@promptbook/azure-openai
Advanced tools
PromptbookBuild responsible, controlled and transparent applications on top of LLM models!
npx ptbk run path/to/your/book.docx, .doc and .pdf documents@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, assertsExecutionSuccessful } from '@promptbook/core';
import {
createCollectionFromDirectory,
$provideExecutionToolsForNode,
$provideFilesystemForNode,
} from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { AzureOpenAiExecutionTools } from '@promptbook/azure-openai';
// โถ Prepare tools
const fs = $provideFilesystemForNode();
const llm = new AzureOpenAiExecutionTools(
// <- TODO: [๐งฑ] 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()],
};
// โถ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./promptbook-collection', tools);
// โถ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book.md`);
// โถ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });
// โถ Prepare input parameters
const inputParameters = { word: 'crocodile' };
// ๐โถ Execute the Pipeline
const result = await pipelineExecutor(inputParameters);
// โถ Fail if the execution was not successful
assertsExecutionSuccessful(result);
// โถ 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,
$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';
// โถ 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: [๐งฑ] 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: [๐งฑ] Implement in a functional (not new Class) way
{
apiKey: process.env.OPENAI_API_KEY,
},
),
new AnthropicClaudeExecutionTools(
// <- TODO: [๐งฑ] 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()],
};
// โถ Create whole pipeline collection
const collection = await createCollectionFromDirectory('./promptbook-collection', tools);
// โถ Get single Pipeline
const pipeline = await collection.getPipelineByUrl(`https://promptbook.studio/my-collection/write-article.book.md`);
// โถ Create executor - the function that will execute the Pipeline
const pipelineExecutor = createPipelineExecutor({ pipeline, tools });
// โถ Prepare input parameters
const inputParameters = { word: 'snake' };
// ๐โถ Execute the Pipeline
const result = await pipelineExecutor(inputParameters);
// โถ Fail if the execution was not successful
assertsExecutionSuccessful(result);
// โถ 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:
If you have a simple, single prompt for ChatGPT, GPT-4, Anthropic Claude, Google Gemini, Llama 3, 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 and prompt drift, word repetition repetition repetition repetition or misuse, lack of context, or just plain w๐๐ขrd resp0nses. When this happens, you generally have three options:
In all of these situations, but especially in 3., the โจ Promptbook can make your life waaaaaaaaaay easier.
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.:) 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 project is ecosystem of multiple projects and tools, following is a list of most important pieces of the project:
| Project | Description | Link |
|---|---|---|
| Core | Promptbook core is a description and documentation of basic innerworkings how should be Promptbook implemented and defines which fetures must be descriable by book language | https://ptbk.io https://github.com/webgptorg/book |
| Book language | Book is a markdown-like language to define core entities like projects, pipelines, knowledge,.... It is designed to be understandable by non-programmers and non-technical people | |
| Promptbook typescript project | Implementation of Promptbook in TypeScript published into multiple packages to NPM | https://github.com/webgptorg/promptbook + Multiple packages on NPM |
| Promptbook studio | No-code studio to write book without need to write even the markdown | https://promptbook.studio https://github.com/hejny/promptbook-studio |
| Promptbook miniapps | Builder of LLM miniapps from book notation |
Following is the documentation and blueprint of the Book language.
# ๐ 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}
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.
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.
Foo bar
Reserved words:
PERSONA, EXPECT, KNOWLEDGE, etc.contentcontextknowledgeexamplesmodelNamecurrentDateTodo todo
Todo todo
Todo todo
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:
โญ Marked packages are worth to try first
ptbk.pdf documents.docx, .odt,โฆ.doc, .rtf,โฆThe 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 by Pavol Hejnรฝ 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 โ buy me a coffee or donate via cryptocurrencies.
You can also โญ star the promptbook package, follow me on GitHub or various other social networks.
FAQs
Supercharge your use of large language models
The npm package @promptbook/azure-openai receives a total of 766 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.ย It has 0 open source maintainers collaborating on the project.
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