From f533902c755b5bc9e95c7df6122cc70cbe078ecf Mon Sep 17 00:00:00 2001 From: Aaron Pham Date: Sun, 5 Oct 2025 19:50:52 -0400 Subject: [PATCH] feat: semantic search (1/n) Signed-off-by: Aaron Pham --- docs/embeddings/hnsw.bin | Bin 0 -> 8480 bytes docs/embeddings/manifest.json | 1 + docs/embeddings/vectors-000.bin | Bin 0 -> 261120 bytes index.d.ts | 1 + package-lock.json | 810 +++++++++++------- package.json | 2 + quartz.config.ts | 16 +- quartz/cfg.ts | 28 + quartz/components/Search.tsx | 63 +- quartz/components/renderPage.tsx | 7 + quartz/components/scripts/search.inline.ts | 802 +++++++++++++---- quartz/components/scripts/semantic.inline.ts | 182 ++++ quartz/components/scripts/util.ts | 65 ++ quartz/components/styles/search.scss | 97 ++- quartz/embed_build.py | 542 ++++++++++++ quartz/plugins/emitters/404.tsx | 2 +- quartz/plugins/emitters/componentResources.ts | 47 +- quartz/plugins/emitters/contentPage.tsx | 2 +- quartz/plugins/emitters/folderPage.tsx | 2 +- quartz/plugins/emitters/index.ts | 3 +- quartz/plugins/emitters/semantic.ts | 235 +++++ quartz/plugins/emitters/tagPage.tsx | 2 +- quartz/workers/semantic.worker.ts | 548 ++++++++++++ 23 files changed, 2955 insertions(+), 502 deletions(-) create mode 100644 docs/embeddings/hnsw.bin create mode 100644 docs/embeddings/manifest.json create mode 100644 docs/embeddings/vectors-000.bin create mode 100644 quartz/components/scripts/semantic.inline.ts create mode 100644 quartz/embed_build.py create mode 100644 quartz/plugins/emitters/semantic.ts create mode 100644 quartz/workers/semantic.worker.ts diff --git a/docs/embeddings/hnsw.bin b/docs/embeddings/hnsw.bin new file mode 100644 index 0000000000000000000000000000000000000000..3cdd4c1185497b61870c1671d7121621d5198021 GIT binary patch literal 8480 zcmZQzU|*SU|=v{U|?`yU|jKU|=Xg0FgP$WFa$6%FeETCFcdH{Ff=eSFic=% zU|7J&z_5Xlf#Con1H%PI28IWW3=AI_85kIt7#IYY7#I|o7#Iwg7#JLw7#ISW7#I?m z7#Iqe7#JFu7#JonF)%D(Vqn<7#K3TXiGkq)69dBoCI*HNObiSR%nS?y%nS?)%nS?$ 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"preact-render-to-string": "^6.6.1", @@ -647,6 +649,47 @@ "integrity": "sha512-aGTxbpbg8/b5JfU1HXSrbH3wXZuLPJcNEcZQFMxLs3oSzgtVu6nFPkbbGGUvBcUjKV2YyB9Wxxabo+HEH9tcRQ==", "license": "MIT" }, + "node_modules/@huggingface/jinja": { + "version": "0.5.1", + "resolved": "https://registry.npmjs.org/@huggingface/jinja/-/jinja-0.5.1.tgz", + "integrity": "sha512-yUZLld4lrM9iFxHCwFQ7D1HW2MWMwSbeB7WzWqFYDWK+rEb+WldkLdAJxUPOmgICMHZLzZGVcVjFh3w/YGubng==", + "license": "MIT", + "engines": { + "node": ">=18" + } + }, + "node_modules/@huggingface/transformers": { + "version": "3.7.5", + "resolved": "https://registry.npmjs.org/@huggingface/transformers/-/transformers-3.7.5.tgz", + "integrity": "sha512-5jvrIwHyRXfOKVaGKYvUZM6ZjJKQXWeKzIOdKBE5pdzPSNzTwBNx5NdWcGElf4Ddv7Dl2mWsvJh+G5RnCUxMmA==", + "license": "Apache-2.0", + "dependencies": { + "@huggingface/jinja": "^0.5.1", + "onnxruntime-node": "1.21.0", + "onnxruntime-web": "1.22.0-dev.20250409-89f8206ba4", + "sharp": "^0.34.1" + } + }, + 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"engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/serve-handler": { "version": "6.1.6", "resolved": "https://registry.npmjs.org/serve-handler/-/serve-handler-6.1.6.tgz", @@ -6771,6 +6901,22 @@ "node": ">=14" } }, + "node_modules/tar": { + "version": "7.5.1", + "resolved": "https://registry.npmjs.org/tar/-/tar-7.5.1.tgz", + "integrity": "sha512-nlGpxf+hv0v7GkWBK2V9spgactGOp0qvfWRxUMjqHyzrt3SgwE48DIv/FhqPHJYLHpgW1opq3nERbz5Anq7n1g==", + "license": "ISC", + "dependencies": { + "@isaacs/fs-minipass": "^4.0.0", + "chownr": "^3.0.0", + "minipass": "^7.1.2", + "minizlib": "^3.1.0", + "yallist": "^5.0.0" + }, + "engines": { + "node": ">=18" + } + }, "node_modules/tiny-inflate": { "version": "1.0.3", "resolved": "https://registry.npmjs.org/tiny-inflate/-/tiny-inflate-1.0.3.tgz", @@ -6858,6 +7004,18 @@ "fsevents": "~2.3.3" } }, + "node_modules/type-fest": { + "version": "0.13.1", + "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.13.1.tgz", + "integrity": "sha512-34R7HTnG0XIJcBSn5XhDd7nNFPRcXYRZrBB2O2jdKqYODldSzBAqzsWoZYYvduky73toYS/ESqxPvkDf/F0XMg==", + "license": "(MIT OR CC0-1.0)", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/typescript": { "version": "5.9.2", "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.2.tgz", @@ -6876,7 +7034,6 @@ "version": "7.13.0", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.13.0.tgz", "integrity": "sha512-Ov2Rr9Sx+fRgagJ5AX0qvItZG/JKKoBRAVITs1zk7IqZGTJUwgUr7qoYBpWwakpWilTZFM98rG/AFRocu10iIQ==", - "dev": true, "license": "MIT" }, "node_modules/unicode-trie": { @@ -7211,6 +7368,15 @@ "node": ">=10" } }, + "node_modules/yallist": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/yallist/-/yallist-5.0.0.tgz", + "integrity": "sha512-YgvUTfwqyc7UXVMrB+SImsVYSmTS8X/tSrtdNZMImM+n7+QTriRXyXim0mBrTXNeqzVF0KWGgHPeiyViFFrNDw==", + "license": "BlueOak-1.0.0", + "engines": { + "node": ">=18" + } + }, "node_modules/yargs": { "version": "18.0.0", "resolved": "https://registry.npmjs.org/yargs/-/yargs-18.0.0.tgz", diff --git a/package.json b/package.json index fdbc8c434..1cf3a0f40 100644 --- a/package.json +++ b/package.json @@ -37,6 +37,7 @@ "dependencies": { "@clack/prompts": "^0.11.0", "@floating-ui/dom": "^1.7.4", + "@huggingface/transformers": "^3.7.5", "@myriaddreamin/rehype-typst": "^0.6.0", "@napi-rs/simple-git": "0.1.22", "@tweenjs/tween.js": "^25.0.0", @@ -61,6 +62,7 @@ "mdast-util-to-string": "^4.0.0", "micromorph": "^0.4.5", "minimatch": "^10.0.3", + "onnxruntime-web": "^1.23.0", "pixi.js": "^8.13.2", "preact": "^10.27.2", "preact-render-to-string": "^6.6.1", diff --git a/quartz.config.ts b/quartz.config.ts index b3db3d60d..6d3fcb4e9 100644 --- a/quartz.config.ts +++ b/quartz.config.ts @@ -1,6 +1,18 @@ -import { QuartzConfig } from "./quartz/cfg" +import { GlobalConfiguration, QuartzConfig } from "./quartz/cfg" import * as Plugin from "./quartz/plugins" +const semanticSearch: GlobalConfiguration["semanticSearch"] = { + enable: true, + model: "onnx-community/embeddinggemma-300m-ONNX", + aot: true, + dims: 768, + dtype: "fp32", + shardSizeRows: 1024, + hnsw: { M: 16, efConstruction: 200 }, + chunking: { chunkSize: 256, chunkOverlap: 64 }, + vllm: { enable: true, concurrency: 16, batchSize: 128 }, +} + /** * Quartz 4 Configuration * @@ -52,6 +64,7 @@ const config: QuartzConfig = { }, }, }, + semanticSearch, }, plugins: { transformers: [ @@ -84,6 +97,7 @@ const config: QuartzConfig = { enableSiteMap: true, enableRSS: true, }), + Plugin.SemanticIndex(semanticSearch), Plugin.Assets(), Plugin.Static(), Plugin.Favicon(), diff --git a/quartz/cfg.ts b/quartz/cfg.ts index 57dff5c75..734f0a3d8 100644 --- a/quartz/cfg.ts +++ b/quartz/cfg.ts @@ -78,6 +78,34 @@ export interface GlobalConfiguration { * Region Codes: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2 */ locale: ValidLocale + /** Semantic search configuration */ + semanticSearch?: { + enable: boolean + model: string + aot: boolean + dtype: "fp32" | "fp16" + dims: number + shardSizeRows: number + manifestUrl?: string + manifestBaseUrl?: string + disableCache?: boolean + hnsw: { + M: number + efConstruction: number + efSearch?: number + } + chunking: { + chunkSize: number + chunkOverlap: number + noChunking?: boolean + } + vllm?: { + enable: boolean + vllmUrl?: string + concurrency: number + batchSize: number + } + } } export interface QuartzConfig { diff --git a/quartz/components/Search.tsx b/quartz/components/Search.tsx index 6e932d2ef..6dbd38a0f 100644 --- a/quartz/components/Search.tsx +++ b/quartz/components/Search.tsx @@ -7,10 +7,12 @@ import { i18n } from "../i18n" export interface SearchOptions { enablePreview: boolean + includeButton: boolean } const defaultOptions: SearchOptions = { enablePreview: true, + includeButton: true, } export default ((userOpts?: Partial) => { @@ -29,19 +31,54 @@ export default ((userOpts?: Partial) => {

