AI-Connectify

AI-Connectify is a JavaScript library for connecting to various AI services at the same time. With AI-Connectify, you can easily access to AI services to perform a variety of tasks, including natural language processing, image processing, and more.

Table of Contents

• AI-Connectify
  • Table of Contents
  • Features
  • Installation
  • Currently supported AI services
    • ChatGPT instance
    • Claude instance
    • Cohere instance
    • DALL-E instance
    • Mistral instance
    • Stability instance
    • TensorFlow instance
  • Credits
  • Contributing
  • License

Features

• A modular architecture that allows for easy integration with various AI services.
• Support for natural language processing, computer vision and machine learning use cases (Just to start).
• Clean and simple library for each AI service.
• Well-documented and extensively tested codebase.

Installation

This is a Node.js module available through the npm registry. If this is a brand new project, make sure to create a package.json first with the npm init command.

Using npm:

$ npm install ai-connectify

Or if you're using Yarn, run:

$ yarn add ai-connectify

Once the package is installed, you can import the library using import or require approach:

const AIConnectify = require('ai-connectify');

Currently supported AI services

Here are some examples of how to use AI-Connectify:

Start a new ChatGPT instance

const chatGPT = new AIConnectify("ChatGPT", "YOUR_OPENAI_API_KEY").connector;

List of Available Methods: | Method | Description | |---|---| | setOrganizationId | Set the Organization ID to the ChatGPTClient instance | | setProjectId | Set the Project ID to the ChatGPTClient instance | | cancelFineTuningJob | Immediately cancel a fine-tune job | | createChatCompletion | Creates a model response for the given chat conversation | | createEmbeddings | Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms | | createFineTuningJob | Creates a fine-tuning job which begins the process of creating a new model from a given dataset | | createModeration | Given some input text, outputs if the model classifies it as potentially harmful across several categories | | createSpeech | Generates audio from the input text | | createTranscription | Transcribes audio into the input language | | createTranslation | Translates audio into English | | deleteFineTunedModel | Delete a fine-tuned model. You must have the Owner role in your organization to delete a model | | getFineTuningJob | Get info about a fine-tuning job | | getFineTuningJobCheckpoints | List checkpoints for a fine-tuning job | | getFineTuningJobEvents | Get status updates for a fine-tuning job | | getFineTuningJobs | List your organization's fine-tuning jobs | | getModel | Retrieves a model instance, providing basic information about the model such as the owner and permissioning | | getModels | Lists the currently available models, and provides basic information about each one such as the owner and availability |

Note: OpenAI API Reference You can interact with a ChatGPT instance using like reference the parameters and returns specified in the OpenAI API Reference.

Start a new Claude instance

const claude = new AIConnectify("Claude", "YOUR_CLAUDE_API_KEY").connector;

List of Available Methods: | Method | Description | |---|---| | setAnthropicVersion | Set the anthropic-version request header | | cancelMessageBatch | (Beta) Batches may be canceled any time before processing ends | | createMessage | Send a structured list of input messages with text and/or image content | | createMessageBatch | (Beta) The Message Batches API can be used to process multiple Messages API requests at once | | createMessageStream | Send a structured list of input messages with text and/or image content | | getMessageBatch | (Beta) The Message Batches API can be used to process multiple Messages API requests at once | | getMessageBatchList | (Beta) List all Message Batches within a Workspace | | getMessageBatchResults | (Beta) Streams the results of a Message Batch as a .jsonl file |

Note: Anthropic Claude API Reference You can interact with a Claude instance using like reference the parameters and returns specified in the Anthropic Claude API Reference.

Start a new Cohere instance

const cohere = new AIConnectify("Cohere", "YOUR_COHERE_API_KEY").connector;

List of Available Methods: | Method | Description | |---|---| | setClientName | Set the name of the project that is making the request | | authorizeConnector | Authorize with oAuth a connector by ID | | cancelEmbedJob | Allows to cancel an active embed job | | chat | Generates a text response to a user message | | chatWithStreaming | Generates a text response to a user message with streaming (stream of events) | | checkApiKey | Checks that the api key in the Authorization header is valid and active | | classify | Makes a prediction about which label fits the specified text inputs best | | createConnector | Creates a new connector | | createDataset | Create a dataset by uploading a file | | createEmbedJob | Launches an async Embed job for a Dataset of type embed-input | | createFineTunedModel | Trains and deploys a fine-tuned model | | deleteConnector | Delete a connector by ID | | deleteDataset | Delete a dataset by ID | | deleteFineTunedModel | Delete a fine-tuned model by ID | | detokenize | Takes tokens using byte-pair encoding and returns their text representation | | embed | Generates an embedding list of floating point numbers that captures semantic information about the text that it represents | | getConnector | Retrieve a connector by ID | | getConnectors | Returns a list of connectors ordered by descending creation date (newer first) | | getDataset | Retrieve a dataset by ID | | getDatasets | Generates a list datasets that have been created | | getDatasetUsage | Retrieves the dataset storage usage for your Organization | | getEmbedJob | Retrieves the details about an embed job started by the same user | | getEmbedJobs | Generates a list embed job endpoint allows users to view all embed jobs history for that specific user | | getFineTunedModel | Retrieve a fine-tuned model by ID | | getFineTunedModelChronology | Retrieves the chronology of statuses the fine-tuned model has been through | | getFineTunedModelMetrics | Retrieves metrics measured during the training of a fine-tuned model | | getFineTunedModels | Returns a list of fine-tuned models available for use | | getModel | Returns the details of a model, provided its name | | getModels | List of models available for use. The list contains models from Cohere as well as your fine-tuned models | | rerank | Takes in a query and a list of texts and produces an ordered array with each text assigned a relevance score | | tokenize | Generates a splits input text into smaller units called tokens using byte-pair encoding (BPE) | | updateConnector | Update a connector by ID | | updateFineTunedModel | Updates a fine-tuned model by ID |

Note: Cohere API Reference You can interact with a Cohere instance using like reference the parameters and returns specified in the Cohere API Reference.

