docs: update Python package README.md

Author: donghaoren

packages/backend/README.md

@@ -1,17 +1,57 @@
-# embedding-atlas
+# Embedding Atlas
 
-A Python package that provides a command line tool to visualize a dataset with embeddings.
+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.
 
-## Development Setup
+- Documentation: https://apple.github.io/embedding-atlas
+- GitHub: https://github.com/apple/embedding-atlas
 
-Install the [uv](https://github.com/astral-sh/uv) package manager.
+## Installation
 
-To launch the command line tool, run it with `uv run`.
+```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
-uv run embedding-atlas --help
+embedding-atlas path_to_dataset.parquet --x projection_x --y projection_y
 ```
 
-Run `./start.sh` to launch a test server with a online dataset.
+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.
+
+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
+```
 
-To build the wheel, run `./build.sh` to build the wheel.
+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.