AI Smart Paste
Paste CSV / TSV / free-form text - the assistant parses it into typed rows with a preview panel. Swap mockAssistant for your LLM endpoint and ship. (requires @svgrid/enterprise)
A live, editable Svelte 5 data grid example from the SvGrid gallery (AI). See the SvGrid documentation for the full API.
About this example
Smart paste for the Svelte 5 data grid: drop any shape of contact data into the zone, CSV with a header, tab-separated cells from a spreadsheet, a JSON array or free-form lines, and the assistant works out which column is which by inspecting the cells. It detects the separator, decides whether the first line is a header, detects email, phone, name, company and role per column with content patterns, falls back to a prose parser that anchors on the email, scores each row's confidence, and previews before api.addRows commits.
Drop ANY shape of contact data into the zone - CSV with header, tab-separated from Excel, JSON array, free-form lines - and the assistant figures out which column is which by INSPECTING THE CELLS, not by demanding a fixed format.
What makes this "smart":
- Auto-detects the separator (tab / comma / semicolon / pipe)
- Auto-detects whether the first line is a header (by name match OR by column-type contrast with line 2)
- Per-column TYPE detection - email / phone / name / company / role - using content regex, then maps each detected type to the matching target field
- Falls back to a free-form parser for prose lines: extracts the email first (definitive marker) and infers the rest by position relative to it
- Per-row confidence score so the user can see what's solid
Swap assistant() for a real LLM call - the rest of the wiring is identical (preview panel, commit, etc.).
Imports, features and API used
Imports: @svgrid/grid, @svgrid/enterprise
Table features registered: rowSortingFeature, rowSelectionFeature
Columns: fullName (Name), email (Email), company (Company), role (Role), phone (Phone)
SvGridApi methods called: api.addRows(), api.setCellValue()
Frequently asked questions
How is the header line detected?
By matching the first line's tokens against known field names, or by contrasting its column types with the second line: a line of words above a line with an email and a phone is a header.
How are columns mapped without a fixed format?
Each column's content is tested against patterns for email, phone, name, company and role, and the detected type is mapped to the matching target field; leftover columns are offered for manual mapping.
How would I use a real language model?
Replace the assistant function with a call to your model that returns typed rows; the preview panel, confidence display and api.addRows commit stay the same.
Related documentation
Related articles
- Paste from Excel into a Svelte Data Grid - How to wire up clipboard paste so users can drop a copied Excel or Google Sheets block directly into SvGrid - TSV parsing, type coercion, validation, and row growth all covered.
- 15 Years of UI Components - The Story Behind jQWidgets and Smart UI - How a team spent 15 years shipping data grids - from jQuery widgets in 2011 to web components to a Svelte 5 native grid - and what actually changed each time.
- Smart UI Wins Gold in the 2025 Visual Studio Magazine Readers' Choice Awards - Smart UI, the web-components suite from the team behind SvGrid, earned Gold in the 2025 Visual Studio Magazine Readers' Choice Awards. Here is what that vote actually tells you about the codebase you are running.
Source code (75-ai-smart-paste.svelte)
<script lang="ts">
/**
* 75. AI Smart Paste (Pro)
* -----------------------
* Drop ANY shape of contact data into the zone - CSV with header,
* tab-separated from Excel, JSON array, free-form lines - and the
* assistant figures out which column is which by INSPECTING THE CELLS,
* not by demanding a fixed format.
*
* What makes this "smart":
* - Auto-detects the separator (tab / comma / semicolon / pipe)
* - Auto-detects whether the first line is a header (by name match
* OR by column-type contrast with line 2)
* - Per-column TYPE detection - email / phone / name / company /
* role - using content regex, then maps each detected type to the
* matching target field
* - Falls back to a free-form parser for prose lines: extracts the
* email first (definitive marker) and infers the rest by position
* relative to it
* - Per-row confidence score so the user can see what's solid
*
* Swap `assistant()` for a real LLM call - the rest of the wiring is
* identical (preview panel, commit, etc.).
*/
import {
SvGrid,
tableFeatures,
rowSortingFeature,
rowSelectionFeature,
type GridColumns,
} from '@svgrid/grid'
import { installEnterprise, setLicenseKey, type EnterpriseGridApi } from '@svgrid/enterprise'
setLicenseKey('SVENTERPRISE-DEV-DEMO')
type Contact = {
id: string
fullName: string
email: string
company: string
role: string
phone: string
}
let rows = $state<Contact[]>([
{ id: 'c1', fullName: 'Ada Lovelace', email: '[email protected]', company: 'Analytic Engine', role: 'Founder', phone: '+44 20 1234 5678' },
{ id: 'c2', fullName: 'Linus Torvalds', email: '[email protected]', company: 'Linux Foundation', role: 'Maintainer', phone: '+1 503 555 0101' },
])
const features = tableFeatures({ rowSortingFeature, rowSelectionFeature })
let api = $state<EnterpriseGridApi<typeof features, Contact> | null>(null)
// ---- Smart paste state ----------------------------------------------
let pasteText = $state('')
let preview = $state<{ rows: ParsedRow[]; log: string[] }>({ rows: [], log: [] })
let working = $state(false)
type Field = 'fullName' | 'email' | 'company' | 'role' | 'phone'
type ParsedRow = {
id: string
source: string // original line for the preview
values: Partial<Record<Field, string>>
confidence: 'high' | 'medium' | 'low'
issues: string[]
}
// ---- Detection helpers ----------------------------------------------
const RX_EMAIL = /^[a-z0-9._%+\-]+@[a-z0-9.\-]+\.[a-z]{2,}$/i
const RX_PHONE = /^[+]?\(?\d[\d\s\-().]{5,}\d$/
const RX_NAME = /^[A-ZÀ-Ý][\p{L}.''\-]+(\s+[A-ZÀ-Ý][\p{L}.''\-]+)+$/u
const RX_ROLE_WORDS = /\b(CEO|CTO|CFO|COO|CMO|CIO|CSO|VP|EVP|SVP|Head|Director|Manager|Lead|Engineer|Architect|Founder|Co-?founder|President|Officer|Specialist|Analyst|Designer|Developer|Consultant|Maintainer|Professor|Administrator|Operator|Owner|Partner|Principal|Senior|Junior|Staff|Distinguished|Fellow|Researcher|Scientist|Strategist|Coordinator|Supervisor)\b/i
const RX_COMPANY_HINT = /\b(Inc\.?|LLC|Ltd\.?|Corp\.?|Co\.?|GmbH|AG|S\.A\.?|S\.r\.l\.?|S\.p\.A\.?|Pty|K\.K\.|B\.V\.|N\.V\.|Holdings|Industries|Foundation|Labs?|Group|Systems|Solutions|Technologies|University|College|Institute|Networks?|Studios?|Partners|Capital|Bank|Trust|Bureau)\b/i
// ---- Smart normalization helpers ------------------------------------
/** Common email-domain typos → canonical. Real systems catch a long
* tail of these by Levenshtein; the demo handles the obvious ones. */
const EMAIL_DOMAIN_FIXES: Record<string, string> = {
'gmial.com': 'gmail.com', 'gmaill.com': 'gmail.com', 'gnail.com': 'gmail.com',
'gmal.com': 'gmail.com', 'gmail.con': 'gmail.com', 'gmail.co': 'gmail.com',
'gmail.cm': 'gmail.com', 'gmaim.com': 'gmail.com',
'yaho.com': 'yahoo.com', 'yhaoo.com': 'yahoo.com', 'yahoo.con': 'yahoo.com',
'outlok.com': 'outlook.com', 'outloo.com': 'outlook.com', 'outlock.com': 'outlook.com',
'hotmial.com': 'hotmail.com', 'hotmal.com': 'hotmail.com', 'hotmai.com': 'hotmail.com',
'icould.com': 'icloud.com', 'iclod.com': 'icloud.com',
}
function correctEmail(raw: string): { email: string; corrected: boolean } {
const trimmed = raw.trim().toLowerCase()
const at = trimmed.lastIndexOf('@')
if (at < 1) return { email: trimmed, corrected: false }
const user = trimmed.slice(0, at)
const domain = trimmed.slice(at + 1)
const fixed = EMAIL_DOMAIN_FIXES[domain]
if (fixed) return { email: `${user}@${fixed}`, corrected: true }
return { email: trimmed, corrected: false }
}
/** Normalize a phone to "+CC AAA BBB CCCC" style. Falls back to the
* trimmed input if no recognizable country shape is found. */
function normalizePhone(raw: string): string {
const cleaned = raw.replace(/[^\d+]/g, '')
if (!cleaned) return raw.trim()
const startsPlus = cleaned.startsWith('+')
const digits = startsPlus ? cleaned.slice(1) : cleaned
// E.164-ish formatting per country code.
