semvec/pypi-packages huggingface dataset:
zap-protocol 1.0.0
ZAP - Zero-copy Application Protocol for AI agent communication. See zap-schema for the main package.
Requires Python: >=3.9
Keywords: ai-agents, capnproto, mcp, protocol, rpc, zap

# zap-protocol

This package re-exports [`zap-schema`](https://pypi.org/project/zap-schema/).

## Installation

```bash
pip install zap-schema
```

## About ZAP

ZAP (Zero-copy Application Protocol) is a high-performance RPC protocol for AI agent communication featuring:

- Clean whitespace-significant schema language
- Zero-copy serialization via Cap'n Proto
- Multi-transport support (TCP, WebSocket, HTTP/SSE, Unix socket)
- MCP gateway for aggregating Model Context Protocol servers
- Post-quantum cryptography (ML-KEM, ML-DSA)
- Agent consensus voting
- W3C DID identity

## Links

- [GitHub](https://github.com/zap-protocol/zap)
- [Documentation](https://zap-protocol.github.io/zap)
- [PyPI](https://pypi.org/project/zap-schema/)
zap-proto 1.0.0
ZAP - Zero-copy Application Protocol for AI agent communication. See zap-schema for the main package.
Requires Python: >=3.9
Keywords: ai-agents, capnproto, mcp, protocol, rpc, zap

# zap-proto

This package re-exports [`zap-schema`](https://pypi.org/project/zap-schema/).

## Installation

```bash
pip install zap-schema
```

## About ZAP

ZAP (Zero-copy Application Protocol) is a high-performance RPC protocol for AI agent communication featuring:

- Clean whitespace-significant schema language
- Zero-copy serialization via Cap'n Proto
- Multi-transport support (TCP, WebSocket, HTTP/SSE, Unix socket)
- MCP gateway for aggregating Model Context Protocol servers
- Post-quantum cryptography (ML-KEM, ML-DSA)
- Agent consensus voting
- W3C DID identity

## Links

- [GitHub](https://github.com/zap-protocol/zap)
- [Documentation](https://zap-protocol.github.io/zap)
- [PyPI](https://pypi.org/project/zap-schema/)
smpclient 6.1.0
Simple Management Protocol (SMP) Client for remotely managing MCU firmware
Requires Python: <4,>=3.10

# Simple Management Protocol (SMP) Client 

`smpclient` implements the transport layer of the Simple Management Protocol.  This library can be
used as a dependency in applications that use SMP over **serial (UART or USB)**, **Bluetooth (BLE)**,
or **UDP** connections.  Some abstractions are provided for common routines like upgrading device
firmware.

If you don't need a library with the transport layer implemented, then you might prefer to use
[smp](https://github.com/JPHutchins/smp) instead.  The SMP specification can be found
[here](https://docs.zephyrproject.org/latest/services/device_mgmt/smp_protocol.html).

If you'd like an SMP CLI application instead of a library, then you should try
[smpmgr](https://github.com/intercreate/smpmgr).

## Install

`smpclient` is [distributed by PyPI](https://pypi.org/project/smpclient/) and can be installed with `poetry`, `pip`, and other dependency managers.

## User Documentation

Documentation is in the source code so that it is available to your editor.
An online version is generated and available [here](https://intercreate.github.io/smpclient/).

## Development Quickstart

> Assumes that you've already [setup your development environment](#development-environment-setup).

1. activate [envr](https://github.com/JPhutchins/envr), the environment manager for **bash**, **zsh**, and **PS**:
   ```
   . ./envr.ps1
   ```
2. run `poetry install` when pulling in new changes
3. run `lint` after making changes
4. run `test` after making changes
5. add library dependencies with `poetry`:
   ```
   poetry add <my_new_dependency>
   ```
6. add test or other development dependencies using [poetry groups](https://python-poetry.org/docs/managing-dependencies#dependency-groups):
   ```
   poetry add -G dev <my_dev_dependency>
   ```
7. run tests for all supported python versions:
   ```
   tox
   ```

## Development Environment Setup

### Install Dependencies

- poetry: https://python-poetry.org/docs/#installation

### Create the venv

```
poetry install
```

The `venv` should be installed to `.venv`.

### Activate envr

> [envr](https://github.com/JPhutchins/envr) supports **bash**, **zsh**, and **PS** in Linux, MacOS, and Windows.  If you are using an unsupported shell, you can activate the `.venv` environment manually, use `poetry run` and `poetry shell`, and refer to `envr-default` for useful aliases.

```
. ./envr.ps1
```

### Verify Your Setup

To verify the installation, make sure that all of the tests are passing using these envr aliases:

```
lint
test
```

### Enable the githooks

> The pre-commit hook will run the linters but not the unit tests.

```
git config core.hooksPath .githooks
```
sharegpt-dataset 0.1.0
Local package containing the ShareGPT V3 unfiltered cleaned split dataset.
Requires Python: >=3.8

# ShareGPT dataset package

This package bundles `ShareGPT_V3_unfiltered_cleaned_split.json` so it can be
installed with `pip` and accessed via `importlib.resources`.

## Prepare the data file

Place the dataset here before building/installing:

```
sharegpt_dataset/src/sharegpt_dataset/data/ShareGPT_V3_unfiltered_cleaned_split.json
```

## Build a wheel

```
cd sharegpt_dataset
python -m pip install --upgrade build
python -m build --wheel
```

## Install

```
pip install sharegpt_dataset/dist/sharegpt_dataset-0.1.0-py3-none-any.whl
```

## Use

```python
from sharegpt_dataset import dataset_path, open_dataset

with dataset_path() as path:
    print(path)

with open_dataset("rb") as f:
    first_bytes = f.read(64)
```