diff --git a/404.html b/404.html index b072dcdc..3700e0ce 100644 --- a/404.html +++ b/404.html @@ -12,7 +12,7 @@ - + @@ -920,7 +920,7 @@

404 - Not found

- + diff --git a/assets/javascripts/workers/search.07f07601.min.js b/assets/javascripts/workers/search.6ce7567c.min.js similarity index 94% rename from assets/javascripts/workers/search.07f07601.min.js rename to assets/javascripts/workers/search.6ce7567c.min.js index f3dbf56b..8e7e5500 100644 --- a/assets/javascripts/workers/search.07f07601.min.js +++ b/assets/javascripts/workers/search.6ce7567c.min.js @@ -1,4 +1,4 @@ -"use strict";(()=>{var xe=Object.create;var U=Object.defineProperty,ve=Object.defineProperties,Se=Object.getOwnPropertyDescriptor,Te=Object.getOwnPropertyDescriptors,Qe=Object.getOwnPropertyNames,Y=Object.getOwnPropertySymbols,Ee=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,be=Object.prototype.propertyIsEnumerable;var Z=Math.pow,J=(t,e,r)=>e in t?U(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,A=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(Y)for(var r of Y(e))be.call(e,r)&&J(t,r,e[r]);return t},G=(t,e)=>ve(t,Te(e));var 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a/assets/javascripts/workers/search.07f07601.min.js.map b/assets/javascripts/workers/search.6ce7567c.min.js.map similarity index 78% rename from assets/javascripts/workers/search.07f07601.min.js.map rename to assets/javascripts/workers/search.6ce7567c.min.js.map index 7fdd4d08..e7c69d21 100644 --- a/assets/javascripts/workers/search.07f07601.min.js.map +++ b/assets/javascripts/workers/search.6ce7567c.min.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["node_modules/lunr/lunr.js", "src/templates/assets/javascripts/integrations/search/worker/main/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/polyfills/index.ts", "src/templates/assets/javascripts/integrations/search/config/index.ts", "src/templates/assets/javascripts/integrations/search/internal/_/index.ts", "src/templates/assets/javascripts/integrations/search/internal/extract/index.ts", "src/templates/assets/javascripts/integrations/search/internal/highlight/index.ts", "src/templates/assets/javascripts/integrations/search/internal/tokenize/index.ts", "src/templates/assets/javascripts/integrations/search/query/transform/index.ts", "src/templates/assets/javascripts/integrations/search/query/_/index.ts", "src/templates/assets/javascripts/integrations/search/query/segment/index.ts", "src/templates/assets/javascripts/integrations/search/_/index.ts"], - "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @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.\n * @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.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport { getElement } from \"~/browser/element/_\"\nimport \"~/polyfills\"\n\nimport { Search } from \"../../_\"\nimport { SearchConfig } from \"../../config\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for `iframe-worker` shim\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous shim implementation. The cool\n * thing is that awaiting a non-Promise will convert it into a Promise, so\n * extending the type definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n *\n * @param urls - Scripts to load\n *\n * @returns Promise resolving with no result\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages which are defined as part of the search configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = getElement(\"script[src]\")\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n const query = message.data\n try {\n return {\n type: SearchMessageType.RESULT,\n data: index.search(query)\n }\n\n /* Return empty result in case of error */\n } catch (err) {\n console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n console.warn(err)\n return {\n type: SearchMessageType.RESULT,\n data: { items: [] }\n }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* Expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return (\n document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n document.activeElement as HTMLElement ??\n undefined\n )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node === \"string\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search configuration\n */\nexport interface SearchConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n pipeline: SearchPipelineFn[] /* Search pipeline */\n}\n\n/**\n * Search document\n */\nexport interface SearchDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n boost?: number /* Document boost */\n parent?: SearchDocument /* Document parent */\n}\n\n/**\n * Search options\n */\nexport interface SearchOptions {\n suggest: boolean /* Search suggestions */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport interface SearchIndex {\n config: SearchConfig /* Search configuration */\n docs: SearchDocument[] /* Search documents */\n options: SearchOptions /* Search options */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search pipeline function\n */\ntype SearchPipelineFn =\n | \"trimmer\" /* Trimmer */\n | \"stopWordFilter\" /* Stop word filter */\n | \"stemmer\" /* Stemmer */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document map\n *\n * This function creates a mapping of URLs (including anchors) to the actual\n * articles and sections. It relies on the invariant that the search index is\n * ordered with the main article appearing before all sections with anchors.\n * If this is not the case, the logic music be changed.\n *\n * @param docs - Search documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchDocument[]\n): Map {\n const map = new Map()\n for (const doc of docs) {\n const [path] = doc.location.split(\"#\")\n\n /* Add document article */\n const article = map.get(path)\n if (typeof article === \"undefined\") {\n map.set(path, doc)\n\n /* Add document section */\n } else {\n map.set(doc.location, doc)\n doc.parent = article\n }\n }\n\n /* Return search document map */\n return map\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string using the given separator\n *\n * @param input - Input value\n * @param separator - Separator\n * @param fn - Visitor function\n */\nexport function split(\n input: string, separator: RegExp, fn: VisitorFn\n): void {\n separator = new RegExp(separator, \"g\")\n\n /* Split string using separator */\n let match: RegExpExecArray | null\n let index = 0\n do {\n match = separator.exec(input)\n\n /* Emit non-empty range */\n const until = match?.index ?? input.length\n if (index < until)\n fn(index, until)\n\n /* Update last index */\n if (match) {\n const [term] = match\n index = match.index + term.length\n\n /* Support zero-length lookaheads */\n if (term.length === 0)\n separator.lastIndex = match.index + 1\n }\n } while (match)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Extraction type\n *\n * This type defines the possible values that are encoded into the first two\n * bits of a section that is part of the blocks of a tokenization table. There\n * are three types of interest: HTML opening and closing tags, as well as the\n * actual text content we need to extract for indexing.\n */\nexport const enum Extract {\n TAG_OPEN = 0, /* HTML opening tag */\n TEXT = 1, /* Text content */\n TAG_CLOSE = 2 /* HTML closing tag */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param block - Block index\n * @param type - Extraction type\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n block: number, type: Extract, start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string into markup and text sections\n *\n * This function scans a string and divides it up into sections of markup and\n * text. For each section, it invokes the given visitor function with the block\n * index, extraction type, as well as start and end offsets. Using a visitor\n * function (= streaming data) is ideal for minimizing pressure on the GC.\n *\n * @param input - Input value\n * @param fn - Visitor function\n */\nexport function extract(\n input: string, fn: VisitorFn\n): void {\n\n let block = 0 /* Current block */\n let start = 0 /* Current start offset */\n let end = 0 /* Current end offset */\n\n /* Split string into sections */\n for (let stack = 0; end < input.length; end++) {\n\n /* Opening tag after non-empty section */\n if (input.charAt(end) === \"<\" && end > start) {\n fn(block, Extract.TEXT, start, start = end)\n\n /* Closing tag */\n } else if (input.charAt(end) === \">\") {\n if (input.charAt(start + 1) === \"/\") {\n if (--stack === 0)\n fn(block++, Extract.TAG_CLOSE, start, end + 1)\n\n /* Tag is not self-closing */\n } else if (input.charAt(end - 1) !== \"/\") {\n if (stack++ === 0)\n fn(block, Extract.TAG_OPEN, start, end + 1)\n }\n\n /* New section */\n start = end + 1\n }\n }\n\n /* Add trailing section */\n if (end > start)\n fn(block, Extract.TEXT, start, end)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Position table\n */\nexport type PositionTable = number[][]\n\n/**\n * Position\n */\nexport type Position = number\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Highlight all occurrences in a string\n *\n * This function receives a field's value (e.g. like `title` or `text`), it's\n * position table that was generated during indexing, and the positions found\n * when executing the query. It then highlights all occurrences, and returns\n * their concatenation. In case of multiple blocks, two are returned.\n *\n * @param input - Input value\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string value\n */\nexport function highlight(\n input: string, table: PositionTable, positions: Position[], full = false\n): string {\n return highlightAll([input], table, positions, full).pop()!\n}\n\n/**\n * Highlight all occurrences in a set of strings\n *\n * @param inputs - Input values\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n inputs: string[], table: PositionTable, positions: Position[], full = false\n): string[] {\n\n /* Map blocks to input values */\n const mapping = [0]\n for (let t = 1; t < table.length; t++) {\n const prev = table[t - 1]\n const next = table[t]\n\n /* Check if table points to new block */\n const p = prev[prev.length - 1] >>> 2 & 0x3FF\n const q = next[0] >>> 12\n\n /* Add block to mapping */\n mapping.push(+(p > q) + mapping[mapping.length - 1])\n }\n\n /* Highlight strings one after another */\n return inputs.map((input, i) => {\n let cursor = 0\n\n /* Map occurrences to blocks */\n const blocks = new Map()\n for (const p of positions.sort((a, b) => a - b)) {\n const index = p & 0xFFFFF\n const block = p >>> 20\n if (mapping[block] !== i)\n continue\n\n /* Ensure presence of block group */\n let group = blocks.get(block)\n if (typeof group === \"undefined\")\n blocks.set(block, group = [])\n\n /* Add index to group */\n group.push(index)\n }\n\n /* Just return string, if no occurrences */\n if (blocks.size === 0)\n return input\n\n /* Compute slices */\n const slices: string[] = []\n for (const [block, indexes] of blocks) {\n const t = table[block]\n\n /* Extract positions and length */\n const start = t[0] >>> 12\n const end = t[t.length - 1] >>> 12\n const length = t[t.length - 1] >>> 2 & 0x3FF\n\n /* Add prefix, if full results are desired */\n if (full && start > cursor)\n slices.push(input.slice(cursor, start))\n\n /* Extract and highlight slice */\n let slice = input.slice(start, end + length)\n for (const j of indexes.sort((a, b) => b - a)) {\n\n /* Retrieve offset and length of match */\n const p = (t[j] >>> 12) - start\n const q = (t[j] >>> 2 & 0x3FF) + p\n\n /* Wrap occurrence */\n slice = [\n slice.slice(0, p),\n \"\",\n slice.slice(p, q),\n \"\",\n slice.slice(q)\n ].join(\"\")\n }\n\n /* Update cursor */\n cursor = end + length\n\n /* Append slice and abort if we have two */\n if (slices.push(slice) === 2)\n break\n }\n\n /* Add suffix, if full results are desired */\n if (full && cursor < input.length)\n slices.push(input.slice(cursor))\n\n /* Return highlighted slices */\n return slices.join(\"\")\n })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../_\"\nimport {\n Extract,\n extract\n} from \"../extract\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string or set of strings into tokens\n *\n * This tokenizer supersedes the default tokenizer that is provided by Lunr.js,\n * as it is aware of HTML tags and allows for multi-character splitting.\n *\n * It takes the given inputs, splits each of them into markup and text sections,\n * tokenizes and segments (if necessary) each of them, and then indexes them in\n * a table by using a compact bit representation. Bitwise techniques are used\n * to write and read from the table during indexing and querying.\n *\n * @see https://bit.ly/3W3Xw4J - Search: better, faster, smaller\n *\n * @param input - Input value(s)\n *\n * @returns Tokens\n */\nexport function tokenize(\n input?: string | string[]\n): lunr.Token[] {\n const tokens: lunr.Token[] = []\n if (typeof input === \"undefined\")\n return tokens\n\n /* Tokenize strings one after another */\n const inputs = Array.isArray(input) ? input : [input]\n for (let i = 0; i < inputs.length; i++) {\n const table = lunr.tokenizer.table\n const total = table.length\n\n /* Split string into sections and tokenize content blocks */\n extract(inputs[i], (block, type, start, end) => {\n table[block += total] ||= []\n switch (type) {\n\n /* Handle markup */\n case Extract.TAG_OPEN:\n case Extract.TAG_CLOSE:\n table[block].push(\n start << 12 |\n end - start << 2 |\n type\n )\n break\n\n /* Handle text content */\n case Extract.TEXT:\n const section = inputs[i].slice(start, end)\n split(section, lunr.tokenizer.separator, (index, until) => {\n\n /**\n * Apply segmenter after tokenization. Note that the segmenter will\n * also split words at word boundaries, which is not what we want,\n * so we need to check if we can somehow mitigate this behavior.\n */\n if (typeof lunr.segmenter !== \"undefined\") {\n const subsection = section.slice(index, until)\n if (/^[MHIK]$/.test(lunr.segmenter.ctype_(subsection))) {\n const segments = lunr.segmenter.segment(subsection)\n for (let s = 0, l = 0; s < segments.length; s++) {\n\n /* Add block to section */\n table[block] ||= []\n table[block].push(\n start + index + l << 12 |\n segments[s].length << 2 |\n type\n )\n\n /* Add token with position */\n tokens.push(new lunr.Token(\n segments[s].toLowerCase(), {\n position: block << 20 | table[block].length - 1\n }\n ))\n\n /* Keep track of length */\n l += segments[s].length\n }\n return\n }\n }\n\n /* Add block to section */\n table[block].push(\n start + index << 12 |\n until - index << 2 |\n type\n )\n\n /* Add token with position */\n tokens.push(new lunr.Token(\n section.slice(index, until).toLowerCase(), {\n position: block << 20 | table[block].length - 1\n }\n ))\n })\n }\n })\n }\n\n /* Return tokens */\n return tokens\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param value - String value\n *\n * @returns String term(s)\n */\ntype VisitorFn = (\n value: string\n) => string | string[]\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Trim excess whitespace from left and right.\n *\n * 2. Search for parts in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all parts, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect parts enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the parts to an `AND` query.\n *\n * 3. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 4. Split the query string at whitespace, then pass each part to the visitor\n * function for tokenization, and append a wildcard to every resulting term\n * that is not explicitly marked with a `+`, `-`, `~` or `^` modifier, since\n * it ensures consistent and stable ranking when multiple terms are entered.\n * Also, if a fuzzy or boost modifier are given, but no numeric value has\n * been entered, default to 1 to not induce a query error.\n *\n * @param query - Query value\n * @param fn - Visitor function\n *\n * @returns Transformed query value\n */\nexport function transform(\n query: string, fn: VisitorFn = term => term\n): string {\n return query\n\n /* => 1 */\n .trim()\n\n /* => 2 */\n .split(/\"([^\"]+)\"/g)\n .map((parts, index) => index & 1\n ? parts.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : parts\n )\n .join(\"\")\n\n /* => 3 */\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\")\n\n /* => 4 */\n .split(/\\s+/g)\n .reduce((prev, term) => {\n const next = fn(term)\n return [...prev, ...Array.isArray(next) ? next : [next]]\n }, [] as string[])\n .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../../internal\"\nimport { transform } from \"../transform\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Transform search query\n *\n * This function lexes the given search query and applies the transformation\n * function to each term, preserving markup like `+` and `-` modifiers.\n *\n * @param query - Search query\n *\n * @returns Search query\n */\nexport function transformSearchQuery(\n query: string\n): string {\n\n /* Split query terms with tokenizer */\n return transform(query, part => {\n const terms: string[] = []\n\n /* Initialize lexer and analyze part */\n const lexer = new lunr.QueryLexer(part)\n lexer.run()\n\n /* Extract and tokenize term from lexeme */\n for (const { type, str: term, start, end } of lexer.lexemes)\n switch (type) {\n\n /* Hack: remove colon - see https://bit.ly/3wD3T3I */\n case \"FIELD\":\n if (![\"title\", \"text\", \"tags\"].includes(term))\n part = [\n part.slice(0, end),\n \" \",\n part.slice(end + 1)\n ].join(\"\")\n break\n\n /* Tokenize term */\n case \"TERM\":\n split(term, lunr.tokenizer.separator, (...range) => {\n terms.push([\n part.slice(0, start),\n term.slice(...range),\n part.slice(end)\n ].join(\"\"))\n })\n }\n\n /* Return terms */\n return terms\n })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * Lunr.js itself has a bug where it doesn't detect or remove wildcards for\n * query clauses, so we must do this here.\n *\n * @see https://bit.ly/3DpTGtz - GitHub issue\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new lunr.Query([\"title\", \"text\", \"tags\"])\n const parser = new lunr.QueryParser(value, query)\n\n /* Parse Search query */\n parser.parse()\n for (const clause of query.clauses) {\n clause.usePipeline = true\n\n /* Handle leading wildcard */\n if (clause.term.startsWith(\"*\")) {\n clause.wildcard = lunr.Query.wildcard.LEADING\n clause.term = clause.term.slice(1)\n }\n\n /* Handle trailing wildcard */\n if (clause.term.endsWith(\"*\")) {\n clause.wildcard = lunr.Query.wildcard.TRAILING\n clause.term = clause.term.slice(0, -1)\n }\n }\n\n /* Return query clauses */\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Segment a search query using the inverted index\n *\n * This function implements a clever approach to text segmentation for Asian\n * languages, as it used the information already available in the search index.\n * The idea is to greedily segment the search query based on the tokens that are\n * already part of the index, as described in the linked issue.\n *\n * @see https://bit.ly/3lwjrk7 - GitHub issue\n *\n * @param query - Query value\n * @param index - Inverted index\n *\n * @returns Segmented query value\n */\nexport function segment(\n query: string, index: object\n): Iterable {\n const segments = new Set()\n\n /* Segment search query */\n const wordcuts = new Uint16Array(query.length)\n for (let i = 0; i < query.length; i++)\n for (let j = i + 1; j < query.length; j++) {\n const value = query.slice(i, j)\n if (value in index)\n wordcuts[i] = j - i\n }\n\n /* Compute longest matches with minimum overlap */\n const stack = [0]\n for (let s = stack.length; s > 0;) {\n const p = stack[--s]\n for (let q = 1; q < wordcuts[p]; q++)\n if (wordcuts[p + q] > wordcuts[p] - q) {\n segments.add(query.slice(p, p + q))\n stack[s++] = p + q\n }\n\n /* Continue at end of query string */\n const q = p + wordcuts[p]\n if (wordcuts[q] && q < query.length - 1)\n stack[s++] = q\n\n /* Add current segment */\n segments.add(query.slice(p, q))\n }\n\n // @todo fix this case in the code block above, this is a hotfix\n if (segments.has(\"\"))\n return new Set([query])\n\n /* Return segmented query value */\n return segments\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchIndex,\n SearchOptions,\n setupSearchDocumentMap\n} from \"../config\"\nimport {\n Position,\n PositionTable,\n highlight,\n highlightAll,\n tokenize\n} from \"../internal\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery,\n segment,\n transformSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search item\n */\nexport interface SearchItem\n extends SearchDocument\n{\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchItem[][] /* Search items */\n suggest?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create field extractor factory\n *\n * @param table - Position table map\n *\n * @returns Extractor factory\n */\nfunction extractor(table: Map) {\n return (name: keyof SearchDocument) => {\n return (doc: SearchDocument) => {\n if (typeof doc[name] === \"undefined\")\n return undefined\n\n /* Compute identifier and initialize table */\n const id = [doc.location, name].join(\":\")\n table.set(id, lunr.tokenizer.table = [])\n\n /* Return field value */\n return doc[name]\n }\n }\n}\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document map\n */\n protected map: Map\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Internal position table map\n */\n protected table: Map\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n const field = extractor(this.table = new Map())\n\n /* Set up document map and options */\n this.map = setupSearchDocumentMap(docs)\n this.options = options\n\n /* Set up document index */\n this.index = lunr(function () {\n this.metadataWhitelist = [\"position\"]\n this.b(0)\n\n /* Set up (multi-)language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n // @ts-expect-error - namespace indexing not supported\n this.use(lunr[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use(lunr.multiLanguage(...config.lang))\n }\n\n /* Set up custom tokenizer (must be after language setup) */\n this.tokenizer = tokenize as typeof lunr.tokenizer\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Set up custom segmenter, if loaded */\n lunr.segmenter = \"TinySegmenter\" in lunr\n ? new lunr.TinySegmenter()\n : undefined\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], config.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n // @ts-expect-error - namespace indexing not supported\n language === \"en\" ? lunr : lunr[language]\n )))\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n\n /* Set up index reference */\n this.ref(\"location\")\n\n /* Set up index fields */\n this.field(\"title\", { boost: 1e3, extractor: field(\"title\") })\n this.field(\"text\", { boost: 1e0, extractor: field(\"text\") })\n this.field(\"tags\", { boost: 1e6, extractor: field(\"tags\") })\n\n /* Add documents to index */\n for (const doc of docs)\n this.add(doc, { boost: doc.boost })\n })\n }\n\n /**\n * Search for matching documents\n *\n * @param query - Search query\n *\n * @returns Search result\n */\n public search(query: string): SearchResult {\n\n // Experimental Chinese segmentation\n query = query.replace(/\\p{sc=Han}+/gu, value => {\n return [...segment(value, this.index.invertedIndex)]\n .join(\"* \")\n })\n\n // @todo: move segmenter (above) into transformSearchQuery\n query = transformSearchQuery(query)\n if (!query)\n return { items: [] }\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(query)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n let doc = this.map.get(ref)\n if (typeof doc !== \"undefined\") {\n\n /* Shallow copy document */\n doc = { ...doc }\n if (doc.tags)\n doc.tags = [...doc.tags]\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight matches in fields */\n for (const field of this.index.fields) {\n if (typeof doc[field] === \"undefined\")\n continue\n\n /* Collect positions from matches */\n const positions: Position[] = []\n for (const match of Object.values(matchData.metadata))\n if (typeof match[field] !== \"undefined\")\n positions.push(...match[field].position)\n\n /* Skip highlighting, if no positions were collected */\n if (!positions.length)\n continue\n\n /* Load table and determine highlighting method */\n const table = this.table.get([doc.location, field].join(\":\"))!\n const fn = Array.isArray(doc[field])\n ? highlightAll\n : highlight\n\n // @ts-expect-error - stop moaning, TypeScript!\n doc[field] = fn(doc[field], table, positions, field !== \"text\")\n }\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!doc.parent +\n Object.values(terms)\n .filter(t => t).length /\n Object.keys(terms).length\n\n /* Append item */\n item.push({\n ...doc,\n score: score * (1 + boost ** 2),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by article */\n .reduce((items, result) => {\n const doc = this.map.get(result.location)\n if (typeof doc !== \"undefined\") {\n const ref = doc.parent\n ? doc.parent.location\n : doc.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Ensure that every item set has an article */\n for (const [ref, items] of groups)\n if (!items.find(item => item.location === ref)) {\n const doc = this.map.get(ref)!