if (startsPlus) {
if (digits.length === 11 && digits.startsWith('1'))
return `+1 ${digits.slice(1, 4)} ${digits.slice(4, 7)} ${digits.slice(7)}`
if (digits.length >= 12 && digits.startsWith('44'))
return `+44 ${digits.slice(2, 4)} ${digits.slice(4, 8)} ${digits.slice(8)}`
if (digits.length >= 11 && digits.startsWith('49'))
return `+49 ${digits.slice(2, 4)} ${digits.slice(4)}`
if (digits.length >= 11 && digits.startsWith('33'))
return `+33 ${digits.slice(2, 3)} ${digits.slice(3, 5)} ${digits.slice(5, 7)} ${digits.slice(7, 9)} ${digits.slice(9)}`
if (digits.length >= 12 && digits.startsWith('81'))
return `+81 ${digits.slice(2, 4)} ${digits.slice(4, 8)} ${digits.slice(8)}`
// Fall back: split country code (2 digits) + rest.
return `+${digits.slice(0, 2)} ${digits.slice(2)}`.replace(/\s+$/, '')
}
// No leading +: assume North American 10 digits, or 11 with leading 1.
if (digits.length === 10)
return `+1 ${digits.slice(0, 3)} ${digits.slice(3, 6)} ${digits.slice(6)}`
if (digits.length === 11 && digits.startsWith('1'))
return `+1 ${digits.slice(1, 4)} ${digits.slice(4, 7)} ${digits.slice(7)}`
return raw.trim()
}
/** Strip titles (Dr., Mr.), suffixes (Jr., III), and rewrite
* "Last, First Middle" into "First Middle Last". */
const RX_NAME_TITLE = /^(Mr|Mrs|Ms|Mx|Dr|Prof|Sir|Lord|Lady|Madam|Rev|Hon)\.?\s+/i
const RX_NAME_SUFFIX = /,?\s+(Jr|Sr|II|III|IV|V|Esq|PhD|MD|MBA|CPA|RN)\.?$/i
function cleanName(raw: string): string {
let name = raw.trim().replace(/\s+/g, ' ')
if (!name) return name
// "Last, First Middle" → "First Middle Last" (only when no '@' to
// avoid mangling "smith, john (acme)" style mixed lines).
if (!name.includes('@') && /^[^,]+,\s*[^,]+$/.test(name)) {
const [last, first] = name.split(/,\s*/)
if (last && first) name = `${first} ${last}`
}
name = name.replace(RX_NAME_TITLE, '')
name = name.replace(RX_NAME_SUFFIX, '')
return name.trim()
}
/** "Boyko Markov <[email protected]>" → { name, email }. Also
* handles `mailto:` prefixes and unquoted names. */
function extractEmailFromAngle(line: string): { name?: string; email: string } | null {
const m = line.match(/^\s*"?([^<"]*?)"?\s*<\s*(?:mailto:)?([^>\s]+@[^>\s]+)\s*>/i)
if (m && m[2]) return { name: cleanName(m[1] || ''), email: m[2] }
const mail = line.match(/mailto:([^\s"']+@[^\s"']+)/i)
if (mail && mail[1]) return { email: mail[1] }
return null
}
// Multi-language field-name dictionary. Picks up CSV exports from
// German / French / Spanish / Italian / Portuguese / Dutch CRMs.
const HEADER_FIELD: Array<{ field: Field; rx: RegExp }> = [
{ field: 'email', rx: /^(e[\s_-]?mail|mail|email[\s_-]?address|electronic[\s_-]?mail|courriel|m[éeê]l|correo|correo[\s_-]?electr[oó]nico|posta|e[\s_-]?posta|epost|epost[\s_-]?adresse)$/i },
{ field: 'phone', rx: /^(phone|tel|telephone|tel[ée]phone|mobile|cell|cell[\s_-]?phone|handy|telefon|telefono|tel[eé]fono|tel\.|m[oó]vil|portable|port[aá]til|numero|n[uú]mero|tlf|gsm)$/i },
{ field: 'fullName', rx: /^(full[\s_-]?name|name|first[\s_-]?name|last[\s_-]?name|given[\s_-]?name|family[\s_-]?name|surname|nom|pr[eé]nom|nome|nombre|cliente|customer|contact|kontakt|nom[\s_-]?complet|nombre[\s_-]?completo|naam|navn)$/i },
{ field: 'company', rx: /^(company|organi[sz]ation|org|firma|firm|entreprise|empresa|sociedad|soci[eé]t[eé]|business|account|account[\s_-]?name|department|abteilung|dept|azienda|bedrijf|virksomhed)$/i },
{ field: 'role', rx: /^(role|title|position|job[\s_-]?title|jobtitle|poste|cargo|titre|funci[oó]n|funktion|puesto|rol|carica|titel|stilling)$/i },
]
function fieldFromHeader(label: string): Field | null {
const v = label.trim()
for (const { field, rx } of HEADER_FIELD) if (rx.test(v)) return field
return null
}
function classifyCell(v: string): Field | 'unknown' {
const t = v.trim()
if (!t) return 'unknown'
if (RX_EMAIL.test(t)) return 'email'
if (RX_PHONE.test(t.replace(/\s/g, ''))) return 'phone'
if (RX_ROLE_WORDS.test(t) && t.length < 60) return 'role'
if (RX_COMPANY_HINT.test(t)) return 'company'
if (RX_NAME.test(t)) return 'fullName'
return 'unknown'
}
/** Build a column-index → field map by voting on each column's cells. */
function inferColumnMap(rows: string[][]): Map<number, Field> {
const map = new Map<number, Field>()
const taken = new Set<Field>()
const colCount = Math.max(...rows.map((r) => r.length))
const tally: Array<Record<Field | 'unknown', number>> = Array.from({ length: colCount }, () => ({
fullName: 0, email: 0, company: 0, role: 0, phone: 0, unknown: 0,
}))
for (const row of rows) {
row.forEach((cell, i) => {
const k = classifyCell(cell)
tally[i]![k] += 1
})
}
// Assign each column to its top-voted field (skipping fields already
// assigned to a different column).
const ranked = tally.map((t, i) => {
const entries = (Object.entries(t) as Array<[Field | 'unknown', number]>)
.filter((e): e is [Field, number] => e[0] !== 'unknown')
.sort((a, b) => b[1] - a[1])
return { i, entries }
})
// Sort columns by how confident their top-vote is (descending) so
// strong matches (email, phone) get picked first.
ranked.sort((a, b) => (b.entries[0]?.[1] ?? 0) - (a.entries[0]?.[1] ?? 0))
for (const { i, entries } of ranked) {
const winner = entries.find(([k, n]) => n > 0 && !taken.has(k))
if (winner) {
map.set(i, winner[0])
taken.add(winner[0])
}
}
// Fill any unmapped column with the first remaining target field
// (company → role → fullName order, since those are the least
// distinguishable by regex alone).
