@promptbook/azure-openai
Advanced tools
It's time for a paradigm shift. The future of software in plain English, French or Latin
.book file extensiono3-mini model by OpenAI@promptbook/azure-openai@promptbook/azure-openai is 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/azure-openai
@promptbook/azure-openai integrates Azure OpenAI API with Promptbook. It allows to execute Promptbooks with Azure OpenAI GPT models.
Note: This is similar to @promptbook/openai but more useful for Enterprise customers who use Azure OpenAI to ensure strict data privacy and compliance.
import { createPipelineExecutor } from '@promptbook/core';
import {
createCollectionFromDirectory,
$provideExecutionToolsForNode,
$provideFilesystemForNode,
} from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { AzureOpenAiExecutionTools } from '@promptbook/azure-openai';
// ▶ Prepare tools
const fs = $provideFilesystemForNode();
const llm = new AzureOpenAiExecutionTools(
// <- TODO: [🧱] Implement in a functional (not new Class) way
{
isVerbose: true,
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: 'crocodile' };
// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });
// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);
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);
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);
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 } from '@promptbook/core';
import {
createCollectionFromDirectory,
$provideExecutionToolsForNode,
$provideFilesystemForNode,
} from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { AzureOpenAiExecutionTools } from '@promptbook/azure-openai';
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 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,
},
),
new OpenAiExecutionTools(
// <- TODO: [🧱] Implement in a functional (not new Class) way
{
apiKey: process.env.OPENAI_API_KEY,
},
),
new AnthropicClaudeExecutionTools(
// <- TODO: [🧱] Implement in a functional (not new Class) way
{
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: 'snake' };
// 🚀▶ Execute the Pipeline
const result = await pipelineExecutor(inputParameters).asPromise({ isCrashedOnError: true });
// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);
See the other model integrations:
Rest of the documentation is common for entire promptbook ecosystem:
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!
Take a look at the simple starter kit with books integrated into the Hello World sample applications:
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. |
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/
Following is the documentation and blueprint of the Book language.
Book is a language that can be used to write AI applications, agents, workflows, automations, knowledgebases, translators, sheet processors, email automations and more. It allows you to harness the power of AI models in human-like terms, without the need to know the specifics and technicalities of the models.
# 🌟 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 writing articles about technology and artificial intelligence
- 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 3 circles:
What work needs to be done. Each book defines a workflow, 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:
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:
The resources used by the personas are used to do the work.
Related commands:
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 .book extension. It uses UTF-8 non BOM encoding.
Book has two variants: flat - which is just a prompt with no structure, and full - which has a structure 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.
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/wizzard - Wizzard 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/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/vercel - Adapter for Vercel functionalities
@promptbook/google - Integration with Google's Gemini API
@promptbook/deepseek - Integration with DeepSeek 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 .pdf documents
@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 - Usefull templates and examples of books which can be used as a starting point
@promptbook/types - Just typescript types used in the library
⭐ @promptbook/cli - Command line interface utilities for promptbooks
🐋 Docker image - Promptbook server
The following glossary is used to clarify certain concepts:
Note: Thos section is not complete dictionary, more list of general AI / LLM terms that has connection with Promptbook
If you have a question start a discussion, open an issue or write me an email.
See CHANGELOG.md
Promptbook project is under BUSL 1.1 is an SPDX license
See TODO.md
We are open to pull requests, feedback, and suggestions.
You can also ⭐ star the project, follow us on GitHub or various other social networks.
FAQs
It's time for a paradigm shift. The future of software in plain English, French or Latin
The npm package @promptbook/azure-openai receives a total of 662 weekly downloads. As such, @promptbook/azure-openai popularity was classified as not popular.
We found that @promptbook/azure-openai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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