Ax Signature

[description] input1:type, input2:type -> output1:type, output2:type

'tags:string[] -> processedTags:string[]'  // arrays
'query:string, context?:string -> response:string'  // optional with ?
'problem:string -> reasoning!:string, solution:string'  // internal with !

`userAge:number(min 0, max 120), contactEmail:string(format email, cache), codeSnippet:code(python)
 -> userName:string(pattern "^[a-z_]+$" "lowercase name"), tagList:string(item "a short tag")[] "all tags",
    profileList:object{ fullName:string, userAge?:number(min 0) }[] "matched profiles"`

# Support triage: several class outputs plus a capped reply draft
ticketText:string -> priorityClass:class "p0, p1, p2", sentimentClass:class "angry, neutral, happy", replyDraft:string(max 500)

# Invoice extraction: regex-validated id, bounded totals, structured line items
invoiceText:string -> invoiceNumber:string(pattern "^INV-\\d+$" "INV- then digits"), totalAmount:number(min 0), lineItems:object{ description:string, quantity:number(min 1), unitPrice:number }[]

# Contact enrichment: optional format-validated outputs
bioText:string -> contactEmail?:string(format email), websiteUrl?:string(format uri), birthDate?:string(format date)

# RAG: cached corpus input plus per-item described citations
corpusText:string(cache), userQuestion:string -> answerText:string, citedChunks:string(item "verbatim quote")[]

# Code generation: language-tagged code outputs
taskBrief:string -> pythonScript:code(python), testCases:code(python), riskNotes?:string

# Chain of thought: internal reasoning stripped from the result
problemText:string -> reasoning!:string, solutionText:string

# Resume parsing: nested objects inside nested arrays
resumeText:string -> candidateProfile:object{ fullName:string, yearsExperience:number(min 0), skillList:string[], education:object{ schoolName:string, degreeName?:string }[] }

# Lead scoring: signature-level description, bounded score, class next step
"Score sales leads" leadNotes:string -> fitScore:number(min 0, max 100) "0-100 fit", nextStep:class "call, email, drop"

# Multimodal: top-level image input with an optional question
productPhoto:image, question?:string -> productDescription:string, detectedObjects:string[]

# Meeting audio: audio input, capped summary, per-item action list
meetingAudio:audio -> meetingSummary:string(max 1000), actionItems:string(item "one action item")[]

# Moderation: class verdict plus structured flagged spans
postText:string -> moderationVerdict:class "allow, review, block", flaggedSpans:object{ spanText:string, reasonNote:string }[]

# Translation: optional locale input
sourceText:string, targetLocale?:string -> translatedText:string, glossaryHits:string[]

# Text-to-SQL: cached schema plus SQL-tagged output
schemaText:string(cache), questionText:string -> sqlQuery:code(sql), queryNotes?:string(max 200)

# Calendar extraction: datetime fields and an optional end
emailText:string -> eventTitle:string, startsAt:datetime, endsAt?:datetime, attendeeNames:string[]

# Booking window: date range, bounded party size, and flexibility flag
requestText:string -> stayWindow:dateRange, partySize:number(min 1, max 12), flexibleDates:boolean

# Contract dates: date fields plus bounded notice period
contractText:string -> effectiveDate:date, expiryDate?:date, autoRenews:boolean, noticeDays?:number(min 0)

# Link audit: URL arrays and an optional primary URL
pageText:string -> referencedUrls:url[], primaryUrl?:url

# Config generation: JSON output plus per-item warnings
requirementsText:string -> serviceConfig:json, setupWarnings:string(item "one warning")[]

# Claims gate: cached policy, bounded confidence, and optional citation
claimText:string, policyText:string(cache) -> isCovered:boolean, confidenceScore:number(min 0, max 1), citedClause?:string

# Earnings extraction: structured period data plus a class outlook
filingText:string(cache) -> revenueByPeriod:object{ periodLabel:string, amountUsd:number }[], guidanceTone:class "raise, hold, cut"