\n items.push({ ...doc, score: 0, terms: {} })\n }\n\n /* Generate search suggestions, if desired */\n let suggest: string[] | undefined\n if (this.options.suggest) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggest = titles.length\n ? 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+ "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @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.\n * @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.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport { getElement } from \"~/browser/element/_\"\nimport \"~/polyfills\"\n\nimport { Search } from \"../../_\"\nimport { SearchConfig } from \"../../config\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for `iframe-worker` shim\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous shim implementation. The cool\n * thing is that awaiting a non-Promise will convert it into a Promise, so\n * extending the type definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n *\n * @param urls - Scripts to load\n *\n * @returns Promise resolving with no result\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages which are defined as part of the search configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = getElement(\"script[src]\")\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n const query = message.data\n try {\n return {\n type: SearchMessageType.RESULT,\n data: index.search(query)\n }\n\n /* Return empty result in case of error */\n } catch (err) {\n console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n console.warn(err)\n return {\n type: SearchMessageType.RESULT,\n data: { items: [] }\n }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* Expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Monkey-patch Lunr.js to mitigate https://t.ly/68TLq */\nlunr.utils.warn = console.warn\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return (\n document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n document.activeElement as HTMLElement ??\n undefined\n )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node === \"string\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search configuration\n */\nexport interface SearchConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n pipeline: SearchPipelineFn[] /* Search pipeline */\n}\n\n/**\n * Search document\n */\nexport interface SearchDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n boost?: number /* Document boost */\n parent?: SearchDocument /* Document parent */\n}\n\n/**\n * Search options\n */\nexport interface SearchOptions {\n suggest: boolean /* Search suggestions */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport interface SearchIndex {\n config: SearchConfig /* Search configuration */\n docs: SearchDocument[] /* Search documents */\n options: SearchOptions /* Search options */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search pipeline function\n */\ntype SearchPipelineFn =\n | \"trimmer\" /* Trimmer */\n | \"stopWordFilter\" /* Stop word filter */\n | \"stemmer\" /* Stemmer */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document map\n *\n * This function creates a mapping of URLs (including anchors) to the actual\n * articles and sections. It relies on the invariant that the search index is\n * ordered with the main article appearing before all sections with anchors.\n * If this is not the case, the logic music be changed.\n *\n * @param docs - Search documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchDocument[]\n): Map {\n const map = new Map()\n for (const doc of docs) {\n const [path] = doc.location.split(\"#\")\n\n /* Add document article */\n const article = map.get(path)\n if (typeof article === \"undefined\") {\n map.set(path, doc)\n\n /* Add document section */\n } else {\n map.set(doc.location, doc)\n doc.parent = article\n }\n }\n\n /* Return search document map */\n return map\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string using the given separator\n *\n * @param input - Input value\n * @param separator - Separator\n * @param fn - Visitor function\n */\nexport function split(\n input: string, separator: RegExp, fn: VisitorFn\n): void {\n separator = new RegExp(separator, \"g\")\n\n /* Split string using separator */\n let match: RegExpExecArray | null\n let index = 0\n do {\n match = separator.exec(input)\n\n /* Emit non-empty range */\n const until = match?.index ?? input.length\n if (index < until)\n fn(index, until)\n\n /* Update last index */\n if (match) {\n const [term] = match\n index = match.index + term.length\n\n /* Support zero-length lookaheads */\n if (term.length === 0)\n separator.lastIndex = match.index + 1\n }\n } while (match)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Extraction type\n *\n * This type defines the possible values that are encoded into the first two\n * bits of a section that is part of the blocks of a tokenization table. There\n * are three types of interest: HTML opening and closing tags, as well as the\n * actual text content we need to extract for indexing.\n */\nexport const enum Extract {\n TAG_OPEN = 0, /* HTML opening tag */\n TEXT = 1, /* Text content */\n TAG_CLOSE = 2 /* HTML closing tag */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param block - Block index\n * @param type - Extraction type\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n block: number, type: Extract, start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string into markup and text sections\n *\n * This function scans a string and divides it up into sections of markup and\n * text. For each section, it invokes the given visitor function with the block\n * index, extraction type, as well as start and end offsets. Using a visitor\n * function (= streaming data) is ideal for minimizing pressure on the GC.\n *\n * @param input - Input value\n * @param fn - Visitor function\n */\nexport function extract(\n input: string, fn: VisitorFn\n): void {\n\n let block = 0 /* Current block */\n let start = 0 /* Current start offset */\n let end = 0 /* Current end offset */\n\n /* Split string into sections */\n for (let stack = 0; end < input.length; end++) {\n\n /* Opening tag after non-empty section */\n if (input.charAt(end) === \"<\" && end > start) {\n fn(block, Extract.TEXT, start, start = end)\n\n /* Closing tag */\n } else if (input.charAt(end) === \">\") {\n if (input.charAt(start + 1) === \"/\") {\n if (--stack === 0)\n fn(block++, Extract.TAG_CLOSE, start, end + 1)\n\n /* Tag is not self-closing */\n } else if (input.charAt(end - 1) !== \"/\") {\n if (stack++ === 0)\n fn(block, Extract.TAG_OPEN, start, end + 1)\n }\n\n /* New section */\n start = end + 1\n }\n }\n\n /* Add trailing section */\n if (end > start)\n fn(block, Extract.TEXT, start, end)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Position table\n */\nexport type PositionTable = number[][]\n\n/**\n * Position\n */\nexport type Position = number\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Highlight all occurrences in a string\n *\n * This function receives a field's value (e.g. like `title` or `text`), it's\n * position table that was generated during indexing, and the positions found\n * when executing the query. It then highlights all occurrences, and returns\n * their concatenation. In case of multiple blocks, two are returned.\n *\n * @param input - Input value\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string value\n */\nexport function highlight(\n input: string, table: PositionTable, positions: Position[], full = false\n): string {\n return highlightAll([input], table, positions, full).pop()!\n}\n\n/**\n * Highlight all occurrences in a set of strings\n *\n * @param inputs - Input values\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n inputs: string[], table: PositionTable, positions: Position[], full = false\n): string[] {\n\n /* Map blocks to input values */\n const mapping = [0]\n for (let t = 1; t < table.length; t++) {\n const prev = table[t - 1]\n const next = table[t]\n\n /* Check if table points to new block */\n const p = prev[prev.length - 1] >>> 2 & 0x3FF\n const q = next[0] >>> 12\n\n /* Add block to mapping */\n mapping.push(+(p > q) + mapping[mapping.length - 1])\n }\n\n /* Highlight strings one after another */\n return inputs.map((input, i) => {\n let cursor = 0\n\n /* Map occurrences to blocks */\n const blocks = new Map()\n for (const p of positions.sort((a, b) => a - b)) {\n const index = p & 0xFFFFF\n const block = p >>> 20\n if (mapping[block] !== i)\n continue\n\n /* Ensure presence of block group */\n let group = blocks.get(block)\n if (typeof group === \"undefined\")\n blocks.set(block, group = [])\n\n /* Add index to group */\n group.push(index)\n }\n\n /* Just return string, if no occurrences */\n if (blocks.size === 0)\n return input\n\n /* Compute slices */\n const slices: string[] = []\n for (const [block, indexes] of blocks) {\n const t = table[block]\n\n /* Extract positions and length */\n const start = t[0] >>> 12\n const end = t[t.length - 1] >>> 12\n const length = t[t.length - 1] >>> 2 & 0x3FF\n\n /* Add prefix, if full results are desired */\n if (full && start > cursor)\n slices.push(input.slice(cursor, start))\n\n /* Extract and highlight slice */\n let slice = input.slice(start, end + length)\n for (const j of indexes.sort((a, b) => b - a)) {\n\n /* Retrieve offset and length of match */\n const p = (t[j] >>> 12) - start\n const q = (t[j] >>> 2 & 0x3FF) + p\n\n /* Wrap occurrence */\n slice = [\n slice.slice(0, p),\n \"\",\n slice.slice(p, q),\n \"\",\n slice.slice(q)\n ].join(\"\")\n }\n\n /* Update cursor */\n cursor = end + length\n\n /* Append slice and abort if we have two */\n if (slices.push(slice) === 2)\n break\n }\n\n /* Add suffix, if full results are desired */\n if (full && cursor < input.length)\n slices.push(input.slice(cursor))\n\n /* Return highlighted slices */\n return slices.join(\"\")\n })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../_\"\nimport {\n Extract,\n extract\n} from \"../extract\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string or set of strings into tokens\n *\n * This tokenizer supersedes the default tokenizer that is provided by Lunr.js,\n * as it is aware of HTML tags and allows for multi-character splitting.\n *\n * It takes the given inputs, splits each of them into markup and text sections,\n * tokenizes and segments (if necessary) each of them, and then indexes them in\n * a table by using a compact bit representation. Bitwise techniques are used\n * to write and read from the table during indexing and querying.\n *\n * @see https://bit.ly/3W3Xw4J - Search: better, faster, smaller\n *\n * @param input - Input value(s)\n *\n * @returns Tokens\n */\nexport function tokenize(\n input?: string | string[]\n): lunr.Token[] {\n const tokens: lunr.Token[] = []\n if (typeof input === \"undefined\")\n return tokens\n\n /* Tokenize strings one after another */\n const inputs = Array.isArray(input) ? input : [input]\n for (let i = 0; i < inputs.length; i++) {\n const table = lunr.tokenizer.table\n const total = table.length\n\n /* Split string into sections and tokenize content blocks */\n extract(inputs[i], (block, type, start, end) => {\n table[block += total] ||= []\n switch (type) {\n\n /* Handle markup */\n case Extract.TAG_OPEN:\n case Extract.TAG_CLOSE:\n table[block].push(\n start << 12 |\n end - start << 2 |\n type\n )\n break\n\n /* Handle text content */\n case Extract.TEXT:\n const section = inputs[i].slice(start, end)\n split(section, lunr.tokenizer.separator, (index, until) => {\n\n /**\n * Apply segmenter after tokenization. Note that the segmenter will\n * also split words at word boundaries, which is not what we want,\n * so we need to check if we can somehow mitigate this behavior.\n */\n if (typeof lunr.segmenter !== \"undefined\") {\n const subsection = section.slice(index, until)\n if (/^[MHIK]$/.test(lunr.segmenter.ctype_(subsection))) {\n const segments = lunr.segmenter.segment(subsection)\n for (let s = 0, l = 0; s < segments.length; s++) {\n\n /* Add block to section */\n table[block] ||= []\n table[block].push(\n start + index + l << 12 |\n segments[s].length << 2 |\n type\n )\n\n /* Add token with position */\n tokens.push(new lunr.Token(\n segments[s].toLowerCase(), {\n position: block << 20 | table[block].length - 1\n }\n ))\n\n /* Keep track of length */\n l += segments[s].length\n }\n return\n }\n }\n\n /* Add block to section */\n table[block].push(\n start + index << 12 |\n until - index << 2 |\n type\n )\n\n /* Add token with position */\n tokens.push(new lunr.Token(\n section.slice(index, until).toLowerCase(), {\n position: block << 20 | table[block].length - 1\n }\n ))\n })\n }\n })\n }\n\n /* Return tokens */\n return tokens\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param value - String value\n *\n * @returns String term(s)\n */\ntype VisitorFn = (\n value: string\n) => string | string[]\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Trim excess whitespace from left and right.\n *\n * 2. Search for parts in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all parts, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect parts enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the parts to an `AND` query.\n *\n * 3. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 4. Split the query string at whitespace, then pass each part to the visitor\n * function for tokenization, and append a wildcard to every resulting term\n * that is not explicitly marked with a `+`, `-`, `~` or `^` modifier, since\n * it ensures consistent and stable ranking when multiple terms are entered.\n * Also, if a fuzzy or boost modifier are given, but no numeric value has\n * been entered, default to 1 to not induce a query error.\n *\n * @param query - Query value\n * @param fn - Visitor function\n *\n * @returns Transformed query value\n */\nexport function transform(\n query: string, fn: VisitorFn = term => term\n): string {\n return query\n\n /* => 1 */\n .trim()\n\n /* => 2 */\n .split(/\"([^\"]+)\"/g)\n .map((parts, index) => index & 1\n ? parts.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : parts\n )\n .join(\"\")\n\n /* => 3 */\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\")\n\n /* => 4 */\n .split(/\\s+/g)\n .reduce((prev, term) => {\n const next = fn(term)\n return [...prev, ...Array.isArray(next) ? next : [next]]\n }, [] as string[])\n .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../../internal\"\nimport { transform } from \"../transform\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Transform search query\n *\n * This function lexes the given search query and applies the transformation\n * function to each term, preserving markup like `+` and `-` modifiers.\n *\n * @param query - Search query\n *\n * @returns Search query\n */\nexport function transformSearchQuery(\n query: string\n): string {\n\n /* Split query terms with tokenizer */\n return transform(query, part => {\n const terms: string[] = []\n\n /* Initialize lexer and analyze part */\n const lexer = new lunr.QueryLexer(part)\n lexer.run()\n\n /* Extract and tokenize term from lexeme */\n for (const { type, str: term, start, end } of lexer.lexemes)\n switch (type) {\n\n /* Hack: remove colon - see https://bit.ly/3wD3T3I */\n case \"FIELD\":\n if (![\"title\", \"text\", \"tags\"].includes(term))\n part = [\n part.slice(0, end),\n \" \",\n part.slice(end + 1)\n ].join(\"\")\n break\n\n /* Tokenize term */\n case \"TERM\":\n split(term, lunr.tokenizer.separator, (...range) => {\n terms.push([\n part.slice(0, start),\n term.slice(...range),\n part.slice(end)\n ].join(\"\"))\n })\n }\n\n /* Return terms */\n return terms\n })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * Lunr.js itself has a bug where it doesn't detect or remove wildcards for\n * query clauses, so we must do this here.\n *\n * @see https://bit.ly/3DpTGtz - GitHub issue\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new lunr.Query([\"title\", \"text\", \"tags\"])\n const parser = new lunr.QueryParser(value, query)\n\n /* Parse Search query */\n parser.parse()\n for (const clause of query.clauses) {\n clause.usePipeline = true\n\n /* Handle leading wildcard */\n if (clause.term.startsWith(\"*\")) {\n clause.wildcard = lunr.Query.wildcard.LEADING\n clause.term = clause.term.slice(1)\n }\n\n /* Handle trailing wildcard */\n if (clause.term.endsWith(\"*\")) {\n clause.wildcard = lunr.Query.wildcard.TRAILING\n clause.term = clause.term.slice(0, -1)\n }\n }\n\n /* Return query clauses */\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Segment a search query using the inverted index\n *\n * This function implements a clever approach to text segmentation for Asian\n * languages, as it used the information already available in the search index.\n * The idea is to greedily segment the search query based on the tokens that are\n * already part of the index, as described in the linked issue.\n *\n * @see https://bit.ly/3lwjrk7 - GitHub issue\n *\n * @param query - Query value\n * @param index - Inverted index\n *\n * @returns Segmented query value\n */\nexport function segment(\n query: string, index: object\n): Iterable {\n const segments = new Set()\n\n /* Segment search query */\n const wordcuts = new Uint16Array(query.length)\n for (let i = 0; i < query.length; i++)\n for (let j = i + 1; j < query.length; j++) {\n const value = query.slice(i, j)\n if (value in index)\n wordcuts[i] = j - i\n }\n\n /* Compute longest matches with minimum overlap */\n const stack = [0]\n for (let s = stack.length; s > 0;) {\n const p = stack[--s]\n for (let q = 1; q < wordcuts[p]; q++)\n if (wordcuts[p + q] > wordcuts[p] - q) {\n segments.add(query.slice(p, p + q))\n stack[s++] = p + q\n }\n\n /* Continue at end of query string */\n const q = p + wordcuts[p]\n if (wordcuts[q] && q < query.length - 1)\n stack[s++] = q\n\n /* Add current segment */\n segments.add(query.slice(p, q))\n }\n\n // @todo fix this case in the code block above, this is a hotfix\n if (segments.has(\"\"))\n return new Set([query])\n\n /* Return segmented query value */\n return segments\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchIndex,\n SearchOptions,\n setupSearchDocumentMap\n} from \"../config\"\nimport {\n Position,\n PositionTable,\n highlight,\n highlightAll,\n tokenize\n} from \"../internal\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery,\n segment,\n transformSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search item\n */\nexport interface SearchItem\n extends SearchDocument\n{\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchItem[][] /* Search items */\n suggest?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create field extractor factory\n *\n * @param table - Position table map\n *\n * @returns Extractor factory\n */\nfunction extractor(table: Map) {\n return (name: keyof SearchDocument) => {\n return (doc: SearchDocument) => {\n if (typeof doc[name] === \"undefined\")\n return undefined\n\n /* Compute identifier and initialize table */\n const id = [doc.location, name].join(\":\")\n table.set(id, lunr.tokenizer.table = [])\n\n /* Return field value */\n return doc[name]\n }\n }\n}\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document map\n */\n protected map: Map\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Internal position table map\n */\n protected table: Map\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n const field = extractor(this.table = new Map())\n\n /* Set up document map and options */\n this.map = setupSearchDocumentMap(docs)\n this.options = options\n\n /* Set up document index */\n this.index = lunr(function () {\n this.metadataWhitelist = [\"position\"]\n this.b(0)\n\n /* Set up (multi-)language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n // @ts-expect-error - namespace indexing not supported\n this.use(lunr[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use(lunr.multiLanguage(...config.lang))\n }\n\n /* Set up custom tokenizer (must be after language setup) */\n this.tokenizer = tokenize as typeof lunr.tokenizer\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Set up custom segmenter, if loaded */\n lunr.segmenter = \"TinySegmenter\" in lunr\n ? new lunr.TinySegmenter()\n : undefined\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], config.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n // @ts-expect-error - namespace indexing not supported\n language === \"en\" ? lunr : lunr[language]\n )))\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n\n /* Set up index reference */\n this.ref(\"location\")\n\n /* Set up index fields */\n this.field(\"title\", { boost: 1e3, extractor: field(\"title\") })\n this.field(\"text\", { boost: 1e0, extractor: field(\"text\") })\n this.field(\"tags\", { boost: 1e6, extractor: field(\"tags\") })\n\n /* Add documents to index */\n for (const doc of docs)\n this.add(doc, { boost: doc.boost })\n })\n }\n\n /**\n * Search for matching documents\n *\n * @param query - Search query\n *\n * @returns Search result\n */\n public search(query: string): SearchResult {\n\n // Experimental Chinese segmentation\n query = query.replace(/\\p{sc=Han}+/gu, value => {\n return [...segment(value, this.index.invertedIndex)]\n .join(\"* \")\n })\n\n // @todo: move segmenter (above) into transformSearchQuery\n query = transformSearchQuery(query)\n if (!query)\n return { items: [] }\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(query)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n let doc = this.map.get(ref)\n if (typeof doc !== \"undefined\") {\n\n /* Shallow copy document */\n doc = { ...doc }\n if (doc.tags)\n doc.tags = [...doc.tags]\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight matches in fields */\n for (const field of this.index.fields) {\n if (typeof doc[field] === \"undefined\")\n continue\n\n /* Collect positions from matches */\n const positions: Position[] = []\n for (const match of Object.values(matchData.metadata))\n if (typeof match[field] !== \"undefined\")\n positions.push(...match[field].position)\n\n /* Skip highlighting, if no positions were collected */\n if (!positions.length)\n continue\n\n /* Load table and determine highlighting method */\n const table = this.table.get([doc.location, field].join(\":\"))!\n const fn = Array.isArray(doc[field])\n ? highlightAll\n : highlight\n\n // @ts-expect-error - stop moaning, TypeScript!\n doc[field] = fn(doc[field], table, positions, field !== \"text\")\n }\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!doc.parent +\n Object.values(terms)\n .filter(t => t).length /\n Object.keys(terms).length\n\n /* Append item */\n item.push({\n ...doc,\n score: score * (1 + boost ** 2),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by article */\n .reduce((items, result) => {\n const doc = this.map.get(result.location)\n if (typeof doc !== \"undefined\") {\n const ref = doc.parent\n ? doc.parent.location\n : doc.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Ensure that every item set has an article */\n for (const [ref, items] of groups)\n if (!items.find(item => item.location === ref)) {\n const doc = this.map.get(ref)!\n items.push({ ...doc, score: 0, terms: {} })\n }\n\n /* Generate search suggestions, if desired */\n let suggest: string[] | undefined\n if (this.options.suggest) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggest = titles.length\n ? 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"block", "start", "end", "stack", "highlight", "input", "table", "positions", "full", "highlightAll", "inputs", "mapping", "t", "prev", "next", "p", "q", "i", "cursor", "blocks", "a", "b", "index", "block", "group", "slices", "indexes", "start", "end", "length", "slice", "j", "tokenize", "input", "tokens", "inputs", "i", "table", "total", "extract", "block", "type", "start", "end", "_a", "section", "split", "index", "until", "subsection", "segments", "s", "l", "transform", "query", "fn", "term", "parts", "index", "prev", "next", "transformSearchQuery", "query", "transform", "part", "terms", "lexer", "type", "term", "start", "end", "split", "range", "parseSearchQuery", "value", "clause", "getSearchQueryTerms", "_a", "clauses", "result", "t", "segment", "query", "index", "segments", "wordcuts", "i", "j", "stack", "p", "q", "extractor", "table", "name", "doc", "id", "difference", "a", "b", "x", "y", "value", "Search", "config", "docs", "options", "field", "setupSearchDocumentMap", "tokenize", "fns", "lang", "language", "fn", "query", "segment", "transformSearchQuery", "clauses", "parseSearchQuery", "clause", "groups", "item", "ref", "score", "matchData", "__spreadValues", "terms", "getSearchQueryTerms", "positions", "match", "highlightAll", "highlight", "boost", "t", "__spreadProps", "__pow", "items", "result", "suggest", "titles", "builder", "index", "setupSearchLanguages", "config", "__async", "base", "worker", "getElement", "path", "scripts", "lang", "handler", "message", "Search", "query", "err", "lunr", "ev"] } diff --git a/concepts/autoscaling/index.html b/concepts/autoscaling/index.html index 1d72956f..14343ff1 100644 --- a/concepts/autoscaling/index.html +++ b/concepts/autoscaling/index.html @@ -18,7 +18,7 @@ - + @@ -1024,7 +1024,7 @@

