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feat: Adds in prompt.yml support
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‎examples/ai/package.json‎

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"dependencies": {
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"@github/models": "file:../../",
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"ai": "beta",
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"confbox": "^0.2",
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"dotenv": "^17"
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}
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}

‎examples/ai/prompt.ts‎

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import {readFile} from 'node:fs/promises'
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import {parseYAML} from 'confbox/yaml'
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import {createObjectPrompt} from '@github/models/prompt'
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import {generateObject} from 'ai'
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import 'dotenv/config'
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const promptFile = parseYAML(await readFile('./teacher.prompt.yml', 'utf8'))
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const prompt = createObjectPrompt(promptFile)
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const result = await generateObject(
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prompt({
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subject: 'balloon popping',
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}),
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)
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console.log('Object:')
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console.log(result.object)
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console.log()
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console.log('Token usage:', result.usage)
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console.log('Finish reason:', result.finishReason)

‎examples/ai/teacher.prompt.yml‎

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name: teacher
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model: openai/gpt-4.1
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responseFormat: json_schema
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jsonSchema: |-
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{
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"name": "explanation",
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"strict": true,
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"schema": {
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"type": "object",
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"properties": {
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"lesson": {
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"type": "string",
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"description": "The lesson about the subject"
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}
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},
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"additionalProperties": false,
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"required": [
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"lesson"
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]
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}
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}
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messages:
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- role: system
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content: You're an elementary school teacher who loves to make learning fun.
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- role: user
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content: Please explain {{subject}} in as little as 5 sentences and give it to be as json.

‎package-lock.json‎

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‎package.json‎

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"types": "./dist/index.d.ts",
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"default": "./dist/index.js"
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},
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"./prompt": {
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"types": "./dist/prompt.d.ts",
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"default": "./dist/prompt.js"
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},
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"./package.json": "./package.json"
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},
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"scripts": {
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"dependencies": {
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"@ai-sdk/openai-compatible": "beta",
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"@ai-sdk/provider": "beta",
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"@ai-sdk/provider-utils": "beta"
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"@ai-sdk/provider-utils": "beta",
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"templite": "^1.2.0",
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"tiny-invariant": "^1.3.3",
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"zod": "^3 || ^4"
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},
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"devDependencies": {
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"@github/prettier-config": "0.0.6",
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"@types/node": "^24",
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"dotenv": "^17.2.0",
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"eslint": "^9",
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"eslint-plugin-github": "^6",
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"eslint-plugin-simple-import-sort": "12.1.1",
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"prettier": "^3",
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"tsup": "^8.3.0",
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"typescript": "^5.7.2",
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"vitest": "^3",
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"dotenv": "^17.2.0"
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"vitest": "^3"
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},
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"engines": {
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"node": ">=20"

‎readme.md‎

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You can use it as a middleware to intercept requests,
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or to provide a custom fetch implementation for e.g. testing.
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## Prompt Management
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The `@github/models/prompt` sub-module provides a powerful way to integrate with AI SDK methods like `generateText` and `generateObject` using your `prompt.yml` files.
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Prompt YAML files are designed to create reusable artifacts that integrate with GitHub's suite of AI tools. Check out the [Models tab](https://github.com/github/models-ai-sdk/models).
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### Example
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Create a `.prompt.yml` file:
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```yaml
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name: teacher
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description: An elementary school teacher who explains concepts simply
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model: openai/gpt-4o
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modelParameters:
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temperature: 0.7
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maxTokens: 500
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messages:
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- role: system
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content: You're an elementary school teacher who loves making learning fun.
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- role: user
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content: Explain {{subject}} in exactly {{sentences}} sentences for a 10-year-old.
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```
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Use `createTextPrompt` for text-based responses:
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```ts
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import {readFile} from 'node:fs/promises'
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import {parseYAML} from 'confbox/yaml'
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import {createTextPrompt} from '@github/models/prompt'
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import {generateText} from 'ai'
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const config = parseYAML(await readFile('./teacher.prompt.yml', 'utf8'))
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const prompt = createTextPrompt(config)
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const result = await generateText(
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prompt({
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subject: 'photosynthesis',
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sentences: '5',
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}),
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)
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console.log(result.text)
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```
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<details><summary>Structured Generation</summary>
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Use `createObjectPrompt` for JSON responses with schema validation:
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```yaml
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name: recipe-generator
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model: openai/gpt-4o
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responseFormat: json_schema
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jsonSchema: |-
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{
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"name": "recipe",
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"strict": true,
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"schema": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"ingredients": {
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"type": "array",
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"items": {"type": "string"}
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},
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"instructions": {
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"type": "array",
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"items": {"type": "string"}
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}
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},
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"required": ["title", "ingredients", "instructions"],
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"additionalProperties": false
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}
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}
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messages:
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- role: user
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content: Create a recipe for {{dish}} that serves {{servings}} people.
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```
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```ts
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import {createObjectPrompt} from '@github/models/prompt'
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import {generateObject} from 'ai'
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const config = parseYAML(await readFile('./recipe.prompt.yml', 'utf8'))
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const prompt = createObjectPrompt(config)
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const result = await generateObject(
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prompt({
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dish: 'chocolate chip cookies',
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servings: '4',
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}),
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)
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console.log(result.object.title)
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console.log(result.object.ingredients)
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```
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</details>
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## API Reference
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The GitHub Models provider uses the [GitHub Models Inference API](https://docs.github.com/en/rest/models/inference?apiVersion=2022-11-28#run-an-inference-request).

