ai-driven

v5.0.6

Published

2 months ago

AI-driven tool for translation, summarization, text moderation, and sensitive image detection.

Downloads

208

ai-driven is a versatile module that integrates two cutting-edge AI platforms: Claude AI and OpenAI's GPT. This powerful combination offers a wide array of natural language processing and computer vision capabilities.

Key features include:

Advanced text analysis and generation
Comprehensive image recognition and description
Multi-language translation and interpretation
Sophisticated question-answering systems

List of all features

Example

import { Assistant } from 'ai-driven';

const assistant = new Assistant({ apiVendor: 'OpenAI', apiKey: 'your_api_key_here' });

const translatedText = await assistant.translateText('Hello, world!', 'it');

console.log(translatedText); // => Ciao, mondo!

You can find more usage examples here

All Features

ai-driven offers easy-to-use methods (API Methods list) for a wide range of tasks including:

Text Processing:
- Content moderation
- Text translation
- Language detection
- Grammar and spelling correction
- Text summarization
- Text generation
- Text paraphrasing
- Text classification
- Keyword extraction
- Named Entity Recognition (NER)
- Sentiment analysis
- Emotion detection in text
- Question answering
Image Analysis:
- Image captioning
- Optical Character Recognition (OCR)
- Object detection
- Object search
- Facial expression analysis
- Violence detection
- Pornography detection
Audio Processing:
- Emotion detection in voice
- Speech-to-text conversion
Free-form ask [more]
- return only the answer (by default) or answer with AI comments
- show answer in the specified language
- act as a specific role [list of roles]
- result in response format [response formats]
- use a specific tone [list of tones]
- apply a particular writing style [list of writing styles]
- convey a specific emotion [list of emotions]

This versatile module simplifies complex AI tasks, making it easier for developers to integrate advanced AI capabilities into their applications.

Installation

To install the ai-driven module, run the following command:

npm i -S ai-driven

Setup

You can configure the assistant in two ways:

Option 1: Direct Initialization

Provide the configuration when creating the assistant:

const assistant = new Assistant({
  apiKey: 'your_api_key_here',
  apiVendor: 'OpenAI', // 'OpenAI' or 'Claude'
  apiUrl: 'https://api.anthropic.com/v1/messages', // optional
  apiModel: 'claude-3-haiku-20240307' // optional
});

Option 2: Using Environment Variables

Create a .env file in your project's root directory.
Add the following variables to the .env file: 2.1. For OpenAI:

OPENAI_API_KEY=your_OpenAI_api_key_here
OPENAI_API_URL=https://api.openai.com/v1/chat/completions
OPENAI_API_MODEL=gpt-3.5-turbo

2.2. For Claude:

CLAUDE_API_KEY=your_Claude_api_key_here
CLAUDE_API_URL=https://api.anthropic.com/v1/messages
CLAUDE_API_MODEL=claude-3-haiku-20240307

The assistant will automatically use these environment variables if no configuration is provided during initialization.

Usage

Here's a basic example of how to use the ai-driven module:

import { Assistant } from 'ai-driven';
import fs from 'fs/promises';

async function main() {
  const assistant = new Assistant({ apiKey: 'your_api_key_here' });

  // Translate text
  const translatedText = await assistant.translateText('Hello, world!', 'it');
  console.log('Translated text:', translatedText);

  // Check for offensive language
  const offensiveLevel = await assistant.checkForOffensiveLanguage('You are stupid!');
  console.log('Offensive level:', offensiveLevel);

  // Check for profanity
  const profanityLevel = await assistant.checkForProfanity('Damn it!');
  console.log('Profanity level:', profanityLevel);

  // Check an image for violence
  const imageBuffer = await fs.readFile('path/to/your/image.jpg');
  const violenceLevel = await assistant.checkImageForViolence(imageBuffer);
  console.log('Violence level in image:', violenceLevel);

  // Check an image for pornography
  const pornographyLevel = await assistant.checkImageForPornography(imageBuffer);
  console.log('Pornography level in image:', pornographyLevel);
}

main().catch(console.error);

Vendors

OpenAI Vendor

How much does it cost?

