semvec/pypi-packages huggingface dataset:
persistentsql 0.1.2
Peristent SQLite Database using S3 on Cloud Workspaces
Requires Python: >=3.6

# Persistent SQLite Database on Cloud Workspaces 

As a data science enthusiast, I have been using Lightning AI for my analytics and upskilling. It’s a great and cost-effective platform, but I’ve often faced one major limitation—the absence of SQL Server or any other persistent database.

This isn’t just a Lightning AI issue; it’s a common challenge with Google Colab, Codespaces, and other cloud workspaces. One common workaround is using SQLite, but there’s a catch—since cloud workspaces are ephemeral, all data is lost after every session, forcing me to recreate tables from scratch each time.

In Lightning AI, I could reuse the same database file within the same studio, but what if I wanted to access it across different studios? While there is a way around this, it’s neither easy nor straightforward.

Exploring Alternatives

I considered using AWS RDS or Azure Databases, but they are expensive and not ideal for someone using them for personal projects or learning.

The Solution: Persistent SQL Database for Cloud Workspaces

To address this, I built a python module 'persist-sql' that allows users to maintain a persistent SQL database in cloud environments using:
✅ SQLite Database for local queries
✅ AWS S3 Bucket for persistent storage

How to Use - 
1. pip install persistentsql
2. import persistentsql as ps
	ps.configure_aws(AWS_KEY, AWS_SECRET, AWS_BUCKET_NAME) 
	ps.connect_db('DEV')
	%sql select * from tablename
	ps.close_connection()

Limitations & Considerations

🔹 This works best when your database size is small.
🔹 If your data grows significantly, consider splitting tables across multiple database files to optimize performance.

GitHub Repository & Demo Notebook

I have shared sample notebook demonstrating how to use this solution. Feel free to explore, contribute, and share your feedback! 🚀
fiblat 0.7.0
A package for generating evenly distributed points on a sphere
Requires Python: >=3.13

# Fibonacci Lattice

[![pypi](https://img.shields.io/pypi/v/fiblat)](https://pypi.org/project/fiblat/)
[![build](https://github.com/erikbrinkman/fibonacci_lattice/actions/workflows/build.yml/badge.svg)](https://github.com/erikbrinkman/fibonacci_lattice/actions/workflows/build.yml)
[![docs](https://img.shields.io/badge/api-docs-blue)](https://erikbrinkman.github.io/fibonacci_lattice/)

A simple small python package for generating uniform points on the sphere.
This module provides to functions `fiblat.cube_lattice` and `fiblat.sphere_lattice`.
Both functions take a dimension and a number of points and return numpy arrays that are roughly evenly spaced in either the `[0, 1]` hypercube or the unit hypersphere.

## Installation

```bash
pip install fiblat
```

## Usage

```python
from fiblat import sphere_lattice, cube_lattice

cube = cube_lattice(3, 100)
sphere = sphere_lattice(3, 100)
```

## Development

```sh
uv run ruff format --check
uv run ruff check
uv run pyright
uv run pytest
```

## Publishing

```sh
rm -rf dist
uv build
uv publish --username __token__
```
difypy 0.1.2
Python SDK for Dify API
Requires Python: >=3.7

# DifyPy

Python SDK for Dify API- 简单易用的 Dify API 客户端

## 安装

```bash
pip install difypy
```

## 快速开始

```python
from dify_client import DifyClient

# 初始化客户端
client = DifyClient(
    api_key="your_api_key",
    base_url="https://your-dify-instance.com/v1"
)

# 运行工作流
response = client.workflows.run(
    inputs={"query": "你好"},
    user="user123"
)

# 打印结果
print(response.data)
```

## 功能特性

- 支持同步和异步操作
- 完整的工作流 API 支持
- 文件上传和管理
- 数据集操作
- 错误处理和重试机制
- 速率限制支持

## 文档

详细的 API 文档请参考 [Dify 官方文档](https://docs.dify.ai/)。

## 许可证

MIT