hive/mcp-server/node_modules/lunr/lib/builder.js

/*!
 * lunr.Builder
 * Copyright (C) @YEAR Oliver Nightingale
 */

/**
 * lunr.Builder performs indexing on a set of documents and
 * returns instances of lunr.Index ready for querying.
 *
 * All configuration of the index is done via the builder, the
 * fields to index, the document reference, the text processing
 * pipeline and document scoring parameters are all set on the
 * builder before indexing.
 *
 * @constructor
 * @property {string} _ref - Internal reference to the document reference field.
 * @property {string[]} _fields - Internal reference to the document fields to index.
 * @property {object} invertedIndex - The inverted index maps terms to document fields.
 * @property {object} documentTermFrequencies - Keeps track of document term frequencies.
 * @property {object} documentLengths - Keeps track of the length of documents added to the index.
 * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.
 * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.
 * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.
 * @property {number} documentCount - Keeps track of the total number of documents indexed.
 * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.
 * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.
 * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.
 * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.
 */
lunr.Builder = function () {
  this._ref = "id"
  this._fields = Object.create(null)
  this._documents = Object.create(null)
  this.invertedIndex = Object.create(null)
  this.fieldTermFrequencies = {}
  this.fieldLengths = {}
  this.tokenizer = lunr.tokenizer
  this.pipeline = new lunr.Pipeline
  this.searchPipeline = new lunr.Pipeline
  this.documentCount = 0
  this._b = 0.75
  this._k1 = 1.2
  this.termIndex = 0
  this.metadataWhitelist = []
}

/**
 * Sets the document field used as the document reference. Every document must have this field.
 * The type of this field in the document should be a string, if it is not a string it will be
 * coerced into a string by calling toString.
 *
 * The default ref is 'id'.
 *
 * The ref should _not_ be changed during indexing, it should be set before any documents are
 * added to the index. Changing it during indexing can lead to inconsistent results.
 *
 * @param {string} ref - The name of the reference field in the document.
 */
lunr.Builder.prototype.ref = function (ref) {
  this._ref = ref
}

/**
 * A function that is used to extract a field from a document.
 *
 * Lunr expects a field to be at the top level of a document, if however the field
 * is deeply nested within a document an extractor function can be used to extract
 * the right field for indexing.
 *
 * @callback fieldExtractor
 * @param {object} doc - The document being added to the index.
 * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.
 * @example <caption>Extracting a nested field</caption>
 * function (doc) { return doc.nested.field }
 */

/**
 * Adds a field to the list of document fields that will be indexed. Every document being
 * indexed should have this field. Null values for this field in indexed documents will
 * not cause errors but will limit the chance of that document being retrieved by searches.
 *
 * All fields should be added before adding documents to the index. Adding fields after
 * a document has been indexed will have no effect on already indexed documents.
 *
 * Fields can be boosted at build time. This allows terms within that field to have more
 * importance when ranking search results. Use a field boost to specify that matches within
 * one field are more important than other fields.
 *
 * @param {string} fieldName - The name of a field to index in all documents.
 * @param {object} attributes - Optional attributes associated with this field.
 * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.
 * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.
 * @throws {RangeError} fieldName cannot contain unsupported characters '/'
 */
lunr.Builder.prototype.field = function (fieldName, attributes) {
  if (/\//.test(fieldName)) {
    throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'")
  }

  this._fields[fieldName] = attributes || {}
}

/**
 * A parameter to tune the amount of field length normalisation that is applied when
 * calculating relevance scores. A value of 0 will completely disable any normalisation
 * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b
 * will be clamped to the range 0 - 1.
 *
 * @param {number} number - The value to set for this tuning parameter.
 */
lunr.Builder.prototype.b = function (number) {
  if (number < 0) {
    this._b = 0
  } else if (number > 1) {
    this._b = 1
  } else {
    this._b = number
  }
}

/**
 * A parameter that controls the speed at which a rise in term frequency results in term
 * frequency saturation. The default value is 1.2. Setting this to a higher value will give
 * slower saturation levels, a lower value will result in quicker saturation.
 *
 * @param {number} number - The value to set for this tuning parameter.
 */
lunr.Builder.prototype.k1 = function (number) {
  this._k1 = number
}

/**
 * Adds a document to the index.
 *
 * Before adding fields to the index the index should have been fully setup, with the document
 * ref and all fields to index already having been specified.
 *
 * The document must have a field name as specified by the ref (by default this is 'id') and
 * it should have all fields defined for indexing, though null or undefined values will not
 * cause errors.
 *
 * Entire documents can be boosted at build time. Applying a boost to a document indicates that
 * this document should rank higher in search results than other documents.
 *
 * @param {object} doc - The document to add to the index.
 * @param {object} attributes - Optional attributes associated with this document.
 * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.
 */
lunr.Builder.prototype.add = function (doc, attributes) {
  var docRef = doc[this._ref],
      fields = Object.keys(this._fields)

  this._documents[docRef] = attributes || {}
  this.documentCount += 1

  for (var i = 0; i < fields.length; i++) {
    var fieldName = fields[i],
        extractor = this._fields[fieldName].extractor,
        field = extractor ? extractor(doc) : doc[fieldName],
        tokens = this.tokenizer(field, {
          fields: [fieldName]
        }),
        terms = this.pipeline.run(tokens),
        fieldRef = new lunr.FieldRef (docRef, fieldName),
        fieldTerms = Object.create(null)

    this.fieldTermFrequencies[fieldRef] = fieldTerms
    this.fieldLengths[fieldRef] = 0

