[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-73633":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},73633,"embedding-atlas","apple\u002Fembedding-atlas","apple","Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.","https:\u002F\u002Fapple.github.io\u002Fembedding-atlas\u002F",null,"TypeScript",4800,300,34,20,0,3,14,26,9,75.04,"MIT License",false,"main",true,[27,28],"embedding","visualization","2026-06-12 04:01:10","# Embedding Atlas\n\n[![NPM Version](https:\u002F\u002Fimg.shields.io\u002Fnpm\u002Fv\u002Fembedding-atlas)](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fembedding-atlas)\n[![PyPI - Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fembedding-atlas)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fembedding-atlas\u002F)\n[![Paper](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpaper-arXiv:2505.06386-b31b1b.svg)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.06386)\n![Build](https:\u002F\u002Fgithub.com\u002Fapple\u002Fembedding-atlas\u002Factions\u002Fworkflows\u002Fci.yml\u002Fbadge.svg)\n[![GitHub License](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fapple\u002Fembedding-atlas)](.\u002FLICENSE)\n\nEmbedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.\n\n**Features**\n\n- 🏷️ **Automatic data clustering & labeling:**\n  Interactively visualize and navigate overall data structure.\n\n- 🫧 **Kernel density estimation & density contours:**\n  Easily explore and distinguish between dense regions of data and outliers.\n\n- 🧊 **Order-independent transparency:**\n  Ensure clear, accurate rendering of overlapping points.\n\n- 🔍 **Real-time search & nearest neighbors:**\n  Find similar data to a given query or existing data point.\n\n- 🚀 **WebGPU implementation (with WebGL 2 fallback):**\n  Fast, smooth performance (up to few million points) with modern rendering stack.\n\n- 📊 **Multi-coordinated views for metadata exploration:**\n  Interactively link and filter data across metadata columns.\n\nPlease visit \u003Chttps:\u002F\u002Fapple.github.io\u002Fembedding-atlas> for a demo and documentation.\n\n\u003Cpicture>\n  \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\".\u002Fpackages\u002Fdocs\u002Fpublic\u002Fassets\u002Fembedding-atlas-dark.png\">\n  \u003Cimg alt=\"screenshot of Embedding Atlas\" src=\".\u002Fpackages\u002Fdocs\u002Fpublic\u002Fassets\u002Fembedding-atlas-light.png\">\n\u003C\u002Fpicture>\n\n## Get started\n\nTo use Embedding Atlas with Python:\n\n```bash\npip install embedding-atlas\n\nembedding-atlas \u003Cyour-dataset.parquet>\n```\n\nIn addition to the command line tool, Embedding Atlas is also available as a Python Notebook (e.g., Jupyter) widget:\n\n```python\nfrom embedding_atlas.widget import EmbeddingAtlasWidget\n\n# Show the Embedding Atlas widget for your data frame:\nEmbeddingAtlasWidget(df)\n```\n\nFinally, components from Embedding Atlas are also available in an npm package:\n\n```bash\nnpm install embedding-atlas\n```\n\n```js\nimport { EmbeddingAtlas, EmbeddingView } from \"embedding-atlas\";\n\n\u002F\u002F or with React:\nimport { EmbeddingAtlas, EmbeddingView } from \"embedding-atlas\u002Freact\";\n\n\u002F\u002F or Svelte:\nimport { EmbeddingAtlas, EmbeddingView } from \"embedding-atlas\u002Fsvelte\";\n```\n\nFor more information, please visit \u003Chttps:\u002F\u002Fapple.github.io\u002Fembedding-atlas\u002Foverview.html>.\n\n## BibTeX\n\nFor the Embedding Atlas tool:\n\n```bibtex\n@misc{ren2025embedding,\n  title={Embedding Atlas: Low-Friction, Interactive Embedding Visualization},\n  author={Donghao Ren and Fred Hohman and Halden Lin and Dominik Moritz},\n  year={2025},\n  eprint={2505.06386},\n  archivePrefix={arXiv},\n  primaryClass={cs.HC},\n  url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.06386},\n}\n```\n\nFor the algorithm that automatically produces clusters and labels in the embedding view:\n\n```bibtex\n@misc{ren2025scalable,\n  title={A Scalable Approach to Clustering Embedding Projections},\n  author={Donghao Ren and Fred Hohman and Dominik Moritz},\n  year={2025},\n  eprint={2504.07285},\n  archivePrefix={arXiv},\n  primaryClass={cs.HC},\n  url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.07285},\n}\n```\n\n## Development\n\nFor development instructions, please visit \u003Chttps:\u002F\u002Fapple.github.io\u002Fembedding-atlas\u002Fdevelop.html>, or checkout `packages\u002Fdocs\u002Fdevelop.md`.\n\n## License\n\nThis code is released under the [`MIT license`](LICENSE).\n","Embedding Atlas 是一个用于大型嵌入向量的交互式可视化工具，它支持嵌入向量及其元数据的可视化、交叉过滤和搜索。该工具采用TypeScript编写，具备自动数据聚类与标记、核密度估计及密度轮廓绘制等功能，能够清晰准确地渲染重叠点，并提供实时搜索与最近邻查询。此外，Embedding Atlas 使用WebGPU（并支持WebGL 2回退）以实现高性能的数据处理能力，适合处理数百万级别的数据点。其多视图协调功能使得用户可以在不同元数据列之间进行交互式的链接和过滤操作，非常适合需要对大规模嵌入向量进行探索分析的研究人员或开发者使用。",2,"2026-06-11 03:46:29","high_star"]