npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@rxtk/genai

v1.0.0

Published

⚡️ Generative AI tools for RxJS

Downloads

15

Readme

@rxtk/genai

⚡️ Generative AI toolkit for RxJS

This package is inspired by Python's Langchain toolkit. It provides convenient operators for composing generative AI pipelines in a way that is:

  • Opinionated: Pipelines use sensible defaults for common use cases to avoid repititious code.
  • Flexible: Advanced configuration options and LLM controls are exposed for use cases that require them.
  • Concise: Syntax allows pipelines to be composed with as few lines of code as possible.
  • Functional: Rather than using complex class-based abstractions like Langchain, this package uses RxJS to provide underlying FP abstractions.
  • Vendor Agnostic: The toolkit works with various GenAI vendors (OpenAI, Anthropic, etc) and normalizes the data. New vendors or custom services can be easily integrated! (Example provided below.)
  • DRY: Stages of the pipeline can be easily abstracted away and reused.
  • Readable: FP pipes and functional composition make pipelines easy to read and reason about quickly.
  • Decoupled: Stages of a pipeline can be easily abstracted into one or more independent modules.

Installation

npm i @rxtk/genai
yarn add @rxtk/genai

Quick Examples

🔒 Authentication: These examples assume you already have an API key for each vendor and that it is stored in the conventional environment variable (e.g. OPENAI_API_KEY). You can also provide a key by passing options.apiKey to the toModel operator.

Simple GenAI Pipeline

import {of} from 'rxjs';
import {map} from 'rxjs/operators';
import {toPrompt,toModel,toCompletionString} from '@rxtk/genai';

const input = [
  {language: 'german', phrase: 'hello'},
  {language: 'french', phrase: 'goodbye'},
  {language: 'pirate', phrase: 'yes, my friend'},
  {language: 'doublespeak', phrase: 'They are saying rebellious things.'},
];
const completionString$ = of(...input).pipe(
  // inject variables into a prompt template
  toPrompt('Translate the phrase into the language: {{language}}.\nPhrase: {{phrase}}.\nTranslation: '),
  // send the prompt to the desired vendor and model
  toModel({vendor: 'openai', model: 'gpt-4o'}),
  // retrieve the string value of the completion
  toCompletionString()
);
completionString$.subscribe(console.log);
// Hallo
// au revoir
// Yarr matey
// They are saying quack speak

With Templates for Multiple Roles

import {of} from 'rxjs';
import {map} from 'rxjs/operators';
import {toPrompt,toModel,toCompletionString} from '@rxtk/genai';

const input = [
  {language: 'german', phrase: 'hello'},
  {language: 'french', phrase: 'goodbye'},
];
const completionString$ = of(...input).pipe(
  // inject variables into a prompt template
  toPrompt([
    ['system', 'Translate the phrase into the language: {{language}}'],
    ['user', '{{phrase}}'],
  ]),
  // send the prompt to the desired vendor and model
  toModel({vendor: 'openai', model: 'gpt-4o'}),
  // retrieve the string value of the completion
  toCompletionString()
);
completionString$.subscribe(console.log);
// Hallo
// au revoir

Generate Completions from Multiple Vendors

import {concat,of} from 'rxjs';
import {map} from 'rxjs/operators';
import {toPrompt,toModel,toCompletionString} from '@rxtk/genai';

const pipelines = [
  {
    vendor: 'openai',
    model: 'gpt-4o',
  },
  {
    vendor: 'anthropic',
    model: 'claude-3-opus-20240229',
  },
  {
    vendor: 'cohere',
    model: 'r-plus',
  },
];

const input = [
  {language: 'german', phrase: 'hello'},
  {language: 'french', phrase: 'goodbye'}
];
const input$ = of(...input);

const workflows = pipelines.map(p => 
  input$.pipe(
    toPrompt([
      ['system', 'Translate the phrase into the language: {{language}}'],
      ['user', '{{phrase}}'],
    ]),
    toModel(p),
    toCompletionString(),
    map(c => `vendor=${p.vendor}, model=${p.model} completion='${c}'`)
  )
);

const output$ = concat(...workflows);
output$.subscribe(console.log);
// vendor=openai, model=gpt-40, completion='Hallo'
// ...
// vendor=anthropic, model=claude-3-opus-20240229, completion='Hallo'
// ...

