semvec/pypi-packages huggingface dataset:
nbsfoodpricecleaner 0.0.3
A Python package for cleaning and processing food price data.
License: MIT
Requires Python: >=3.7

# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini
We use if for the testing in the python package testing against different version of the python 

## how tox works tox enviornment creation
1. Install depedencies and packages 
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox


# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application
*types of testing*
1. Automated testing 
2. Manual testing

*Mode of testing*
1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle
llm-wrapper-yl 0.1.3
A flexible wrapper for LLM providers with caching, async support, and cost tracking
License: MIT
Requires Python: >=3.11

# LLM Wrapper

A flexible wrapper for LLM providers with caching, async support, and cost logging.

<https://pypi.org/project/llm-wrapper-yl/>

## Installation

```bash
pip install llm-wrapper-yl
```

## Quick Start

```python
from llm_wrapper import Provider

# Initialize the provider
provider = Provider(model="gpt-4o-mini")

# Generate a single response
response = provider.generate("What is the capital of France?")

# Generate multiple responses with caching
responses = provider.generate(
    ["What is 2+2?", "What is the weather?"],
    system_prompt="You are a helpful assistant.",
    temperature=0.7
)

# For the second call, you will retrieve responses from cache
responses = provider.generate(
    ["What is 2+2?", "What is the weather?"],
    system_prompt="You are a helpful assistant.",
    temperature=0.7
)
```

## Usage with Local Models

```python
from llm_wrapper import Provider

# Initialize the provider with a local model
provider = Provider(
    model="your-model-name",
    host="your-server-host",
    port=8000
)

# Generate responses
responses = provider.generate(["Your prompt here"])
```

## Testing

```bash
# Install local package with test dependencies
uv pip install '.[test]'
# Run tests with output
python -m pytest -s tests
```

## Deploying

```bash
hatch build
twine upload dist/*
```