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chains"],"repository":{"type":"git","url":"git+https://github.com/makinteract/baby-prompts.git"},"description":"Providing basic prompt techniques and chains for OpenAI's response API","maintainers":[{"name":"makinteract","email":"andrea@kaist.ac.kr"}],"readme":"# Baby Prompts\n\n[![NPM Version](https://img.shields.io/npm/v/baby-prompts.svg?style=flat)](https://www.npmjs.org/package/baby-prompts)\n[![NPM Downloads](https://img.shields.io/npm/dm/baby-prompts.svg?style=flat)](https://npmcharts.com/compare/baby-prompts?minimal=true)\n\nA NodeJS library providing super basic prompt techniques and chains for OpenAI's response API.\n\n- [Baby Prompts](#baby-prompts)\n  - [Overview](#overview)\n  - [Installation](#installation)\n  - [Prompt techniques examples](#prompt-techniques-examples)\n    - [1. Zero-shot prompting](#1-zero-shot-prompting)\n    - [2. Few-shot prompting](#2-few-shot-prompting)\n    - [3. Prompt chaining](#3-prompt-chaining)\n  - [Streaming](#streaming)\n  - [Structured output](#structured-output)\n  - [Conversational history](#conversational-history)\n  - [Notes for version 2.2.x](#notes-for-version-22x)\n  - [Credits](#credits)\n\n![example](./assets/baby-prompts.png)\n\n## Overview\n\n👉 _Baby Prompts_ is a NodeJS library that allows you to easily create different prompt techniques (see [below](#prompt-techniques-examples)) and choose formatting output. It also supports streaming and conversational history.\n\nA full list of examples is available [here](examples), and [this is a tutorial article](https://medium.com/@andrea_36977/baby-steps-with-prompting-techniques-in-javascript-fb8ad332cf71) to help you with the setup and explain the different prompting techniques.\n\n## Installation\n\nInstall the library by typing\n\n```sh\nnpm install baby-prompts\n```\n\nTo run the examples, you also need to have an account with [OpenAI](https://platform.openai.com) and sufficient credits in your account to run the models.\n\nYou then need to create a `.env` file with the variable `OPENAI_API_KEY` set to your own OpenAI API key and have that in your root folder. You can find an API key [here](https://platform.openai.com/api-keys).\n\n> ⚠️ This is a library for NodeJS and relies on having a .env file with the OPEN_API_KEY in it. It won't work directly from the browser.\n\n## Prompt techniques examples\n\nBefore you can invoke any prompt, you need to configure your prompt by choosing a model.\n\n```js\n// Import the necessary features\nimport {\n  getPrompt,\n  promptChain,\n  invoke,\n  outputText,\n  user,\n  assistant,\n  developer,\n  tap,\n  json,\n  withJsonFormatter,\n  withPreviousResponse,\n} from 'baby-prompts';\n\n// Get the prompt function with default settings\nconst prompt = getPrompt();\n\n// ...or select custom options\nconst prompt = getPrompt({\n  model: 'gpt-5',\n  reasoning: { effort: 'low' },\n  stream: false,\n});\n```\n\nThe default model is `gpt-4.1-mini`.\n\nFollow the [OpenAI documentation](https://platform.openai.com/docs/api-reference/introduction) for choosing models and options.\n\nHere are some examples of how to use different prompting techniques:\n\n- _Zero-shot_ prompting\n- _Few-shot_ prompting\n- Prompt _chains_\n\n### 1. Zero-shot prompting\n\nHere is a simple example of invoking a prompt.\n\n```js\nprompt(developer('Be a funny assistant'), 'Tell me a joke') // setup the prompt\n  .pipe(invoke) // execute it\n  .pipe(outputText) // extract the output_text from the response\n  .pipe(tap); // print it\n```\n\nNote that the `pipe` method is basically a `then` call (i.e., method of a Promise).\nIf you prefer to _async/await_, here is the same code.\n\n```js\nconst result = await invoke(\n  prompt(developer('Be a funny assistant'), 'Tell me a joke'),\n);\nconsole.log(outputText(result)); // or result.output_text\n```\n\nYou can find a full working example [here](./examples/zero_shot.js).