enum.Enum
lunr.query.QueryPresence
- Defines possible behaviours for the term's presence in a document.Exception
lunr.exceptions.BaseLunrException
- Undocumentedlunr.exceptions.QueryParseError
- Undocumentedlunr.builder.Builder
- Performs indexing on a set of documents and returns instances of lunr.Index ready for querying.lunr.builder.Field
- Represents a field with boost and extractor functions.lunr.field_ref.FieldRef
- Undocumentedlunr.index.Index
- An index contains the built index of all documents and provides a query interface to the index.lunr.match_data.MatchData
- Contains and collects metadata about a matching document.lunr.pipeline.Pipeline
- lunr.Pipelines maintain a list of functions to be applied to all tokens in documents entering the search index and queries ran agains the index.lunr.query.Clause
- A single clause in a `lunr.Query` contains a term and details on how to match that term against a `lunr.Index`lunr.query.Query
- A `lunr.Query` provides a programmatic way of defining queries to be performed against a `lunr.Index`.lunr.query_lexer.QueryLexer
- Undocumentedlunr.query_parser.QueryParser
- Undocumentedlunr.stemmer.PorterStemmer
- No class docstring; 0/4 instance variable, 16/16 methods documentedlunr.token.Token
- No class docstring; 0/2 instance variable, 2/5 methods documentedlunr.token_set.TokenSet
- A token set is used to store the unique list of all tokens within an index. Token sets are also used to represent an incoming query to the index, this query token set and index token set are then intersected to find which tokens to look up in the inverted index.lunr.token_set_builder.TokenSetBuilder
- Undocumentedlunr.vector.Vector
- A vector is used to construct the vector space of documents and queries. These vectors support operations to determine the similarity between two documents or a document and a query.set
lunr.utils.CompleteSet
- Undocumented