .build();
     Query featureQuery = FeatureField.newSaturationQuery("features", "pagerank");
     Query query = new BooleanQuery.Builder()
         .add(originalQuery, Occur.MUST)
         .add(new BoostQuery(featureQuery, 0.7f), Occur.SHOULD)
         .build();

A less efficient yet more flexible way of modifying scores is to index scoring features into doc-value fields and then combine them with the similarity score using a FunctionScoreQuery from the queries module. For instance the below example shows how to compute scores as `similarityScore * Math.log(popularity)` using the expressions module and assuming that values for the `popularity` field have been set in a `NumericDocValuesField` at index time:

       // compile an expression:
       Expression expr = JavascriptCompiler.compile("_score * ln(popularity)");

       // SimpleBindings just maps variables to DoubleValuesSource instances
       SimpleBindings bindings = new SimpleBindings();
       bindings.add("_score", DoubleValuesSource.SCORES);
       bindings.add("popularity", DoubleValuesSource.fromIntField("popularity"));

       // create a query that matches based on 'originalQuery' but
       // scores using expr
       Query query = new FunctionScoreQuery(
           originalQuery,
           expr.getDoubleValuesSource(bindings));

### Multi-stage retrieval pipelines

The above explains how to influence the score when evaluating all matches of the query. This is expensive by design since it applies to all matches of the query, which could be millions. In order to apply more sophisticated ranking logic, a good approach consists of having a retrieval pipeline that runs a simple candidate retrieval stage that retrieves e.g. 1,000 hits, followed by a more sophisticated reranking stage that reranks these 1,000 hits to select the best 100 hits among them. Since the number of hits that this retrieval stage needs to operate on is bounded, it allows it to be more sophisticated.

Lucene exposes reranking via the `Rescorer` abstract class, which has two main sub-classes:

- `QueryRescorer`, to rescore using a query. For instance, the query string could be parsed as phrase query using `QueryBuilder.createPhraseQuery(java.lang.String, java.lang.String)` instead of a boolean query in order to help boost hits which also match the query string as a phrase.
- `SortRescorer`, to rescore using a `Sort`. For instance, the best 1,000 hits by BM25 score may be sorted by descending popularity in order to compute the final top-100 hits.

### Top hits fusion

Sometimes, multiple retrieval pipelines may make sense, having their own pros and cons. A typical example would be a lexical retrieval pipeline, matching exactly what the user requested, and a semantic retrieval pipeline, matching documents that are closest to the user's query from a semantic perspective. Combining scores is hazardous as different retrieval pipelines often produce scores that not only have different ranges, but also different distributions within this range. A robust way of combining multiple retrieval pipelines consists of combining the top hits that they produce through their ranks rather than through their scores using reciprocal rank fusion. This is exposed via `TopDocs.rrf(int topN, int k, TopDocs[] hits)`.

## Custom Queries — Expert Level

Custom queries are an expert level task, so tread carefully and be prepared to share your code if you want help.

With the warning out of the way, it is possible to change a lot more than just the Similarity when it comes to matching and scoring in Lucene. Lucene's search is a complex mechanism that is grounded by three main classes:

1. `Query` — The abstract object representation of the user's information need.
2. `Weight` — A specialization of a Query for a given index. This typically associates a Query object with index statistics that are later used to compute document scores.
3. `Scorer` — The core class of the scoring process: for a given segment, scorers return `iterators` over matches and give a way to compute the `score` of these matches.
4. `BulkScorer` — An abstract class that scores a range of documents. A default implementation simply iterates through the hits from `Scorer`, but some queries such as `BooleanQuery` have more efficient implementations.

Details on each of these classes, and their children, can be found in the subsections below.

