webgptorg
✨ Promptbook: AI apps in plain Language
Write AI applications using plain human language across multiple models and platforms.
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](https://www.npmjs.com/package/promptbook)
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🌟 New Features
- 🚀 GPT-5 Support - Now includes OpenAI's most advanced language model with unprecedented reasoning capabilities and 200K context window
- 💡 VS Code support for
.bookfiles with syntax highlighting and IntelliSense - 🐳 Official Docker image (
hejny/promptbook) for seamless containerized usage - 🔥 Native support for OpenAI
o3-mini, GPT-4 and other leading LLMs - 🔍 DeepSeek integration for advanced knowledge search
⚠ Warning: This is a pre-release version of the library. It is not yet ready for production use. Please look at latest stable release.
📦 Package @promptbook/utils
- Promptbooks are divided into several packages, all are published from single monorepo.
- This package
@promptbook/utilsis one part of the promptbook ecosystem.
To install this package, run:
# Install entire promptbook ecosystem
npm i ptbk
# Install just this package to save space
npm install @promptbook/utilsComprehensive utility functions for text processing, validation, normalization, and LLM input/output handling in the Promptbook ecosystem.
🎯 Purpose and Motivation
The utils package provides a rich collection of utility functions that are essential for working with LLM inputs and outputs. It handles common tasks like text normalization, parameter templating, validation, and postprocessing, eliminating the need to implement these utilities from scratch in every promptbook application.
🔧 High-Level Functionality
This package offers utilities across multiple domains:
- Text Processing: Counting, splitting, and analyzing text content
- Template System: Secure parameter substitution and prompt formatting
- Normalization: Converting text to various naming conventions and formats
- Validation: Comprehensive validation for URLs, emails, file paths, and more
- Serialization: JSON handling, deep cloning, and object manipulation
- Environment Detection: Runtime environment identification utilities
- Format Parsing: Support for CSV, JSON, XML validation and parsing
✨ Key Features
- 🔒 Secure Templating - Prompt injection protection with template functions
- 📊 Text Analysis - Count words, sentences, paragraphs, pages, and characters
- 🔄 Case Conversion - Support for kebab-case, camelCase, PascalCase, SCREAMING_CASE
- ✅ Comprehensive Validation - Email, URL, file path, UUID, and format validators
- 🧹 Text Cleaning - Remove emojis, quotes, diacritics, and normalize whitespace
- 📦 Serialization Tools - Deep cloning, JSON export, and serialization checking
- 🌐 Environment Aware - Detect browser, Node.js, Jest, and Web Worker environments
- 🎯 LLM Optimized - Functions specifically designed for LLM input/output processing
Simple templating
The prompt template tag function helps format prompt strings for LLM interactions. It handles string interpolation and maintains consistent formatting for multiline strings and lists and also handles a security to avoid prompt injection.
import { prompt } from '@promptbook/utils';
const promptString = prompt`
Correct the following sentence:
> ${unsecureUserInput}
`;The prompt name could be overloaded by multiple things in your code. If you want to use the promptTemplate which is alias for prompt:
import { promptTemplate } from '@promptbook/utils';
const promptString = promptTemplate`
Correct the following sentence:
> ${unsecureUserInput}
`;Advanced templating
There is a function templateParameters which is used to replace the parameters in given template optimized to LLM prompt templates.
import { templateParameters } from '@promptbook/utils';
templateParameters('Hello, {name}!', { name: 'world' }); // 'Hello, world!'And also multiline templates with blockquotes
import { templateParameters, spaceTrim } from '@promptbook/utils';
templateParameters(
spaceTrim(`
Hello, {name}!
> {answer}
`),
{
name: 'world',
answer: spaceTrim(`
I'm fine,
thank you!
And you?
`),
},
);
// Hello, world!
//
// > I'm fine,
// > thank you!