const fallback: Field[] = ['fullName', 'company', 'role', 'phone', 'email']
for (let i = 0; i < colCount; i += 1) {
if (map.has(i)) continue
const next = fallback.find((f) => !taken.has(f))
if (next) { map.set(i, next); taken.add(next) }
}
return map
}
// ---- Tokeniser -------------------------------------------------------
/** Detect the delimiter. Counts occurrences across all lines, picks the
* one with the highest median count > 0. */
function detectDelimiter(lines: string[]): string | null {
const candidates = ['\t', ',', ';', '|']
let best: { d: string; n: number } | null = null
for (const d of candidates) {
const counts = lines.map((l) => splitRespectingQuotes(l, d).length - 1)
counts.sort((a, b) => a - b)
const median = counts[Math.floor(counts.length / 2)] ?? 0
if (median >= 1 && (!best || median > best.n)) best = { d, n: median }
}
return best?.d ?? null
}
/** Split on delimiter, respecting "double-quoted" cells. */
function splitRespectingQuotes(line: string, delim: string): string[] {
const out: string[] = []
let buf = ''
let inQ = false
for (let i = 0; i < line.length; i += 1) {
const ch = line[i]
if (ch === '"') {
if (inQ && line[i + 1] === '"') { buf += '"'; i += 1 }
else inQ = !inQ
} else if (ch === delim && !inQ) {
out.push(buf); buf = ''
} else {
buf += ch
}
}
out.push(buf)
return out.map((s) => s.trim())
}
// ---- vCard parser ----------------------------------------------------
/** Parse RFC 6350 vCards. Contacts apps copy in this format on macOS,
* iOS, Android, Outlook export, and Google Contacts download. */
function parseVCards(text: string): ParsedRow[] | null {
if (!/BEGIN:VCARD/i.test(text)) return null
const out: ParsedRow[] = []
const cards = text.split(/BEGIN:VCARD/i).slice(1)
cards.forEach((card, i) => {
const values: Partial<Record<Field, string>> = {}
// vCard unfolding: lines starting with whitespace continue the prior.
const unfolded: string[] = []
for (const raw of card.split(/\r?\n/)) {
if (/^\s/.test(raw) && unfolded.length) unfolded[unfolded.length - 1]! += raw.trim()
else unfolded.push(raw)
}
for (const line of unfolded) {
if (/^END:VCARD/i.test(line)) break
const m = line.match(/^([A-Z]+)(?:;[^:]*)?:(.+)$/i)
if (!m) continue
const key = m[1]!.toUpperCase()
const val = m[2]!.trim()
if (key === 'FN' && !values.fullName) values.fullName = cleanName(val)
else if (key === 'N' && !values.fullName) values.fullName = cleanName(val.split(';').reverse().filter(Boolean).join(' '))
else if (key === 'EMAIL' && !values.email) values.email = correctEmail(val).email
else if (key === 'ORG' && !values.company) values.company = val.split(';')[0]!.trim()
else if (key === 'TITLE' && !values.role) values.role = val
else if (key === 'TEL' && !values.phone) values.phone = normalizePhone(val)
}
if (Object.keys(values).length > 0) {
out.push(finalise(`vcard-${i}`, `vCard · ${values.fullName ?? '(unnamed)'}`, values))
}
})
return out.length > 0 ? out : null
}
// ---- Markdown table parser ------------------------------------------
/** GitHub-flavored Markdown tables: `| header | header |` followed by
* `|---|---|` separator, then data rows. Common when pasting from Slack
* threads, GitHub issues, or docs. */
function parseMarkdownTable(text: string): ParsedRow[] | null {
const lines = text.split(/\r?\n/).map((l) => l.trim()).filter((l) => l.startsWith('|') && l.endsWith('|'))
if (lines.length < 2) return null
const rowsAsArr = lines.map((l) =>
l.replace(/^\||\|$/g, '').split('|').map((c) => c.trim()),
)
const sepIdx = rowsAsArr.findIndex((r) => r.length > 0 && r.every((c) => /^:?-+:?$/.test(c)))
if (sepIdx < 1) return null
const headerLine = rowsAsArr[0]!
const data = rowsAsArr.slice(sepIdx + 1).filter((r) => r.some((c) => c))
if (data.length === 0) return null
const colMap = new Map<number, Field>()
headerLine.forEach((h, i) => {
const f = fieldFromHeader(h)
if (f) colMap.set(i, f)
})
const inferred = inferColumnMap(data)
for (const [i, f] of inferred) if (!colMap.has(i)) colMap.set(i, f)
return data.map((cells, i) => {
const values: Partial<Record<Field, string>> = {}
cells.forEach((cell, ix) => {
const f = colMap.get(ix)
if (f && cell) values[f] = cell
})
return finalise(`md-${i}`, cells.join(' | '), values)
})
}
// ---- Signature-block parser -----------------------------------------
/** Multi-line "email signature" blocks separated by blank lines.
* Each block becomes one contact. Recognises:
* - "Name <email>" or `mailto:` first line
* - Title + Company on the same line ("CEO, Acme")
* - Phone with international or local format
*/
function parseSignatureBlocks(text: string): ParsedRow[] | null {
const blocks = text.split(/\n\s*\n/).map((b) => b.trim()).filter(Boolean)
if (blocks.length < 1) return null
const out: ParsedRow[] = []
for (const block of blocks) {
const lines = block.split(/\r?\n/).map((l) => l.trim()).filter(Boolean)
if (lines.length < 2 || lines.length > 14) continue
const hasEmail = lines.some((l) => /[\w.+-]+@[\w.-]+\.\w{2,}/.test(l))
if (!hasEmail) continue
const values: Partial<Record<Field, string>> = {}
for (const line of lines) {
// "Name <email>" pattern - capture both.
const angle = extractEmailFromAngle(line)
if (angle?.email && !values.email) values.email = correctEmail(angle.email).email
if (angle?.name && !values.fullName) values.fullName = angle.name
// Bare email.
if (!values.email) {
const em = line.match(/[\w.+-]+@[\w.-]+\.\w{2,}/)
if (em) values.email = correctEmail(em[0]).email
}
// Phone.
if (!values.phone) {
const ph = line.match(/[+]?\(?\d[\d\s\-().]{6,}\d/)
if (ph) values.phone = normalizePhone(ph[0])
}
// Name.
if (!values.fullName) {
const candidate = cleanName(line)
if (RX_NAME.test(candidate)) values.fullName = candidate
}
// "Role, Company" or "Role at Company" patterns.
if (!values.role && RX_ROLE_WORDS.test(line) && line.length < 100) {
const split = line.split(/\s+at\s+|\s*[,|]\s*/i).map((p) => p.trim()).filter(Boolean)
if (split[0]) values.role = split[0]
if (split[1] && !values.company) values.company = split[1]
}
// Standalone company line with hint.
if (!values.company && RX_COMPANY_HINT.test(line) && line.length < 100) {
values.company = line
}
}
const matched = Object.keys(values).length
if (matched >= 2) out.push(finalise(`sig-${out.length}`, lines[0] || 'signature', values))
}
return out.length > 0 ? out : null
}
// ---- Free-form parser ------------------------------------------------
/** For prose lines with no consistent delimiter, extract by signals. */
function parseProseLine(line: string): { values: Partial<Record<Field, string>>; issues: string[] } {
const issues: string[] = []
const values: Partial<Record<Field, string>> = {}
// 0. "Name <email>" / `mailto:` - extract both at once.
const angle = extractEmailFromAngle(line)
if (angle?.email) values.email = correctEmail(angle.email).email
if (angle?.name) values.fullName = angle.name
// 1. Email - definitive marker. Skip if angle form already captured it.
