e89f2f4b7b
Created 10 detailed GitHub issues covering: - Project activation and management UI (#1-2) - Worker node coordination and visualization (#3-4) - Automated GitHub repository scanning (#5) - Intelligent model-to-issue matching (#6) - Multi-model task execution system (#7) - N8N workflow integration (#8) - Hive-Bzzz P2P bridge (#9) - Peer assistance protocol (#10) Each issue includes detailed specifications, acceptance criteria, technical implementation notes, and dependency mapping. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
493 lines
18 KiB
JavaScript
493 lines
18 KiB
JavaScript
/*!
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* lunr.Index
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* Copyright (C) @YEAR Oliver Nightingale
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*/
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/**
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* An index contains the built index of all documents and provides a query interface
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* to the index.
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*
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* Usually instances of lunr.Index will not be created using this constructor, instead
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* lunr.Builder should be used to construct new indexes, or lunr.Index.load should be
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* used to load previously built and serialized indexes.
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*
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* @constructor
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* @param {Object} attrs - The attributes of the built search index.
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* @param {Object} attrs.invertedIndex - An index of term/field to document reference.
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* @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors
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* @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.
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* @param {string[]} attrs.fields - The names of indexed document fields.
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* @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.
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*/
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lunr.Index = function (attrs) {
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this.invertedIndex = attrs.invertedIndex
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this.fieldVectors = attrs.fieldVectors
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this.tokenSet = attrs.tokenSet
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this.fields = attrs.fields
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this.pipeline = attrs.pipeline
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}
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/**
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* A result contains details of a document matching a search query.
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* @typedef {Object} lunr.Index~Result
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* @property {string} ref - The reference of the document this result represents.
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* @property {number} score - A number between 0 and 1 representing how similar this document is to the query.
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* @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.
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*/
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/**
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* Although lunr provides the ability to create queries using lunr.Query, it also provides a simple
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* query language which itself is parsed into an instance of lunr.Query.
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*
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* For programmatically building queries it is advised to directly use lunr.Query, the query language
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* is best used for human entered text rather than program generated text.
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*
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* At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported
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* and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'
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* or 'world', though those that contain both will rank higher in the results.
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*
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* Wildcards can be included in terms to match one or more unspecified characters, these wildcards can
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* be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding
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* wildcards will increase the number of documents that will be found but can also have a negative
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* impact on query performance, especially with wildcards at the beginning of a term.
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*
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* Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term
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* hello in the title field will match this query. Using a field not present in the index will lead
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* to an error being thrown.
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*
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* Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term
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* boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported
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* to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.
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* Avoid large values for edit distance to improve query performance.
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*
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* Each term also supports a presence modifier. By default a term's presence in document is optional, however
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* this can be changed to either required or prohibited. For a term's presence to be required in a document the
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* term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and
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* optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not
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* appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.
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*
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* To escape special characters the backslash character '\' can be used, this allows searches to include
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* characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead
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* of attempting to apply a boost of 2 to the search term "foo".
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*
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* @typedef {string} lunr.Index~QueryString
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* @example <caption>Simple single term query</caption>
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* hello
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* @example <caption>Multiple term query</caption>
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* hello world
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* @example <caption>term scoped to a field</caption>
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* title:hello
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* @example <caption>term with a boost of 10</caption>
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* hello^10
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* @example <caption>term with an edit distance of 2</caption>
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* hello~2
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* @example <caption>terms with presence modifiers</caption>
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* -foo +bar baz
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*/
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/**
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* Performs a search against the index using lunr query syntax.
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*
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* Results will be returned sorted by their score, the most relevant results
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* will be returned first. For details on how the score is calculated, please see
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* the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.
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*
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* For more programmatic querying use lunr.Index#query.
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*
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* @param {lunr.Index~QueryString} queryString - A string containing a lunr query.
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* @throws {lunr.QueryParseError} If the passed query string cannot be parsed.
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* @returns {lunr.Index~Result[]}
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*/
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lunr.Index.prototype.search = function (queryString) {
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return this.query(function (query) {
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var parser = new lunr.QueryParser(queryString, query)
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parser.parse()
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})
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}
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/**
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* A query builder callback provides a query object to be used to express
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* the query to perform on the index.
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*
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* @callback lunr.Index~queryBuilder
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* @param {lunr.Query} query - The query object to build up.
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* @this lunr.Query
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*/
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/**
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* Performs a query against the index using the yielded lunr.Query object.
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*
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* If performing programmatic queries against the index, this method is preferred
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* over lunr.Index#search so as to avoid the additional query parsing overhead.
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*
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* A query object is yielded to the supplied function which should be used to
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* express the query to be run against the index.
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*
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* Note that although this function takes a callback parameter it is _not_ an
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* asynchronous operation, the callback is just yielded a query object to be
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* customized.
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*
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* @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.