\n\n### 2. Few-shot prompting\n\nMultiple messages can be combined before invocation, and you can choose the `user` (default), `developer`, or `assistant` roles. Note that this is still a single prompt (a single _invoke_ method is called).\n\n```js\nprompt(\n  developer('Ask a question following this style'),\n  user('How are you?'),\n  assistant('How are you, human?'),\n  user('What time is it?'),\n  assistant('What time is it, human?'),\n  user('Where are you from?'),\n  assistant('Where are you from, human?'),\n  user('What is your age?'), // expected: \"What is your age, human?\"\n)\n  .pipe(invoke)\n  .pipe(outputText)\n  .pipe(console.log);\n```\n\nYou can find a full working example [here](./examples/few_shot.js).\n\n### 3. Prompt chaining\n\nWith prompt chaining, you can chain the output of a prompt directly into the input of the next one.\n\n```js\npromptChain(\n  prompt(user('What is 1+1?')),\n  prompt(user('Say that without using numbers.')),\n  prompt(user('Add an emoji at the end.')),\n)\n  .pipe(outputText)\n  .pipe(console.log);\n```\n\nPlease note that when using chains, you do not need to call the _invoke_ method manually, as it is called for you by the _promptChain_ function.\n\nFor more complex examples, involving the usage of the `tap` function and formatted output, look at [this](./examples/chain.js).\n\n## Streaming\n\nWhen you create a prompt at first you can pass the `stream: true` option to enable streaming.\n\n```js\nconst stream = await prompt(user('Write a paragraph about the ocean'))\n  .pipe(withOptions({ stream: true })) // require to have a streamed response\n  .pipe(invoke);\n\nfor await (const event of stream) {\n  if (event.type == 'response.output_text.delta')\n    process.stdout.write(event.delta);\n}\n```\n\nThis is an [example](./examples/stream.js).\n\n## Structured output\n\nYou can structure the output of a prompt just before invocation. For that, you need to use the [zod](https://www.npmjs.com/package/zod) library, which is already included as a dependency.\n\n```js\nimport { z } from 'zod';\n\nconst Person = z.object({\n  name: z.string(),\n  age: z.number(),\n});\n\nconst PeopleList = z.object({\n  people: z.array(Person).length(10), // exactly 10 people\n});\n\n// prompt\nprompt(\n  developer('You are a helpful assistant'), //\n  'Write a list of 10 people with name and age',\n)\n  .pipe(withJsonFormatter(PeopleList)) // format the output\n  .pipe(invoke)\n  .pipe(json)\n  .pipe(console.log);\n```\n\n[Here](./examples/structured_output.js) a full working example.\n\n## Conversational history\n\nYou can preserve information across multiple messages or turns in a conversation by passing a response as an input to the next prompt using the `withPreviousResponse` function.\n\nHere a couple of examples.\n\n```js\n// Get the first prompt\nconst res = await prompt('My name is Jon Snow.').pipe(invoke);\n\n// Using a single prompt\nprompt('What is my name?')\n  .pipe(withPreviousResponse(res)) // pass in the previous response\n  .pipe(invoke)\n  .pipe(outputText)\n  .pipe(console.log); // \"Jon Snow\"\n\n// Using a chain\npromptChain(\n  prompt('What is my name?').pipe(withPreviousResponse(res)), // pass in the previous response\n  prompt('Add an emoji to my name.'),\n)\n  .pipe(outputText)\n  .pipe(console.log); // \"Jon Snow 🐺\"\n```\n\nHere the [full example](./examples/conversation.js).\n\n## Notes for version 2.2.x\n\nThere a few breaking changes from version 2.2.x onward.\n\nThe function `getPrompt` now only takes as parameter the default options to tweak the model.\n\nThe function `jsonFormatter` has been renamed to `withJsonFormatter` for consistency with other similar functions.\n\n## Credits\n\nDeveloped by [MAKinteract](https://make.kaist.ac.kr/andrea) with ♥️.\n","readmeFilename":"README.md"}