let rawEmail: string | null = null
if (!values.email) {
const m = line.match(/[a-z0-9._%+\-]+@[a-z0-9.\-]+\.[a-z]{2,}/i)
if (m) { rawEmail = m[0]; values.email = correctEmail(m[0]).email }
else issues.push('No email found')
}
// 2. Phone - the leftmost +?(...)?digits group of 7+ chars.
const phoneMatch = line.match(/[+]?\(?\d[\d\s\-().]{5,}\d/)
if (phoneMatch && phoneMatch[0] !== rawEmail) values.phone = normalizePhone(phoneMatch[0])
// 3. Strip the email + phone + angle form, then split.
const stripped = line
.replace(/^\s*"?([^<"]*?)"?\s*<\s*[^>]+\s*>/i, '$1')
.replace(rawEmail ?? '', '')
.replace(phoneMatch?.[0] ?? '', '')
.trim()
const parts = stripped
.split(/\s*[-–|,;]\s*|\s{2,}/)
.map((p) => p.trim())
.filter(Boolean)
// 4. Identify name (RX_NAME) - usually the first qualifying chunk.
if (!values.fullName) {
const cleaned = parts.map((p) => cleanName(p))
const nameIx = cleaned.findIndex((p) => RX_NAME.test(p))
if (nameIx !== -1) { values.fullName = cleaned[nameIx]!; parts.splice(nameIx, 1) }
else if (parts.length > 0) values.fullName = cleanName(parts.shift()!)
}
// 5. Role - chunk that matches role words. Same chunk can mention
// company ("CEO at Acme") - split on " at ".
const roleIx = parts.findIndex((p) => RX_ROLE_WORDS.test(p))
if (roleIx !== -1) {
const chunk = parts.splice(roleIx, 1)[0]!
const atSplit = chunk.split(/\s+at\s+/i)
values.role = atSplit[0]!.trim()
if (atSplit[1] && !values.company) values.company = atSplit[1]!.trim()
}
// 6. Whatever's left is the company.
if (!values.company && parts.length > 0) {
values.company = parts.join(' ').replace(/\s*\(\)\s*$/, '').trim()
}
return { values, issues }
}
// ---- Main assistant --------------------------------------------------
async function assistant(text: string): Promise<{ rows: ParsedRow[]; log: string[] }> {
await new Promise((r) => setTimeout(r, 250 + Math.random() * 300))
const log: string[] = []
const t = text.replace(/^/, '').replace(/\r\n?/g, '\n').trim()
if (!t) { log.push('Nothing to paste.'); return { rows: [], log } }
// ---- 1. vCard - most specific format, try first ----
const vcards = parseVCards(t)
if (vcards) {
log.push(`Detected vCard · ${vcards.length} record${vcards.length === 1 ? '' : 's'}.`)
return { rows: dedup(vcards, log), log }
}
// ---- 2. Markdown table ----
const md = parseMarkdownTable(t)
if (md) {
log.push(`Detected Markdown table · ${md.length} row${md.length === 1 ? '' : 's'}.`)
return { rows: dedup(md, log), log }
}
// ---- 3. JSON ----
if (t.startsWith('[') || t.startsWith('{')) {
try {
const obj = JSON.parse(t)
const arr = Array.isArray(obj) ? obj : [obj]
log.push(`Detected JSON · ${arr.length} record${arr.length === 1 ? '' : 's'}.`)
const out: ParsedRow[] = arr.map((entry, i) => {
const values: Partial<Record<Field, string>> = {}
for (const [k, v] of Object.entries(entry as Record<string, unknown>)) {
const field = fieldFromHeader(k)
if (field) values[field] = String(v ?? '')
}
return finalise(`json-${i}`, JSON.stringify(entry), values)
})
log.push(`Parsed ${out.length} record${out.length === 1 ? '' : 's'} from JSON.`)
return { rows: dedup(out, log), log }
} catch { log.push('Looked like JSON but did not parse, falling back to text mode.') }
}
const lines = t.split('\n').filter((l) => l.trim())
// ---- 4. Tabular ----
const delim = detectDelimiter(lines)
if (delim) {
log.push(`Detected delimiter: ${delim === '\t' ? 'TAB' : `"${delim}"`}.`)
const rawRows = lines.map((l) => splitRespectingQuotes(l, delim))
const headerLine = rawRows[0]!
const headerLooksLikeData = headerLine.some((c) => RX_EMAIL.test(c) || RX_PHONE.test(c.replace(/\s/g, '')))
const headerHasFieldNames = headerLine.some((c) => fieldFromHeader(c) !== null)
const hasHeader = headerHasFieldNames && !headerLooksLikeData
log.push(hasHeader ? 'First line treated as header (field names, multi-language).' : 'No header detected - sampling content to infer columns.')
const data = hasHeader ? rawRows.slice(1) : rawRows
// Map columns by header name OR by content inference.
const colMap = new Map<number, Field>()
if (hasHeader) {
headerLine.forEach((h, i) => {
const f = fieldFromHeader(h)
if (f) colMap.set(i, f)
})
}
// Inference for any column the header didn't cover.
const inferred = inferColumnMap(data)
for (const [i, f] of inferred) if (!colMap.has(i)) colMap.set(i, f)
const mapDescription = Array.from(colMap.entries())
.sort(([a], [b]) => a - b)
.map(([i, f]) => `col${i}→${f}`).join(', ')
log.push(`Column map: ${mapDescription || '(none)'}.`)
const out: ParsedRow[] = data.map((cells, i) => {
const values: Partial<Record<Field, string>> = {}
cells.forEach((cell, ix) => {
const field = colMap.get(ix)
if (field && cell) values[field] = cell
})
return finalise(`tab-${i}`, cells.join(delim === '\t' ? ' › ' : ` ${delim} `), values)
})
log.push(`Parsed ${out.length} row${out.length === 1 ? '' : 's'}.`)
return { rows: dedup(out, log), log }
}
// ---- 5. Signature blocks ----
const sigs = parseSignatureBlocks(t)
if (sigs && sigs.length > 0) {
log.push(`Detected ${sigs.length} signature block${sigs.length === 1 ? '' : 's'}.`)
return { rows: dedup(sigs, log), log }
}
// ---- 6. Free-form prose (one line per record) ----
log.push('No consistent delimiter or block structure - parsing each line as free-form prose.')
const out: ParsedRow[] = lines.map((line, i) => {
const r = parseProseLine(line)
return finalise(`prose-${i}`, line, r.values, r.issues)
})
log.push(`Parsed ${out.length} line${out.length === 1 ? '' : 's'}.`)
return { rows: dedup(out, log), log }
}
/** De-duplicate parsed rows by normalised email so a paste with the
* same contact in two formats (CSV + signature, JSON + vCard) collapses
* cleanly. The first occurrence wins; subsequent duplicates are merged
* (filling any missing fields). */
function dedup(rows: ParsedRow[], log: string[]): ParsedRow[] {
const seen = new Map<string, ParsedRow>()
const out: ParsedRow[] = []
let duplicates = 0
for (const r of rows) {
const key = r.values.email?.toLowerCase().trim()
if (!key) { out.push(r); continue }
const existing = seen.get(key)
if (!existing) { seen.set(key, r); out.push(r); continue }
// Merge missing fields into the existing row.
for (const f of ['fullName', 'company', 'role', 'phone'] as const) {
if (!existing.values[f] && r.values[f]) existing.values[f] = r.values[f]
}
duplicates += 1
}
if (duplicates > 0) log.push(`Merged ${duplicates} duplicate${duplicates === 1 ? '' : 's'} by email.`)
return out
}
function finalise(
id: string,
source: string,
values: Partial<Record<Field, string>>,
extraIssues: string[] = [],
): ParsedRow {
const issues = [...extraIssues]
// Auto-correct email typos (gmial.com → gmail.com) and surface what
// was corrected so the user can see the assistant fixed it.
