semvec/pypi-packages huggingface dataset:
pydantic-firebase-remoteconfig 0.3.0
A simple Firebase Remote Config binding for Pydantic
Requires Python: >=3.12

# Firebase Remote config validator for Pydantic

[![PyPI - Version](https://img.shields.io/pypi/v/pydantic-firebase-remoteconfig)](https://pypi.org/project/pydantic-firebase-remoteconfig/) [![Pydantic v2](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json)](https://pydantic.dev)

A simple Firebase Remote Config binding for Pydantic, using
remote server configuration only.

## Installation

Package is publicly available on PyPi and can be install using any
supported package manager:

```bash
pip install pydantic-firebase-remoteconfig
```

## Usage

You simply need to implement your `BaseRemoteConfigModel` class
and use the appropriate async validation class method. Let's imagine
you have a server remote configuration with a `foo` variable with `bar`
value:

```python
from pydantic_firebase_remoteconfig import BaseRemoteConfigModel


class MyRemoteConfigModel(BaseRemoteConfigModel):
    foo: str


config = await MyRemoteConfigModel.model_validate_remoteconfig()
print(config.foo)
# bar
```

### With custom model configuration

A custom model configuration dict is also available.

#### Key prefix

```python
from pydantic_firebase_remoteconfig import RemoteConfigConfigDict


class MyRemoteConfigModel(BaseRemoteConfigModel):
    model_config = RemoteConfigConfigDict(
        # This would evaluate remote configuration
        # variable `myprefix_foo`.
        rc_prefix="myprefix_",
    )
    foo: str
```

#### Parameter group selection


```python
from pydantic_firebase_remoteconfig import RemoteConfigConfigDict


class MyRemoteConfigModel(BaseRemoteConfigModel):
    model_config = RemoteConfigConfigDict(
        # This would evaluate remote configuration
        # variable `foo` under group `my_group`.
        rc_group="my_group",
    )
    foo: str
```

#### Complex model using nested delimiter

Using the same pattern than [pydantic_settings](), nested
configuration can be evaluated, based on either:

- A submodel is designed as a JSON parameter on RemoteConfig.
- A submodel is designed as combinaison of flat value with delimiter.

```python
from pydantic import BaseModel
from pydantic_firebase_remoteconfig import RemoteConfigConfigDict


class MyNestedConfig(BaseModel):
    foo: str


class MyRemoteConfigModel(BaseRemoteConfigModel):
    model_config = RemoteConfigConfigDict(
        # This would evaluate remote configuration
        # variable `nested_config__foo`.
        rc_nested_delimiter="__",
    )

    nested_config: MyNestedConfig
```
wagtail-ai 3.0.0
Get a hand writing your content with AI super powers!
Requires Python: >=3.11

![wagtail-ai](https://user-images.githubusercontent.com/27112/223072917-8354f8f2-b687-44dd-9db7-33f2cc340233.png)

# Wagtail AI

Get help with your content using AI superpowers.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/wagtail-ai.svg)](https://badge.fury.io/py/wagtail-ai)
[![ai CI](https://github.com/wagtail/wagtail-ai/actions/workflows/test.yml/badge.svg)](https://github.com/wagtail/wagtail-ai/actions/workflows/test.yml)

Wagtail AI integrates Wagtail with AI's APIs (think ChatGPT) to help you write and correct your content.

Right now, it can:

* Finish what you've started - write some text and tell Wagtail AI to finish it off for you
* Correct your spelling/grammar
* Let you add your own custom prompts
* Automatically generate alt-tags for your uploaded images
* Work with multiple LLM providers including local models, OpenAI, Mistral, Claude and many others

## Demos

### Rich-text integration

https://user-images.githubusercontent.com/27112/223072938-8cb5ccff-4835-489a-8be4-cca85001885e.mp4

### Alt-text generation

https://github.com/wagtail/wagtail-ai/assets/27617/5ffd5493-b39c-4d38-bed8-fdd243920eb5

## Requirements & Costs

Wagtail AI supports [many different LLMs](https://wagtail-ai.readthedocs.io/latest/ai-backends/), with OpenAI models
available by default. To use these, you'll need an OpenAI account and an API key. There'll also be some cost involved.

For the OpenAI API used here (`gpt-3.5-turbo`), the [cost](https://openai.com/pricing) is

- $0.0005 per 1000 tokens for input tokens (prompt)
- $0.0015 per 1000 tokens for output tokens (answer)

Here is an estimated cost breakdown for the `correction` prompt on a 1000-word paragraph.