// >
// > And you?Counting
These functions are useful to count stats about the input/output in human-like terms not tokens and bytes, you can use
countCharacters, countLines, countPages, countParagraphs, countSentences, countWords
import { countWords } from '@promptbook/utils';
console.log(countWords('Hello, world!')); // 2Splitting
Splitting functions are similar to counting but they return the split parts of the input/output, you can use
splitIntoCharacters, splitIntoLines, splitIntoPages, splitIntoParagraphs, splitIntoSentences, splitIntoWords
import { splitIntoWords } from '@promptbook/utils';
console.log(splitIntoWords('Hello, world!')); // ['Hello', 'world']Normalization
Normalization functions are used to put the string into a normalized form, you can use
kebab-case
PascalCase
SCREAMING_CASE
snake_case
kebab-case
import { normalizeTo } from '@promptbook/utils';
console.log(normalizeTo['kebab-case']('Hello, world!')); // 'hello-world'- There are more normalization functions like
capitalize,decapitalize,removeDiacritics,... - These can be also used as postprocessing functions in the
POSTPROCESScommand in promptbook
Postprocessing
Sometimes you need to postprocess the output of the LLM model, every postprocessing function that is available through POSTPROCESS command in promptbook is exported from @promptbook/utils. You can use:
spaceTrimextractAllBlocksFromMarkdown, <- Note: Exported from@promptbook/markdown-utilsextractAllListItemsFromMarkdown<- Note: Exported from@promptbook/markdown-utilsextractBlockextractOneBlockFromMarkdown<- Note: Exported from@promptbook/markdown-utilsprettifyPipelineStringremoveMarkdownCommentsremoveEmojisremoveMarkdownFormatting<- Note: Exported from@promptbook/markdown-utilsremoveQuotestrimCodeBlocktrimEndOfCodeBlockunwrapResult
Very often you will use unwrapResult, which is used to extract the result you need from output with some additional information:
import { unwrapResult } from '@promptbook/utils';
unwrapResult('Best greeting for the user is "Hi Pavol!"'); // 'Hi Pavol!'📦 Exported Entities
Version Information
BOOK_LANGUAGE_VERSION- Current book language versionPROMPTBOOK_ENGINE_VERSION- Current engine version
Configuration Constants
VALUE_STRINGS- Standard value stringsSMALL_NUMBER- Small number constant
Visualization
renderPromptbookMermaid- Render promptbook as Mermaid diagram
Error Handling
deserializeError- Deserialize error objectsserializeError- Serialize error objects
Async Utilities
forEachAsync- Async forEach implementation
Format Validation
isValidCsvString- Validate CSV string formatisValidJsonString- Validate JSON string formatjsonParse- Safe JSON parsingisValidXmlString- Validate XML string format
Template Functions
prompt- Template tag for secure prompt formattingpromptTemplate- Alias for prompt template tag
Environment Detection
$getCurrentDate- Get current date (side effect)$isRunningInBrowser- Check if running in browser$isRunningInJest- Check if running in Jest$isRunningInNode- Check if running in Node.js$isRunningInWebWorker- Check if running in Web Worker
Text Counting and Analysis
CHARACTERS_PER_STANDARD_LINE- Characters per standard line constantLINES_PER_STANDARD_PAGE- Lines per standard page constantcountCharacters- Count characters in textcountLines- Count lines in textcountPages- Count pages in textcountParagraphs- Count paragraphs in textsplitIntoSentences- Split text into sentencescountSentences- Count sentences in textcountWords- Count words in textCountUtils- Utility object with all counting functions
Text Normalization
capitalize- Capitalize first letterdecapitalize- Decapitalize first letterDIACRITIC_VARIANTS_LETTERS- Diacritic variants mappingstring_keyword- Keyword string type (type)Keywords- Keywords type (type)isValidKeyword- Validate keyword formatnameToUriPart- Convert name to URI partnameToUriParts- Convert name to URI partsstring_kebab_case- Kebab case string type (type)normalizeToKebabCase- Convert to kebab-casestring_camelCase- Camel case string type (type)normalizeTo_camelCase- Convert to camelCasestring_PascalCase- Pascal case string type (type)normalizeTo_PascalCase- Convert to PascalCasestring_SCREAMING_CASE- Screaming case string type (type)normalizeTo_SCREAMING_CASE- Convert to SCREAMING_CASEnormalizeTo_snake_case- Convert to snake_casenormalizeWhitespaces- Normalize whitespace charactersorderJson- Order JSON object propertiesparseKeywords- Parse keywords from inputparseKeywordsFromString- Parse keywords from stringremoveDiacritics- Remove diacritic markssearchKeywords- Search within keywordssuffixUrl- Add suffix to URLtitleToName- Convert title to name format
Text Organization
spaceTrim- Trim spaces while preserving structure
Parameter Processing
extractParameterNames- Extract parameter names from templatenumberToString- Convert number to stringtemplateParameters- Replace template parametersvalueToString- Convert value to string
Parsing Utilities
parseNumber- Parse number from string
Text Processing
removeEmojis- Remove emoji charactersremoveQuotes- Remove quote characters
