semvec/pypi-packages huggingface dataset:
atlas-compute 0.1.13
Atlas Compute SDK
Requires Python: >=3.11

# Atlas Compute

Atlas Compute is a Python SDK for interacting with the Atlas platform's compute resources, including ClickHouse, MinIO, and OpenSearch.

## Installation

```bash
pip install atlas-compute
```

## Usage

### Connecting to Resources

```python
from atlas_compute.compute import ComputeClient

# Initialize the client
computeclient = ComputeClient(
    username="your_username",
    password="your_password",
    warehouse_id="your_warehouse_id"
)

# Get credentials
creds = computeclient.get_credentials()

# Create a data warehouse client
client = computeclient.create_client("warehouse")
result = client.execute("SELECT 1")
print(result)

# Create a storage client
client = computeclient.create_client("storage")
buckets = client.list_buckets()
print(buckets)

# Create a search and analytics client
client = computeclient.create_client("search_and_analytics")
buckets = client.search()
print(buckets)
```

## Features

- **Unified Authentication**: Authenticate once and access all resources.
- **Resource Clients**: Easy creation of clients for data warehouse, storage, and search & analytics.
- **Secure**: Handles credential retrieval and management securely.
vizro-ai 0.4.0
Vizro-AI provides pre-configured agents that help with data visualizations
Requires Python: >=3.10

<h1 align="center">Vizro-AI</h1>

<div align="center" markdown="1">

[![Python version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fmckinsey%2Fvizro%2Frefs%2Fheads%2Fmain%2Fvizro-ai%2Fpyproject.toml)](https://pypi.org/project/vizro/) [![PyPI version](https://badge.fury.io/py/vizro_ai.svg)](https://badge.fury.io/py/vizro_ai) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/mckinsey/vizro/blob/main/LICENSE.md) [![Documentation](https://readthedocs.org/projects/vizro-ai/badge/?version=latest)](https://vizro-ai.readthedocs.io/) [![OpenSSF Best Practices](https://www.bestpractices.dev/projects/7858/badge)](https://www.bestpractices.dev/projects/7858)

<picture>
  <source srcset="https://raw.githubusercontent.com/mckinsey/vizro/main/.github/images/toolkit_vizro_ai.gif">
  <img alt="Gif to demonstrate Vizro-AI" src="https://raw.githubusercontent.com/mckinsey/vizro/main/.github/images/toolkit_vizro_ai_fallback.png" width="600">
</picture>

<p>
<font size="+1">
Vizro-AI is a tool designed for generating data visualizations.
It serves as an extension to Vizro, leveraging natural language capabilities to empower users in creating interactive charts, making it a comprehensive solution for visual data exploration.
</font>
</p>

</div>

## Why Vizro-AI?

### Easy-to-use

One of the key strengths of Vizro-AI lies in its natural language capabilities, making it accessible to coding novices. Vizro-AI provides a user-friendly way to create interactive charts while offering detailed insights about the data and the generated code.

### Visually-optimized

Vizro-AI also caters to data practitioners who often find themselves spending more time in formatting rather than actual visualization creation. Vizro-AI enables users to speed up the process of creating visually appealing charts by using Vizro's themes which incorporate design best practices by default.

### End-to-End solution

Vizro-AI leverages the power of [Plotly](https://plotly.com/python/) to produce interactive charts, making it a complete solution for visual data exploration.

## Disclaimer

Please review the [Disclaimer](https://vizro-ai.readthedocs.io/en/latest/pages/explanation/disclaimer/) before using `vizro-ai` package.

## Documentation

For more information, please refer to our [Documentation](https://vizro.readthedocs.io/projects/vizro-ai/).

## Acknowledgment

Vizro-AI is powered by [Pydantic AI](https://ai.pydantic.dev/).