Next&par - + diff --git a/concepts/backend-servers/index.html b/concepts/backend-servers/index.html index 11d59730..5628b500 100644 --- a/concepts/backend-servers/index.html +++ b/concepts/backend-servers/index.html @@ -18,7 +18,7 @@ - + @@ -1023,7 +1023,7 @@

Next&par - + diff --git a/concepts/resource-profiles/index.html b/concepts/resource-profiles/index.html index 7882cc1d..2cc279d7 100644 --- a/concepts/resource-profiles/index.html +++ b/concepts/resource-profiles/index.html @@ -18,7 +18,7 @@ - + @@ -1032,7 +1032,7 @@

Next&par - + diff --git a/concepts/storage-caching/index.html b/concepts/storage-caching/index.html index e03c2d56..3f42a963 100644 --- a/concepts/storage-caching/index.html +++ b/concepts/storage-caching/index.html @@ -18,7 +18,7 @@ - + @@ -1088,7 +1088,7 @@

C. Model on read-only-many disk - + diff --git a/contributing/development-environment/index.html b/contributing/development-environment/index.html index 9bdcbe2b..c9b1ad1e 100644 --- a/contributing/development-environment/index.html +++ b/contributing/development-environment/index.html @@ -18,7 +18,7 @@ - + @@ -1190,7 +1190,7 @@

Messaging Integration{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}} + diff --git a/contributing/documentation/index.html b/contributing/documentation/index.html index 9ef9b93a..e28d9f1f 100644 --- a/contributing/documentation/index.html +++ b/contributing/documentation/index.html @@ -18,7 +18,7 @@ - + @@ -1052,7 +1052,7 @@

How to serve kubeai.org locally - + diff --git a/contributing/release-process/index.html b/contributing/release-process/index.html index 37456f6b..cd7cbcf4 100644 --- a/contributing/release-process/index.html +++ b/contributing/release-process/index.html @@ -18,7 +18,7 @@ - + @@ -1069,7 +1069,7 @@

Helm Chart{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}} + diff --git a/how-to/build-models-into-containers/index.html b/how-to/build-models-into-containers/index.html index 1d05511e..0d0f6c28 100644 --- a/how-to/build-models-into-containers/index.html +++ b/how-to/build-models-into-containers/index.html @@ -18,7 +18,7 @@ - + @@ -992,7 +992,7 @@

Build models into containers{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}} + diff --git a/how-to/configure-autoscaling/index.html b/how-to/configure-autoscaling/index.html index da87b657..78749cae 100644 --- a/how-to/configure-autoscaling/index.html +++ b/how-to/configure-autoscaling/index.html @@ -18,7 +18,7 @@ - + @@ -1041,7 +1041,7 @@

Configure autoscaling

This guide with cover how to configure KubeAI autoscaling parameters.

System Settings

-

KubeAI administrators can define system-wide autoscaling settings by setting the following helm values:

+

KubeAI administrators can define system-wide autoscaling settings by setting the following Helm values (for the kubeai/kubeai chart):

Example:

# helm-values.yaml
 modelAutoscaling:
@@ -1053,19 +1053,18 @@ 
Model SettingsModel settings: helm

-
If you are managing models via Helm, you can use:

+
If you are managing models via the kubeai/models Helm chart, you can use:

 
# helm-values.yaml
-models:
-  catalog:
-    model-a:
-      # ...
-      minReplicas: 1
-      maxReplicas: 9
-      targetRequests: 250
-      scaleDownDelaySeconds: 45
-    model-b:
-      # ...
-      disableAutoscaling: true
+catalog:
+  model-a:
+    # ...
+    minReplicas: 1
+    maxReplicas: 9
+    targetRequests: 250
+    scaleDownDelaySeconds: 45
+  model-b:
+    # ...
+    disableAutoscaling: true
 # ...
 

 
Re-running helm upgrade with these additional parameters will update model settings in the cluster.

@@ -1129,7 +1128,7 @@ 
Model settings: kubectl
     
     
-    
+    
     
     
       
diff --git a/how-to/configure-resource-profiles/index.html b/how-to/configure-resource-profiles/index.html
index 8a6518b1..b23ced54 100644
--- a/how-to/configure-resource-profiles/index.html
+++ b/how-to/configure-resource-profiles/index.html
@@ -18,7 +18,7 @@
       
       
       
-      
+      
     
     
       
@@ -1076,7 +1076,7 @@ 
Next&par
- + diff --git a/how-to/configure-speech-to-text/index.html b/how-to/configure-speech-to-text/index.html index 98add41b..bfba6721 100644 --- a/how-to/configure-speech-to-text/index.html +++ b/how-to/configure-speech-to-text/index.html @@ -18,7 +18,7 @@ - + @@ -1043,12 +1043,12 @@

Configure speech-to-textEnable Speech to Text model

You can create new models by creating a Model CRD object or by enabling a model from the model catalog.

Enable from model catalog

-

KubeAI provides predefined models in the model catalog. To enable the Speech to Text model, you can set the enabled flag to true in the helm-values.yaml file.

-
models:
-  catalog:
-    faster-whisper-medium-en-cpu:
-      enabled: true
-      minReplicas: 1
+
KubeAI provides predefined models in the kubeai/models Helm chart. To enable the Speech to Text model, you can set the enabled flag to true in your values file.

+
# models-helm-values.yaml
+catalog:
+  faster-whisper-medium-en-cpu:
+    enabled: true
+    minReplicas: 1
 

 
Enable by creating Model CRD

 
You can also create a Model CRD object to enable the Speech to Text model. Here is an example of a Model CRD object for the Speech to Text model:

@@ -1118,7 +1118,7 @@ 
Usage&
- + diff --git a/how-to/install-models/index.html b/how-to/install-models/index.html index 849db96c..005a52ee 100644 --- a/how-to/install-models/index.html +++ b/how-to/install-models/index.html @@ -18,7 +18,7 @@ - + @@ -1059,33 +1059,31 @@

Install models

This guide provides instructions on how to configure KubeAI models.

Installing models with helm

+

KubeAI provides a chart that contains preconfigured models.

Preconfigured models with helm

-

When you are defining KubeAI Helm values, you can install a preconfigured Model by setting enabled: true. You can view a list of all preconfigured models here. NOTE: When you are installing the KubeAI chart, the catalog is accessed under .models.catalog.<model-name>:

+

When you are defining Helm values for the kubeai/models chart you can install a preconfigured Model by setting enabled: true. You can view a list of all preconfigured models in the chart's default values file. 

# helm-values.yaml
-models:
-  catalog:
-    llama-3.1-8b-instruct-fp8-l4:
-      enabled: true
+catalog:
+  llama-3.1-8b-instruct-fp8-l4:
+    enabled: true

You can optionally override preconfigured settings, for example, resourceProfile:

# helm-values.yaml
-models:
-  catalog:
-    llama-3.1-8b-instruct-fp8-l4:
-      enabled: true
-      resourceProfile: nvidia-gpu-l4:2 # Require "2 NVIDIA L4 GPUs"
+catalog:
+  llama-3.1-8b-instruct-fp8-l4:
+    enabled: true
+    resourceProfile: nvidia-gpu-l4:2 # Require "2 NVIDIA L4 GPUs"

Custom models with helm

-

If you prefer to add a custom model via the same Helm chart you use for installed KubeAI, you can add your custom model entry into the .models.catalog array of your existing Helm values file:

+

If you prefer to add a custom model via the same Helm chart you use for installed KubeAI, you can add your custom model entry into the .catalog array of your existing values file for the kubeai/models Helm chart:

# helm-values.yaml
-models:
-  catalog:
-    my-custom-model-name:
-      enabled: true
-      features: ["TextEmbedding"]
-      owner: me
-      url: "hf://me/my-custom-model"
-      resourceProfile: CPU:1
+catalog:
+  my-custom-model-name:
+    enabled: true
+    features: ["TextEmbedding"]
+    owner: me
+    url: "hf://me/my-custom-model"
+    resourceProfile: CPU:1

Installing models with kubectl

You can add your own model by defining a Model yaml file and applying it using kubectl apply -f model.yaml.

@@ -1142,7 +1140,7 @@

Feedback welcome: A model manage - + diff --git a/index.html b/index.html index 30f5de2e..a01ccc2d 100644 --- a/index.html +++ b/index.html @@ -16,7 +16,7 @@ - + @@ -1166,21 +1166,22 @@

Local Quickstart repo update

Install KubeAI and wait for all components to be ready (may take a minute).

-
cat <<EOF > helm-values.yaml
-models:
-  catalog:
-    gemma2-2b-cpu:
-      enabled: true
-      minReplicas: 1
-    qwen2-500m-cpu:
-      enabled: true
-    nomic-embed-text-cpu:
-      enabled: true
+
helm install kubeai kubeai/kubeai --wait --timeout 10m
+

+
Install some predefined models.

+
cat <<EOF > kubeai-models.yaml
+catalog:
+  gemma2-2b-cpu:
+    enabled: true
+    minReplicas: 1
+  qwen2-500m-cpu:
+    enabled: true
+  nomic-embed-text-cpu:
+    enabled: true
 EOF
 
-helm upgrade --install kubeai kubeai/kubeai \
-    -f ./helm-values.yaml \
-    --wait --timeout 10m
+helm install kubeai-models kubeai/models \
+    -f ./kubeai-models.yaml
 

 
Before progressing to the next steps, start a watch on Pods in a standalone terminal to see how KubeAI deploys models. 

 
kubectl get pods --watch
@@ -1303,7 +1304,7 @@ 
Contact{"base": ".", "features": [], "search": "assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}
+    
     
     
       
diff --git a/installation/gke/index.html b/installation/gke/index.html
index 30764769..97ff9440 100644
--- a/installation/gke/index.html
+++ b/installation/gke/index.html
@@ -18,7 +18,7 @@
       
       
       
-      
+      
     
     
       
@@ -367,6 +367,15 @@
     
   
   
+
+      
+        
  • 
+  
+    
+      3. Optionally configure models
+    
+  
+  
 
    • 
       
     
@@ -1018,6 +1027,15 @@
     
   
   
+
+      
+        
  • 
+  
+    
+      3. Optionally configure models
+    
+  
+  
 
    • 
       
     
@@ -1083,30 +1101,36 @@ 
    Option: GKE AutopilotOption: GKE Standard
    
 
    TODO: Reference gcloud commands for creating a GKE standard cluster.
    
 
    2. Install KubeAI
    
-
    Define the installation values for GKE.
    
-
    cat <<EOF > helm-values.yaml
-models:
-  catalog:
-    llama-3.1-8b-instruct-fp8-l4:
-      enabled: true
-
+
    Add KubeAI Helm repository.
    
+
    helm repo add kubeai https://www.kubeai.org
+helm repo update
+
    
+
    Make sure you have a HuggingFace Hub token set in your environment (HUGGING_FACE_HUB_TOKEN).
    
+
    Install KubeAI with Helm.
    
+
    cat <<EOF > kubeai.yaml
 resourceProfiles:
   nvidia-gpu-l4:
     nodeSelector:
       cloud.google.com/gke-accelerator: "nvidia-l4"
       cloud.google.com/gke-spot: "true"
 EOF
-
    
-
    Make sure you have a HuggingFace Hub token set in your environment (HUGGING_FACE_HUB_TOKEN).
    
-
    Install KubeAI with Helm.
    
-
    helm repo add kubeai https://substratusai.github.io/kubeai/
-helm repo update
 
 helm upgrade --install kubeai kubeai/kubeai \
-    -f ./helm-values.yaml \
+    -f ./kubeai.yaml \
     --set secrets.huggingface.token=$HUGGING_FACE_HUB_TOKEN \
     --wait
 
    
+
    3. Optionally configure models
    
+
    Optionally install preconfigured models.
    
+
    cat <<EOF > kubeai-models.yaml
+catalog:
+  llama-3.1-8b-instruct-fp8-l4:
+    enabled: true
+EOF
+
+helm install kubeai-models kubeai/models \
+    -f ./kubeai-models.yaml
+
    
 
 
 
@@ -1153,7 +1177,7 @@ 
    2. Install KubeAI{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}
+    
     
     
       
diff --git a/reference/kubernetes-api/index.html b/reference/kubernetes-api/index.html
index cfbb2b0f..4fb9e428 100644
--- a/reference/kubernetes-api/index.html
+++ b/reference/kubernetes-api/index.html
@@ -18,7 +18,7 @@
       
       
       
-      
+      
     
     
       
@@ -1378,7 +1378,7 @@ 
    ModelStatusReplicas{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}
+    
     
     
       
diff --git a/reference/openai-api-compatibility/index.html b/reference/openai-api-compatibility/index.html
index 46c75c74..f3797bd4 100644
--- a/reference/openai-api-compatibility/index.html
+++ b/reference/openai-api-compatibility/index.html
@@ -16,7 +16,7 @@
       
       
       
-      
+      
     
     
       
@@ -1159,7 +1159,7 @@ 
    Speech-to-Text{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}
+    
     
     
       
diff --git a/search/search_index.json b/search/search_index.json
index 83dfc851..d5335fc1 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"KubeAI: Private Open AI on Kubernetes","text":"
    Get inferencing running on Kubernetes: LLMs, Embeddings, Speech-to-Text.
     
    \u2705\ufe0f  Drop-in replacement for OpenAI with API compatibility \ud83e\udde0  Serve top OSS models (LLMs, Whisper, etc.) \ud83d\ude80  Multi-platform: CPU-only, GPU, coming soon: TPU \u2696\ufe0f  Scale from zero, autoscale based on load \ud83d\udee0\ufe0f  Zero dependencies (does not depend on Istio, Knative, etc.)  \ud83d\udcac  Chat UI included (OpenWebUI) \ud83e\udd16  Operates OSS model servers (vLLM, Ollama, FasterWhisper) \u2709  Stream/batch inference via messaging integrations (Kafka, PubSub, etc.)  
     
