EnergeticAI's Overview

EnergeticAI is TensorFlow.js optimized for serverless functions. It offers fast cold-start, small module size, and pre-trained models, making it ideal for incorporating open-source AI in your Node.js applications.

How to use EnergeticAI?

To use EnergeticAI in your Node.js apps, follow these steps:

1. Install EnergeticAI from NPM: npm install @energetic-ai/core
2. Require and initialize the model using the provided API methods.
3. Utilize pre-trained models, such as embeddings, classifiers, or semantic search, based on your specific use case.

EnergeticAI's Core Features

Optimized for serverless environments Fast cold-start performance Small module size Pre-trained models available Supports embeddings, classifiers, and semantic search

EnergeticAI's Use Cases

1 Building recommendations with sentence embeddings#2 Classifying text into categories with minimal training examples#3 Providing answers based on meaning with question-answering models

FAQ from EnergeticAI

What is EnergeticAI?

EnergeticAI is TensorFlow.js optimized for serverless functions. It provides a fast cold-start, small module size, and pre-trained models for integrating open-source AI in Node.js apps.

How do I use EnergeticAI in my Node.js apps?

To use EnergeticAI, you need to install it from NPM and then initialize the model using the provided API methods. You can then leverage the pre-trained models, such as embeddings, classifiers, or semantic search, based on your specific requirements.

What are the core features of EnergeticAI?

EnergeticAI is optimized for serverless environments and offers fast cold-start performance. It has a small module size and provides access to pre-trained models. It supports embeddings, classifiers, and semantic search.

What are the use cases for EnergeticAI?

EnergeticAI can be used for various tasks, including building recommendations with sentence embeddings, classifying text into categories with minimal training examples, and providing answers based on meaning with question-answering models.