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* @returns {lunr.Index~Result[]}
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*/
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lunr.Index.prototype.query = function (fn) {
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// for each query clause
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// * process terms
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// * expand terms from token set
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// * find matching documents and metadata
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// * get document vectors
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// * score documents
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var query = new lunr.Query(this.fields),
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matchingFields = Object.create(null),
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queryVectors = Object.create(null),
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termFieldCache = Object.create(null),
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requiredMatches = Object.create(null),
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prohibitedMatches = Object.create(null)
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/*
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* To support field level boosts a query vector is created per
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* field. An empty vector is eagerly created to support negated
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* queries.
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*/
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for (var i = 0; i < this.fields.length; i++) {
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queryVectors[this.fields[i]] = new lunr.Vector
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}
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fn.call(query, query)
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for (var i = 0; i < query.clauses.length; i++) {
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/*
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* Unless the pipeline has been disabled for this term, which is
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* the case for terms with wildcards, we need to pass the clause
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* term through the search pipeline. A pipeline returns an array
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* of processed terms. Pipeline functions may expand the passed
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* term, which means we may end up performing multiple index lookups
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* for a single query term.
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*/
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var clause = query.clauses[i],
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terms = null,
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clauseMatches = lunr.Set.empty
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if (clause.usePipeline) {
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terms = this.pipeline.runString(clause.term, {
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fields: clause.fields
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})
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} else {
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terms = [clause.term]
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}
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for (var m = 0; m < terms.length; m++) {
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var term = terms[m]
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/*
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* Each term returned from the pipeline needs to use the same query
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* clause object, e.g. the same boost and or edit distance. The
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* simplest way to do this is to re-use the clause object but mutate
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* its term property.
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*/
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clause.term = term
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/*
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* From the term in the clause we create a token set which will then
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* be used to intersect the indexes token set to get a list of terms
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* to lookup in the inverted index
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*/
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var termTokenSet = lunr.TokenSet.fromClause(clause),
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expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()
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/*
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* If a term marked as required does not exist in the tokenSet it is
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* impossible for the search to return any matches. We set all the field
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* scoped required matches set to empty and stop examining any further
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* clauses.
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*/
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if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {
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for (var k = 0; k < clause.fields.length; k++) {
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var field = clause.fields[k]
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requiredMatches[field] = lunr.Set.empty
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}
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break
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}
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for (var j = 0; j < expandedTerms.length; j++) {
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/*
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* For each term get the posting and termIndex, this is required for
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* building the query vector.
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*/
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var expandedTerm = expandedTerms[j],
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posting = this.invertedIndex[expandedTerm],
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termIndex = posting._index
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for (var k = 0; k < clause.fields.length; k++) {
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/*
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* For each field that this query term is scoped by (by default
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* all fields are in scope) we need to get all the document refs
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* that have this term in that field.
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*
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* The posting is the entry in the invertedIndex for the matching
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* term from above.
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*/
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var field = clause.fields[k],
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fieldPosting = posting[field],
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matchingDocumentRefs = Object.keys(fieldPosting),
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termField = expandedTerm + "/" + field,
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matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)
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/*
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* if the presence of this term is required ensure that the matching
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* documents are added to the set of required matches for this clause.
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*
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*/
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if (clause.presence == lunr.Query.presence.REQUIRED) {
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clauseMatches = clauseMatches.union(matchingDocumentsSet)
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if (requiredMatches[field] === undefined) {
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requiredMatches[field] = lunr.Set.complete
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}
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}
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/*
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* if the presence of this term is prohibited ensure that the matching
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* documents are added to the set of prohibited matches for this field,
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* creating that set if it does not yet exist.
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*/
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if (clause.presence == lunr.Query.presence.PROHIBITED) {
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if (prohibitedMatches[field] === undefined) {
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prohibitedMatches[field] = lunr.Set.empty
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}
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prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)
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/*
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* Prohibited matches should not be part of the query vector used for
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* similarity scoring and no metadata should be extracted so we continue
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* to the next field
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*/
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continue
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}
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/*
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* The query field vector is populated using the termIndex found for
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* the term and a unit value with the appropriate boost applied.
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* Using upsert because there could already be an entry in the vector
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* for the term we are working with. In that case we just add the scores
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* together.
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*/
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queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })
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/**
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* If we've already seen this term, field combo then we've already collected
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* the matching documents and metadata, no need to go through all that again
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*/
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if (termFieldCache[termField]) {
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continue
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}
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for (var l = 0; l < matchingDocumentRefs.length; l++) {
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/*
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* All metadata for this term/field/document triple
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* are then extracted and collected into an instance
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* of lunr.MatchData ready to be returned in the query
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* results
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*/
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var matchingDocumentRef = matchingDocumentRefs[l],
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matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),
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metadata = fieldPosting[matchingDocumentRef],
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fieldMatch
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if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {
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matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)
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} else {
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fieldMatch.add(expandedTerm, field, metadata)
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}
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}
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termFieldCache[termField] = true
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}
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}
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}
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/**
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* If the presence was required we need to update the requiredMatches field sets.
