semvec/pypi-packages huggingface dataset:
docugami-langchain 0.0.22
Docugami partner package for LangChain
License: MIT
Requires Python: <4.0,>=3.9

# docugami-langchain

This package contains the LangChain integrations for Docugami.

## Installation

To install with pip:

`pip install docugami-langchain`

To install with poetry:

`poetry add docugami-langchain`

## Development and Testing

1. To install with development and testing dependencies, install as `poetry install --with test,lint,typing,codespell`.
1. Add an API key:
    1. Ensure that you have a FIREWORKS_API_KEY set in your .env file.
    1. Ensure that you have OPENAI_API_KEY set in your .env file (platform.openai.com).
1. To send traces to langsmith, add the following to your .env file:
    1. LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
    1. LANGCHAIN_API_KEY=... # get from LangSmith
    1. LANGCHAIN_PROJECT=alias-dev # change to your alias
mk8dx 1.2.3
To help to develop something about mk8dx
License: MIT

# mk8dx

This library has Mogi, Race, Track, and Cup classes.
And mk8dx.lounge_api is fully lounge api wrapper.

## install

```bash
pip install mk8dx
```

## sample

### Track

```python
from mk8dx import Track

mks = Track.from_nick('マリカス') # => Track.MKS
dcl = Track.from_nick('dcl') # => Track.DCL

print(mks.abbr)         # MKS
print(mks.name)         # Mario Kart Stadium
print(mks.full_name)    # Mario Kart Stadium
print(dcl.abbr_ja)      # チーズ
print(dcl.name_ja)      # チーズランド
print(dcl.full_name_ja) # GBA チーズランド
```

### lounge_api

```python
from mk8dx import lounge_api

async def getMaxScore(name: str, season: int) -> Optional[int]:
    player = await lounge_api.get_player_details(name=name, season=season)
    max_score = -1
    for mmr_change in player.mmr_changes:
        if mmr_change.score is not None and max_score < mmr_change.score:
            max_score = mmr_change.score
    if max_score == -1:
        return None
    return max_score
```
rhythmFramework 0.1.1
A Framework for faster development with AI Agents.
License: MIT
Requires Python: >=3.12

# Rhythm Framework

Rhythm Framework is a package that speeds up development with AI agents in phython by providing built-in agents (Robots) and integration classes for easier API interations. This allows users to build complex systems based on the ALAN (Autonomous layered AI network) technology in no time and start to automate their life today. ALAN technology allows linking multiple AI systems together in a net based structure and ensure cooperation between them to work towards a greater goal and push the limits of what AI can do.

Check the [documentation folder](https://github.com/RhymeAI/Rhythm-Framework/tree/main/doc) for help.

## Installation

Install rhythm framework with pip

```bash
  pip install rhythmFramework
```

## Authors

- [@Dan-The-Man-350](https://github.com/Dan-The-Man-350)

- [@EKV](https://github.com/EKVgd)

- [@Sanki1001](https://github.com/sanki1001)

## License

[MIT](https://choosealicense.com/licenses/mit/)
tenops 0.2.0
Python package for creating a common interface for using numpy, pytorch, etc.
License: MIT
Requires Python: <4.0,>=3.9

# Ten(sor) Op(eration)s

The basic idea behind this repository is to allow for seamlessly switching between libraries like numpy, pytorch, and tensorflow by checking the input types and returning the proper function for use in some operation.

## Installation
Installation can be done over pip
```
pip install tenops
```

Note that for the library to work you'll need to have at least one of the following libraries installed:
- numpy
- torch
- tensorflow

## Usage

Functions work like regular functions, one just needs to pass in the object and the function will identify the corresponding module and use the appropriate function:

```
>>> import torch, numpy, tensorflow as tf
>>> from tenops.special import exp
>>> exp(numpy.array([0]))
array([1.])
>>> exp(torch.tensor([0]))
tensor([1.])
>>> exp(tf.constant([0.]))
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)>
```

One can also just specify the module using the `default` parameter rather than typecasting directly (note that this requires that the specified library is installed):

```
>>> from tenops.special import exp
>>> exp([0], default="numpy")
array([1.])
>>> exp([0], default="torch")
tensor([1.])
>>> exp([0.], default="tensorflow")
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)>
```


## Development

[Poetry](https://python-poetry.org/docs/) is used to manage the build process.