The cost for using OpenAI's models varies depending on the model and usage. As of now, for the GPT-4o model, the pricing is as follows:

$0.005 per 1,000 tokens for input
$0.015 per 1,000 tokens for output

For more detailed and up-to-date pricing, please refer to the OpenAI Pricing page.

If you use the text examples from example.ts, and you consume 739 tokens for input and 384 tokens for output, the cost would be approximately $0,009.

However, the cost will increase significantly if you use image and audio processing models, as the pricing for these services depends on the size and complexity of the files you're working with—larger files incur higher costs.

Rate Limits

For the most up-to-date information on rate limits, please refer to the OpenAI Rate Limits page.

API Key

To use this library, you'll need an API key. You can obtain one from the OpenAI console: https://platform.openai.com/account/api-keys

List of Models

GPT Models:
- gpt-4
- gpt-4-turbo
- gpt-4-vision-preview
- gpt-4o
- gpt-4-32k
- gpt-3.5-turbo
- gpt-3.5-turbo-16k
- gpt-3.5-turbo-instruct
DALL-E Models:
- dall-e-3
- dall-e-2
Whisper Models:
- whisper
Embedding Models:
- text-embedding-3-large
- text-embedding-3-small
- text-embedding-ada-002
Text-to-Speech Models:
- tts-1
- tts-1-hd

More about models: https://platform.openai.com/docs/models

Claude Vendor

How much does it cost?

Currently, the most affordable model costs $0.25 per million tokens (MTok) for input and $1.25 per MTok for output. More details here

If you only use the text examples from example.ts, you'll consume 739 tokens for input and 384 tokens for output, resulting in a cost of approximately $0.0007.

However, this cost will increase significantly if you use image and audio processing, as it depends entirely on the size of the files you're working with—larger files incur higher costs.

Rate Limits

For the most up-to-date information on rate limits, please refer to the Rate Limits page

API Key

To use this library, you'll need an API key. You can obtain one from the Anthropic console: https://console.anthropic.com/settings/keys

List of Models

claude-3-5-sonnet-20240620
claude-3-opus-20240229
claude-3-sonnet-20240229
claude-3-haiku-20240307

More about models: https://docs.anthropic.com/en/docs/about-claude/models#model-names

Description

ai-driven leverages the power of Claude AI and OpenAI's GPT models to perform various tasks such as:

Text translation: Convert text from one language to another while preserving meaning and context - translateText(text: string, lang?: string, context?: string ): Promise<string>
Language detection: Automatically identify the language of a given text. Return 2-letters ISO_639-1 language code - detectLanguage(text: string): Promise<string>
Grammar and spelling correction: Identify and correct grammatical errors and spelling mistakes in text - correctText(text: string): Promise<string>
Text Summarization: Generate concise summaries of longer text documents - summarizeText(text: string, maxWords?: number): Promise<string>
Text Generation: Create coherent and contextually relevant text based on given prompts - generateText(prompt: string, maxWords?: number): Promise<string>
Text paraphrasing: Rewrite text to convey the same meaning using different words and sentence structures - paraphraseText(text: string): Promise<string>
Text classification: Categorize text into predefined classes or topics - classifyText(text: string, categories: string[]): Promise<string>
Keyword extraction: Identify and extract the most important or relevant words or phrases from a text - extractKeywords(text: string, count?: number): Promise<string[]>
Named Entity Recognition (NER): Extract entities like names, dates, locations, and organizations from text - extractEntities(text: string): Promise<Record<string, string[]>>
Sentiment Analysis: Detect the sentiment (positive, negative, neutral) in text data - analyzeSentiment(text: string): Promise<string>
Offensive language detection: Identify and flag inappropriate, offensive, or harmful language in text - checkForOffensiveLanguage(text: string): Promise<number>
Profanity checking: Detect and filter out profane or vulgar words and expressions in text - checkForProfanity(text: string): Promise<number>
Emotion Detection: Identify specific emotions (e.g., joy, sadness, anger) in text - detectEmotion(text: string): Promise<string>
Question Answering: Provide accurate answers to questions based on a given context or dataset - answerQuestion(question: string, context: string): Promise<string>
Image Captioning: Generate descriptive captions for images - captionImage(imageBuffer: Buffer): Promise<string>
Optical Character Recognition (OCR): Extract text from images of documents or handwritten notes (not supported by OpenAI vendor) - extractTextFromImage(imageBuffer: Buffer): Promise<string>
Object Detection in Images: Identify and locate objects within images - detectObjectsInImage(imageBuffer: Buffer): Promise<Record<string, number[]>>
Search Object in Images: Locate specific objects within images based on user queries - searchObjectInImage(imageBuffer: Buffer, objectQuery: string): Promise<number[] | null>
Violence detection in images: Identify and flag images containing violent content or scenes - checkImageForViolence(imageBuffer: Buffer): Promise<number>
Pornographic content detection in images: Detect and filter out images containing explicit or pornographic content - checkImageForPornography(imageBuffer: Buffer): Promise<number>
Facial expression analysis in images: Recognize and categorize facial expressions in images to determine emotions - analyzeFacialExpression(imageBuffer: Buffer): Promise<Record<string, string>>
Emotion Detection in Voice: Identify specific emotions (e.g., joy, sadness, anger) in voice data (not supported by OpenAI vendor) - detectEmotionInVoice(audioBuffer: Buffer): Promise<string>
Speech-to-text conversion: Transcribe spoken words from audio recordings into written text (not supported by OpenAI vendor) - speechToText(audioBuffer: Buffer): Promise<string>