{i18n(cfg.locale).components.search.title}

-
-
- -
-
-
+ +
+
+ +
+ + +
+
+ + +
) } diff --git a/quartz/components/renderPage.tsx b/quartz/components/renderPage.tsx index 3ebfe4879..4e19d1238 100644 --- a/quartz/components/renderPage.tsx +++ b/quartz/components/renderPage.tsx @@ -25,6 +25,7 @@ const headerRegex = new RegExp(/h[1-6]/) export function pageResources( baseDir: FullSlug | RelativeURL, staticResources: StaticResources, + cfg?: GlobalConfiguration, ): StaticResources { const contentIndexPath = joinSegments(baseDir, "static/contentIndex.json") const contentIndexScript = `const fetchData = fetch("${contentIndexPath}").then(data => data.json())` @@ -48,6 +49,12 @@ export function pageResources( spaPreserve: true, script: contentIndexScript, }, + { + loadTime: "beforeDOMReady", + contentType: "inline", + spaPreserve: true, + script: `const semanticCfg = ${JSON.stringify(cfg?.semanticSearch ?? {})};`, + }, ...staticResources.js, ], additionalHead: staticResources.additionalHead, diff --git a/quartz/components/scripts/search.inline.ts b/quartz/components/scripts/search.inline.ts index 6a84a50e0..eedbe871f 100644 --- a/quartz/components/scripts/search.inline.ts +++ b/quartz/components/scripts/search.inline.ts @@ -1,6 +1,14 @@ import FlexSearch, { DefaultDocumentSearchResults } from "flexsearch" -import { ContentDetails } from "../../plugins/emitters/contentIndex" -import { registerEscapeHandler, removeAllChildren } from "./util" +import type { ContentDetails } from "../../plugins/emitters/contentIndex" +import { SemanticClient, type SemanticResult } from "./semantic.inline" +import { + registerEscapeHandler, + removeAllChildren, + highlight, + tokenizeTerm, + encode, + fetchCanonical, +} from "./util" import { FullSlug, normalizeRelativeURLs, resolveRelative } from "../../util/path" interface Item { @@ -14,17 +22,110 @@ interface Item { // Can be expanded with things like "term" in the future type SearchType = "basic" | "tags" -let searchType: SearchType = "basic" -let currentSearchTerm: string = "" -const encoder = (str: string) => { - return str - .toLowerCase() - .split(/\s+/) - .filter((token) => token.length > 0) +type SearchMode = "lexical" | "semantic" +const SEARCH_MODE_STORAGE_KEY = "quartz:search:mode" + +const loadStoredSearchMode = (): SearchMode | null => { + if (typeof window === "undefined") { + return null + } + + try { + const stored = window.localStorage.getItem(SEARCH_MODE_STORAGE_KEY) + return stored === "lexical" || stored === "semantic" ? stored : null + } catch (err) { + console.warn("[Search] failed to read stored search mode:", err) + return null + } } -let index = new FlexSearch.Document({ - encode: encoder, +const persistSearchMode = (mode: SearchMode) => { + if (typeof window === "undefined") { + return + } + + try { + window.localStorage.setItem(SEARCH_MODE_STORAGE_KEY, mode) + } catch (err) { + console.warn("[Search] failed to persist search mode:", err) + } +} + +let searchMode: SearchMode = "lexical" +let currentSearchTerm: string = "" +let rawSearchTerm: string = "" +let semantic: SemanticClient | null = null +let semanticReady = false +let semanticInitFailed = false +type SimilarityResult = { item: Item; similarity: number } +let chunkMetadata: Record = {} +let manifestIds: string[] = [] + +/** + * Get parent document slug for a chunk ID + */ +function getParentSlug(slug: string): string { + const meta = chunkMetadata[slug] + return meta ? meta.parentSlug : slug +} + +/** + * Aggregate semantic search results from chunks to documents using RRF + * @param results Raw semantic results (chunk-level) + * @param slugToDocIndex Map from document slug to index in idDataMap + * @returns Object with rrfScores (for ranking) and maxScores (for display) + */ +function aggregateChunkResults( + results: SemanticResult[], + slugToDocIndex: Map, +): { rrfScores: Map; maxScores: Map } { + // Group chunks by parent document + const docChunks = new Map>() + + results.forEach(({ id, score }) => { + // id is an index into manifestIds (the chunk IDs from embeddings) + const chunkSlug = manifestIds[id] + if (!chunkSlug) return + + // Get parent document slug + const parentSlug = getParentSlug(chunkSlug) + + if (!docChunks.has(parentSlug)) { + docChunks.set(parentSlug, []) + } + + docChunks.get(parentSlug)!.push({ score }) + }) + + // Apply RRF for ranking and track max similarity for display + const rrfScores = new Map() + const maxScores = new Map() + const RRF_K = 60 + + for (const [parentSlug, chunks] of docChunks) { + const docIdx = slugToDocIndex.get(parentSlug as FullSlug) + if (typeof docIdx !== "number") continue + + // Sort chunks by score descending to assign per-document ranks + chunks.sort((a, b) => b.score - a.score) + + // RRF formula: sum(1 / (k + rank)) across all chunks, using per-document ranks + const rrfScore = chunks.reduce((sum, _, rank) => sum + 1.0 / (RRF_K + rank), 0) + + // Max similarity score for display (original 0-1 range) + const maxScore = chunks[0].score + + rrfScores.set(docIdx, rrfScore) + maxScores.set(docIdx, maxScore) + } + + return { rrfScores, maxScores } +} + +// Initialize the FlexSearch Document instance with the appropriate configuration +const index = new FlexSearch.Document({ + tokenize: "forward", + encode, document: { id: "id", tag: "tags", @@ -41,73 +142,18 @@ let index = new FlexSearch.Document({ field: "tags", tokenize: "forward", }, + { + field: "aliases", + tokenize: "forward", + }, ], }, }) const p = new DOMParser() const fetchContentCache: Map = new Map() -const contextWindowWords = 30 -const numSearchResults = 8 -const numTagResults = 5 - -const tokenizeTerm = (term: string) => { - const tokens = term.split(/\s+/).filter((t) => t.trim() !== "") - const tokenLen = tokens.length - if (tokenLen > 1) { - for (let i = 1; i < tokenLen; i++) { - tokens.push(tokens.slice(0, i + 1).join(" ")) - } - } - - return tokens.sort((a, b) => b.length - a.length) // always highlight longest terms first -} - -function highlight(searchTerm: string, text: string, trim?: boolean) { - const tokenizedTerms = tokenizeTerm(searchTerm) - let tokenizedText = text.split(/\s+/).filter((t) => t !== "") - - let startIndex = 0 - let endIndex = tokenizedText.length - 1 - if (trim) { - const includesCheck = (tok: string) => - tokenizedTerms.some((term) => tok.toLowerCase().startsWith(term.toLowerCase())) - const occurrencesIndices = tokenizedText.map(includesCheck) - - let bestSum = 0 - let bestIndex = 0 - for (let i = 0; i < Math.max(tokenizedText.length - contextWindowWords, 0); i++) { - const window = occurrencesIndices.slice(i, i + contextWindowWords) - const windowSum = window.reduce((total, cur) => total + (cur ? 1 : 0), 0) - if (windowSum >= bestSum) { - bestSum = windowSum - bestIndex = i - } - } - - startIndex = Math.max(bestIndex - contextWindowWords, 0) - endIndex = Math.min(startIndex + 2 * contextWindowWords, tokenizedText.length - 1) - tokenizedText = tokenizedText.slice(startIndex, endIndex) - } - - const slice = tokenizedText - .map((tok) => { - // see if this tok is prefixed by any search terms - for (const searchTok of tokenizedTerms) { - if (tok.toLowerCase().includes(searchTok.toLowerCase())) { - const regex = new RegExp(searchTok.toLowerCase(), "gi") - return tok.replace(regex, `$&`) - } - } - return tok - }) - .join(" ") - - return `${startIndex === 0 ? "" : "..."}${slice}${ - endIndex === tokenizedText.length - 1 ? "" : "..." - }` -} - +const numSearchResults = 10 +const numTagResults = 10 function highlightHTML(searchTerm: string, el: HTMLElement) { const p = new DOMParser() const tokenizedTerms = tokenizeTerm(searchTerm) @@ -149,7 +195,11 @@ function highlightHTML(searchTerm: string, el: HTMLElement) { return html.body } -async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: ContentIndex) { +async function setupSearch( + searchElement: HTMLDivElement, + currentSlug: FullSlug, + data: ContentIndex, +) { const container = searchElement.querySelector(".search-container") as HTMLElement if (!container) return @@ -164,12 +214,183 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: const searchLayout = searchElement.querySelector(".search-layout") as HTMLElement if (!searchLayout) return + const searchSpace = searchElement?.querySelector(".search-space") as HTMLFormElement + if (!searchSpace) return + + // Create semantic search progress bar + const progressBar = document.createElement("div") + progressBar.className = "semantic-search-progress" + progressBar.style.cssText = ` + position: absolute; + bottom: 0; + left: 0; + height: 2px; + width: 0; + background: var(--secondary); + transition: width 0.3s ease, opacity 0.3s ease; + opacity: 0; + z-index: 9999; + ` + searchBar.parentElement?.appendChild(progressBar) + + const startSemanticProgress = () => { + progressBar.style.opacity = "1" + progressBar.style.width = "0" + setTimeout(() => { + progressBar.style.width = "100%" + }, 10) + } + + const completeSemanticProgress = () => { + progressBar.style.opacity = "0" + setTimeout(() => { + progressBar.style.width = "0" + }, 300) + } + + const resetProgressBar = () => { + progressBar.style.opacity = "0" + progressBar.style.width = "0" + } + const idDataMap = Object.keys(data) as FullSlug[] + const slugToIndex = new Map() + idDataMap.forEach((slug, idx) => slugToIndex.set(slug, idx)) + const modeToggle = searchSpace.querySelector(".search-mode-toggle") as HTMLDivElement | null + const modeButtons = modeToggle + ? Array.from(modeToggle.querySelectorAll(".mode-option")) + : [] + const appendLayout = (el: HTMLElement) => { searchLayout.appendChild(el) } const enablePreview = searchLayout.dataset.preview === "true" + if (!semantic && !semanticInitFailed) { + const client = new SemanticClient(semanticCfg) + try { + await client.ensureReady() + semantic = client + semanticReady = true + + // Load chunk metadata and IDs from manifest + try { + const manifestUrl = "/embeddings/manifest.json" + const res = await fetch(manifestUrl) + if (res.ok) { + const manifest = await res.json() + chunkMetadata = manifest.chunkMetadata || {} + manifestIds = manifest.ids || [] + console.debug( + `[Search] Loaded manifest: ${manifestIds.length} chunks, ${Object.keys(chunkMetadata).length} chunked documents`, + ) + } + } catch (err) { + console.warn("[Search] failed to load chunk metadata:", err) + chunkMetadata = {} + manifestIds = [] + } + } catch (err) { + console.warn("[SemanticClient] initialization failed:", err) + client.dispose() + semantic = null + semanticReady = false + semanticInitFailed = true + } + } else if (semantic && !semanticReady) { + try { + await semantic.ensureReady() + semanticReady = true + } catch (err) { + console.warn("[SemanticClient] became unavailable:", err) + semantic.dispose() + semantic = null + semanticReady = false + semanticInitFailed = true + } + } + const storedMode = loadStoredSearchMode() + if (storedMode === "semantic") { + if (semanticReady) { + searchMode = storedMode + } + } else if (storedMode === "lexical") { + searchMode = storedMode + } + if (!semanticReady && searchMode === "semantic") { + searchMode = "lexical" + } + let searchSeq = 0 + let runSearchTimer: number | null = null + let lastInputAt = 0 + searchLayout.dataset.mode = searchMode + + const updateModeUI = (mode: SearchMode) => { + modeButtons.forEach((button) => { + const btnMode = (button.dataset.mode as SearchMode) ?? "lexical" + const isActive = btnMode === mode + button.classList.toggle("active", isActive) + button.setAttribute("aria-pressed", String(isActive)) + }) + if (modeToggle) { + modeToggle.dataset.mode = mode + } + searchLayout.dataset.mode = mode + } + + const computeDebounceDelay = (term: string): number => { + const trimmed = term.trim() + const lastTerm = currentSearchTerm + const isExtension = + lastTerm.length > 0 && trimmed.length > lastTerm.length && trimmed.startsWith(lastTerm) + const isRetraction = lastTerm.length > trimmed.length + const isReplacement = + lastTerm.length > 0 && !trimmed.startsWith(lastTerm) && !lastTerm.startsWith(trimmed) + const baseFullQueryDelay = 200 + const semanticPenalty = searchMode === "semantic" ? 60 : 0 + + if (isExtension && trimmed.length > 2) { + return baseFullQueryDelay + semanticPenalty + } + + if (isReplacement && trimmed.length > 3) { + return Math.max(90, baseFullQueryDelay - 80) + } + + if (isRetraction) { + return 90 + } + + return baseFullQueryDelay + (searchMode === "semantic" ? 40 : 0) + } + + const triggerSearchWithMode = (mode: SearchMode) => { + if (mode === "semantic" && !semanticReady) { + return + } + if (searchMode === mode) return + searchMode = mode + updateModeUI(mode) + persistSearchMode(searchMode) + if (rawSearchTerm.trim() !== "") { + searchLayout.classList.add("display-results") + const token = ++searchSeq + void runSearch(rawSearchTerm, token) + } + } + + updateModeUI(searchMode) + + modeButtons.forEach((button) => { + const btnMode = (button.dataset.mode as SearchMode) ?? "lexical" + if (btnMode === "semantic") { + button.disabled = !semanticReady + button.setAttribute("aria-disabled", String(!semanticReady)) + } + const handler = () => triggerSearchWithMode(btnMode) + button.addEventListener("click", handler) + window.addCleanup(() => button.removeEventListener("click", handler)) + }) let preview: HTMLDivElement | undefined = undefined let previewInner: HTMLDivElement | undefined = undefined const results = document.createElement("div") @@ -193,18 +414,25 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: searchLayout.classList.remove("display-results") searchType = "basic" // reset search type after closing searchButton.focus() + resetProgressBar() } - function showSearch(searchTypeNew: SearchType) { - searchType = searchTypeNew - if (sidebar) sidebar.style.zIndex = "1" + function showSearch(type: SearchType) { container.classList.add("active") + if (type === "tags") { + searchBar.value = "#" + rawSearchTerm = "#" + } searchBar.focus() } let currentHover: HTMLInputElement | null = null + async function shortcutHandler(e: HTMLElementEventMap["keydown"]) { - if (e.key === "k" && (e.ctrlKey || e.metaKey) && !e.shiftKey) { + const paletteOpen = document.querySelector("search#palette-container") as HTMLDivElement + if (paletteOpen && paletteOpen.classList.contains("active")) return + + if ((e.key === "/" || e.key === "k") && (e.ctrlKey || e.metaKey) && !e.shiftKey) { e.preventDefault() const searchBarOpen = container.classList.contains("active") searchBarOpen ? hideSearch() : showSearch("basic") @@ -214,9 +442,6 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: e.preventDefault() const searchBarOpen = container.classList.contains("active") searchBarOpen ? hideSearch() : showSearch("tags") - - // add "#" prefix for tag search - searchBar.value = "#" return } @@ -226,20 +451,29 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: // If search is active, then we will render the first result and display accordingly if (!container.classList.contains("active")) return - if (e.key === "Enter" && !e.isComposing) { + if (e.key === "Enter") { // If result has focus, navigate to that one, otherwise pick first result + let anchor: HTMLAnchorElement | undefined if (results.contains(document.activeElement)) { - const active = document.activeElement as HTMLInputElement - if (active.classList.contains("no-match")) return - await displayPreview(active) - active.click() + anchor = document.activeElement as HTMLAnchorElement + if (anchor.classList.contains("no-match")) return + await displayPreview(anchor) + e.preventDefault() + anchor.click() } else { - const anchor = document.getElementsByClassName("result-card")[0] as HTMLInputElement | null + anchor = document.getElementsByClassName("result-card")[0] as HTMLAnchorElement if (!anchor || anchor.classList.contains("no-match")) return await displayPreview(anchor) + e.preventDefault() anchor.click() } - } else if (e.key === "ArrowUp" || (e.shiftKey && e.key === "Tab")) { + if (anchor !== undefined) + window.spaNavigate(new URL(new URL(anchor.href).pathname, window.location.toString())) + } else if ( + e.key === "ArrowUp" || + (e.shiftKey && e.key === "Tab") || + (e.ctrlKey && e.key === "p") + ) { e.preventDefault() if (results.contains(document.activeElement)) { // If an element in results-container already has focus, focus previous one @@ -252,7 +486,7 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: if (prevResult) currentHover = prevResult await displayPreview(prevResult) } - } else if (e.key === "ArrowDown" || e.key === "Tab") { + } else if (e.key === "ArrowDown" || e.key === "Tab" || (e.ctrlKey && e.key === "n")) { e.preventDefault() // The results should already been focused, so we need to find the next one. // The activeElement is the search bar, so we need to find the first result and focus it. @@ -269,25 +503,40 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: } } - const formatForDisplay = (term: string, id: number) => { + const formatForDisplay = (term: string, id: number, renderType: SearchType) => { const slug = idDataMap[id] + const aliases: string[] = data[slug].aliases ?? [] + const target = aliases.find((alias) => alias.toLowerCase().includes(term.toLowerCase())) ?? "" + + // Check if query contains title words (for boosting exact matches) + const queryTokens = tokenizeTerm(term) + const titleTokens = tokenizeTerm(data[slug].title ?? "") + const titleMatch = titleTokens.some((t) => queryTokens.includes(t)) + return { id, slug, - title: searchType === "tags" ? data[slug].title : highlight(term, data[slug].title ?? ""), + title: + renderType === "tags" || target + ? data[slug].title + : highlight(term, data[slug].title ?? ""), + target, content: highlight(term, data[slug].content ?? "", true), - tags: highlightTags(term.substring(1), data[slug].tags), + tags: highlightTags(term, data[slug].tags, renderType), + aliases: aliases, + titleMatch, // Add title match flag for boosting } } - function highlightTags(term: string, tags: string[]) { - if (!tags || searchType !== "tags") { + function highlightTags(term: string, tags: string[], renderType: SearchType) { + if (!tags || renderType !== "tags") { return [] } + const tagTerm = term.toLowerCase() return tags .map((tag) => { - if (tag.toLowerCase().includes(term.toLowerCase())) { + if (tag.toLowerCase().includes(tagTerm)) { return `