    Quotes from the community:
     
    reusable, well abstracted solution to run LLMs - Mike Ensor
    "},{"location":"#architecture","title":"Architecture","text":"
    KubeAI serves an OpenAI compatible HTTP API. Admins can configure ML models via kind: Model Kubernetes Custom Resources. KubeAI can be thought of as a Model Operator (See Operator Pattern) that manages vLLM and Ollama servers.
     
    "},{"location":"#local-quickstart","title":"Local Quickstart","text":"
    Create a local cluster using kind or minikube.
     TIP: If you are using Podman for kind... Make sure your Podman machine can use up to 6G of memory (by default it is capped at 2G):  
    # You might need to stop and remove the existing machine:\npodman machine stop\npodman machine rm\n\n# Init and start a new machine:\npodman machine init --memory 6144\npodman machine start\n
     
    kind create cluster # OR: minikube start\n
     
    Add the KubeAI Helm repository.
     
    helm repo add kubeai https://www.kubeai.org\nhelm repo update\n
     
    Install KubeAI and wait for all components to be ready (may take a minute).
     
    cat <<EOF > helm-values.yaml\nmodels:\n  catalog:\n    gemma2-2b-cpu:\n      enabled: true\n      minReplicas: 1\n    qwen2-500m-cpu:\n      enabled: true\n    nomic-embed-text-cpu:\n      enabled: true\nEOF\n\nhelm upgrade --install kubeai kubeai/kubeai \\\n    -f ./helm-values.yaml \\\n    --wait --timeout 10m\n
     
    Before progressing to the next steps, start a watch on Pods in a standalone terminal to see how KubeAI deploys models. 
     
    kubectl get pods --watch\n
    "},{"location":"#interact-with-gemma2","title":"Interact with Gemma2","text":"
    Because we set minReplicas: 1 for the Gemma model you should see a model Pod already coming up.
     
    Start a local port-forward to the bundled chat UI.
     
    kubectl port-forward svc/openwebui 8000:80\n
     
    Now open your browser to localhost:8000 and select the Gemma model to start chatting with.
    "},{"location":"#scale-up-qwen2-from-zero","title":"Scale up Qwen2 from Zero","text":"
    If you go back to the browser and start a chat with Qwen2, you will notice that it will take a while to respond at first. This is because we set minReplicas: 0 for this model and KubeAI needs to spin up a new Pod (you can verify with kubectl get models -oyaml qwen2-500m-cpu).
     
    NOTE: Autoscaling after initial scale-from-zero is not yet supported for the Ollama backend which we use in this local quickstart. KubeAI relies upon backend-specific metrics and the Ollama project has an open issue: https://github.com/ollama/ollama/issues/3144. To see autoscaling in action, checkout the GKE install guide which uses the vLLM backend and autoscales across GPU resources.
    "},{"location":"#documentation","title":"Documentation","text":"
    Checkout our documentation on kubeai.org to find info on:
     
       
    • Installing KubeAI in the cloud
      •  
    • How to guides (e.g. how to manage models and resource profiles).
      •  
    • Concepts (how the components of KubeAI work).
      •  
    • How to contribute
      •  
    "},{"location":"#adopters","title":"Adopters","text":"
    List of known adopters:
     Name Description Link Telescope Telescope uses KubeAI for multi-region large scale batch LLM inference. trytelescope.ai Google Cloud Distributed Edge KubeAI is included as a reference architecture for inferencing at the edge. LinkedIn, GitLab 
    If you are using KubeAI and would like to be listed as an adopter, please make a PR.
    "},{"location":"#openai-api-compatibility","title":"OpenAI API Compatibility","text":"
    # Implemented #\n/v1/chat/completions\n/v1/completions\n/v1/embeddings\n/v1/models\n/v1/audio/transcriptions\n\n# Planned #\n# /v1/assistants/*\n# /v1/batches/*\n# /v1/fine_tuning/*\n# /v1/images/*\n# /v1/vector_stores/*\n
    "},{"location":"#immediate-roadmap","title":"Immediate Roadmap","text":"
       
    • Model caching
      •  
    • LoRA finetuning (compatible with OpenAI finetuning API)
      •  
    • Image generation (compatible with OpenAI images API)
      •  
     
    NOTE: KubeAI was born out of a project called Lingo which was a simple Kubernetes LLM proxy with basic autoscaling. We relaunched the project as KubeAI (late August 2024) and expanded the roadmap to what it is today.
     
    \ud83c\udf1f Don't forget to drop us a star on GitHub and follow the repo to stay up to date!
     
    "},{"location":"#contact","title":"Contact","text":"
    Let us know about features you are interested in seeing or reach out with questions. Visit our Discord channel to join the discussion!
     
    Or just reach out on LinkedIn if you want to connect:
     
       
    • Nick Stogner
      •  
    • Sam Stoelinga
      •  
    "},{"location":"concepts/autoscaling/","title":"Autoscaling","text":"
    KubeAI proxies HTTP and messaging (i.e. Kafka, etc) requests and messages to models. It will adjust the number Pods serving a given model based on metrics reported by those servers. If no Pods are running when a request comes in, KubeAI will hold the request, scale up a Pod and forward the request when the Pod is ready. This process happens in a manner that is transparent to the end client (other than the added delay from a cold-start).
     
     
    "},{"location":"concepts/autoscaling/#next","title":"Next","text":"
    Read about how to configure autoscaling.
    "},{"location":"concepts/backend-servers/","title":"Backend Servers","text":"
    KubeAI serves ML models by launching Pods on Kubernetes. The configuration and lifecycle of these Pods are managed by the KubeAI controller. Every model server Pod loads exactly one model on startup.
     
    In a Model manifest you can define what server to use for inference (VLLM, OLlama). Any model-specific settings can be passed to the server process via the args and env fields.
    "},{"location":"concepts/backend-servers/#next","title":"Next","text":"
    Read about how to install models.
    "},{"location":"concepts/resource-profiles/","title":"Resource Profiles","text":"
    A resource profile maps a type of compute resource (i.e. NVIDIA L4 GPU) to a collection of Kubernetes settings that are configured on inference server Pods. These profiles are defined in the KubeAI config.yaml file (via a ConfigMap). Each model specifies the resource profile that it requires.
     
    Kubernetes Model resources specify a resource profile and the count of that resource that they require (for example resourceProfile: nvidia-gpu-l4:2 - 2x L4 GPUs).
     
    A given profile might need to contain slightly different settings based on the cluster/cloud that KubeAI is deployed in.
     
    Example: A resource profile named nvidia-gpu-l4 might contain the following node selectors when installing KubeAI on a GKE Kubernetes cluster:
     
    cloud.google.com/gke-accelerator: \"nvidia-l4\"\ncloud.google.com/gke-spot: \"true\"\n
     
    and add the following resource requests to the model server Pods:
     
    nvidia.com/gpu: \"1\"\n
     
    In addition to node selectors and resource requirements, a resource profile may optionally specify an image name. This name maps to the container image that will be selected when serving a model on that resource.
    "},{"location":"concepts/resource-profiles/#next","title":"Next","text":"
    Read about how to configure resource profiles.
    "},{"location":"concepts/storage-caching/","title":"Storage / Caching","text":"
    With \"Large\" in the name, caching is a critical part of serving LLMs.
     
    The best caching technique may very depending on your environment:
     
       
    • What cloud features are available?
      •  
    • Is your cluster deployed in an air-gapped environment?
      •  
    "},{"location":"concepts/storage-caching/#a-model-built-into-container","title":"A. Model built into container","text":"
    Status: Supported
     
    Building a model into a container image can provide a simple way to take advantage of image-related optimizations built into Kubernetes:
     
       
    •  
      Relaunching a model server on the same Node that it ran on before will likely be able to reuse the previously pulled image.
       
      •  
    •  
      Secondary boot disks on GKE can be used to avoid needing to pull images.
       
      •  
    •  
      Image streaming on GKE can allow for containers to startup before the entire image is present on the Node.
       
      •  
    •  
      Container images can be pre-installed on Nodes in air-gapped environments (example: k3s airgap installation).
       
      •  
     
    Guides:
     
       
    • How to build models into container images
      •  
    "},{"location":"concepts/storage-caching/#b-model-on-shared-filesystem-read-write-many","title":"B. Model on shared filesystem (read-write-many)","text":"
    Status: Planned.
     
    Examples: AWS EFS
    "},{"location":"concepts/storage-caching/#c-model-on-read-only-many-disk","title":"C. Model on read-only-many disk","text":"
    Status: Planned.
     
    Examples: GCP Hyperdisk ML
    "},{"location":"contributing/development-environment/","title":"Development environment","text":"
    This document provides instructions for setting up an environment for developing KubeAI.
    "},{"location":"contributing/development-environment/#optional-cloud-setup","title":"Optional: Cloud Setup","text":""},{"location":"contributing/development-environment/#gcp-pubsub","title":"GCP PubSub","text":"
    If you are develop PubSub messaging integration on GCP, setup test topics and subscriptions and uncomment the .messaging.streams in ./hack/dev-config.yaml.
     
    gcloud auth login --update-adc\n\ngcloud pubsub topics create test-kubeai-requests\ngcloud pubsub subscriptions create test-kubeai-requests-sub --topic test-kubeai-requests\ngcloud pubsub topics create test-kubeai-responses\ngcloud pubsub subscriptions create test-kubeai-responses-sub --topic test-kubeai-responses\n
    "},{"location":"contributing/development-environment/#run-in-local-cluster","title":"Run in Local Cluster","text":"
    kind create cluster\n# OR\n#./hack/create-dev-gke-cluster.yaml\n\n# Generate CRDs from Go code.\nmake generate && make manifests\n\n# When CRDs are changed reapply using kubectl:\nkubectl apply -f ./charts/kubeai/charts/crds/crds\n\n# Model with special address annotations:\nkubectl apply -f ./hack/dev-model.yaml\n\n# OPTION A #\n# Run KubeAI inside cluster\n# Change `-f` based on the cluster environment.\nhelm upgrade --install kubeai ./charts/kubeai \\\n    --set openwebui.enabled=true \\\n    --set image.tag=latest \\\n    --set image.pullPolicy=Always \\\n    --set image.repository=us-central1-docker.pkg.dev/substratus-dev/default/kubeai \\\n    --set secrets.huggingface.token=$HUGGING_FACE_HUB_TOKEN \\\n    --set replicaCount=1 -f ./hack/dev-gke-helm-values.yaml\n\n# OPTION B #\n# For quick local interation (run KubeAI outside of cluster)\nCONFIG_PATH=./hack/dev-config.yaml POD_NAMESPACE=default go run ./cmd/main.go\n\n# In another terminal:\nwhile true; do kubectl port-forward service/dev-model 7000:7000; done\n############\n
    "},{"location":"contributing/development-environment/#running","title":"Running","text":""},{"location":"contributing/development-environment/#completions-api","title":"Completions API","text":"
    # If you are running kubeai in-cluster:\n# kubectl port-forward svc/kubeai 8000:80\n\ncurl http://localhost:8000/openai/v1/completions -H \"Content-Type: application/json\" -d '{\"prompt\": \"Hi\", \"model\": \"dev\"}' -v\n
    "},{"location":"contributing/development-environment/#messaging-integration","title":"Messaging Integration","text":"
    gcloud pubsub topics publish test-kubeai-requests \\                  \n  --message='{\"path\":\"/v1/completions\", \"metadata\":{\"a\":\"b\"}, \"body\": {\"model\": \"dev\", \"prompt\": \"hi\"}}'\n\ngcloud pubsub subscriptions pull test-kubeai-responses-sub --auto-ack\n
    "},{"location":"contributing/documentation/","title":"Documentation","text":"
    We are grateful for anyone who takes the time to improve KubeAI documentation! In order to keep our docs clear and consistent we ask that you first read about the approach to documentation that we have standardized on...
    "},{"location":"contributing/documentation/#read-before-writing","title":"Read before writing!","text":"
    The KubeAI approach to documentation is loosely inspired by the Diataxis method.
     
    TLDR on how KubeAI docs are organized:
     
       
    • Installation: How-to guides specific to installing KubeAI.
      •  
    • How To: Directions that guide the reader through a problem or towards a result. How-to guides are goal-oriented. They assume the user is familiar with general concepts, tools, and has already installed KubeAI.
      •  
    • Concepts: A reflective explanation of KubeAI topics with a focus on giving the reader an understanding of the why.
      •  
    • Tutorials: Learning oriented experiences. Lessons that often guide a user from beginning to end. The goal is to help the reader learn something (compared to a how-to guide that is focused on helping the reader do something).
      •  
    • Contributing: The docs in here differ from the rest of the docs by audience: these docs are for anyone who will be contributing code or docs to the KubeAI project.
      •  
    "},{"location":"contributing/documentation/#how-to-serve-kubeaiorg-locally","title":"How to serve kubeai.org locally","text":"
    Make sure you have python3 installed and run:
     
    make docs\n
    "},{"location":"contributing/release-process/","title":"Release Process","text":"
    This document describes the process for releasing a new version of the project.
    "},{"location":"contributing/release-process/#docs","title":"Docs","text":"
    The docs are automatically published whenever a PR updates the docs and the PR is merged into the main branch. The docs are published to the gh-pages branch, which is the source for the Github Pages site.
    "},{"location":"contributing/release-process/#docker-images","title":"Docker images","text":"
    The Docker image latest tag always points to the latest released version. The main tag points to the latest commit on the main branch.
     
    If you push a tag vX.Y.Z to the repository, the Docker image with the tag vX.Y.Z is built and pushed to Docker Hub. Afterwards, the latest tag is updated to point to the new version.
    "},{"location":"contributing/release-process/#helm-chart","title":"Helm Chart","text":"
    The Helm chart only gets released when a git tag is pushed to the repository with the format helm-v*.
     
    The appVersion in the Helm chart does not have to point to the latest released version. This allows us to first publish a new version of the Docker image without updating the Helm chart. The Helm chart is updated when we are ready to release a new version.
     
    This is important when a new appVersion isn't compatible with the current Helm chart. In those cases, we can first merge the PR, thoroughly test, release new container image, and then in a separate PR update the Helm chart and the appVersion.
    "},{"location":"how-to/build-models-into-containers/","title":"Build models into containers","text":"
    In this guide we will preload a LLM into a custom built Ollama serving image. You can follow the same steps for other models and other serving engines.
     
    Define some values 
    export MODEL_URL=ollama://qwen2:0.5b\n\n# Customize with your own image repo.\nexport IMAGE=us-central1-docker.pkg.dev/substratus-dev/default/ollama-builtin-qwen2-05b:latest\n
     
    Build and push image. Note: building (downloading base image & model) and pushing (uploading image & model) can take a while depending on the size of the model.
     
    git clone https://github.com/substratusai/kubeai\ncd ./kubeai/images/ollama-builtin\n\ndocker build --build-arg MODEL_URL=$MODEL_URL -t $IMAGE .\ndocker push $IMAGE\n
     
    Create a model manifest & apply into a cluster with KubeAI installed. NOTE: The only difference between an built-in model image and otherwise is the addition of the image: field.
     
    kubectl apply -f - << EOF\napiVersion: kubeai.org/v1\nkind: Model\nmetadata:\n  name: builtin-model-example\nspec:\n  features: [\"TextGeneration\"]\n  owner: alibaba\n  image: $IMAGE # <-- The image with model built-in\n  url: \"$MODEL_URL\"\n  engine: OLlama\n  resourceProfile: cpu:1\nEOF\n
    "},{"location":"how-to/configure-autoscaling/","title":"Configure autoscaling","text":"
    This guide with cover how to configure KubeAI autoscaling parameters.
    "},{"location":"how-to/configure-autoscaling/#system-settings","title":"System Settings","text":"
    KubeAI administrators can define system-wide autoscaling settings by setting the following helm values:
     
    Example:
     
    # helm-values.yaml\nmodelAutoscaling:\n  interval: 15s\n  timeWindow: 10m\n# ...\n
    "},{"location":"how-to/configure-autoscaling/#model-settings","title":"Model Settings","text":"
    The following settings can be configured on a model-by-model basis.
     
    NOTE: Updates to model settings will be applied upon scale up of additional model Pods. You can force-restart model servers by running kubectl delete pods -l model=<model-name>.
    "},{"location":"how-to/configure-autoscaling/#model-settings-helm","title":"Model settings: helm","text":"
    If you are managing models via Helm, you can use:
     
    # helm-values.yaml\nmodels:\n  catalog:\n    model-a:\n      # ...\n      minReplicas: 1\n      maxReplicas: 9\n      targetRequests: 250\n      scaleDownDelaySeconds: 45\n    model-b:\n      # ...\n      disableAutoscaling: true\n# ...\n
     
    Re-running helm upgrade with these additional parameters will update model settings in the cluster.
    "},{"location":"how-to/configure-autoscaling/#model-settings-kubectl","title":"Model settings: kubectl","text":"
    You can also specify the autoscaling profile directly via the Models custom resource in the Kubernetes API:
     
    apiVersion: kubeai.org/v1\nkind: Model\nmetadata:\n  name: my-model\nspec:\n  # ...\n  minReplicas: 1\n  maxReplicas: 9\n  targetRequests: 250\n  scaleDownDelaySeconds: 45\n
     
    If you are already managing models using Model manifest files, you can make the update to your file and reapply it using kubectl apply -f <filename>.yaml.
    "},{"location":"how-to/configure-resource-profiles/","title":"Configure resource profiles","text":"
    This guide will cover modifying preconfigured resource profiles and adding your own.
    "},{"location":"how-to/configure-resource-profiles/#modifying-preconfigured-resource-profiles","title":"Modifying preconfigured resource profiles","text":"
    The KubeAI helm chart comes with preconfigured resource profiles for common resource types such as NVIDIA L4 GPUs. You can view these profiles in the default helm values file.
     
    These profiles usually require some additional settings based on the cluster/cloud that KubeAI is installed into. You can modify a resource profile by setting custom helm values and runing helm install or helm upgrade. For example, if you are installing KubeAI on GKE you will need to set GKE-specific node selectors:
     
    # helm-values.yaml\nresourceProfiles:\n  nvidia-gpu-l4:\n    nodeSelector:\n      cloud.google.com/gke-accelerator: \"nvidia-l4\"\n      cloud.google.com/gke-spot: \"true\"\n
     
    NOTE: See the cloud-specific installation guide for a comprehensive list of settings.
    "},{"location":"how-to/configure-resource-profiles/#adding-additional-resource-profiles","title":"Adding additional resource profiles","text":"
    If the preconfigured resource profiles do not meet your needs you can add additional profiles by appending to the .resourceProfiles object in the helm values file you use to install KubeAI.
     
