The reference contextual AI assistant for ixo networks.
Run:
pip install -r requirements.txt
Use rasa train
to train a model.
Set up the action server in one terminal window:
rasa run actions
In another window, run the duckling server (for entity extraction):
docker run -p 8000:8000 rasa/duckling
Then to talk to the assistant, run:
rasa shell --debug
Note that --debug
mode will produce a lot of output meant to help you understand how the assistant is working
under the hood. To simply talk to the assistant, you can remove this flag.
data/core.md
- contains stories
data/nlu.md
- contains NLU training data
actions.py
- contains custom action/api code
domain.yml
- the domain file, including bot response templates
config.yml
- training configurations for the NLU pipeline and policy ensemble
tests/e2e.md
- end-to-end test stories
The assistant currently has a growing range of skills. You can ask it to:
- Fuel an ixo entity (project or cell) with IXO Credits
- Apply to join a cell
- Apply to work on a project
- Stake into a bond
- Send and receive tokens of various denominations
- Create a cell or project from a template
It also has a limited ability to switch skills mid-transaction and then return to the transaction at hand.
For the purposes of illustration, the assistant recognises the following entitites:
ixo-entity
// this could be a cell, project, investment, oracle, data-asset, or templatetoken
// IXO, IXOS, xUSDidentity
wallet
You can change any of these by modifying actions.py
and the corresponding NLU data.
You can test the assistant on the test conversations by running rasa test
.
Note that if duckling is running when you do this, you'll probably see some "failures" because of entities; that's ok!