### We assume that:

- Prompt is 30 words and the existing paragraph is 1000 words (Input)
- Each word is 1.3 tokens (Tokens multiplier)
- We get back 1000 words back (Output)

### Then:

- **Input tokens :** (35 + 1000) x 1.3 = 1345.5 tokens.
- **Output tokens :** 1000 x 1.3 = 1300
- **Input tokens cost :** 1345.5 / 1000 * $0.0005 = $0.00067275
- **Output tokens cost :** 1300 / 1000 * $0.0015 = $0.00195
- **Total cost :** $0.00262275

## Links

- [Documentation](https://wagtail-ai.readthedocs.io/)
- [Changelog](https://github.com/wagtail/wagtail-ai/blob/main/CHANGELOG.md)
- [Contributing](https://wagtail-ai.readthedocs.io/latest/contributing/)
- [Discussions](https://github.com/wagtail/wagtail-ai/discussions)
- [Security](https://github.com/wagtail/wagtail-ai/security)

## Supported Versions

* Wagtail 7.1+
* Django 4.2+
* Python 3.11+
qfeval_data 0.1.1
Data structures for quantitative finance
Requires Python: >=3.9

# qfeval-data
[![python](https://img.shields.io/badge/python-%3E=3.9-blue.svg)](https://pypi.org/project/qfeval_data/)
[![pypi](https://img.shields.io/pypi/v/qfeval_data.svg)](https://pypi.org/project/qfeval_data/)
[![CI](https://github.com/pfnet-research/qfeval-data/actions/workflows/ci-python.yaml/badge.svg)](https://github.com/pfnet-research/qfeval-data/actions/workflows/ci-python.yaml)
[![codecov](https://codecov.io/gh/pfnet-research/qfeval-data/graph/badge.svg?token=5A02B1JV7V)](https://codecov.io/gh/pfnet-research/qfeval-data)
[![downloads](https://img.shields.io/pypi/dm/qfeval_data)](https://pypi.org/project/qfeval_data)
[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

qfevalは、Preferred Networks 金融チームが開発している、金融時系列処理のためのフレームワークです。
データ形式の仕様定義、金融時系列データを効率的に扱うためのクラス/関数群、および金融時系列モデルの評価フレームワークが含まれます。

qfeval-dataは、qfevalの中でも、金融時系列データを効率的に扱うためのデータフレームを提供します。

---

qfeval is a framework developed by Preferred Networks' Financial Solutions team for processing financial time series data.
It includes: data format specification definitions, a set of classes/functions for efficiently handling financial time series data, and a framework for evaluating financial time series models.

qfeval-data provides data frames specifically designed for efficiently handling financial time series data within qfeval.

## Installation

```bash
pip install qfeval_data
```

## Usage
TBD
azure-mgmt-disconnectedoperations 1.0.0b2
Microsoft Azure Disconnectedoperations Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Disconnectedoperations Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-disconnectedoperations
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.disconnectedoperations import DisconnectedOperationsMgmtClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = DisconnectedOperationsMgmtClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Disconnectedoperations Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b2 (2025-10-09)

### Bugs Fixed

- Exclude `generated_samples` and `generated_tests` from wheel

## 1.0.0b1 (2025-09-15)

### Other Changes

  - Initial version
azure-mgmt-sitemanager 1.0.1
Microsoft Azure Sitemanager Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Sitemanager Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-sitemanager
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.sitemanager import SiteManagerMgmtClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = SiteManagerMgmtClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Sitemanager Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.1 (2025-10-09)

### Bugs Fixed

- Exclude `generated_samples` and `generated_tests` from wheel

## 1.0.0 (2025-09-04)

### Features Added

  - Client `SiteManagerMgmtClient` added optional signature `cloud_setting` in method `__init__`

### Other Changes

  - First GA

## 1.0.0b1 (2025-05-15)

### Other Changes

  - Initial version
azure-mgmt-storagediscovery 1.0.1
Microsoft Azure Storagediscovery Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Storagediscovery Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-storagediscovery
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.storagediscovery import StorageDiscoveryMgmtClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = StorageDiscoveryMgmtClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Storagediscovery Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.1 (2025-10-09)

### Bugs Fixed

- Exclude `generated_samples` and `generated_tests` from wheel

## 1.0.0 (2025-09-29)

### Features Added

  - First GA

## 1.0.0b2 (2025-09-03)

### Features Added

  - Client `StorageDiscoveryMgmtClient` added parameter `cloud_setting` in method `__init__`

## 1.0.0b1 (2025-07-17)

### Other Changes

  - Initial version
stserve 0.1.0
Light-weight server exposing sentence-transformer models for embeddings
Requires Python: >=3.13

## Sentence Transformer Server

Lightweight FastAPI wrapper that exposes any `sentence-transformers` model over HTTP for easy embedding generation.

