semvec/pypi-packages huggingface dataset:
fastapi-tools-bootstrap 0.1.5
Bootstrap tool
Requires Python: <4.0,>=3.12

### Bootstrap

```python
from fastapi import FastAPI
from fastapi_tools import bootstrap


@bootstrap.on_exit(kind=["server"])
async def shutdown1(app: FastAPI) -> None:
    print("Shutdown")
    
@bootstrap.on_exit
async def shutdown2(app: FastAPI) -> None:
    print("Shutdown")
    
    
app = FastAPI(lifespan=bootstrap.lifespan("server"))
```
gdorker 1.0.4
Script for automation search/dorking with Google and Duckduckgo
Keywords: Duckduckgo, dorking, google, search

# GDORKER

## What is it

CLI tool for searching with DuckDuckgo and Google Search API in friendly format 

## Installation and run
```
pipx install gdorker
gdorker --help
```

## Get API Keys for Google Search API

You can skip this step if you are going to use DDG only.

https://programmablesearchengine.google.com/controlpanel/

place keys here `~/.config/gdorker/config.json`

Config file will be created automatically after initial run

## Caveats

Might be glitchy. Gdorker was created for personal use and was not tested well, niether it has automated tests.
twelve-angry-llms 0.1.0
Add your description here
Requires Python: >=3.11

# Twelve Angry LLMs

An extensible Python library for creating and using LLMs as judges. This library provides a framework for defining different types of judges, from those that return a simple score to those that provide a detailed, descriptive evaluation. It's designed to be modular, allowing you to easily connect or use different LLM providers like OpenAI, Hugging Face, or your own local models.

<!-- [![PyPI version](https://badge.fury.io/py/twelve-angry-llms.svg)](https://badge.fury.io/py/twelve-angry-llms) -->
<!-- [![Build Status](https://github.com/meghdadFar/twelve-angry-llms/actions/workflows/python-package.yml/badge.svg)](https://github.com/meghdadFar/twelve-angry-llms/actions/workflows/python-package.yml) -->
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Code style: ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
<!-- [![Python Version](https://img.shields.io/pypi/pyversions/twelve-angry-llms.svg)](https://pypi.org/project/twelve-angry-llms) -->

## Installation

```bash
pip install twelve-angry-llms
```

## Usage

Here's how to use the `DescriptiveJudge` with the `OpenAIProvider`:

```python
from twelve_angry_llms.providers.openai import OpenAIProvider
from twelve_angry_llms.judges.descriptive import DescriptiveJudge

# Make sure to set the OPENAI_API_KEY environment variable
openai_provider = OpenAIProvider()
descriptive_judge = DescriptiveJudge(llm_provider=openai_provider)

result = descriptive_judge.evaluate(
    generation="This is a test generation.",
    reference="This is a test reference.",
    context="This is a test context."
)

print(result.evaluation)
```

## Contributing

Contributions are welcome! Please see the [Contributing Guide](CONTRIBUTING.md) for more information.
sfst-transduce 1.3.1
Python bindings for SFST focusing on transducer usage
Requires Python: >=3.8
Keywords: Finite-state Transducer, Morphological Analysis, Natural Language Processing, NLP, SFST


# sfst-transduce

_Python bindings for SFST focusing on transducer usage_

[![PyPI – Version](https://badge.fury.io/py/sfst-transduce.svg)](https://badge.fury.io/py/sfst-transduce)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sfst-transduce)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/sfst-transduce)
![GitHub License](https://img.shields.io/github/license/zentrum-lexikographie/sfst-transduce)


A Python library providing bindings for the Stuttgart Finite State Transducer
system with a focus on the usage of compiled and serialized transducers,
excluding code for transducer development and testing, which reduces compile and
runtime dependencies.

Should you need the [SFST
tools](https://www.cis.uni-muenchen.de/~schmid/tools/SFST/) for transducer
generation, please take a look at their homepage for installation and usage
instructions.

## Installation

`sfst-transduce` is available at
[PyPI](https://pypi.org/project/sfst-transduce/):

```bash
pip install sfst-transduce
```

## Usage

```python
import sfst_transduce

# Create instance from compiled transducer
transducer = sfst_transduce.Transducer('path/to/fst.a')

# Analysis
analysis_results = transducer.analyse("easier")
# Returns ['easy<ADJ><comp>'] for example.

# Generate a string. results are a list of analysis.
generate_results = transducer.generate("easy<ADJ><comp>")
# Returns ['easier'] for example.

# Create instance from compiled transducer (compacted serialisation)
transducer = sfst_transduce.CompactTransducer('path/to/fst.ca')
# Activate output of aligned input and output layer
transducer.both_layers = True
# Analysis
transducer.analyse('easier')
# Returns ['easy:i<ADJ>:<><comp>:e<>:r'] for example.
```

## Development

```bash
pip install -e .
pip install -r requirements-dev.txt
pre-commit install
```

## Release

```bash
scripts/release $NEW_VERSION
git push && git push --tags
```

## Credits

The [SFST tools](https://www.cis.uni-muenchen.de/~schmid/tools/SFST/) have been
implemented by Helmut Schmid, Institute for Computational Linguistics,
University of Stuttgart, Germany and they are available under the GNU public
license version 2 or higher.

Please cite the following publication if you want to refer to the SFST tools:

Schmid, Helmut. "A programming language for finite state transducers." FSMNLP.
Vol. 4002. 2005.
[pdf](https://www.cis.uni-muenchen.de/~schmid/papers/SFST-PL.pdf)

This Python library is a fork of the excellent [SFST
adaptation](https://github.com/santhoshtr/sfst) by Santhosh Thottingal, changing
the focus of the Python part.
ampyobs 0.0.2
Ampy Observability SDK (Python) — logs, metrics, tracing
Requires Python: >=3.10

# Ampy Observability SDK (Python)

Python SDK for Ampy observability stack - logs, metrics, and tracing.

## Installation

```bash
pip install -e ./python  # Editable install for development
```

## Quick Start

```python
from ampyobs import Config, init, shutdown
from ampyobs.logger import L
from ampyobs.tracing import BusAttrs, start_bus_publish
from ampyobs.metrics import init_instruments, bus_produced

# Initialize
init(Config(service_name="my-service", collector_endpoint="localhost:4317"))
init_instruments()

# Logging with trace context
L.info("Processing order", extra={"order_id": "12345"})

# Tracing
attrs = BusAttrs(topic="orders", schema_fqdn="order.v1", message_id="123", partition_key="AAPL", run_id="run1")
ctx, span = start_bus_publish(attrs)
try:
    # Your business logic
    pass
finally:
    span.end()

# Metrics
bus_produced("orders", 1, service="my-service", env="dev")

# Cleanup
shutdown()
```

## Examples

See `python/examples/` for complete producer/consumer and metrics demos.

## Configuration

The SDK sends telemetry to your local OTel Collector (localhost:4317 by default).