### The Query Class

In some sense, the `Query` class is where it all begins. Without a Query, there would be nothing to score. Furthermore, the Query class is the catalyst for the other scoring classes as it is often responsible for creating them or coordinating the functionality between them. The `Query` class has several methods that are important for derived classes:

1. `createWeight(IndexSearcher searcher, ScoreMode scoreMode, float boost)` — A `Weight` is the internal representation of the Query, so each Query implementation must provide an implementation of Weight. See the subsection on The Weight Interface below for details on implementing the Weight interface.
2. `rewrite(IndexReader reader)` — Rewrites queries into primitive queries. Primitive queries are: `TermQuery`, `BooleanQuery`, and other queries that implement `createWeight(IndexSearcher searcher,ScoreMode scoreMode, float boost)`

### The Weight Interface

The `Weight` interface provides an internal representation of the Query so that it can be reused. Any `IndexSearcher` dependent state should be stored in the Weight implementation, not in the Query class. The interface defines four main methods:

1. `scorer()` — Construct a new `Scorer` for this Weight. See The Scorer Class below for help defining a Scorer. As the name implies, the Scorer is responsible for doing the actual scoring of documents given the Query.
2. `explain(LeafReaderContext context, int doc)` — Provide a means for explaining why a given document was scored the way it was. Typically a weight such as TermWeight that scores via a `Similarity` will make use of the Similarity's implementation: `SimScorer#explain(Explanation freq, long norm)`.
3. `matches(LeafReaderContext context, int doc)` — Give information about positions and offsets of matches. This is typically useful to implement highlighting.

### The Scorer Class

The `Scorer` abstract class provides common scoring functionality for all Scorer implementations and is the heart of the Lucene scoring process. The Scorer defines the following methods which must be implemented:

1. `iterator()` — Return a `DocIdSetIterator` that can iterate over all document that matches this Query.
2. `docID()` — Returns the id of the `Document` that contains the match.
3. `score()` — Return the score of the current document. This value can be determined in any appropriate way for an application. For instance, the `TermScorer` simply defers to the configured Similarity: `SimScorer.score(float freq, long norm)`.
4. `getChildren()` — Returns any child subscorers underneath this scorer. This allows for users to navigate the scorer hierarchy and receive more fine-grained details on the scoring process.

### The BulkScorer Class

The `BulkScorer` scores a range of documents. There is only one abstract method:

1. `score(LeafCollector,Bits,int,int)` — Score all documents up to but not including the specified max document.

### Why would I want to add my own Query?

In a nutshell, you want to add your own custom Query implementation when you think that Lucene's aren't appropriate for the task that you want to do. You might be doing some cutting edge research or you need more information back out of Lucene (similar to Doug adding SpanQuery functionality).

## Appendix: Search Algorithm

This section is mostly notes on stepping through the Scoring process and serves as fertilizer for the earlier sections.

In the typical search application, a `Query` is passed to the `IndexSearcher`, beginning the scoring process.

Once inside the IndexSearcher, a `Collector` is used for the scoring and sorting of the search results. These important objects are involved in a search:

1. The `Weight` object of the Query. The Weight object is an internal representation of the Query that allows the Query to be reused by the IndexSearcher.
2. The IndexSearcher that initiated the call.
3. A `Sort` object for specifying how to sort the results if the standard score-based sort method is not desired.

Assuming we are not sorting (since sorting doesn't affect the raw Lucene score), we call one of the search methods of the IndexSearcher, passing in the `Weight` object created by `IndexSearcher.createWeight(Query,ScoreMode,float)` and the number of results we want. This method returns a `TopDocs` object, which is an internal collection of search results. The IndexSearcher creates a `TopScoreDocCollector` and passes it along with the Weight to another expert search method (for more on the `Collector` mechanism, see `IndexSearcher`). The TopScoreDocCollector uses a `PriorityQueue` to collect the top results for the search.

At last, we are actually going to score some documents. The score method takes in the Collector (most likely the TopScoreDocCollector or TopFieldCollector) and does its business. Of course, here is where things get involved. The `Scorer` that is returned by the `Weight` object depends on what type of Query was submitted. In most real world applications with multiple query terms, the `Scorer` is going to be a `BooleanScorer2` created from `BooleanWeight` (see the section on custom queries for info on changing this).

