promptbook

Build responsible, controlled and transparent applications on top of LLM models!

Stars: 62

Visit

Promptbook is a library designed to build responsible, controlled, and transparent applications on top of large language models (LLMs). It helps users overcome limitations of LLMs like hallucinations, off-topic responses, and poor quality output by offering features such as fine-tuning models, prompt-engineering, and orchestrating multiple prompts in a pipeline. The library separates concerns, establishes a common format for prompt business logic, and handles low-level details like model selection and context size. It also provides tools for pipeline execution, caching, fine-tuning, anomaly detection, and versioning. Promptbook supports advanced techniques like Retrieval-Augmented Generation (RAG) and knowledge utilization to enhance output quality.

README:

Promptbook

Build responsible, controlled and transparent applications on top of LLM models!

✨ New Features

✨ Support of OpenAI o1 model

🤍 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.

PIPELINE URL https://promptbook.studio/webgpt/write-website-content.ptbk.md

INPUT  PARAM {rawTitle} Automatically suggested a site name or empty text

INPUT  PARAM {rawAssigment} Automatically generated site entry from image recognition

OUTPUT PARAM {websiteContent} Web content

OUTPUT PARAM {keywords} Keywords

👤 Specifying the assigment

What is your web about?

DIALOG TEMPLATE
{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?

DIALOG TEMPLATE
{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

SIMPLE TEMPLATE
# {title}

> {claim}
-> {contentBeginning} Beginning of web content

🖋 Write the content

PERSONA Jane
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

SIMPLE TEMPLATE
{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

⭐ ptbk - Bundle of all packages, when you want to install everything and you don't care about the size
promptbook - Same as ptbk
@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/wizzard - Wizard for creating+running promptbooks in single line
@promptbook/execute-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/azure-openai - Execution tools for Azure OpenAI API
@promptbook/langtail - Execution tools for Langtail API, wrapper around Langtail SDK
@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/types - Just typescript types used in the library
@promptbook/cli - Command line interface utilities for promptbooks

📚 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

➖ 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.

⌚ 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.

For Tasks:

Click tags to check more tools for each tasks

create website content improve title generate claim analyze keywords write web content

For Jobs:

software developer data scientist ai engineer content creator digital marketer

Alternative AI tools for promptbook

Similar Open Source Tools

promptbook

github

: 62

trustgraph

TrustGraph is a tool that deploys private GraphRAG pipelines to build a RDF style knowledge graph from data, enabling accurate and secure `RAG` requests compatible with cloud LLMs and open-source SLMs. It showcases the reliability and efficiencies of GraphRAG algorithms, capturing contextual language flags missed in conventional RAG approaches. The tool offers features like PDF decoding, text chunking, inference of various LMs, RDF-aligned Knowledge Graph extraction, and more. TrustGraph is designed to be modular, supporting multiple Language Models and environments, with a plug'n'play architecture for easy customization.

github

: 219

easydiffusion

Easy Diffusion 3.0 is a user-friendly tool for installing and using Stable Diffusion on your computer. It offers hassle-free installation, clutter-free UI, task queue, intelligent model detection, live preview, image modifiers, multiple prompts file, saving generated images, UI themes, searchable models dropdown, and supports various image generation tasks like 'Text to Image', 'Image to Image', and 'InPainting'. The tool also provides advanced features such as custom models, merge models, custom VAE models, multi-GPU support, auto-updater, developer console, and more. It is designed for both new users and advanced users looking for powerful AI image generation capabilities.

github

: 9.7k

UglyFeed

UglyFeed is a simple Python application designed to retrieve, aggregate, filter, rewrite, evaluate, and serve content (RSS feeds) written by a large language model. It provides features such as retrieving RSS feeds, aggregating feed items by similarity, rewriting content using various APIs, saving rewritten feeds to JSON files, converting JSON to valid RSS feed, serving XML feed via an HTTP server, deploying XML feed to GitHub or GitLab, and evaluating generated content. The tool can be used for smart content curation, dynamic blog generation, interactive educational tools, personalized reading experiences, brand monitoring, multilingual content delivery, enhanced RSS feeds, creative writing assistance, content repurposing, and fake news detection datasets. It is modular, extensible, and aims to empower users in content manipulation and delivery.

