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Integrating Rasa into Live Helper Chat (intent)

Rasa is just AI bot which does all the hard work. Integration once you have rasa is dead simple. Once you have Rasa running.

We will need few things

Install instructions for docker version

git clone && cd intent-rasa

Now you can edit data/nlu.yml and write your model data in this file.

Build docker image

docker-compose build

Run one time

docker-compose up

Run as a service

docker-compose up -d

You can try out Rasa rest API using curl commands

curl -i http://localhost:5005

# Send demo request
curl localhost:5005/model/parse -d '{"text":"who are you"}'

# Response. In this case intent is bot_challange
{"text":"who are you","intent":{"id":234572354186811386,"name":"bot_challenge","confidence":0.9868453741073608},"entities":[],"intent_ranking":[{"id":234572354186811386,"name":"bot_challenge","confidence":0.9868453741073608},{"id":6404620717205297070,"name":"goodbye","confidence":0.005530951544642448},{"id":-411671348428771358,"name":"affirm","confidence":0.0027931963559240103},{"id":-6453914516151693962,"name":"mood_great","confidence":0.002673292765393853},{"id":3246239079246662505,"name":"deny","confidence":0.0013442487688735127},{"id":-2677704442101564553,"name":"greet","confidence":0.00047634306247346103},{"id":3489442963776345962,"name":"mood_unhappy","confidence":0.00033664595684967935}],"response_selector":{"all_retrieval_intents":[],"default":{"response":{"id":null,"response_templates":null,"confidence":0.0,"intent_response_key":null,"template_name":"utter_None"},"ranking":[]}}}

Install instructions for non docker version

This tutorial is partly based on

If you want to find out more about intent and how to configure it please read.

Here is a quick version how to run Rasa

mkdir rasa
cd rasa

# Change to your python version
python3.6m -m venv ./venv
source ./venv/bin/activate
pip3 install -U pip
pip3 install rasa

# Optional, if you get some errors you can try this
pip3 --use-feature=2020-resolver install rasa

mkdir intent
cd ./intent

# Choose yes to train initial model
rasa init --no-prompt

# Now you can edit
# data/nlu.yml and write your model data in this file.

# After you did above changes you can train your Rasa
rasa train nlu

# To try out your model run. [Replace nlu-20190515-144445.tar.gz with your trained model.]
rasa shell -m models/nlu-20190515-144445.tar.gz

# Run it as API server
rasa run --enable-api -m models/nlu-20190515-144445.tar.gz

With Rasa we have now everything we need now.

Configuring Rest API in Live Helper Chat

Create a new Rest API by navigating to

System configuration > Live help configuration > Rest API Calls

Just create a new. Configuration looks like this

We set body request as JSON and set content.

We also set Outpout parsing

We set that confidence would be greater than 85% (0.85). As for output parsing you can also in condition to use check that intent is greeting. So in bot you would have separate response type by intent.

Configuration bot in Live Helper Chat

For bot configuration we only need three triggers

  • Default it has checked Default, Default for unknown message
  • Intent parser We search for a message by returned response.
  • To low confidence - this will be send if response has to low confidence value.
  • nlu_fallback - it's Rasa internal return response when no intent was found.
  • greet - just greeting intent response

Default configuration

Intent parser configuration

To low confidence configuration

nlu_fallback configuration

greet configuration

Same way you can define other intents. E.g hi, who are you?

Conversation example

Don't forget to set your bot as default department bot.