    # helm-values.yaml\nresourceProfiles:\n  my-custom-gpu:\n    imageName: \"optional-custom-image-name\"\n    nodeSelector:\n      my-custom-node-pool: \"some-value\"\n    limits:\n      custom.com/gpu: \"1\"\n    requests:\n      custom.com/gpu: \"1\"\n      cpu: \"3\"\n      memory: \"12Gi\"\n
     
    If you need to run custom model server images on your resource profile, make sure to also add those in the modelServers section:
     
    # helm-values.yaml\nmodelServers:\n  VLLM:\n    images:\n      optional-custom-image-name: \"my-repo/my-vllm-image:v1.2.3\"\n  OLlama:\n    images:\n      optional-custom-image-name: \"my-repo/my-ollama-image:v1.2.3\"\n
    "},{"location":"how-to/configure-resource-profiles/#next","title":"Next","text":"
    See the guide on how to install models which includes how to configure the resource profile to use for a given model.
    "},{"location":"how-to/configure-speech-to-text/","title":"Configure speech-to-text","text":"
    KubeAI provides a Speech to Text endpoint that can be used to transcribe audio files. This guide will walk you through the steps to enable this feature.
    "},{"location":"how-to/configure-speech-to-text/#enable-speech-to-text-model","title":"Enable Speech to Text model","text":"
    You can create new models by creating a Model CRD object or by enabling a model from the model catalog.
    "},{"location":"how-to/configure-speech-to-text/#enable-from-model-catalog","title":"Enable from model catalog","text":"
    KubeAI provides predefined models in the model catalog. To enable the Speech to Text model, you can set the enabled flag to true in the helm-values.yaml file.
     
    models:\n  catalog:\n    faster-whisper-medium-en-cpu:\n      enabled: true\n      minReplicas: 1\n
    "},{"location":"how-to/configure-speech-to-text/#enable-by-creating-model-crd","title":"Enable by creating Model CRD","text":"
    You can also create a Model CRD object to enable the Speech to Text model. Here is an example of a Model CRD object for the Speech to Text model:
     
    apiVersion: kubeai.org/v1\nkind: Model\nmetadata:\n  name: faster-whisper-medium-en-cpu\nspec:\n  features: [SpeechToText]\n  owner: Systran\n  url: hf://Systran/faster-whisper-medium.en\n  engine: FasterWhisper\n  resourceProfile: cpu:1\n
    "},{"location":"how-to/configure-speech-to-text/#usage","title":"Usage","text":"
    The Speech to Text endpoint is available at /openai/v1/transcriptions.
     
    Example usage using curl:
     
    curl -L -o kubeai.mp4 https://github.com/user-attachments/assets/711d1279-6af9-4c6c-a052-e59e7730b757\ncurl http://localhost:8000/openai/v1/audio/transcriptions \\\n  -F \"file=@kubeai.mp4\" \\\n  -F \"language=en\" \\\n  -F \"model=faster-whisper-medium-en-cpu\"\n
    "},{"location":"how-to/install-models/","title":"Install models","text":"
    This guide provides instructions on how to configure KubeAI models.
    "},{"location":"how-to/install-models/#installing-models-with-helm","title":"Installing models with helm","text":""},{"location":"how-to/install-models/#preconfigured-models-with-helm","title":"Preconfigured models with helm","text":"
    When you are defining KubeAI Helm values, you can install a preconfigured Model by setting enabled: true. You can view a list of all preconfigured models here. NOTE: When you are installing the KubeAI chart, the catalog is accessed under .models.catalog.<model-name>:
     
    # helm-values.yaml\nmodels:\n  catalog:\n    llama-3.1-8b-instruct-fp8-l4:\n      enabled: true\n
     
    You can optionally override preconfigured settings, for example, resourceProfile:
     
    # helm-values.yaml\nmodels:\n  catalog:\n    llama-3.1-8b-instruct-fp8-l4:\n      enabled: true\n      resourceProfile: nvidia-gpu-l4:2 # Require \"2 NVIDIA L4 GPUs\"\n
    "},{"location":"how-to/install-models/#custom-models-with-helm","title":"Custom models with helm","text":"
    If you prefer to add a custom model via the same Helm chart you use for installed KubeAI, you can add your custom model entry into the .models.catalog array of your existing Helm values file:
     
    # helm-values.yaml\nmodels:\n  catalog:\n    my-custom-model-name:\n      enabled: true\n      features: [\"TextEmbedding\"]\n      owner: me\n      url: \"hf://me/my-custom-model\"\n      resourceProfile: CPU:1\n
    "},{"location":"how-to/install-models/#installing-models-with-kubectl","title":"Installing models with kubectl","text":"
    You can add your own model by defining a Model yaml file and applying it using kubectl apply -f model.yaml.
     
    If you have a running cluster with KubeAI installed you can inspect the schema for a Model using kubectl explain:
     
    kubectl explain models\nkubectl explain models.spec\nkubectl explain models.spec.engine\n
    "},{"location":"how-to/install-models/#feedback-welcome-a-model-management-ui","title":"Feedback welcome: A model management UI","text":"
    We are considering adding a UI for managing models in a running KubeAI instance. Give the GitHub Issue a thumbs up if you would be interested in this feature.
    "},{"location":"installation/gke/","title":"Install on GKE","text":"TIP: Make sure you have enough quota in your GCP project. 
    Open the cloud console quotas page: https://console.cloud.google.com/iam-admin/quotas. Make sure your project is selected in the top left.
     
    There are 3 critical quotas you will need to verify for this guide. The minimum value here is assuming that you have nothing else running in your project.
     Quota Location Min Value Preemptible NVIDIA L4 GPUs <your-region> 2 GPUs (all regions) - 2 CPUs (all regions) - 24 
    See the following screenshot examples of how these quotas appear in the console:
     
     
     
    "},{"location":"installation/gke/#1-create-a-cluster","title":"1. Create a cluster","text":""},{"location":"installation/gke/#option-gke-autopilot","title":"Option: GKE Autopilot","text":"
    Create an Autopilot cluster (replace us-central1 with a region that you have quota).
     
    gcloud container clusters create-auto cluster-1 \\\n    --location=us-central1\n
    "},{"location":"installation/gke/#option-gke-standard","title":"Option: GKE Standard","text":"
    TODO: Reference gcloud commands for creating a GKE standard cluster.
    "},{"location":"installation/gke/#2-install-kubeai","title":"2. Install KubeAI","text":"
    Define the installation values for GKE.
     
    cat <<EOF > helm-values.yaml\nmodels:\n  catalog:\n    llama-3.1-8b-instruct-fp8-l4:\n      enabled: true\n\nresourceProfiles:\n  nvidia-gpu-l4:\n    nodeSelector:\n      cloud.google.com/gke-accelerator: \"nvidia-l4\"\n      cloud.google.com/gke-spot: \"true\"\nEOF\n
     
    Make sure you have a HuggingFace Hub token set in your environment (HUGGING_FACE_HUB_TOKEN).
     
    Install KubeAI with Helm.
     
    helm repo add kubeai https://substratusai.github.io/kubeai/\nhelm repo update\n\nhelm upgrade --install kubeai kubeai/kubeai \\\n    -f ./helm-values.yaml \\\n    --set secrets.huggingface.token=$HUGGING_FACE_HUB_TOKEN \\\n    --wait\n
    "},{"location":"reference/kubernetes-api/","title":"Kubernetes API","text":""},{"location":"reference/kubernetes-api/#packages","title":"Packages","text":"
       
    • kubeai.org/v1
      •  
    "},{"location":"reference/kubernetes-api/#kubeaiorgv1","title":"kubeai.org/v1","text":"
    Package v1 contains API Schema definitions for the kubeai v1 API group
    "},{"location":"reference/kubernetes-api/#resource-types","title":"Resource Types","text":"
       
    • Model
      •  
    "},{"location":"reference/kubernetes-api/#model","title":"Model","text":"
    Model resources define the ML models that will be served by KubeAI.
     Field Description Default Validation apiVersion string kubeai.org/v1 kind string Model metadata ObjectMeta Refer to Kubernetes API documentation for fields of metadata. spec ModelSpec status ModelStatus"},{"location":"reference/kubernetes-api/#modelfeature","title":"ModelFeature","text":"
    Underlying type: string
     
    Validation: - Enum: [TextGeneration TextEmbedding SpeechToText]
     
    Appears in: - ModelSpec
    "},{"location":"reference/kubernetes-api/#modelspec","title":"ModelSpec","text":"
    ModelSpec defines the desired state of Model.
     
    Appears in: - Model
     Field Description Default Validation url string URL of the model to be served.Currently only the following formats are supported:For VLLM & FasterWhisper engines: \"hf:///\"For OLlama engine: \"ollama:// features ModelFeature array Features that the model supports.Dictates the APIs that are available for the model. Enum: [TextGeneration TextEmbedding SpeechToText]  engine string Engine to be used for the server process. Enum: [OLlama VLLM FasterWhisper]  resourceProfile string ResourceProfile required to serve the model.Use the format \":\".Example: \"nvidia-gpu-l4:2\" - 2x NVIDIA L4 GPUs.Must be a valid ResourceProfile defined in the system config. image string Image to be used for the server process.Will be set from ResourceProfile + Engine if not specified. args string array Args to be added to the server process. env object (keys:string, values:string) Env variables to be added to the server process. replicas integer Replicas is the number of Pod replicas that should be activelyserving the model. KubeAI will manage this field unless AutoscalingDisabledis set to true. minReplicas integer MinReplicas is the minimum number of Pod replicas that the model can scale down to.Note: 0 is a valid value. Minimum: 0 Optional: {}  maxReplicas integer MaxReplicas is the maximum number of Pod replicas that the model can scale up to.Empty value means no limit. Minimum: 1  autoscalingDisabled boolean AutoscalingDisabled will stop the controller from managing the replicasfor the Model. When disabled, metrics will not be collected on server Pods. targetRequests integer TargetRequests is average number of active requests that the autoscalerwill try to maintain on model server Pods. 100 Minimum: 1  scaleDownDelaySeconds integer ScaleDownDelay is the minimum time before a deployment is scaled down afterthe autoscaling algorithm determines that it should be scaled down. 30 owner string Owner of the model. Used solely to populate the owner field in theOpenAI /v1/models endpoint.DEPRECATED. Optional: {}"},{"location":"reference/kubernetes-api/#modelstatus","title":"ModelStatus","text":"
    ModelStatus defines the observed state of Model.
     
    Appears in: - Model
     Field Description Default Validation replicas ModelStatusReplicas"},{"location":"reference/kubernetes-api/#modelstatusreplicas","title":"ModelStatusReplicas","text":"
    Appears in: - ModelStatus
     Field Description Default Validation all integer ready integer"},{"location":"reference/openai-api-compatibility/","title":"OpenAI API Compatibility","text":"
    KubeAI provides an OpenAI API compatiblity layer.
    "},{"location":"reference/openai-api-compatibility/#general","title":"General:","text":""},{"location":"reference/openai-api-compatibility/#models","title":"Models","text":"
    GET /v1/models\n
     
       
    • Lists all kind: Model object installed in teh Kubernetes API Server.
      •  
    "},{"location":"reference/openai-api-compatibility/#inference","title":"Inference","text":""},{"location":"reference/openai-api-compatibility/#text-generation","title":"Text Generation","text":"
    POST /v1/chat/completions\nPOST /v1/completions\n
     
       
    • Supported for Models with .spec.features: [\"TextGeneration\"].
      •  
    "},{"location":"reference/openai-api-compatibility/#embeddings","title":"Embeddings","text":"
    POST /v1/embeddings\n
     
       
    • Supported for  Models with .spec.features: [\"TextEmbedding\"].
      •  
    "},{"location":"reference/openai-api-compatibility/#speech-to-text","title":"Speech-to-Text","text":"
    POST /v1/audio/transcriptions\n
     
       
    • Supported for Models with .spec.features: [\"SpeechToText\"].
      •  
    "},{"location":"tutorials/langchain/","title":"Using LangChain with KubeAI","text":"
    LangChain makes it easy to build applications powered by LLMs. KubeAI makes it easy to deploy and manage LLMs at scale. Together, they make it easy to build and deploy private and secure AI applications.
     
    In this tutorial, we'll show you how to use LangChain with KubeAI's OpenAI compatible API. The beauty of KubeAI's OpenAI compatibility is that you can use KubeAI with any framework that supports OpenAI.
    "},{"location":"tutorials/langchain/#prerequisites","title":"Prerequisites","text":"
    A K8s cluster. You can use a local cluster like kind.
    "},{"location":"tutorials/langchain/#installing-kubeai-with-gemma-2b","title":"Installing KubeAI with Gemma 2B","text":"
    Run the following command to install KubeAI with Gemma 2B:
     
    helm repo add kubeai https://www.kubeai.org\ncat <<EOF > helm-values.yaml\nmodels:\n  catalog:\n    gemma2-2b-cpu:\n      enabled: true\n      minReplicas: 1\nEOF\n\nhelm upgrade --install kubeai kubeai/kubeai \\\n    -f ./helm-values.yaml \\\n    --wait --timeout 10m\n
    "},{"location":"tutorials/langchain/#using-langchain","title":"Using LangChain","text":"
    Install the required Python packages: 
    pip install langchain_openai\n
     
    Let's access the KubeAI OpenAI compatible API locally to make it easier.
     
    Run the following command to port-forward to the KubeAI service: 
    kubectl port-forward svc/kubeai 8000:80\n
     Now the KubeAI OpenAI compatible API is available at http://localhost:8000/openai from your local machine.
     
    Let's create a simple Python script that uses LangChain and is connected to KubeAI.
     
    Create a file named test-langchain.py with the following content: 
    from langchain_openai import ChatOpenAI\n\nllm = ChatOpenAI(\n    model=\"gemma2-2b-cpu\",\n    temperature=0,\n    max_tokens=None,\n    timeout=None,\n    max_retries=2,\n    api_key=\"thisIsIgnored\",\n    base_url=\"http://localhost:8000/openai/v1\",\n)\n\nmessages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nprint(ai_msg.content)\n
     
    Run the Python script: 
    python test-langchain.py\n
     
    Notice that we set base_url to http://localhost:8000/openai/v1. This tells LangChain to use our local KubeAI OpenAI compatible AP instead of the default OpenAI public API.
     
    If you run langchain within the K8s cluster, you can use the following base_url instead: http://kubeai/openai/v1. So the code would look like this: 
    llm = ChatOpenAI(\n    ...\n    base_url=\"http://kubeai/openai/v1\",\n)\n
     
    That's it! You've successfully used LangChain with KubeAI. Now you can build and deploy private and secure AI applications with ease.
    "},{"location":"tutorials/langtrace/","title":"Deploying KubeAI with Langtrace","text":"
    Langtrace is an open source tool that helps you with tracing and monitoring your AI calls. It includes a self-hosted UI that for example shows you the estimated costs of your LLM calls.
     
    KubeAI is used for deploying LLMs with an OpenAI compatible endpoint.
     
    In this tutorial you will learn how to deploy KubeAI and Langtrace end-to-end. Both KubeAI and Langtrace are installed in your Kubernetes cluster. No cloud services or external dependencies are required.
     
    If you don't have a K8s cluster yet, you can create one using kind or minikube. 
    kind create cluster # OR: minikube start\n
     
    Install Langtrace: 
    helm repo add langtrace https://Scale3-Labs.github.io/langtrace-helm-chart\nhelm repo update\nhelm install langtrace langtrace/langtrace\n
     
    Install KubeAI: 
    helm repo add kubeai https://substratusai.github.io/kubeai/\nhelm repo update\ncat <<EOF > helm-values.yaml\nmodels:\n  catalog:\n    gemma2-2b-cpu:\n      enabled: true\n      minReplicas: 1\nEOF\n\nhelm upgrade --install kubeai kubeai/kubeai \\\n    --wait --timeout 10m \\\n    -f ./helm-values.yaml\n
     
    Create a local Python environment and install dependencies: 
    python3 -m venv .venv\nsource .venv/bin/activate\npip install langtrace-python-sdk openai\n
     
    Expose the KubeAI service to your local port: 
    kubectl port-forward service/kubeai 8000:80\n
     
    Expose the Langtrace service to your local port: 
    kubectl port-forward service/langtrace-app 3000:3000\n
     
    A Langtrace API key is required to use the Langtrace SDK. So lets get one by visiting your self hosted Langtace UI.
     
    Open your browser to http://localhost:3000, create a project and get the API keys for your langtrace project.
     
    In the Python script below, replace langtrace_api_key with your API key.
     
    Create file named langtrace-example.py with the following content: 
    # Replace this with your langtrace API key by visiting http://localhost:3000\nlangtrace_api_key=\"f7e003de19b9a628258531c17c264002e985604ca9fa561debcc85c41f357b09\"\n\nfrom langtrace_python_sdk import langtrace\nfrom langtrace_python_sdk.utils.with_root_span import with_langtrace_root_span\n# Paste this code after your langtrace init function\n\nfrom openai import OpenAI\n\nlangtrace.init(\n    api_key=api_key,\n    api_host=\"http://localhost:3000/api/trace\",\n)\n\nbase_url = \"http://localhost:8000/openai/v1\"\nmodel = \"gemma2-2b-cpu\"\n\n@with_langtrace_root_span()\ndef example():\n    client = OpenAI(base_url=base_url, api_key=\"ignored-by-kubeai\")\n    response = client.chat.completions.create(\n        model=model,\n        messages=[\n            {\n                \"role\": \"system\",\n                \"content\": \"How many states of matter are there?\"\n            }\n        ],\n    )\n    print(response.choices[0].message.content)\n\nexample()\n
     
    Run the Python script: 
    python3 langtrace-example.py\n
     
    Now you should see the trace in your Langtrace UI. Take a look by visiting http://localhost:3000.
     
    "},{"location":"tutorials/weaviate/","title":"Weaviate with local autoscaling embedding and generative models","text":"
    Weaviate is a vector search engine that can integrate seamlessly with KubeAI's embedding and generative models. This tutorial demonstrates how to deploy both KubeAI and Weaviate in a Kubernetes cluster, using KubeAI as the OpenAI endpoint for Weaviate.
     
    Why use KubeAI with Weaviate?
     
       
    • Security and privacy: KubeAI runs locally in your Kubernetes cluster, so your data never leaves your infrastructure.
      •  
    • Cost savings: KubeAI can run on your existing hardware, reducing the need for paying for embeddings and generative models.
      •  
     
    This tutorial uses CPU only models, so it should work even on your laptop.
     
    As you go go through this tutorial, you will learn how to:
     
       
    • Deploy KubeAI with embedding and generative models
      •  
    • Install Weaviate and connect it to KubeAI
      •  
    • Import data into Weaviate
      •  
    • Perform semantic search using the embedding model
      •  
    • Perform generative search using the generative model
      •  
    "},{"location":"tutorials/weaviate/#prerequisites","title":"Prerequisites","text":"
    A Kubernetes cluster. You can use kind or minikube.
     
    kind create cluster\n
    "},{"location":"tutorials/weaviate/#kubeai-configuration","title":"KubeAI Configuration","text":"
    Let's start by deploying KubeAI with the models we want to use. Nomic embedding model is used instead of text-embedding-ada-002. Gemma 2 2B is used instead of gpt-3.5-turbo. You could choose to use bigger models depending on your available hardware.
     
    Create a file named kubeai-values.yaml with the following content: 
    models:\n  catalog:\n    text-embedding-ada-002:\n      enabled: true\n      minReplicas: 1\n      features: [\"TextEmbedding\"]\n      owner: nomic\n      url: \"ollama://nomic-embed-text\"\n      engine: OLlama\n      resourceProfile: cpu:1\n    gpt-3.5-turbo:\n      enabled: true\n      minReplicas: 1\n      features: [\"TextGeneration\"]\n      owner: google\n      url: \"ollama://gemma2:2b\"\n      engine: OLlama\n      resourceProfile: cpu:2\n
     
    Note: It's important that you name the models as text-embedding-ada-002 and gpt-3.5-turbo as Weaviate expects these names.
     