(Beta): Generate Completions from a Custom Model

// If you want to use an LLM service that is not supported, you can write your own:
import axios from 'axios';
import {map,mergeMap} from 'rxjs/operators';

// This is just an RxJS operator
const toMyService = (params, options = {}) => source$ => source$.pipe(
  mergeMap(messages => {
    const {model, apiKey} = params;
    const data$ = from(
      axios({
        method: 'post',
        url: `https://api.myfancyllm.com/v1/messages/${model}`,
        data: { 
          model: model || 'default-model',
          messages,
          ...options
        },
        headers: {
          'Authorization': `Bearer ${options?.apiKey || process.env?.CUSTOM_LLM_API_KEY}`,
        }
      })
    );
    return data$.pipe(
      map(
        options?.normalize 
        // write a custom parser to normalize the response. 
        // for examples see ./src/internals/toOpenAI.js, ./src/internals/toAnthropic.js, etc.
        ? response => response.data 
        : x => x
      )
    );
  }),
);

const input = [
  {language: 'german', phrase: 'hello'},
  {language: 'french', phrase: 'goodbye'}
];
const completionString$ = of(...input).pipe(
  // inject variables into a prompt template
  toPrompt([
    ['system', 'Translate the phrase into the language: {{language}}'],
    ['user', '{{phrase}}'],
  ]),
  // send the prompt to your custom integration
  toModel({customOperator: toMyService}),
  // retrieve the string value of the completion
  toCompletionString()
);
completionString$.subscribe(console.log);
// Hallo
// au revoir

API

toModel({vendor='openai', model='gpt-4o'}, options)([{role<String>,content<String>}])

  • vendor!<String>: Like openai, anthropic, cohere, or huggingface. Check ./lib/toModel.js for the complete, up-to-date list.
  • model: The vendor's name for the model being used. Example: gpt-3.5-turbo.
  • options.apiKey: API key for the vendor. If not provided, the operator will attempt to find the environment variable for the vendor using the default name for the variable.
  • options.normalize(default=true): whether to normalize responses or return raw responses.
  • options.llm: Configuration options to pass directly to the model (like temperature, etc).
  • options.batchSize: If provided, requests to the LLM will be batched and returned as an array of completions instead of individual completions.
import {from} from 'rxjs';
import {map} from 'rxjs/operators';
import {toModel} from '@rxtk/genai';

// Note on API keys: Each model will look for the default environment variable for each vendor API key and other settings. Those can also be passed in using the apiKey configuration variable
const string$ = from(['hello', 'goodbye']);
const output$ = string$.pipe(
  map(str => [
    {role: 'system', content: 'Translate the input text into German.'},
    {role: 'user', content: str}
  ]),
  // accepts an array of messages
  toModel({vendor: 'openai', model: 'gpt-4o'})
);
output$.subscribe(console.log);
// Outputs the normalized response data from each api call

toPrompt(params)(dictionary{})

  • params[[role<String>, template<String>]]: can accept an array of messages with the shape [role, template] like toPrompt([ ['user', 'Hello {{name}}'], ['system', 'Answer nicely but be cool and edgy.']])({name: 'Woody'})
  • params<String>: For simple use cases, roles do not need to be specified. If a string is passed, then it will simply be fed in as the user role. Example: toPrompt("Hello {{name}}.")({name: 'Buzz'}).
  • dictionary{}: A set of key value pairs to interpolate into the template.

The content can wrap variable names in curly braces like 'Insert a variable here: {{myVar}}'. If the object passed into the operator at each iteration contains any of those those keys, the template will inject the value of each key into the string.

import {from} from 'rxjs';
import {toModel, toPrompt} from '@rxtk/genai';

const promptTemplate = [
  ['system', 'Translate the phrase into the language: {{language}}'],
  ['user', '{{phrase}}'],
];
const inputString$ = from([
  {language: 'german', phrase: 'hello'}, 
  {language: 'french', phrase: 'goodbye'},
]);
const output$ = string$.pipe(
  toPrompt(promptTemplate),
  toModel({vendor: 'openai', model: 'gpt-4o'})
);
output$.subscribe(console.log); 
// Output:
// foo
// bar

toCompletionString()(rawResponse)

Outputs the completion string from a completion object.

import {from} from 'rxjs';
import {toModel, toCompletionString} from '@rxtk/genai';

const prompt$ = from([
  {role: 'user', content: 'What is the capital of Michigan?'},
  {role: 'user', content: 'What is the capital of Ohio?'},
]);
const output$ = prompt$.pipe(
  toModel({vendor: 'anthropic'}),
  toCompletionString()
);
output$.subscribe(console.log);
// Output:
// The capital of Michigan is Lansing.
// Who really cares what the capital of Ohio is? Just kidding. That was mean.