  • #${tag}

    • ` } else { return `

  • #${tag}

    • ` @@ -300,24 +549,40 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: return new URL(resolveRelative(currentSlug, slug), location.toString()) } - const resultToHTML = ({ slug, title, content, tags }: Item) => { + const resultToHTML = ({ item, percent }: { item: Item; percent: number | null }) => { + const { slug, title, content, tags, target } = item const htmlTags = tags.length > 0 ? `
      ${tags.join("")}
    ` : `` const itemTile = document.createElement("a") + const titleContent = target ? highlight(currentSearchTerm, target) : title + const subscript = target ? `${slug}` : `` + let percentLabel = "—" + let percentAttr = "" + if (percent !== null && Number.isFinite(percent)) { + const bounded = Math.max(0, Math.min(100, percent)) + percentLabel = `${bounded.toFixed(1)}%` + percentAttr = bounded.toFixed(3) + } itemTile.classList.add("result-card") itemTile.id = slug itemTile.href = resolveUrl(slug).toString() - itemTile.innerHTML = ` -

    ${title}

    - ${htmlTags} -

    ${content}

    - ` - itemTile.addEventListener("click", (event) => { - if (event.altKey || event.ctrlKey || event.metaKey || event.shiftKey) return - hideSearch() - }) + itemTile.innerHTML = `
    +

    ${titleContent}

    + ${subscript}${htmlTags} + ${searchMode === "semantic" ? ` ${percentLabel}` : ""} + ${enablePreview && window.innerWidth > 600 ? "" : `

    ${content}

    `} +
    ` + if (percentAttr) itemTile.dataset.scorePercent = percentAttr + else delete itemTile.dataset.scorePercent - const handler = (event: MouseEvent) => { - if (event.altKey || event.ctrlKey || event.metaKey || event.shiftKey) return + const handler = (evt: MouseEvent) => { + if (evt.altKey || evt.ctrlKey || evt.metaKey || evt.shiftKey) return + const anchor = evt.currentTarget as HTMLAnchorElement | null + if (!anchor) return + evt.preventDefault() + const href = anchor.getAttribute("href") + if (!href) return + const url = new URL(href, window.location.toString()) + window.spaNavigate(url) hideSearch() } @@ -335,15 +600,22 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: return itemTile } - async function displayResults(finalResults: Item[]) { + async function displayResults(finalResults: SimilarityResult[]) { removeAllChildren(results) if (finalResults.length === 0) { results.innerHTML = `

    No results.

    Try another search term?

