Chatette

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A powerful dataset generator for Rasa NLU, inspired by Chatito

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<h1 align="center">
 <a href="https://pypi.org/project/chatette">
 <img src="https://raw.githubusercontent.com/SimGus/Chatette/master/public/images/chatette-logo.png" alt="Chatette logo" width=300>
 </a>
 
 Chatette
 
</h1>

<h4 align="center">A data generator for Rasa NLU</h4>

<a href="#installation">Installation</a> •
 <a href="#uninstallation">Uninstallation</a> •
 <a href="#how-to-use-chatette">How to use Chatette?</a> •
 <a href="#chatette-vs-chatito">Chatette vs Chatito?</a> •
 <a href="#development">Development</a> •
 <a href="#credits">Credits</a>

*Chatette* is a Python program that generates training datasets for [*Rasa NLU*](https://github.com/RasaHQ/rasa_nlu "rasa-nlu GitHub repository") given template files.
If you want to make large datasets of example data for Natural Language Understanding tasks without too much of a headache, *Chatette* is a project for you.

Specifically, *Chatette* implements a Domain Specific Language (*DSL*) that allows you to define templates to generate a large number of sentences, which are then saved in the input format(s) of *Rasa NLU*.

The *DSL* used is a near-superset of the excellent project [*Chatito*](https://github.com/rodrigopivi/Chatito "Chatito's GitHub repository") created by Rodrigo Pimentel. (*Note: the DSL is actually a superset of Chatito v2.1.x for Rasa NLU, not for all possible adapters.*)

An interactive mode is available as well:

## Installation
To run *Chatette*, you will need to have [Python](https://www.python.org/) installed.
*Chatette* works with both Python 2.7 and 3.x (>= 3.4).

*Chatette* is available on [PyPI](https://pypi.org/project/chatette), and can thus be installed using `pip`:
```sh
pip install chatette
```

**Alternatively**, you can clone the [GitHub repository](https://github.com/SimGus/Chatette) and install the requirements:
```sh
pip install -r requirements/common.txt
```
You can then install the project (as an editable package) using pip, by executing the following command from the directory `Chatette/chatette/`:
```sh
pip install -e .
```
You can then run the module by using the commands below in the cloned directory.

## Uninstallation
You can just use pip to uninstall *Chatette*:
```sh
pip uninstall chatette
```

## How to use *Chatette*?

### Input and output data

The data that *Chatette* uses and generates is loaded from and saved to files. You will thus have:
- One or several **input file(s)** containing the templates.
  There is no need for a specific file extension. The syntax of the *DSL* to make those templates is described on the [wiki](https://github.com/SimGus/Chatette/wiki).

- One or several **output file(s)**, which will be generated by *Chatette* and will contain the generated examples. Those files can be formatted in *JSON* (by default) or in *Markdown* and can be directly fed to *Rasa NLU*. It is also possible to use a *JSONL* format.

### Running *Chatette*
Once *Chatette* is installed and you created the template files, run the following command:
```bash
python -m chatette <path_to_template>
```
where `python` is your Python interpreter (some operating systems use `python3` as the alias to the Python 3.x interpreter).

You can specify the name of the output file as follows:
```bash
python -m chatette <path_to_template> -o <output_directory_path>
```

`<output_directory_path>` is specified relatively to the directory from which the script is being executed.
The output file(s) will then be saved in numbered `.json` files in `<output_directory_path>/train` and `<output_directory_path>/test`. If you didn't specify a path for the output directory, the default one is `output`.

Other program arguments and are described [in the wiki](https://github.com/SimGus/Chatette/wiki).

## *Chatette* vs *Chatito*?
*TL;DR: **main selling point**: it is easier to deal with large projects using* Chatette, *and you can transform most* Chatito *projects into a* Chatette *one without any modification.*

A perfectly legitimate question is:
> Why does *Chatette* exist when *Chatito* already fulfills the same purposes?