    Run the following command to deploy KubeAI: 
    helm install kubeai kubeai/kubeai \\\n    -f ./kubeai-values.yaml\n
    "},{"location":"tutorials/weaviate/#weaviate-installation","title":"Weaviate Installation","text":"
    For this tutorial, we will use the Weaviate Helm chart to deploy Weaviate.
     
    Let's enable the text2vec-openai and generative-openai modules in Weaviate. We will also set the default vectorizer module to text2vec-openai.
     
    The apiKey is ignored in this case as we are using KubeAI as the OpenAI endpoint.
     
    Create a file named weaviate-values.yaml with the following content: 
    modules:\n  text2vec-openai:\n    enabled: true\n    apiKey: thisIsIgnored\n  generative-openai:\n    enabled: true\n    apiKey: thisIsIgnored\n  default_vectorizer_module: text2vec-openai\nservice:\n  # To prevent Weaviate being exposed publicly\n  type: ClusterIP\n
     
    Install Weaviate by running the following command: 
    helm repo add weaviate https://weaviate.github.io/weaviate-helm\nhelm upgrade --install \\\n  \"weaviate\" \\\n  weaviate/weaviate \\\n  -f weaviate-values.yaml\n
    "},{"location":"tutorials/weaviate/#usage","title":"Usage","text":"
    We will be using Python to interact with Weaviate. The 2 use cases we will cover are: - Semantic search using the embedding model - Generative search using the generative model
    "},{"location":"tutorials/weaviate/#connectivity","title":"Connectivity","text":"
    The remaining steps require connectivity to the Weaviate service. However, Weaviate is not exposed publicly in this setup. So we setup a local port forwards to access the Weaviate services.
     
    Setup a local port forwards to the Weaviate services by running: 
    kubectl port-forward svc/weaviate 8080:80\nkubectl port-forward svc/weaviate-grpc 50051:50051\n
    "},{"location":"tutorials/weaviate/#weaviate-client-python-setup","title":"Weaviate client Python Setup","text":"
    Create a virtual environment and install the Weaviate client: 
    python -m venv .venv\nsource .venv/bin/activate\npip install -U weaviate-client requests\n
    "},{"location":"tutorials/weaviate/#collection-and-data-import","title":"Collection and Data Import","text":"
    Create a file named create-collection.py with the following content: 
    import json\nimport weaviate\nimport requests\nfrom weaviate.classes.config import Configure\n\n# This works due to port forward in previous step\nwith weaviate.connect_to_local(port=8080, grpc_port=50051) as client:\n\n    client.collections.create(\n        \"Question\",\n        vectorizer_config=Configure.Vectorizer.text2vec_openai(\n                model=\"text-embedding-ada-002\",\n                base_url=\"http://kubeai/openai\",\n        ),\n        generative_config=Configure.Generative.openai(\n            model=\"gpt-3.5-turbo\",\n            base_url=\"http://kubeai/openai\",\n        ),\n    )\n\n    # import data\n    resp = requests.get('https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny.json')\n    data = json.loads(resp.text)  # Load data\n\n    question_objs = list()\n    for i, d in enumerate(data):\n        question_objs.append({\n            \"answer\": d[\"Answer\"],\n            \"question\": d[\"Question\"],\n            \"category\": d[\"Category\"],\n        })\n\n    questions = client.collections.get(\"Question\")\n    questions.data.insert_many(question_objs)\n    print(\"Data imported successfully\")\n
     
    Create a collection that uses KubeAI as the openAI endpoint: 
    python create-collection.py\n
     You should see a message Data imported successfully.
     
    The collection is now created and data is imported. The vectors are generated by KubeAI and stored in Weaviate.
    "},{"location":"tutorials/weaviate/#semantic-search","title":"Semantic Search","text":"
    Now let's do semantic search, which uses the embeddings. Create a file named search.py with the following content: 
    import weaviate\nfrom weaviate.classes.config import Configure\n\n# This works due to port forward in previous step\nwith weaviate.connect_to_local(port=8080, grpc_port=50051) as client:\n    questions = client.collections.get(\"Question\")\n    response = questions.query.near_text(\n        query=\"biology\",\n        limit=2\n    )\n    print(response.objects[0].properties)  # Inspect the first object\n
     
    Execute the python script: 
    python search.py\n
     
    You should see the following output: 
    {\n  \"answer\": \"DNA\",\n  \"question\": \"In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance\",\n  \"category\": \"SCIENCE\"\n}\n
    "},{"location":"tutorials/weaviate/#generative-search-rag","title":"Generative Search (RAG)","text":"
    Now let's do generative search, which uses the generative model (Text generation LLM). The generative model is run locally and managed by KubeAI.
     
    Create a file named generate.py with the following content: 
    import weaviate\nfrom weaviate.classes.config import Configure\n\n# This works due to port forward in previous step\nwith weaviate.connect_to_local(port=8080, grpc_port=50051) as client:\n    questions = client.collections.get(\"Question\")\n\n    response = questions.generate.near_text(\n        query=\"biology\",\n        limit=2,\n        grouped_task=\"Write a tweet with emojis about these facts.\"\n    )\n\n    print(response.generated)  # Inspect the generated text\n
     
    Run the python script: 
    python generate.py\n
     
    You should see something similar to this:
     
    \ud83e\uddec Watson & Crick cracked the code in 1953!  \ud83e\udd2f They built a model of DNA, the blueprint of life. \ud83e\uddec \ud83e\udde0 Liver power! \ud83d\udcaa This organ keeps your blood sugar balanced by storing glucose as glycogen. \ud83e\ude78 #ScienceFacts #Biology
    "},{"location":"tutorials/weaviate/#conclusion","title":"Conclusion","text":"
    You've now successfully set up KubeAI with Weaviate for both embedding-based semantic search and generative tasks. You've also learned how to import data, perform searches, and generate content using KubeAI-managed models.
    "}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"KubeAI: Private Open AI on Kubernetes","text":"
    Get inferencing running on Kubernetes: LLMs, Embeddings, Speech-to-Text.
     
    \u2705\ufe0f  Drop-in replacement for OpenAI with API compatibility \ud83e\udde0  Serve top OSS models (LLMs, Whisper, etc.) \ud83d\ude80  Multi-platform: CPU-only, GPU, coming soon: TPU \u2696\ufe0f  Scale from zero, autoscale based on load \ud83d\udee0\ufe0f  Zero dependencies (does not depend on Istio, Knative, etc.)  \ud83d\udcac  Chat UI included (OpenWebUI) \ud83e\udd16  Operates OSS model servers (vLLM, Ollama, FasterWhisper) \u2709  Stream/batch inference via messaging integrations (Kafka, PubSub, etc.)  
     
    Quotes from the community:
     
    reusable, well abstracted solution to run LLMs - Mike Ensor
    "},{"location":"#architecture","title":"Architecture","text":"
    KubeAI serves an OpenAI compatible HTTP API. Admins can configure ML models via kind: Model Kubernetes Custom Resources. KubeAI can be thought of as a Model Operator (See Operator Pattern) that manages vLLM and Ollama servers.
     
    "},{"location":"#local-quickstart","title":"Local Quickstart","text":"
    Create a local cluster using kind or minikube.
     TIP: If you are using Podman for kind... Make sure your Podman machine can use up to 6G of memory (by default it is capped at 2G):  
    # You might need to stop and remove the existing machine:\npodman machine stop\npodman machine rm\n\n# Init and start a new machine:\npodman machine init --memory 6144\npodman machine start\n
     
    kind create cluster # OR: minikube start\n
     
    Add the KubeAI Helm repository.
     
    helm repo add kubeai https://www.kubeai.org\nhelm repo update\n
     
    Install KubeAI and wait for all components to be ready (may take a minute).
     
    helm install kubeai kubeai/kubeai --wait --timeout 10m\n
     
    Install some predefined models.
     
    cat <<EOF > kubeai-models.yaml\ncatalog:\n  gemma2-2b-cpu:\n    enabled: true\n    minReplicas: 1\n  qwen2-500m-cpu:\n    enabled: true\n  nomic-embed-text-cpu:\n    enabled: true\nEOF\n\nhelm install kubeai-models kubeai/models \\\n    -f ./kubeai-models.yaml\n
     
    Before progressing to the next steps, start a watch on Pods in a standalone terminal to see how KubeAI deploys models. 
     
    kubectl get pods --watch\n
    "},{"location":"#interact-with-gemma2","title":"Interact with Gemma2","text":"
    Because we set minReplicas: 1 for the Gemma model you should see a model Pod already coming up.
     
    Start a local port-forward to the bundled chat UI.
     
    kubectl port-forward svc/openwebui 8000:80\n
     
    Now open your browser to localhost:8000 and select the Gemma model to start chatting with.
    "},{"location":"#scale-up-qwen2-from-zero","title":"Scale up Qwen2 from Zero","text":"
    If you go back to the browser and start a chat with Qwen2, you will notice that it will take a while to respond at first. This is because we set minReplicas: 0 for this model and KubeAI needs to spin up a new Pod (you can verify with kubectl get models -oyaml qwen2-500m-cpu).
     
    NOTE: Autoscaling after initial scale-from-zero is not yet supported for the Ollama backend which we use in this local quickstart. KubeAI relies upon backend-specific metrics and the Ollama project has an open issue: https://github.com/ollama/ollama/issues/3144. To see autoscaling in action, checkout the GKE install guide which uses the vLLM backend and autoscales across GPU resources.
    "},{"location":"#documentation","title":"Documentation","text":"
    Checkout our documentation on kubeai.org to find info on:
     
       
    • Installing KubeAI in the cloud
      •  
    • How to guides (e.g. how to manage models and resource profiles).
      •  
    • Concepts (how the components of KubeAI work).
      •  
    • How to contribute
      •  
    "},{"location":"#adopters","title":"Adopters","text":"
    List of known adopters:
     Name Description Link Telescope Telescope uses KubeAI for multi-region large scale batch LLM inference. trytelescope.ai Google Cloud Distributed Edge KubeAI is included as a reference architecture for inferencing at the edge. LinkedIn, GitLab 
    If you are using KubeAI and would like to be listed as an adopter, please make a PR.
    "},{"location":"#openai-api-compatibility","title":"OpenAI API Compatibility","text":"
    # Implemented #\n/v1/chat/completions\n/v1/completions\n/v1/embeddings\n/v1/models\n/v1/audio/transcriptions\n\n# Planned #\n# /v1/assistants/*\n# /v1/batches/*\n# /v1/fine_tuning/*\n# /v1/images/*\n# /v1/vector_stores/*\n
    "},{"location":"#immediate-roadmap","title":"Immediate Roadmap","text":"
       
    • Model caching
      •  
    • LoRA finetuning (compatible with OpenAI finetuning API)
      •  
    • Image generation (compatible with OpenAI images API)
      •  
     
    NOTE: KubeAI was born out of a project called Lingo which was a simple Kubernetes LLM proxy with basic autoscaling. We relaunched the project as KubeAI (late August 2024) and expanded the roadmap to what it is today.
     
    \ud83c\udf1f Don't forget to drop us a star on GitHub and follow the repo to stay up to date!
     
    "},{"location":"#contact","title":"Contact","text":"
    Let us know about features you are interested in seeing or reach out with questions. Visit our Discord channel to join the discussion!
     
    Or just reach out on LinkedIn if you want to connect:
     
       
    • Nick Stogner
      •  
    • Sam Stoelinga
      •  
    "},{"location":"concepts/autoscaling/","title":"Autoscaling","text":"
    KubeAI proxies HTTP and messaging (i.e. Kafka, etc) requests and messages to models. It will adjust the number Pods serving a given model based on metrics reported by those servers. If no Pods are running when a request comes in, KubeAI will hold the request, scale up a Pod and forward the request when the Pod is ready. This process happens in a manner that is transparent to the end client (other than the added delay from a cold-start).
     
     
    "},{"location":"concepts/autoscaling/#next","title":"Next","text":"
    Read about how to configure autoscaling.
    "},{"location":"concepts/backend-servers/","title":"Backend Servers","text":"
    KubeAI serves ML models by launching Pods on Kubernetes. The configuration and lifecycle of these Pods are managed by the KubeAI controller. Every model server Pod loads exactly one model on startup.
     
    In a Model manifest you can define what server to use for inference (VLLM, OLlama). Any model-specific settings can be passed to the server process via the args and env fields.
    "},{"location":"concepts/backend-servers/#next","title":"Next","text":"
    Read about how to install models.
    "},{"location":"concepts/resource-profiles/","title":"Resource Profiles","text":"
    A resource profile maps a type of compute resource (i.e. NVIDIA L4 GPU) to a collection of Kubernetes settings that are configured on inference server Pods. These profiles are defined in the KubeAI config.yaml file (via a ConfigMap). Each model specifies the resource profile that it requires.
     
    Kubernetes Model resources specify a resource profile and the count of that resource that they require (for example resourceProfile: nvidia-gpu-l4:2 - 2x L4 GPUs).
     
    A given profile might need to contain slightly different settings based on the cluster/cloud that KubeAI is deployed in.
     
    Example: A resource profile named nvidia-gpu-l4 might contain the following node selectors when installing KubeAI on a GKE Kubernetes cluster:
     
    cloud.google.com/gke-accelerator: \"nvidia-l4\"\ncloud.google.com/gke-spot: \"true\"\n
     
    and add the following resource requests to the model server Pods:
     
    nvidia.com/gpu: \"1\"\n
     
    In addition to node selectors and resource requirements, a resource profile may optionally specify an image name. This name maps to the container image that will be selected when serving a model on that resource.
    "},{"location":"concepts/resource-profiles/#next","title":"Next","text":"
    Read about how to configure resource profiles.
    "},{"location":"concepts/storage-caching/","title":"Storage / Caching","text":"
    With \"Large\" in the name, caching is a critical part of serving LLMs.
     
    The best caching technique may very depending on your environment:
     
       
    • What cloud features are available?
      •  
    • Is your cluster deployed in an air-gapped environment?
      •  
    "},{"location":"concepts/storage-caching/#a-model-built-into-container","title":"A. Model built into container","text":"
    Status: Supported
     
    Building a model into a container image can provide a simple way to take advantage of image-related optimizations built into Kubernetes:
     
       
    •  
      Relaunching a model server on the same Node that it ran on before will likely be able to reuse the previously pulled image.
       
      •  
    •  
      Secondary boot disks on GKE can be used to avoid needing to pull images.
       
      •  
    •  
      Image streaming on GKE can allow for containers to startup before the entire image is present on the Node.
       
      •  
    •  
      Container images can be pre-installed on Nodes in air-gapped environments (example: k3s airgap installation).
       
      •  
     
    Guides:
     
       
    • How to build models into container images
      •  
    "},{"location":"concepts/storage-caching/#b-model-on-shared-filesystem-read-write-many","title":"B. Model on shared filesystem (read-write-many)","text":"
    Status: Planned.
     
    Examples: AWS EFS
    "},{"location":"concepts/storage-caching/#c-model-on-read-only-many-disk","title":"C. Model on read-only-many disk","text":"
    Status: Planned.
     
    Examples: GCP Hyperdisk ML
    "},{"location":"contributing/development-environment/","title":"Development environment","text":"
    This document provides instructions for setting up an environment for developing KubeAI.
    "},{"location":"contributing/development-environment/#optional-cloud-setup","title":"Optional: Cloud Setup","text":""},{"location":"contributing/development-environment/#gcp-pubsub","title":"GCP PubSub","text":"
    If you are develop PubSub messaging integration on GCP, setup test topics and subscriptions and uncomment the .messaging.streams in ./hack/dev-config.yaml.
     
    gcloud auth login --update-adc\n\ngcloud pubsub topics create test-kubeai-requests\ngcloud pubsub subscriptions create test-kubeai-requests-sub --topic test-kubeai-requests\ngcloud pubsub topics create test-kubeai-responses\ngcloud pubsub subscriptions create test-kubeai-responses-sub --topic test-kubeai-responses\n
    "},{"location":"contributing/development-environment/#run-in-local-cluster","title":"Run in Local Cluster","text":"
    kind create cluster\n# OR\n#./hack/create-dev-gke-cluster.yaml\n\n# Generate CRDs from Go code.\nmake generate && make manifests\n\n# When CRDs are changed reapply using kubectl:\nkubectl apply -f ./charts/kubeai/charts/crds/crds\n\n# Model with special address annotations:\nkubectl apply -f ./hack/dev-model.yaml\n\n# OPTION A #\n# Run KubeAI inside cluster\n# Change `-f` based on the cluster environment.\nhelm upgrade --install kubeai ./charts/kubeai \\\n    --set openwebui.enabled=true \\\n    --set image.tag=latest \\\n    --set image.pullPolicy=Always \\\n    --set image.repository=us-central1-docker.pkg.dev/substratus-dev/default/kubeai \\\n    --set secrets.huggingface.token=$HUGGING_FACE_HUB_TOKEN \\\n    --set replicaCount=1 -f ./hack/dev-gke-helm-values.yaml\n\n# OPTION B #\n# For quick local interation (run KubeAI outside of cluster)\nCONFIG_PATH=./hack/dev-config.yaml POD_NAMESPACE=default go run ./cmd/main.go\n\n# In another terminal:\nwhile true; do kubectl port-forward service/dev-model 7000:7000; done\n############\n
    "},{"location":"contributing/development-environment/#running","title":"Running","text":""},{"location":"contributing/development-environment/#completions-api","title":"Completions API","text":"
    # If you are running kubeai in-cluster:\n# kubectl port-forward svc/kubeai 8000:80\n\ncurl http://localhost:8000/openai/v1/completions -H \"Content-Type: application/json\" -d '{\"prompt\": \"Hi\", \"model\": \"dev\"}' -v\n
    "},{"location":"contributing/development-environment/#messaging-integration","title":"Messaging Integration","text":"
    gcloud pubsub topics publish test-kubeai-requests \\                  \n  --message='{\"path\":\"/v1/completions\", \"metadata\":{\"a\":\"b\"}, \"body\": {\"model\": \"dev\", \"prompt\": \"hi\"}}'\n\ngcloud pubsub subscriptions pull test-kubeai-responses-sub --auto-ack\n
    "},{"location":"contributing/documentation/","title":"Documentation","text":"
    We are grateful for anyone who takes the time to improve KubeAI documentation! In order to keep our docs clear and consistent we ask that you first read about the approach to documentation that we have standardized on...
    "},{"location":"contributing/documentation/#read-before-writing","title":"Read before writing!","text":"
    The KubeAI approach to documentation is loosely inspired by the Diataxis method.
     
    TLDR on how KubeAI docs are organized:
     
       
    • Installation: How-to guides specific to installing KubeAI.
      •  
    • How To: Directions that guide the reader through a problem or towards a result. How-to guides are goal-oriented. They assume the user is familiar with general concepts, tools, and has already installed KubeAI.
      •  
    • Concepts: A reflective explanation of KubeAI topics with a focus on giving the reader an understanding of the why.
      •  
    • Tutorials: Learning oriented experiences. Lessons that often guide a user from beginning to end. The goal is to help the reader learn something (compared to a how-to guide that is focused on helping the reader do something).
      •  
    • Contributing: The docs in here differ from the rest of the docs by audience: these docs are for anyone who will be contributing code or docs to the KubeAI project.
      •  
    "},{"location":"contributing/documentation/#how-to-serve-kubeaiorg-locally","title":"How to serve kubeai.org locally","text":"
    Make sure you have python3 installed and run:
     
    make docs\n
    "},{"location":"contributing/release-process/","title":"Release Process","text":"
    This document describes the process for releasing a new version of the project.
    "},{"location":"contributing/release-process/#docs","title":"Docs","text":"
    The docs are automatically published whenever a PR updates the docs and the PR is merged into the main branch. The docs are published to the gh-pages branch, which is the source for the Github Pages site.
    "},{"location":"contributing/release-process/#docker-images","title":"Docker images","text":"
    The Docker image latest tag always points to the latest released version. The main tag points to the latest commit on the main branch.
     
