semvec/pypi-packages huggingface dataset:
oracle_utils 1.1.1
Oracle SQLPlus Automation for Database Scripting
Requires Python: >=3.8
Keywords: automation, backup, database, oracle, sqlplus

oracle_utils: local sqlplus query module
========================================

Python module to automate the interaction with sqlplus for database scripting. It is designed to connect to a local database instance as defined by the ORACLE_SID environment variable.

The sqlplus program is called once and run in a subprocess so multiple queries can be quickly run. Performance is much better than Bash with ````cat <<EOF```` for integrating database data into scripts. The query results are returned in JSON format.

Example
-------

```python
from oracle_utils import Sqlplus
import json

sql_session = Sqlplus()
sql_session.start()
result = sql_session.run_query('select * from dual;')
print(json.dumps(result, indent=4))
sql_session.end()
```

You can pass a query to the module if you want to only execute one query and output the results to the terminal:

```python
Sqlplus(query="select * from v$instance;")
```

This also enables a Bash one-linter to run a quick query:

```shell
python -c "from oracle_utils import Sqlplus; Sqlplus(query='select * from v\$instance;')"
```
DatasetSpliter 0.1.1
This utility splits an image dataset into train, validation, and test subsets while preserving the class folder structure.
Requires Python: >=3.8

## Dataset Splitter

This utility splits an image dataset into train, validation, and test subsets while preserving the class folder structure.

## Description

The split_dataset function takes a list of source folders, where each folder represents a class containing image files. The function randomly splits the images into training, validation, and testing sets and copies them into a new output directory.

The resulting directory structure looks like this:

output_folder/
  train/
    class_name/
  val/
    class_name/
  test/
    class_name/

The split is random but reproducible due to a fixed random seed.

## Supported Image Formats

.jpg
.jpeg
.png
.bmp
.gif
.tiff
.webp

## Usage

from dataset_splitter import split_dataset

source_folders = [
    "data/class_1",
    "data/class_2"
]

output_folder = "dataset_split"

split_dataset(
    source_folders=source_folders,
    output_folder=output_folder,
    train_ratio=0.7,
    val_ratio=0.15,
    test_ratio=0.15,
    seed=42
)

## Function Signature

split_dataset(
    source_folders: list[str],
    output_folder: str,
    train_ratio: float = 0.7,
    val_ratio: float = 0.15,
    test_ratio: float = 0.15,
    seed: int = 42
)

## Parameters

source_folders
List of paths to class directories containing images.

output_folder
Path to the directory where the split dataset will be saved.

train_ratio
Fraction of images used for training.

val_ratio
Fraction of images used for validation.

test_ratio
Fraction of images used for testing.

seed
Random seed to ensure reproducible splits.

The sum of train_ratio, val_ratio, and test_ratio must equal 1.0.

## Output

Image files are copied, not moved.
Original data remains unchanged.
Class folder names are preserved.
Progress information is printed to the console.

## Requirements

Python 3.8 or newer.
Uses only Python standard library modules.
django-dotnetid 1.0.0
A django-allauth dotnetid provider
Requires Python: >=3.13

# django-dotnetid

django-dotnetid is a Django app extending django-allauth.
It implements an OpenID connect provider able to map extra_info from the id_token
into Django User model. It is also capable of attributing groups automatically.

You need to install django-allauth to be able to use this extension.

A full working example can be found here: https://github.com/sitn/django-dotnetid-example

## Quick start

1. Install requiremed packages in your project:

```sh
python -m pip install django-allauth pyjwt cryptography django-dotnetid
```

2. Add `django_dotnetid` to your INSTALLED_APPS setting like this:

```python
    INSTALLED_APPS = [
        ...,
        "django_dotnetid",
        "allauth",
        "allauth.account",
        "allauth.socialaccount",
        "allauth.socialaccount.providers.openid_connect",
    ]
```

3. Include the dotnetidprovider URL conf in your project urls.py like this:

```python
from django.urls import include, path
...
    path('accounts/', include('allauth.urls')),
```

4. Run `python manage.py migrate` to create the models.

5. In your settings.py, you can add the provider like that:

```python
    AUTHENTICATION_BACKENDS = [
        "django.contrib.auth.backends.ModelBackend",
        "allauth.account.auth_backends.AuthenticationBackend",
    ]

    SOCIALACCOUNT_PROVIDERS = {
        "dotnetidprovider": {
            "APP": {
                "provider_id": "dotnetid",
                "name": "Etat de Neuchâtel",
                "client_id": os.environ['DOTNETID_CLIENT_ID'],
                "secret": os.environ['DOTNETID_CLIENT_SECRET'],
                "settings": {
                    "server_url": os.environ['DOTNETID_SERVER_URL'],
                },
            },
            "SCOPE": [
                "profile",
                "openid",
                "glados",
            ],
            "EXTRA_ATTRIBUTES_PREFIX": "djangoopenid",
            "EXTRA_ATTRIBUTES_NAMES": [
                "groups",
                "admin",
            ],
            "OAUTH_PKCE_ENABLED": True,
            "ID_TOKEN_ISSUER": os.environ['DOTNETID_SERVER_URL'],
        }
    }
    SOCIALACCOUNT_EMAIL_VERIFICATION = None
    SOCIALACCOUNT_ADAPTER  = 'django_dotnetid.adapter.DotnetIdAccountAdapter'
    LOGIN_REDIRECT_URL = 'index'
    ACCOUNT_LOGOUT_REDIRECT = 'index'
    SITE_ID = 1
```

6.

