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<html> |
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<head> |
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<meta content="text/html;charset=utf-8" http-equiv="Content-Type" /> |
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<title>Candle Bert</title> |
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</head> |
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<body></body> |
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</html> |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<meta charset="UTF-8" /> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /> |
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<style> |
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@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap"); |
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html, |
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body { |
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font-family: "Source Sans 3", sans-serif; |
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} |
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</style> |
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<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script> |
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<script src="https://cdn.tailwindcss.com"></script> |
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<script type="module" src="./code.js"></script> |
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<script type="module"> |
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import { hcl } from "https://cdn.skypack.dev/d3-color@3"; |
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import { interpolateReds } from "https://cdn.skypack.dev/d3-scale-chromatic@3"; |
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import { scaleLinear } from "https://cdn.skypack.dev/d3-scale@4"; |
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import { |
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getModelInfo, |
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getEmbeddings, |
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getWikiText, |
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cosineSimilarity, |
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} from "./utils.js"; |
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const bertWorker = new Worker("./bertWorker.js", { |
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type: "module", |
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}); |
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const inputContainerEL = document.querySelector("#input-container"); |
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const textAreaEl = document.querySelector("#input-area"); |
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const outputAreaEl = document.querySelector("#output-area"); |
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const formEl = document.querySelector("#form"); |
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const searchInputEl = document.querySelector("#search-input"); |
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const formWikiEl = document.querySelector("#form-wiki"); |
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const searchWikiEl = document.querySelector("#search-wiki"); |
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const outputStatusEl = document.querySelector("#output-status"); |
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const modelSelectEl = document.querySelector("#model"); |
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const sentencesRegex = |
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/(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z]\.)(?<=\.|\?)\s/gm; |
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let sentenceEmbeddings = []; |
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let currInputText = ""; |
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let isCalculating = false; |
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function toggleTextArea(state) { |
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if (state) { |
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textAreaEl.hidden = false; |
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textAreaEl.focus(); |
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} else { |
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textAreaEl.hidden = true; |
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} |
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} |
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inputContainerEL.addEventListener("focus", (e) => { |
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toggleTextArea(true); |
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}); |
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textAreaEl.addEventListener("blur", (e) => { |
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toggleTextArea(false); |
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}); |
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textAreaEl.addEventListener("focusout", (e) => { |
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toggleTextArea(false); |
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if (currInputText === textAreaEl.value || isCalculating) return; |
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populateOutputArea(textAreaEl.value); |
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calculateEmbeddings(textAreaEl.value); |
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}); |
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modelSelectEl.addEventListener("change", (e) => { |
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const query = new URLSearchParams(window.location.search); |
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query.set("model", modelSelectEl.value); |
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window.history.replaceState( |
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{}, |
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"", |
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`${window.location.pathname}?${query}` |
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); |
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window.parent.postMessage({ queryString: "?" + query }, "*") |
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if (currInputText === "" || isCalculating) return; |
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populateOutputArea(textAreaEl.value); |
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calculateEmbeddings(textAreaEl.value); |
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}); |
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function populateOutputArea(text) { |
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currInputText = text; |
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const sentences = text.split(sentencesRegex); |
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outputAreaEl.innerHTML = ""; |
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for (const [id, sentence] of sentences.entries()) { |
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const sentenceEl = document.createElement("span"); |
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sentenceEl.id = `sentence-${id}`; |
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sentenceEl.innerText = sentence + " "; |
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outputAreaEl.appendChild(sentenceEl); |
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} |
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} |
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formEl.addEventListener("submit", async (e) => { |
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e.preventDefault(); |
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if (isCalculating || currInputText === "") return; |
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toggleInputs(true); |
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const modelID = modelSelectEl.value; |
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const { modelURL, tokenizerURL, configURL, search_prefix } = |
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getModelInfo(modelID); |
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const text = searchInputEl.value; |
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const query = search_prefix + searchInputEl.value; |
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outputStatusEl.classList.remove("invisible"); |
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outputStatusEl.innerText = "Calculating embeddings for query..."; |
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isCalculating = true; |
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const out = await getEmbeddings( |
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bertWorker, |
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modelURL, |
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tokenizerURL, |
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configURL, |
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modelID, |
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[query] |
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); |
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outputStatusEl.classList.add("invisible"); |
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const queryEmbeddings = out.output[0]; |
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const distances = sentenceEmbeddings |
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.map((embedding, id) => ({ |
