What we did…
With the help of Hallwaze beat’s RTM api and a bot engine that uses popular Wit.ai for NLU and NLP. We cooked some very interesting bots.
We mashed up IoT and conversational bots to create a system where a bot was acting like an interface to IoT ecosystem.
Beats is the messaging part of Enterprise Collaboration Ecosystem, Hallwaze. Now that it has opened up access to send and receive messages at real time, many new opportunities open up.
- Beats RTM api
- ai – Chatbot engine
- Smart devices
Once Wit.ai is ready for Natural Language processing and Natural Language Understanding, next is to make bot engine functional. Bot engine passes the received message to Wit.ai which returns score for user defined entities and sentiments, upon which Bot.ai decides the next step is to integrate Wit.ai into the code for our bot’s engine. Wit.ai has well-documented open source libraries and SDKs for iOS, Ruby, Node.js, and Python which you can access at the Wit.ai Github page (https://github.com/wit-ai).
Workflow for Bot conversation
Step 1. Fetch a Bot identity from Beats Server.
- Log into beats and form setting add bot.
- Get the connection string.
Step 2. Connect to messaging system using connection string provided over bot registeration
Step 3. Get bot’s Natural language Understanding done right using Wit.ai.
- A detailed quick start with wit.ai can be found at https://wit.ai/docs/quickstart.
Step 4. Once Wit.ai is ready for Natural Language processing and Natural Language Understanding. Next is to make bot engine functional.
Bot engine passes the received message further to Wit.ai which returns score for user defined entities, its state and sentiments, upon which Bot engine rule works.
Bot engine returns confidence or probability for the entity and its state upon which rule engine works.
Step 5. Was to finally get Rule engine done right by checking probability scores for entities and intents of sentence passed to Wit.ai
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