feat: release contextual omnichannel customer platform

Add Telegram, WhatsApp and email account management, realtime messaging, contextual multilingual AI, customer intelligence, knowledge workflows, enterprise controls, a unified design system and complete repository documentation.
2026-07-30 02:29:53 +08:00
parent 9804341463
commit af49d22ca7
66 changed files with 3181 additions and 554 deletions
@@ -14,6 +14,7 @@
        "helmet": "^8.1.0",
        "imapflow": "^1.6.1",
        "jsonwebtoken": "^9.0.2",
+        "libphonenumber-js": "^1.11.20",
        "mailparser": "^3.9.14",
        "mammoth": "^1.9.0",
        "nodemailer": "^7.0.5",
@@ -891,6 +892,12 @@
        "libqp": "2.1.1"
      }
    },
+    "node_modules/libphonenumber-js": {
+      "version": "1.11.20",
+      "resolved": "https://registry.npmmirror.com/libphonenumber-js/-/libphonenumber-js-1.11.20.tgz",
+      "integrity": "sha512-/ipwAMvtSZRdiQBHqW1qxqeYiBMzncOQLVA+62MWYr7N4m7Q2jqpJ0WgT7zlOEOpyLRSqrMXidbJpC0J77AaKA==",
+      "license": "MIT"
+    },
    "node_modules/libqp": {
      "version": "2.1.1",
      "resolved": "https://registry.npmmirror.com/libqp/-/libqp-2.1.1.tgz",
@@ -16,6 +16,7 @@
    "helmet": "^8.1.0",
    "imapflow": "^1.6.1",
    "jsonwebtoken": "^9.0.2",
+    "libphonenumber-js": "^1.11.20",
    "mailparser": "^3.9.14",
    "mammoth": "^1.9.0",
    "nodemailer": "^7.0.5",
@@ -7,7 +7,11 @@ function parseJson(text) {
  return JSON.parse(cleaned)
 }

-export async function deepseek(system, user, json = false) {
+export async function deepseek(system, user, jsonOrOptions = false) {
+  const options = typeof jsonOrOptions === 'boolean'
+    ? { json: jsonOrOptions }
+    : { ...(jsonOrOptions || {}) }
+  const json = Boolean(options.json)
  const apiKey = process.env.DEEPSEEK_API_KEY
  if (!apiKey) throw Object.assign(new Error('DEEPSEEK_API_KEY 尚未配置'), { status: 503 })
  const endpoint = `${(process.env.DEEPSEEK_BASE_URL || 'https://api.deepseek.com/anthropic').replace(/\/$/, '')}/v1/messages`
@@ -17,7 +21,10 @@ export async function deepseek(system, user, json = false) {

  for (let attempt = 0; attempt < attempts; attempt += 1) {
    const controller = new AbortController()
-    const timeout = setTimeout(() => controller.abort(), Number(process.env.AI_TIMEOUT_MS || 45000))
+    const timeout = setTimeout(
+      () => controller.abort(),
+      Number(options.timeoutMs || process.env.AI_TIMEOUT_MS || 30000),
+    )
    try {
      const response = await fetch(endpoint, {
        method: 'POST',
@@ -29,8 +36,11 @@ export async function deepseek(system, user, json = false) {
        },
        body: JSON.stringify({
          model: process.env.DEEPSEEK_MODEL || 'deepseek-v4-flash',
-          max_tokens: Number(process.env.AI_MAX_TOKENS || 4096),
-          temperature: json ? 0 : 0.3,
+          max_tokens: Number(options.maxTokens || process.env.AI_MAX_TOKENS || 4096),
+          temperature: options.temperature ?? (json ? 0 : 0.3),
+          thinking: options.thinking === true
+            ? { type: 'enabled' }
+            : { type: 'disabled' },
          system,
          messages: [{ role: 'user', content: [{ type: 'text', text: prompt }] }],
        }),
@@ -47,6 +57,11 @@ export async function deepseek(system, user, json = false) {
        lastError = error
        prompt = `${user}\n\n上一次响应不是合法 JSON。请重新生成完整结果：只允许输出一个可被 JSON.parse 直接解析的 JSON 值；不得使用 Markdown；字符串中的双引号、换行和反斜杠必须正确转义。`
      }
+    } catch (error) {
+      if (error?.name === 'AbortError') {
+        throw Object.assign(new Error('AI 响应超时，请稍后重试'), { status: 504 })
+      }
+      throw error
    } finally {
      clearTimeout(timeout)
    }
@@ -54,8 +69,8 @@ export async function deepseek(system, user, json = false) {
  throw Object.assign(new Error('AI 返回格式异常，自动重试后仍无法解析'), { status: 502, cause: lastError })
 }

