Need help in choosing the pipeline

Hi All,

I am new to ML & AI / Rasa and trying to develop a company-policy specific faq chatbot.

My requirements are to develop a bot:-

  1. Learn and chat with company-specific policies faq.

  2. Learns and chats with small chitchat conversation.

I am not sure which policies and pipeline so i have used basic settings from weather bot or other tutorials.

 # Configuration for Rasa NLU.
 language: en
 pipeline: supervised_embeddings
 # Configuration for Rasa Core.
   - name: MemoizationPolicy
     max_history: 5
   - name: KerasPolicy
     epochs: 400
     batch_size: 100
     validation_split: 0.2
     max_history: 5
   - name: MappingPolicy

supervised_embeddings internally uses below component.

language: "en"

- name: "WhitespaceTokenizer"
- name: "RegexFeaturizer"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"
- name: "CountVectorsFeaturizer"
  1. How can i choose which component should be used and in which order’s.

  2. when i ask questions for chitchat using the keyword from faq intent NLU choose faq intent for it.

After reading lots of articles i am confused which policy to and pipeline to use.

Can any body please help

I would recommend to use default nlu pipeline and core config

Ghostvv - I am already using nlu pipeline .I wanted to know which is better supervised or pretrained for company specific bot which will have faq question and small talk chittalk.

it depends on your type of text you have. It is better to perform comparison tests to see which pipeline performs better