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added basic and ugly but working search
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Original file line number | Diff line number | Diff line change |
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function debounce(func, wait) { | ||
var timeout; | ||
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return function () { | ||
var context = this; | ||
var args = arguments; | ||
clearTimeout(timeout); | ||
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timeout = setTimeout(function () { | ||
timeout = null; | ||
func.apply(context, args); | ||
}, wait); | ||
}; | ||
} | ||
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// Taken from mdbook | ||
// The strategy is as follows: | ||
// First, assign a value to each word in the document: | ||
// Words that correspond to search terms (stemmer aware): 40 | ||
// Normal words: 2 | ||
// First word in a sentence: 8 | ||
// Then use a sliding window with a constant number of words and count the | ||
// sum of the values of the words within the window. Then use the window that got the | ||
// maximum sum. If there are multiple maximas, then get the last one. | ||
// Enclose the terms in <b>. | ||
function makeTeaser(body, terms) { | ||
var TERM_WEIGHT = 40; | ||
var NORMAL_WORD_WEIGHT = 2; | ||
var FIRST_WORD_WEIGHT = 8; | ||
var TEASER_MAX_WORDS = 30; | ||
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var stemmedTerms = terms.map(function (w) { | ||
return elasticlunr.stemmer(w.toLowerCase()); | ||
}); | ||
var termFound = false; | ||
var index = 0; | ||
var weighted = []; // contains elements of ["word", weight, index_in_document] | ||
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// split in sentences, then words | ||
var sentences = body.toLowerCase().split(". "); | ||
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for (var i in sentences) { | ||
var words = sentences[i].split(" "); | ||
var value = FIRST_WORD_WEIGHT; | ||
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for (var j in words) { | ||
var word = words[j]; | ||
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if (word.length > 0) { | ||
for (var k in stemmedTerms) { | ||
if (elasticlunr.stemmer(word).startsWith(stemmedTerms[k])) { | ||
value = TERM_WEIGHT; | ||
termFound = true; | ||
} | ||
} | ||
weighted.push([word, value, index]); | ||
value = NORMAL_WORD_WEIGHT; | ||
} | ||
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index += word.length; | ||
index += 1; // ' ' or '.' if last word in sentence | ||
} | ||
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index += 1; // because we split at a two-char boundary '. ' | ||
} | ||
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if (weighted.length === 0) { | ||
return body; | ||
} | ||
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var windowWeights = []; | ||
var windowSize = Math.min(weighted.length, TEASER_MAX_WORDS); | ||
// We add a window with all the weights first | ||
var curSum = 0; | ||
for (var i = 0; i < windowSize; i++) { | ||
curSum += weighted[i][1]; | ||
} | ||
windowWeights.push(curSum); | ||
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for (var i = 0; i < weighted.length - windowSize; i++) { | ||
curSum -= weighted[i][1]; | ||
curSum += weighted[i + windowSize][1]; | ||
windowWeights.push(curSum); | ||
} | ||
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// If we didn't find the term, just pick the first window | ||
var maxSumIndex = 0; | ||
if (termFound) { | ||
var maxFound = 0; | ||
// backwards | ||
for (var i = windowWeights.length - 1; i >= 0; i--) { | ||
if (windowWeights[i] > maxFound) { | ||
maxFound = windowWeights[i]; | ||
maxSumIndex = i; | ||
} | ||
} | ||
} | ||
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var teaser = []; | ||
var startIndex = weighted[maxSumIndex][2]; | ||
for (var i = maxSumIndex; i < maxSumIndex + windowSize; i++) { | ||
var word = weighted[i]; | ||
if (startIndex < word[2]) { | ||
// missing text from index to start of `word` | ||
teaser.push(body.substring(startIndex, word[2])); | ||
startIndex = word[2]; | ||
} | ||
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// add <em/> around search terms | ||
if (word[1] === TERM_WEIGHT) { | ||
teaser.push("<b>"); | ||
} | ||
startIndex = word[2] + word[0].length; | ||
teaser.push(body.substring(word[2], startIndex)); | ||
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if (word[1] === TERM_WEIGHT) { | ||
teaser.push("</b>"); | ||
} | ||
} | ||
teaser.push("…"); | ||
return teaser.join(""); | ||
} | ||
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function formatSearchResultItem(item, terms) { | ||
return '<div class="search-results__item">' | ||
+ `<a href="${item.ref}">${item.doc.title}</a>` | ||
+ `<div>${makeTeaser(item.doc.body, terms)}</div>` | ||
+ '</div>'; | ||
} | ||
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function initSearch() { | ||
var $searchInput = document.getElementById("search"); | ||
var $searchResults = document.querySelector(".search-results"); | ||
var $searchResultsItems = document.querySelector(".search-results__items"); | ||
var MAX_ITEMS = 10; | ||
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var options = { | ||
bool: "AND", | ||
fields: { | ||
title: {boost: 2}, | ||
body: {boost: 1}, | ||
} | ||
}; | ||
var currentTerm = ""; | ||
var index; | ||
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var initIndex = async function () { | ||
if (index === undefined) { | ||
index = fetch("/search_index.en.json") | ||
.then( | ||
async function(response) { | ||
return await elasticlunr.Index.load(await response.json()); | ||
} | ||
); | ||
} | ||
let res = await index; | ||
return res; | ||
} | ||
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$searchInput.addEventListener("keyup", debounce(async function() { | ||
var term = $searchInput.value.trim(); | ||
if (term === currentTerm) { | ||
return; | ||
} | ||
$searchResults.style.display = term === "" ? "none" : "block"; | ||
$searchResultsItems.innerHTML = ""; | ||
currentTerm = term; | ||
if (term === "") { | ||
return; | ||
} | ||
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var results = (await initIndex()).search(term, options); | ||
if (results.length === 0) { | ||
$searchResults.style.display = "none"; | ||
return; | ||
} | ||
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for (var i = 0; i < Math.min(results.length, MAX_ITEMS); i++) { | ||
var item = document.createElement("li"); | ||
item.innerHTML = formatSearchResultItem(results[i], term.split(" ")); | ||
$searchResultsItems.appendChild(item); | ||
} | ||
}, 150)); | ||
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window.addEventListener('click', function(e) { | ||
if ($searchResults.style.display == "block" && !$searchResults.contains(e.target)) { | ||
$searchResults.style.display = "none"; | ||
} | ||
}); | ||
} | ||
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if (document.readyState === "complete" || | ||
(document.readyState !== "loading" && !document.documentElement.doScroll) | ||
) { | ||
initSearch(); | ||
} else { | ||
document.addEventListener("DOMContentLoaded", initSearch); | ||
} |
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