if (values.email) {
const trimmed = values.email.trim().toLowerCase()
const { email: fixed, corrected } = correctEmail(trimmed)
values.email = fixed
if (corrected) issues.push(`Fixed domain typo → ${fixed.split('@')[1]}`)
if (!RX_EMAIL.test(fixed)) issues.push('Email looks malformed')
}
// Normalize phone to a consistent E.164-ish format.
if (values.phone) values.phone = normalizePhone(values.phone)
// Clean name (strip titles, "Last, First" → "First Last").
if (values.fullName) values.fullName = cleanName(values.fullName)
if (!values.fullName) issues.push('No name detected')
if (!values.email) issues.push('No email detected')
const filled = Object.values(values).filter((v) => v && v.trim()).length
// Confidence: high if all 5 fields present and no real issues; medium
// if 3-4 fields with minor issues; low otherwise. A "Fixed domain
// typo" warning is informational, so don't count it against high.
const seriousIssues = issues.filter((i) => !i.startsWith('Fixed domain typo')).length
const confidence: ParsedRow['confidence'] =
seriousIssues === 0 && filled >= 4 ? 'high'
: seriousIssues <= 1 && filled >= 3 ? 'medium'
: 'low'
return { id, source, values, confidence, issues }
}
// ---- Clipboard + samples + commit -----------------------------------
async function runFromTextarea() { await runAssistant(pasteText) }
async function runAssistant(text: string) {
pasteText = text
working = true
preview = { rows: [], log: [] }
preview = await assistant(text)
working = false
}
async function pasteFromClipboard() {
try {
const text = await navigator.clipboard.readText()
if (text) await runAssistant(text)
} catch {
preview = { rows: [], log: ['Clipboard read blocked - paste the text into the box instead.'] }
}
}
function onPaste(e: ClipboardEvent) {
const text = e.clipboardData?.getData('text') ?? ''
if (!text) return
e.preventDefault()
void runAssistant(text)
}
// ---- Per-row apply mode: insert new, update existing, or skip -------
type RowMode = 'insert' | 'update' | 'skip'
let rowMode = $state<Record<string, RowMode>>({})
let rowMatch = $state<Record<string, string | null>>({}) // parsedRowId → existing contact id
/** Decide a default action per parsed row: if the email matches an
* existing contact, default to "update"; otherwise if we have at least
* an email, "insert"; else "skip". */
$effect(() => {
const mode: Record<string, RowMode> = {}
const match: Record<string, string | null> = {}
for (const r of preview.rows) {
const email = r.values.email?.toLowerCase().trim()
const existing = email
? rows.find((c) => c.email.toLowerCase().trim() === email)
: undefined
if (existing) { mode[r.id] = 'update'; match[r.id] = existing.id }
else if (email) { mode[r.id] = 'insert'; match[r.id] = null }
else { mode[r.id] = 'skip'; match[r.id] = null }
}
rowMode = mode
rowMatch = match
})
const applyCounts = $derived.by(() => {
let insert = 0, update = 0, skip = 0
for (const r of preview.rows) {
const m = rowMode[r.id] ?? 'skip'
if (m === 'insert') insert += 1
else if (m === 'update') update += 1
else skip += 1
}
return { insert, update, skip, total: insert + update }
})
function setRowMode(id: string, mode: RowMode) {
rowMode = { ...rowMode, [id]: mode }
}
function commit() {
if (!api || preview.rows.length === 0) return
let inserted = 0, updated = 0
const toInsert: Contact[] = []
for (const r of preview.rows) {
const mode = rowMode[r.id] ?? 'skip'
if (mode === 'skip') continue
const payload: Contact = {
id: rowMatch[r.id] ?? `c_${Math.random().toString(36).slice(2, 9)}`,
fullName: r.values.fullName ?? '',
email: r.values.email ?? '',
company: r.values.company ?? '',
role: r.values.role ?? '',
phone: r.values.phone ?? '',
}
if (mode === 'update' && rowMatch[r.id]) {
// Find existing row's index in the source data; rewrite each cell
// through the imperative API so the grid emits change events.
const ix = rows.findIndex((c) => c.id === rowMatch[r.id])
if (ix !== -1) {
for (const f of ['fullName', 'email', 'company', 'role', 'phone'] as const) {
const v = payload[f]
if (v) api.setCellValue(ix, f, v)
}
updated += 1
}
} else {
toInsert.push(payload)
inserted += 1
}
}
if (toInsert.length > 0) api.addRows(toInsert)
const parts: string[] = []
if (inserted > 0) parts.push(`Inserted ${inserted}`)
if (updated > 0) parts.push(`Updated ${updated}`)
preview = { rows: [], log: [parts.join(' · ') || 'No changes applied.'] }
pasteText = ''
}
function cancel() { preview = { rows: [], log: [] }; pasteText = '' }
const SAMPLES: Array<{ label: string; text: string; hint: string; icon: string; tag: string }> = [
{
label: 'CSV with header',
icon: '📑',
tag: '3 contacts · commas',
hint: 'Standard CSV - field names in the first line.',
text:
`name,email,company,role,phone
Grace Hopper,[email protected],US Navy,Rear Admiral,+1 202 555 0144
Margaret Hamilton,[email protected],NASA,Lead Programmer,+1 281 555 0177
Edsger Dijkstra,[email protected],CWI,Researcher,+31 20 555 0188`,
},
{
label: 'TSV from Excel (no header)',
icon: '📊',
tag: '3 contacts · tabs · scrambled columns',
hint: 'Columns in a different order, no header - assistant infers each field from content.',
text:
`[email protected]\tAnders Hejlsberg\tDistinguished Engineer\tMicrosoft\t+1 425 555 0198
[email protected]\tBrendan Eich\tCEO\tBrave\t+1 415 555 0123
[email protected]\tGuido van Rossum\tEmeritus BDFL\tPython\t+1 650 555 0177`,
},
{
label: 'Free-form prose',
icon: '✍️',
tag: '3 contacts · mixed punctuation',
hint: 'Sentences with dashes and parens - assistant pulls email first, then infers the rest.',
text:
`Tim Berners-Lee – [email protected] – W3C – Director (+44 7700 900 123)
Bjarne Stroustrup, [email protected], Morgan Stanley, MD (+1 212 555 0188)
Contact Brian Kernighan at [email protected], Princeton, Professor +1 609 555 0166`,
},
{
label: 'JSON array',
icon: '{ }',
tag: '2 contacts · API export',
hint: 'API export - field names map automatically.',
text:
`[
{"fullName":"Ken Thompson","email":"[email protected]","company":"Bell Labs","title":"Computer scientist","tel":"+1 908 555 0144"},
{"fullName":"Dennis Ritchie","email":"[email protected]","company":"Bell Labs","title":"Inventor of C","tel":"+1 908 555 0166"}
]`,
},
{
label: 'Semicolon CSV',
icon: '🇪🇺',
tag: '2 new contacts · semicolons',
hint: 'European-style CSV uses semicolons. Same parser, different delimiter. These 2 emails are new, so the assistant defaults to Insert - applying adds them as new rows.',
text:
`Edsger Dijkstra;[email protected];UT Austin;Professor;+1 512 555 0144
