rasa_chatbot

A Chinese task oriented chatbot in IVR(Interactive Voice Response) domain， Implement by rasa nlu and rasa core. This is a demo with toy dataset.

install dependency:

• follow here

dir tree

rasa_chatbot/
├── data
│   ├── mobile_nlu_data.json  # rasa nlu train data
│   ├── mobile_story.md       # rasa core train data
│   └── total_word_feature_extractor.dat  # mitie word vector feature
├── __init__.py               # init file
├── httpserver.py             # rasa nlu httpserver
├── bot.py                    # ivr bot main script.
├── mobile_domain.yml         # rasa core domain file
├── mobile_nlu_model_config.json  # rasa nlu config file
├── models                    # directory to save trained models
└── README.md                 # readme file

train nlu model

python -m rasa_nlu.train --config  mobile_nlu_model_config.json --data data/mobile_nlu_data.json  --path models

test rasa nlu

$python -m rasa_nlu.server --path projects
$ curl -X POST localhost:5000/parse -d '{"q":"hello"}' | python -m json.tool
{
    "intent": {
        "name": "greet",
        "confidence": 1.0
    },
    "entities": [],
    "text": "hello",
    "project": "default",
    "model": "fallback"
}

train dialogue

python bot.py train-dialogue

train dialogue in online mode

python bot.py online_train

test

Run command below:

python bot.py run

Example1:

Bot loaded. Type a message and press enter : 
YOU：你是谁
BOT：您好!，我是机器人小热，很高兴为您服务。
YOU：我想看一下消费情况
BOT：您想查询哪个月的消费？
YOU：十月份的
BOT：好，请稍等
BOT：您好，您十月份共消费二十八元。
YOU：好谢谢
BOT：您开心我就开心
BOT：您还想干什么
YOU：没什么了
BOT：Bye， 下次再见

Example2:

Bot loaded. Type a message and press enter : 
YOU：给我看看我上月用了多少话费
BOT：好，请稍等
BOT：您好，您上月共消费二十八元。
BOT：您还想干什么

train word vector

You can train your own MITIE model using following method:

$ git clone https://github.com/mit-nlp/MITIE.git
$ cd MITIE/tools/wordrep
$ mkdir build
$ cd build
$ cmake ..
$ cmake --build . --config Release
$ ./wordrep -e /path/to/your/folder_of_cutted_text_files

/path/to/your/folder_of_cutted_text_files above is a directory path in which has word cutted data files to train. This process may cost one or two days.