Start a new DALL-E instance

const dalle = new AIConnectify("DALLE", "YOUR_OPENAI_API_KEY").connector;

List of Available Methods: | Method | Description | |---|---| | setOrganizationId | Set the Organization ID to the ChatGPTClient instance | | setProjectId | Set the Project ID to the ChatGPTClient instance | | createImageVariation | Creates a variation of a given image | | createImage | Given a prompt and/or an input image, the model will generate a new image | | createImageEdit | Creates an edited or extended image given an original image and a prompt | | getModel | Retrieves a model instance, providing basic information about the model such as the owner and permissioning | | getModels | Lists the currently available models, and provides basic information about each one such as the owner and availability |

Note: OpenAI API Reference You can interact with a DALL-E instance using like reference the parameters and returns specified in the OpenAI API Reference.

Start a new Mistral instance

const mistral = new AIConnectify("Mistral", "YOUR_OPENAI_API_KEY").connector;

List of Available Methods: | Method | Description | |---|---| | agentsCompletion | Agents API | | archiveFineTuningModel | Archive a fine-tuned model | | cancelFineTuningJob | Request the cancellation of a fine tuning job | | createChatCompletion | Chat Completion API | | createFineTuningJob | Creates a fine-tuning job which begins the process of creating a new model from a given dataset | | deleteFineTuningModel | Delete a fine-tuned model | | embeddings | Embeddings API | | fimCompletion | Fill-in-the-middle API | | getFineTuningJob | Get a fine-tuned job details by its UUID | | getFineTuningJobs | Get a list of fine-tuning jobs for your organization and user | | getModel | Retrieve a model information | | getModels | List all models available to the user | | startFineTuningJob | Request the start of a validated fine tuning job | | unarchiveFineTuningModel | Archive a fine-tuned model | | updateFineTuningModel | Update a model name or description |

Note: Mistral API Reference You can interact with a Mistral instance using like reference the parameters and returns specified in the Mistral API Reference.

Start a new Stability instance

const stability = new AIConnectify("Stability", "YOUR_STABILITY_API_KEY").connector;

List of Available Methods: | Method | Description | |---|---| | setClientId | Set the client ID of your application | | setClientUserId | Set an unique identifier for your end user | | setClientVersion | Set the version of your application | | controlSketch | This service offers an ideal solution for design projects that require brainstorming and frequent iterations | | controlStructure | This service excels in generating images by maintaining the structure of an input image | | controlStyle | This service extracts stylistic elements from an input image (control image) and uses it to guide the creation of an output image based on the prompt | | erase | Removes unwanted objects, such as blemishes on portraits or items on desks, using image masks | | generateImageCore | Tools for generating new images with the best quality achievable at high speed | | generateImageDiffusion | Tools for generating new images using a Stable Diffusion 3 model | | generateImageUltra | Creates the highest quality images with unprecedented prompt understanding | | getImageToVideo | Fetch the result of an image-to-video generation by ID | | getUpscaleCreative | Fetch the result of an upscale generation by ID | | imageToVideo | Generate a short video based on an initial image with Stable Video Diffusion, a latent video diffusion model | | inpaint | Intelligently modify images by filling in or replacing specified areas with new content based on the content of a "mask" image | | outpaint | The Outpaint service inserts additional content in an image to fill in the space in any direction | | removeBackground | The Remove Background service accurately segments the foreground from an image and implements and removes the background | | searchAndRecolor | The Search and Recolor service provides the ability to change the color of a specific object in an image using a prompt | | searchAndReplace | The Search and Replace service is a specific version of inpainting that does not require a mask | | upscaleConservative | Tools for increasing the size of your existing images | | upscaleCreative | Takes images between 64x64 and 1 megapixel and upscales them all the way to 4K resolution | | videoStableFast | Stable Fast 3D generates high-quality 3D assets from a single 2D input image | | upscaleFast | Fast Upscaler service enhances image resolution by 4x using predictive and generative AI |

Note: Stability API Reference You can interact with a Stability instance using like reference the parameters and returns specified in the Stability API Reference.

Start a new TensorFlow instance

const tf = new AIConnectify("TensorFlow").connector;

Note: TensorFlow API Reference You can interact with a TensorFlow instance using like reference the parameters and returns specified in the TensorFlow API Reference.

Credits

The original author of AI-Connectify is Brian Elizondo

Contributing

Welcome to AI-Connectify! This is my first library, and I'm excited to see it grow and improve with the help of the community. I'm grateful for any contributions you might have to offer, whether it's reporting an issue, submitting a pull request, or just giving some feedback.

Please don't hesitate to reach out if you have any questions, comments, or concerns. I'm open to suggestions and eager to collaborate with other developers to make AI-Connectify even better. Thank you for your support!

List of all contributors

License

AI-Connectify is an open-source library released under the MIT License. This means you are free to use, modify, and distribute the software for personal and commercial purposes, as long as you retain the original copyright and license notice. No fees are charged for using this library. The library is provided as-is, without any warranty, and there is no financial gain or charge for its use. For more details, refer to the MIT file.