    ` + currentHover = null } else { - results.append(...finalResults.map(resultToHTML)) + const decorated = finalResults.map(({ item, similarity }) => { + if (!Number.isFinite(similarity)) return { item, percent: null } + const bounded = Math.max(-1, Math.min(1, similarity)) + const percent = ((bounded + 1) / 2) * 100 + return { item, percent } + }) + results.append(...decorated.map(resultToHTML)) } if (finalResults.length === 0 && preview) { @@ -363,8 +635,8 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: return fetchContentCache.get(slug) as Element[] } - const targetUrl = resolveUrl(slug).toString() - const contents = await fetch(targetUrl) + const targetUrl = resolveUrl(slug) + const contents = await fetchCanonical(targetUrl) .then((res) => res.text()) .then((contents) => { if (contents === undefined) { @@ -394,73 +666,297 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: const highlights = [...preview.getElementsByClassName("highlight")].sort( (a, b) => b.innerHTML.length - a.innerHTML.length, ) - highlights[0]?.scrollIntoView({ block: "start" }) + if (highlights.length > 0) { + const highlight = highlights[0] + const container = preview + if (container && highlight) { + // Get the relative positions + const containerRect = container.getBoundingClientRect() + const highlightRect = highlight.getBoundingClientRect() + // Calculate the scroll position relative to the container + const relativeTop = highlightRect.top - containerRect.top + container.scrollTop - 20 // 20px buffer + // Smoothly scroll the container + container.scrollTo({ + top: relativeTop, + behavior: "smooth", + }) + } + } } - async function onType(e: HTMLElementEventMap["input"]) { + async function runSearch(rawTerm: string, token: number) { if (!searchLayout || !index) return - currentSearchTerm = (e.target as HTMLInputElement).value - searchLayout.classList.toggle("display-results", currentSearchTerm !== "") - searchType = currentSearchTerm.startsWith("#") ? "tags" : "basic" + const trimmed = rawTerm.trim() + if (trimmed === "") { + removeAllChildren(results) + if (preview) { + removeAllChildren(preview) + } + currentHover = null + searchLayout.classList.remove("display-results") + resetProgressBar() + return + } - let searchResults: DefaultDocumentSearchResults - if (searchType === "tags") { - currentSearchTerm = currentSearchTerm.substring(1).trim() - const separatorIndex = currentSearchTerm.indexOf(" ") - if (separatorIndex != -1) { - // search by title and content index and then filter by tag (implemented in flexsearch) - const tag = currentSearchTerm.substring(0, separatorIndex) - const query = currentSearchTerm.substring(separatorIndex + 1).trim() - searchResults = await index.searchAsync({ - query: query, - // return at least 10000 documents, so it is enough to filter them by tag (implemented in flexsearch) + const modeForRanking: SearchMode = searchMode + const initialType: SearchType = trimmed.startsWith("#") ? "tags" : "basic" + let workingType: SearchType = initialType + let highlightTerm = trimmed + let tagTerm = "" + let searchResults: DefaultDocumentSearchResults = [] + + if (initialType === "tags") { + tagTerm = trimmed.substring(1).trim() + const separatorIndex = tagTerm.indexOf(" ") + if (separatorIndex !== -1) { + const tag = tagTerm.substring(0, separatorIndex).trim() + const query = tagTerm.substring(separatorIndex + 1).trim() + const results = await index.searchAsync({ + query, limit: Math.max(numSearchResults, 10000), - index: ["title", "content"], + index: ["title", "content", "aliases"], tag: { tags: tag }, }) - for (let searchResult of searchResults) { - searchResult.result = searchResult.result.slice(0, numSearchResults) - } - // set search type to basic and remove tag from term for proper highlightning and scroll - searchType = "basic" - currentSearchTerm = query + if (token !== searchSeq) return + searchResults = Object.values(results) + workingType = "basic" + highlightTerm = query } else { - // default search by tags index - searchResults = await index.searchAsync({ - query: currentSearchTerm, + const results = await index.searchAsync({ + query: tagTerm, limit: numSearchResults, index: ["tags"], }) + if (token !== searchSeq) return + searchResults = Object.values(results) + highlightTerm = tagTerm } - } else if (searchType === "basic") { - searchResults = await index.searchAsync({ - query: currentSearchTerm, + } else { + const results = await index.searchAsync({ + query: highlightTerm, limit: numSearchResults, - index: ["title", "content"], + index: ["title", "content", "aliases"], }) + if (token !== searchSeq) return + searchResults = Object.values(results) + } + + const coerceIds = (hit?: DefaultDocumentSearchResults[number]): number[] => { + if (!hit) return [] + return hit.result + .map((value: Id) => { + if (typeof value === "number") { + return value + } + const parsed = Number.parseInt(String(value), 10) + return Number.isNaN(parsed) ? null : parsed + }) + .filter((value): value is number => value !== null) } const getByField = (field: string): number[] => { - const results = searchResults.filter((x) => x.field === field) - return results.length === 0 ? [] : ([...results[0].result] as number[]) + const hit = searchResults.find((x) => x.field === field) + return coerceIds(hit) } - // order titles ahead of content const allIds: Set = new Set([ + ...getByField("aliases"), ...getByField("title"), ...getByField("content"), ...getByField("tags"), ]) - const finalResults = [...allIds].map((id) => formatForDisplay(currentSearchTerm, id)) - await displayResults(finalResults) + + currentSearchTerm = highlightTerm + + const candidateItems = new Map() + const ensureItem = (id: number): Item | null => { + const slug = idDataMap[id] + if (!slug) return null + const cached = candidateItems.get(slug) + if (cached) return cached + const item = formatForDisplay(highlightTerm, id, workingType) + if (item) { + candidateItems.set(slug, item) + return item + } + return null + } + + const baseIndices: number[] = [] + for (const id of allIds) { + const item = ensureItem(id) + if (!item) continue + const idx = slugToIndex.get(item.slug) + if (typeof idx === "number") { + baseIndices.push(idx) + } + } + + let semanticIds: number[] = [] + const semanticSimilarity = new Map() + + const integrateIds = (ids: number[]) => { + ids.forEach((docId) => { + ensureItem(docId) + }) + } + + const orchestrator = semanticReady && semantic ? semantic : null + + const resolveSimilarity = (item: Item): number => { + const semanticHit = semanticSimilarity.get(item.id) + return semanticHit ?? Number.NaN + } + + const render = async () => { + if (token !== searchSeq) return + const useSemantic = semanticReady && semanticIds.length > 0 + const weights = + modeForRanking === "semantic" && useSemantic + ? { base: 0.3, semantic: 1.0 } + : { base: 1.0, semantic: useSemantic ? 0.3 : 0 } + const rrf = new Map() + const push = (ids: number[], weight: number, applyTitleBoost: boolean = false) => { + if (!ids.length || weight <= 0) return + ids.forEach((docId, rank) => { + const slug = idDataMap[docId] + if (!slug) return + const item = ensureItem(docId) + if (!item) return + + // Apply title boost for FlexSearch results (1.5x boost for exact title matches) + let effectiveWeight = weight + if (applyTitleBoost && item.titleMatch) { + effectiveWeight *= 1.5 + } + + const prev = rrf.get(slug) ?? 0 + rrf.set(slug, prev + effectiveWeight / (1 + rank)) + }) + } + + push(baseIndices, weights.base, true) // FlexSearch with title boost + push(semanticIds, weights.semantic, false) // Semantic without boost + + const rankedEntries = Array.from(candidateItems.values()) + .map((item) => ({ item, score: rrf.get(item.slug) ?? 0 })) + .sort((a, b) => b.score - a.score) + .slice(0, numSearchResults) + + const displayEntries: SimilarityResult[] = [] + for (const entry of rankedEntries) { + const similarity = resolveSimilarity(entry.item) + displayEntries.push({ item: entry.item, similarity }) + } + + await displayResults(displayEntries) + } + + await render() + + if (workingType === "tags" || !orchestrator || !semanticReady || highlightTerm.length < 2) { + return + } + + const showProgress = modeForRanking === "semantic" + if (showProgress) { + startSemanticProgress() + } + + try { + const { semantic: semRes } = await orchestrator.search( + highlightTerm, + numSearchResults * 3, // Request more chunks to ensure good document coverage + ) + if (token !== searchSeq) { + if (showProgress) completeSemanticProgress() + return + } + + // Aggregate chunk results to document level using RRF + const { rrfScores: semRrfScores, maxScores: semMaxScores } = aggregateChunkResults( + semRes, + slugToIndex, + ) + + // Use RRF scores for ranking + semanticIds = Array.from(semRrfScores.entries()) + .sort((a, b) => b[1] - a[1]) + .slice(0, numSearchResults) + .map(([docIdx]) => docIdx) + + // Use max chunk similarity for display (0-1 range) + semanticSimilarity.clear() + semMaxScores.forEach((score, docIdx) => { + semanticSimilarity.set(docIdx, score) + }) + + integrateIds(semanticIds) + if (showProgress) completeSemanticProgress() + } catch (err) { + console.warn("[SemanticClient] search failed:", err) + if (showProgress) completeSemanticProgress() + orchestrator.dispose() + semantic = null + semanticReady = false + semanticInitFailed = true + if (searchMode === "semantic") { + searchMode = "lexical" + updateModeUI(searchMode) + } + modeButtons.forEach((button) => { + if ((button.dataset.mode as SearchMode) === "semantic") { + button.disabled = true + button.setAttribute("aria-disabled", "true") + } + }) + } + + await render() + } + + function onType(e: HTMLElementEventMap["input"]) { + if (!searchLayout || !index) return + rawSearchTerm = (e.target as HTMLInputElement).value + const hasQuery = rawSearchTerm.trim() !== "" + searchLayout.classList.toggle("display-results", hasQuery) + const term = rawSearchTerm + const token = ++searchSeq + if (runSearchTimer !== null) { + window.clearTimeout(runSearchTimer) + runSearchTimer = null + } + if (!hasQuery) { + void runSearch("", token) + return + } + const now = performance.now() + lastInputAt = now + const delay = computeDebounceDelay(term) + const scheduledAt = lastInputAt + runSearchTimer = window.setTimeout(() => { + if (scheduledAt !== lastInputAt) { + return + } + runSearchTimer = null + void runSearch(term, token) + }, delay) } document.addEventListener("keydown", shortcutHandler) window.addCleanup(() => document.removeEventListener("keydown", shortcutHandler)) - searchButton.addEventListener("click", () => showSearch("basic")) - window.addCleanup(() => searchButton.removeEventListener("click", () => showSearch("basic"))) + const openHandler = () => showSearch("basic") + searchButton.addEventListener("click", openHandler) + window.addCleanup(() => searchButton.removeEventListener("click", openHandler)) searchBar.addEventListener("input", onType) window.addCleanup(() => searchBar.removeEventListener("input", onType)) + window.addCleanup(() => { + if (runSearchTimer !