User input
    -> Input guardrails (safety, PII, token limits)
    -> Prompt construction (system prompt, context, few-shots, retrieved docs)
    -> Model call (streaming or batch)
    -> Output guardrails (schema validation, content check, hallucination detection)
    -> Post-processing (formatting, citations, structured extraction)
    -> Response to user

import OpenAI from 'openai'

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })

async function callLLM(systemPrompt: string, userMessage: string, model = 'gpt-4o-mini'): Promise<string> {
  const controller = new AbortController()
  const timeout = setTimeout(() => controller.abort(), 30_000)
  try {
    const res = await client.chat.completions.create(
      { model, max_tokens: 1024, messages: [{ role: 'system', content: systemPrompt }, { role: 'user', content: userMessage }] },
      { signal: controller.signal },
    )
    return res.choices[0].message.content ?? ''
  } finally {
    clearTimeout(timeout)
  }
}

import { z } from 'zod'

const PII_PATTERNS = [
  /\b\d{3}-\d{2}-\d{4}\b/g,                              // SSN
  /\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b/gi,        // email
  /\b(?:\d{4}[ -]?){3}\d{4}\b/g,                         // credit card
]

function scrubPII(text: string): string {
  return PII_PATTERNS.reduce((t, re) => t.replace(re, '[REDACTED]'), text)
}

function validateInput(text: string): { ok: boolean; reason?: string } {
  if (text.split(/\s+/).length > 4000) return { ok: false, reason: 'Input too long' }
  return { ok: true }
}

const SummarySchema = z.object({
  summary: z.string().min(10).max(500),
  keyPoints: z.array(z.string()).min(1).max(10),
  confidence: z.number().min(0).max(1),
})

async function getSummaryWithGuardrails(text: string) {
  const v = validateInput(text)
  if (!v.ok) throw new Error(`Input rejected: ${v.reason}`)
  const raw = await callLLM('Respond only with valid JSON.', `Summarize as JSON: ${scrubPII(text)}`)
  return SummarySchema.parse(JSON.parse(raw))  // throws ZodError if schema invalid
}

interface EvalCase {
  id: string
  input: string
  expectedContains?: string[]
  expectedNotContains?: string[]
  scoreThreshold?: number  // 0-1 for LLM-as-judge
}

async function runEval(ec: EvalCase, modelFn: (input: string) => Promise<string>) {
  const output = await modelFn(ec.input)
  for (const s of ec.expectedContains ?? [])
    if (!output.includes(s)) return { id: ec.id, passed: false, details: `Missing: "${s}"` }
  for (const s of ec.expectedNotContains ?? [])
    if (output.includes(s)) return { id: ec.id, passed: false, details: `Forbidden: "${s}"` }
  if (ec.scoreThreshold !== undefined) {
    const score = await judgeOutput(ec.input, output)
    if (score < ec.scoreThreshold) return { id: ec.id, passed: false, details: `Score ${score} < ${ec.scoreThreshold}` }
  }
  return { id: ec.id, passed: true, details: 'OK' }
}

async function judgeOutput(input: string, output: string): Promise<number> {
  const score = await callLLM(
    'You are a strict evaluator. Reply with only a number from 0.0 to 1.0.',
    `Input: ${input}\n\nOutput: ${output}\n\nScore quality (0.0=poor, 1.0=excellent):`,
    'gpt-4o',
  )
  return Math.min(1, Math.max(0, parseFloat(score)))
}

import OpenAI from 'openai'

const client = new OpenAI()

function chunkText(text: string, size = 512, overlap = 64): string[] {
  const words = text.split(/\s+/)
  const chunks: string[] = []
  for (let i = 0; i < words.length; i += size - overlap) {
    chunks.push(words.slice(i, i + size).join(' '))
    if (i + size >= words.length) break
  }
  return chunks
}

async function embedTexts(texts: string[]): Promise<number[][]> {
  const res = await client.embeddings.create({ model: 'text-embedding-3-small', input: texts })
  return res.data.map(d => d.embedding)
}

function cosine(a: number[], b: number[]): number {
  const dot = a.reduce((s, v, i) => s + v * b[i], 0)
  return dot / (Math.sqrt(a.reduce((s, v) => s + v * v, 0)) * Math.sqrt(b.reduce((s, v) => s + v * v, 0)))
}

