semvec/pypi-packages huggingface dataset:
joinly 0.1.1
Join two keyed lists with the use of LLMs
Requires Python: >=3.11
Keywords: python



# Introduction

Joinly is a library that is mean to help assist in the joining of two different lists of items using a LLM. Like a
human data annotator you can provide context through a prompt on how you want the task achieved.  The library supports
inner, left, right, and full joins.

By default the library uses OpenAI's gpt-4o model.  You will need to set your OpenAI credentials prior to using the
library with:

```
 OPENAI_API_KEY=XXXX
```

# Installation

```
pip install llm-joinly
```


# Quickstart

Let's say you have two lists of fruits.  One is a bit more specific with types of each fruit while the second
one is more general.  Joinly allows you to describe the task you want and join in a fuzzy way using LLMs.

```
from joinly import join

context = '''
You are joining fruits so make sure to be aware of that fruits
have different types but make sure the catagory is correct
'''

left = [
    ("apple", 1),
    ("banana", 2),
    ("orange", 3),
]

right = [
    ("honeycrisp", 4),
    ("florida orange", 5),
    ("gala", 6),
    ("grannysmith", 7)
]

results = join.inner_join(left, right, context=context)
> [
 (('apple', 1), ('honeycrisp', 4)),
 (('apple', 1), ('gala', 6)),
 (('apple', 1), ('grannysmith', 7)),
 (('orange', 3), ('florida orange', 5))
]
```

# Other Models

You can bring in any custom model as BaseAI with an embedding method and a prompt.

```
from joinly.ai import BaseAI


class MyAI(BaseAI):
    def __init__(self) -> None:
        self.client = ...

    def __call__(self, system_message: str, user_message: str) -> Optional[str]
        pass

    def embed(self, text: str) -> Optional[ArrayLike]:
        pass


results = join.inner_join(left, right, context=context, llm=MyAI())
```
supersynthetic-data-generator 0.1.3
A library to generate synthetic datasets for testing ML models
Requires Python: >=3.6

# Super Synthetic Data Generator

A Python library to generate synthetic datasets for testing machine learning models. It supports:
- Tabular Data
- Text Data
- Categorical Data
- Image Data

## Installation

pip install supersynthetic-data-generator

## Usage
from supersynthetic_data_generator import get_synthetic_data

synthetic_data = get_synthetic_data(
    rows=10, cols=3, categories=["Red", "Green", "Blue"],
    imagefolder_path="image_output_folderpath",
    image_count=10, image_size=(100, 100)
)
print(synthetic_data)
amrita-converter 0.1.0
A simple function that can calculate the series and factorial of a number
Requires Python: >=3.6

# Temp Converter

A simple Python library to calculate basic series and factorial of a given number 
 Eg - n = 5
 series (5) = 5+4+3+2+1
 factorial (5) = 5*4*3*2*1

## Installation

```bash
pip install converter