The two projects actually have different goals:

*Chatito* aims to be a generic but powerful *DSL*, that should stay very legible. While it is perfectly fine for small projects, when projects get larger, the simplicity of its *DSL* may become a burden: your template file becomes overwhelmingly large, to the point you get lost inside it.

*Chatette* defines a more complex *DSL* to be able to manage larger projects and tries to stay as interoperable with *Chatito* as possible.
Here is a non-exhaustive list of features *Chatette* has and that *Chatito* does not have:

- Ability to break down templates into **multiple files**
- Possibility to specify the **probability of generating some parts** of the sentences
- **Conditional generation of some parts** of the sentences, given which other parts were generated
- **Choice syntax** to prevent copy-pasting rules with only a few changes and to easily modify the generation behavior of parts of sentences
- Ability to **define the value of each slot (entity)** whatever the generated example
- Syntax for generating words with **different case** for the leading letter
- **Argument support** so that some templates may be filled by different strings in different situations
- **Indentation is permissive** and must only be somewhat coherent
- Support for **synonyms**
- **Interactive command interpreter**
- Output for Rasa in ***JSON* or in *Markdown* formats**

As the *Chatette*'s DSL is a superset of *Chatito*'s one, input files used for *Chatito* are most of the time completely usable with *Chatette* (not the other way around). Hence, it is easy to start using *Chatette* if you used *Chatito* before.

As an example, this *Chatito* data:
```
// This template defines different ways to ask for the location of toilets (Chatito version)
%[ask_toilet]('training': '3')
    ~[sorry?] ~[tell me] where the @[toilet#singular] is ~[please?]?
    ~[sorry?] ~[tell me] where the @[toilet#plural] are ~[please?]?

~[sorry]
    sorry
    Sorry
    excuse me
    Excuse me

~[tell me]
    ~[can you?] tell me
    ~[can you?] show me
~[can you]
    can you
    could you
    would you

~[please]
    please

@[toilet#singular]
    toilet
    loo
@[toilet#plural]
    toilets
```
could be directly given as input to *Chatette*, but this *Chatette* template would produce the same results:
```
// This template defines different ways to ask for the location of toilets (Chatette version)
%[&ask_toilet](3)
    ~[sorry?] ~[tell me] where the @[toilet#singular] is [please?]?
    ~[sorry?] ~[tell me] where the @[toilet#plural] are [please?]?

~[sorry]
    sorry
    excuse me

~[tell me]
    ~[can you?] [tell|show] me
~[can you]
    [can|could|would] you

@[toilet#singular]
    toilet
    loo
@[toilet#plural]
    toilets
```

The *Chatito* version is arguably easier to read, but the *Chatette* version is shorter, which may be very useful when dealing with lots of templates and potential repetition.

Beware that, as always with machine learning, having too much data may cause your models to perform less well because of overfitting. While this script can be used to generate thousands upon thousands of examples, it isn't advised for machine learning tasks.

*Chatette* is named after *Chatito*: *-ette* in French could be translated to *-ita* or *-ito* in Spanish. Note that the last *e* in *Chatette* is not pronouced (as is the case in "note").

## Development
For developers, you can clone the [repo](https://github.com/SimGus/Chatette) and install the development requirements:
```pip install -r requirements/develop.txt```
Then, install the module as editable:
```pip install -e <path-to-chatette-module>```

- Run [pylint](https://www.pylint.org/):
```tox -e pylint```

- Run [pycodestyle](https://github.com/PyCQA/pycodestyle):
```tox -e pycodestyle```

- Run [pytest](https://docs.pytest.org/en/latest/):
```tox -e pytest```

## Credits
### Author and maintainer
- [SimGus](https://github.com/SimGus)

*Disclaimer: This is a side-project I'm not paid for, don't expect me to work 24/7 on it.*

### Contributors
- [meiblorn](https://github.com/meiblorn)
- [Valentin Calomme](https://github.com/ValentinCalomme)
- [Jeff Greenberg](https://github.com/ziligy)
- [Dávid Kizivat](https://github.com/kizivat)

Many thanks to them!

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