semvec/pypi-packages huggingface dataset:
ezpyai 0.0.1a8
A useful utility library for AI applications
Requires Python: >=3.8

# ezpyai

Welcome to `ezpyai`, your new best friend in the wild, wild world of AI! Whether you're a coding wizard, a data sorcerer, or just someone who likes to mess around with powerful tools, this Python utility library is here to make your life easier and your AI dreams a reality. Hack the planet, one prompt at a time!

## Features

- **Easy Integration**: Plug and play with OpenAI's API(and others in the future like LM Studio, Groq, HuggingFace, custom ones, etc). No need to sacrifice any goats.
- **Simplified Prompts**: Wrangle those prompts like a boss. Customize to your heart's content.
- **Structured Responses**: Get clean, validated JSON responses without the hassle. Because who has time for messy data?

## Installation

Getting started is a breeze. Just run this magic spell in your terminal:

```bash
pip install ezpyai
```

Boom! You're ready to rock.

## Usage

TODO

## Documentation

TODO

## Contributing

We love contributions like hackers love caffeine. Found a bug? Have a brilliant idea? Head over to the [issues page](https://github.com/psyb0t/ezpyai/issues) and let us know. Pull requests are always welcome!

## License

**ezpyai** is unleashed under the WTFPL (Do What The Fuck You Want To Public License). Copy it, change it, or repurpose it to start your own digital riot.

## Development notes

TODO

## TODO

- knowledge gatherer - implement semantic chunking
- prompt - add prompt enhancer
- prompt - add prompt compression using LLMLingua
- prompt - add history support
- llm provider - optionally use NuExtract text to json model to get structured response(like instead of embedding the instruction to the base llm, take the resp and send it to nuextract)
- llm provider - text generation web ui - support multiple instances hosted in diff locations
to-sequential 0.2.0
A utility to generate sequential data for sequential models from a Pandas DataFrame.
Requires Python: >=3.6

## To Sequential

`to_sequential` is a simple utility function to generate sequential data from a Pandas DataFrame, making it suitable for
preparing data for sequential models.

### Usage

Here's a basic example of how to use the generate_sequences function:

```python
import pandas as pd
from to_sequential import generate_sequences

df = pd.read_csv('Example.csv')

# Generate sequences
sequences, targets = generate_sequences(df, window_size=10, column_names=['feature1', 'feature2'])

print("Sequences:\n", sequences)
print("Targets:\n", targets)
```

### Function Documentation
`generate_sequences`
Generate sequential data for sequential model.

#### Parameters
* df (pd.DataFrame): The input data frame.
* window_size (int): The size of the window for the sequences.
* column_names (List[str]): List of column names to use for generating sequences.

#### Returns
* np.ndarray: Array of sequences of shape (number_of_sequences, window_size, number_of_columns).
* np.ndarray: Array of targets of shape (number_of_sequences, number_of_columns).

#### Raises
* ValueError: If any column in column_names is not found in the DataFrame.