@promptbook/anthropic-claude
Advanced tools
PromptbookSupercharge your use of large language models
@promptbook/anthropic-claude@promptbook/anthropic-claude 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/anthropic-claude
@promptbook/anthropic-claude integrates Anthropic's Claude API with Promptbook. It allows to execute Promptbooks with OpenAI Claude 2 and 3 models.
import { createPipelineExecutor, createCollectionFromDirectory, assertsExecutionSuccessful } from '@promptbook/core';
import { createCollectionFromDirectory } from '@promptbook/node';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
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 tools
const tools = {
llm: new AnthropicClaudeExecutionTools({
isVerbose: true,
apiKey: process.env.ANTHROPIC_CLAUDE_API_KEY,
}),
script: [new JavascriptExecutionTools()],
};
// ▶ 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);
// ▶ Fail if the execution was not successful
assertsExecutionSuccessful(result);
// ▶ Handle the result
const { isSuccessful, errors, outputParameters, executionReport } = result;
console.info(outputParameters);
You can just use createLlmToolsFromEnv function to create LLM tools from environment variables like ANTHROPIC_CLAUDE_API_KEY and OPENAI_API_KEY automatically.
import { createPipelineExecutor, createCollectionFromDirectory, assertsExecutionSuccessful } from '@promptbook/core';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { createLlmToolsFromEnv } from '@promptbook/node';
// ▶ 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 = {
// Note: 🧙♂️ Just call `createLlmToolsFromEnv` to automatically connect to all configured providers
llm: createLlmToolsFromEnv(),
script: [new JavascriptExecutionTools()],
};
// ▶ 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);
// ▶ 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, createCollectionFromDirectory, assertsExecutionSuccessful } from '@promptbook/core';
import { JavascriptExecutionTools } from '@promptbook/execute-javascript';
import { OpenAiExecutionTools } from '@promptbook/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 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 AnthropicClaudeExecutionTools({
apiKey: process.env.ANTHROPIC_CLAUDE_API_KEY,
}),
new OpenAiExecutionTools({
apiKey: process.env.OPENAI_API_KEY,
}),
new AzureOpenAiExecutionTools({
resourceName: process.env.AZUREOPENAI_RESOURCE_NAME,
deploymentName: process.env.AZUREOPENAI_DEPLOYMENT_NAME,
apiKey: process.env.AZUREOPENAI_API_KEY,
}),
],
script: [new JavascriptExecutionTools()],
};
// ▶ 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);
// ▶ 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.
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 basic concepts:
Prompt in a text along with model requirements, but without any execution or templating logic.
For example:
{
"request": "Which sound does a cat make?",
"modelRequirements": {
"variant": "CHAT"
}
}
{
"request": "I am a cat.\nI like to eat fish.\nI like to sleep.\nI like to play with a ball.\nI l",
"modelRequirements": {
"variant": "COMPLETION"
}
}
Similar concept to Prompt, but with templating logic.
For example:
{
"request": "Which sound does a {animalName} make?",
"modelRequirements": {
"variant": "CHAT"
}
}
Abstract way to specify the LLM. It does not specify the LLM with concrete version itself, only the requirements for the LLM. NOT chatgpt-3.5-turbo BUT CHAT variant of GPT-3.5.
For example:
{
"variant": "CHAT",
"version": "GPT-3.5",
"temperature": 0.7
}
Each block of promptbook can have a different execution type.
It is specified in list of requirements for the block.
By default, it is Prompt template
Prompt template The block is a prompt template and is executed by LLM (OpenAI, Azure,...)SIMPLE TEMPLATE The block is a simple text template which is just filled with parametersScript The block is a script that is executed by some script runtime, the runtime is determined by block type, currently only javascript is supported but we plan to add python and typescript in the future.PROMPT DIALOG Ask user for inputParameters that are placed in the prompt template and replaced to create the prompt. It is a simple key-value object.
{
"animalName": "cat",
"animalSound": "Meow!"
}
There are three types of template parameters, depending on how they are used in the promptbook:
Note: Parameter can be both intermedite and output at the same time.
Promptbook is core concept of this library. It represents a series of prompt templates chained together to form a pipeline / one big prompt template with input and result parameters.
Internally it can have multiple formats:
Library of all promptbooks used in your application.
Each promptbook is a separate .ptbk.md file with unique PIPELINE URL. Theese urls are used to reference promptbooks in other promptbooks or in the application code.
Prompt result is the simplest concept of execution. It is the result of executing one prompt (NOT a template).
For example:
{
"response": "Meow!",
"model": "chatgpt-3.5-turbo"
}
ExecutionTools is an interface which contains all the tools needed to execute prompts.