Serialization
$deepFreeze- Deep freeze object (side effect)checkSerializableAsJson- Check if serializable as JSONclonePipeline- Clone pipeline objectdeepClone- Deep clone objectexportJson- Export object as JSONisSerializableAsJson- Check if object is JSON serializablejsonStringsToJsons- Convert JSON strings to objects
Set Operations
difference- Set difference operationintersection- Set intersection operationunion- Set union operation
Code Processing
trimCodeBlock- Trim code block formattingtrimEndOfCodeBlock- Trim end of code blockunwrapResult- Extract result from wrapped output
Validation
isValidEmail- Validate email address formatisRootPath- Check if path is root pathisValidFilePath- Validate file path formatisValidJavascriptName- Validate JavaScript identifierisValidPromptbookVersion- Validate promptbook versionisValidSemanticVersion- Validate semantic versionisHostnameOnPrivateNetwork- Check if hostname is on private networkisUrlOnPrivateNetwork- Check if URL is on private networkisValidPipelineUrl- Validate pipeline URL formatisValidUrl- Validate URL formatisValidUuid- Validate UUID format
💡 This package provides utility functions for promptbook applications. For the core functionality, see @promptbook/core or install all packages with
npm i ptbk
Rest of the documentation is common for entire promptbook ecosystem:
🤍 The Book Abstract
It's time for a paradigm shift! The future of software is written in plain English, French, or Latin.
During the computer revolution, we have seen multiple generations of computer languages, from the physical rewiring of the vacuum tubes through low-level machine code to the high-level languages like Python or JavaScript. And now, we're on the edge of the next revolution!
It's a revolution of writing software in plain human language that is understandable and executable by both humans and machines – and it's going to change everything!
The incredible growth in power of microprocessors and the Moore's Law have been the driving force behind the ever-more powerful languages, and it's been an amazing journey! Similarly, the large language models (like GPT or Claude) are the next big thing in language technology, and they're set to transform the way we interact with computers.
This shift will happen whether we're ready or not. Our mission is to make it excellent, not just good.
Join us in this journey!
🚀 Get started
Take a look at the simple starter kit with books integrated into the Hello World sample applications:
💜 The Promptbook Project
Promptbook project is ecosystem of multiple projects and tools, following is a list of most important pieces of the project:
| Project | About |
|---|---|
| Book language |
Book is a human-understandable markup language for writing AI applications such as chatbots, knowledge bases, agents, avarars, translators, automations and more.
There is also a plugin for VSCode to support .book file extension
|
| Promptbook Engine | Promptbook engine can run applications written in Book language. It is released as multiple NPM packages and Docker HUB |
| Promptbook Studio | Promptbook.studio is a web-based editor and runner for book applications. It is still in the experimental MVP stage. |
Hello world examples:
🌐 Community & Social Media
Join our growing community of developers and users:
| Platform | Description |
|---|---|
| 💬 Discord | Join our active developer community for discussions and support |
| 🗣️ GitHub Discussions | Technical discussions, feature requests, and community Q&A |
| Professional updates and industry insights | |
| General announcements and community engagement | |
| 🔗 ptbk.io | Official landing page with project information |
🖼️ Product & Brand Channels
Promptbook.studio
| 📸 Instagram @promptbook.studio | Visual updates, UI showcases, and design inspiration |
📘 Book Language Blueprint
A concise, Markdown-based DSL for crafting AI workflows and automations.
Introduction
Book is a Markdown-based language that simplifies the creation of AI applications, workflows, and automations. With human-readable commands, you can define inputs, outputs, personas, knowledge sources, and actions—without needing model-specific details.
Example
# 🌟 My First Book
- BOOK VERSION 1.0.0
- URL https://promptbook.studio/hello.book
- INPUT PARAMETER {topic}
- OUTPUT PARAMETER {article}
# Write an Article
- PERSONA Jane, marketing specialist with prior experience in tech and AI writing
- KNOWLEDGE https://wikipedia.org/
- KNOWLEDGE ./journalist-ethics.pdf
- EXPECT MIN 1 Sentence
- EXPECT MAX 5 Pages
> Write an article about {topic}
→ {article}Each part of the book defines one of three circles:
1. What: Workflows, Tasks and Parameters
What work needs to be done. Each book defines a workflow (scenario or pipeline), which is one or more tasks. Each workflow has a fixed input and output. For example, you have a book that generates an article from a topic. Once it generates an article about AI, once about marketing, once about cooking. The workflow (= your AI program) is the same, only the input and output change.