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* We do this after all fields for the term have collected their matches because
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* the clause terms presence is required in _any_ of the fields not _all_ of the
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* fields.
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*/
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if (clause.presence === lunr.Query.presence.REQUIRED) {
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for (var k = 0; k < clause.fields.length; k++) {
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var field = clause.fields[k]
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requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)
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}
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}
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}
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/**
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* Need to combine the field scoped required and prohibited
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* matching documents into a global set of required and prohibited
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* matches
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*/
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var allRequiredMatches = lunr.Set.complete,
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allProhibitedMatches = lunr.Set.empty
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for (var i = 0; i < this.fields.length; i++) {
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var field = this.fields[i]
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if (requiredMatches[field]) {
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allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])
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}
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if (prohibitedMatches[field]) {
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allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])
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}
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}
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var matchingFieldRefs = Object.keys(matchingFields),
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results = [],
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matches = Object.create(null)
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/*
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* If the query is negated (contains only prohibited terms)
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* we need to get _all_ fieldRefs currently existing in the
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* index. This is only done when we know that the query is
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* entirely prohibited terms to avoid any cost of getting all
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* fieldRefs unnecessarily.
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*
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* Additionally, blank MatchData must be created to correctly
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* populate the results.
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*/
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if (query.isNegated()) {
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matchingFieldRefs = Object.keys(this.fieldVectors)
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for (var i = 0; i < matchingFieldRefs.length; i++) {
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var matchingFieldRef = matchingFieldRefs[i]
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var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)
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matchingFields[matchingFieldRef] = new lunr.MatchData
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}
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}
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for (var i = 0; i < matchingFieldRefs.length; i++) {
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/*
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* Currently we have document fields that match the query, but we
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* need to return documents. The matchData and scores are combined
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* from multiple fields belonging to the same document.
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*
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* Scores are calculated by field, using the query vectors created
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* above, and combined into a final document score using addition.
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*/
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var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),
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docRef = fieldRef.docRef
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if (!allRequiredMatches.contains(docRef)) {
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continue
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}
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if (allProhibitedMatches.contains(docRef)) {
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continue
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}
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var fieldVector = this.fieldVectors[fieldRef],
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score = queryVectors[fieldRef.fieldName].similarity(fieldVector),
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docMatch
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if ((docMatch = matches[docRef]) !== undefined) {
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docMatch.score += score
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docMatch.matchData.combine(matchingFields[fieldRef])
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} else {
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var match = {
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ref: docRef,
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score: score,
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matchData: matchingFields[fieldRef]
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}
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matches[docRef] = match
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results.push(match)
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}
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}
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/*
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* Sort the results objects by score, highest first.
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*/
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return results.sort(function (a, b) {
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return b.score - a.score
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})
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}
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/**
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* Prepares the index for JSON serialization.
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*
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* The schema for this JSON blob will be described in a
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* separate JSON schema file.
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*
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* @returns {Object}
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*/
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lunr.Index.prototype.toJSON = function () {
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var invertedIndex = Object.keys(this.invertedIndex)
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.sort()
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.map(function (term) {
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return [term, this.invertedIndex[term]]
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}, this)
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var fieldVectors = Object.keys(this.fieldVectors)
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.map(function (ref) {
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return [ref, this.fieldVectors[ref].toJSON()]
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}, this)
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return {
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version: lunr.version,
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fields: this.fields,
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fieldVectors: fieldVectors,
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invertedIndex: invertedIndex,
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pipeline: this.pipeline.toJSON()
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}
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}
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/**
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* Loads a previously serialized lunr.Index
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*
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* @param {Object} serializedIndex - A previously serialized lunr.Index
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* @returns {lunr.Index}
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*/
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lunr.Index.load = function (serializedIndex) {
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var attrs = {},
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fieldVectors = {},
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serializedVectors = serializedIndex.fieldVectors,
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invertedIndex = Object.create(null),
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serializedInvertedIndex = serializedIndex.invertedIndex,
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tokenSetBuilder = new lunr.TokenSet.Builder,
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pipeline = lunr.Pipeline.load(serializedIndex.pipeline)
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if (serializedIndex.version != lunr.version) {
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lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'")
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}
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for (var i = 0; i < serializedVectors.length; i++) {
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var tuple = serializedVectors[i],
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ref = tuple[0],
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elements = tuple[1]
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fieldVectors[ref] = new lunr.Vector(elements)
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}
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for (var i = 0; i < serializedInvertedIndex.length; i++) {
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var tuple = serializedInvertedIndex[i],
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term = tuple[0],
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posting = tuple[1]
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tokenSetBuilder.insert(term)
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invertedIndex[term] = posting
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}
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tokenSetBuilder.finish()
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attrs.fields = serializedIndex.fields
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attrs.fieldVectors = fieldVectors
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attrs.invertedIndex = invertedIndex
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attrs.tokenSet = tokenSetBuilder.root
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attrs.pipeline = pipeline
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return new lunr.Index(attrs)
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}
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