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@promptbook/anthropic-claude

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  • License BUSL-1.1

It's time for a paradigm shift. The future of software in plain English, French or Latin

Package Exports

  • @promptbook/anthropic-claude
  • @promptbook/anthropic-claude/esm/index.es.js
  • @promptbook/anthropic-claude/umd/index.umd.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@promptbook/anthropic-claude) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

✨ Promptbook

[NPM Version of ![Promptbook logo - cube with letters P and B](./other/design/logo-h1.png) Promptbook](https://www.npmjs.com/package/promptbook) [Quality of package ![Promptbook logo - cube with letters P and B](./other/design/logo-h1.png) Promptbook](https://packagequality.com/#?package=promptbook) Known Vulnerabilities Issues

🌟 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.

📦 Package @promptbook/anthropic-claude

To install this package, run:

# Install entire promptbook ecosystem
npm i ptbk

# Install just this package to save space
npm install @promptbook/anthropic-claude

@promptbook/anthropic-claude integrates Anthropic's Claude API with Promptbook. It allows to execute Promptbooks with OpenAI Claude 2 and 3 models.

🧡 Usage

import { createPipelineExecutor, createCollectionFromDirectory } from '@promptbook/core';
import {
    createCollectionFromDirectory,
    $provideExecutionToolsForNode,
    $provideFilesystemForNode,
} from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { AnthropicClaudeExecutionTools } from '@promptbook/anthropic-claude';

// ▶ Prepare tools
const fs = $provideFilesystemForNode();
const llm = new AnthropicClaudeExecutionTools(
    //            <- TODO: [🧱] Implement in a functional (not new Class) way
    {
        isVerbose: true,
        apiKey: process.env.ANTHROPIC_CLAUDE_API_KEY,
    },
);
const executables = await $provideExecutablesForNode();
const tools = {
    llm,
    fs,
    scrapers: await $provideScrapersForNode({ fs, llm, executables }),
    script: [new JavascriptExecutionTools()],
};

// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./books', tools);

// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book`);

// ▶ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });

// ▶ Prepare input parameters
const inputParameters = { word: 'rabbit' };

// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });

// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);

🧙‍♂️ Wizard

Run books without any settings, boilerplate or struggle in Node.js:

import { wizzard } from '@promptbook/wizzard';

const {
    outputParameters: { joke },
} = await wizzard.execute(`https://github.com/webgptorg/book/blob/main/books/templates/generic.book`, {
    topic: 'Prague',
});

console.info(joke);

🧙‍♂️ Connect to LLM providers automatically

You can just use $provideExecutionToolsForNode function to create all required tools from environment variables like ANTHROPIC_CLAUDE_API_KEY and OPENAI_API_KEY automatically.

import { createPipelineExecutor, createCollectionFromDirectory } from '@promptbook/core';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { $provideExecutionToolsForNode } from '@promptbook/node';
import { $provideFilesystemForNode } from '@promptbook/node';

// ▶ Prepare tools
const tools = await $provideExecutionToolsForNode();

// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./books', tools);

// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book`);

// ▶ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });

// ▶ Prepare input parameters
const inputParameters = { word: 'dog' };

// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });

// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);

💕 Usage of multiple LLM providers

You can use multiple LLM providers in one Promptbook execution. The best model will be chosen automatically according to the prompt and the model's capabilities.

import { createPipelineExecutor, createCollectionFromDirectory } from '@promptbook/core';
import { $provideExecutionToolsForNode } from '@promptbook/node';
import { $provideFilesystemForNode } from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { OpenAiExecutionTools } from '@promptbook/openai';
import { AnthropicClaudeExecutionTools } from '@promptbook/anthropic-claude';