github

: 111

chonkie

Chonkie is a lightweight and fast RAG chunking library designed to efficiently split text for RAG (Retrieval-Augmented Generation) applications. It offers various chunking methods like TokenChunker, WordChunker, SentenceChunker, SemanticChunker, SDPMChunker, and an experimental LateChunker. Chonkie is feature-rich, easy to use, fast, supports multiple tokenizers, and comes with a cute pygmy hippo mascot. It aims to provide a no-nonsense solution for chunking text without the need to worry about dependencies or bloat.

github

: 2.2k

polyfire-js

Polyfire is an all-in-one managed backend for AI apps that allows users to build AI applications directly from the frontend, eliminating the need for a separate backend. It simplifies the process by providing most backend services in just a few lines of code. With Polyfire, users can easily create chatbots, transcribe audio files, generate simple text, manage long-term memory, and generate images. The tool also offers starter guides and tutorials to help users get started quickly and efficiently.

github

: 126

airweave

Airweave is an open-core tool that simplifies the process of making data searchable by unifying apps, APIs, and databases into a vector database with minimal configuration. It offers over 120 integrations, simplicity in syncing data from diverse sources, extensibility through 'sources', 'destinations', and 'embedders', and an async-first approach for large-scale data synchronization. With features like no-code setup, white-labeled multi-tenant support, chunk generators, automated sync, versioning & hashing, multi-source support, and scalability, Airweave provides a comprehensive solution for building applications that require semantic search.

github

: 52

personal-assistant

Obsidian Personal Assistant is a plugin designed to help users manage their Obsidian notes more efficiently. It offers features like automatically creating notes in specified directories, opening related graph views, managing plugins and themes, setting graph view colors, and more. The plugin aims to streamline note-taking and organization within the Obsidian app, catering to users who seek automation and customization in their note management workflow.

github

: 89

crawlee

Crawlee is a web scraping and browser automation library that helps you build reliable scrapers quickly. Your crawlers will appear human-like and fly under the radar of modern bot protections even with the default configuration. Crawlee gives you the tools to crawl the web for links, scrape data, and store it to disk or cloud while staying configurable to suit your project's needs.

github

: 16.5k

open-webui

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. For more information, be sure to check out our Open WebUI Documentation.

github

: 55.9k

AiR

AiR is an AI tool built entirely in Rust that delivers blazing speed and efficiency. It features accurate translation and seamless text rewriting to supercharge productivity. AiR is designed to assist non-native speakers by automatically fixing errors and polishing language to sound like a native speaker. The tool is under heavy development with more features on the horizon.

github

: 118

Devon

Devon is an open-source pair programmer tool designed to facilitate collaborative coding sessions. It provides features such as multi-file editing, codebase exploration, test writing, bug fixing, and architecture exploration. The tool supports Anthropic, OpenAI, and Groq APIs, with plans to add more models in the future. Devon is community-driven, with ongoing development goals including multi-model support, plugin system for tool builders, self-hostable Electron app, and setting SOTA on SWE-bench Lite. Users can contribute to the project by developing core functionality, conducting research on agent performance, providing feedback, and testing the tool.

github

: 2.6k

GPTPortal

github

: 184

Local-File-Organizer

The Local File Organizer is an AI-powered tool designed to help users organize their digital files efficiently and securely on their local device. By leveraging advanced AI models for text and visual content analysis, the tool automatically scans and categorizes files, generates relevant descriptions and filenames, and organizes them into a new directory structure. All AI processing occurs locally using the Nexa SDK, ensuring privacy and security. With support for multiple file types and customizable prompts, this tool aims to simplify file management and bring order to users' digital lives.

github

: 1.0k

spaCy

spaCy is an industrial-strength Natural Language Processing (NLP) library in Python and Cython. It incorporates the latest research and is designed for real-world applications. The library offers pretrained pipelines supporting 70+ languages, with advanced neural network models for tasks such as tagging, parsing, named entity recognition, and text classification. It also facilitates multi-task learning with pretrained transformers like BERT, along with a production-ready training system and streamlined model packaging, deployment, and workflow management. spaCy is commercial open-source software released under the MIT license.

github

: 30.7k

CushyStudio

CushyStudio is a generative AI platform designed for creatives of any level to effortlessly create stunning images, videos, and 3D models. It offers CushyApps, a collection of visual tools tailored for different artistic tasks, and CushyKit, an extensive toolkit for custom apps development and task automation. Users can dive into the AI revolution, unleash their creativity, share projects, and connect with a vibrant community. The platform aims to simplify the AI art creation process and provide a user-friendly environment for designing interfaces, adding custom logic, and accessing various tools.

github

: 641

For similar tasks

promptbook

github

: 62

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 593

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.2k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k