Generation

These TypeScript examples are real runnable files. Edit the source file first; this page is rebuilt from the checked-in example and its metadata header.

TypeScript Typed Generation

Runs a small typed generation program against OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: beginner
Run: npm run example -- typescript src/examples/typescript/generation/axgen-openai.ts
Source: src/examples/typescript/generation/axgen-openai.ts

TypeScript

import { AxAIOpenAIModel, ai, ax } from '@ax-llm/ax';

const apiKey = process.env.OPENAI_API_KEY ?? process.env.OPENAI_APIKEY;
if (!apiKey) {
  throw new Error('Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.');
}

const llm = ai({
  name: 'openai',
  apiKey,
  config: {
    model: AxAIOpenAIModel.GPT54Mini,
    temperature: 0,
  },
});

const program = ax('question:string -> answer:string');
const result = await program.forward(llm, {
  question:
    'In one sentence, explain Ax as a language-agnostic LLM programming library.',
});

console.log(JSON.stringify(result, null, 2));

TypeScript Structured Extraction

Extracts structured fields and labels from support text with OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: intermediate
Run: npm run example -- typescript src/examples/typescript/generation/structured.ts
Source: src/examples/typescript/generation/structured.ts

TypeScript

import { AxAIOpenAIModel, ai, ax } from '@ax-llm/ax';

const apiKey = process.env.OPENAI_API_KEY ?? process.env.OPENAI_APIKEY;
if (!apiKey) {
  throw new Error('Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.');
}

const llm = ai({
  name: 'openai',
  apiKey,
  config: {
    model: AxAIOpenAIModel.GPT54Mini,
    temperature: 0,
  },
});

const program = ax(
  'ticket:string -> priority:class "high, normal, low", summary:string, labels:string[]'
);
const result = await program.forward(llm, {
  ticket:
    'Checkout has failed for enterprise customers since 09:00. Support wants a concise summary and tags.',
});

console.log(JSON.stringify(result, null, 2));

TypeScript Contextual Generation

Answers from supplied context and returns compact citations with OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: advanced
Run: npm run example -- typescript src/examples/typescript/generation/context.ts
Source: src/examples/typescript/generation/context.ts

TypeScript

import { AxAIOpenAIModel, ai, ax } from '@ax-llm/ax';

const apiKey = process.env.OPENAI_API_KEY ?? process.env.OPENAI_APIKEY;
if (!apiKey) {
  throw new Error('Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.');
}

const llm = ai({
  name: 'openai',
  apiKey,
  config: {
    model: AxAIOpenAIModel.GPT54Mini,
    temperature: 0,
  },
});

const program = ax(
  'context:string, question:string -> answer:string, citations:string[]'
);
const result = await program.forward(llm, {
  context:
    'Ax uses signatures for typed IO, ai() for providers, ax() for generation, agent() for runtime loops, flow() for orchestration, and optimize() for GEPA tuning.',
  question: 'How should a new developer think about Ax?',
});

console.log(JSON.stringify(result, null, 2));