Margaret Hamilton;[email protected];NASA Apollo;Lead Engineer;+1 281 555 0199`,
},
{
label: 'vCard (.vcf)',
icon: '🪪',
tag: '2 contacts · BEGIN:VCARD',
hint: 'macOS / iOS / Google Contacts export this RFC 6350 format on copy. The assistant unfolds wrapped lines, reads FN / EMAIL / ORG / TITLE / TEL, and normalises the phone number.',
text:
`BEGIN:VCARD
VERSION:3.0
FN:Brendan Eich
ORG:Brave Software
TITLE:CEO
EMAIL;TYPE=WORK:[email protected]
TEL;TYPE=CELL:+1 415 555 0188
END:VCARD
BEGIN:VCARD
VERSION:3.0
FN:Anders Hejlsberg
ORG:Microsoft
TITLE:Technical Fellow
EMAIL;TYPE=WORK:[email protected]
TEL;TYPE=WORK:+1 425 555 0177
END:VCARD`,
},
{
label: 'Markdown table',
icon: '📝',
tag: '3 contacts · | pipes |',
hint: 'Pasted from a GitHub issue, a Slack thread, or VS Code docs. The assistant locates the `|---|---|` separator, reads multi-language column headers, and parses every data row.',
text:
`| Name | Email | Company | Rol | Téléphone |
|---|---|---|---|---|
| Grace Hopper | [email protected] | US Navy | Rear Admiral | +1 202 555 0133 |
| Ken Thompson | [email protected] | Bell Labs | Researcher | +1 908 555 0124 |
| Bjarne Stroustrup | [email protected] | Morgan Stanley | Managing Director | +1 212 555 0166 |`,
},
{
label: 'Signature blocks',
icon: '✉️',
tag: '2 contacts · email signatures',
hint: 'Paste two email signatures separated by a blank line. The assistant finds "Name <email>", reads the role / company on the line below, and normalises the phone format.',
text:
`Donald E. Knuth <[email protected]>
Professor Emeritus, Stanford University
Phone: (650) 555-0111
Barbara Liskov <[email protected]>
Institute Professor at MIT CSAIL
Cell: +1.617.555.0142`,
},
{
label: 'Messy paste',
icon: '🪄',
tag: 'typos · titles · "Last, First"',
hint: 'Real-world clipboard chaos: title prefixes ("Dr."), name inversion ("Wirth, Niklaus"), email typo (gmial.com), and varied phone formats. The assistant cleans, corrects, and normalises every field.',
text:
`Dr. Margaret Rhodes; [email protected]; Rhodes Capital LLC; Managing Partner; (212) 555-0188
Wirth, Niklaus; [email protected]; ETH Zurich; Professor Emeritus; +41 44 555 0166
"James Gosling" <[email protected]>; Amazon Web Services; Distinguished Engineer; 1-415-555-0177`,
},
]
function loadSample(s: (typeof SAMPLES)[number]) {
void runAssistant(s.text)
}
const FIELD_LABEL: Record<Field, string> = {
fullName: 'Name', email: 'Email', company: 'Company', role: 'Role', phone: 'Phone',
}
const FIELD_ORDER: Field[] = ['fullName', 'email', 'company', 'role', 'phone']
const columns: GridColumns<Contact> = [
{ field: 'fullName', header: 'Name', editorType: 'text', width: 180 },
{ field: 'email', header: 'Email', editorType: 'text', width: 220 },
{ field: 'company', header: 'Company', editorType: 'text', width: 180 },
{ field: 'role', header: 'Role', editorType: 'text', width: 160 },
{ field: 'phone', header: 'Phone', editorType: 'text', width: 160 },
]
</script>
<section class="ai-demo flex flex-col flex-1 min-h-0 gap-3">
<!-- Smart paste zone -->
<div class="ai-zone shrink-0">
<header class="ai-zone-head">
<div class="ai-zone-title">
<span class="ai-spark">✨</span>
<div>
<div class="ai-zone-h1">Smart paste with AI <span class="ai-pill">Pro</span></div>
<div class="ai-zone-h2">Paste anything - the assistant detects the format and maps it into typed rows.</div>
</div>
</div>
<button type="button" class="ai-clipboard" onclick={pasteFromClipboard}>
<span>📋</span> Paste from clipboard
</button>
</header>
<div class="ai-sample-grid">
{#each SAMPLES as s (s.label)}
<button type="button" class="ai-sample-card" onclick={() => loadSample(s)} title={s.hint}>
<span class="ai-sample-icon">{s.icon}</span>
<span class="ai-sample-body">
<span class="ai-sample-label">{s.label}</span>
<span class="ai-sample-tag">{s.tag}</span>
</span>
</button>
{/each}
</div>
<textarea
class="ai-textarea"
placeholder="…or paste your own data here. CSV / TSV / JSON / semicolon-CSV / free-form prose - all supported. The assistant detects delimiters and infers columns from cell content (email, phone, name, role keywords)."
bind:value={pasteText}
onpaste={onPaste}
rows="4"
></textarea>
<div class="ai-actions">
<button type="button" class="ai-primary"
disabled={working || !pasteText.trim()}
onclick={runFromTextarea}
>{working ? 'Analysing…' : '✨ Parse with AI'}</button>
{#if preview.rows.length > 0}
{@const total = applyCounts.insert + applyCounts.update}
{@const tip =
(applyCounts.insert > 0 ? `${applyCounts.insert} new` : '') +
(applyCounts.insert > 0 && applyCounts.update > 0 ? ' · ' : '') +
(applyCounts.update > 0 ? `${applyCounts.update} update` : '') +
(applyCounts.skip > 0 ? ` · ${applyCounts.skip} skip` : '')}
<button
type="button"
class="ai-accept"
onclick={commit}
disabled={applyCounts.total === 0}
title={tip || 'Apply'}
>
<span class="ai-accept-icon" aria-hidden="true">
<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
stroke-width="2.4" stroke-linecap="round" stroke-linejoin="round">
<path d="M20 6L9 17l-5-5" />
</svg>
</span>
<span class="ai-accept-label">Apply</span>
<span class="ai-accept-spacer"></span>
{#if total > 0}
<span class="ai-accept-badge" aria-label={tip}>
{total} row{total === 1 ? '' : 's'}
</span>
{/if}
</button>
{/if}
{#if preview.rows.length > 0 || preview.log.length > 0}
<button type="button" class="ai-cancel" onclick={cancel}>Discard</button>
{/if}
{#if preview.log.length > 0}
<span class="ai-log-summary" title={preview.log.join(' • ')}>
{preview.log[preview.log.length - 1] ?? ''}
</span>
{/if}
</div>
</div>
<!-- Preview rows panel -->
{#if preview.rows.length > 0}
<div class="ai-preview shrink-0">
<div class="ai-preview-head">
<div>
<strong>Preview · {preview.rows.length} row{preview.rows.length === 1 ? '' : 's'}</strong>
<span class="ai-counts">
{#if applyCounts.insert > 0}<span class="ai-count-pill ai-count-insert">+{applyCounts.insert} new</span>{/if}
{#if applyCounts.update > 0}<span class="ai-count-pill ai-count-update">↻{applyCounts.update} update</span>{/if}
{#if applyCounts.skip > 0}<span class="ai-count-pill ai-count-skip">⊘{applyCounts.skip} skip</span>{/if}
</span>
</div>
<span class="ai-legend">pick an action per row · Update is only available when the email matches an existing row</span>
</div>
<div class="ai-card-list">
{#each preview.rows as r (r.id)}
{@const mode = rowMode[r.id] ?? 'skip'}
{@const matchId = rowMatch[r.id]}
{@const matchRow = matchId ? rows.find((c) => c.id === matchId) : null}
<article class="ai-row-card ai-mode-{mode}">
<header class="ai-row-card-head">
