Rasa is an open source machine learning framework for automated text and voice-based conversations. Rasa is helpful in understanding messages, holding conversations and connecting to messaging channels and APIs.
There are two main components of Rasa: 1> Rasa NLU 2> Rasa core
Rasa NLU is an open-source natural language processing tool for intent classification and entity extraction in chatbots. Here is an example:
“I need to book flight for 4 people”
ans struture of above will look like: { "intent": "Flight_Booking", "entities": { "service" : "Flight", "COUNT" : 4 } } Rasa NLU is using Bag-of-word(BoW) algorithm to find intents and Conditional Random Field(CRF) to find the entities internally.
Rasa Core playsessntial role in generating reply for chatbots. It consider the output of Rasa NLU (intents and entities) as an input and applies machine learning models to response with a bot reply.
Below fig shows step by step working of Rasa:
Below steps are involved in building the basic chatbot: 1> Create virtual enviorment and activate it using below commands: python3 -m venv --system-site-packages ./rasa # Creating virtual env for rasa folder source ./rasa/bin/activate #Activate it
2> Install dependencies for spacy using below: pip install "rasa[spacy]" python -m spacy download en_core_web_md python -m spacy link en_core_web_md en
3> Install rasa -x to your system: pip install rasa-x --extra-index-url https://pypi.rasa.com/simple
4> Building your basic simle bot: rasa init --no-prompt
Above command will create some configuration files in your system that we can customize as per our needs.Below files will be created:
| Column Name | Description |
|---|---|
| init.py | an empty file that helps python find your actions |
| actions.py | code for your custom actions |
| config.yml | configuration of your NLU and Core models |
| credentials.yml | details for connecting to other services |
| data/nlu.md | Training data for NLU model |
| data/stories.md | stories |
| domain.yml | assistant’s domain |
| endpoints.yml | details for connecting to channels like fb messenger |
| models/tstmp.tar.gz | your initial model |
Here is the last step just write rasa x or rasa shell in your terminal and start chatting.