semvec/pypi-packages huggingface dataset:
q1-storage 0.1.0
Simple local file storage backed by SQLite with optional AES-GCM encryption
Requires Python: >=3.9

# Q1: Simple Local File Storage

A production-grade, pip-installable Python package that offers simple local file storage backed by SQLite and optional AES-GCM encryption.

## Features

- Store and retrieve files with a simple API
- Transactional operations with ACID guarantees
- Optional at-rest AES-256-GCM encryption
- Integrity verification with SHA-256
- File deduplication
- Soft and hard delete with recovery options
- Concurrency support with SQLite WAL mode

## Installation

```bash
pip install q1
```

For encryption support:
```bash
pip install q1[crypto]
```

## Quick Start

```python
from q1 import Q1

# Create or open a storage location
with Q1("./my_store") as store:
    # Store a file
    file_id = store.put("example.txt", b"Hello, world!")
    
    # Retrieve the file
    content = store.get(file_id)
    
    # Get file information
    info = store.info(file_id)
    print(f"File name: {info.name}, size: {info.size} bytes")
    
    # List all files
    for file_info in store.list():
        print(f"ID: {file_info.id}, Name: {file_info.name}")
    
    # Delete a file (soft delete by default)
    store.delete(file_id)
    
    # Undelete a file
    store.undelete(file_id)
    
    # Permanently delete
    store.delete(file_id, hard=True)
```

## Using Encryption

```python
from q1 import Q1
from q1.crypto import AesGcmCrypto

# Create an encryption provider with a secure key
crypto = AesGcmCrypto(key=b"a" * 32)  # Use a proper secure key in production!

# Open storage with encryption
with Q1("./encrypted_store", crypto_provider=crypto) as store:
    # All operations work the same as before, but data is encrypted at rest
    file_id = store.put("secret.txt", b"Confidential information")
    content = store.get(file_id)  # Automatically decrypted
```

## Development

### Setting up the development environment

```bash
# Clone the repository
git clone https://github.com/yourusername/q1.git
cd q1

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev,crypto]"
```

### Running tests

```bash
pytest
```

### Type checking

```bash
mypy --strict q1
```

### Building the package

```bash
flit build
```

## Continuous Integration

This project uses GitHub Actions for continuous integration, running tests on:
- Python 3.9, 3.10, 3.11, and 3.12
- Windows, macOS, and Linux

The CI pipeline performs:
1. Test execution with coverage reporting
2. Type checking with mypy
3. Package building
4. Optional PyPI deployment for tagged releases

## License

MIT License - See LICENSE file for details.
talkabout 0.1.3
AI-powered object query proxy using Claude AI
Requires Python: >=3.8

# talkabout

[![PyPI version](https://badge.fury.io/py/talkabout.svg)](https://badge.fury.io/py/talkabout)

_(inspired by [https://github.com/awwaiid/gremllm](https://github.com/awwaiid/gremllm))_

Ask anything about any Python object using natural language and get Python code executed automatically. Passes the object's `repr()` and your query to Claude API. For now, Claude responds to the system query with single-line code statements only

## Installation

```bash
pip install talkabout
```

## Setup

You'll need an Anthropic API key. Set it as an environment variable:

```bash
export ANTHROPIC_API_KEY="your-api-key-here"
```

Or pass it directly when creating a Talk instance:

```python
from talkabout import Talk
talk = Talk(my_object, api_key="your-api-key-here")
```

## Usage

A simple example:

```python
import numpy as np
from talkabout import Talk

x = np.random.uniform(size=50)

talk = Talk(x)
talk('90th percentile')

# printed output
Executing code: np.percentile(obj, 90)

Out[1]: np.float64(0.8442100946036629)
```

You can also use the .chat() method to just ask a question and get a textual response:

```python
import torch
from talkabout import Talk

optimizer = torch.optim.Adam([torch.Tensor([0.0])], lr=0.001)
talk = Talk(optimizer)
talk.chat('is this the best optimiser ever?')

# printed output
Out[6]: 'Let me help you evaluate the Adam optimizer:\n\nAdam is generally considered ...'
```

A more complicated example - inspect financials of a company using Yahoo Finance API (this use-case actually motivated this package, because those yfinance dataframes are messy):

```python
import yfinance as yf
from talkabout import Talk

pypl = yf.Ticker('PYPL')

talk = Talk(pypl)
talk('qoq Oper CF over debt; use .loc')

# printed output
Executing code: pypl.quarterly_cash_flow.loc['Operating Cash Flow'].pct_change() / pypl.quarterly_balancesheet.loc['Total Debt']

Out[6]:
2025-03-31             NaN
2024-12-31    1.076823e-10
2024-09-30   -3.265984e-11
2024-06-30   -5.669014e-12
2024-03-31    2.654644e-11
2023-12-31             NaN
dtype: float64
```
lhcli 0.1.0
LightHope CLI Tools - Enterprise Developer Tools
Requires Python: >=3.10

# LightHope CLI Tools (lhcli)

<p align="center">
  <img src="https://lighthope.com/logo.png" alt="LightHope Logo" width="200">
</p>

<p align="center">
  <strong>企业级开发者生产力工具套件</strong>
</p>

<p align="center">
  <a href="https://pypi.org/project/lhcli/"><img src="https://img.shields.io/pypi/v/lhcli.svg" alt="PyPI version"></a>
  <a href="https://pypi.org/project/lhcli/"><img src="https://img.shields.io/pypi/pyversions/lhcli.svg" alt="Python versions"></a>
  <a href="https://github.com/lighthope-tech/lhcli/blob/main/LICENSE"><img src="https://img.shields.io/github/license/lighthope-tech/lhcli.svg" alt="License"></a>
  <a href="https://github.com/lighthope-tech/lhcli/actions"><img src="https://github.com/lighthope-tech/lhcli/workflows/CI/badge.svg" alt="CI"></a>
</p>

## 🚀 快速开始

### 安装

```bash
pip install lhcli
```

### 使用示例

```bash
# Mermaid 图表生成
lh diagram mermaid flowchart.mmd

# 代码格式化
lh dev format src/

# Base64 编码
lh util base64 "Hello LightHope"
```

## 📚 文档

完整文档请访问: [https://docs.lighthope.com/cli](https://docs.lighthope.com/cli)

## 🤝 贡献

欢迎提交 Pull Request！请查看 [CONTRIBUTING.md](CONTRIBUTING.md) 了解详情。

## 📄 许可证

本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件。

---

<p align="center">Made with ❤️ by LightHope Technologies</p>