semvec/pypi-packages huggingface dataset:
infisical-python 2.3.6
Official Infisical SDK for Python (New)
Requires Python: >=3.7

<h1 align="center">
    <a href="https://github.com/Infisical/infisical">
        <img width="300" src="https://raw.githubusercontent.com/Infisical/infisical-node/main/img/logoname-white.svg#gh-dark-mode-only" alt="infisical">
    </a>
</h1>

<p align="center">
  <p align="center">Open-source, end-to-end encrypted tool to manage secrets and configs across your team and infrastructure.</p>
</p>

# Deprecation Notice (Important)

Please be aware that this SDK is officially fully deprecated and is no longer supported by Infisical.
Please move to use the [Official Infisical Python SDK](https://github.com/Infisical/python-sdk-official) to receive the latest security updates.


# Documentation
[You can find the documentation here](https://infisical.com/docs/sdks/languages/python)
echo-mcp-server-for-testing 0.1.5
A simple echo MCP (Model Context Protocol) Server for testing MCP Clients
Requires Python: >=3.10
Keywords: mcp, server, testing

# echo-mcp-server-for-testing

A simple echo MCP (Model Context Protocol) Server with a simple `echo_tool` for testing MCP Clients.
It is also great as a template for new MCP Servers.

## Usage

Install [uv](https://docs.astral.sh/uv/) and add the server to an MCP config using `uvx`:

```json
{
    "name": "echo-mcp-server-for-testing",
    "command": "uvx",
    "args": [
        "echo-mcp-server-for-testing"
    ],
    "env": {
        "SECRET_KEY": "123456789"
    }
}
```

or clone the repo and use `uv` with a directory:

```json
{
    "name": "echo-mcp-server-for-testing",
    "command": "uv",
    "args": [
        "--directory",
        "path/to/root/dir/",
        "run",
        "main.py"
    ],
    "env": {
        "SECRET_KEY": "123456789"
    }
}
```

## Development

### Testing

Clone the repo and use [mcp-client-for-testing](https://github.com/piebro/mcp-client-for-testing) to test the tools of the server.

```bash
uvx mcp-client-for-testing \
    --config '
    [
        {
            "name": "echo-mcp-server-for-testing",
            "command": "uv",
            "args": [
                "--directory", 
                "path/to/root/dir/", 
                "run", 
                "main.py"
            ],
            "env": {
                "SECRET_KEY": "123456789"
            }
        }
    ]
    ' \
    --tool_call '{"name": "echo_tool", "arguments": {"message": "Hello, world!"}}'
```

### Formatting and Linting

The code is formatted and linted with ruff:

```bash
uv run ruff format
uv run ruff check --fix
```

### Building with uv

Build the package using uv:

```bash
uv build
```

### Releasing a New Version

To release a new version of the package to PyPI:

1. Create and push a new Git tag following semantic versioning:
   ```bash
   git tag v0.2.0
   git push origin v0.2.0
   ```

The GitHub Actions workflow will automatically build and publish the package to PyPI when a new tag is pushed. The version number will be derived directly from the Git tag.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
human-rag 0.0.1.dev0
A RAG system that operates like a human
Requires Python: >=3.11

<div align="center">
  <h1>📒 HumanRAG</h1>
  <p><strong>A RAG system that operates like a human</strong></p>
  <p>
    <a href="https://pypi.org/project/human_rag/" > 
    	<img src="https://img.shields.io/badge/python->=3.11-blue">
    </a>
    <a href="https://pypi.org/project/human_rag/">
      <img src="https://img.shields.io/pypi/v/human_rag.svg">
    </a>
  </p>
</div>
complextorch 1.2.0
A lightweight complex-valued neural network package built on PyTorch
Requires Python: >=3.6

<h1 align="center">ComplexTorch</h1>
<p align="center">
  <img src="https://img.shields.io/badge/complextorch-black?style=for-the-badge">
  
</p>
<h3>

[Homepage](https://github.com/josiahwsmith10/complextorch) | [Documentation](https://complextorch.readthedocs.io/en/latest/)

</h3>

# Complex PyTorch
(Available on [PyPI](https://pypi.org/project/complextorch/))

Author: Josiah W. Smith, Ph.D.

A lightweight complex-valued neural network package built on PyTorch.

This is a package built on [PyTorch](https://pytorch.org/) with the intention of implementing light-weight interfaces for common complex-valued neural network operations and architectures. 
Notably, we include efficient implementations for linear, convolution, and attention modules in addition to activation functions and normalization layers such as batchnorm and layernorm.

Although there is an emphasis on 1-D data tensors, due to a focus on signal processing, communications, and radar data, many of the routines are implemented for 2-D and 3-D data as well.

### Version 1.1 Release Notes:
- Methods have been renamed to reflect identical names in PyTorch, e.g., `complextorch.nn.CVConv1d` was renamed to `complextorch.nn.Conv1d`. This change was implemented for quick conversion from PyTorch to `complextorch`. 
- Use of `torch.Tensor` is now recommended over `complextorch.CVTensor`. Previous speed advantages of `complextorch.CVTensor` are no longer present if using a version of PyTorch newer than 2.1.0. 
- Similarly, previous implementations of `complextorch.nn.Conv1d` (for 1-D, 2-D, 3-D, and transposed convolution) and `complextorch.nn.Linear` have been renamed with the prefix `Slow` as PyTorch's native convolution and linear operators now outperform that of `complextorch`. Now, `complextorch.nn.Conv1d`, for example, uses `torch.nn.Conv1d` with `dtype=torch.float` for maximum efficiency. 

## Documentation

Please see [Read the Docs](https://complextorch.readthedocs.io/en/latest/index.html) or our [arXiv](https://github.com/josiahwsmith10/complextorch) paper, which is also located at ```docs/complextorch_paper.pdf```.

## Dependencies

This library requires numpy and PyTorch.[PyTorch](https://pytorch.org/get-started/locally/) should be installed to your environment using the compute platform (CPU/GPU) settings for your machine. PyTorch will not be automatically installed with the installation of complextorch and MUST be installed manually by the user.

## Installation:

IMPORTANT:
Prior to installation, [install PyTorch](https://pytorch.org/get-started/locally/) to your environment using your preferred method using the compute platform (CPU/GPU) settings for your machine.

Using [pip](https://pypi.org/project/complextorch/)

```sh
pip install complextorch
```
From the source:
```sh
git clone https://github.com/josiahwsmith10/complextorch.git
cd complextorch
pip install -r requirements.txt
pip install . --use-pep517
```

## Basic Usage

``` python
import complextorch as cT

x = cT.randn(64, 5, 7)
```