‎src/prompt.test.ts‎

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import {describe, expect, test} from 'vitest'
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import {createObjectPrompt, createTextPrompt} from './prompt'
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describe('#createTextPrompt', () => {
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test('can create a text prompt', () => {
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const prompt = createTextPrompt(mockTextPrompt)
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expect(
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prompt({
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name: 'mona',
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}),
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).toMatchObject({
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model: expect.anything(),
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messages: [
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{role: 'system', content: 'You are a helpful assistant.'},
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{role: 'user', content: 'Hello mona!'},
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],
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temperature: 0.7,
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})
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})
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test('fails because responseFormat is not text', () => {
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expect(() => createTextPrompt(mockObjectPrompt)).toThrowError(/responseFormat must be "text"/)
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})
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})
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describe('createObjectPrompt', () => {
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test('can create a object prompt', () => {
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const prompt = createObjectPrompt(mockObjectPrompt)
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expect(
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prompt({
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name: 'mona',
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}),
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).toMatchObject({
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model: expect.anything(),
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messages: [
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{role: 'system', content: 'You are a helpful assistant.'},
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{role: 'user', content: 'Hello mona!'},
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],
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temperature: 0.7,
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})
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})
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test('fails because responseFormat is not json like', () => {
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expect(() => createObjectPrompt(mockTextPrompt)).toThrowError(/responseFormat must be either "json_object"/)
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})
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test('allows json_object without a schema', () => {
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const prompt = createObjectPrompt(mockJsonSchemaPrompt)
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expect(
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prompt({
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name: 'mona',
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}),
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).toMatchObject({
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model: expect.anything(),
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messages: [
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{role: 'system', content: 'You are a helpful assistant.'},
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{role: 'user', content: 'Hello mona!'},
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],
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temperature: 0.7,
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schema: expect.any(Object),
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schemaName: 'schema_name',
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})
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})
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})
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const mockTextPrompt = {
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model: 'abc-123',
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messages: [
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{role: 'system', content: 'You are a helpful assistant.'},
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{role: 'user', content: 'Hello {{name}}!'},
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],
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modelParameters: {
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temperature: 0.7,
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},
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}
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const mockObjectPrompt = {
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model: 'abc-123',
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messages: [
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{role: 'system', content: 'You are a helpful assistant.'},
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{role: 'user', content: 'Hello {{name}}!'},
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],
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modelParameters: {
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temperature: 0.7,
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},
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responseFormat: 'json_object',
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}
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const mockJsonSchemaPrompt = {
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model: 'abc-123',
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messages: [
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{role: 'system', content: 'You are a helpful assistant.'},
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{role: 'user', content: 'Hello {{name}}!'},
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],
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modelParameters: {
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temperature: 0.7,
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},
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responseFormat: 'json_schema',
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jsonSchema: JSON.stringify({
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name: 'schema_name',
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schema: {},
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}),
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}

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