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id, |
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similarity: cosineSimilarity(queryEmbeddings, embedding), |
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})) |
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.sort((a, b) => b.similarity - a.similarity) |
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.slice(0, 10); |
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const colorScale = scaleLinear() |
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.domain([ |
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distances[distances.length - 1].similarity, |
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distances[0].similarity, |
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]) |
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.range([0, 1]) |
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.interpolate(() => interpolateReds); |
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outputAreaEl.querySelectorAll("span").forEach((el) => { |
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el.style.color = "unset"; |
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el.style.backgroundColor = "unset"; |
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}); |
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distances.forEach((d) => { |
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const el = outputAreaEl.querySelector(`#sentence-${d.id}`); |
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const color = colorScale(d.similarity); |
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const fontColor = hcl(color).l < 70 ? "white" : "black"; |
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el.style.color = fontColor; |
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el.style.backgroundColor = color; |
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}); |
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outputAreaEl |
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.querySelector(`#sentence-${distances[0].id}`) |
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.scrollIntoView({ |
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behavior: "smooth", |
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block: "center", |
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inline: "nearest", |
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}); |
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isCalculating = false; |
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toggleInputs(false); |
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}); |
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async function calculateEmbeddings(text) { |
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isCalculating = true; |
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toggleInputs(true); |
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const modelID = modelSelectEl.value; |
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const { modelURL, tokenizerURL, configURL, document_prefix } = |
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getModelInfo(modelID); |
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const sentences = text.split(sentencesRegex); |
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const allEmbeddings = []; |
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outputStatusEl.classList.remove("invisible"); |
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for (const [id, sentence] of sentences.entries()) { |
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const query = document_prefix + sentence; |
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outputStatusEl.innerText = `Calculating embeddings: sentence ${ |
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id + 1 |
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} of ${sentences.length}`; |
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const embeddings = await getEmbeddings( |
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bertWorker, |
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modelURL, |
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tokenizerURL, |
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configURL, |
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modelID, |
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[query], |
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updateStatus |
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); |
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allEmbeddings.push(embeddings); |
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} |
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outputStatusEl.classList.add("invisible"); |
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sentenceEmbeddings = allEmbeddings.map((e) => e.output[0]); |
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isCalculating = false; |
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toggleInputs(false); |
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} |
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function updateStatus(data) { |
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if ("status" in data) { |
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if (data.status === "loading") { |
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outputStatusEl.innerText = data.message; |
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outputStatusEl.classList.remove("invisible"); |
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} |
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} |
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} |
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function toggleInputs(state) { |
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const interactive = document.querySelectorAll(".interactive"); |
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interactive.forEach((el) => { |
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if (state) { |
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el.disabled = true; |
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} else { |
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el.disabled = false; |
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} |
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}); |
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} |
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searchWikiEl.addEventListener("input", () => { |
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searchWikiEl.setCustomValidity(""); |
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}); |
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formWikiEl.addEventListener("submit", async (e) => { |
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e.preventDefault(); |
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if ("example" in e.submitter.dataset) { |
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searchWikiEl.value = e.submitter.innerText; |
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} |
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const text = searchWikiEl.value; |
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if (isCalculating || text === "") return; |
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try { |
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const wikiText = await getWikiText(text); |
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searchWikiEl.setCustomValidity(""); |
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textAreaEl.innerHTML = wikiText; |
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populateOutputArea(wikiText); |
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calculateEmbeddings(wikiText); |
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searchWikiEl.value = ""; |
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} catch { |
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searchWikiEl.setCustomValidity("Invalid Wikipedia article name"); |
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searchWikiEl.reportValidity(); |
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} |
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}); |
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document.addEventListener("DOMContentLoaded", () => { |
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const query = new URLSearchParams(window.location.search); |
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const modelID = query.get("model"); |
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if (modelID) { |
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modelSelectEl.value = modelID; |
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modelSelectEl.dispatchEvent(new Event("change")); |
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} |
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}); |
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</script> |
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</head> |
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<body class="container max-w-4xl mx-auto p-4"> |
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<main class="grid grid-cols-1 gap-5 relative"> |
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<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span> |
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<div> |
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<h1 class="text-5xl font-bold">Candle BERT</h1> |
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<h2 class="text-2xl font-bold">Rust/WASM Demo</h2> |
