semvec/pypi-packages huggingface dataset:
bashate-mod-ds 0.0.1.dev206
A pep8 equivalent for bash scripts (modified)
Requires Python: >=3.5

===============================
bashate-mod-ds
===============================

A modified version of bashate

- modified requirement for function declaration (require brackets)
- check that local variables are in lower case

========================================
Description of original bashate is below
========================================


A pep8 equivalent for bash scripts

This program attempts to be an automated style checker for bash scripts
to fill the same part of code review that pep8 does in most OpenStack
projects. It started from humble beginnings in the DevStack project,
and will continue to evolve over time.

The output format aims to follow `pycodestyle (pep8) default output format
<https://github.com/PyCQA/pycodestyle/blob/master/pycodestyle.py#L108>`_.


- Free software: Apache license
- Documentation: https://docs.openstack.org/bashate
- Source: https://opendev.org/openstack/bashate/
- Bugs: https://bugs.launchpad.net/bash8
- Release notes: https://docs.openstack.org/releasenotes/bashate/index.html
- Contributing: https://docs.openstack.org/bashate/latest/contributor/index.html

Currently Supported Checks
--------------------------

Errors
~~~~~~

Basic white space errors, for consistent indenting

- E001: check that lines do not end with trailing whitespace
- E002: ensure that indents are only spaces, and not hard tabs
- E003: ensure all indents are a multiple of 4 spaces
- E004: file did not end with a newline
- E005: file does not begin with #! or have a .sh prefix
- E006: check for lines longer than 79 columns

Structure Errors
~~~~~~~~~~~~~~~~

A set of rules that help keep things consistent in control blocks.
These are ignored on long lines that have a continuation, because
unrolling that is kind of "interesting"

- E010: *do* not on the same line as *for*
- E011: *then* not on the same line as *if* or *elif*
- E012: heredoc didn't end before EOF
- E020: Function declaration not in format ``^function name() {$``

Obsolete, deprecated or unsafe syntax
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Rules to identify obsolete, deprecated or unsafe syntax that should
not be used

- E040: Syntax errors reported by `bash -n`
- E041: Usage of $[ for arithmetic is deprecated for $((
- E042: local declaration hides errors
- E043: arithmetic compound has inconsistent return semantics
- E044: Use [[ for =~,<,> comparisions
odk-mailer 0.7.3
A simple CLI for sending form related mails in the context of Open Data Kit (ODK) 
Requires Python: >=3.10,<4.0

# ODK Mailer

A simple CLI to send Mails for ODK.
Built with Typer and packaged with Poetry.

## Requirements
- Python 3
- PIP
- cron

## Setup
**Install the cli via pip**
```bash
pip install odk-mailer
```

**Configuration example**
Config location: `~/.env`:
```
    SMTP_HOST=smtp.freesmtpservers.com
    SMTP_PORT=25
    SMTP_USER=
    SMTP_PASS=
    CRON_FREQUENCY=15
    NOTIFY_TO=foo@bar.com,faz@baz.com
    NOTIFY_FROM=odk-mailer@freesmtpservers.com
    ODK_HOST=https://your.odk-central.host.tld
```

## Usage
**Create and run a new mail job**
```bash
    # Create a new mail job with a unique ID
    odk-mailer jobs create

    # Run the job by ID
    odk-mailer jobs run <job-id>

    # Remove a job
    odk-mailer jobs delete <job-id>
```

**List mail jobs and evaluate them**
```bash
    # Show all jobs
    odk-mailer jobs list

    # Run all jobs that are ready to be sent
    odk-mailer jobs evaluate
```

**Initiate crontab to periodically evaluate mailjobs and run them directly after**
```bash
    odk-mailer cron init
```

## Development
Building a Package - Guide: https://typer.tiangolo.com/tutorial/package/#create-your-app

**Requirements**
- Python 3.10
- Poetry 1.7

```bash
    # clone the repo and cd into
    poetry shell
    poetry install
    # Run it with
    poetry run odk-mailer 
```

**Push to PyPi**

```
    poetry build
    poetry publish
    # requires credentials to be setup: poetry config pypi-token.pypi <my-token>
```
bastion-integration 0.2.8
Модуль интеграции с Бастион
Requires Python: >=3.10,<4.0

# Example Package

This is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.

Данный модуль предназначен для интеграции с АПК Бастион 3 версии. ВОзможно интеграция как через Бастион-ИКС,
так и через web-socket.
ChatAgent-py 0.0.1
Pure Python Based Agents for Large Language Models
Requires Python: >=3.8
Keywords: OpenAIAPILLMAgent

# ChatAgent

[中文说明](./README_CN.md)

A Python-based large language model agent framework. 
The online agents deployed through ChatAgent have provided over a million stable API calls for the internal OpenRL team.