API Methods

The ai-driven module provides the following methods:

| Method | Description | Parameters | Return Promise Type | |--------|-------------|------------|-------------| | ask | Ask a question with customizable options [more] | text: string, options?: askOptionsType | string | | translateText | Translates the given text to selected language (English by default) | text: string, lang?: string, context?: string | string | | detectLanguage | Detects the language of the provided text | text: string | string | | correctText | Corrects grammar and spelling errors in the given text | text: string | string | | summarizeText | Generates a summary of the provided text, optionally limiting the summary length | text: string, maxWords?: number | string | | generateText | Creates coherent and contextually relevant text based on the given prompt | prompt: string, maxWords?: number | string | | paraphraseText | Rewrites the given text to convey the same meaning using different words and sentence structures | text: string | string | | classifyText | Categorizes the given text into one of the predefined classes or topics | text: string, categories: string[] | string | | extractKeywords | Identifies and extracts the most important or relevant words or phrases from the text | text: string, count?: number | string[] | | extractEntities | Extracts named entities (names, dates, locations, organizations) from the text | text: string | Record<string, string[]> | | analyzeSentiment | Detects the sentiment (positive, negative, neutral) in the given text | text: string | string | | checkForOffensiveLanguage | Checks the given text for offensive language and returns a score from 1 to 10 | text: string | number | | checkForProfanity | Checks the given text for profanity and returns a score from 1 to 10 | text: string | number | | detectEmotion | Identifies specific emotions (e.g., joy, sadness, anger) in the given text | text: string | string | | answerQuestion | Provides an accurate answer to the question based on the given context | question: string, context: string | string | | captionImage | Generates a descriptive caption for the given image | imageBuffer: Buffer | string | | extractTextFromImage | Extracts text from images of documents or handwritten notes (not supported by OpenAI vendor) | imageBuffer: Buffer | string | | detectObjectsInImage | Identifies and locates objects within the given image | imageBuffer: Buffer | Record<string, number[]> | | searchObjectInImage | Locates a specific object within the image based on the user query | imageBuffer: Buffer, objectQuery: string | number[] \| null | | checkImageForViolence | Analyzes the given image for violent content and returns a score from 1 to 10 | imageBuffer: Buffer | number | | checkImageForPornography | Analyzes the given image for pornographic content and returns a score from 1 to 10 | imageBuffer: Buffer | number | | analyzeFacialExpression | Recognizes and categorizes facial expressions in the given image to determine emotions | imageBuffer: Buffer | Record<string, string> | | detectEmotionInVoice | Identifies specific emotions in the given voice data (not supported by OpenAI vendor) | audioBuffer: Buffer | string | | speechToText | Transcribes spoken words from the given audio recording into written text (not supported by OpenAI vendor) | audioBuffer: Buffer | string |

Free-form ask

Method: ask

Description:

This method is used to ask a question with customizable options.

