semvec/pypi-packages huggingface dataset:
lastmileai 0.0.3
Python library for LastMile AI API
Requires Python: >=3.7

# LastMile AI Python Library

This library provides access to the LastMile AI API from Python. The code should reflect the same API endpoints documented here: https://lastmileai.dev/docs/api

## API Token

This library requires a LastMile AI API Token, which can be obtained from https://lastmileai.dev/settings?page=tokens.

Important note: this library should only be used from a server-side context, where the API key can be securely accessed. Using this library from client-side browser code will expose your private API key!

## Installation

```
pip install lastmileai
```

## Usage

### Initialize Library with API Key

This library needs to be configured with your API Token (aka API key) obtained above. You can store the API key in an environment variable or alternative secure storage that can be accessed in your server-side code. For example, to initialize the library with the API key loaded from environment variable:

```python
import os
from lastmileai import LastMile

lastmile = LastMile(api_key=os.environ["LASTMILEAI_API_KEY"])
```

### Completions -- Open AI Models

OpenAI completions are supported out-of the box for ChatGPT and GPT3 models:

```python
completion = lastmile.create_openai_completion(
  completion_params = {
    model: "text-davinci-003",
    prompt: "Your prompt here",
  }
)
```

```python
completion = lastmile.create_openai_chat_completion(
  completion_params = {
    model: "gpt-3.5-turbo",
    messages: [
      { role: "user", content: "Your prompt here" },
    ],
  }
)
```

### Completions -- Custom Models

```python
completion = lastmile.create_openai_completion(
  completion_params = {
    model: "text-davinci-003",
    prompt: "Your prompt here",
  },
  embedding_collection_id = "clfpqyvpp004npmzgp1d4j4fw"
)
```

## Run tests

After setting `LASTMILEAI_API_KEY` in your shell environment, run the following:

```
pip3 install -e .
python3 -m unittest discover test
```
calling-rate 0.5.0
Calculate population size from calling rate of seabirds
Requires Python: >=3.9

<a href="https://www.islas.org.mx/"><img src="https://www.islas.org.mx/img/logo.svg" align="right" width="256" /></a>
# Calling Rate
[![codecov](https://codecov.io/gh/IslasGECI/calling_rate/graph/badge.svg?token=ZRhomhz7XH)](https://codecov.io/gh/IslasGECI/calling_rate)
![example branch
parameter](https://github.com/IslasGECI/population_trend/actions/workflows/actions.yml/badge.svg)
![licencia](https://img.shields.io/github/license/IslasGECI/population_trend)
![languages](https://img.shields.io/github/languages/top/IslasGECI/population_trend)
![commits](https://img.shields.io/github/commit-activity/y/IslasGECI/population_trend)
![GitHub contributors](https://img.shields.io/github/contributors/IslasGECI/population_trend)

Este repositorio lo usamos para calcular la densidad de individuos usando las tasas de vocalización.
cururo 1.1.3
Review an item using OpenAI.
Requires Python: >=3.6

# Cururo: Automated Review Tool

## Description

Based on [Condor](https://github.com/CarloGauss33/Condor) (automated PR review tool) this tool leverages the power of generative AI to offer insightful and constructive feedback. This CLI software is designed to facilitate item reviews using OpenAI's services. It allows users to specify an item for review, authenticate with OpenAI, and customize the review process through various command-line arguments.

## Features

- **AI-Powered Review Process**: Cururo uses generative AI to offer constructive criticism and suggestions.
- **CI/CD Integration (Coming Soon)**: Cururo will soon support integration with Continuous Integration/Continuous Deployment (CI/CD) systems, allowing automated reviews to be triggered by events such as PR creation or code push.

## Usage

To use Cururo, you must have an OpenAI API key. You can obtain an API key by signing up for an account on the [OpenAI website](https://platform.openai.com/).

To run Cururo, you need to specify the input arguments:

- `--item`: The item you want to review.
- `--openai-key`: Your OpenAI API key.
- `--assistant-id`: The ID of the OpenAI assistant you want to use.
- `--action` (callable, optional): A custom action to perform on the OpenAI response.
- `--publisher` (Publisher, optional): The publisher to use for the review.

## Requirements

- Python 3.6 or higher
- OpenAI Python client
- PyGithub

You can install the required Python packages using pip:
`pip install openai PyGithub`
bambot 0.4.5
Containers for AI agents
Requires Python: >=3.6

# Bam

[![PyPI version](https://badge.fury.io/py/bam.svg)](https://badge.fury.io/py/bambot)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/bambot)](https://pepy.tech/project/bambot)

Bam is a lightweight and easy-to-use command-line interface (CLI) tool for creating and running AI agent containers. It is currently Docker-based.

## Installation

You can install Bam using pip:

```bash
pip install bambot
```
## Getting Started

To get started with bambot, follow these simple steps:

1. Install bambot using pip:
```bash
pip install bambot
```
2. Initialize a new Bam project:
```bash
bam init [CONTAINER_NAME]
```
3. Build the Bam project locally:
```bash
cd [CONTAINER_NAME]
bam build
```
4. Start the AI agent container locally:
```bash
bam start
```

That's it! You're now ready to start using Bam to create and run AI agent containers.

## Documentation

For detailed documentation and additional features, please visit our website at [bam.bot](https://bam.bot) (coming soon).

## License

bambot is released under the [MIT License](LICENSE).

## Support

If you have any questions, issues, or suggestions, please [open an issue](https://github.com/Bam-Corp/bambot/issues) on our GitHub repository or email spencer@bam.bot.

Happy coding!

*Note: Bam is VERY much a work-in-progress. more to come very soon!*
h9nt 1.0.1
A Simple Algorithm utils with protobuf in python
Keywords: python, dev, free, algo


## Installation



Install h9nt using pip:



```bash

pip install h9nt

```





```python

from h9nt import PbEnc



# Example usage:



def __get__payload() -> str:

    payload = {

        1: "Hello World!",

        2: 234,

        3: 556,

        4: 67,

        5: {

            1: 11,

        },

        11: "outpout"

    }

    return payload



def __to_buff(payload: str) -> str:

    try:

        return PbEnc().encrypt_pb_payload(payload)

    except:

        return None



`its just a basic example how its could be used.`