Promptbook
Build responsible, controlled and transparent applications on top of LLM models!
✨ New Features
⚠ Warning: This is a pre-release version of the library. It is not yet ready for production use. Please look at latest stable release.
⚠ 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
To install this package, run:
npm i ptbk
npm install @promptbook/utils
Utility functions used in the library but also useful for individual use in preprocessing and postprocessing LLM inputs and outputs
Here is a overview of the functions which are exported from the @promptbook/utils
package and can be used in your own projects:
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:
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!"');
Templating
There is a function replaceParameters
which is used to replace the parameters in given template optimized to LLM prompt templates.
import { replaceParameters } from '@promptbook/utils';
replaceParameters('Hello, {name}!', { name: 'world' });
And also multiline templates with blockquotes
import { replaceParameters, spaceTrim } from '@promptbook/utils';
replaceParameters(
spaceTrim(`
Hello, {name}!
> {answer}
`),
{
name: 'world',
answer: spaceTrim(`
I'm fine,
thank you!
And you?
`),
},
);
Counting
Theese functions are usefull 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!'));
Splitting
Splitting functions are similar to counting but they return the splitted parts of the input/output, you can use
splitIntoCharacters
, splitIntoLines
, splitIntoPages
, splitIntoParagraphs
, splitIntoSentences
, splitIntoWords
import { splitIntoWords } from '@promptbook/utils';
console.log(splitIntoWords('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!'));
- There are more normalization functions like
capitalize
, decapitalize
, removeDiacritics
,... - Theese can be also used as postprocessing functions in the
POSTPROCESS
command in promptbook
Misc
See also the documentation for all the functions in the @promptbook/utils
package, every function is documented by jsdoc, typed by typescript and tested by jest.
assertsExecutionSuccessful
,
checkExpectations
,
executionReportJsonToString
,
isPassingExpectations
,
isValidJsonString
,
parseNumber
Rest of the documentation is common for entire promptbook ecosystem:
🤍 The Promptbook Whitepaper
If you have a simple, single prompt for ChatGPT, GPT-4, Anthropic Claude, Google Gemini, Llama 2, or whatever, it doesn't matter how you integrate it. Whether it's calling a REST API directly, using the SDK, hardcoding the prompt into the source code, or importing a text file, the process remains the same.
But often you will struggle with the limitations of LLMs, such as hallucinations, off-topic responses, poor quality output, language drift, word repetition repetition repetition repetition or misuse, lack of context, or just plain w𝒆𝐢rd responses. When this happens, you generally have three options:
- Fine-tune the model to your specifications or even train your own.
- Prompt-engineer the prompt to the best shape you can achieve.
- Orchestrate multiple prompts in a pipeline to get the best result.
In all of these situations, but especially in 3., the Promptbook library can make your life easier.
- Separates concerns between prompt-engineer and programmer, between code files and prompt files, and between prompts and their execution logic.
- Establishes a common format
.ptbk.md
that can be used to describe your prompt business logic without having to write code or deal with the technicalities of LLMs. - Forget about low-level details like choosing the right model, tokens, context size, temperature, top-k, top-p, or kernel sampling. Just write your intent and persona who should be responsible for the task and let the library do the rest.
- Has built-in orchestration of pipeline execution and many tools to make the process easier, more reliable, and more efficient, such as caching, compilation+preparation, just-in-time fine-tuning, expectation-aware generation, agent adversary expectations, and more.
- Sometimes even the best prompts with the best framework like Promptbook
:)
can't avoid the problems. In this case, the library has built-in anomaly detection and logging to help you find and fix the problems. - Promptbook has built in versioning. You can test multiple A/B versions of pipelines and see which one works best.
- Promptbook is designed to do RAG (Retrieval-Augmented Generation) and other advanced techniques. You can use knowledge to improve the quality of the output.
🧔 Pipeline (for prompt-engeneers)
Prompt book markdown file (or .ptbk.md
file) is document that describes a pipeline - a series of prompts that are chained together to form somewhat reciepe for transforming natural language input.
- Multiple pipelines forms a collection which will handle core know-how of your LLM application.
- Theese pipelines are designed such as they can be written by non-programmers.
Sample:
File write-website-content.ptbk.md
:
🌍 Create website content
Instructions for creating web page content.
👤 Specifying the assigment
What is your web about?
{rawAssigment}
-> {assigment}
Website assignment and specification
✨ Improving the title
- PERSONA Jane, Copywriter and Marketing Specialist.
As an experienced marketing specialist, you have been entrusted with improving the name of your client's business.
A suggested name from a client:
"{rawTitle}"
Assignment from customer:
> {assigment}
## Instructions:
- Write only one name suggestion
- The name will be used on the website, business cards, visuals, etc.
-> {enhancedTitle}
Enhanced title
👤 Website title approval
Is the title for your website okay?
