At the core every chatbot there is a intent recognition component in charge of matching user utterances to one of the available chatbot intents. This is Xatkit's own NLU server. Note that:
- Following Xatkit's chatbot orchestration platform philosophy, you can use any available Intent Recognition engines to build your Xatkit bots.
- Thanks to its public REST API, this NLU Engine can be integrated in other NLP solutions, not only in Xatkit.
Note that this NLU engine is still in an alpha phase. So, great for learning and playing. Not ready for any type of production use. Keep also in mind the project is quickly evolving, this includes its public APIs. Until a first stable version, breaking changes can occur at any time.
If there is something we have learnt is that there is no one size fits all solution when it comes to the Natural Language processing requirements for a chatbot project.
And we wanted to offer a solution that was easy to adapt to any chatbot requirement and that offer a range of intent matching options, mostly adopting a rather pragmatic approach, instead of the typical solution of building a large neural network for the whole bot.
Some Xatkit's NLU specific characteristics:
We see bots as having different conversation contexts (e.g. as part of a bot state machine). When in a given context, only the intents that make sense in that context should be evaluated when considering possible matches.
A Xatkit bot is composed of contexts where each contexts may include a number of intents (see the dsl package). During the training phase, a NLP model is trained on those intents' training sentences and attached to the context for future predictions).
What if the user input text is full of words the NN has never seen before? It's safe to assume that we can directly determine there is no matching and trigger a bot move to the a default fallback state.
Or what if the input text is a perfect literal match to one of the training sentences? Shouldn't we assume that's the intent to be returned with maximum confidence?
This type of pragmatic decisions are at the core of Xatkit to make it a really useful chatbot-specific intent matching project.
For more detail on how Xatkit's NLU has been built and how to use it, see also this blog post
Right now, the engine focus on intent matching and NER (named-entity recognition).
We allow you to define your own set of entities with their respective values and synonyms.
An example entity could be 'CityEntity' with values 'Barcelona' (with synonyms: 'BCN', 'Barna') and 'Madrid'.
We develop predefined entity types that can be used within any intent:
- Number (e.g. '3', '3.5', '-3.5', 'minus three point five', etc.)
- Date-time (e.g. 'December 1st 2022 at 15:00', 'December', '4pm', 'today', 'in 3 months', etc.)
See base_entities.py to check all the Base Entity Types.
Check also the tests folder for some examples of how to use the engine and its configuration options (described below).
Xatkit NLU engine has been tested with Python 3.9.
Other key requirements are:
- numpy~=1.22.2
- fastapi~=0.74.0
- tensorflow~=2.8.0
- pydantic~=1.9.0
- matplotlib~=3.5.1
- stanza~=1.3.0
- PyStemmer~=2.0.1
- text2num~=2.5.0
- dateparser~=1.1.3
- python-dateutil~=2.8.2
FastAPI is the web framework that we use to expose the NLU engine as a REST API. You will probably recognize most of the other dependencies :-)
We use Stanza's language-dependent tokenizer. You'll need to download the language models you'll be using in your bots before running the server
FastAPI relies on uvicorn as ASGI web server implementation.
To expose Xatkit as a web API write:
python -m uvicorn main:app --log-level trace
where main is the module where the FastAPI app resides.
List of configuration options and default values (see nlp_configuration.py)
| Key | Values | Description | Constraint |
|---|---|---|---|
country |
String | The country language used by the bot | Optional (default en) |
region |
String | The region code used by the bot | Optional (default US) |
timezone |
String | The timezone used by the bot | Optional (default Europe/Madrid) |
num_words |
int | Max number of words to keep track of in the word index | Optional (default 1000) |
lower |
Boolean | Whether all strings should be transformed to lowercase | Optional (default true) |
oov_token |
String | Token to represent out of vocabulary words during prediction | Optional (default <OOV>) |
num_epochs |
int | Number of epochs to run during training | Optional (default 300) |
embedding_dim |
int | Number of dimensions to be sued during the embedding of word tokens | Optional (default 128) |
input_max_num_tokens |
int | Max length (in terms of number of tokens) to keep for all sentences | Optional (default 30) |
stemmer |
Boolean | Whether to use a Stemmer as part of the training sentences (and user utterances) processing | Optional (default True) |
discard_oov_sentences |
Boolean | Automatically assign a zero probability to all intents when the user utterance is all OOV tokens | Optional (default True) |
check_exact_prediction_match |
Boolean | Whether to check for exact match between the sentence to predict and one of the training sentences | Optional (default True) |
use_ner_in_prediction |
Boolean | Should entity matches be used during prediction? | Optional (default True) |
activation_last_layer |
String | The activation function of the last layer | Optional (default sigmoid) |
activation_hidden_layers |
String | The activation function of the hidden layers | Optional (default tanh) |
Do you want to contribute to Xatkit? We would love to hear from you. Remember that there are many ways to support open source projects beyond committing code!. Talking about Xatkit, writing documentation, contributing examples,... all are great ways to help us.
When contributing code, please first discuss the change you wish to make with us. Start by opening a descriptive issue so that we can advise on the best way to proceed with your bug fix or new feature idea.
Thanks for reading until the end! If you like what you see, don't forget to star/watch this repository, you'll make us very happy!
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