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<p class="max-w-lg"> |
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Running sentence embeddings and similarity search in the browser using |
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the Bert Model written with |
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<a |
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href="https://github.com/huggingface/candle/" |
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target="_blank" |
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class="underline hover:text-blue-500 hover:no-underline" |
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>Candle |
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</a> |
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and compiled to Wasm. Embeddings models from are from |
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<a |
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href="https://huggingface.co/sentence-transformers/" |
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target="_blank" |
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class="underline hover:text-blue-500 hover:no-underline"> |
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Sentence Transformers |
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</a> |
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and |
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<a |
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href="https://huggingface.co/intfloat/" |
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target="_blank" |
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class="underline hover:text-blue-500 hover:no-underline"> |
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Liang Wang - e5 Models |
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</a> |
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</p> |
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</div> |
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<div> |
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<label for="model" class="font-medium block">Models Options: </label> |
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<select |
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id="model" |
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class="border-2 border-gray-500 rounded-md font-light interactive disabled:cursor-not-allowed w-full max-w-max"> |
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<option value="gte_tiny">gte_tiny (45.5 MB)</option> |
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<option value="intfloat_e5_small_v2" selected> |
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intfloat/e5-small-v2 (133 MB) |
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</option> |
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<option value="intfloat_e5_base_v2"> |
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intfloat/e5-base-v2 (438 MB) |
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</option> |
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<option value="intfloat_multilingual_e5_small"> |
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intfloat/multilingual-e5-small (471 MB) |
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</option> |
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<option value="sentence_transformers_all_MiniLM_L6_v2"> |
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sentence-transformers/all-MiniLM-L6-v2 (90.9 MB) |
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</option> |
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<option value="sentence_transformers_all_MiniLM_L12_v2"> |
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sentence-transformers/all-MiniLM-L12-v2 (133 MB) |
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</option> |
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</select> |
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</div> |
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<div> |
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<h3 class="font-medium">Examples:</h3> |
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<form |
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id="form-wiki" |
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class="flex text-xs rounded-md justify-between w-min gap-3"> |
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<input type="submit" hidden /> |
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<button data-example class="disabled:cursor-not-allowed interactive"> |
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Pizza |
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</button> |
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<button data-example class="disabled:cursor-not-allowed interactive"> |
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Paris |
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</button> |
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<button data-example class="disabled:cursor-not-allowed interactive"> |
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Physics |
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</button> |
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<input |
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type="text" |
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id="search-wiki" |
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title="Search Wikipedia article by title" |
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class="font-light py-0 mx-1 resize-none outline-none w-32 disabled:cursor-not-allowed interactive" |
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placeholder="Load Wikipedia article..." /> |
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<button |
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title="Search Wikipedia article and load into input" |
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class="bg-gray-700 hover:bg-gray-800 text-white font-normal px-2 py-1 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"> |
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Load |
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</button> |
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</form> |
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</div> |
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<form |
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id="form" |
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class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"> |
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<input type="submit" hidden /> |
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<input |
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type="text" |
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id="search-input" |
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class="font-light w-full px-3 py-2 mx-1 resize-none outline-none interactive disabled:cursor-not-allowed" |
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placeholder="Search query here..." /> |
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<button |
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class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"> |
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Search |
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</button> |
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</form> |
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<div> |
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<h3 class="font-medium">Input text:</h3> |
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<div class="flex justify-between items-center"> |
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<div class="rounded-md inline text-xs"> |
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<span id="output-status" class="m-auto font-light invisible" |
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>C</span |
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> |
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</div> |
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</div> |
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<div |
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id="input-container" |
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tabindex="0" |
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class="min-h-[250px] bg-slate-100 text-gray-500 rounded-md p-4 flex flex-col gap-2 relative"> |
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<textarea |
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id="input-area" |
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hidden |
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value="" |
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placeholder="Input text to perform semantic similarity search..." |
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class="flex-1 resize-none outline-none left-0 right-0 top-0 bottom-0 m-4 absolute interactive disabled:invisible"></textarea> |
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<p id="output-area" class="grid-rows-2"> |
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Input text to perform semantic similarity search... |
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</p> |
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</div> |
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</div> |
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</main> |
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</body> |
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</html> |
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