Related commands:
2. Who: Personas
Who does the work. Each task is performed by a persona. A persona is a description of your virtual employee. It is a higher abstraction than the model, tokens, temperature, top-k, top-p and other model parameters.
You can describe what you want in human language like Jane, creative writer with a sense of sharp humour instead of gpt-4-2024-13-31, temperature 1.2, top-k 40, STOP token ".\n",....
Personas can have access to different knowledge, tools and actions. They can also consult their work with other personas or user, if allowed.
Related commands:
3. How: Knowledge, Instruments and Actions
The resources used by the personas are used to do the work.
Related commands:
- KNOWLEDGE of documents, websites, and other resources
- INSTRUMENT for real-time data like time, location, weather, stock prices, searching the internet, calculations, etc.
- ACTION for actions like sending emails, creating files, ending a workflow, etc.
General Principles
Book language is based on markdown. It is subset of markdown. It is designed to be easy to read and write. It is designed to be understandable by both humans and machines and without specific knowledge of the language.
The file has a .book extension and uses UTF-8 encoding without BOM.
Books have two variants: flat — just a prompt without structure, and full — with tasks, commands, and prompts.
As it is source code, it can leverage all the features of version control systems like git and does not suffer from the problems of binary formats, proprietary formats, or no-code solutions.
But unlike programming languages, it is designed to be understandable by non-programmers and non-technical people.
📚 Documentation
See detailed guides and API reference in the docs or online.
🔒 Security
For information on reporting security vulnerabilities, see our Security Policy.
📦 Packages (for developers)
This library is divided into several packages, all are published from single monorepo. You can install all of them at once:
npm i ptbkOr you can install them separately:
⭐ Marked packages are worth to try first
- ⭐ ptbk - Bundle of all packages, when you want to install everything and you don't care about the size
- promptbook - Same as
ptbk - ⭐🧙♂️ @promptbook/wizard - Wizard to just run the books in node without any struggle
- @promptbook/core - Core of the library, it contains the main logic for promptbooks
- @promptbook/node - Core of the library for Node.js environment
- @promptbook/browser - Core of the library for browser environment
- ⭐ @promptbook/utils - Utility functions used in the library but also useful for individual use in preprocessing and postprocessing LLM inputs and outputs
- @promptbook/markdown-utils - Utility functions used for processing markdown
- (Not finished) @promptbook/wizard - Wizard for creating+running promptbooks in single line
- @promptbook/javascript - Execution tools for javascript inside promptbooks
- @promptbook/openai - Execution tools for OpenAI API, wrapper around OpenAI SDK
- @promptbook/anthropic-claude - Execution tools for Anthropic Claude API, wrapper around Anthropic Claude SDK
- @promptbook/vercel - Adapter for Vercel functionalities
- @promptbook/google - Integration with Google's Gemini API
- @promptbook/deepseek - Integration with DeepSeek API
- @promptbook/ollama - Integration with Ollama API
@promptbook/azure-openai - Execution tools for Azure OpenAI API
@promptbook/fake-llm - Mocked execution tools for testing the library and saving the tokens
- @promptbook/remote-client - Remote client for remote execution of promptbooks
- @promptbook/remote-server - Remote server for remote execution of promptbooks
- @promptbook/pdf - Read knowledge from
.pdfdocuments - @promptbook/documents - Integration of Markitdown by Microsoft
- @promptbook/documents - Read knowledge from documents like
.docx,.odt,… - @promptbook/legacy-documents - Read knowledge from legacy documents like
.doc,.rtf,… - @promptbook/website-crawler - Crawl knowledge from the web
- @promptbook/editable - Editable book as native javascript object with imperative object API
- @promptbook/templates - Useful templates and examples of books which can be used as a starting point
- @promptbook/types - Just typescript types used in the library
- @promptbook/color - Color manipulation library
- ⭐ @promptbook/cli - Command line interface utilities for promptbooks
- 🐋 Docker image - Promptbook server
📚 Dictionary
The following glossary is used to clarify certain concepts:
General LLM / AI terms
- Prompt drift is a phenomenon where the AI model starts to generate outputs that are not aligned with the original prompt. This can happen due to the model's training data, the prompt's wording, or the model's architecture.
