semvec/pypi-packages huggingface dataset:
retryer 1.0.4
A retry decorator for error handling

# Retryr
[![PyPI version](https://badge.fury.io/py/retryer.svg)](https://badge.fury.io/py/retryer)

A Python decorator that allows you to retry a function, and customise the action if it runs into error.

## Installation
You can install this package using pip:

```bash
pip install retryer

# Usage

from retryr import retryer

# Define your custom_action function
def custom_action(exception):

    print(f"Custom error handling: {exception}")
    # You can perform any action you want here

@retryer(max_retries=5, retry_delay=2, on_error=custom_action)
def your_function():
    # Your code here
    # If this function raises an error, it will retry up to 5 times with a 2-second delay.

if __name__ == "__main__":
    your_function()
```
## Parameters
* max_retries: The maximum number of times the function should be retried on error.
* retry_delay: The delay in seconds between retry attempts.
* on_error: A custom error handling function that will be executed when an error occurs.

## License
This project is licensed under the MIT License - see the LICENSE file for details.
quantiprot 0.2.5
Quantitative characteristics of protein sequences
Requires Python: >=2.7
Keywords: protein,sequence,quantitative,analysis,rqa,n-gram

# Quantiprot
##### current version: 0.2.5

The Quantiprot package is a python package designed to facilitate quantitative analysis of protein sequences.

The Quantiprot package is developed and maintained at [Politechnika Wroclawska](http://pwr.edu.pl) with financial support from [National Science Centre](https://www.ncn.gov.pl?language=en) grant no. 2015/17/D/ST6/04054 (*Formal linguistics for proteomics - modeling, analysis and hypotheses testing*).

## License
The Quantiprot package is distributed under [the MIT license](/LICENSE.txt).

## Citing

If you publish results obtained using Quantiprot, please cite:

B.M. Konopka, M. Marciniak, and W. Dyrka. Quantiprot – a Python package for quantitative analysis of protein sequences. BMC Bioinformatics 18:339, 2017

## Code

Currently, the code is hosted by [E-SCIENCE.PL](https://git.e-science.pl/wdyrka/quantiprot).

## Documentation

More information on the package and **examples** can be found in the [USER MANUAL](/docs/manual.md).

## Contact

Please contact **witold** dot **dyrka** at **pwr** dot **edu** dot **pl**.
vectorim 0.0.1
A simple, no bs vector database for experimentation and prototyping
Requires Python: >=3.7

# Vectorim

A simple, no BS vector database for experimentation and prototyping.
This Vector "DB" is not designed for performance or scale, and will probably choke your machine under more than a few thousand entries.
It is to be used in scripting and experimenting with new ideas, without the hassle of actually using a database. It just keeps the vectors next to your data in a class, and pickles it on each update (if enabled). This way the db can be saved across runs avoiding re-embeddding.
Usage:

```python
from vectorim import Vectorim

# create a database
vector_db = Vectorim([[1.0, 0.4, ...], ...], ["Vectorim", "Is", "The", "Best", "Database", ...], file_path="my_db.pkl")

# search!
scores, data = vector_db.search([0.69, 0.42, ...], top_k=3)

# that's it really
```

For the sake of laziness, vectors/matrices can be either lists/nested-lists of floats or numpy arrays of corresponding dimensions. OpenAI API returns a list of floats as of now, but working with numpy is nicer so internally everything is converted to that format. Shouldn't really bother you as it's hidden, just use any one of them.
Vectorim 0.0.1
A simple, no bs vector database for experimentation and prototyping
Requires Python: >=3.7

# Vectorim

A simple, no BS vector database for experimentation and prototyping.
This Vector "DB" is not designed for performance or scale, and will probably choke your machine under more than a few thousand entries.
It is to be used in scripting and experimenting with new ideas, without the hassle of actually using a database. It just keeps the vectors next to your data in a class, and pickles it on each update (if enabled). This way the db can be saved across runs avoiding re-embeddding.
Usage:

```python
from vectorim import Vectorim

# create a database
vector_db = Vectorim([[1.0, 0.4, ...], ...], ["Vectorim", "Is", "The", "Best", "Database", ...], file_path="my_db.pkl")

# search!
scores, data = vector_db.search([0.69, 0.42, ...], top_k=3)

# that's it really
```

For the sake of laziness, vectors/matrices can be either lists/nested-lists of floats or numpy arrays of corresponding dimensions. OpenAI API returns a list of floats as of now, but working with numpy is nicer so internally everything is converted to that format. Shouldn't really bother you as it's hidden, just use any one of them.