== null) { + window.clearTimeout(runSearchTimer) + runSearchTimer = null + } + resetProgressBar() + }) registerEscapeHandler(container, hideSearch) await fillDocument(data) @@ -468,24 +964,26 @@ async function setupSearch(searchElement: Element, currentSlug: FullSlug, data: /** * Fills flexsearch document with data - * @param index index to fill * @param data data to fill index with */ let indexPopulated = false async function fillDocument(data: ContentIndex) { if (indexPopulated) return let id = 0 - const promises: Array> = [] + const promises = [] for (const [slug, fileData] of Object.entries(data)) { promises.push( - index.addAsync(id++, { + //@ts-ignore + index.addAsync({ id, slug: slug as FullSlug, title: fileData.title, content: fileData.content, tags: fileData.tags, + aliases: fileData.aliases, }), ) + id++ } await Promise.all(promises) @@ -495,7 +993,9 @@ async function fillDocument(data: ContentIndex) { document.addEventListener("nav", async (e: CustomEventMap["nav"]) => { const currentSlug = e.detail.url const data = await fetchData - const searchElement = document.getElementsByClassName("search") + const searchElement = document.getElementsByClassName( + "search", + ) as HTMLCollectionOf for (const element of searchElement) { await setupSearch(element, currentSlug, data) } diff --git a/quartz/components/scripts/semantic.inline.ts b/quartz/components/scripts/semantic.inline.ts new file mode 100644 index 000000000..167091c02 --- /dev/null +++ b/quartz/components/scripts/semantic.inline.ts @@ -0,0 +1,182 @@ +export type SemanticResult = { id: number; score: number } + +type ProgressMessage = { + type: "progress" + loadedRows: number + totalRows: number +} + +type ReadyMessage = { type: "ready" } + +type ResultMessage = { + type: "search-result" + seq: number + semantic: SemanticResult[] +} + +type ErrorMessage = { type: "error"; seq?: number; message: string } + +type SearchPayload = { + semantic: SemanticResult[] +} + +type PendingResolver = { + resolve: (payload: SearchPayload) => void + reject: (err: Error) => void +} + +export class SemanticClient { + private ready: Promise + private resolveReady!: () => void + private worker: Worker | null = null + private pending = new Map() + private seq = 0 + private disposed = false + private readySettled = false + private configured = false + private lastError: Error | null = null + + constructor(private cfg?: any) { + this.ready = new Promise((resolve) => { + this.resolveReady = () => { + if (this.readySettled) return + this.readySettled = true + resolve() + } + }) + + if (this.cfg?.enable === false) { + this.lastError = new Error("semantic search disabled by configuration") + this.resolveReady() + return + } + + this.boot() + } + + private boot() { + try { + this.worker = new Worker("/semantic.worker.js", { type: "module" }) + } catch (err) { + this.handleFatal(err) + return + } + this.setupWorker() + this.startInit() + } + + private setupWorker() { + if (!this.worker) return + this.worker.onmessage = ( + event: MessageEvent, + ) => { + const msg = event.data + if (msg.type === "progress") { + // Progress updates during initialization - can be logged if needed + return + } + if (msg.type === "ready") { + this.configured = true + this.lastError = null + this.resolveReady() + return + } + if (msg.type === "search-result") { + const pending = this.pending.get(msg.seq) + if (pending) { + this.pending.delete(msg.seq) + pending.resolve({ semantic: msg.semantic ?? [] }) + } + return + } + if (msg.type === "error") { + if (typeof msg.seq === "number") { + const pending = this.pending.get(msg.seq) + if (pending) { + this.pending.delete(msg.seq) + pending.reject(new Error(msg.message)) + } + } else { + this.handleFatal(msg.message) + } + } + } + } + + private startInit() { + if (!this.worker) return + const manifestUrl = + typeof this.cfg?.manifestUrl === "string" && this.cfg.manifestUrl.length > 0 + ? this.cfg.manifestUrl + : "/embeddings/manifest.json" + const disableCache = Boolean(this.cfg?.disableCache) + const baseUrl = + typeof this.cfg?.manifestBaseUrl === "string" ? this.cfg.manifestBaseUrl : undefined + this.worker.postMessage({ + type: "init", + cfg: this.cfg, + manifestUrl, + baseUrl, + disableCache, + }) + } + + private rejectAll(err: Error, fatal = false) { + for (const [id, pending] of this.pending.entries()) { + pending.reject(err) + this.pending.delete(id) + } + if (fatal) { + this.lastError = err + this.configured = false + if (!this.readySettled) { + this.resolveReady() + } + } + } + + private handleFatal(err: unknown) { + const error = err instanceof Error ? err : new Error(String(err)) + console.error("[SemanticClient] initialization failure:", error) + this.rejectAll(error, true) + if (this.worker) { + this.worker.postMessage({ type: "reset" }) + this.worker.terminate() + this.worker = null + } + } + + async ensureReady() { + await this.ready + if (!this.configured) { + throw this.lastError ?? new Error("semantic search unavailable") + } + } + + async search(text: string, k: number): Promise { + if (this.disposed) { + throw new Error("semantic client has been disposed") + } + await this.ensureReady() + if (!this.worker || !this.configured) { + throw this.lastError ?? new Error("worker unavailable") + } + return new Promise((resolve, reject) => { + const seq = ++this.seq + this.pending.set(seq, { resolve, reject }) + this.worker?.postMessage({ type: "search", text, k, seq }) + }) + } + + dispose() { + if (this.disposed) return + this.disposed = true + this.rejectAll(new Error("semantic client disposed")) + if (this.worker) { + this.worker.postMessage({ type: "reset" }) + this.worker.terminate() + } + this.worker = null + this.configured = false + } +} diff --git a/quartz/components/scripts/util.ts b/quartz/components/scripts/util.ts index f71790104..de0681f78 100644 --- a/quartz/components/scripts/util.ts +++ b/quartz/components/scripts/util.ts @@ -44,3 +44,68 @@ export async function fetchCanonical(url: URL): Promise { const [_, redirect] = text.match(canonicalRegex) ?? [] return redirect ? fetch(`${new URL(redirect, url)}`) : res } + +const contextWindowWords = 30 +export const tokenizeTerm = (term: string) => { + const tokens = term.split(/\s+/).filter((t) => t.trim() !== "") + const tokenLen = tokens.length + if (tokenLen > 1) { + for (let i = 1; i < tokenLen; i++) { + tokens.push(tokens.slice(0, i + 1).join(" ")) + } + } + + return tokens.sort((a, b) => b.length - a.length) // always highlight longest terms first +} + +export function highlight(searchTerm: string, text: string, trim?: boolean) { + const tokenizedTerms = tokenizeTerm(searchTerm) + let tokenizedText = text.split(/\s+/).filter((t) => t !== "") + + let startIndex = 0 + let endIndex = tokenizedText.length - 1 + if (trim) { + const includesCheck = (tok: string) => + tokenizedTerms.some((term) => tok.toLowerCase().startsWith(term.toLowerCase())) + const occurrencesIndices = tokenizedText.map(includesCheck) + + let bestSum = 0 + let bestIndex = 0 + for (let i = 0; i < Math.max(tokenizedText.length - contextWindowWords, 0); i++) { + const window = occurrencesIndices.slice(i, i + contextWindowWords) + const windowSum = window.reduce((total, cur) => total + (cur ? 1 : 0), 0) + if (windowSum >= bestSum) { + bestSum = windowSum + bestIndex = i + } + } + + startIndex = Math.max(bestIndex - contextWindowWords, 0) + endIndex = Math.min(startIndex + 2 * contextWindowWords, tokenizedText.length - 1) + tokenizedText = tokenizedText.slice(startIndex, endIndex) + } + + const slice = tokenizedText + .map((tok) => { + // see if this tok is prefixed by any search terms + for (const searchTok of tokenizedTerms) { + if (tok.toLowerCase().includes(searchTok.toLowerCase())) { + const regex = new RegExp(searchTok.toLowerCase(), "gi") + return tok.replace(regex, `$&`) + } + } + return tok + }) + .join(" ") + + return `${startIndex === 0 ? "" : "..."}${slice}${ + endIndex === tokenizedText.length - 1 ? "" : "..." + }` +} + +// To be used with search and everything else with flexsearch +export const encode = (str: string) => + str + .toLowerCase() + .split(/\s+/) + .filter((token) => token.length > 0) diff --git a/quartz/components/styles/search.scss b/quartz/components/styles/search.scss index bb58948ae..685f0fc31 100644 --- a/quartz/components/styles/search.scss +++ b/quartz/components/styles/search.scss @@ -77,16 +77,97 @@ margin-bottom: 2em; } - & > input { + & > .input-container { + align-items: center; + gap: 0.5rem; + display: flex; + flex-wrap: wrap; + position: relative; box-sizing: border-box; - padding: 0.5em 1em; - font-family: var(--bodyFont); - color: var(--dark); - font-size: 1.1em; - border: 1px solid var(--lightgray); - &:focus { - outline: none; + .search-bar { + flex: 1 1 auto; + min-width: 0; + box-sizing: border-box; + padding: 0.5em 1em; + font-family: var(--bodyFont); + color: var(--dark); + font-size: 1.1em; + border: none; + background: transparent; + + &:focus { + outline: none; + } + } + + .semantic-search-progress { + position: absolute; + bottom: 0; + left: 0; + right: 0; + height: 2px; + background-color: var(--secondary); + width: 0; + opacity: 0; + transition: + width 0.3s ease, + opacity 0.2s ease; + pointer-events: none; + } + + .search-mode-toggle { + display: inline-flex; + align-items: center; + border-radius: 9999px; + height: 1.4rem; + background-color: color-mix(in srgb, var(--darkgray) 12%, transparent); + margin-right: 1rem; + + .mode-option { + border: none; + background: transparent; + font: inherit; + color: var(--gray); + border-radius: 9999px; + cursor: pointer; + transition: + background-color 0.2s ease, + color 0.2s ease; + display: inline-flex; + align-items: center; + justify-content: center; + width: 1.5rem; + height: 1.5rem; + position: relative; + + &:focus-visible { + outline: 2px solid var(--tertiary); + outline-offset: 2px; + } + + &.active { + background-color: var(--secondary); + color: var(--light); + } + + svg { + width: 18px; + height: 18px; + } + + .sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border: 0; + } + } } } diff --git a/quartz/embed_build.py b/quartz/embed_build.py new file mode 100644 index 000000000..63ade1be5 --- /dev/null +++ b/quartz/embed_build.py @@ -0,0 +1,542 @@ +# /// script +# requires-python = ">=3.11" +# dependencies = [ +# "langchain-text-splitters", +# "numpy", +# "openai", +# "sentence-transformers", +# "tiktoken", +# ] +# /// + +from __future__ import annotations + +import os, json, argparse, hashlib, math, random, logging + +from pathlib import Path +from functools import lru_cache +from collections.abc import Iterable +from concurrent.futures import ThreadPoolExecutor, as_completed + +import tiktoken, numpy as np + +from openai import OpenAI +from langchain_text_splitters import RecursiveCharacterTextSplitter + + +logger = logging.getLogger(__name__) +DEFAULT_VLLM_URL = os.environ.get("VLLM_URL") or os.environ.get("VLLM_EMBED_URL") or "http://127.0.0.1:8000/v1" + + +def resolve_vllm_base_url(url: str) -> str: + if not url: + raise ValueError("vLLM URL must be non-empty") + + trimmed = url.rstrip("/") + if trimmed.endswith("/v1/embeddings"): + trimmed = trimmed[: -len("/embeddings")] + elif trimmed.endswith("/embeddings"): + trimmed = trimmed[: trimmed.rfind("/")] + + if not trimmed.endswith("/v1"): + trimmed = f"{trimmed}/v1" + + return trimmed + + +def load_jsonl(fp: str) -> Iterable[dict]: + with open(fp, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + yield json.loads(line) + + +def l2_normalize_rows(x: np.ndarray) -> np.ndarray: + # x: [N, D] + norms = np.linalg.norm(x, ord=2, axis=1, keepdims=True) + norms[norms == 0] = 1.0 + return x / norms + + +@lru_cache(maxsize=1) +def get_tiktoken_encoder(): + # Get the o200k_base tokenizer (GPT-4o) with caching + # change this if you want something else. + return tiktoken.get_encoding("o200k_base") + + +def count_tokens(text: str) -> int: + # Count tokens using o200k_base encoding + encoder = get_tiktoken_encoder() + return len(encoder.encode(text)) + + +def get_text_splitter(chunk_size: int, overlap: int): + encoder = get_tiktoken_encoder() + return RecursiveCharacterTextSplitter( + chunk_size=chunk_size * 4, # character approximation + chunk_overlap=overlap * 4, + separators=["\n\n", "\n", ". ", " ", ""], + length_function=lambda t: len(encoder.encode(t)), + is_separator_regex=False, + ) + + +def chunk_document( + doc: dict, max_tokens: int = 512, overlap_tokens: int = 128, min_chunk_size: int = 100 +) -> list[dict]: + """ + Chunk a document if it exceeds max_tokens + + Args: + doc: {'slug': str, 'title': str, 'text': str} + max_tokens: Maximum tokens per chunk + overlap_tokens: Overlap between chunks + min_chunk_size: Minimum chunk size (avoid tiny chunks) + + Returns: + List of chunk dicts with metadata + """ + text = doc["text"] + token_count = count_tokens(text) + + # No chunking needed + if token_count <= max_tokens: + return [ + { + "slug": doc["slug"], + "title": doc.get("title", doc["slug"]), + "text": text, + "chunk_id": 0, + "parent_slug": doc["slug"], + "is_chunked": False, + } + ] + + # Apply chunking + splitter = get_text_splitter(max_tokens, overlap_tokens) + raw_chunks = splitter.split_text(text) + + # Filter out tiny chunks + valid_chunks = [c for c in raw_chunks if count_tokens(c) >= min_chunk_size] + + return [ + { + "slug": f"{doc['slug']}#chunk{i}", + "title": doc.get("title", doc["slug"]), + "text": chunk, + "chunk_id": i, + "parent_slug": doc["slug"], + "is_chunked": True, + } + for i, chunk in enumerate(valid_chunks) + ] + + +def write_shards(vectors: np.ndarray, shard_size: int, dtype: str, out_dir: Path) -> list[dict]: + out_dir.mkdir(parents=True, exist_ok=True) + rows, dims = vectors.shape + shards_meta: list[dict] = [] + np_dtype = np.float16 if dtype == "fp16" else np.float32 + bytes_per_value = np.dtype(np_dtype).itemsize + row_offset = 0 + for si, start in enumerate(range(0, rows, shard_size)): + end = min(start + shard_size, rows) + shard = vectors[start:end] # [n, dims] + bin_path = out_dir / f"vectors-{si:03d}.bin" + payload = shard.astype(np_dtype, copy=False).tobytes(order="C") + digest = hashlib.sha256(payload).hexdigest() + with open(bin_path, "wb") as f: + f.write(payload) + shard_rows = int(shard.shape[0]) + shards_meta.append( + { + "path": f"/embeddings/{bin_path.name}", + "rows": shard_rows, + "rowOffset": row_offset, + "byteLength": len(payload), + "sha256": digest, + "byteStride": dims * bytes_per_value, + }, + ) + row_offset += shard_rows + return shards_meta + + +def write_hnsw_graph(levels: list[list[list[int]]], rows: int, out_path: Path) -> tuple[list[dict], str]: + out_path.parent.mkdir(parents=True, exist_ok=True) + offset = 0 + meta: list[dict] = [] + digest = hashlib.sha256() + with open(out_path, "wb") as f: + for lvl in levels: + indptr = np.zeros(rows + 1, dtype=np.uint32) + edge_accum: list[int] = [] + for idx in range(rows): + neighbors = lvl[idx] if idx < len(lvl) else [] + indptr[idx + 1] = indptr[idx] + len(neighbors) + edge_accum.extend(neighbors) + indptr_bytes = indptr.tobytes(order="C") + indptr_offset = offset + f.write(indptr_bytes) + digest.update(indptr_bytes) + offset += len(indptr_bytes) + + if edge_accum: + indices = np.asarray(edge_accum, dtype=np.uint32) + indices_bytes = indices.tobytes(order="C") + else: + indices = np.zeros(0, dtype=np.uint32) + indices_bytes = indices.tobytes(order="C") + indices_offset = offset + f.write(indices_bytes) + digest.update(indices_bytes) + offset += len(indices_bytes) + + meta.append( + { + "level": len(meta), + "indptr": { + "offset": indptr_offset, + "elements": int(indptr.shape[0]), + "byteLength": len(indptr_bytes), + }, + "indices": { + "offset": indices_offset, + "elements": int(indices.shape[0]), + "byteLength": len(indices_bytes), + }, + }, + ) + return meta, digest.hexdigest() + + + +def embed_vllm( + texts: list[str], + model_id: str, + vllm_url: str, + batch_size: int = 64, + concurrency: int = 8, +) -> np.ndarray: + base_url = resolve_vllm_base_url(vllm_url) + api_key = os.environ.get("VLLM_API_KEY") or os.environ.get("OPENAI_API_KEY") or "not-set" + client = OpenAI(base_url=base_url, api_key=api_key, timeout=300) + + def list_available_models() -> list[str]: + models: list[str] = [] + page = client.models.list() + models.extend(model.id for model in page.data) + while getattr(page, "has_more", False) and page.data: + cursor = page.data[-1].id + page = client.models.list(after=cursor) + models.extend(model.id for model in page.data) + return models + + try: + available_models = list_available_models() + except Exception as exc: + raise RuntimeError(f"failed to query {base_url}/models: {exc}") from exc + + if model_id not in available_models: + suggestions = ", ".join(sorted(available_models)) if available_models else "" + logger.warning( + "model '%s' not served by vLLM at %s. Available models: %s. Use the first model, results may differ during semantic search (you can omit this message if your weights is a ONNX checkpoint of the same model.)", model_id, base_url, suggestions, + ) + model_id = available_models[0] + + # Apply model-specific prefixes for documents (asymmetric search) + model_lower = model_id.lower() + if "e5" in model_lower: + # E5 models: use "passage:" prefix for documents + prefixed = [f"passage: {t}" for t in texts] + elif "qwen" in model_lower and "embedding" in model_lower: + # Qwen3-Embedding: documents use plain text (no prefix) + prefixed = texts + elif "embeddinggemma" in model_lower: + # embeddinggemma: use "title: none | text:" prefix for documents + prefixed = [f"title: none | text: {t}" for t in texts] + else: + # Default: no prefix for unknown models + prefixed = texts + + print( + "Embedding" + f" {len(prefixed)} texts with vLLM" + f" (model={model_id}, batch_size={batch_size}, concurrency={concurrency})", + ) + + # Create batches + batches = [] + for i in range(0, len(prefixed), batch_size): + batch = prefixed[i : i + batch_size] + batches.append((i, batch)) + + # Function to send a single batch request + def send_batch(batch_info: tuple[int, list[str]]) -> tuple[int, list[np.ndarray]]: + idx, batch = batch_info + response = client.embeddings.create(model=model_id, input=batch) + embeddings = [np.asarray(item.embedding, dtype=np.float32) for item in response.data] + return (idx, embeddings) + + # Send batches concurrently (or sequentially if only 1 batch) + results: dict[int, list[np.ndarray]] = {} + if len(batches) == 1: + # Single batch - no need for threading + idx, embeddings = send_batch(batches[0]) + results[idx] = embeddings + else: + # Multiple batches - use concurrent requests + with ThreadPoolExecutor(max_workers=concurrency) as executor: + futures = {executor.submit(send_batch, batch_info): batch_info[0] for batch_info in batches} + completed = 0 + for future in as_completed(futures): + idx, embeddings = future.result() + results[idx] = embeddings + completed += 1 + if completed % max(1, len(batches) // 10) == 0 or completed == len(batches): + print(f" Completed {completed}/{len(batches)} batches ({completed * 100 // len(batches)}%)") + + # Reconstruct in order + out: list[np.ndarray] = [] + for i in sorted(results.keys()): + out.extend(results[i]) + + return np.stack(out, axis=0) + + +def embed_hf(texts: list[str], model_id: str, device: str) -> np.ndarray: + # Prefer sentence-transformers for E5 and similar embed models + from sentence_transformers import SentenceTransformer + + model = SentenceTransformer(model_id, device=device) + + # Apply model-specific prefixes for documents (asymmetric search) + model_lower = model_id.lower() + if "e5" in model_lower: + # E5 models: use "passage:" prefix for documents + prefixed = [f"passage: {t}" for t in texts] + elif "qwen" in model_lower and "embedding" in model_lower: + # Qwen3-Embedding: documents use plain text (no prefix) + prefixed = texts + elif "embeddinggemma" in model_lower: + # embeddinggemma: use "title: none | text:" prefix for documents + prefixed = [f"title: none | text: {t}" for t in texts] + else: + # Default: no prefix for unknown models + prefixed = texts + + vecs = model.encode( + prefixed, + batch_size=64, + normalize_embeddings=True, + convert_to_numpy=True, + show_progress_bar=True, + ) + return vecs.astype(np.float32, copy=False) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--jsonl", default="public/embeddings-text.jsonl") + ap.add_argument("--model", default=os.environ.get("SEM_MODEL", "intfloat/multilingual-e5-large")) + ap.add_argument("--dims", type=int, default=int(os.environ.get("SEM_DIMS", "1024"))) + ap.add_argument("--dtype", choices=["fp16", "fp32"], default=os.environ.get("SEM_DTYPE", "fp32")) + ap.add_argument("--shard-size", type=int, default=int(os.environ.get("SEM_SHARD", "1024"))) + ap.add_argument("--out", default="public/embeddings") + ap.add_argument("--use-vllm", action="store_true", default=bool(os.environ.get("USE_VLLM", ""))) + ap.add_argument( + "--vllm-url", + default=DEFAULT_VLLM_URL, + help="Base URL for the vLLM OpenAI-compatible server (accepts either /v1 or /v1/embeddings)", + ) + ap.add_argument("--chunk-size", type=int, default=512, help="Max tokens per chunk") + ap.add_argument("--chunk-overlap", type=int, default=128, help="Overlap tokens between chunks") + ap.add_argument("--no-chunking", action="store_true", help="Disable chunking (embed full docs)") + ap.add_argument( + "--concurrency", + type=int, + default=int(os.environ.get("VLLM_CONCURRENCY", "8")), + help="Number of concurrent requests to vLLM (default: 8)", + ) + ap.add_argument( + "--batch-size", + type=int, + default=int(os.environ.get("VLLM_BATCH_SIZE", "64")), + help="Batch size for vLLM requests (default: 64)", + ) + args = ap.parse_args() + + recs = list(load_jsonl(args.jsonl)) + if not recs: + print("No input found in public/embeddings-text.jsonl; run the site build first to emit JSONL.") + return + + # Apply chunking + if args.no_chunking: + chunks = recs + chunk_metadata = {} + print(f"Chunking disabled. Processing {len(chunks)} full documents") + else: + chunks = [] + chunk_metadata = {} + for rec in recs: + doc_chunks = chunk_document(rec, max_tokens=args.chunk_size, overlap_tokens=args.chunk_overlap) + chunks.extend(doc_chunks) + # Build chunk metadata map + for chunk in doc_chunks: + if chunk["is_chunked"]: + chunk_metadata[chunk["slug"]] = { + "parentSlug": chunk["parent_slug"], + "chunkId": chunk["chunk_id"], + } + chunked_count = sum(1 for c in chunks if c.get("is_chunked", False)) + print(f"Chunked {len(recs)} documents into {len(chunks)} chunks ({chunked_count} chunked, {len(chunks) - chunked_count} unchanged)") + print(f" Chunk size: {args.chunk_size} tokens, overlap: {args.chunk_overlap} tokens") + + ids = [c["slug"] for c in chunks] + titles = [c.get("title", c["slug"]) for c in chunks] + texts = [c["text"] for c in chunks] + + if args.use_vllm: + vecs = embed_vllm( + texts, + args.model, + args.vllm_url, + batch_size=args.batch_size, + concurrency=args.concurrency, + ) + else: + device = "cuda" if os.environ.get("CUDA_VISIBLE_DEVICES") else "cpu" + vecs = embed_hf(texts, args.model, device) + + # Coerce dims and re-normalize + if vecs.shape[1] != args.dims: + if vecs.shape[1] > args.dims: + vecs = vecs[:, : args.dims] + else: + vecs = np.pad(vecs, ((0, 0), (0, args.dims - vecs.shape[1]))) + vecs = l2_normalize_rows(vecs.astype(np.float32, copy=False)) + + out_dir = Path(args.out) + shards = write_shards(vecs, args.shard_size, args.dtype, out_dir) + + # Build a lightweight HNSW graph and store it in a compact binary layout + def hnsw_build(data: np.ndarray, M: int = 16, efC: int = 200, seed: int = 0) -> dict: + rng = random.Random(seed) + N, D = data.shape + levels: list[list[list[int]]] = [] # levels[L][i] = neighbors of node i at level L + + # random level assignment using 1/e distribution + node_levels = [] + for _ in range(N): + lvl = 0 + while rng.random() < 1 / math.e: + lvl += 1 + node_levels.append(lvl) + max_level = max(node_levels) if N > 0 else 0 + for _ in range(max_level + 1): + levels.append([[] for _ in range(N)]) + + def sim(i: int, j: int) -> float: + return float((data[i] * data[j]).sum()) + + entry = 0 if N > 0 else -1 + + def search_layer(q: int, ep: int, ef: int, L: int) -> list[int]: + if ep < 0: + return [] + visited = set() + cand: list[tuple[float, int]] = [] + top: list[tuple[float, int]] = [] + def push(node: int): + if node in visited: + return + visited.add(node) + cand.append((sim(q, node), node)) + push(ep) + while cand: + cand.sort(reverse=True) + s, v = cand.pop(0) + if len(top) >= ef and s <= top[-1][0]: + break + top.append((s, v)) + for u in levels[L][v]: + push(u) + top.sort(reverse=True) + return [n for _, n in top] + + for i in range(N): + if i == 0: + continue + lvl = node_levels[i] + ep = entry + for L in range(max_level, lvl, -1): + c = search_layer(i, ep, 1, L) + if c: + ep = c[0] + for L in range(min(max_level, lvl), -1, -1): + W = search_layer(i, ep, efC, L) + # Select top M by similarity + neigh = sorted(((sim(i, j), j) for j in W if j != i), reverse=True)[:M] + for _, e in neigh: + if e not in levels[L][i]: + levels[L][i].append(e) + if i not in levels[L][e]: + levels[L][e].append(i) + + # trim neighbors to M + for L in range(len(levels)): + for i in range(N): + if len(levels[L][i]) > M: + # keep top M by sim + nb = levels[L][i] + nb = sorted(nb, key=lambda j: sim(i, j), reverse=True)[:M] + levels[L][i] = nb + + return { + "M": M, + "efConstruction": efC, + "entryPoint": entry, + "maxLevel": max_level, + "levels": levels, + } + + hnsw = hnsw_build(vecs, M=16, efC=200) + hnsw_meta, hnsw_sha = write_hnsw_graph(hnsw["levels"], int(vecs.shape[0]), out_dir / "hnsw.bin") + + manifest = { + "version": 2, + "dims": args.dims, + "dtype": args.dtype, + "normalized": True, + "rows": int(vecs.shape[0]), + "shardSizeRows": args.shard_size, + "vectors": { + "dtype": args.dtype, + "rows": int(vecs.shape[0]), + "dims": args.dims, + "shards": shards, + }, + "ids": ids, + "titles": titles, + "chunkMetadata": chunk_metadata, + "hnsw": { + "M": hnsw["M"], + "efConstruction": hnsw["efConstruction"], + "entryPoint": hnsw["entryPoint"], + "maxLevel": hnsw["maxLevel"], + "graph": { + "path": "/embeddings/hnsw.bin", + "sha256": hnsw_sha, + "levels": hnsw_meta, + }, + }, + } + (out_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False), encoding="utf-8") + print(f"Wrote {len(shards)} vector shard(s), HNSW graph, and manifest to {out_dir}") + +if __name__ == "__main__": + main() diff --git a/quartz/plugins/emitters/404.tsx b/quartz/plugins/emitters/404.tsx index 04a006dd1..8eba6ba2f 100644 --- a/quartz/plugins/emitters/404.tsx +++ b/quartz/plugins/emitters/404.tsx @@ -40,7 +40,7 @@ export const NotFoundPage: QuartzEmitterPlugin = () => { description: notFound, frontmatter: { title: notFound, tags: [] }, }) - const externalResources = pageResources(path, resources) + const externalResources = pageResources(path, resources, ctx.cfg.configuration) const componentData: QuartzComponentProps = { ctx, fileData: vfile.data, diff --git a/quartz/plugins/emitters/componentResources.ts b/quartz/plugins/emitters/componentResources.ts index f97fddec2..4340af50f 100644 --- a/quartz/plugins/emitters/componentResources.ts +++ b/quartz/plugins/emitters/componentResources.ts @@ -1,5 +1,8 @@ import { FullSlug, joinSegments } from "../../util/path" import { QuartzEmitterPlugin } from "../types" +import path from "path" +import fs from "node:fs/promises" +import { globby } from "globby" // @ts-ignore import spaRouterScript from "../../components/scripts/spa.inline" @@ -16,7 +19,7 @@ import { processGoogleFonts, } from "../../util/theme" import { Features, transform } from "lightningcss" -import { transform as transpile } from "esbuild" +import { transform as transpile, build as bundle } from "esbuild" import { write } from "./helpers" type ComponentResources = { @@ -357,7 +360,47 @@ export const ComponentResources: QuartzEmitterPlugin = () => { ext: ".js", content: postscript, }) + + // Bundle all worker files + const workerFiles = await globby(["quartz/**/*.worker.ts"]) + for (const src of workerFiles) { + const result = await bundle({ + entryPoints: [src], + bundle: true, + minify: true, + platform: "browser", + format: "esm", + write: false, + }) + const code = result.outputFiles[0].text + const name = path.basename(src).replace(/\.ts$/, "") + yield write({ ctx, slug: name as FullSlug, ext: ".js", content: code }) + } + }, + async *partialEmit(ctx, _content, _resources, changeEvents) { + // Handle worker file changes in incremental builds + for (const changeEvent of changeEvents) { + if (!