npm install @botpress/zai @botpress/client @bpinternal/zui

import { Client } from '@botpress/client'
import { Zai } from '@botpress/zai'
import { z } from '@bpinternal/zui'

// Initialize
const client = new Client({ botId: 'YOUR_BOT_ID', token: 'YOUR_TOKEN' })
const zai = new Zai({ client })

// Extract structured data
const person = await zai.extract(
  'John Doe is 30 years old and lives in New York',
  z.object({
    name: z.string(),
    age: z.number(),
    location: z.string(),
  })
)
// Result: { name: 'John Doe', age: 30, location: 'New York' }

// Check content
const isPositive = await zai.check('This product is amazing!', 'expresses positive sentiment')
// Result: true

// Generate text
const story = await zai.text('Write a short story about AI', { length: 200 })

// Summarize documents
const summary = await zai.summarize(longDocument, { length: 500 })

// Extract single object
const product = await zai.extract(
  text,
  z.object({
    name: z.string(),
    price: z.number(),
    inStock: z.boolean(),
  })
)

// Extract array of items
const products = await zai.extract(text, z.array(productSchema))

const result = await zai.check(email, 'is spam')
const { value, explanation } = await result.full()

const labels = await zai.label(review, {
  positive: 'expresses positive sentiment',
  technical: 'mentions technical details',
  verified: 'from verified purchaser',
})
// Result: { positive: true, technical: false, verified: true }

// Translate
const french = await zai.rewrite(text, 'translate to French')

// Change tone
const formal = await zai.rewrite('Hey! What's up?', 'make it professional')

const techCompanies = await zai.filter(companies, 'are technology companies')
const recentPosts = await zai.filter(posts, 'were published this week')

// Group items automatically
const grouped = await zai.group(tasks, {
  instructions: 'Group by priority level',
})
// Result: { 'High Priority': [...], 'Medium Priority': [...], 'Low Priority': [...] }

// Group with initial categories
const categorized = await zai.group(emails, {
  instructions: 'Group by topic',
  initialGroups: [
    { id: 'work', label: 'Work' },
    { id: 'personal', label: 'Personal' },
  ],
})

// Group large datasets efficiently
const organized = await zai.group(largeArray, {
  instructions: 'Group by date',
  chunkLength: 8000, // Process in chunks for better performance
})

// Auto-generate criteria (returns total score)
const scores = await zai.rate(products, 'is it a good value product?')
// Result: [12, 8, 15] (total scores for each item)

// Get detailed ratings
const { output } = await zai.rate(products, 'is it a good value product?').result()
// Result: [
//   { affordability: 4, quality: 5, features: 3, total: 12 },
//   { affordability: 3, quality: 2, features: 3, total: 8 },
//   ...
// ]

// Use fixed criteria
const ratings = await zai.rate(passwords, {
  length: 'password length (12+ chars = very_good, 8-11 = good, 6-7 = average, 4-5 = bad, <4 = very_bad)',
  complexity: 'character variety (all types = very_good, 3 types = good, 2 types = average, 1 type = bad)',
  strength: 'overall password strength',
})
// Result: [
//   { length: 5, complexity: 5, strength: 5, total: 15 },
//   { length: 1, complexity: 1, strength: 1, total: 3 },
// ]

// Rate large datasets efficiently (parallelized)
const allRatings = await zai.rate(Array(500).fill(item), 'how complete is this?')
// Processes ~500 items in ~120ms with automatic chunking

// Sort by natural criteria
const sorted = await zai.sort(emails, 'sort by urgency')
// LLM determines criteria and orders items accordingly

// Sort with detailed results
const { output } = await zai.sort(tasks, 'sort by priority').result()
// output includes scoring breakdown for each item

// Complex multi-criteria sorting
const prioritized = await zai.sort(tickets, 'sort by customer importance and issue severity')

// Sort large datasets efficiently (parallelized with chunking)
const orderedItems = await zai.sort(Array(500).fill(item), 'sort by relevance')

const blogPost = await zai.text('Write about the future of AI', {
  length: 1000,
  temperature: 0.7,
})

// Simple summary
const summary = await zai.summarize(article)

// With custom prompt
const technicalSummary = await zai.summarize(paper, {
  length: 500,
  prompt: 'Focus on technical implementation details',
})

const zai = new Zai({
  client,
  activeLearning: {
    enable: true,
    tableName: 'ai_learning_data',
    taskId: 'sentiment-analysis',
  },
})

// Use with task ID for learning
const result = await zai.learn('sentiment-analysis').check(text, 'is positive')

// Use the best model (default)
const zai = new Zai({ client, model: 'best' })

// Use fast model for speed
const fastZai = new Zai({ client, model: 'fast' })

// Use specific model
const customZai = new Zai({ client, model: 'gpt-4-turbo' })

const response = zai.summarize(veryLongDocument)

// Track progress
response.on('progress', (progress) => {
  console.log(`${progress.percent}% complete`)
})

const summary = await response

const result = await zai.extract(text, schema)
const usage = await result.usage()

console.log({
  tokens: usage.totalTokens,
  cost: usage.totalCost,
  latency: usage.totalLatency,
})

const processedData = await zai.with({ temperature: 0.3 }).learn('data-extraction').extract(document, complexSchema)

// Automatically chunks and processes in parallel
const extractedData = await zai.extract(
  hugeDocument, // 100k+ tokens
  z.array(recordSchema),
  { chunkSize: 4000 }
)

const controller = new AbortController()
const response = zai.summarize(document, { signal: controller.signal })

// Cancel if needed
setTimeout(() => controller.abort(), 5000)