- Pipeline, workflow scenario or chain is a sequence of tasks that are executed in a specific order. In the context of AI, a pipeline can refer to a sequence of AI models that are used to process data.
- Fine-tuning is a process where a pre-trained AI model is further trained on a specific dataset to improve its performance on a specific task.
- Zero-shot learning is a machine learning paradigm where a model is trained to perform a task without any labeled examples. Instead, the model is provided with a description of the task and is expected to generate the correct output.
- Few-shot learning is a machine learning paradigm where a model is trained to perform a task with only a few labeled examples. This is in contrast to traditional machine learning, where models are trained on large datasets.
- Meta-learning is a machine learning paradigm where a model is trained on a variety of tasks and is able to learn new tasks with minimal additional training. This is achieved by learning a set of meta-parameters that can be quickly adapted to new tasks.
- Retrieval-augmented generation is a machine learning paradigm where a model generates text by retrieving relevant information from a large database of text. This approach combines the benefits of generative models and retrieval models.
- Longtail refers to non-common or rare events, items, or entities that are not well-represented in the training data of machine learning models. Longtail items are often challenging for models to predict accurately.
Note: This section is not a complete dictionary, more list of general AI / LLM terms that has connection with Promptbook
💯 Core concepts
- 📚 Collection of pipelines
- 📯 Pipeline
- 🙇♂️ Tasks and pipeline sections
- 🤼 Personas
- ⭕ Parameters
- 🚀 Pipeline execution
- 🧪 Expectations - Define what outputs should look like and how they're validated
- ✂️ Postprocessing - How outputs are refined after generation
- 🔣 Words not tokens - The human-friendly way to think about text generation
- ☯ Separation of concerns - How Book language organizes different aspects of AI workflows
Advanced concepts
| Data & Knowledge Management | Pipeline Control |
|---|---|
|
|
| Language & Output Control | Advanced Generation |
|
|
🚂 Promptbook Engine
➕➖ When to use Promptbook?
➕ When to use
- When you are writing app that generates complex things via LLM - like websites, articles, presentations, code, stories, songs,...
- When you want to separate code from text prompts
- When you want to describe complex prompt pipelines and don't want to do it in the code
- When you want to orchestrate multiple prompts together
- When you want to reuse parts of prompts in multiple places
- When you want to version your prompts and test multiple versions
- When you want to log the execution of prompts and backtrace the issues
➖ When not to use
- When you have already implemented single simple prompt and it works fine for your job
- When OpenAI Assistant (GPTs) is enough for you
- When you need streaming (this may be implemented in the future, see discussion).
- When you need to use something other than JavaScript or TypeScript (other languages are on the way, see the discussion)
- When your main focus is on something other than text - like images, audio, video, spreadsheets (other media types may be added in the future, see discussion)
- When you need to use recursion (see the discussion)
🐜 Known issues
🧼 Intentionally not implemented features
❔ FAQ
If you have a question start a discussion, open an issue or write me an email.
- ❔ Why not just use the OpenAI SDK / Anthropic Claude SDK / ...?
- ❔ How is it different from the OpenAI`s GPTs?
- ❔ How is it different from the Langchain?
- ❔ How is it different from the DSPy?
- ❔ How is it different from anything?
- ❔ Is Promptbook using RAG (Retrieval-Augmented Generation)?
- ❔ Is Promptbook using function calling?
📅 Changelog
See CHANGELOG.md
📜 License
This project is licensed under BUSL 1.1.
🤝 Contributing
We welcome contributions! See CONTRIBUTING.md for guidelines.
You can also ⭐ star the project, follow us on GitHub or various other social networks.We are open to pull requests, feedback, and suggestions.
🆘 Support & Community
Need help with Book language? We're here for you!
- 💬 Join our Discord community for real-time support
- 📝 Browse our GitHub discussions for FAQs and community knowledge
- 🐛 Report issues for bugs or feature requests
- 📚 Visit ptbk.io for more resources and documentation
- 📧 Contact us directly through the channels listed in our signpost
We welcome contributions and feedback to make Book language better for everyone!