    If you push a tag vX.Y.Z to the repository, the Docker image with the tag vX.Y.Z is built and pushed to Docker Hub. Afterwards, the latest tag is updated to point to the new version.
    "},{"location":"contributing/release-process/#helm-chart","title":"Helm Chart","text":"
    The Helm chart only gets released when a git tag is pushed to the repository with the format helm-v*.
     
    The appVersion in the Helm chart does not have to point to the latest released version. This allows us to first publish a new version of the Docker image without updating the Helm chart. The Helm chart is updated when we are ready to release a new version.
     
    This is important when a new appVersion isn't compatible with the current Helm chart. In those cases, we can first merge the PR, thoroughly test, release new container image, and then in a separate PR update the Helm chart and the appVersion.
    "},{"location":"how-to/build-models-into-containers/","title":"Build models into containers","text":"
    In this guide we will preload a LLM into a custom built Ollama serving image. You can follow the same steps for other models and other serving engines.
     
    Define some values 
    export MODEL_URL=ollama://qwen2:0.5b\n\n# Customize with your own image repo.\nexport IMAGE=us-central1-docker.pkg.dev/substratus-dev/default/ollama-builtin-qwen2-05b:latest\n
     
    Build and push image. Note: building (downloading base image & model) and pushing (uploading image & model) can take a while depending on the size of the model.
     
    git clone https://github.com/substratusai/kubeai\ncd ./kubeai/images/ollama-builtin\n\ndocker build --build-arg MODEL_URL=$MODEL_URL -t $IMAGE .\ndocker push $IMAGE\n
     
    Create a model manifest & apply into a cluster with KubeAI installed. NOTE: The only difference between an built-in model image and otherwise is the addition of the image: field.
     
    kubectl apply -f - << EOF\napiVersion: kubeai.org/v1\nkind: Model\nmetadata:\n  name: builtin-model-example\nspec:\n  features: [\"TextGeneration\"]\n  owner: alibaba\n  image: $IMAGE # <-- The image with model built-in\n  url: \"$MODEL_URL\"\n  engine: OLlama\n  resourceProfile: cpu:1\nEOF\n
    "},{"location":"how-to/configure-autoscaling/","title":"Configure autoscaling","text":"
    This guide with cover how to configure KubeAI autoscaling parameters.
    "},{"location":"how-to/configure-autoscaling/#system-settings","title":"System Settings","text":"
    KubeAI administrators can define system-wide autoscaling settings by setting the following Helm values (for the kubeai/kubeai chart):
     
    Example:
     
    # helm-values.yaml\nmodelAutoscaling:\n  interval: 15s\n  timeWindow: 10m\n# ...\n
    "},{"location":"how-to/configure-autoscaling/#model-settings","title":"Model Settings","text":"
    The following settings can be configured on a model-by-model basis.
     
    NOTE: Updates to model settings will be applied upon scale up of additional model Pods. You can force-restart model servers by running kubectl delete pods -l model=<model-name>.
    "},{"location":"how-to/configure-autoscaling/#model-settings-helm","title":"Model settings: helm","text":"
    If you are managing models via the kubeai/models Helm chart, you can use:
     
    # helm-values.yaml\ncatalog:\n  model-a:\n    # ...\n    minReplicas: 1\n    maxReplicas: 9\n    targetRequests: 250\n    scaleDownDelaySeconds: 45\n  model-b:\n    # ...\n    disableAutoscaling: true\n# ...\n
     
    Re-running helm upgrade with these additional parameters will update model settings in the cluster.
    "},{"location":"how-to/configure-autoscaling/#model-settings-kubectl","title":"Model settings: kubectl","text":"
    You can also specify the autoscaling profile directly via the Models custom resource in the Kubernetes API:
     
    apiVersion: kubeai.org/v1\nkind: Model\nmetadata:\n  name: my-model\nspec:\n  # ...\n  minReplicas: 1\n  maxReplicas: 9\n  targetRequests: 250\n  scaleDownDelaySeconds: 45\n
     
    If you are already managing models using Model manifest files, you can make the update to your file and reapply it using kubectl apply -f <filename>.yaml.
    "},{"location":"how-to/configure-resource-profiles/","title":"Configure resource profiles","text":"
    This guide will cover modifying preconfigured resource profiles and adding your own.
    "},{"location":"how-to/configure-resource-profiles/#modifying-preconfigured-resource-profiles","title":"Modifying preconfigured resource profiles","text":"
    The KubeAI helm chart comes with preconfigured resource profiles for common resource types such as NVIDIA L4 GPUs. You can view these profiles in the default helm values file.
     
    These profiles usually require some additional settings based on the cluster/cloud that KubeAI is installed into. You can modify a resource profile by setting custom helm values and runing helm install or helm upgrade. For example, if you are installing KubeAI on GKE you will need to set GKE-specific node selectors:
     
    # helm-values.yaml\nresourceProfiles:\n  nvidia-gpu-l4:\n    nodeSelector:\n      cloud.google.com/gke-accelerator: \"nvidia-l4\"\n      cloud.google.com/gke-spot: \"true\"\n
     
    NOTE: See the cloud-specific installation guide for a comprehensive list of settings.
    "},{"location":"how-to/configure-resource-profiles/#adding-additional-resource-profiles","title":"Adding additional resource profiles","text":"
    If the preconfigured resource profiles do not meet your needs you can add additional profiles by appending to the .resourceProfiles object in the helm values file you use to install KubeAI.
     
    # helm-values.yaml\nresourceProfiles:\n  my-custom-gpu:\n    imageName: \"optional-custom-image-name\"\n    nodeSelector:\n      my-custom-node-pool: \"some-value\"\n    limits:\n      custom.com/gpu: \"1\"\n    requests:\n      custom.com/gpu: \"1\"\n      cpu: \"3\"\n      memory: \"12Gi\"\n
     
    If you need to run custom model server images on your resource profile, make sure to also add those in the modelServers section:
     
    # helm-values.yaml\nmodelServers:\n  VLLM:\n    images:\n      optional-custom-image-name: \"my-repo/my-vllm-image:v1.2.3\"\n  OLlama:\n    images:\n      optional-custom-image-name: \"my-repo/my-ollama-image:v1.2.3\"\n
    "},{"location":"how-to/configure-resource-profiles/#next","title":"Next","text":"
    See the guide on how to install models which includes how to configure the resource profile to use for a given model.
    "},{"location":"how-to/configure-speech-to-text/","title":"Configure speech-to-text","text":"
    KubeAI provides a Speech to Text endpoint that can be used to transcribe audio files. This guide will walk you through the steps to enable this feature.
    "},{"location":"how-to/configure-speech-to-text/#enable-speech-to-text-model","title":"Enable Speech to Text model","text":"
    You can create new models by creating a Model CRD object or by enabling a model from the model catalog.
    "},{"location":"how-to/configure-speech-to-text/#enable-from-model-catalog","title":"Enable from model catalog","text":"
    KubeAI provides predefined models in the kubeai/models Helm chart. To enable the Speech to Text model, you can set the enabled flag to true in your values file.
     
    # models-helm-values.yaml\ncatalog:\n  faster-whisper-medium-en-cpu:\n    enabled: true\n    minReplicas: 1\n
    "},{"location":"how-to/configure-speech-to-text/#enable-by-creating-model-crd","title":"Enable by creating Model CRD","text":"
    You can also create a Model CRD object to enable the Speech to Text model. Here is an example of a Model CRD object for the Speech to Text model:
     
    apiVersion: kubeai.org/v1\nkind: Model\nmetadata:\n  name: faster-whisper-medium-en-cpu\nspec:\n  features: [SpeechToText]\n  owner: Systran\n  url: hf://Systran/faster-whisper-medium.en\n  engine: FasterWhisper\n  resourceProfile: cpu:1\n
    "},{"location":"how-to/configure-speech-to-text/#usage","title":"Usage","text":"
    The Speech to Text endpoint is available at /openai/v1/transcriptions.
     
    Example usage using curl:
     
    curl -L -o kubeai.mp4 https://github.com/user-attachments/assets/711d1279-6af9-4c6c-a052-e59e7730b757\ncurl http://localhost:8000/openai/v1/audio/transcriptions \\\n  -F \"file=@kubeai.mp4\" \\\n  -F \"language=en\" \\\n  -F \"model=faster-whisper-medium-en-cpu\"\n
    "},{"location":"how-to/install-models/","title":"Install models","text":"
    This guide provides instructions on how to configure KubeAI models.
    "},{"location":"how-to/install-models/#installing-models-with-helm","title":"Installing models with helm","text":"
    KubeAI provides a chart that contains preconfigured models.
    "},{"location":"how-to/install-models/#preconfigured-models-with-helm","title":"Preconfigured models with helm","text":"
    When you are defining Helm values for the kubeai/models chart you can install a preconfigured Model by setting enabled: true. You can view a list of all preconfigured models in the chart's default values file. 
     
    # helm-values.yaml\ncatalog:\n  llama-3.1-8b-instruct-fp8-l4:\n    enabled: true\n
     
    You can optionally override preconfigured settings, for example, resourceProfile:
     
    # helm-values.yaml\ncatalog:\n  llama-3.1-8b-instruct-fp8-l4:\n    enabled: true\n    resourceProfile: nvidia-gpu-l4:2 # Require \"2 NVIDIA L4 GPUs\"\n
    "},{"location":"how-to/install-models/#custom-models-with-helm","title":"Custom models with helm","text":"
    If you prefer to add a custom model via the same Helm chart you use for installed KubeAI, you can add your custom model entry into the .catalog array of your existing values file for the kubeai/models Helm chart:
     
    # helm-values.yaml\ncatalog:\n  my-custom-model-name:\n    enabled: true\n    features: [\"TextEmbedding\"]\n    owner: me\n    url: \"hf://me/my-custom-model\"\n    resourceProfile: CPU:1\n
    "},{"location":"how-to/install-models/#installing-models-with-kubectl","title":"Installing models with kubectl","text":"
    You can add your own model by defining a Model yaml file and applying it using kubectl apply -f model.yaml.
     
    If you have a running cluster with KubeAI installed you can inspect the schema for a Model using kubectl explain:
     
    kubectl explain models\nkubectl explain models.spec\nkubectl explain models.spec.engine\n
    "},{"location":"how-to/install-models/#feedback-welcome-a-model-management-ui","title":"Feedback welcome: A model management UI","text":"
    We are considering adding a UI for managing models in a running KubeAI instance. Give the GitHub Issue a thumbs up if you would be interested in this feature.
    "},{"location":"installation/gke/","title":"Install on GKE","text":"TIP: Make sure you have enough quota in your GCP project. 
    Open the cloud console quotas page: https://console.cloud.google.com/iam-admin/quotas. Make sure your project is selected in the top left.
     
    There are 3 critical quotas you will need to verify for this guide. The minimum value here is assuming that you have nothing else running in your project.
     Quota Location Min Value Preemptible NVIDIA L4 GPUs <your-region> 2 GPUs (all regions) - 2 CPUs (all regions) - 24 
    See the following screenshot examples of how these quotas appear in the console:
     
     
     
    "},{"location":"installation/gke/#1-create-a-cluster","title":"1. Create a cluster","text":""},{"location":"installation/gke/#option-gke-autopilot","title":"Option: GKE Autopilot","text":"
    Create an Autopilot cluster (replace us-central1 with a region that you have quota).
     
    gcloud container clusters create-auto cluster-1 \\\n    --location=us-central1\n
    "},{"location":"installation/gke/#option-gke-standard","title":"Option: GKE Standard","text":"
    TODO: Reference gcloud commands for creating a GKE standard cluster.
    "},{"location":"installation/gke/#2-install-kubeai","title":"2. Install KubeAI","text":"
    Add KubeAI Helm repository.
     
    helm repo add kubeai https://www.kubeai.org\nhelm repo update\n
     
    Make sure you have a HuggingFace Hub token set in your environment (HUGGING_FACE_HUB_TOKEN).
     
    Install KubeAI with Helm.
     
    cat <<EOF > kubeai.yaml\nresourceProfiles:\n  nvidia-gpu-l4:\n    nodeSelector:\n      cloud.google.com/gke-accelerator: \"nvidia-l4\"\n      cloud.google.com/gke-spot: \"true\"\nEOF\n\nhelm upgrade --install kubeai kubeai/kubeai \\\n    -f ./kubeai.yaml \\\n    --set secrets.huggingface.token=$HUGGING_FACE_HUB_TOKEN \\\n    --wait\n
    "},{"location":"installation/gke/#3-optionally-configure-models","title":"3. Optionally configure models","text":"
    Optionally install preconfigured models.
     
    cat <<EOF > kubeai-models.yaml\ncatalog:\n  llama-3.1-8b-instruct-fp8-l4:\n    enabled: true\nEOF\n\nhelm install kubeai-models kubeai/models \\\n    -f ./kubeai-models.yaml\n
    "},{"location":"reference/kubernetes-api/","title":"Kubernetes API","text":""},{"location":"reference/kubernetes-api/#packages","title":"Packages","text":"
       
    • kubeai.org/v1
      •  
    "},{"location":"reference/kubernetes-api/#kubeaiorgv1","title":"kubeai.org/v1","text":"
    Package v1 contains API Schema definitions for the kubeai v1 API group
    "},{"location":"reference/kubernetes-api/#resource-types","title":"Resource Types","text":"
       
    • Model
      •  
    "},{"location":"reference/kubernetes-api/#model","title":"Model","text":"
    Model resources define the ML models that will be served by KubeAI.
     Field Description Default Validation apiVersion string kubeai.org/v1 kind string Model metadata ObjectMeta Refer to Kubernetes API documentation for fields of metadata. spec ModelSpec status ModelStatus"},{"location":"reference/kubernetes-api/#modelfeature","title":"ModelFeature","text":"
    Underlying type: string
     
    Validation: - Enum: [TextGeneration TextEmbedding SpeechToText]
     
    Appears in: - ModelSpec
    "},{"location":"reference/kubernetes-api/#modelspec","title":"ModelSpec","text":"
    ModelSpec defines the desired state of Model.
     
    Appears in: - Model
     Field Description Default Validation url string URL of the model to be served.Currently only the following formats are supported:For VLLM & FasterWhisper engines: \"hf:///\"For OLlama engine: \"ollama:// features ModelFeature array Features that the model supports.Dictates the APIs that are available for the model. Enum: [TextGeneration TextEmbedding SpeechToText]  engine string Engine to be used for the server process. Enum: [OLlama VLLM FasterWhisper]  resourceProfile string ResourceProfile required to serve the model.Use the format \":\".Example: \"nvidia-gpu-l4:2\" - 2x NVIDIA L4 GPUs.Must be a valid ResourceProfile defined in the system config. image string Image to be used for the server process.Will be set from ResourceProfile + Engine if not specified. args string array Args to be added to the server process. env object (keys:string, values:string) Env variables to be added to the server process. replicas integer Replicas is the number of Pod replicas that should be activelyserving the model. KubeAI will manage this field unless AutoscalingDisabledis set to true. minReplicas integer MinReplicas is the minimum number of Pod replicas that the model can scale down to.Note: 0 is a valid value. Minimum: 0 Optional: {}  maxReplicas integer MaxReplicas is the maximum number of Pod replicas that the model can scale up to.Empty value means no limit. Minimum: 1  autoscalingDisabled boolean AutoscalingDisabled will stop the controller from managing the replicasfor the Model. When disabled, metrics will not be collected on server Pods. targetRequests integer TargetRequests is average number of active requests that the autoscalerwill try to maintain on model server Pods. 100 Minimum: 1  scaleDownDelaySeconds integer ScaleDownDelay is the minimum time before a deployment is scaled down afterthe autoscaling algorithm determines that it should be scaled down. 30 owner string Owner of the model. Used solely to populate the owner field in theOpenAI /v1/models endpoint.DEPRECATED. Optional: {}"},{"location":"reference/kubernetes-api/#modelstatus","title":"ModelStatus","text":"
    ModelStatus defines the observed state of Model.
     
    Appears in: - Model
     Field Description Default Validation replicas ModelStatusReplicas"},{"location":"reference/kubernetes-api/#modelstatusreplicas","title":"ModelStatusReplicas","text":"
    Appears in: - ModelStatus
     Field Description Default Validation all integer ready integer"},{"location":"reference/openai-api-compatibility/","title":"OpenAI API Compatibility","text":"
    KubeAI provides an OpenAI API compatiblity layer.
    "},{"location":"reference/openai-api-compatibility/#general","title":"General:","text":""},{"location":"reference/openai-api-compatibility/#models","title":"Models","text":"
    GET /v1/models\n
     
       
    • Lists all kind: Model object installed in teh Kubernetes API Server.
      •  
    "},{"location":"reference/openai-api-compatibility/#inference","title":"Inference","text":""},{"location":"reference/openai-api-compatibility/#text-generation","title":"Text Generation","text":"
    POST /v1/chat/completions\nPOST /v1/completions\n
     
       
    • Supported for Models with .spec.features: [\"TextGeneration\"].
      •  
    "},{"location":"reference/openai-api-compatibility/#embeddings","title":"Embeddings","text":"
    POST /v1/embeddings\n
     
       
    • Supported for  Models with .spec.features: [\"TextEmbedding\"].
      •  
    "},{"location":"reference/openai-api-compatibility/#speech-to-text","title":"Speech-to-Text","text":"
    POST /v1/audio/transcriptions\n
     
       
    • Supported for Models with .spec.features: [\"SpeechToText\"].
      •  
    "},{"location":"tutorials/langchain/","title":"Using LangChain with KubeAI","text":"
    LangChain makes it easy to build applications powered by LLMs. KubeAI makes it easy to deploy and manage LLMs at scale. Together, they make it easy to build and deploy private and secure AI applications.
     
    In this tutorial, we'll show you how to use LangChain with KubeAI's OpenAI compatible API. The beauty of KubeAI's OpenAI compatibility is that you can use KubeAI with any framework that supports OpenAI.
    "},{"location":"tutorials/langchain/#prerequisites","title":"Prerequisites","text":"
    A K8s cluster. You can use a local cluster like kind.
    "},{"location":"tutorials/langchain/#installing-kubeai-with-gemma-2b","title":"Installing KubeAI with Gemma 2B","text":"
    Run the following command to install KubeAI with Gemma 2B:
     
    helm repo add kubeai https://www.kubeai.org\nhelm repo update\n\ncat <<EOF > models-helm-values.yaml\ncatalog:\n  gemma2-2b-cpu:\n    enabled: true\n    minReplicas: 1\nEOF\n\nhelm install kubeai kubeai/kubeai \\\n    -f ./helm-values.yaml \\\n    --wait --timeout 10m\n\nhelm install kubeai-models kubeai/models \\\n    -f ./models-helm-values.yaml\n
    "},{"location":"tutorials/langchain/#using-langchain","title":"Using LangChain","text":"
    Install the required Python packages: 
    pip install langchain_openai\n
     
    Let's access the KubeAI OpenAI compatible API locally to make it easier.
     
    Run the following command to port-forward to the KubeAI service: 
    kubectl port-forward svc/kubeai 8000:80\n
     Now the KubeAI OpenAI compatible API is available at http://localhost:8000/openai from your local machine.
     