## Features

- [x] Supports multimodal large language models
- [x] Supports OpenAI API
- [x] Supports API calls to Qwen on Alibaba Cloud, Zhipu AI's GLM, Microsoft Azure, etc.
- [x] Supports parallel and sequential calls of different agents
- [x] Supports adding an api key for access control
- [x] Supports setting a maximum number of concurrent requests, i.e., the maximum number of requests a model can handle at the same time
- [x] Supports customizing complex agent interaction strategies

## Installation

```bash
pip install ChatAgent-py
```

## Usage

We provide some examples in the `examples` directory, which you can run them directly to explore ChatAgent's abilities.

### 1. Example for Qwen/ZhiPu API to OpenAI API

With just over a dozen lines of code, you can convert the Qwen/ZhiPu API to the OpenAI API. 
For specific code and test cases, please refer to [examples/qwen2openai](./examples/qwen2openai) and [examples/glm2openai](./examples/glm2openai).
```python
import os
from ChatAgent import serve
from ChatAgent.chat_models.base_chat_model import BaseChatModel
from ChatAgent.agents.dashscope_chat_agent import DashScopeChatAgent
from ChatAgent.protocol.openai_api_protocol import MultimodalityChatCompletionRequest
class QwenMax(BaseChatModel):
    def init_agent(self):
        self.agent = DashScopeChatAgent(model_name='qwen-max',api_key=os.getenv("QWEN_API_KEY"))
    def create_chat_completion(self, request):
        return self.agent.act(request)
@serve.create_chat_completion()
async def implement_completions(request: MultimodalityChatCompletionRequest):
    return QwenMax().create_chat_completion(request)
serve.run(host="0.0.0.0", port=6367)
```

### 2. Ensemble with Multiple Agents

We provide an example in [examples/multiagent_ensemble](./examples/multiagent_ensemble) where multiple agents perform ensemble to answer user questions.

### 3. Agent Q&A Based on RAG Query Results

We provide an example in [examples/rag](./examples/rag) of agent Q&A based on RAG query results.

## Citation

If you use ChatAgent, please cite us:
```bibtex
@misc{ChatAgent2024,
    title={ChatAgent},
    author={Shiyu Huang},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/OpenRL-Lab/ChatAgent}},
    year={2024},
}
```
ChatAgent-python 0.0.1
Pure Python Based Agents for Large Language Models
Requires Python: >=3.8
Keywords: OpenAIAPILLMAgent

# ChatAgent

[中文说明](./README_CN.md)

A Python-based large language model agent framework. 
The online agents deployed through ChatAgent have provided over a million stable API calls for the internal OpenRL team.

## Features

- [x] Supports multimodal large language models
- [x] Supports OpenAI API
- [x] Supports API calls to Qwen on Alibaba Cloud, Zhipu AI's GLM, Microsoft Azure, etc.
- [x] Supports parallel and sequential calls of different agents
- [x] Supports adding an api key for access control
- [x] Supports setting a maximum number of concurrent requests, i.e., the maximum number of requests a model can handle at the same time
- [x] Supports customizing complex agent interaction strategies

## Installation

```bash
pip install ChatAgent
```

## Usage

We provide some examples in the `examples` directory, which you can run them directly to explore ChatAgent's abilities.

### 1. Example for Qwen/ZhiPu API to OpenAI API

With just over a dozen lines of code, you can convert the Qwen/ZhiPu API to the OpenAI API. 
For specific code and test cases, please refer to [examples/qwen2openai](./examples/qwen2openai) and [examples/glm2openai](./examples/glm2openai).
```python
import os
from ChatAgent import serve
from ChatAgent.chat_models.base_chat_model import BaseChatModel
from ChatAgent.agents.dashscope_chat_agent import DashScopeChatAgent
from ChatAgent.protocol.openai_api_protocol import MultimodalityChatCompletionRequest
class QwenMax(BaseChatModel):
    def init_agent(self):
        self.agent = DashScopeChatAgent(model_name='qwen-max',api_key=os.getenv("QWEN_API_KEY"))
    def create_chat_completion(self, request):
        return self.agent.act(request)
@serve.create_chat_completion()
async def implement_completions(request: MultimodalityChatCompletionRequest):
    return QwenMax().create_chat_completion(request)
serve.run(host="0.0.0.0", port=6367)
```

### 2. Ensemble with Multiple Agents

We provide an example in [examples/multiagent_ensemble](./examples/multiagent_ensemble) where multiple agents perform ensemble to answer user questions.

### 3. Agent Q&A Based on RAG Query Results

We provide an example in [examples/rag](./examples/rag) of agent Q&A based on RAG query results.

## Citation

If you use ChatAgent, please cite us:
```bibtex
@misc{ChatAgent2024,
    title={ChatAgent},
    author={Shiyu Huang},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/OpenRL-Lab/ChatAgent}},
    year={2024},
}
```