interface DocChunk { text: string; embedding: number[] }

async function ragQuery(question: string, store: DocChunk[], topK = 5): Promise<string> {
  const [qEmbed] = await embedTexts([question])
  const context = store
    .map(c => ({ text: c.text, score: cosine(qEmbed, c.embedding) }))
    .sort((a, b) => b.score - a.score).slice(0, topK).map(r => r.text)
  return callLLM(
    'Answer using only the provided context. If not found, say "I don\'t know."',
    `Context:\n${context.join('\n---\n')}\n\nQuestion: ${question}`,
  )
}

import OpenAI from 'openai'

const client = new OpenAI()
type ToolHandlers = Record<string, (args: Record<string, unknown>) => Promise<string>>

const tools: OpenAI.ChatCompletionTool[] = [{
  type: 'function',
  function: {
    name: 'get_weather',
    description: 'Get current weather for a city.',
    parameters: {
      type: 'object',
      properties: { city: { type: 'string' }, units: { type: 'string', enum: ['celsius', 'fahrenheit'] } },
      required: ['city'],
    },
  },
}]

async function runWithTools(userMessage: string, handlers: ToolHandlers): Promise<string> {
  const messages: OpenAI.ChatCompletionMessageParam[] = [{ role: 'user', content: userMessage }]
  for (let step = 0; step < 5; step++) {  // cap tool-use loops to prevent infinite recursion
    const res = await client.chat.completions.create({ model: 'gpt-4o', tools, messages })
    const choice = res.choices[0]
    messages.push(choice.message)
    if (choice.finish_reason === 'stop') return choice.message.content ?? ''
    for (const tc of choice.message.tool_calls ?? []) {
      const fn = handlers[tc.function.name]
      if (!fn) throw new Error(`Unknown tool: ${tc.function.name}`)
      const result = await fn(JSON.parse(tc.function.arguments) as Record<string, unknown>)
      messages.push({ role: 'tool', tool_call_id: tc.id, content: result })
    }
  }
  throw new Error('Tool call loop exceeded max steps')
}

import OpenAI from 'openai'
import type { Response } from 'express'

const client = new OpenAI()

async function streamToResponse(prompt: string, res: Response): Promise<void> {
  res.setHeader('Content-Type', 'text/event-stream')
  res.setHeader('Cache-Control', 'no-cache')
  res.setHeader('Connection', 'keep-alive')
  const stream = await client.chat.completions.create({
    model: 'gpt-4o-mini', stream: true,
    messages: [{ role: 'user', content: prompt }],
  })
  let fullText = ''
  for await (const chunk of stream) {
    const token = chunk.choices[0]?.delta?.content
    if (token) { fullText += token; res.write(`data: ${JSON.stringify({ token })}\n\n`) }
  }
  runOutputGuardrails(fullText)  // validate after stream completes
  res.write('data: [DONE]\n\n')
  res.end()
}

// Client-side consumption
function consumeStream(url: string, onToken: (t: string) => void): void {
  const es = new EventSource(url)
  es.onmessage = (e) => {
    if (e.data === '[DONE]') { es.close(); return }
    onToken((JSON.parse(e.data) as { token: string }).token)
  }
}

function runOutputGuardrails(_text: string): void { /* content policy / schema checks */ }

import crypto from 'crypto'

const cache = new Map<string, { value: string; expiresAt: number }>()

async function cachedLLMCall(prompt: string, model = 'gpt-4o-mini', ttlMs = 3_600_000): Promise<string> {
  const key = crypto.createHash('sha256').update(`${model}:${prompt}`).digest('hex')
  const cached = cache.get(key)
  if (cached && cached.expiresAt > Date.now()) return cached.value
  const result = await callLLM('', prompt, model)
  cache.set(key, { value: result, expiresAt: Date.now() + ttlMs })
  return result
}

// Route to cheaper model based on prompt complexity
function routeModel(prompt: string): string {
  const words = prompt.split(/\s+/).length
  if (words < 50) return 'gpt-4o-mini'
  if (words < 300) return 'gpt-4o-mini'
  return 'gpt-4o'
}

// Strip redundant whitespace to reduce token count
const compressPrompt = (p: string): string => p.replace(/\s{2,}/g, ' ').trim()