Assuming a BooleanScorer2, we get a internal Scorer based on the required, optional and prohibited parts of the query. Using this internal Scorer, the BooleanScorer2 then proceeds into a while loop based on the `DocIdSetIterator.nextDoc()` method. The nextDoc() method advances to the next document matching the query. This is an abstract method in the Scorer class and is thus overridden by all derived implementations. If you have a simple OR query your internal Scorer is most likely a DisjunctionSumScorer, which essentially combines the scorers from the sub scorers of the OR'd terms.

- Related Packages
  Package
  Description
  org.apache.lucene.search.comparators
  Comparators, used to compare hits so as to determine their sort order when collecting the top results with `TopFieldCollector`.
  org.apache.lucene.search.knn
  Classes related to vector search: knn and vector fields.
  org.apache.lucene.search.similarities
  This package contains the various ranking models that can be used in Lucene.
- Class
  Description
  AbstractDocIdSetIterator
  Abstract implementation of a `DocIdSetIterator` that tracks the current doc ID.
  AbstractKnnCollector
  AbstractKnnCollector is the default implementation for a knn collector used for gathering kNN results and providing topDocs from the gathered neighbors
  AcceptDocs
  Higher-level abstraction for document acceptance filtering.
  AutomatonQuery
  A `Query` that will match terms against a finite-state machine.
  BayesianScoreEstimator
  Estimates `BayesianScoreQuery` parameters (alpha, beta, base rate) from corpus statistics via pseudo-query sampling.
  BayesianScoreEstimator.Parameters
  Estimated parameters for `BayesianScoreQuery`.
  BayesianScoreQuery
  A query wrapper that transforms the inner query's score into a calibrated probability via sigmoid calibration: `P = sigmoid(alpha * (score - beta))`.
  BinarySortField
  SortField for `BinaryDocValues`, usable as an index sort.
  BinarySortField.Provider
  A SortFieldProvider for this sort
  BlendedTermQuery
  A `Query` that blends index statistics across multiple terms.
  BlendedTermQuery.Builder
  A Builder for `BlendedTermQuery`.
  BlendedTermQuery.DisjunctionMaxRewrite
  A `BlendedTermQuery.RewriteMethod` that creates a `DisjunctionMaxQuery` out of the sub queries.
  BlendedTermQuery.RewriteMethod
  A `BlendedTermQuery.RewriteMethod` defines how queries for individual terms should be merged.
  BooleanClause
  A clause in a BooleanQuery.
  BooleanClause.Occur
  Specifies how clauses are to occur in matching documents.
  BooleanQuery
  A Query that matches documents matching boolean combinations of other queries, e.g.
  BooleanQuery.Builder
  A builder for boolean queries.
  BoostAttribute
  Add this `Attribute` to a `TermsEnum` returned by `MultiTermQuery.getTermsEnum(Terms,AttributeSource)` and update the boost on each returned term.
  BoostAttributeImpl
  Implementation class for `BoostAttribute`.
  BoostQuery
  A `Query` wrapper that allows to give a boost to the wrapped query.
  BulkScorer
  This class is used to score a range of documents at once, and is returned by `Weight.bulkScorer(org.apache.lucene.index.LeafReaderContext)`.
  ByteVectorSimilarityQuery
  Search for all (approximate) byte vectors above a similarity threshold.
  CachingCollector
  Caches all docs, and optionally also scores, coming from a search, and is then able to replay them to another collector.
  CheckedIntConsumer<T extends Exception>
  Like `IntConsumer`, but may throw checked exceptions.
  CollectionStatistics
  Contains statistics for a collection (field).
  CollectionTerminatedException
  Throw this exception in `LeafCollector.collect(int)` to prematurely terminate collection of the current leaf.
  Collector
  Expert: Collectors are primarily meant to be used to gather raw results from a search, and implement sorting or custom result filtering, collation, etc.
  CollectorManager<C extends Collector,T>
  A manager of collectors.
  CombinedFieldQuery
