semvec/pypi-packages huggingface dataset:
EmbedSelection 1.0
Embedding selection: A tool for selecting the best embedding model for your use case
Requires Python: >=3.6

## Embedding Selector Framework


#### - This framework helps you automatically select the most suitable text embedding model for a given downstream use case.

#### - It analyzes task requirements (e.g., retrieval, classification, summarization), matches them against available embedding models, and evaluates performance on relevant benchmarks.
---

##  Features


- **Use Case–Driven Selection:** Takes a natural-language description of a use case and extracts structured metadata (e.g., languages, token limits, complexity).
- **Metadata Extraction:** Uses advanced LLM models to normalize requirements into a standardized schema (parameters, memory, licensing, etc.).
- **Model Matching:** Filters embedding models based on attributes like size, efficiency, license, and language coverage.
- **Task Alignment:** Selects relevant evaluation tasks from MTEB (Massive Text Embedding Benchmark).
- **Performance Evaluation:** Loads benchmark results and computes average scores per candidate model.
---

##  How It Works

The pipeline runs in sequential steps:

- **Use Case Selection**
Choose from predefined scenarios (chatbots, legal retrieval, recommendations, sentiment analysis, summarization, etc.) or provide your own description.
- **Requirement Extraction (LLM Agent)**
GPT-4o parses the description into structured metadata, including:
Supported languages
Max token length
Memory usage & parameter limits
Task/domain classification
- **Model Filtering**
Candidate models from MTEB are filtered according to the extracted attributes.
- **Task Evaluation**
Candidate models are benchmarked on the most relevant MTEB tasks (retrieval, classification, summarization, etc.).
- **Ranking & Export**
Models are ranked by performance (with ties broken by efficiency) and exported to CSV for inspection.
## Usage 


To use the tool, follow these steps:

 ```bash
   pip install EmbedSelection

   EmbedSelection 



```

  
   
## Contributing

Contributions to improve the tool are welcome! Feel free to open issues for bugs or feature requests, or submit pull requests for enhancements.



## Acknowledgements

This project utilizes MTEB benchmark from huggingface : https://huggingface.co/spaces/mteb/leaderboard
petcmd 1.0.5
Library for creating a command line tools
Requires Python: >=3.12

# petcmd

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/petcmd)
![PyPI - Version](https://img.shields.io/pypi/v/petcmd)
![Documentation Status](https://readthedocs.org/projects/petcmd/badge/?version=latest)
![PyPI - License](https://img.shields.io/pypi/l/petcmd)
![Downloads](https://static.pepy.tech/badge/petcmd)

## Installation

**petcmd** can be installed with [pip](http://pypi.python.org/pypi/pip):

```bash
python -m pip install petcmd
```

You can also download the project source and do:

```bash
pip install .
```

## Dependencies

**petcmd** was created as a lightweight package, so there are no dependencies.

## Examples

```python
from petcmd import Commander

commander = Commander()

@commander.command("calc")
def calculate(a: int, b: int, operator: str = "+"):
	print(eval(f"{a} {operator} {b}"))

if __name__ == "__main__":
	commander.process()
```

```bash
$ python app.py calc 1 2
3
$ python app.py calc 10 2 /
5.0
$ python app.py calc 10 2 -o /
5.0
$ python app.py calc 10 2 --operator /
5.0
$ python app.py calc -a 10 -b 2 --operator /
5.0
```

### Documentation

Documentation is available at https://petcmd.readthedocs.io/en/latest/

## Testing

```bash
python -m tests
```
simuth 0.1.0
Collection of sorting algorithms in Python I did while I was bored
Requires Python: >=3.8
Keywords: sorting, algorithms, bubble sort, quick sort, merge sort, heap sort

# simuth

A simple Python package built while bored.
Currently contains only basic sorts, will do more as I progress more :D

---

## Installation

You can install from PyPI:

```bash
pip install simuth
```

## Usage
After installing, import the `Sorting` class and call any method.

```python
from simuth import Sorting

arr = [5, 3, 8, 4, 2]

print(Sorting.bubble_sort(arr))
print(Sorting.quick_sort_lomuto(arr))

# Special Dutch National Flag (only for 0s, 1s and 2s)
colors = [2, 0, 1, 2, 0, 0]
print(Sorting.dutch_national_flag(colors)) 
```

## Currently available algorithms
1. Bubble Sort
2. Selection Sort
3. Insertion Sort
4. Merge Sort
5. Quick Sort (Lomuto and Hoare)
6. Dutch National Flag Algorithm
7. An Easter Egg

## License
This project is licensed under the [MIT License](LICENSE)