semvec/pypi-packages huggingface dataset:
inline-snapshot-django 1.4.0
Extensions for using inline-snapshot to test Django projects.
Requires Python: >=3.10
Keywords: Django

======================
inline-snapshot-django
======================

.. image:: https://img.shields.io/readthedocs/inline-snapshot-django?style=for-the-badge
   :target: https://inline-snapshot-django.readthedocs.io/en/latest/

.. image:: https://img.shields.io/github/actions/workflow/status/adamchainz/inline-snapshot-django/main.yml.svg?branch=main&style=for-the-badge
   :target: https://github.com/adamchainz/inline-snapshot-django/actions?workflow=CI

.. image:: https://img.shields.io/badge/Coverage-100%25-success?style=for-the-badge
   :target: https://github.com/adamchainz/inline-snapshot-django/actions?workflow=CI

.. image:: https://img.shields.io/pypi/v/inline-snapshot-django.svg?style=for-the-badge
   :target: https://pypi.org/project/inline-snapshot-django/

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge
   :target: https://github.com/psf/black

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   :target: https://github.com/pre-commit/pre-commit
   :alt: pre-commit

----

Extensions for using `inline-snapshot <https://github.com/15r10nk/inline-snapshot>`__ to test `Django <https://www.djangoproject.com/>`__ projects.

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

Please see https://inline-snapshot-django.readthedocs.io/.
fxutil 2.2
Some handy tools...
Requires Python: >=3.10
Keywords: utilities, python, science

[![Tests](https://github.com/fxjung/fxutil/actions/workflows/test.yml/badge.svg)](https://github.com/fxjung/fxutil/actions/workflows/test.yml)
[![PyPI release](https://github.com/fxjung/fxutil/actions/workflows/release.yml/badge.svg)](https://github.com/fxjung/fxutil/actions/workflows/release.yml)

# f(x)util---A bunch of utilities to do things.

Scarcely [documented](https://fjung.com/fxutil).

## Some hints

### Getting started

Needs Python 3.10 or later.

```bash
pip install fxutil
```

### Contributing

Happy to take pull requests!

```bash
git clone git@github.com:fxjung/fxutil.git
cd fxutil
pip install -e .
pre-commit install
pytest
```

Code style is ruff.

### CLI commands

In general, try `fxutil <cmd> --help` to get more help.

- `fxutil manuscript package` -- Package a scientfic manuscript into a
  journal-digestible zip file

### `SaveFigure`

Use like:

```python
from fxutil.imports.general import *

sf = SaveFigure()


def draw_plot():
    fig, ax = figax()
    ax.plot(*evf(np.r_[-1:1:100j], lambda x: x**2))


sf(draw_plot, "my cute figure")
```


### Releasing a new version

```bash
git push
gh release create v<version> --generate-notes
```
republic 0.4.0
Build LLM workflows like normal Python while keeping a full audit trail by default.
Requires Python: <4.0,>=3.11
Keywords: agents, ai, any-llm, context, llm, openai, openrouter, python, routing, tape, tools

# Republic

[![Release](https://img.shields.io/github/v/release/psiace/republic)](https://img.shields.io/github/v/release/psiace/republic)
[![Build status](https://img.shields.io/github/actions/workflow/status/psiace/republic/main.yml?branch=main)](https://github.com/psiace/republic/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/psiace/republic/branch/main/graph/badge.svg)](https://codecov.io/gh/psiace/republic)
[![Commit activity](https://img.shields.io/github/commit-activity/m/psiace/republic)](https://img.shields.io/github/commit-activity/m/psiace/republic)
[![License](https://img.shields.io/github/license/psiace/republic)](https://img.shields.io/github/license/psiace/republic)

Build LLM workflows like normal Python while keeping a full audit trail by default.

> Visit https://getrepublic.org for concepts, guides, and API reference.

Republic is a **tape-first** LLM client: messages, tool calls, tool results, errors, and usage are all recorded as structured data. You can make the workflow explicit first, then decide where intelligence should be added.

## Quick Start

```bash
pip install republic
```

```python
from __future__ import annotations

import os

from republic import LLM

api_key = os.getenv("LLM_API_KEY")
if not api_key:
    raise RuntimeError("Set LLM_API_KEY before running this example.")

llm = LLM(model="openrouter:openrouter/free", api_key=api_key)
result = llm.chat("Describe Republic in one sentence.", max_tokens=48)

if result.error:
    print(result.error.kind, result.error.message)
else:
    print(result.value)
```

## Why It Feels Natural

- **Plain Python**: The main flow is regular functions and branches, no extra DSL.
- **Structured Result**: Core interfaces return `StructuredOutput`, with stable `ErrorKind` values.
- **Tools without magic**: Supports both automatic and manual tool execution with clear debugging and auditing.
- **Tape-first memory**: Use anchor/handoff to bound context windows and replay full evidence.
- **Event streaming**: Subscribe to text deltas, tool calls, tool results, usage, and final state.

## Development

```bash
make check
make test
```

See [CONTRIBUTING.md](./CONTRIBUTING.md) for local setup, testing, and release guidance.

## License

[Apache 2.0](./LICENSE)

---

> This project is derived from [lightning-ai/litai](https://github.com/lightning-ai/litai) and inspired by [pydantic/pydantic-ai](https://github.com/pydantic/pydantic-ai); we hope you like them too.
amsterdam-django-oidc 0.2.4
Extension of the authenctication backend of the mozilla-django-oidc package, with extra validation.
Requires Python: <4.0,>=3.10

# amsterdam-django-oidc
This package contains an authentication backend for Django.
It is currently based on the authentication backend provided by Mozilla through the `mozilla-django-oidc` package.
The Mozilla package however does not validate the `iss`, `aud` and `exp` claims of the access token and always calls
the `userinfo` endpoint on the identity provider. Unfortunately that is not adequate for the use case within the 
landscape of applications of the city of Amsterdam. Hence, the reason for this solution.

Instead of calling the `userinfo` endpoint, it will validate the aforementioned claims.

# Install
The package can be installed using your favorite package manager for python.
For example using uv:
```shell
uv add amsterdam-django-oidc
```

Or using pip:
```shell
pip install amsterdam-django-oidc
```

# Usage
Add the backend to the setting `AUTHENTICATION_BACKENDS`:
```python
# settings.py
AUTHENTICATION_BACKENDS = [
    # ...
    "amsterdam_django_oidc.OIDCAuthenticationBackend",
]
```

There are also a few settings required in addition to those of the Mozilla package:

| Name                   | Type      | Description                                                                                                     |
|------------------------|-----------|-----------------------------------------------------------------------------------------------------------------|
| OIDC_OP_ISSUER         | str       | The allowed issuer, the value of the `iss` claim in the access token must match the value of this setting       |
| OIDC_TRUSTED_AUDIENCES | list[str] | Audiences that we trust, at least one of the values of the `aud` claim must match one the values of this setting |
| OIDC_VERIFY_AUDIENCE   | bool      | Controls wether or not to verify the `aud` claim, default: `True`                                               |

# Development
In order to facilitate further development of this package a containerized setup is provided. 

## Building the container images
```shell
docker compose build
```

## Running development tools
It's recommended to start a container and use the shell inside the container:
```shell
docker compose run --rm amsterdam-django-oidc bash
```
Once you see the shell it's possible to run commands like: 
```shell
uv run ruff check
uv run mypy .
uv run pytest
```