@promptbook/azure-openai
Advanced tools
PromptbookSupercharge your use of large language models
@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 i @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, assertsExecutionSuccessful } from '@promptbook/core';
import { createCollectionFromDirectory } from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { AzureOpenAiExecutionTools } from '@promptbook/azure-openai';
// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./promptbook-collection');
// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.ptbk.md`);
// ▶ Prepare tools
const tools = {
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,
},
),
script: [
new JavascriptExecutionTools(),
// <- TODO: [🧱] Implement in a functional (not new Class) way
],
};
// ▶ 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);
// ▶ Fail if the execution was not successful
assertsExecutionSuccessful(result);
// ▶ 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, assertsExecutionSuccessful } from '@promptbook/core';
import { createCollectionFromDirectory } 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';
// ▶ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./promptbook-collection');
// ▶ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.ptbk.md`);
// ▶ Prepare multiple tools
const tools = {
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,
},
),
],
script: [
new JavascriptExecutionTools(),
// <- TODO: [🧱] Implement in a functional (not new Class) way
],
};
// ▶ 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);
// ▶ Fail if the execution was not successful
assertsExecutionSuccessful(result);
// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);
See the other models available in the Promptbook package:
Rest of the documentation is common for entire promptbook ecosystem:
When you have a simple, single prompt for ChatGPT, GPT-4, Anthropic Claude, Google Gemini, Llama 2, or whatever, it doesn't matter how it is integrated. Whether it's the direct calling of a REST API, using the SDK, hardcoding the prompt in the source code, or importing a text file, the process remains the same.
If you need something more advanced or want to extend the capabilities of LLMs, you generally have three ways to proceed:
In any of these situations, but especially in (3), the Promptbook library can make your life easier and make orchestraror for your prompts.
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.
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
- PROMPTBOOK VERSION 0.0.1
- 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?
- PROMPT DIALOG
{rawAssigment}
-> {assigment}Website assignment and specification✨ Improving the title
- MODEL VARIANT Chat
- MODEL NAME
gpt-4- POSTPROCESSING
unwrapResultAs 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?
- PROMPT DIALOG
{enhancedTitle}
-> {title}Title for the website🐰 Cunning subtitle
- MODEL VARIANT Chat
- MODEL NAME
gpt-4- POSTPROCESSING
unwrapResultAs 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
- MODEL VARIANT Chat
- MODEL NAME
gpt-4As 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
- MODEL VARIANT Completion
- MODEL NAME
gpt-3.5-turbo-instructAs 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.
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
ptbkThe following glossary is used to clarify certain concepts:
If you have a question start a discussion, open an issue or write me an email.
Different levels of abstraction. OpenAI library is for direct use of OpenAI API. This library is for a higher level of abstraction. It is for creating prompt templates and promptbooks that are independent of the underlying library, LLM model, or even LLM provider.
Langchain is primarily aimed at ML developers working in Python. This library is for developers working in javascript/typescript and creating applications for end users.
We are considering creating a bridge/converter between these two libraries.
GPTs are chat assistants that can be assigned to specific tasks and materials. But they are still chat assistants. Promptbooks are a way to orchestrate many more predefined tasks to have much tighter control over the process. Promptbooks are not a good technology for creating human-like chatbots, GPTs are not a good technology for creating outputs with specific requirements.
If you use raw SDKs, you just put prompts in the sourcecode, mixed in with typescript, javascript, python or whatever programming language you use.
If you use promptbooks, you can store them in several places, each with its own advantages and disadvantages:
As source code, typically git-committed. In this case you can use the versioning system and the promptbooks will be tightly coupled with the version of the application. You still get the power of promptbooks, as you separate the concerns of the prompt-engineer and the programmer.
As data in a database In this case, promptbooks are like posts / articles on the blog. They can be modified independently of the application. You don't need to redeploy the application to change the promptbooks. You can have multiple versions of promptbooks for each user. You can have a web interface for non-programmers to create and modify promptbooks. But you lose the versioning system and you still have to consider the interface between the promptbooks and the application (= input and output parameters).
In a configuration in environment variables. This is a good way to store promptbooks if you have an application with multiple deployments and you want to have different but simple promptbooks for each deployment and you don't need to change them often.
A single promptbook can be written for several (human) languages at once. However, we recommend that you have separate promptbooks for each language.
In large language models, you will get better results if you have prompts in the same language as the user input.
The best way to manage this is to have suffixed promptbooks like write-website-content.en.ptbk.md and write-website-content.cs.ptbk.md for each supported language.
See CHANGELOG.md
Promptbook by Pavol Hejný is licensed under CC BY 4.0
See TODO.md
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.
0.66.0 (2024-08-19)
[🎰] Model updates and registers
$claude-3-5-sonnet-20240620 to AnthropicClaudeExecutionToolsAnthropicClaudeExecutionTools@anthropic-ai/sdk from 0.21.1 to 0.26.1@azure/openai from 1.0.0-beta.12 to 2.0.0-beta.1openai from 4.46.1 to 4.55.9LlmExecutionToolsConstructor$llmToolsConfigurationBoilerplatesRegister$llmToolsRegisterOpenai ->OpenAiFAQs
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 256 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 0 open source maintainers collaborating on the project.
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