<div class="ai-row-id">
<span class={`ai-badge ai-badge-${r.confidence}`}>{r.confidence}</span>
<span class="ai-row-name">{r.values.fullName || '(unnamed)'}</span>
{#if r.values.email}
<code class="ai-row-email">{r.values.email}</code>
{/if}
</div>
<div class="ai-mode-picker" role="group" aria-label="Apply mode">
<button type="button"
class="ai-tag ai-tag-insert" class:is-active={mode === 'insert'}
onclick={() => setRowMode(r.id, 'insert')}>+ Insert</button>
<button type="button"
class="ai-tag ai-tag-update"
disabled={!matchId}
title={matchId ? `Will overwrite ${matchRow?.fullName ?? matchId}` : 'No matching email in the grid'}
class:is-active={mode === 'update'}
onclick={() => matchId && setRowMode(r.id, 'update')}>↻ Update</button>
<button type="button"
class="ai-tag ai-tag-skip" class:is-active={mode === 'skip'}
onclick={() => setRowMode(r.id, 'skip')}>⊘ Skip</button>
</div>
</header>
<dl class="ai-row-fields">
<div class="ai-field"><dt>Company</dt><dd class:ai-empty={!r.values.company}>{r.values.company || '-'}</dd></div>
<div class="ai-field"><dt>Role</dt> <dd class:ai-empty={!r.values.role} >{r.values.role || '-'}</dd></div>
<div class="ai-field"><dt>Phone</dt> <dd class:ai-empty={!r.values.phone} >{r.values.phone || '-'}</dd></div>
</dl>
{#if matchRow}
<div class="ai-row-match">
↻ Will overwrite existing row <strong>{matchRow.fullName}</strong>
</div>
{/if}
{#if r.issues.length > 0}
<div class="ai-row-issues">⚠ {r.issues.join(' · ')}</div>
{/if}
</article>
{/each}
</div>
</div>
{/if}
<div class="flex-1 min-h-0">
<SvGrid responsive={true}
columnResize
data={rows}
columns={columns}
features={features}
filterMode="menu"
selectionMode="row"
showRowNumbers={true}
enableInlineEditing={true}
enableCellSelection={false}
rowHeight={32}
containerHeight="100%"
fitColumns={true}
onApiReady={(next) => (api = installEnterprise(next))}
/>
</div>
</section>
<style>
/* Status colours (insert / update / warn / error) carry meaning, so they
stay off the theme tokens; only their dark ramp is adjusted here. */
.ai-demo {
--ai-pos-bg: rgba(16, 185, 129, 0.15);
--ai-pos-fg: #047857;
--ai-info-bg: rgba(59, 130, 246, 0.15);
--ai-info-fg: #1d4ed8;
--ai-info-soft: rgba(59, 130, 246, 0.05);
--ai-warn-bg: rgba(245, 158, 11, 0.15);
--ai-warn-fg: #b45309;
--ai-neg-bg: rgba(239, 68, 68, 0.15);
--ai-neg-fg: #b91c1c;
}
:global([data-theme="dark"]) .ai-demo {
--ai-pos-bg: rgba(16, 185, 129, 0.22);
--ai-pos-fg: #6ee7b7;
--ai-info-bg: rgba(59, 130, 246, 0.22);
--ai-info-fg: #93c5fd;
--ai-info-soft: rgba(59, 130, 246, 0.10);
--ai-warn-bg: rgba(245, 158, 11, 0.22);
--ai-warn-fg: #fcd34d;
--ai-neg-bg: rgba(239, 68, 68, 0.22);
--ai-neg-fg: #fca5a5;
}
/* ---- Paste zone ---- */
.ai-zone {
border: 1px solid color-mix(in srgb, var(--sg-accent, #6366f1) 28%, transparent);
background: color-mix(in srgb, var(--sg-accent, #6366f1) 5%, transparent);
border-radius: 12px;
padding: 14px;
display: flex; flex-direction: column; gap: 12px;
}
.ai-zone-head { display: flex; align-items: flex-start; justify-content: space-between; gap: 12px; flex-wrap: wrap; }
.ai-zone-title { display: inline-flex; align-items: flex-start; gap: 10px; }
.ai-zone-h1 { font-weight: 700; font-size: 15px; color: var(--sg-fg, #0f172a); display: inline-flex; align-items: center; gap: 8px; }
.ai-zone-h2 { font-size: 12px; color: var(--sg-muted, #64748b); margin-top: 1px; }
.ai-spark { font-size: 22px; line-height: 1; filter: drop-shadow(0 0 6px color-mix(in srgb, var(--sg-accent, #a855f7) 55%, transparent)); padding-top: 2px; }
.ai-pill {
background: var(--sg-accent, #6366f1); color: var(--sg-on-accent, #fff);
font-size: 10px; font-weight: 700; letter-spacing: 0.06em;
padding: 2px 7px; border-radius: 999px; text-transform: uppercase;
}
.ai-clipboard {
display: inline-flex; align-items: center; gap: 6px;
border: 1px solid var(--sg-border, #cbd5e1); background: var(--sg-bg, #fff);
color: var(--sg-fg, #0f172a);
border-radius: 8px; padding: 6px 12px; font-size: 12px; font-weight: 500; cursor: pointer;
}
.ai-clipboard:hover { background: var(--sg-row-hover-bg, rgba(148,163,184,0.10)); }
.ai-clipboard span { font-size: 14px; }
/* ---- Sample cards ---- */
.ai-sample-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 8px;
}
.ai-sample-card {
display: flex; align-items: center; gap: 10px;
padding: 10px 12px;
background: var(--sg-bg, #fff);
border: 1px solid var(--sg-border, #cbd5e1);
border-radius: 8px;
text-align: left;
cursor: pointer;
transition: border-color 80ms, background 80ms, transform 80ms;
}
.ai-sample-card:hover {
border-color: var(--sg-accent, #6366f1);
background: color-mix(in srgb, var(--sg-accent, #6366f1) 4%, transparent);
transform: translateY(-1px);
}
.ai-sample-icon {
width: 32px; height: 32px;
display: inline-grid; place-items: center;
border-radius: 8px;
background: color-mix(in srgb, var(--sg-accent, #6366f1) 10%, transparent);
color: var(--sg-accent, #4338ca);
font-size: 16px; font-weight: 700; flex: none;
}
.ai-sample-body { display: flex; flex-direction: column; min-width: 0; }
.ai-sample-label { font-size: 13px; font-weight: 600; color: var(--sg-fg, #0f172a); }
.ai-sample-tag { font-size: 11px; color: var(--sg-muted, #64748b); }
/* ---- Textarea ---- */
.ai-textarea {
width: 100%;
border: 1px solid var(--sg-border, #cbd5e1);
background: var(--sg-bg, #fff);
color: var(--sg-fg, #0f172a);
border-radius: 8px;
padding: 10px 12px;
font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
font-size: 12px;
resize: vertical;
outline: none;
}
.ai-textarea:focus {
border-color: var(--sg-accent, #6366f1);
box-shadow: var(--sg-focus-ring, 0 0 0 3px color-mix(in srgb, var(--sg-accent, #6366f1) 18%, transparent));
}
/* ---- Action bar ---- */
.ai-actions { display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
.ai-primary {
background: var(--sg-accent, #6366f1); color: var(--sg-on-accent, #fff);
border: 0; border-radius: 8px;
padding: 8px 14px; font-weight: 600; cursor: pointer;
box-shadow: 0 1px 2px color-mix(in srgb, var(--sg-accent, #6366f1) 25%, transparent);
}
.ai-primary:disabled { opacity: 0.50; cursor: default; box-shadow: none; }
.ai-accept {
display: flex;
height: 100%;
overflow: hidden;
align-items: center;
gap: 8px;
background: linear-gradient(135deg, #10b981 0%, #059669 100%);
color: #fff;
border: 0;
border-radius: 8px;
padding: 7px 8px 7px 14px;
font-weight: 600;
font-size: 13.5px;
line-height: 1.2;
white-space: nowrap;
flex-shrink: 0;
cursor: pointer;
width: 170px;
box-shadow:
0 1px 0 rgba(255, 255, 255, 0.18) inset,
0 -1px 0 rgba(0, 0, 0, 0.10) inset,
