Rasa will not save slot from entity of intent

new to the rasa framework. I've been trying to make a simple bot that would simply recognize the intent of me providing my name and then rasa responding with a phrase including my name. The problem is that it does seem to recognize the intent but if the name I give it is not included in one of the examples then the response just includes none instead of my name (meaning the slot is empty). I thought its because of the lack of examples but I added more than 20. It would only fill that slot if the name provided was one of the names provided in the examples.

Furthermore, I can't seem to use rules. I was under the assumption that on a technical level I can just use rules for one-off questions such as providing the name and the bot responding hello, name. I can only get responses if it was a story.

rules.yml:

# ask name confirmation
- rule: ask name confirmation
  steps:
  - intent: inform_name
  - action: utter_greet_with_name

# respond with name
- rule: respond with name
  steps:
  - action: utter_respond_with_name

# respond without name
- rule: respond without name
  condition:
  - slot_was_set:
      - name: null
  steps:
  - action: utter_respond_without_name

nlu.yml:

- intent: inform_name
  examples: |
    - I'm [Sarah](name).
    - Hi, my name is [Chris](name).
    - You can call me [Alex](name).

stories.yml:

stories:

- story: path 1
  steps:
  - intent: greet
  - action: utter_greet
  - intent: inform_name
  - action: utter_greet_with_name
  - intent: ask_name
  - action: utter_respond_with_name

- story: path 2
  steps:
  - intent: greet
  - action: utter_greet
  - intent: ask_name
  - action: utter_respond_without_name

part of domain.yml

entities:
  - name

# Define the 'name' slot with the 'from_text' mapping
slots:
  name:
    type: text
    influence_conversation: true
    mappings:
      - type: from_entity
        entity: name

Thank you!

Using name mentioned in example.

Generally, based on your entity category, you can benefit from different entity extractors. You can add Duckling, SpaCy, and CRF to your Rasa pipeline. Here I suggest going with SpacyEntityExtractor as it has a pre-trained model good for recognizing Names, Companies, etc.

Here you need to first install Spacy lib.

python -m pip install -U rasa[spacy]
python -m spacy download en_core_web_lg

Then you can set up your pipeline in config.yml and add the following lines to deploy the entity extractor.

name: "SpacyNLP"
# language model to load
model: "en_core_web_lg"

and

- name: "SpacyEntityExtractor"
  dimensions: ["PERSON"]

For a better explanation of how to set up your config, pls refer to Rasa docs