226 lines
6.9 KiB
TypeScript
226 lines
6.9 KiB
TypeScript
// Search worker - runs search in background thread
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// Receives document updates and search queries, returns incremental results
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interface DocData {
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loc: string
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name: string
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key: string
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size: number
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mtime: number
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dir: boolean
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}
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interface WorkerDoc extends DocData {
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haystack: string
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}
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interface SearchMessage {
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type: 'search'
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query: string
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loc: string
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id: number
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}
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interface UpdateMessage {
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type: 'update'
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documents: DocData[]
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}
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type IncomingMessage = SearchMessage | UpdateMessage
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interface ResultMessage {
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type: 'results'
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docs: DocData[]
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id: number
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done: boolean
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}
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// Worker state
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let recentDocuments: WorkerDoc[] = [] // Sorted by mtime descending
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let currentSearchId = 0
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// Search result cache - cleared when documents change
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interface CacheEntry {
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query: string // Normalized query string
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results: WorkerDoc[] // Matched results (up to limit)
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complete: boolean // True if search scanned all documents
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}
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const searchCache: CacheEntry[] = []
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const MAX_CACHE_SIZE = 10
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const RESULT_LIMIT = 100
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// Normalize string for search (remove diacritics, lowercase)
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// Haystack adds ^ and $ markers to allow matching start/end of name
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function normalizeHaystack(str: string): string {
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return '^' + str.normalize('NFKD').replace(/[\u0300-\u036f]/g, '').toLowerCase() + '$'
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}
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function normalizeQuery(str: string): string {
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return str.normalize('NFKD').replace(/[\u0300-\u036f]/g, '').toLowerCase()
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}
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// Test if document matches search query
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function matches(haystack: string, query: string, words: string[]): boolean {
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return haystack.includes(query) || words.every(word => haystack.includes(word))
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}
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// Collator for sorting
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const collator = new Intl.Collator('en', { sensitivity: 'base', numeric: true })
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// Yield control to allow new messages to be processed
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const yieldControl = (): Promise<void> => new Promise(resolve => setTimeout(resolve, 0))
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// Find best cache entry to filter from
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// Returns entry if new query's results are guaranteed to be a subset of cached results
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// Only valid if the cached search was complete (scanned all documents)
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function findCacheSubset(query: string): CacheEntry | null {
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// Look for a cached query that the new query starts with
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// e.g., cached "foo" can be used for "foobar" or "foo bar"
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// The longer the prefix, the better (fewer items to filter)
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// IMPORTANT: Only use complete cache entries - incomplete ones may have
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// missed results that would match the more specific query
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let best: CacheEntry | null = null
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for (const entry of searchCache) {
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if (entry.complete && query.startsWith(entry.query)) {
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if (!best || entry.query.length > best.query.length) {
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best = entry
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}
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}
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}
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return best
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}
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// Add result to cache
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function addToCache(query: string, results: WorkerDoc[], complete: boolean) {
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// Remove existing entry for same query if any
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const idx = searchCache.findIndex(e => e.query === query)
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if (idx !== -1) searchCache.splice(idx, 1)
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// Add to front (most recent)
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searchCache.unshift({ query, results, complete })
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// Trim cache
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if (searchCache.length > MAX_CACHE_SIZE) searchCache.pop()
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}
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// Clear cache (called when documents change)
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function clearCache() {
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searchCache.length = 0
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}
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// Perform search with incremental results
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async function performSearch(rawQuery: string, loc: string, searchId: number) {
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const query = normalizeQuery(rawQuery)
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const words = query.split(/\s+/)
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const results: WorkerDoc[] = []
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let lastResultCount = 0
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// Check cache for exact match
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const exactMatch = searchCache.find(e => e.query === query)
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if (exactMatch) {
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if (currentSearchId === searchId) {
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postResults(exactMatch.results, rawQuery, loc, searchId, true)
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}
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return
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}
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// Check if we can filter from a cached superset
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const cacheEntry = findCacheSubset(query)
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if (cacheEntry) {
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// Fast path: filter from cached results (only used for complete cache entries)
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for (const doc of cacheEntry.results) {
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if (matches(doc.haystack, query, words)) {
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results.push(doc)
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}
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}
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// Cache entry was complete, so filtered results are also complete
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addToCache(query, results, true)
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if (currentSearchId === searchId) {
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postResults(results, rawQuery, loc, searchId, true)
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}
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return
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}
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// Slow path: scan all documents
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const batchSize = 500
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for (let i = 0; i < recentDocuments.length && results.length < RESULT_LIMIT; i += batchSize) {
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if (currentSearchId !== searchId) return // Superseded
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// Process batch
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const end = Math.min(i + batchSize, recentDocuments.length)
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for (let j = i; j < end && results.length < RESULT_LIMIT; j++) {
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const doc = recentDocuments[j]!
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if (matches(doc.haystack, query, words)) {
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results.push(doc)
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}
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}
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// Post incremental results if we found new matches
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if (results.length > lastResultCount && currentSearchId === searchId) {
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lastResultCount = results.length
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postResults(results, rawQuery, loc, searchId, false)
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}
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// Yield control between batches
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if (i + batchSize < recentDocuments.length && results.length < RESULT_LIMIT) {
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await yieldControl()
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}
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}
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// Cache and post final results
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addToCache(query, results, results.length < RESULT_LIMIT)
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if (currentSearchId === searchId) {
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postResults(results, rawQuery, loc, searchId, true)
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}
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}
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// Post results to main thread
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function postResults(docs: WorkerDoc[], query: string, loc: string, id: number, done: boolean) {
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const sorted = sortResults(docs, query, loc)
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postMessage({
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type: 'results',
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docs: sorted.map(({ haystack, ...rest }) => rest),
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id,
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done
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} as ResultMessage)
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}
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// Sort results by relevance
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function sortResults(docs: WorkerDoc[], query: string, loc: string): WorkerDoc[] {
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const locsub = loc + '/'
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return [...docs].sort((a, b) => (
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// Current folder first
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Number(b.loc === loc) - Number(a.loc === loc) ||
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// Then subfolders
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Number(b.loc.startsWith(locsub)) - Number(a.loc.startsWith(locsub)) ||
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// Then by location
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collator.compare(a.loc, b.loc) ||
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// Folders before files
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Number(b.dir) - Number(a.dir) ||
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// Exact name match first
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Number(b.name.includes(query)) - Number(a.name.includes(query)) ||
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// Finally by name
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collator.compare(a.name, b.name)
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))
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}
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// Handle incoming messages
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self.onmessage = async (e: MessageEvent<IncomingMessage>) => {
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const msg = e.data
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if (msg.type === 'update') {
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// Update document list with haystacks, sorted by mtime descending
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recentDocuments = msg.documents
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.map(doc => ({ ...doc, haystack: normalizeHaystack(doc.name) }))
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.sort((a, b) => b.mtime - a.mtime)
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clearCache()
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} else if (msg.type === 'search') {
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currentSearchId = msg.id
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if (msg.query) {
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await performSearch(msg.query, msg.loc, msg.id)
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} else {
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// Empty query - no results needed
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postMessage({ type: 'results', docs: [], id: msg.id, done: true } as ResultMessage)
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}
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}
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}
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