0 2px 5px rgba(16, 185, 129, 0.35);
transition: transform 80ms ease, box-shadow 140ms ease, filter 140ms ease;
}
.ai-accept:hover:not(:disabled) {
filter: brightness(1.06);
box-shadow:
0 1px 0 rgba(255,255,255,0.20) inset,
0 -1px 0 rgba(0,0,0,0.10) inset,
0 3px 10px rgba(16,185,129,0.45);
}
.ai-accept:focus-visible {
outline: none;
box-shadow:
0 0 0 3px rgba(16,185,129,0.30),
0 1px 0 rgba(255,255,255,0.18) inset,
0 -1px 0 rgba(0,0,0,0.10) inset,
0 2px 5px rgba(16,185,129,0.35);
}
.ai-accept:active:not(:disabled) { transform: translateY(1px); }
.ai-accept-icon {
display: inline-flex; align-items: center; justify-content: center;
width: 16px; height: 16px; flex-shrink: 0;
color: rgba(255,255,255,0.95);
}
.ai-accept-label { letter-spacing: 0.01em; flex-shrink: 0; }
.ai-accept-spacer { flex: 1; min-width: 4px; }
.ai-accept-badge {
background: rgba(255,255,255,0.22);
border-radius: 999px;
padding: 2px 8px;
font-size: 11.5px; font-weight: 700;
line-height: 1;
white-space: nowrap;
flex-shrink: 0;
}
.ai-accept:disabled {
background: var(--sg-header-bg, #f1f5f9);
color: var(--sg-muted, #94a3b8);
box-shadow: 0 0 0 1px var(--sg-border, #cbd5e1) inset;
cursor: default;
}
.ai-accept:disabled .ai-accept-badge {
background: var(--sg-border, rgba(148, 163, 184, 0.18));
color: var(--sg-muted, #94a3b8);
}
.ai-cancel {
background: transparent; color: var(--sg-muted, #64748b);
border: 1px solid var(--sg-border, #cbd5e1); border-radius: 8px;
padding: 7px 12px; cursor: pointer;
}
.ai-cancel:hover { background: var(--sg-row-hover-bg, rgba(148,163,184,0.10)); }
.ai-log-summary {
margin-left: auto;
font-size: 11px; color: var(--sg-muted, #64748b);
max-width: 50%; overflow: hidden; text-overflow: ellipsis; white-space: nowrap;
}
.ai-skip-count { color: var(--sg-muted, rgba(148, 163, 184, 0.9)); font-weight: 500; margin-left: 4px; }
/* ---- Preview panel ---- */
.ai-preview {
border: 1px solid var(--sg-border, #cbd5e1);
background: var(--sg-bg, #fff);
border-radius: 12px; overflow: hidden;
max-height: 360px;
display: flex; flex-direction: column;
}
.ai-preview-head {
display: flex; align-items: center; justify-content: space-between; gap: 12px; flex-wrap: wrap;
padding: 10px 14px;
font-size: 13px;
color: var(--sg-fg, #0f172a);
background: var(--sg-header-bg, #f8fafc);
border-bottom: 1px solid var(--sg-border, #e2e8f0);
}
.ai-counts { display: inline-flex; gap: 4px; margin-left: 10px; }
.ai-count-pill {
font-size: 11px; font-weight: 600;
padding: 1px 8px; border-radius: 999px;
}
.ai-count-insert { background: var(--ai-pos-bg); color: var(--ai-pos-fg); }
.ai-count-update { background: var(--ai-info-bg); color: var(--ai-info-fg); }
.ai-count-skip { background: var(--sg-border, rgba(148, 163, 184, 0.18)); color: var(--sg-muted, #475569); }
.ai-legend { font-size: 11px; color: var(--sg-muted, #64748b); }
.ai-card-list {
display: flex; flex-direction: column; gap: 8px;
padding: 10px 14px;
overflow: auto;
min-height: 0;
}
.ai-row-card {
border: 1px solid var(--sg-border, #e2e8f0);
border-radius: 10px;
background: var(--sg-bg, #fff);
padding: 10px 12px;
display: flex; flex-direction: column; gap: 6px;
transition: border-color 80ms, background 80ms;
}
.ai-row-card.ai-mode-insert { border-left: 3px solid #10b981; }
.ai-row-card.ai-mode-update { border-left: 3px solid #3b82f6; background: var(--ai-info-soft); }
.ai-row-card.ai-mode-skip { border-left: 3px solid var(--sg-border, #cbd5e1); opacity: 0.65; }
.ai-row-card-head { display: flex; align-items: center; justify-content: space-between; gap: 10px; flex-wrap: wrap; }
.ai-row-id { display: inline-flex; align-items: center; gap: 8px; min-width: 0; flex-wrap: wrap; }
.ai-row-name { font-weight: 600; font-size: 13px; color: var(--sg-fg, #0f172a); }
.ai-row-email {
font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
font-size: 11px;
background: color-mix(in srgb, var(--sg-accent, #6366f1) 10%, transparent);
color: var(--sg-accent, #4338ca);
padding: 1px 6px; border-radius: 4px;
}
/* Action picker */
.ai-mode-picker {
display: inline-flex; gap: 2px;
border: 1px solid var(--sg-border, #cbd5e1);
background: var(--sg-bg, #fff);
border-radius: 8px;
padding: 2px;
}
.ai-tag {
display: inline-flex; align-items: center; gap: 3px;
font-size: 11px; font-weight: 600; letter-spacing: 0.02em;
padding: 3px 9px; border-radius: 6px;
border: 0; cursor: pointer;
background: transparent;
color: var(--sg-muted, #64748b);
}
.ai-tag:disabled { opacity: 0.40; cursor: not-allowed; }
.ai-tag-insert.is-active { background: var(--ai-pos-bg); color: var(--ai-pos-fg); }
.ai-tag-update.is-active { background: var(--ai-info-bg); color: var(--ai-info-fg); }
.ai-tag-skip.is-active { background: var(--sg-bg-subtle, var(--sg-header-bg, #e2e8f0)); color: var(--sg-fg, #334155); }
.ai-tag:hover:not(:disabled):not(.is-active) { background: var(--sg-row-hover-bg, rgba(148,163,184,0.10)); color: var(--sg-fg, #0f172a); }
/* Confidence badge */
.ai-badge { font-size: 9px; font-weight: 700; letter-spacing: 0.06em; text-transform: uppercase;
padding: 2px 6px; border-radius: 4px; }
.ai-badge-high { background: var(--ai-pos-bg); color: var(--ai-pos-fg); }
.ai-badge-medium { background: var(--ai-warn-bg); color: var(--ai-warn-fg); }
.ai-badge-low { background: var(--ai-neg-bg); color: var(--ai-neg-fg); }
/* Field grid */
.ai-row-fields {
display: grid;
grid-template-columns: repeat(3, minmax(0, 1fr));
gap: 8px 16px;
margin: 0;
}
.ai-field { display: flex; flex-direction: column; min-width: 0; }
.ai-field dt { font-size: 10px; letter-spacing: 0.04em; text-transform: uppercase; color: var(--sg-muted, #94a3b8); }
.ai-field dd { margin: 0; font-size: 12px; color: var(--sg-fg, #0f172a); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }
.ai-empty { color: var(--sg-muted, #cbd5e1); font-style: italic; }
/* Inline match / issues */
.ai-row-match {
font-size: 11px; color: var(--ai-info-fg);
background: var(--ai-info-bg);
padding: 4px 8px; border-radius: 6px;
}
.ai-row-issues {
font-size: 11px; color: var(--ai-warn-fg);
background: var(--ai-warn-bg);
padding: 4px 8px; border-radius: 6px;
}
</style>More AI examples
- AI assistant - NL filter / smart-fill / summarise / classify driven by a BYO model adapter. Runs end-to-end against the bundled mock provider so no API key is required.
- NL filter bar (AI) - Type "EMEA active over 50k" - the AI Platform parses your phrase into api.setFilter / setSort / topN calls. Demo ships a rule-based fallback so you can evaluate without a key; wire your own model with setAIProvider.