// ▶ Prepare multiple tools
const fs = $provideFilesystemForNode();
const llm = [
    // Note: 💕 You can use multiple LLM providers in one Promptbook execution.
    //       The best model will be chosen automatically according to the prompt and the model's capabilities.
    new AnthropicClaudeExecutionTools(
        //            <- TODO: [🧱] Implement in a functional (not new Class) way
        {
            apiKey: process.env.ANTHROPIC_CLAUDE_API_KEY,
        },
    ),
    new OpenAiExecutionTools(
        //            <- TODO: [🧱] Implement in a functional (not new Class) way
        {
            apiKey: process.env.OPENAI_API_KEY,
        },
    ),
    new AzureOpenAiExecutionTools(
        //            <- TODO: [🧱] Implement in a functional (not new Class) way
        {
            resourceName: process.env.AZUREOPENAI_RESOURCE_NAME,
            deploymentName: process.env.AZUREOPENAI_DEPLOYMENT_NAME,
            apiKey: process.env.AZUREOPENAI_API_KEY,
        },
    ),
];
const executables = await $provideExecutablesForNode();
const tools = {
    llm,
    fs,
    scrapers: await $provideScrapersForNode({ fs, llm, executables }),
    script: [new JavascriptExecutionTools()],
};

// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./books', tools);

// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book`);

// ▶ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });

// ▶ Prepare input parameters
const inputParameters = { word: 'bunny' };

// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });

// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);

💙 Integration with other models

See the other model integrations:

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 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 is going to happen, whether we are ready for it or not. Our mission is to make it excellently, 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 markdown-like language to define core entities like personas, knowledge, tasks,.... It is designed to be understandable by non-programmers and non-technical people
There is also a plugin for VSCode to support .book file extension
Promptbook Engine Promptbook implementation in TypeScript released as multiple NPM packages and Docker HUB
Promptbook Studio Studio to write Books and instantly publish them as miniapps

We also have a community of developers and users of Promptbook:

And Promptbook.studio branded socials:

And Promptujeme sub-brand:

/Subbrand for Czech clients/

And Promptbook.city branded socials:

/Sub-brand for images and graphics generated via Promptbook prompting/

💙 Book language (for prompt-engineer)

💙 The blueprint of book language

Following is the documentation and blueprint of the Book language.

Example

# 🌟 My first Book

-   PERSONA Jane, marketing specialist with prior experience in writing articles about technology and artificial intelligence
-   KNOWLEDGE https://ptbk.io
-   KNOWLEDGE ./promptbook.pdf
-   EXPECT MIN 1 Sentence
-   EXPECT MAX 1 Paragraph

> Write an article about the future of artificial intelligence in the next 10 years and how metalanguages will change the way AI is used in the world.
> Look specifically at the impact of Promptbook on the AI industry.

-> {article}

Goals and principles of book language

File is designed to be easy to read and write. It is strict subset of markdown. It is designed to be understandable by both humans and machines and without specific knowledge of the language.

It has file with .book.md or .book extension with UTF-8 non BOM encoding.

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.

Structure

Book is divided into sections. Each section starts with heading. The language itself is not sensitive to the type of heading (h1, h2, h3, ...) but it is recommended to use h1 for header section and h2 for other sections.

Header is the first section of the book. It contains metadata about the pipeline. It is recommended to use h1 heading for header section but it is not required.

Parameter

Foo bar

Parameter names

Reserved words:

  • each command like PERSONA, EXPECT, KNOWLEDGE, etc.
  • content
  • context
  • knowledge
  • examples
  • modelName
  • currentDate

Parameter notation

Task

Task type

Todo todo

Command

Todo todo

Block

Todo todo

Return parameter

Examples

📦 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 ptbk

Or you can install them separately:

⭐ Marked packages are worth to try first

📚 Dictionary

📚 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 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: Thos section is not complete dictionary, more list of general AI / LLM terms that has connection with Promptbook

Promptbook core

  • Organization (legacy name collection) group jobs, workforce, knowledge, instruments, and actions into one package. Entities in one organization can share resources (= import resources from each other).
    • Jobs
      • Task
      • Subtask
    • Workforce
      • Persona
      • Team
      • Role
    • Knowledge
      • Public
      • Private
      • Protected
    • Instruments
    • Actions

Book language

  • Book file
    • Section
      • Heading
      • Description
      • Command
      • Block
      • Return statement
    • Comment
    • Import
    • Scope

💯 Core concepts

Advanced concepts

Terms specific to Promptbook TypeScript implementation

  • Anonymous mode
  • Application mode

🔌 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

🤝 Partners

🖋️ 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.