clipkit-embeddings

Fast CLIP text and image embedding toolkit for Node.js and browser using ONNX.

clipkit-embeddings is a TypeScript library and CLI that enables high-performance CLIP embeddings for both text and images, powered by onnxruntime-node. It allows you to use CLIP models offline for semantic search, similarity computation, or zero-shot classification — both programmatically and via the command line.

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✨ Features

• 🔤 Embed text using clip-text.onnx
• 🖼️ Embed images using clip-image.onnx
• 🧠 Cosine similarity for comparing embeddings
• ⚡️ Fast inference with ONNXRuntime
• 🧰 CLI tool for quick use in scripts
• 🌐 Works in Node.js and browser (with bundler)

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📦 Installation

As a library:

npm install clipkit-embeddings

As a library:

npm install -g clipkit-embeddings

🚀 Usage

🧠 Text Embedding

import { loadTextModel, embedText } from "clipkit-embeddings";

await loadTextModel("onnx/clip-text.onnx");
const embedding = await embedText("a photo of a dog");
console.log(embedding);

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🖼️ Image Embedding

import { loadImageModel, embedImage } from "clipkit-embeddings";
import { preprocessImage } from "clipkit-embeddings/utils";

await loadImageModel("onnx/clip-image.onnx");
const tensor = await preprocessImage("test/cat.jpg");
const embedding = await embedImage(tensor);
console.log(embedding);

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🔁 Cosine Similarity

import { cosineSimilarity } from "clipkit-embeddings/utils";

const similarity = cosineSimilarity(imageEmbedding, textEmbedding);
console.log("Cosine similarity:", similarity.toFixed(4));

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🧪 CLI Usage

Embed text:

clipkit text "a photo of a cat"

Embed image:

clipkit image ./path/to/image.jpg

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📁 Project Structure

clipkit-embeddings/
├── cli/              # CLI logic
├── src/              # Core logic (text/image/utils)
├── test/             # Sample test files and images
├── onnx/             # Model files (.onnx)
│   ├── clip-text.onnx
│   └── clip-image.onnx
├── package.json
├── tsconfig.json
├── README.md
└── LICENSE

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👨‍💻 Contributing

Contributions, issues and feature requests are welcome!
Please open an issue to discuss any major changes beforehand.

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📚 References

• CLIP Paper (OpenAI)
• ONNX Runtime
• onnxruntime-node on npm
• Sharp image processing

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📝 License

This project is licensed under the MIT License.
See the LICENSE file for full text.

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✍️ Author

Divyansh Saraswat
GitHub • LinkedIn • Twitter