  A `Query` that treats multiple fields as a single stream and scores terms as if they had been indexed in a single field whose values would be the union of the values of the provided fields.
  CombinedFieldQuery.Builder
  A builder for `CombinedFieldQuery`.
  ConjunctionUtils
  Helper methods for building conjunction iterators
  ConstantScoreQuery
  A query that wraps another query and simply returns a constant score equal to 1 for every document that matches the query.
  ConstantScoreQuery.ConstantBulkScorer
  We return this as our `BulkScorer` so that if the CSQ wraps a query with its own optimized top-level scorer (e.g.
  ConstantScoreScorer
  A constant-scoring `Scorer`.
  ConstantScoreScorerSupplier
  Specialization of `ScorerSupplier` for queries that produce constant scores.
  ConstantScoreWeight
  A Weight that has a constant score equal to the boost of the wrapped query.
  ControlledRealTimeReopenThread<T>
  Utility class that runs a thread to manage periodicc reopens of a `ReferenceManager`, with methods to wait for a specific index changes to become visible.
  DisiPriorityQueue
  A priority queue of DocIdSetIterators that orders by current doc ID.
  DisiWrapper
  Wrapper used in `DisiPriorityQueue`.
  DisjunctionDISIApproximation
  A `DocIdSetIterator` which is a disjunction of the approximations of the provided iterators.
  DisjunctionMaxQuery
  A query that generates the union of documents produced by its subqueries, and that scores each document with the maximum score for that document as produced by any subquery, plus a tie breaking increment for any additional matching subqueries.
  DocAndFloatFeatureBuffer
  Wrapper around parallel arrays storing doc IDs and their corresponding features, stored as Java floats.
  DocAndScoreAccBuffer
  Wrapper around parallel arrays storing doc IDs and their corresponding score accumulators.
  DocIdSet
  A DocIdSet contains a set of doc ids.
  DocIdSetBulkIterator
  Bulk iterator over a `DocIdSetIterator`.
  DocIdSetIterator
  This abstract class defines methods to iterate over a set of non-decreasing doc ids.
  DocIdStream
  A stream of doc IDs.
  DocValuesRangeIterator
  A `TwoPhaseIterator` over doc values that skips documents with values that lie outside a specific range, using a `SkipBlockRangeIterator` as an approximation
  DocValuesRewriteMethod
  Rewrites MultiTermQueries into a filter, using DocValues for term enumeration.
  DoubleValues
  Per-segment, per-document double values, which can be calculated at search-time
  DoubleValuesSource
  Base class for producing `DoubleValues`
  DoubleValuesSourceRescorer
  A `Rescorer` that uses provided DoubleValuesSource to rescore first pass hits.
  ExactPhraseMatcher
  Expert: Find exact phrases
  Explanation
  Expert: Describes the score computation for document and query.
  FieldComparator<T>
  Expert: a FieldComparator compares hits so as to determine their sort order when collecting the top results with `TopFieldCollector`.
  FieldComparator.RelevanceComparator
  Sorts by descending relevance.
  FieldComparator.TermValComparator
  Sorts by field's natural Term sort order.
  FieldComparatorSource
  Provides a `FieldComparator` for custom field sorting.
  FieldDoc
  Expert: A ScoreDoc which also contains information about how to sort the referenced document.
  FieldExistsQuery
  A `Query` that matches documents that contain either a `KnnFloatVectorField`, `KnnByteVectorField` or a field that indexes norms or doc values.
  FieldValueHitQueue<T extends FieldValueHitQueue.Entry>
  Expert: A hit queue for sorting by hits by terms in more than one field.
  FieldValueHitQueue.Entry
  Extension of ScoreDoc to also store the `FieldComparator` slot.
  FilterCollector
  `Collector` delegator.
  FilterDocIdSetIterator
  Wrapper around a `DocIdSetIterator`.
  FilteredDocIdSetIterator
  Abstract decorator class of a DocIdSetIterator implementation that provides on-demand filter/validation mechanism on an underlying DocIdSetIterator.
  FilterLeafCollector
  `LeafCollector` delegator.
  FilterMatchesIterator
  A MatchesIterator that delegates all calls to another MatchesIterator