### Quick Start

1. Install the package from PyPI (Python 3.13+):
   ```bash
   pip install stserve
   ```
2. Launch the server with the default MiniLM model (choose your preferred runner):
   ```bash
   stserve
   # or via uvx
   uvx stserve
   ```
3. Request embeddings from another terminal:
   ```bash
   curl -X POST http://127.0.0.1:8501/embed \
     -H "Content-Type: application/json" \
     -d '{"texts": ["hello world", "how are you?"]}'
   ```

### Configuration

- `--model`: sentence-transformers model name or local path (defaults to `all-MiniLM-L6-v2`).
- `--device`: target Torch device; auto-detects CUDA.
- `--batch-size`: batches encode calls for throughput.
- `--normalize`: toggles L2 normalization on embeddings.
- `--show-progress`: prints encode progress in the server logs.
- `--host` / `--port`: Uvicorn bind address (defaults `127.0.0.1:8501`).

Example with GPU and normalization:
```bash
uvx stserve --model sentence-transformers/all-MiniLM-L12-v2 --device cuda --normalize
```

### API

- `POST /embed`: Body `{ "texts": [str, ...] }` → `{ "embeddings": [[float, ...], ...] }`.
- `GET /health`: Returns current configuration (model, device, batch size, etc.).

### Development Notes

- The CLI entry point is `stserve.app:main`.
- Embedding work happens in a thread pool so the event loop stays responsive.
- CUDA usage requires PyTorch with GPU support.
inginx 0.3.18
Add your description here
Requires Python: >=3.12

<p align="center">
  <img src="https://github.com/richard-hajek/inginx/blob/main/docs/inginx.png" />
</p>

nginx is a really powerful software, but it requires non-trivial configuration and its own working directory. Command-line configuration is possible but verbose.

**inginx** creates a temporary working directory and assembles an nginx configuration optimized for common use cases.

> [!TIP]
> The name “inginx” is derived from “instant nginx”

## Examples

### Serve file system

Serve a directory `./dir` on `8787`

```
inginx --serve ./dir --listen 0.0.0.0:8787
```

Output:

```
Running /usr/bin/freenginx in /tmp/tmpu8151mtc
127.0.0.1 - - [25/Aug/2025:11:18:50 +0200] "GET / HTTP/1.1" 200 263 "-" "Mozilla/5.0 (X11; Linux x86_64; rv:141.0) Gecko/20100101 Firefox/141.0"
....
```

### Reverse proxy

Start a reverse proxy up to `https://echo.free.beeceptor.com` on `8080`

```
inginx --reverse https://echo.free.beeceptor.com --listen 0.0.0.0:8080
```

Output:
```
Running /usr/bin/freenginx in /tmp/tmpo66xmvm_
127.0.0.1 - - [25/Aug/2025:11:21:03 +0200] "GET / HTTP/1.1" 200 844 "-" "Mozilla/5.0 (X11; Linux x86_64; rv:141.0) Gecko/20100101 Firefox/141.0"
...
```

## Common Flags

- `--serve <directory>` - Serve a local filesystem directory

- `--reverse <remote>` - Set up a reverse proxy

- `--listen <address[:port]>` - Tell Nginx what address/port to use

# Installation

This is a self-contained script that has no dependencies, besides nginx itself. It is aiming to work on as old Python as 3.7.

## Managed installation (uvx, pipx)

Ideal solution, but needs uvx, or pipx pre-installed.

```
uvx inginx --help
```

```
pipx install inginx
inginx --help
```

## pip

```
pip install --user inginx --break-system-packages
```

> [!WARNING]
> This is generally not recommended. It will work and not break anything in this case because inginx does not have any dependencies.


## Manual

```
wget https://raw.githubusercontent.com/richard-hajek/inginx/refs/heads/main/inginx/inginx.py -O ~/.local/bin
chmod +x ~/.local/bin/inginx
```

> [!WARNING]
> Check that ~/.local/bin is in your path by doing echo $PATH and checking, with your eyes, if '.local/bin' is there
> 
> If not, good luck, have fun, see https://askubuntu.com/questions/440691/add-a-binary-to-my-path for detailed instructions how to manage your $PATH


# Advanced configuration

This tool is not indended for advanced configuration. It will however provide you with a good starting point. If you want to further customize the nginx instance, do

```
mkdir ./prefix
cd ./prefix
inginx <your configuration> --dry > nginx.conf
```

and then make any changes you want to the generated nginx.conf

And run your nginx with

```
nginx -p $(realpath .) -c $(realpath ./nginx.conf)
```

# nginx binary

This software officially supports and recommends `freenginx`. It was built and tested for `freenginx/1.29.0`
emlib 1.20.2
Miscellaneous utilities (text handling, maths, iterators, etc.)
Requires Python: >=3.10

emlib
=====

emlib is a set of modules with miscelaneous functionality:

- maths: 
    - base conversion, euclidian distance, min. common denominator, etc.
    - combinatorics
    - number theory
    - number series
    - markov chains
    - utilities to traverse graphs
- matplotlib enhancements
- csv utilities
- iterator tools (similar to more_itertools)
- containers: RecordList (a list of named tuples)
- doctools: tools to generate documentation (for mkdocs particularly)
- etc

-----

Documentation
-------------

https://emlib.readthedocs.io


-----

Installation

.. code:: bash

    pip install emlib