Free-form ask example

import { Assistant } from 'ai-driven';

const assistant = new Assistant({ apiVendor: 'OpenAI', apiKey: 'your_api_key_here' });

const result = await assistant.ask(
  'bubble sort function',
  {
    format: 'TypeScript',
    answerOnly: false,
    language: 'en',
    role: 'Fitness Trainer',
    tone: 'Informative',
    style: 'Poetic',
    emotion: 'Love',
    context: 'Sort colors',
  }
);

console.log(result);

Result

Bubble Sort Function for Sorting Colors

Ah, the dance of colors, a captivating sight,

Where hues embrace, in a harmonious flight.

Let us embark on a journey, with grace and might,

To sort these vibrant shades, with all our might.

Fitness Trainer's Perspective:

Just as our bodies crave a well-ordered routine,

Our colors, too, deserve a rhythm, serene.

Through the Bubble Sort, we'll find the way,

To arrange these hues, in a beautiful display.

With each gentle swap, a transformation unfolds,

Allowing the spectrum to shine, its story untold.

From the lightest hue to the darkest hue,

We'll navigate this dance, with love anew.

So, let's dive in, and embrace the flow,

As we sort these colors, with a rhythmic glow.

For in this process, we'll find the art,

Of bringing order to the canvas of our heart.

function bubbleSort(colors: string[]): string[] {
  const n = colors.length;
  for (let i = 0; i < n - 1; i++) {
      for (let j = 0; j < n - i - 1; j++) {
          if (colors[j] > colors[j + 1]) {
              // Swap colors[j] and colors[j+1]
              [colors[j], colors[j + 1]] = [colors[j + 1], colors[j]];
          }
      }
  }
  return colors;
}

Signature

public async ask(question: string, options?: askOptionsType): Promise<string>

Parameters

question (string): The question to ask.
options (askOptionsType): Optional parameters to customize the question.

askOptionsType Interface

answerOnly (boolean): Return only the answer. Default is true.
language (string): Answer in the specified language.
context (string): In the specified context.
role (string): Act as a specific role.
task (string): Create a specific task.
format (string): Response format.
tone (string): Tone of the response.
style (string): Style of writing.
emotion (string): Emotion to convey.

Roles

Translator
Programmer
Data Scientist
Analyst
Researcher
Teacher
Tutor
Historian
Scientist
Mathematician
Statistician
Financial Advisor
Consultant
Coach
Mentor
Content Writer
Editor
Proofreader
Engineer
Architect
Designer
Developer
Marketer
SEO Specialist
Strategist
Project Manager
Product Manager
Customer Support
Technical Support
Salesperson
Psychologist
Therapist
Counselor
Librarian
Legal Advisor
Medical Advisor
Chemist
Physicist
Biologist
Environmentalist
Economist
Entrepreneur
Business Analyst
Investor
Accountant
Auditor
Chef
Bartender
Nutritionist
Fitness Trainer
Artist
Musician
Composer
Poet
Novelist
Critic
Reviewer