    Let's create a simple Python script that uses LangChain and is connected to KubeAI.
     
    Create a file named test-langchain.py with the following content: 
    from langchain_openai import ChatOpenAI\n\nllm = ChatOpenAI(\n    model=\"gemma2-2b-cpu\",\n    temperature=0,\n    max_tokens=None,\n    timeout=None,\n    max_retries=2,\n    api_key=\"thisIsIgnored\",\n    base_url=\"http://localhost:8000/openai/v1\",\n)\n\nmessages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nprint(ai_msg.content)\n
     
    Run the Python script: 
    python test-langchain.py\n
     
    Notice that we set base_url to http://localhost:8000/openai/v1. This tells LangChain to use our local KubeAI OpenAI compatible AP instead of the default OpenAI public API.
     
    If you run langchain within the K8s cluster, you can use the following base_url instead: http://kubeai/openai/v1. So the code would look like this: 
    llm = ChatOpenAI(\n    ...\n    base_url=\"http://kubeai/openai/v1\",\n)\n
     
    That's it! You've successfully used LangChain with KubeAI. Now you can build and deploy private and secure AI applications with ease.
    "},{"location":"tutorials/langtrace/","title":"Deploying KubeAI with Langtrace","text":"
    Langtrace is an open source tool that helps you with tracing and monitoring your AI calls. It includes a self-hosted UI that for example shows you the estimated costs of your LLM calls.
     
    KubeAI is used for deploying LLMs with an OpenAI compatible endpoint.
     
    In this tutorial you will learn how to deploy KubeAI and Langtrace end-to-end. Both KubeAI and Langtrace are installed in your Kubernetes cluster. No cloud services or external dependencies are required.
     
    If you don't have a K8s cluster yet, you can create one using kind or minikube. 
    kind create cluster # OR: minikube start\n
     
    Install Langtrace: 
    helm repo add langtrace https://Scale3-Labs.github.io/langtrace-helm-chart\nhelm repo update\nhelm install langtrace langtrace/langtrace\n
     
    Install KubeAI and wait for all components to be ready (may take a minute). 
    helm repo add kubeai https://www.kubeai.org\nhelm repo update\nhelm install kubeai kubeai/kubeai --wait --timeout 10m\n
     
    Install the gemma2-2b-cpu model:
     
    cat <<EOF > kubeai-models.yaml\ncatalog:\n  gemma2-2b-cpu:\n    enabled: true\n    minReplicas: 1\nEOF\n\nhelm install kubeai-models kubeai/models \\\n    -f ./kubeai-models.yaml\n
     
    Create a local Python environment and install dependencies: 
    python3 -m venv .venv\nsource .venv/bin/activate\npip install langtrace-python-sdk openai\n
     
    Expose the KubeAI service to your local port: 
    kubectl port-forward service/kubeai 8000:80\n
     
    Expose the Langtrace service to your local port: 
    kubectl port-forward service/langtrace-app 3000:3000\n
     
    A Langtrace API key is required to use the Langtrace SDK. So lets get one by visiting your self hosted Langtace UI.
     
    Open your browser to http://localhost:3000, create a project and get the API keys for your langtrace project.
     
    In the Python script below, replace langtrace_api_key with your API key.
     
    Create file named langtrace-example.py with the following content: 
    # Replace this with your langtrace API key by visiting http://localhost:3000\nlangtrace_api_key=\"f7e003de19b9a628258531c17c264002e985604ca9fa561debcc85c41f357b09\"\n\nfrom langtrace_python_sdk import langtrace\nfrom langtrace_python_sdk.utils.with_root_span import with_langtrace_root_span\n# Paste this code after your langtrace init function\n\nfrom openai import OpenAI\n\nlangtrace.init(\n    api_key=api_key,\n    api_host=\"http://localhost:3000/api/trace\",\n)\n\nbase_url = \"http://localhost:8000/openai/v1\"\nmodel = \"gemma2-2b-cpu\"\n\n@with_langtrace_root_span()\ndef example():\n    client = OpenAI(base_url=base_url, api_key=\"ignored-by-kubeai\")\n    response = client.chat.completions.create(\n        model=model,\n        messages=[\n            {\n                \"role\": \"system\",\n                \"content\": \"How many states of matter are there?\"\n            }\n        ],\n    )\n    print(response.choices[0].message.content)\n\nexample()\n
     
    Run the Python script: 
    python3 langtrace-example.py\n
     
    Now you should see the trace in your Langtrace UI. Take a look by visiting http://localhost:3000.
     
    "},{"location":"tutorials/weaviate/","title":"Weaviate with local autoscaling embedding and generative models","text":"
    Weaviate is a vector search engine that can integrate seamlessly with KubeAI's embedding and generative models. This tutorial demonstrates how to deploy both KubeAI and Weaviate in a Kubernetes cluster, using KubeAI as the OpenAI endpoint for Weaviate.
     
    Why use KubeAI with Weaviate?
     
       
    • Security and privacy: KubeAI runs locally in your Kubernetes cluster, so your data never leaves your infrastructure.
      •  
    • Cost savings: KubeAI can run on your existing hardware, reducing the need for paying for embeddings and generative models.
      •  
     
    This tutorial uses CPU only models, so it should work even on your laptop.
     
    As you go go through this tutorial, you will learn how to:
     
       
    • Deploy KubeAI with embedding and generative models
      •  
    • Install Weaviate and connect it to KubeAI
      •  
    • Import data into Weaviate
      •  
    • Perform semantic search using the embedding model
      •  
    • Perform generative search using the generative model
      •  
    "},{"location":"tutorials/weaviate/#prerequisites","title":"Prerequisites","text":"
    A Kubernetes cluster. You can use kind or minikube.
     
    kind create cluster\n
    "},{"location":"tutorials/weaviate/#kubeai-configuration","title":"KubeAI Configuration","text":"
    Let's start by deploying KubeAI with the models we want to use. Nomic embedding model is used instead of text-embedding-ada-002. Gemma 2 2B is used instead of gpt-3.5-turbo. You could choose to use bigger models depending on your available hardware.
     
    Create a file named kubeai-model-values.yaml with the following content: 
    catalog:\n  text-embedding-ada-002:\n    enabled: true\n    minReplicas: 1\n    features: [\"TextEmbedding\"]\n    owner: nomic\n    url: \"ollama://nomic-embed-text\"\n    engine: OLlama\n    resourceProfile: cpu:1\n  gpt-3.5-turbo:\n    enabled: true\n    minReplicas: 1\n    features: [\"TextGeneration\"]\n    owner: google\n    url: \"ollama://gemma2:2b\"\n    engine: OLlama\n    resourceProfile: cpu:2\n
     
    Note: It's important that you name the models as text-embedding-ada-002 and gpt-3.5-turbo as Weaviate expects these names.
     
    Run the following command to deploy KubeAI and install the configured models: 
    helm repo add kubeai https://www.kubeai.org && helm repo update\n\nhelm install kubeai kubeai/kubeai\n\nhelm install kubeai-models kubeai/models \\\n    -f ./kubeai-model-values.yaml\n
    "},{"location":"tutorials/weaviate/#weaviate-installation","title":"Weaviate Installation","text":"
    For this tutorial, we will use the Weaviate Helm chart to deploy Weaviate.
     
    Let's enable the text2vec-openai and generative-openai modules in Weaviate. We will also set the default vectorizer module to text2vec-openai.
     
    The apiKey is ignored in this case as we are using KubeAI as the OpenAI endpoint.
     
    Create a file named weaviate-values.yaml with the following content: 
    modules:\n  text2vec-openai:\n    enabled: true\n    apiKey: thisIsIgnored\n  generative-openai:\n    enabled: true\n    apiKey: thisIsIgnored\n  default_vectorizer_module: text2vec-openai\nservice:\n  # To prevent Weaviate being exposed publicly\n  type: ClusterIP\n
     
    Install Weaviate by running the following command: 
    helm repo add weaviate https://weaviate.github.io/weaviate-helm && helm repo update\n\nhelm install \\\n  \"weaviate\" \\\n  weaviate/weaviate \\\n  -f weaviate-values.yaml\n
    "},{"location":"tutorials/weaviate/#usage","title":"Usage","text":"
    We will be using Python to interact with Weaviate. The 2 use cases we will cover are: - Semantic search using the embedding model - Generative search using the generative model
    "},{"location":"tutorials/weaviate/#connectivity","title":"Connectivity","text":"
    The remaining steps require connectivity to the Weaviate service. However, Weaviate is not exposed publicly in this setup. So we setup a local port forwards to access the Weaviate services.
     
    Setup a local port forwards to the Weaviate services by running: 
    kubectl port-forward svc/weaviate 8080:80\nkubectl port-forward svc/weaviate-grpc 50051:50051\n
    "},{"location":"tutorials/weaviate/#weaviate-client-python-setup","title":"Weaviate client Python Setup","text":"
    Create a virtual environment and install the Weaviate client: 
    python -m venv .venv\nsource .venv/bin/activate\npip install -U weaviate-client requests\n
    "},{"location":"tutorials/weaviate/#collection-and-data-import","title":"Collection and Data Import","text":"
    Create a file named create-collection.py with the following content: 
    import json\nimport weaviate\nimport requests\nfrom weaviate.classes.config import Configure\n\n# This works due to port forward in previous step\nwith weaviate.connect_to_local(port=8080, grpc_port=50051) as client:\n\n    client.collections.create(\n        \"Question\",\n        vectorizer_config=Configure.Vectorizer.text2vec_openai(\n                model=\"text-embedding-ada-002\",\n                base_url=\"http://kubeai/openai\",\n        ),\n        generative_config=Configure.Generative.openai(\n            model=\"gpt-3.5-turbo\",\n            base_url=\"http://kubeai/openai\",\n        ),\n    )\n\n    # import data\n    resp = requests.get('https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny.json')\n    data = json.loads(resp.text)  # Load data\n\n    question_objs = list()\n    for i, d in enumerate(data):\n        question_objs.append({\n            \"answer\": d[\"Answer\"],\n            \"question\": d[\"Question\"],\n            \"category\": d[\"Category\"],\n        })\n\n    questions = client.collections.get(\"Question\")\n    questions.data.insert_many(question_objs)\n    print(\"Data imported successfully\")\n
     
    Create a collection that uses KubeAI as the openAI endpoint: 
    python create-collection.py\n
     You should see a message Data imported successfully.
     
    The collection is now created and data is imported. The vectors are generated by KubeAI and stored in Weaviate.
    "},{"location":"tutorials/weaviate/#semantic-search","title":"Semantic Search","text":"
    Now let's do semantic search, which uses the embeddings. Create a file named search.py with the following content: 
    import weaviate\nfrom weaviate.classes.config import Configure\n\n# This works due to port forward in previous step\nwith weaviate.connect_to_local(port=8080, grpc_port=50051) as client:\n    questions = client.collections.get(\"Question\")\n    response = questions.query.near_text(\n        query=\"biology\",\n        limit=2\n    )\n    print(response.objects[0].properties)  # Inspect the first object\n
     
    Execute the python script: 
    python search.py\n
     
    You should see the following output: 
    {\n  \"answer\": \"DNA\",\n  \"question\": \"In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance\",\n  \"category\": \"SCIENCE\"\n}\n
    "},{"location":"tutorials/weaviate/#generative-search-rag","title":"Generative Search (RAG)","text":"
    Now let's do generative search, which uses the generative model (Text generation LLM). The generative model is run locally and managed by KubeAI.
     
    Create a file named generate.py with the following content: 
    import weaviate\nfrom weaviate.classes.config import Configure\n\n# This works due to port forward in previous step\nwith weaviate.connect_to_local(port=8080, grpc_port=50051) as client:\n    questions = client.collections.get(\"Question\")\n\n    response = questions.generate.near_text(\n        query=\"biology\",\n        limit=2,\n        grouped_task=\"Write a tweet with emojis about these facts.\"\n    )\n\n    print(response.generated)  # Inspect the generated text\n
     
    Run the python script: 
    python generate.py\n
     
    You should see something similar to this:
     
    \ud83e\uddec Watson & Crick cracked the code in 1953!  \ud83e\udd2f They built a model of DNA, the blueprint of life. \ud83e\uddec \ud83e\udde0 Liver power! \ud83d\udcaa This organ keeps your blood sugar balanced by storing glucose as glycogen. \ud83e\ude78 #ScienceFacts #Biology
    "},{"location":"tutorials/weaviate/#conclusion","title":"Conclusion","text":"
    You've now successfully set up KubeAI with Weaviate for both embedding-based semantic search and generative tasks. You've also learned how to import data, perform searches, and generate content using KubeAI-managed models.
    "}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index d890b9a6..0e886057 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,78 +2,78 @@
 
     
          https://www.kubeai.org/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/concepts/autoscaling/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/concepts/backend-servers/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/concepts/resource-profiles/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/concepts/storage-caching/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/contributing/development-environment/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/contributing/documentation/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/contributing/release-process/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/how-to/build-models-into-containers/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/how-to/configure-autoscaling/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/how-to/configure-resource-profiles/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/how-to/configure-speech-to-text/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/how-to/install-models/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/installation/gke/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/reference/kubernetes-api/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/reference/openai-api-compatibility/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/tutorials/langchain/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/tutorials/langtrace/
-         2024-09-17
+         2024-09-19
     
     
          https://www.kubeai.org/tutorials/weaviate/
-         2024-09-17
+         2024-09-19
     
 
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index bd6e688a..db126584 100644
Binary files a/sitemap.xml.gz and b/sitemap.xml.gz differ
diff --git a/tutorials/langchain/index.html b/tutorials/langchain/index.html
index ac425c8f..46643d65 100644
--- a/tutorials/langchain/index.html
+++ b/tutorials/langchain/index.html
@@ -18,7 +18,7 @@
       
       
       
-      
+      
     
     
       
@@ -1021,17 +1021,21 @@ 
    PrerequisitesInstalling KubeAI with Gemma 2B
    
 
    Run the following command to install KubeAI with Gemma 2B:
    
 
    helm repo add kubeai https://www.kubeai.org
-cat <<EOF > helm-values.yaml
-models:
-  catalog:
-    gemma2-2b-cpu:
-      enabled: true
-      minReplicas: 1
+helm repo update
+
+cat <<EOF > models-helm-values.yaml
+catalog:
+  gemma2-2b-cpu:
+    enabled: true
+    minReplicas: 1
 EOF
 
-helm upgrade --install kubeai kubeai/kubeai \
+helm install kubeai kubeai/kubeai \
     -f ./helm-values.yaml \
     --wait --timeout 10m
+
+helm install kubeai-models kubeai/models \
+    -f ./models-helm-values.yaml
 
    
 
    Using LangChain
    
 
    Install the required Python packages:
@@ -1128,7 +1132,7 @@ 
    Using LangChain{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}
+    
     
     
       
diff --git a/tutorials/langtrace/index.html b/tutorials/langtrace/index.html
index b309f3b9..7aa34553 100644
--- a/tutorials/langtrace/index.html
+++ b/tutorials/langtrace/index.html
@@ -18,7 +18,7 @@
       
       
       
-      
+      
     
     
       
@@ -928,25 +928,26 @@ 
    Deploying KubeAI with Langtrace
    kind create cluster # OR: minikube start
 
    
 
    Install Langtrace:
-
    helm repo add langtrace https://Scale3-Labs.github.io/langtrace-helm-chart
-helm repo update
-helm install langtrace langtrace/langtrace
+
    helm repo add langtrace https://Scale3-Labs.github.io/langtrace-helm-chart
+helm repo update
+helm install langtrace langtrace/langtrace
 
    
-
    Install KubeAI:
-
    helm repo add kubeai https://substratusai.github.io/kubeai/
+
    Install KubeAI and wait for all components to be ready (may take a minute).
+
    helm repo add kubeai https://www.kubeai.org
 helm repo update
-cat <<EOF > helm-values.yaml
-models:
-  catalog:
-    gemma2-2b-cpu:
-      enabled: true
-      minReplicas: 1
+helm install kubeai kubeai/kubeai --wait --timeout 10m
+
    
+
    Install the gemma2-2b-cpu model:
    
+
    cat <<EOF > kubeai-models.yaml
+catalog:
+  gemma2-2b-cpu:
+    enabled: true
+    minReplicas: 1
 EOF
 
-helm upgrade --install kubeai kubeai/kubeai \
-    --wait --timeout 10m \
-    -f ./helm-values.yaml
-
    
+helm install kubeai-models kubeai/models \
+    -f ./kubeai-models.yaml
+
    
 
    Create a local Python environment and install dependencies:
 
    python3 -m venv .venv
 source .venv/bin/activate
@@ -1049,7 +1050,7 @@ 
    Deploying KubeAI with Langtrace
     
     
-    
+    
     
     
       
diff --git a/tutorials/weaviate/index.html b/tutorials/weaviate/index.html
index 1796a0e5..9b1c68fe 100644
--- a/tutorials/weaviate/index.html
+++ b/tutorials/weaviate/index.html
@@ -18,7 +18,7 @@
       
       
       
-      
+      
     
     
       
@@ -1171,30 +1171,33 @@ 
    KubeAI Configuration
    models:
-  catalog:
-    text-embedding-ada-002:
-      enabled: true
-      minReplicas: 1
-      features: ["TextEmbedding"]
-      owner: nomic
-      url: "ollama://nomic-embed-text"
-      engine: OLlama
-      resourceProfile: cpu:1
-    gpt-3.5-turbo:
-      enabled: true
-      minReplicas: 1
-      features: ["TextGeneration"]
-      owner: google
-      url: "ollama://gemma2:2b"
-      engine: OLlama
-      resourceProfile: cpu:2
+
    Create a file named kubeai-model-values.yaml with the following content:
+
    catalog:
+  text-embedding-ada-002:
+    enabled: true
+    minReplicas: 1
+    features: ["TextEmbedding"]
+    owner: nomic
+    url: "ollama://nomic-embed-text"
+    engine: OLlama
+    resourceProfile: cpu:1
+  gpt-3.5-turbo:
+    enabled: true
+    minReplicas: 1
+    features: ["TextGeneration"]
+    owner: google
+    url: "ollama://gemma2:2b"
+    engine: OLlama
+    resourceProfile: cpu:2
 
    
 
    Note: It's important that you name the models as text-embedding-ada-002 and gpt-3.5-turbo as Weaviate expects these names.
    
-
    Run the following command to deploy KubeAI:
-
    helm install kubeai kubeai/kubeai \
-    -f ./kubeai-values.yaml
+
    Run the following command to deploy KubeAI and install the configured models:
+
    helm repo add kubeai https://www.kubeai.org && helm repo update
+
+helm install kubeai kubeai/kubeai
+
+helm install kubeai-models kubeai/models \
+    -f ./kubeai-model-values.yaml
 
    
 
    Weaviate Installation
    
 
    For this tutorial, we will use the Weaviate Helm chart to deploy Weaviate.
    
@@ -1215,8 +1218,9 @@ 
    Weaviate Installation  type: ClusterIP
 
    
 
    Install Weaviate by running the following command:
-
    helm repo add weaviate https://weaviate.github.io/weaviate-helm
-helm upgrade --install \
+
    helm repo add weaviate https://weaviate.github.io/weaviate-helm && helm repo update
+
+helm install \
   "weaviate" \
   weaviate/weaviate \
   -f weaviate-values.yaml
@@ -1382,7 +1386,7 @@ 
    Conclusion{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.07f07601.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}
+