Add `allauth.account.middleware.AccountMiddleware` at the end of your middlewares:

```python
MIDDLEWARE = [
...
    "allauth.account.middleware.AccountMiddleware",
]
```

7. Start the development server.

8. Visit the ``/``.
aidee-living-docs 1.0.7
Helpers for generating living documentation with Sphinx and Behave
Requires Python: <4.0,>=3.9

# Aidee Living Documentation Helpers

[![PyPI](https://img.shields.io/pypi/v/aidee-living-docs.svg)][pypi_]
[![Status](https://img.shields.io/pypi/status/aidee-living-docs.svg)][status]
[![Python Version](https://img.shields.io/pypi/pyversions/aidee-living-docs)][python version]
[![License](https://img.shields.io/pypi/l/aidee-living-docs)][license]

[![Tests](https://github.com/aidee-health/aidee-living-docs/workflows/Tests/badge.svg)][tests]

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][pre-commit]
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)][black]

[pypi_]: https://pypi.org/project/aidee-living-docs/
[status]: https://pypi.org/project/aidee-living-docs/
[python version]: https://pypi.org/project/aidee-living-docs
[tests]: https://github.com/aidee-health/aidee-living-docs/actions?workflow=Tests
[pre-commit]: https://github.com/pre-commit/pre-commit
[black]: https://github.com/psf/black

## Features

- Helper functions to generate living documentation with Sphinx and Behave
- Type safe code using [ty](https://docs.astral.sh/ty/) for type checking

## Requirements

- Python 3.9-3.11

## Installation

You can install _Aidee Living Documentation_ via [pip]:

```console
$ pip install aidee-living-docs
```

This adds `aidee-living-docs` as a library, but also provides the CLI application with the same name.

## Using the application from the command line

The application also provides a CLI application that is automatically added to the path when installing via pip.

Once installed with pip, type:

```
aidee-living-docs --help
```

To see which options are available.

## Contributing

Contributions are very welcome.
To learn more, see the [Contributor Guide].

## Issues

If you encounter any problems,
please [file an issue] along with a detailed description.

[file an issue]: https://github.com/aidee-health/aidee-living-docs/issues
[pip]: https://pip.pypa.io/

## Credits

This project has been heavily inspired by [Bluefruit](https://github.com/bluefruit/LivingDocumentationHelpers)

<!-- github-only -->

[license]: https://github.com/aidee-health/aidee-living-docs/blob/main/LICENSE
[contributor guide]: https://github.com/aidee-health/aidee-living-docs/blob/main/CONTRIBUTING.md
epmp-igen-document-search-mcp-server 0.2.1
InfoNgen search MCP server
Requires Python: >=3.11

# InfoNgen Document Search MCP Server

This is a Model Context Protocol (MCP) server that provides semantic search capabilities using the InfoNgen API. It allows AI assistants to search for documents, news, and other content indexed by InfoNgen.

## Features

- **Semantic Search**: Perform semantic searches against the InfoNgen database.
- **Document Retrieval**: Retrieves document details including headline, summary, provider, publication date, and URL.
- **Content Snippets**: Returns relevant content chunks from the documents.

## Configuration

The server requires the following configuration, which can be provided via environment variables or command-line arguments:

| Environment Variable | Command Line Argument | Description |
|----------------------|-----------------------|-------------|
| `API_BASE_URL` | `--api-base-url` | The base URL for the InfoNgen API. |
| `API_KEY` | `--api-key` | Your InfoNgen API Key. |
| `OAUTH_TOKEN_URL` | `--oauth-token-url` | The OAuth2 token endpoint URL. |
| `OAUTH_CLIENT_ID` | `--oauth-client-id` | Your OAuth2 Client ID. |
| `OAUTH_CLIENT_SECRET` | `--oauth-client-secret` | Your OAuth2 Client Secret. |
| `USER_CONTEXT` | `--user-context` | The user context string for API requests. |

## Installation

Ensure you have Python 3.11 or higher installed.

1. Clone the repository.
2. Install dependencies:

```bash
# Install dependencies and set up the environment
uv sync
```

## Usage

### Running the Server

You can run the server using `uvx`. Make sure to set the required environment variables or command line arguments first.

```bash
# Set environment variables (example for PowerShell)
$env:API_BASE_URL="https://api.infongen.com/v1"
$env:API_KEY="your-api-key"
$env:OAUTH_TOKEN_URL="https://auth.infongen.com/oauth/token"
$env:OAUTH_CLIENT_ID="your-client-id"
$env:OAUTH_CLIENT_SECRET="your-client-secret"
$env:USER_CONTEXT="your-user-context"

# Run the server
uvx --index-url https://nexus-ci.core.kuberocketci.io/repository/krci-python-group/simple/ --from epmp-igen-document-search-mcp-server mcp-server
```

### Tools

#### `search`

Performs a semantic search.

- **query** (string, required): The text to search for.
- **limit** (integer, optional): The maximum number of documents to return. Default is 10.

## Development

This project uses `mcp` and `requests` libraries. The main logic is in `main.py` which defines the MCP server and tools, and `api.py` which handles the API communication.