/\.worker\.ts$/.test(changeEvent.path)) continue + if (changeEvent.type === "delete") { + const name = path.basename(changeEvent.path).replace(/\.ts$/, "") + const dest = joinSegments(ctx.argv.output, `${name}.js`) + try { + await fs.unlink(dest) + } catch {} + continue + } + const result = await bundle({ + entryPoints: [changeEvent.path], + bundle: true, + minify: true, + platform: "browser", + format: "esm", + write: false, + }) + const code = result.outputFiles[0].text + const name = path.basename(changeEvent.path).replace(/\.ts$/, "") + yield write({ ctx, slug: name as FullSlug, ext: ".js", content: code }) + } }, - async *partialEmit() {}, } } diff --git a/quartz/plugins/emitters/contentPage.tsx b/quartz/plugins/emitters/contentPage.tsx index c3410ecc3..b07635383 100644 --- a/quartz/plugins/emitters/contentPage.tsx +++ b/quartz/plugins/emitters/contentPage.tsx @@ -25,7 +25,7 @@ async function processContent( ) { const slug = fileData.slug! const cfg = ctx.cfg.configuration - const externalResources = pageResources(pathToRoot(slug), resources) + const externalResources = pageResources(pathToRoot(slug), resources, ctx.cfg.configuration) const componentData: QuartzComponentProps = { ctx, fileData, diff --git a/quartz/plugins/emitters/folderPage.tsx b/quartz/plugins/emitters/folderPage.tsx index f9b181dff..3cd288c05 100644 --- a/quartz/plugins/emitters/folderPage.tsx +++ b/quartz/plugins/emitters/folderPage.tsx @@ -38,7 +38,7 @@ async function* processFolderInfo( const slug = joinSegments(folder, "index") as FullSlug const [tree, file] = folderContent const cfg = ctx.cfg.configuration - const externalResources = pageResources(pathToRoot(slug), resources) + const externalResources = pageResources(pathToRoot(slug), resources, ctx.cfg.configuration) const componentData: QuartzComponentProps = { ctx, fileData: file.data, diff --git a/quartz/plugins/emitters/index.ts b/quartz/plugins/emitters/index.ts index d2de2ed1e..1471544ce 100644 --- a/quartz/plugins/emitters/index.ts +++ b/quartz/plugins/emitters/index.ts @@ -1,7 +1,7 @@ export { ContentPage } from "./contentPage" export { TagPage } from "./tagPage" export { FolderPage } from "./folderPage" -export { ContentIndex as ContentIndex } from "./contentIndex" +export { ContentIndex } from "./contentIndex" export { AliasRedirects } from "./aliases" export { Assets } from "./assets" export { Static } from "./static" @@ -10,3 +10,4 @@ export { ComponentResources } from "./componentResources" export { NotFoundPage } from "./404" export { CNAME } from "./cname" export { CustomOgImages } from "./ogImage" +export { SemanticIndex } from "./semantic" diff --git a/quartz/plugins/emitters/semantic.ts b/quartz/plugins/emitters/semantic.ts new file mode 100644 index 000000000..47442ea9a --- /dev/null +++ b/quartz/plugins/emitters/semantic.ts @@ -0,0 +1,235 @@ +import { write } from "./helpers" +import { QuartzEmitterPlugin } from "../types" +import { FilePath, FullSlug, joinSegments, QUARTZ } from "../../util/path" +import { ReadTimeResults } from "reading-time" +import { GlobalConfiguration } from "../../cfg" +import { spawn } from "child_process" + +const DEFAULT_MODEL_ID = "onnx-community/Qwen3-Embedding-0.6B-ONNX" + +const defaults: GlobalConfiguration["semanticSearch"] = { + enable: true, + model: DEFAULT_MODEL_ID, + aot: false, + dims: 1024, + dtype: "fp32", + shardSizeRows: 1024, + hnsw: { M: 16, efConstruction: 200 }, + chunking: { + chunkSize: 512, + chunkOverlap: 128, + noChunking: false, + }, + vllm: { + enable: false, + vllmUrl: + process.env.VLLM_URL || process.env.VLLM_EMBED_URL || "http://127.0.0.1:8000/v1/embeddings", + concurrency: parseInt(process.env.VLLM_CONCURRENCY || "8", 10), + batchSize: parseInt(process.env.VLLM_BATCH_SIZE || "64", 10), + }, +} + +type ContentDetails = { + slug: string + title: string + filePath: FilePath + content: string + readingTime?: Partial +} + +/** + * Check if uv is installed + */ +function checkUvInstalled(): Promise { + return new Promise((resolve) => { + const proc = spawn("uv", ["--version"], { shell: true }) + proc.on("error", () => resolve(false)) + proc.on("close", (code) => resolve(code === 0)) + }) +} + +/** + * Run the Python embedding build script using uv + * Script uses PEP 723 inline metadata for dependency management + */ +function runEmbedBuild( + jsonlPath: string, + outDir: string, + opts: { + model: string + dtype: string + dims: number + shardSizeRows: number + chunking: { chunkSize: number; chunkOverlap: number; noChunking: boolean } + vllm: { enable: boolean; vllmUrl?: string; concurrency: number; batchSize: number } + }, +): Promise { + return new Promise((resolve, reject) => { + const scriptPath = joinSegments(QUARTZ, "embed_build.py") + const args = [ + "run", + scriptPath, + "--jsonl", + jsonlPath, + "--model", + opts.model, + "--out", + outDir, + "--dtype", + opts.dtype, + "--dims", + String(opts.dims), + "--shard-size", + String(opts.shardSizeRows), + "--chunk-size", + String(opts.chunking.chunkSize), + "--chunk-overlap", + String(opts.chunking.chunkOverlap), + ] + + if (opts.chunking.noChunking) { + args.push("--no-chunking") + } + + if (opts.vllm.enable) { + args.push("--use-vllm") + if (opts.vllm.vllmUrl) { + args.push("--vllm-url", opts.vllm.vllmUrl) + } + args.push("--concurrency", String(opts.vllm.concurrency)) + args.push("--batch-size", String(opts.vllm.batchSize)) + } + + console.log("\nRunning embedding generation:") + console.log(` uv ${args.join(" ")}`) + + const env = { ...process.env } + if (opts.vllm.enable && !env.USE_VLLM) { + env.USE_VLLM = "1" + } + + const proc = spawn("uv", args, { + stdio: "inherit", + shell: true, + env, + }) + + proc.on("error", (err) => { + reject(new Error(`Failed to spawn uv: ${err.message}`)) + }) + + proc.on("close", (code) => { + if (code === 0) { + console.log("Embedding generation completed successfully") + resolve() + } else { + reject(new Error(`embed_build.py exited with code ${code}`)) + } + }) + }) +} + +export const SemanticIndex: QuartzEmitterPlugin> = ( + opts, +) => { + const merged = { ...defaults, ...opts } + const o = { + enable: merged.enable!, + model: merged.model!, + aot: merged.aot!, + dims: merged.dims!, + dtype: merged.dtype!, + shardSizeRows: merged.shardSizeRows!, + hnsw: { + M: merged.hnsw?.M ?? defaults.hnsw!.M!, + efConstruction: merged.hnsw?.efConstruction ?? defaults.hnsw!.efConstruction!, + efSearch: merged.hnsw?.efSearch, + }, + chunking: { + chunkSize: merged.chunking?.chunkSize ?? defaults.chunking!.chunkSize!, + chunkOverlap: merged.chunking?.chunkOverlap ?? defaults.chunking!.chunkOverlap!, + noChunking: merged.chunking?.noChunking ?? defaults.chunking!.noChunking!, + }, + vllm: { + enable: merged.vllm?.enable ?? defaults.vllm!.enable!, + vllmUrl: merged.vllm?.vllmUrl ?? defaults.vllm!.vllmUrl, + concurrency: merged.vllm?.concurrency ?? defaults.vllm!.concurrency!, + batchSize: merged.vllm?.batchSize ?? defaults.vllm!.batchSize!, + }, + } + + if (!o.model) { + throw new Error("Semantic search requires a model identifier") + } + + return { + name: "SemanticIndex", + getQuartzComponents() { + return [] + }, + async *partialEmit() {}, + async *emit(ctx, content, _resources) { + if (!o.enable) return + + const docs: ContentDetails[] = [] + for (const [_, file] of content) { + const slug = file.data.slug! + const title = file.data.frontmatter?.title ?? slug + const text = file.data.text + if (text) { + docs.push({ + slug, + title, + filePath: file.data.filePath!, + content: text, + readingTime: file.data.readingTime, + }) + } + } + + // Emit JSONL with the exact text used for embeddings + const jsonl = docs + .map((d) => ({ slug: d.slug, title: d.title, text: d.content })) + .map((o) => JSON.stringify(o)) + .join("\n") + + const jsonlSlug = "embeddings-text" as FullSlug + yield write({ + ctx, + slug: jsonlSlug, + ext: ".jsonl", + content: jsonl, + }) + + // If aot is false, run the embedding generation script + if (!o.aot) { + console.log("\nGenerating embeddings (aot=false)...") + + // Check for uv + const hasUv = await checkUvInstalled() + if (!hasUv) { + throw new Error( + "uv is required for embedding generation. Install it from https://docs.astral.sh/uv/", + ) + } + + const jsonlPath = joinSegments(ctx.argv.output, "embeddings-text.jsonl") + const outDir = joinSegments(ctx.argv.output, "embeddings") + + try { + await runEmbedBuild(jsonlPath, outDir, o) + } catch (err) { + const message = err instanceof Error ? err.message : String(err) + throw new Error(`Embedding generation failed: ${message}`) + } + } else { + console.log( + "\nSkipping embedding generation (aot=true). Expecting pre-generated embeddings in public/embeddings/", + ) + } + }, + externalResources(_ctx) { + return {} + }, + } +} diff --git a/quartz/plugins/emitters/tagPage.tsx b/quartz/plugins/emitters/tagPage.tsx index 5f238932d..d0ab59295 100644 --- a/quartz/plugins/emitters/tagPage.tsx +++ b/quartz/plugins/emitters/tagPage.tsx @@ -73,7 +73,7 @@ async function processTagPage( const slug = joinSegments("tags", tag) as FullSlug const [tree, file] = tagContent const cfg = ctx.cfg.configuration - const externalResources = pageResources(pathToRoot(slug), resources) + const externalResources = pageResources(pathToRoot(slug), resources, ctx.cfg.configuration) const componentData: QuartzComponentProps = { ctx, fileData: file.data, diff --git a/quartz/workers/semantic.worker.ts b/quartz/workers/semantic.worker.ts new file mode 100644 index 000000000..2d8e46477 --- /dev/null +++ b/quartz/workers/semantic.worker.ts @@ -0,0 +1,548 @@ +// Unified semantic search worker: handles data loading and query execution +import { env, pipeline } from "@huggingface/transformers" +import "onnxruntime-web/webgpu" +import "onnxruntime-web/wasm" + +export {} + +type VectorShardMeta = { + path: string + rows: number + rowOffset: number + byteLength: number + sha256?: string + byteStride: number +} + +type LevelSection = { + level: number + indptr: { offset: number; elements: number; byteLength: number } + indices: { offset: number; elements: number; byteLength: number } +} + +type ChunkMetadata = { + parentSlug: string + chunkId: number +} + +type Manifest = { + version: number + dims: number + dtype: string + normalized: boolean + rows: number + shardSizeRows: number + vectors: { + dtype: string + rows: number + dims: number + shards: VectorShardMeta[] + } + ids: string[] + titles?: string[] + chunkMetadata?: Record + hnsw: { + M: number + efConstruction: number + entryPoint: number + maxLevel: number + graph: { + path: string + sha256?: string + levels: LevelSection[] + } + } +} + +type InitMessage = { + type: "init" + cfg: any + manifestUrl: string + baseUrl?: string + disableCache?: boolean +} + +type SearchMessage = { type: "search"; text: string; k: number; seq: number } +type ResetMessage = { type: "reset" } + +type WorkerMessage = InitMessage | SearchMessage | ResetMessage + +type ReadyMessage = { type: "ready" } + +type ProgressMessage = { + type: "progress" + loadedRows: number + totalRows: number +} + +type SearchHit = { id: number; score: number } + +type SearchResultMessage = { + type: "search-result" + seq: number + semantic: SearchHit[] +} + +type ErrorMessage = { type: "error"; seq?: number; message: string } + +type WorkerState = "idle" | "loading" | "ready" | "error" + +// IndexedDB configuration +const DB_NAME = "semantic-search-cache" +const STORE_NAME = "assets" +const DB_VERSION = 1 +const hasIndexedDB = typeof indexedDB !== "undefined" +const supportsSharedArrayBuffer = typeof SharedArrayBuffer !