Tasks

Essay
Summary
Report
Research paper
Presentation
Speech
Lesson plan
Tutorial
Documentation
Code snippets
Data analysis
Business plan
Marketing plan
Article
Blog post
Product review
User manual
Test cases
Screenplay
Poem
Short story
Character profile
Letter
Resume
Cover letter
Recommendation letter
Project proposal
Interview questions
Survey
Quiz
News article
Social media content
Email template
FAQ
Roadmap
Checklist
Recipe
Meal plan
Workout plan
Book summary
Annotated bibliography
Financial forecast
Grant proposal
SWOT analysis
Strategic plan
Case study
Itinerary
Script for a podcast
Storyboard
Press release
Content calendar
Mind map
Business case
Product description
User story
API documentation
Compliance report
Risk assessment
User journey map
Technical specification
Workflow diagram
Competitive analysis
Literature review
Training module
Onboarding plan
Executive summary
Customer persona
Sales pitch
White paper
Case analysis
Investment proposal
Financial report
Marketing campaign
Content strategy
Value proposition
Partnership proposal
Brand guideline
Community guideline
Action plan
Conflict resolution plan
Safety protocol
Crisis management plan
Disaster recovery plan
Mission statement
Vision statement
Core values statement
Diversity and inclusion plan
Succession plan
Employee handbook
Operational plan
Retention strategy
Compensation plan
Performance review
Employee evaluation
Professional development plan
Retirement plan
Sustainability plan
Environmental impact assessment
Corporate social responsibility report
Governance framework
Ethics policy
Code of conduct
Conflict of interest policy
Whistleblower policy
Privacy policy
Data protection plan
Information security policy
Digital transformation strategy
Technology roadmap
IT disaster recovery plan
Software requirements specification
System architecture
Database schema
Data migration plan
API integration plan
Cloud adoption strategy
DevOps strategy
IT governance framework
Enterprise architecture plan
Business continuity plan
IT service management plan
Incident response plan
Cybersecurity strategy
Network architecture plan
Infrastructure as code (IaC) template
Deployment plan
Monitoring and alerting strategy
Software development lifecycle (SDLC) plan
User acceptance testing (UAT) plan
Change management plan
Configuration management plan
Release management plan
Vendor management plan
Procurement strategy
Supply chain management plan
Logistics plan
Inventory management plan
Quality control plan
Lean manufacturing plan
Six sigma plan
Total quality management (TQM) plan
Maintenance plan
Asset management plan
Facilities management plan
Fleet management plan
Energy management plan
Waste management plan
Water management plan
Air quality management plan
Noise management plan
Land management plan
Biodiversity management plan
Ecosystem management plan
Wildlife management plan
Forestry management plan
Fisheries management plan
Tourism management plan
Cultural heritage management plan
Community development plan
Public health plan
Education plan
Housing plan
Transportation plan
Urban planning plan
Rural development plan
Regional development plan
National development plan
International development plan

Response formats

plain text
JSON
HTML
XML
CSV
Markdown
Table
List
CSV
YAML
LaTeX
SQL
JavaScript
TypeScript
Python
PHP
Java
C#
C++
Ruby
Go
Swift
Kotlin
R
Perl
Shell (Bash)
Code snippets
Summary

Tones

Friendly
Professional
Academic
Casual
Formal
Enthusiastic
Neutral
Concise
Detailed
Humorous
Empathetic
Authoritative
Informative
Encouraging
Diplomatic
Respectful
Analytical
Conversational
Instructional
Matter-of-fact

Writing Styles

Technical
Scientific
Poetic
Narrative
Comparative
Analytical
Descriptive
Persuasive
Expository
Instructional
Journalistic
Historical
Philosophical
Legal
Medical
Business
Educational
Literary
Conversational
Socratic (question-based)
Summary style
Step-by-step guide
Pros and cons analysis
Hypothetical scenarios
Case study approach
Mother
Father
Uncle

Emotions

Happiness
Sadness
Anger
Fear
Surprise
Disgust
Contempt
Joy
Trust
Anticipation
Anxiety
Shame
Guilt
Embarrassment
Excitement
Envy
Jealousy
Pride
Relief
Satisfaction
Frustration
Despair
Hope
Nostalgia
Loneliness
Empathy
Gratitude
Regret
Love
Hatred
Confusion
Interest
Boredom
Contentment
Grief
Courage
Shyness
Enthusiasm
Nervousness
Admiration
Disappointment
Doubt
Optimism
Pessimism
Relaxation
Stress
Determination
Indifference
Resentment
Longing

Note

This module requires a valid Claude API key to function. Ensure you have the necessary permissions and comply with Claude's terms of service when using this module.

License

MIT

Created by

Dimitry Ivanov [email protected] # curl -A cv ivanoff.org.ua

Published

Vulnerabilities

Links

Maintainers

Keywords

Readme

Example

Table of Contents

All Features

Installation

Setup

Option 1: Direct Initialization

Option 2: Using Environment Variables

Usage

Vendors

OpenAI Vendor

How much does it cost?

Rate Limits

API Key

List of Models

Claude Vendor

How much does it cost?

Rate Limits

API Key

List of Models

Description

API Methods

Free-form ask

Description:

Free-form ask example

Result

Signature

Parameters

askOptionsType Interface

Roles

Tasks

Response formats

Tones

Writing Styles

Emotions

Note

License

Created by