{enhancedTitle}
-> {title}
Title for the website
🐰 Cunning subtitle
- PERSONA Josh, a copywriter, tasked with creating a claim for the website.
As an experienced copywriter, you have been entrusted with creating a claim for the "{title}" web page.
A website assignment from a customer:
> {assigment}
## Instructions:
- Write only one name suggestion
- Claim will be used on website, business cards, visuals, etc.
- Claim should be punchy, funny, original
-> {claim}
Claim for the web
🚦 Keyword analysis
- PERSONA Paul, extremely creative SEO specialist.
As an experienced SEO specialist, you have been entrusted with creating keywords for the website "{title}".
Website assignment from the customer:
> {assigment}
## Instructions:
- Write a list of keywords
- Keywords are in basic form
## Example:
- Ice cream
- Olomouc
- Quality
- Family
- Tradition
- Italy
- Craft
-> {keywords}
Keywords
🔗 Combine the beginning
# {title}
> {claim}
-> {contentBeginning}
Beginning of web content
🖋 Write the content
As an experienced copywriter and web designer, you have been entrusted with creating text for a new website {title}.
A website assignment from a customer:
> {assigment}
## Instructions:
- Text formatting is in Markdown
- Be concise and to the point
- Use keywords, but they should be naturally in the text
- This is the complete content of the page, so don't forget all the important information and elements the page should contain
- Use headings, bullets, text formatting
## Keywords:
{keywords}
## Web Content:
{contentBeginning}
-> {contentBody}
Middle of the web content
🔗 Combine the content
{contentBeginning}
{contentBody}
-> {websiteContent}
Following is the scheme how the promptbook above is executed:
%% 🔮 Tip: Open this on GitHub or in the VSCode website to see the Mermaid graph visually
flowchart LR
subgraph "🌍 Create website content"
direction TB
input((Input)):::input
templateSpecifyingTheAssigment(👤 Specifying the assigment)
input--"{rawAssigment}"-->templateSpecifyingTheAssigment
templateImprovingTheTitle(✨ Improving the title)
input--"{rawTitle}"-->templateImprovingTheTitle
templateSpecifyingTheAssigment--"{assigment}"-->templateImprovingTheTitle
templateWebsiteTitleApproval(👤 Website title approval)
templateImprovingTheTitle--"{enhancedTitle}"-->templateWebsiteTitleApproval
templateCunningSubtitle(🐰 Cunning subtitle)
templateWebsiteTitleApproval--"{title}"-->templateCunningSubtitle
templateSpecifyingTheAssigment--"{assigment}"-->templateCunningSubtitle
templateKeywordAnalysis(🚦 Keyword analysis)
templateWebsiteTitleApproval--"{title}"-->templateKeywordAnalysis
templateSpecifyingTheAssigment--"{assigment}"-->templateKeywordAnalysis
templateCombineTheBeginning(🔗 Combine the beginning)
templateWebsiteTitleApproval--"{title}"-->templateCombineTheBeginning
templateCunningSubtitle--"{claim}"-->templateCombineTheBeginning
templateWriteTheContent(🖋 Write the content)
templateWebsiteTitleApproval--"{title}"-->templateWriteTheContent
templateSpecifyingTheAssigment--"{assigment}"-->templateWriteTheContent
templateKeywordAnalysis--"{keywords}"-->templateWriteTheContent
templateCombineTheBeginning--"{contentBeginning}"-->templateWriteTheContent
templateCombineTheContent(🔗 Combine the content)
templateCombineTheBeginning--"{contentBeginning}"-->templateCombineTheContent
templateWriteTheContent--"{contentBody}"-->templateCombineTheContent
templateCombineTheContent--"{websiteContent}"-->output
output((Output)):::output
classDef input color: grey;
classDef output color: grey;
end;
Note: We are using postprocessing functions like unwrapResult
that can be used to postprocess the result.
📦 Packages
This library is divided into several packages, all are published from single monorepo.
You can install all of them at once:
npm i ptbk
Or you can install them separately:
⭐ Marked packages are worth to try first
📚 Dictionary
The following glossary is used to clarify certain concepts:
Core concepts
Advanced concepts
🔌 Usage in Typescript / Javascript
➕➖ 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
See more
➖ 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)
See more
🐜 Known issues
🧼 Intentionally not implemented features
❔ FAQ
If you have a question start a discussion, open an issue or write me an email.
⌚ Changelog
See CHANGELOG.md
📜 License
Promptbook by Pavol Hejný is licensed under CC BY 4.0
🎯 Todos
See TODO.md
🖋️ Contributing
I am open to pull requests, feedback, and suggestions. Or if you like this utility, you can ☕ buy me a coffee or donate via cryptocurrencies.
You can also ⭐ star the promptbook package, follow me on GitHub or various other social networks.