    // store the length of this field for this document
    this.fieldLengths[fieldRef] += terms.length

    // calculate term frequencies for this field
    for (var j = 0; j < terms.length; j++) {
      var term = terms[j]

      if (fieldTerms[term] == undefined) {
        fieldTerms[term] = 0
      }

      fieldTerms[term] += 1

      // add to inverted index
      // create an initial posting if one doesn't exist
      if (this.invertedIndex[term] == undefined) {
        var posting = Object.create(null)
        posting["_index"] = this.termIndex
        this.termIndex += 1

        for (var k = 0; k < fields.length; k++) {
          posting[fields[k]] = Object.create(null)
        }

        this.invertedIndex[term] = posting
      }

      // add an entry for this term/fieldName/docRef to the invertedIndex
      if (this.invertedIndex[term][fieldName][docRef] == undefined) {
        this.invertedIndex[term][fieldName][docRef] = Object.create(null)
      }

      // store all whitelisted metadata about this token in the
      // inverted index
      for (var l = 0; l < this.metadataWhitelist.length; l++) {
        var metadataKey = this.metadataWhitelist[l],
            metadata = term.metadata[metadataKey]

        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {
          this.invertedIndex[term][fieldName][docRef][metadataKey] = []
        }

        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)
      }
    }

  }
}

/**
 * Calculates the average document length for this index
 *
 * @private
 */
lunr.Builder.prototype.calculateAverageFieldLengths = function () {

  var fieldRefs = Object.keys(this.fieldLengths),
      numberOfFields = fieldRefs.length,
      accumulator = {},
      documentsWithField = {}

  for (var i = 0; i < numberOfFields; i++) {
    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
        field = fieldRef.fieldName

    documentsWithField[field] || (documentsWithField[field] = 0)
    documentsWithField[field] += 1

    accumulator[field] || (accumulator[field] = 0)
    accumulator[field] += this.fieldLengths[fieldRef]
  }

  var fields = Object.keys(this._fields)

  for (var i = 0; i < fields.length; i++) {
    var fieldName = fields[i]
    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]
  }

  this.averageFieldLength = accumulator
}

/**
 * Builds a vector space model of every document using lunr.Vector
 *
 * @private
 */
lunr.Builder.prototype.createFieldVectors = function () {
  var fieldVectors = {},
      fieldRefs = Object.keys(this.fieldTermFrequencies),
      fieldRefsLength = fieldRefs.length,
      termIdfCache = Object.create(null)

  for (var i = 0; i < fieldRefsLength; i++) {
    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
        fieldName = fieldRef.fieldName,
        fieldLength = this.fieldLengths[fieldRef],
        fieldVector = new lunr.Vector,
        termFrequencies = this.fieldTermFrequencies[fieldRef],
        terms = Object.keys(termFrequencies),
        termsLength = terms.length


    var fieldBoost = this._fields[fieldName].boost || 1,
        docBoost = this._documents[fieldRef.docRef].boost || 1

    for (var j = 0; j < termsLength; j++) {
      var term = terms[j],
          tf = termFrequencies[term],
          termIndex = this.invertedIndex[term]._index,
          idf, score, scoreWithPrecision

      if (termIdfCache[term] === undefined) {
        idf = lunr.idf(this.invertedIndex[term], this.documentCount)
        termIdfCache[term] = idf
      } else {
        idf = termIdfCache[term]
      }

      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)
      score *= fieldBoost
      score *= docBoost
      scoreWithPrecision = Math.round(score * 1000) / 1000
      // Converts 1.23456789 to 1.234.
      // Reducing the precision so that the vectors take up less
      // space when serialised. Doing it now so that they behave
      // the same before and after serialisation. Also, this is
      // the fastest approach to reducing a number's precision in
      // JavaScript.

      fieldVector.insert(termIndex, scoreWithPrecision)
    }

    fieldVectors[fieldRef] = fieldVector
  }

  this.fieldVectors = fieldVectors
}

/**
 * Creates a token set of all tokens in the index using lunr.TokenSet
 *
 * @private
 */
lunr.Builder.prototype.createTokenSet = function () {
  this.tokenSet = lunr.TokenSet.fromArray(
    Object.keys(this.invertedIndex).sort()
  )
}

/**
 * Builds the index, creating an instance of lunr.Index.
 *
 * This completes the indexing process and should only be called
 * once all documents have been added to the index.
 *
 * @returns {lunr.Index}
 */
lunr.Builder.prototype.build = function () {
  this.calculateAverageFieldLengths()
  this.createFieldVectors()
  this.createTokenSet()

  return new lunr.Index({
    invertedIndex: this.invertedIndex,
    fieldVectors: this.fieldVectors,
    tokenSet: this.tokenSet,
    fields: Object.keys(this._fields),
    pipeline: this.searchPipeline
  })
}

/**
 * Applies a plugin to the index builder.
 *
 * A plugin is a function that is called with the index builder as its context.
 * Plugins can be used to customise or extend the behaviour of the index
 * in some way. A plugin is just a function, that encapsulated the custom
 * behaviour that should be applied when building the index.
 *
 * The plugin function will be called with the index builder as its argument, additional
 * arguments can also be passed when calling use. The function will be called
 * with the index builder as its context.
 *
 * @param {Function} plugin The plugin to apply.
 */
lunr.Builder.prototype.use = function (fn) {
  var args = Array.prototype.slice.call(arguments, 1)
  args.unshift(this)
  fn.apply(this, args)
}