EmbSystem

A lightweight semantic text embedding engine built in TypeScript.

Fast, dependency-free, and perfect for AI pipelines, semantic search, clustering, and NLP tasks.

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🔥 Overview

EmbSystem is a minimal, high-performance embedding engine designed for:

- Semantic Text Encoding
- Similarity Search
- Clustering
- NLP Preprocessing
- Lightweight AI Memory Systems
- Retrieval-Augmented Generation (RAG)
- DeepAIM internal components

Unlike heavy ML frameworks, EmbSystem requires no TensorFlow, PyTorch, or WASM.
It runs purely on TypeScript and CPU — fast, predictable, and extremely portable.

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✨ Key Features

- ⚡ Fast hashing-based embeddings
- 🔍 Semantic similarity scoring
- 📚 Keyword decoding (reverse embedding)
- 🧠 K-Means clustering included
- 🌐 Multi-language support (English & Arabic)
- 🧩 128–1024 dimensional vectors
- 💼 100% TypeScript, zero dependencies
- 🚀 Runs on Node.js 20+ / 24+
- 🧪 Perfect for building lightweight AI pipelines

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📦 Installation

Clone or include in any Node.js project:

``bash
npm install
`
``

(Optional) Build TypeScript:

`bash
npm run build
`

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🧠 Usage Example

`ts
import EmbeddingSystem from "./src/embedding.js";

const texts = [
"Machine learning is amazing",
"Deep learning uses neural networks",
"Python is great for AI",
"Natural language processing",
"التعلم الآلي رائع",
"الشبكات العصبية قوية",
];

EmbeddingSystem.initialize(texts, 128);

const embedding = EmbeddingSystem.encode("machine learning");
console.log("Embedding:", embedding.slice(0, 5), "...");

const decoded = EmbeddingSystem.decode(embedding, 3);
console.log("Decoded words:", decoded);

const results = EmbeddingSystem.semanticSearch("neural networks", texts, 3);
console.log("Semantic Search:", results);

const clusters = EmbeddingSystem.cluster(texts, 2);
console.log("Clusters:", clusters);

console.log("System Info:", EmbeddingSystem.getInfo());
`

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🧪 Sample Output

`
Embedding: [0.01, -0.03, -0.12, 0.07, 0.002 ...]

Decoded words:
[ "machine", "learning", "deep" ]

Semantic Search:
Score: 0.610 - Deep learning uses neural networks
Score: 0.098 - Natural language processing
Score: -0.001 - Python is great for AI

Clusters:
Cluster 0: ["Python is great for AI", "الشبكات العصبية قوية"]
Cluster 1: ["Machine learning is amazing", "Deep learning uses neural networks", "Natural language processing", "التعلم الآلي رائع"]

System Info:
{ vocabularySize: 21, dimension: 128, isInitialized: true }
`

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🗂 Project Structure

`
src/
embedding.ts
test.ts
package.json
README.md
`

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🛠 Scripts

`bash
npm run dev # Runs src/main.js
npm run test # Runs test.js
npm run build # Compiles TypeScript into dist/
npm start # Runs dist/main.js after build
``

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📜 License

MIT License
Created by QuickDigi 💙

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🌟 Contribution

Pull requests and improvements are always welcome!
If you use EmbSystem in your project, feel free to share your work.

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