  FilterScorable
  Filter a `Scorable`, intercepting methods and optionally changing their return values
  FilterScorer
  A `FilterScorer` contains another `Scorer`, which it uses as its basic source of data, possibly transforming the data along the way or providing additional functionality.
  FilterWeight
  A `FilterWeight` contains another `Weight` and implements all abstract methods by calling the contained weight's method.
  FloatVectorSimilarityQuery
  Search for all (approximate) float vectors above a similarity threshold.
  FullPrecisionFloatVectorSimilarityValuesSource
  A `DoubleValuesSource` that computes vector similarity between a query vector and raw full precision vectors indexed in provided `KnnFloatVectorField` in documents.
  FuzzyQuery
  Implements the fuzzy search query.
  FuzzyTermsEnum
  Subclass of TermsEnum for enumerating all terms that are similar to the specified filter term.
  FuzzyTermsEnum.FuzzyTermsException
  Thrown to indicate that there was an issue creating a fuzzy query for a given term.
  HitQueue
  Expert: Priority queue containing hit docs
  HnswQueueSaturationCollector
  A `KnnCollector.Decorator` that early exits when nearest neighbor queue keeps saturating beyond a 'patience' parameter.
  ImpactsDISI
  `DocIdSetIterator` that skips non-competitive docs thanks to the indexed impacts.
  IndexOrDocValuesQuery
  A query that uses either an index structure (points or terms) or doc values in order to run a query, depending which one is more efficient.
  IndexSearcher
  Implements search over a single IndexReader.
  IndexSearcher.LeafReaderContextPartition
  Holds information about a specific leaf context and the corresponding range of doc ids to search within.
  IndexSearcher.LeafSlice
  A class holding a subset of the `IndexSearcher`s leaf contexts to be executed within a single thread.
  IndexSearcher.TooManyClauses
  Thrown when an attempt is made to add more than `IndexSearcher.TooManyClauses.getMaxClauseCount()` clauses.
  IndexSearcher.TooManyNestedClauses
  Thrown when a client attempts to execute a Query that has more than `IndexSearcher.TooManyClauses.getMaxClauseCount()` total clauses cumulatively in all of its children.
  IndexSortSortedNumericDocValuesRangeQuery
  A range query that can take advantage of the fact that the index is sorted to speed up execution.
  IndriAndQuery
  A Query that matches documents matching combinations of subqueries.
  IndriAndScorer
  Combines scores of subscorers.
  IndriAndWeight
  The Weight for IndriAndQuery, used to normalize, score and explain these queries.
  IndriDisjunctionScorer
  The Indri implemenation of a disjunction scorer which stores the subscorers for the child queries.
  IndriQuery
  A Basic abstract query that all IndriQueries can extend to implement toString, equals, getClauses, and iterator.
  IndriScorer
  The Indri parent scorer that stores the boost so that IndriScorers can use the boost outside of the term.
  KnnByteVectorQuery
  Uses `KnnVectorsReader.search(String, byte[], KnnCollector, AcceptDocs)` to perform nearest neighbour search.
  KnnCollector
  KnnCollector is a knn collector used for gathering kNN results and providing topDocs from the gathered neighbors
  KnnCollector.Decorator
  KnnCollector.Decorator is the base class for decorators of KnnCollector objects, which extend the object with new behaviors.
  KnnFloatVectorQuery
  Uses `KnnVectorsReader.search(String, float[], KnnCollector, AcceptDocs)` to perform nearest neighbour search.
  LateInteractionFloatValuesSource
  A `DoubleValuesSource` that scores documents using similarity between a multi-vector query, and indexed document multi-vectors.
  LateInteractionFloatValuesSource.ScoreFunction
  Defines the function to compute similarity score between query and document multi-vectors
  LateInteractionRescorer
  Rescores top N results from a first pass query using a `LateInteractionFloatValuesSource`
  LeafCollector
  Collector decouples the score from the collected doc: the score computation is skipped entirely if it's not needed.