-export function conversationContext(messages) {
-  return messages.slice(-80).map((m) => {
+export function conversationContext(messages, limit = 30) {
+  return messages.slice(-limit).map((m) => {
    const role = m.sender === 'agent' ? '坐席' : m.sender === 'system' ? '系统' : '客户'
    const content = m.originalText || m.attachmentName || `[${m.type || '消息'}]`
    const translation = m.translatedText && m.translatedText !== content ? `（译文：${m.translatedText}）` : ''
@@ -0,0 +1,85 @@
+import { parsePhoneNumberFromString } from 'libphonenumber-js'
+
+const VALID_SOURCE = new Set(['manual', 'account-profile', 'phone-prefix', 'unknown'])
+
+export function normalizeCountryCode(value) {
+  const code = String(value || '').trim().toUpperCase()
+  return /^[A-Z]{2}$/.test(code) ? code : ''
+}
+
+export function inferCountryFromPhone(value) {
+  const raw = String(value || '').trim()
+  if (!raw) return ''
+  const normalized = raw.startsWith('+') ? raw : `+${raw.replace(/[^\d]/g, '')}`
+  try {
+    return normalizeCountryCode(parsePhoneNumberFromString(normalized)?.country)
+  } catch {
+    return ''
+  }
+}
+
+export function resolveCustomerCountry(input = {}, existing = {}, options = {}) {
+  const manualCountry = normalizeCountryCode(input.country)
+  if (options.manual === true) {
+    return {
+      country: manualCountry,
+      countrySource: manualCountry ? 'manual' : 'unknown',
+      countryConfidence: manualCountry ? 1 : 0,
+      countryInferredAt: new Date().toISOString(),
+    }
+  }
+
+  const existingSource = VALID_SOURCE.has(existing.countrySource)
+    ? existing.countrySource
+    : 'unknown'
+  if (existingSource === 'manual' && normalizeCountryCode(existing.country)) {
+    return {
+      country: normalizeCountryCode(existing.country),
+      countrySource: 'manual',
+      countryConfidence: 1,
+      countryInferredAt: existing.countryInferredAt || existing.updatedAt,
+    }
+  }
+
+  const accountCountry = normalizeCountryCode(
+    input.accountCountry || input.countryCode || input.profileCountry,
+  )
+  if (accountCountry) {
+    return {
+      country: accountCountry,
+      countrySource: 'account-profile',
+      countryConfidence: 0.98,
+      countryInferredAt: new Date().toISOString(),
+    }
+  }
+
+  const phoneCountry = inferCountryFromPhone(input.phone || existing.phone)
+  if (phoneCountry) {
+    return {
+      country: phoneCountry,
+      countrySource: 'phone-prefix',
+      countryConfidence: 0.92,
+      countryInferredAt: new Date().toISOString(),
+    }
+  }
+
+  const preserved = normalizeCountryCode(input.country || existing.country)
+  const preservedSource = VALID_SOURCE.has(input.countrySource)
+    ? input.countrySource
+    : existingSource
+  if (preserved && preservedSource !== 'unknown') {
+    return {
+      country: preserved,
+      countrySource: preservedSource,
+      countryConfidence: Number(input.countryConfidence ?? existing.countryConfidence ?? 0.7),
+      countryInferredAt: input.countryInferredAt || existing.countryInferredAt,
+    }
+  }
+
+  return {
+    country: '',
+    countrySource: 'unknown',
+    countryConfidence: 0,
+    countryInferredAt: null,
+  }
+}
@@ -90,8 +90,11 @@ export async function deleteResource(tenantId, kind, id) {
 }

