Embedding Atlas

Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.

Features

• 🏷️ Automatic data clustering & labeling: Interactively visualize and navigate overall data structure.

• 🫧 Kernel density estimation & density contours: Easily explore and distinguish between dense regions of data and outliers.

• 🧊 Order-independent transparency: Ensure clear, accurate rendering of overlapping points.

• 🔍 Real-time search & nearest neighbors: Find similar data to a given query or existing data point.

• 🚀 WebGPU implementation (with WebGL 2 fallback): Fast, smooth performance (up to few million points) with modern rendering stack.

• 📊 Multi-coordinated views for metadata exploration: Interactively link and filter data across metadata columns.

Please visit https://apple.github.io/embedding-atlas for a demo and documentation.

Get started

To use Embedding Atlas with Python:

pip install embedding-atlas

embedding-atlas <your-dataset.parquet>

In addition to the command line tool, Embedding Atlas is also available as a Python Notebook (e.g., Jupyter) widget:

from embedding_atlas.widget import EmbeddingAtlasWidget

# Show the Embedding Atlas widget for your data frame:
EmbeddingAtlasWidget(df)

Finally, components from Embedding Atlas are also available in an npm package:

npm install embedding-atlas

import { EmbeddingAtlas, EmbeddingView } from "embedding-atlas";

// or with React:
import { EmbeddingAtlas, EmbeddingView } from "embedding-atlas/react";

// or Svelte:
import { EmbeddingAtlas, EmbeddingView } from "embedding-atlas/svelte";

For more information, please visit https://apple.github.io/embedding-atlas/overview.html.

BibTeX

For the Embedding Atlas tool:

@misc{ren2025embedding,
  title={Embedding Atlas: Low-Friction, Interactive Embedding Visualization},
  author={Donghao Ren and Fred Hohman and Halden Lin and Dominik Moritz},
  year={2025},
  eprint={2505.06386},
  archivePrefix={arXiv},
  primaryClass={cs.HC},
  url={https://arxiv.org/abs/2505.06386},
}

For the algorithm that automatically produces clusters and labels in the embedding view:

@misc{ren2025scalable,
  title={A Scalable Approach to Clustering Embedding Projections},
  author={Donghao Ren and Fred Hohman and Dominik Moritz},
  year={2025},
  eprint={2504.07285},
  archivePrefix={arXiv},
  primaryClass={cs.HC},
  url={https://arxiv.org/abs/2504.07285},
}

Development

For development instructions, please visit https://apple.github.io/embedding-atlas/develop.html, or checkout packages/docs/develop.md.

License

This code is released under the MIT license.