  LeafFieldComparator
  Expert: comparator that gets instantiated on each leaf from a top-level `FieldComparator` instance.
  LiveFieldValues<S,T>
  Tracks live field values across NRT reader reopens.
  LogOddsFusionQuery
  A query that combines sub-query probability scores via log-odds fusion.
  LongValues
  Per-segment, per-document long values, which can be calculated at search-time
  LongValuesSource
  Base class for producing `LongValues`
  LongValuesSource.ConstantLongValuesSource
  A ConstantLongValuesSource that always returns a constant value
  LRUQueryCache
  A `QueryCache` that evicts queries using a LRU (least-recently-used) eviction policy in order to remain under a given maximum size and number of bytes used.
  LRUQueryCache.CacheAndCount
  Cache of doc ids with a count.
  MatchAllDocsQuery
  A query that matches all documents.
  Matches
  Reports the positions and optionally offsets of all matching terms in a query for a single document
  MatchesIterator
  An iterator over match positions (and optionally offsets) for a single document and field
  MatchesUtils
  Contains static functions that aid the implementation of `Matches` and `MatchesIterator` interfaces.
  MatchNoDocsQuery
  A query that matches no documents.
  MaxNonCompetitiveBoostAttribute
  Add this `Attribute` to a fresh `AttributeSource` before calling `MultiTermQuery.getTermsEnum(Terms,AttributeSource)`.
  MaxNonCompetitiveBoostAttributeImpl
  Implementation class for `MaxNonCompetitiveBoostAttribute`.
  MaxScoreCache
  Compute maximum scores based on `Impacts` and keep them in a cache in order not to run expensive similarity score computations multiple times on the same data.
  MultiCollector
  A `Collector` which allows running a search with several `Collector`s.
  MultiCollectorManager
  A `CollectorManager` implements which wrap a set of `CollectorManager` as `MultiCollector` acts for `Collector`.
  MultiPhraseQuery
  A generalized version of `PhraseQuery`, with the possibility of adding more than one term at the same position that are treated as a disjunction (OR).
  MultiPhraseQuery.Builder
  A builder for multi-phrase queries
  MultiPhraseQuery.UnionFullPostingsEnum
  Slower version of UnionPostingsEnum that delegates offsets and positions, for use by MatchesIterator
  MultiPhraseQuery.UnionPostingsEnum
  Takes the logical union of multiple PostingsEnum iterators.
  Multiset<T>
  A `Multiset` is a set that allows for duplicate elements.
  MultiTermQuery
  An abstract `Query` that matches documents containing a subset of terms provided by a `FilteredTermsEnum` enumeration.
  MultiTermQuery.RewriteMethod
  Abstract class that defines how the query is rewritten.
  MultiTermQuery.TopTermsBlendedFreqScoringRewrite
  A rewrite method that first translates each term into `BooleanClause.Occur.SHOULD` clause in a BooleanQuery, but adjusts the frequencies used for scoring to be blended across the terms, otherwise the rarest term typically ranks highest (often not useful eg in the set of expanded terms in a FuzzyQuery).
  MultiTermQuery.TopTermsBoostOnlyBooleanQueryRewrite
  A rewrite method that first translates each term into `BooleanClause.Occur.SHOULD` clause in a BooleanQuery, but the scores are only computed as the boost.
  MultiTermQuery.TopTermsScoringBooleanQueryRewrite
  A rewrite method that first translates each term into `BooleanClause.Occur.SHOULD` clause in a BooleanQuery, and keeps the scores as computed by the query.
  MultiVectorSimilarity
  Interface to define the similarity function between multi-vectors
  NamedMatches
  Utility class to help extract the set of sub queries that have matched from a larger query.
  NGramPhraseQuery
  This is a `PhraseQuery` which is optimized for n-gram phrase query.
  NumericDocValuesRangeQuery
  A Query over a range of long values
  NumericFieldStats
  Utility class for retrieving global numeric field statistics from index metadata structures, without accessing individual documents.
  NumericFieldStats.Stats
