semvec/pypi-packages huggingface dataset:
djangocms-4-migration 0.0.2
A django CMS 3 to 4 migration package

# django CMS 4 Migration

**Warning:** If the migration process fails to complete you will not be able to undo the changes without reloading a database backup. We cannot be held accountable for any data loss sustained when running the commands provided in this project. Please keep a database backup before running any commands provided by this package. 

## When do I need this package?
This package is designed to migrate a django CMS 3.5+ project to django CMS 4.0.

## What does this package do?
- Keeps any draft and published content, ensuring that any new draft changes are kept as a new draft version in djangocms_versioning. 

## Limitations
Due to the nature of the changes between django CMS 3.5+ and 4.0 the package will fail to function if an incompatible package is installed. 

This may require you to:
 - Fork or copy and modify this package to work with any bespoke requirements your project has (we may accept these changes back for popular packages as a configurable option)
 - Ensure that all installed packages for your project are 

## Prerequisites 
Require knowledge of the changes and new features in 4.0:
- New cms app configuration
- Revised Page, Title (Now named PageContent) and Placeholder relationships

Requires knowledge of django CMS Versioning
- Grouper and content model terms
- Understanding how versioning selects published content

### Install the following packages
The following packages are not yet officially released, they need to be installed directly from the repository. We need your help to make packages v4.0 compatible and to provide documentation for the wider community!

django CMS 4.0
```
pip install http://github.com/divio/django-cms/tarball/release/4.0.x#egg=django-cms
```

djangocms-text-ckeditor
```
pip install https://github.com/divio/djangocms-text-ckeditor/tarball/support/4.0.x#egg=djangocms-text-ckeditor
```

djangocms-versioning
```
pip install https://github.com/divio/djangocms-versioning/tarball/master#egg=djangocms-versioning
```

djangocms-alias
```
pip install https://github.com/divio/djangocms-alias/tarball/master#egg=djangocms-alias
```

## Installation
**Warning**: This package can leave your DB in a corrupted state if used incorrectly, be sure to backup any databases prior to running any commands listed here!

First install this package in your project
```
pip install djangocms-4-migration
```

## Running
Simply run the following command to run the data migration. 
**Note:** This command calls the django migrate command, this is because it has to run commands that save information that would have been lost by running the cms migrations directly.
```
python manage.py cms4_migration
```

## Common solutions for django CMS 4.0 compatibility

Import PageContent in a backwards compatible way (Title).
```python

# django CMS v4
try:
    from cms.models import PageContent
# django CMS 3.x
except ImportError:
    from cms.models import Title as PageContent
```
crelm 0.0.36
Utility that automates turning simple C classes into Python objects
Requires Python: >=3.8,<4.0

# Crelm
The Crelm package provides a relatively simple API around the CFFI system. Give it C code or source files, it gives you a package with bindings to functions and `struct`s. Easiest way to get started is hack up the examples in [the repo](https://github.com/wideopensource/crelm/tree/main/examples).

### Installation

pip3 install crelm

### Simple Demo

```
from crelm import Factory

paste = Factory(debug=False, verbose=True).create_Tube('add2') \
    .add_source_text('int add2(int a, int b) { return a + b; }') \
    .squeeze()

print(f'2 + 3 = {paste.add2(2, 3)}')
```
dbt-cratedb 1.5.3
The crate adapter plugin for dbt (data build tool)
Requires Python: >=3.8.2

<p align="center">
  <img src="https://raw.githubusercontent.com/dbt-labs/dbt/ec7dee39f793aa4f7dd3dae37282cc87664813e4/etc/dbt-logo-full.svg" alt="dbt logo" width="500"/>
</p>

**[dbt](https://www.getdbt.com/)** enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

# dbt-cratedb
dbt adapter for cratedb
magik-prompt-sdk 0.1.0
SDK to write and run tests for your LLM app
Requires Python: >=3.6

_Magik is an LLM Observability SDK that helps you write tests and monitor your app in production_.
<br /><br />

# Overview

Reliability of output is one of the biggest challenges for people trying to use LLM apps in production.<br />

LLM responses are non-deterministic by nature. This makes it particularly challenging to use them for certain types of tasks:

- If you're building a AI assistant that helps answer legal questions, and you cannot afford to have hallucinations, or misinformation.
- If you're building a code generation AI, you might need to make sure the code is correct, and works as expected.
- If you're building a customer support agent, you might need to make it sure it responds with accurate answers in a specified format, and does not contain sensitive information like PII.

We are trying to solve these problems with a **test-driven approach towards LLM observability.**
<br /><br /><br />

# Use Cases

Who is this product meant for?

- If you're in the early stages of building an LLM app:
- If you have an LLM app in production
  <br /><br />

### If you're in the early stages of building an LLM app:

---

Test-driven development can speed up your development very nicely, and can help you engineer your prompts to be more robust.

For example, you can write tests like this:

```
# Test that output contains none of the restricted keywords
{
    "description": "output does not contain restricted keywords",
    "eval_function": contains_none,
    "vars": {},
    "args": [restricted_keywords],
    "failure_labels": ["contains_restricted_words", "critical"],
},
```

<br /><br />

### If you have an LLM app in production:

---

You can use our **evaluation & monitoring platform** to:

- Observe the prompt, response pairs in production, and analyze response times, cost, token usage, etc for different prompts and date ranges.

- Evaluate your production responses against your own tests to get a quantifiable understanding of how well your LLM app is performing.

  - For example, You can run the tests you defined against the LLM responses you are getting in production to measure how your app is performing with real data.

- Filter by failure labels, severity, prompt, etc to identify different types of errors that are occurring in your LLM outputs.

<br />

### Upcoming Features

---

Soon, you will also be able to:

- Fail bad outputs before they get to your users.

  - For example, if the LLM response contains sensitive information like PII, you can detect that in real-time, and cut it off before it reaches the end user.

- Set up alerts to notify you about critical errors in production.

<br /><br />

# Platform

Contact us at hello@magiklabs.app to get access to our LLM observability platform where you can run the tests you've defined here against your LLM responses in production.