== "undefined" + +// State +let state: WorkerState = "idle" +let manifest: Manifest | null = null +let cfg: any = null +let vectorsView: Float32Array | null = null +let dims = 0 +let rows = 0 +let classifier: any = null +let envConfigured = false +let entryPoint = -1 +let maxLevel = 0 +let efDefault = 128 +let levelGraph: { indptr: Uint32Array; indices: Uint32Array }[] = [] +let abortController: AbortController | null = null +let dbPromise: Promise | null = null + +// IndexedDB helpers +function openDatabase(): Promise { + if (!hasIndexedDB) { + return Promise.reject(new Error("indexedDB unavailable")) + } + if (!dbPromise) { + dbPromise = new Promise((resolve, reject) => { + const req = indexedDB.open(DB_NAME, DB_VERSION) + req.onupgradeneeded = () => { + const db = req.result + if (!db.objectStoreNames.contains(STORE_NAME)) { + db.createObjectStore(STORE_NAME) + } + } + req.onsuccess = () => resolve(req.result) + req.onerror = () => reject(req.error ?? new Error("failed to open cache store")) + }) + } + return dbPromise +} + +async function readAsset(hash: string): Promise { + if (!hasIndexedDB) { + return null + } + const db = await openDatabase() + return new Promise((resolve, reject) => { + const tx = db.transaction(STORE_NAME, "readonly") + const store = tx.objectStore(STORE_NAME) + const req = store.get(hash) + req.onsuccess = () => { + const value = req.result + if (value instanceof ArrayBuffer) { + resolve(value) + } else if (value && value.buffer instanceof ArrayBuffer) { + resolve(value.buffer as ArrayBuffer) + } else { + resolve(null) + } + } + req.onerror = () => reject(req.error ?? new Error("failed to read cached asset")) + }) +} + +async function writeAsset(hash: string, buffer: ArrayBuffer): Promise { + if (!hasIndexedDB) { + return + } + const db = await openDatabase() + await new Promise((resolve, reject) => { + const tx = db.transaction(STORE_NAME, "readwrite") + const store = tx.objectStore(STORE_NAME) + const req = store.put(buffer, hash) + req.onsuccess = () => resolve() + req.onerror = () => reject(req.error ?? new Error("failed to cache asset")) + }) +} + +function toAbsolute(path: string, baseUrl?: string): string { + if (path.startsWith("http://") || path.startsWith("https://")) { + return path + } + const base = baseUrl ?? self.location.origin + return new URL(path, base).toString() +} + +async function fetchBinary( + path: string, + disableCache: boolean, + sha?: string, +): Promise { + if (!disableCache && sha && hasIndexedDB) { + try { + const cached = await readAsset(sha) + if (cached) { + return cached + } + } catch { + // fall through to network fetch on cache errors + } + } + const res = await fetch(path, { signal: abortController?.signal ?? undefined }) + if (!res.ok) { + throw new Error(`failed to fetch ${path}: ${res.status} ${res.statusText}`) + } + const payload = await res.arrayBuffer() + if (!disableCache && sha && hasIndexedDB) { + try { + await writeAsset(sha, payload) + } catch { + // ignore cache write failures + } + } + return payload +} + +async function populateVectors( + manifest: Manifest, + baseUrl: string | undefined, + disableCache: boolean | undefined, +): Promise<{ buffer: Float32Array; rowsLoaded: number }> { + if (manifest.vectors.dtype !== "fp32") { + throw new Error(`unsupported embedding dtype '${manifest.vectors.dtype}', regenerate with fp32`) + } + const rows = manifest.rows + const dims = manifest.dims + const totalBytes = rows * dims * Float32Array.BYTES_PER_ELEMENT + const buffer = supportsSharedArrayBuffer + ? new Float32Array(new SharedArrayBuffer(totalBytes)) + : new Float32Array(totalBytes) + let loadedRows = 0 + for (const shard of manifest.vectors.shards) { + const absolute = toAbsolute(shard.path, baseUrl) + const payload = await fetchBinary(absolute, Boolean(disableCache), shard.sha256) + const view = new Float32Array(payload) + if (view.length !== shard.rows * dims) { + throw new Error( + `shard ${shard.path} has mismatched length (expected ${shard.rows * dims}, got ${view.length})`, + ) + } + buffer.set(view, shard.rowOffset * dims) + loadedRows = Math.min(rows, shard.rowOffset + shard.rows) + const progress: ProgressMessage = { + type: "progress", + loadedRows, + totalRows: rows, + } + self.postMessage(progress) + } + return { buffer, rowsLoaded: loadedRows } +} + +async function populateGraph( + manifest: Manifest, + baseUrl: string | undefined, + disableCache: boolean | undefined, +): Promise { + const graphMeta = manifest.hnsw.graph + const absolute = toAbsolute(graphMeta.path, baseUrl) + return await fetchBinary(absolute, Boolean(disableCache), graphMeta.sha256) +} + +function configureRuntimeEnv() { + if (envConfigured) return + env.allowLocalModels = false + env.allowRemoteModels = true + const wasmBackend = env.backends?.onnx?.wasm + if (!wasmBackend) { + throw new Error("transformers.js ONNX runtime backend unavailable") + } + const cdnBase = `https://cdn.jsdelivr.net/npm/@huggingface/transformers@${env.version}/dist/` + wasmBackend.wasmPaths = cdnBase + envConfigured = true +} + +async function ensureEncoder() { + if (classifier) return + if (!cfg?.model) { + throw new Error("semantic worker missing model identifier") + } + configureRuntimeEnv() + const dtype = typeof cfg?.dtype === "string" && cfg.dtype.length > 0 ? cfg.dtype : "fp32" + const pipelineOpts: Record = { + device: "wasm", + dtype, + local_files_only: false, + } + classifier = await pipeline("feature-extraction", cfg.model, pipelineOpts) + cfg.dtype = dtype +} + +function vectorSlice(id: number): Float32Array { + if (!vectorsView) { + throw new Error("vector buffer not configured") + } + const start = id * dims + const end = start + dims + return vectorsView.subarray(start, end) +} + +function dot(a: Float32Array, b: Float32Array): number { + let s = 0 + for (let i = 0; i < dims; i++) { + s += a[i] * b[i] + } + return s +} + +function neighborsFor(level: number, node: number): Uint32Array { + const meta = levelGraph[level] + if (!meta) return new Uint32Array() + const { indptr, indices } = meta + if (node < 0 || node + 1 >= indptr.length) return new Uint32Array() + const start = indptr[node] + const end = indptr[node + 1] + return indices.subarray(start, end) +} + +function insertSortedDescending(arr: SearchHit[], item: SearchHit) { + let idx = arr.length + while (idx > 0 && arr[idx - 1].score < item.score) { + idx -= 1 + } + arr.splice(idx, 0, item) +} + +function bruteForceSearch(query: Float32Array, k: number): SearchHit[] { + if (!vectorsView) return [] + const hits: SearchHit[] = [] + for (let id = 0; id < rows; id++) { + const score = dot(query, vectorSlice(id)) + if (hits.length < k) { + insertSortedDescending(hits, { id, score }) + } else if (score > hits[hits.length - 1].score) { + insertSortedDescending(hits, { id, score }) + hits.length = k + } + } + return hits +} + +function hnswSearch(query: Float32Array, k: number): SearchHit[] { + if (!manifest || !vectorsView || entryPoint < 0 || levelGraph.length === 0) { + return bruteForceSearch(query, k) + } + const ef = Math.max(efDefault, k * 10) + let ep = entryPoint + let epScore = dot(query, vectorSlice(ep)) + for (let level = maxLevel; level > 0; level--) { + let changed = true + while (changed) { + changed = false + const neigh = neighborsFor(level, ep) + for (let i = 0; i < neigh.length; i++) { + const candidate = neigh[i] + if (candidate >= rows) continue + const score = dot(query, vectorSlice(candidate)) + if (score > epScore) { + epScore = score + ep = candidate + changed = true + } + } + } + } + + const visited = new Set() + const candidateQueue: SearchHit[] = [] + const best: SearchHit[] = [] + insertSortedDescending(candidateQueue, { id: ep, score: epScore }) + insertSortedDescending(best, { id: ep, score: epScore }) + visited.add(ep) + + while (candidateQueue.length > 0) { + const current = candidateQueue.shift()! + const worstBest = best.length >= ef ? best[best.length - 1].score : -Infinity + if (current.score < worstBest && best.length >= ef) { + break + } + const neigh = neighborsFor(0, current.id) + for (let i = 0; i < neigh.length; i++) { + const candidate = neigh[i] + if (candidate >= rows || visited.has(candidate)) continue + visited.add(candidate) + const score = dot(query, vectorSlice(candidate)) + const hit = { id: candidate, score } + insertSortedDescending(candidateQueue, hit) + if (best.length < ef || score > best[best.length - 1].score) { + insertSortedDescending(best, hit) + if (best.length > ef) { + best.pop() + } + } + } + } + + best.sort((a, b) => b.score - a.score) + return best.slice(0, k) +} + +async function embed(text: string, isQuery: boolean = false): Promise { + await ensureEncoder() + // Apply model-specific prefixes for asymmetric search + let prefixedText = text + if (cfg?.model) { + const modelName = cfg.model.toLowerCase() + switch (true) { + case modelName.includes("e5"): { + // E5 models require query: or passage: prefix + prefixedText = isQuery ? `query: ${text}` : `passage: ${text}` + break + } + case modelName.includes("qwen") && modelName.includes("embedding"): { + // Qwen3-Embedding requires task instruction for queries only + if (isQuery) { + const task = "Given a web search query, retrieve relevant passages that answer the query" + prefixedText = `Instruct: ${task}\nQuery: ${text}` + } + // Documents use plain text (no prefix) + break + } + case modelName.includes("embeddinggemma"): { + // embeddinggemma requires specific prefixes + prefixedText = isQuery + ? `task: search result | query: ${text}` + : `title: none | text: ${text}` + break + } + default: + break + } + } + const out = await classifier(prefixedText, { pooling: "mean", normalize: true }) + const data = Array.from(out?.data ?? out) as number[] + const vec = new Float32Array(dims) + for (let i = 0; i < dims; i++) vec[i] = data[i] ?? 0 + return vec +} + +async function handleInit(msg: InitMessage) { + if (state === "loading" || state === "ready") { + throw new Error("worker already initialized or loading") + } + + state = "loading" + abortController?.abort() + abortController = new AbortController() + + try { + cfg = msg.cfg + + const manifestUrl = toAbsolute(msg.manifestUrl, msg.baseUrl) + const response = await fetch(manifestUrl, { signal: abortController.signal }) + if (!response.ok) { + throw new Error( + `failed to fetch manifest ${manifestUrl}: ${response.status} ${response.statusText}`, + ) + } + manifest = (await response.json()) as Manifest + + if (manifest.vectors.dtype !== "fp32") { + throw new Error( + `unsupported embedding dtype '${manifest.vectors.dtype}', regenerate with fp32`, + ) + } + + dims = manifest.dims + rows = manifest.rows + + const { buffer: vectorBuffer } = await populateVectors(manifest, msg.baseUrl, msg.disableCache) + vectorsView = vectorBuffer + + const graphBuffer = await populateGraph(manifest, msg.baseUrl, msg.disableCache) + + entryPoint = manifest.hnsw.entryPoint + maxLevel = manifest.hnsw.maxLevel + efDefault = Math.max(64, manifest.hnsw.M * 4) + levelGraph = manifest.hnsw.graph.levels.map((level) => { + const indptr = new Uint32Array(graphBuffer, level.indptr.offset, level.indptr.elements) + const indices = new Uint32Array(graphBuffer, level.indices.offset, level.indices.elements) + return { indptr, indices } + }) + + state = "ready" + const ready: ReadyMessage = { type: "ready" } + self.postMessage(ready) + } catch (err) { + state = "error" + throw err + } +} + +async function handleSearch(msg: SearchMessage) { + if (state !== "ready") { + throw new Error("worker not ready for search") + } + if (!manifest || !vectorsView) { + throw new Error("semantic worker not configured") + } + + const queryVec = await embed(msg.text, true) + const semanticHits = hnswSearch(queryVec, Math.max(1, msg.k)) + const message: SearchResultMessage = { + type: "search-result", + seq: msg.seq, + semantic: semanticHits, + } + self.postMessage(message) +} + +function handleReset() { + abortController?.abort() + abortController = null + state = "idle" + manifest = null + cfg = null + vectorsView = null + dims = 0 + rows = 0 + classifier = null + envConfigured = false + levelGraph = [] + entryPoint = -1 + maxLevel = 0 +} + +self.onmessage = (event: MessageEvent) => { + const data = event.data + + if (data.type === "reset") { + handleReset() + return + } + + if (data.type === "init") { + void handleInit(data).catch((err: unknown) => { + const message: ErrorMessage = { + type: "error", + message: err instanceof Error ? err.message : String(err), + } + self.postMessage(message) + }) + return + } + + if (data.type === "search") { + void handleSearch(data).catch((err: unknown) => { + const message: ErrorMessage = { + type: "error", + seq: data.seq, + message: err instanceof Error ? err.message : String(err), + } + self.postMessage(message) + }) + } +}