  Holds the global statistics for a numeric field.
  PatienceKnnVectorQuery
  This is a version of knn vector query that exits early when HNSW queue saturates over a `#saturationThreshold` for more than `#patience` times.
  PhraseMatcher
  Base class for exact and sloppy phrase matching
  PhraseQuery
  A Query that matches documents containing a particular sequence of terms.
  PhraseQuery.Builder
  A builder for phrase queries.
  PhraseQuery.PostingsAndFreq
  Term postings and position information for phrase matching
  PhraseWeight
  Expert: Weight class for phrase matching
  PointInSetQuery
  Abstract query class to find all documents whose single or multi-dimensional point values, previously indexed with e.g.
  PointInSetQuery.Stream
  Iterator of encoded point values.
  PointRangeQuery
  Abstract class for range queries against single or multidimensional points such as `IntPoint`.
  PositiveScoresOnlyCollector
  A `Collector` implementation which wraps another `Collector` and makes sure only documents with scores > 0 are collected.
  PrefixQuery
  A Query that matches documents containing terms with a specified prefix.
  Pruning
  Controls `LeafFieldComparator` how to skip documents
  Query
  The abstract base class for queries.
  QueryCache
  A cache for queries.
  QueryCachingPolicy
  A policy defining which filters should be cached.
  QueryRescorer
  A `Rescorer` that uses a provided Query to assign scores to the first-pass hits.
  QueryVisitor
  Allows recursion through a query tree
  ReferenceManager<G>
  Utility class to safely share instances of a certain type across multiple threads, while periodically refreshing them.
  ReferenceManager.RefreshListener
  Use to receive notification when a refresh has finished.
  RefreshCommitSupplier
  Expert: Interface to supply commit for searcher refresh.
  RegexpQuery
  A fast regular expression query based on the `org.apache.lucene.util.automaton` package.
  Rescorer
  Re-scores the topN results (`TopDocs`) from an original query.
  RescoreTopNQuery
  A Query that re-scores another Query with a `DoubleValuesSource` function and cut-off the results at top N.
  Scorable
  Allows access to the score of a Query
  Scorable.ChildScorable
  A child Scorer and its relationship to its parent.
  ScoreCachingWrappingScorer
  A `Scorer` which wraps another scorer and caches the score of the current document.
  ScoreDoc
  Holds one hit in `TopDocs`.
  ScoreMode
  Different modes of search.
  Scorer
  Expert: Common scoring functionality for different types of queries.
  ScorerSupplier
  A supplier of `Scorer`.
  ScoringRewrite<B>
  Base rewrite method that translates each term into a query, and keeps the scores as computed by the query.
  SearcherFactory
  Factory class used by `SearcherManager` to create new IndexSearchers.
  SearcherLifetimeManager
  Keeps track of current plus old IndexSearchers, closing the old ones once they have timed out.
  SearcherLifetimeManager.PruneByAge
  Simple pruner that drops any searcher older by more than the specified seconds, than the newest searcher.
  SearcherLifetimeManager.Pruner
  See `SearcherLifetimeManager.prune(org.apache.lucene.search.SearcherLifetimeManager.Pruner)`.
  SearcherManager
  Utility class to safely share `IndexSearcher` instances across multiple threads, while periodically reopening.
  SeededKnnVectorQuery
  This is a version of knn vector query that provides a query seed to initiate the vector search.
  SegmentCacheable
  Interface defining whether or not an object can be cached against a `LeafReader`
  SimpleCollector
  Base `Collector` implementation that is used to collect all contexts.
  SimpleFieldComparator<T>
  Base `FieldComparator` implementation that is used for all contexts.
  SkipBlockRangeIterator
  A DocIdSetIterator that returns all documents within DocValuesSkipper blocks that have minimum and maximum values that fall within a specified range.
  SkipBlockRangeIterator.Match
  The match state of the current positioned block
  SloppyPhraseMatcher