# Pull request review: diff code, cached guide, structured comments, and verdict
diffText:code(diff), styleGuide?:string(cache) -> reviewComments:object{ filePath:string, lineNumber:number(min 1), commentText:string(max 300) }[], overallVerdict:class "approve, revise"

# Incident triage: severity class, optional service, and per-item runbook steps
alertLog:string -> incidentSeverity:class "sev1, sev2, sev3", suspectedService?:string, runbookSteps:string(item "one step")[]

# Product listing: image and file inputs with constrained listing outputs
productPhoto:image, priceSheet?:file -> listingTitle:string(max 80), bulletPoints:string(item "one selling point")[], priceUsd?:number(min 0)

# Study cards: nested object array with an optional difficulty tag
chapterText:string -> flashCards:object{ questionText:string, answerText:string, difficultyTag?:string }[]

import { ax, s } from '@ax-llm/ax';
const gen = ax('input:string -> output:string');
const sig = s('query:string -> response:string');

import { f } from '@ax-llm/ax';
const sig = f()
  .input('userMessage', f.string('User input'))
  .input('contextData', f.string('Additional context').optional())
  .input('tags', f.string('Keywords').array())
  .output('responseText', f.string('AI response'))
  .output('confidenceScore', f.number('Confidence 0-1'))
  .output('debugInfo', f.string('Debug info').internal())
  .build();

import { z } from 'zod';
import { f } from '@ax-llm/ax';

// Shape A: per-field schema — name first, then the schema, then optional ax hints
const sig = f()
  .input('contextData', z.string().describe('Background context'), { cache: true })
  .input('userQuestion', z.string().describe('Question to answer'))
  .output('reasoning', z.string().describe('Step-by-step thinking'), { internal: true })
  .output('answer', z.string().describe('Final answer'))
  .build();

// Shape B: whole-object schema — decomposed into fields in declaration order
const sig2 = f()
  .description('Answer questions from retrieved context')
  .input(
    z.object({
      contextData: z.string().describe('Background context'),
      userQuestion: z.string().describe('Question to answer'),
    }),
    { fields: { contextData: { cache: true } } }  // companion options map
  )
  .output(
    z.object({
      reasoning: z.string().describe('Step-by-step thinking'),
      answer: z.string().describe('Final answer'),
    }),
    { fields: { reasoning: { internal: true } } }
  )
  .build();

// String constraints: .email(), .url(), .min(), .max(), .regex()
// Number constraints: .min(), .max()
// Arrays: z.array(z.string())
// Enums: z.enum([...])  — NOTE: enum maps to ax class type, output fields only
const sig3 = f()
  .input(z.object({
    emailAddress: z.string().email().describe('Contact email'),
    username: z.string().min(3).max(20).describe('Handle'),
    score: z.number().min(0).max(100).describe('Numeric score'),
  }))
  .output(z.object({
    priority: z.enum(['low', 'medium', 'high']).describe('Priority'),
    summary: z.string().describe('Result'),
  }))
  .build();

import { z } from 'zod';
import { fn } from '@ax-llm/ax';

// Whole-object zod on a tool — AI-SDK-style
const lookupProduct = fn('lookupProduct')
  .description('Look up a product by name and return its current details')
  .arg(
    z.object({
      productName: z.string().min(1).describe('Exact product name'),
      includeSpecs: z.boolean().optional(),
    })
  )
  .returns(
    z.object({
      price: z.number(),
      inStock: z.boolean(),
      rating: z.number().min(1).max(5),
    })
  )
  .handler(async ({ productName, includeSpecs }) => ({
    price: 79.99,
    inStock: true,
    rating: 4.3,
  }))
  .build();

// Per-argument form — mix with f.*() args, attach ax hints
const searchDocs = fn('searchDocs')
  .description('Search indexed docs')
  .arg('query', z.string().min(1), { cache: true })
  .arg('limit', z.number().int().positive().optional())
  .returnsField('results', z.array(z.string()))
  .handler(async ({ query }) => [])
  .build();

import { s, f } from '@ax-llm/ax';
const sig = s('base:string -> result:string')
  .appendInputField('extra', f.json('Metadata').optional())
  .appendOutputField('score', f.number('Quality score'));