It contais 3 subtools:
LlmExecutionToolsScriptExecutionToolsUserInterfaceToolsWhich are described below:
LlmExecutionTools is a container for all the tools needed to execute prompts to large language models like GPT-4.
On its interface it exposes common methods for prompt execution.
Internally it calls OpenAI, Azure, GPU, proxy, cache, logging,...
LlmExecutionTools an abstract interface that is implemented by concrete execution tools:
OpenAiExecutionToolsAnthropicClaudeExecutionToolsAzureOpenAiExecutionToolsLangtailExecutionToolsBardExecutionToolsLamaExecutionToolsGpuExecutionToolsRemoteLlmExecutionTools that connect to a remote server and run one of the above execution tools on that server.MockedEchoLlmExecutionTools that is used for testing and mocking.LogLlmExecutionToolsWrapper that is technically also an execution tools but it is more proxy wrapper around other execution tools that logs all calls to execution tools.ScriptExecutionTools is an abstract container that represents all the tools needed to EXECUTE SCRIPTs. It is implemented by concrete execution tools:
JavascriptExecutionTools is a wrapper around vm2 module that executes javascript code in a sandbox.JavascriptEvalExecutionTools is wrapper around eval function that executes javascript. It is used for testing and mocking NOT intended to use in the production due to its unsafe nature, use JavascriptExecutionTools instead.TypescriptExecutionTools executes typescript code in a sandbox.PythonExecutionTools executes python code in a sandbox.There are postprocessing functions that can be used to postprocess the result.
UserInterfaceTools is an abstract container that represents all the tools needed to interact with the user. It is implemented by concrete execution tools:
ConsoleInterfaceTools is a wrapper around readline module that interacts with the user via console.SimplePromptInterfaceTools is a wrapper around window.prompt synchronous function that interacts with the user via browser prompt. It is used for testing and mocking NOT intended to use in the production due to its synchronous nature.CallbackInterfaceTools delagates the user interaction to a async callback function. You need to provide your own implementation of this callback function and its bind to UI.Executor is a simple async function that takes input parameters and returns output parameters. It is constructed by combining execution tools and promptbook to execute together.
Joker is a previously defined parameter that is used to bypass some parts of the pipeline. If the joker is present in the template, it is checked to see if it meets the requirements (without postprocessing), and if so, it is used instead of executing that prompt template. There can be multiple wildcards in a prompt template, if so they are checked in order and the first one that meets the requirements is used.
If none of the jokers meet the requirements, the prompt template is executed as usual.
This can be useful, for example, if you want to use some predefined data, or if you want to use some data from the user, but you are not sure if it is suitable form.
When using wildcards, you must have at least one minimum expectation. If you do not have a minimum expectation, the joker will always fulfil the expectation because it has none, so it makes no logical sense.
Look at jokers.ptbk.md sample.
You can define postprocessing functions when creating JavascriptEvalExecutionTools:
Additionally there are some usefull string-manipulation build-in functions, which are listed here.
Expect command describes the desired output of the prompt template (after post-processing)
It can set limits for the maximum/minimum length of the output, measured in characters, words, sentences, paragraphs,...
Note: LLMs work with tokens, not characters, but in Promptbooks we want to use some human-recognisable and cross-model interoperable units.
# ✨ Sample: Expectations
- INPUT PARAMETER {yourName} Name of the hero
## 💬 Question
- EXPECT MAX 30 CHARACTERS
- EXPECT MIN 2 CHARACTERS
- EXPECT MAX 3 WORDS
- EXPECT EXACTLY 1 SENTENCE
- EXPECT EXACTLY 1 LINE
...
There are two types of expectations which are not strictly symmetrical:
EXPECT MIN 0 ... is not valid minimal expectation. It makes no sense.EXPECT JSON is both minimal and maximal expectationJOKER in same prompt template, you need to have at least one minimal expectationEXPECT MAX 0 ... is valid maximal expectation. For example, you can expect 0 pages and 2 sentences.EXPECT JSON is both minimal and maximal expectationLook at expectations.ptbk.md and expect-json.ptbk.md samples for more.
Execution report is a simple object or markdown that contains information about the execution of the pipeline.
See the example of such a report
Remote server is a proxy server that uses its execution tools internally and exposes the executor interface externally.
You can simply use RemoteExecutionTools on client-side javascript and connect to your remote server.
This is useful to make all logic on browser side but not expose your API keys or no need to use customer's GPU.
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.
FAQs
It's time for a paradigm shift. The future of software in plain English, French or Latin
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