  Find all slop-valid position-combinations (matches) encountered while traversing/hopping the PhrasePositions.
  Sort
  Encapsulates sort criteria for returned hits.
  SortedNumericSelector
  Selects a value from the document's list to use as the representative value
  SortedNumericSelector.Type
  Type of selection to perform.
  SortedNumericSortField
  SortField for `SortedNumericDocValues`.
  SortedNumericSortField.Provider
  A SortFieldProvider for this sort field
  SortedSetSelector
  Selects a value from the document's set to use as the representative value
  SortedSetSelector.Type
  Type of selection to perform.
  SortedSetSortField
  SortField for `SortedSetDocValues`.
  SortedSetSortField.Provider
  A SortFieldProvider for this sort
  SortField
  Stores information about how to sort documents by terms in an individual field.
  SortField.Provider
  A SortFieldProvider for field sorts
  SortField.Type
  Specifies the type of the terms to be sorted, or special types such as CUSTOM
  SortRescorer
  A `Rescorer` that re-sorts according to a provided Sort.
  SynonymQuery
  A query that treats multiple terms as synonyms.
  SynonymQuery.Builder
  A builder for `SynonymQuery`.
  TaskExecutor
  Executor wrapper responsible for the execution of concurrent tasks.
  TermInSetQuery
  Specialization for a disjunction over many terms that, by default, behaves like a `ConstantScoreQuery` over a `BooleanQuery` containing only `BooleanClause.Occur.SHOULD` clauses.
  TermQuery
  A Query that matches documents containing a term.
  TermRangeQuery
  A Query that matches documents within an range of terms.
  TermScorer
  Expert: A `Scorer` for documents matching a `Term`.
  TermStatistics
  Contains statistics for a specific term
  TimeLimitingKnnCollectorManager
  A `KnnCollectorManager` that collects results with a timeout.
  TopDocs
  Represents hits returned by `IndexSearcher.search(Query,int)`.
  TopDocsCollector<T extends ScoreDoc>
  A base class for all collectors that return a `TopDocs` output.
  TopFieldCollector
  A `Collector` that sorts by `SortField` using `FieldComparator`s.
  TopFieldCollectorManager
  Create a TopFieldCollectorManager which uses a shared hit counter to maintain number of hits and a shared `MaxScoreAccumulator` to propagate the minimum score across segments if the primary sort is by relevancy.
  TopFieldDocs
  Represents hits returned by `IndexSearcher.search(Query,int,Sort)`.
  TopKnnCollector
  TopKnnCollector is a specific KnnCollector.
  TopScoreDocCollector
  A `Collector` implementation that collects the top-scoring hits, returning them as a `TopDocs`.
  TopScoreDocCollectorManager
  Create a TopScoreDocCollectorManager which uses a shared hit counter to maintain number of hits and a shared `MaxScoreAccumulator` to propagate the minimum score across segments
  TopTermsRewrite<B>
  Base rewrite method for collecting only the top terms via a priority queue.
  TotalHitCountCollector
  Just counts the total number of hits.
  TotalHitCountCollectorManager
  Collector manager based on `TotalHitCountCollector` that allows users to parallelize counting the number of hits, expected to be used mostly wrapped in `MultiCollectorManager`.
  TotalHits
  Description of the total number of hits of a query.
  TotalHits.Relation
  How the `TotalHits.value` should be interpreted.
  TwoPhaseIterator
  Returned by `Scorer.twoPhaseIterator()` to expose an approximation of a `DocIdSetIterator`.
  UsageTrackingQueryCachingPolicy
  A `QueryCachingPolicy` that tracks usage statistics of recently-used filters in order to decide on which filters are worth caching.
  VectorScorer
  Computes the similarity score between a given query vector and different document vectors.
  VectorScorer.Bulk
  Bulk scorer interface to score multiple vectors at once
  Weight
  Expert: Calculate query weights and build query scorers.
  Weight.DefaultBulkScorer
  Just wraps a Scorer and performs top scoring using it.
  Weight.DefaultScorerSupplier
  A wrap for default scorer supplier.
  WildcardQuery
  Implements the wildcard search query.