 export async function audit(req, action, target, detail = {}) {
+  // pg 会把顶层 JavaScript 数组编码成 PostgreSQL 数组文本（如 {ai}），
+  // jsonb 无法解析该格式。显式 JSON 序列化可同时兼容对象、数组和标量。
+  const serializedDetail = JSON.stringify(detail ?? {})
  await pool.query(
    'INSERT INTO audit_logs(id,tenant_id,user_id,action,target,detail,ip) VALUES($1,$2,$3,$4,$5,$6,$7)',
-    [uid(), req.auth?.tenantId ?? null, req.auth?.userId ?? null, action, target, detail, req.ip],
+    [uid(), req.auth?.tenantId ?? null, req.auth?.userId ?? null, action, target, serializedDetail, req.ip],
  )
 }
@@ -0,0 +1,107 @@
+function normalize(value) {
+  return String(value || '')
+    .normalize('NFKC')
+    .toLowerCase()
+    .replace(/\s+/g, ' ')
+    .trim()
+}
+
+export function knowledgeTokens(value) {
+  const text = normalize(value)
+  const tokens = new Set(text.match(/[a-z0-9][a-z0-9._+-]{1,}/g) || [])
+  const chineseRuns = text.match(/[\u3400-\u9fff]+/g) || []
+  for (const run of chineseRuns) {
+    if (run.length === 1) tokens.add(run)
+    for (let size = 2; size <= 3; size += 1) {
+      for (let index = 0; index <= run.length - size; index += 1) {
+        tokens.add(run.slice(index, index + size))
+      }
+    }
+  }
+  return [...tokens].slice(0, 300)
+}
+
+export function chunkKnowledge(content, maximum = 900, overlap = 120) {
+  const text = String(content || '').replace(/\r\n/g, '\n').trim()
+  if (!text) return []
+  const paragraphs = text.split(/\n{2,}/).map((item) => item.trim()).filter(Boolean)
+  const chunks = []
+  let current = ''
+  const flush = () => {
+    if (!current.trim()) return
+    chunks.push(current.trim())
+    current = current.slice(-overlap)
+  }
+  for (const paragraph of paragraphs.length ? paragraphs : [text]) {
+    if (paragraph.length > maximum) {
+      if (current) flush()
+      for (let start = 0; start < paragraph.length; start += maximum - overlap) {
+        chunks.push(paragraph.slice(start, start + maximum).trim())
+      }
+      current = ''
+      continue
+    }
+    if (current && current.length + paragraph.length + 2 > maximum) flush()
+    current = current ? `${current}\n\n${paragraph}` : paragraph
+  }
+  if (current.trim()) chunks.push(current.trim())
+  return [...new Set(chunks)].filter(Boolean)
+}
+
+function scoreChunk(item, chunk, queryTokens, normalizedQuery) {
+  const chunkText = normalize(chunk)
+  const titleText = normalize(`${item.title || ''} ${(item.tags || []).join(' ')}`)
+  let matched = 0
+  let weighted = 0
+  for (const token of queryTokens) {
+    if (chunkText.includes(token)) {
+      matched += 1
+      weighted += token.length >= 3 ? 1.35 : 1
+    }
+    if (titleText.includes(token)) weighted += 1.8
+  }
+  if (normalizedQuery.length >= 3 && chunkText.includes(normalizedQuery)) weighted += 4
+  const coverage = queryTokens.length ? matched / queryTokens.length : 0
+  return weighted + coverage * 3
+}
+
+export function rankKnowledge(items, query, limit = 5) {
+  const normalizedQuery = normalize(query)
+  const queryTokens = knowledgeTokens(normalizedQuery)
+  if (!normalizedQuery || !queryTokens.length) return []
+  const candidates = []
+  for (const item of items) {
+    if (item.enabled === false || item.status === 'failed') continue
+    const chunks = chunkKnowledge(item.content)
+    chunks.forEach((chunk, index) => {
+      const rawScore = scoreChunk(item, chunk, queryTokens, normalizedQuery)
+      if (rawScore <= 0) return
+      candidates.push({
+        item,
+        chunkIndex: index,
+        snippet: chunk.slice(0, 560),
+        rawScore,
+      })
+    })
+  }
+  candidates.sort((a, b) => b.rawScore - a.rawScore)
+  const bestByItem = new Map()
+  for (const candidate of candidates) {
+    if (!bestByItem.has(candidate.item.id)) bestByItem.set(candidate.item.id, candidate)
+  }
+  const selected = [...bestByItem.values()].slice(0, Math.max(1, limit))
+  const maximum = selected[0]?.rawScore || 1
+  return selected.map((candidate) => ({
+    ...candidate,
+    score: Math.max(0.01, Math.min(1, candidate.rawScore / maximum)),
+  }))
+}
+
+export function knowledgeMetadata(content) {
+  const chunks = chunkKnowledge(content)
+  return {
+    characterCount: String(content || '').length,
+    chunkCount: chunks.length,
+    indexedAt: new Date().toISOString(),
+  }
+}