https://github.com/farizrahman4u/eywa

Open source framework for building conversational agents [WIP]
https://github.com/farizrahman4u/eywa

Last synced: 23 days ago
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Open source framework for building conversational agents [WIP]

• Host: GitHub
• URL: https://github.com/farizrahman4u/eywa
• Owner: farizrahman4u
• License: gpl-3.0
• Created: 2018-01-29T23:57:42.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2021-06-05T19:35:15.000Z (over 3 years ago)
• Last Synced: 2024-10-08T10:25:08.069Z (about 1 month ago)
• Language: Python
• Size: 313 KB
• Stars: 26
• Watchers: 9
• Forks: 15
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Eywa - Framework for Conversational Agents



[![Build Status](https://travis-ci.org/farizrahman4u/eywa.svg?branch=master)](https://travis-ci.org/farizrahman4u/eywa)

Eywa is an open source framework for building and deploying conversational agents (aka chatbots). 

#### Features:

* Requires only few samples for training

* Instant retraining

* Uses word embeddings + heuristics instead of deep learning (better debuggability and interpretability)

## Quickstart

### Classifier

```python

from eywa.nlu import Classifier

x_hotel = ['book a hotel', 'need a nice place to stay', 'any motels near by']

x_weather = ['what is the weather like', 'is it hot outside']

clf = Classifier()

clf.fit(x_hotel, 'hotel')

clf.fit(x_weather, 'weather')

print(clf.predict('will it rain today'))  # >>> 'weather'

print(clf.predict('find a place to stay'))  # >>> 'hotel'

```

### Entity extractor

```python

from eywa.nlu import EntityExtractor

x = ['what is the weather in tokyo', 'what is the weather', 'what is the weather like in kochi']

y = [{'intent': 'weather', 'place': 'tokyo'}, {'intent': 'weather', 'place': 'here'}, {'intent': 'weather', 'place': 'kochi'}]

ex = EntityExtractor()

ex.fit(x, y)

x_test = 'what is the weather in london like'

print(ex.predict(x_test))

```

### Pattern

```python

from eywa.nlu import Pattern

p = Pattern('[fruit: apple, banana] is my favourite fruit')  # create variable [fruit] with sample values {apple, banana}

p('i like grapes')  # >> {'fruit' : 'grapes'}

```

### Requirements

* Python 3.6 or higher

* Eywa requires [Tensorflow 2.0](https://www.tensorflow.org/install/pip) and should be installed manually by the user (is not installed automatically as a dependency)

## Installation

### Via pip:

`pip install eywa`

### Install from source:

```

git clone https://www.github.com/farizrahman4u/eywa.git

cd eywa

python setup.py install

```