embedding-atlas


Nameembedding-atlas JSON
Version 0.9.0 PyPI version JSON
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home_pageNone
SummaryA tool for visualizing embeddings
upload_time2025-09-03 22:29:23
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docs_urlNone
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requires_python>=3.10
licenseNone
keywords embedding visualization
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            # Embedding Atlas

A Python package that provides a command line tool to visualize a dataset with embeddings. It also includes a Jupyter widget and a Streamlit widget.

- Documentation: https://apple.github.io/embedding-atlas
- GitHub: https://github.com/apple/embedding-atlas

## Installation

```bash
pip install embedding-atlas
```

and then launch the command line tool:

```bash
embedding-atlas [OPTIONS] INPUTS...
```

## Loading Data

You can load your data in two ways: locally or from Hugging Face.

### Loading Local Data

To get started with your own data, run:

```bash
embedding-atlas path_to_dataset.parquet
```

### Loading Hugging Face Data

You can instead load datasets from Hugging Face:

```bash
embedding-atlas huggingface_org/dataset_name
```

## Visualizing Embedding Projections

To visual embedding projections, pre-compute the X and Y coordinates, and specify the column names with `--x` and `--y`, such as:

```bash
embedding-atlas path_to_dataset.parquet --x projection_x --y projection_y
```

You may use the [SentenceTransformers](https://sbert.net/) package to compute high-dimensional embeddings from text data, and then use the [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html) package to compute 2D projections.

### Using Pre-computed Vectors

If you already have pre-computed embedding vectors (but not the 2D projections), you can specify the column containing the vectors with `--vector`:

```bash
embedding-atlas path_to_dataset.parquet --vector embedding_vectors
```

This will apply UMAP dimensionality reduction to your pre-existing vectors without recomputing embeddings. The vectors should be stored as lists or numpy arrays in your dataset.

You may also specify a column for pre-computed nearest neighbors:

```bash
embedding-atlas path_to_dataset.parquet --x projection_x --y projection_y --neighbors neighbors
```

The `neighbors` column should have values in the following format: `{"ids": [id1, id2, ...], "distances": [d1, d2, ...]}`.
If this column is specified, you'll be able to see nearest neighbors for a selected point in the tool.

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    "description": "# Embedding Atlas\n\nA Python package that provides a command line tool to visualize a dataset with embeddings. It also includes a Jupyter widget and a Streamlit widget.\n\n- Documentation: https://apple.github.io/embedding-atlas\n- GitHub: https://github.com/apple/embedding-atlas\n\n## Installation\n\n```bash\npip install embedding-atlas\n```\n\nand then launch the command line tool:\n\n```bash\nembedding-atlas [OPTIONS] INPUTS...\n```\n\n## Loading Data\n\nYou can load your data in two ways: locally or from Hugging Face.\n\n### Loading Local Data\n\nTo get started with your own data, run:\n\n```bash\nembedding-atlas path_to_dataset.parquet\n```\n\n### Loading Hugging Face Data\n\nYou can instead load datasets from Hugging Face:\n\n```bash\nembedding-atlas huggingface_org/dataset_name\n```\n\n## Visualizing Embedding Projections\n\nTo visual embedding projections, pre-compute the X and Y coordinates, and specify the column names with `--x` and `--y`, such as:\n\n```bash\nembedding-atlas path_to_dataset.parquet --x projection_x --y projection_y\n```\n\nYou may use the [SentenceTransformers](https://sbert.net/) package to compute high-dimensional embeddings from text data, and then use the [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html) package to compute 2D projections.\n\n### Using Pre-computed Vectors\n\nIf you already have pre-computed embedding vectors (but not the 2D projections), you can specify the column containing the vectors with `--vector`:\n\n```bash\nembedding-atlas path_to_dataset.parquet --vector embedding_vectors\n```\n\nThis will apply UMAP dimensionality reduction to your pre-existing vectors without recomputing embeddings. The vectors should be stored as lists or numpy arrays in your dataset.\n\nYou may also specify a column for pre-computed nearest neighbors:\n\n```bash\nembedding-atlas path_to_dataset.parquet --x projection_x --y projection_y --neighbors neighbors\n```\n\nThe `neighbors` column should have values in the following format: `{\"ids\": [id1, id2, ...], \"distances\": [d1, d2, ...]}`.\nIf this column is specified, you'll be able to see nearest neighbors for a selected point in the tool.\n",
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