semvec/pypi-packages huggingface dataset:
scalar-ninja 0.1.2
This plugin provides an easy way to render a beautiful API reference based on a OpenAPI/Swagger file with Django Ninja.

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# Scalar API Reference Plugin for Django Ninja

[![Version](https://img.shields.io/pypi/v/scalar-ninja)](https://pypi.org/project/scalar-ninja/)
[![Downloads](https://img.shields.io/pypi/dm/scalar-ninja)](https://pypi.org/project/scalar-ninja/)
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[![Discord](https://img.shields.io/discord/1135330207960678410?style=flat&color=5865F2)](https://discord.gg/scalar)

Scalar Django Ninja integration

## Documentation

[Read the documentation here](https://scalar.com/products/api-references/integrations/django-ninja)

## Changelog

See [CHANGELOG.md](https://github.com/scalar/scalar/blob/main/integrations/django-ninja/CHANGELOG.md) for a list of changes.

## Community

We are API nerds. You too? Let’s chat on Discord: <https://discord.gg/scalar>

## License

The source code in this repository is licensed under [MIT](https://github.com/scalar/scalar/blob/main/LICENSE).
scalar-fastapi 1.6.2
This plugin provides an easy way to render a beautiful API reference based on a OpenAPI/Swagger file with FastAPI.

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# Scalar API Reference Plugin for FastAPI

[![Version](https://img.shields.io/pypi/v/scalar-fastapi)](https://pypi.org/project/scalar-fastapi/)
[![Downloads](https://img.shields.io/pypi/dm/scalar-fastapi)](https://pypi.org/project/scalar-fastapi/)
[![License](https://img.shields.io/pypi/l/scalar-fastapi)](https://pypi.org/project/scalar-fastapi/)
[![Discord](https://img.shields.io/discord/1135330207960678410?style=flat&color=5865F2)](https://discord.gg/scalar)

FastAPI plugin to serve beautiful, interactive API documentation from OpenAPI/Swagger documents.

## Documentation

[Read the documentation here](https://scalar.com/products/api-references/integrations/fastapi)

## Changelog

See [CHANGELOG.md](https://github.com/scalar/scalar/blob/main/integrations/fastapi/CHANGELOG.md) for a list of changes.

## Community

We are API nerds. You too? Let’s chat on Discord: <https://discord.gg/scalar>

## License

The source code in this repository is licensed under [MIT](https://github.com/scalar/scalar/blob/main/LICENSE).
embedding-atlas 0.17.0
A tool for visualizing embeddings
Requires Python: >=3.11
Keywords: embedding, visualization

# Embedding Atlas

A Python package that provides a command line tool to visualize a dataset with embeddings. It also includes a Python Notebook (e.g., 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.

## Local Development

Launch Embedding Altas with a wine reviews dataset with `./start.sh` and the MNIST dataset with `./start_image.sh`.