Slot variables are still variables; they’re just a special type. If you want to automatically detect an entity within a Question interaction, you must use a slot variable that’s associated with that entity. Using a standard variable won’t accomplish this; only slot variables have this specialized behavior.

When combined with entities, slots bring dynamic, fluid behavior to storing consumer input. For example, if you add the Question interaction, "What type of shoes are you looking for?", Conversation Builder’s Assist tool will suggest appropriate entities and slots for that interaction. As long as the consumer stays within the bounds of entities that you have created, the slots will automatically adjust and update based upon consumer input throughout the conversation.

Add a slot manually

This section describes how to create a slot manually. However, if you use the Assist tool to assign an entity to a question, Assist automatically creates a slot to store the consumer's response. For info on this, see here.

To add a slot manually

  1. Select the interaction where you'd like to look for entities in the consumer’s input, like a multiple choice question, for example.
  2. Add a custom rule.
  3. In the Add Next Action Rule dialog box, click +Add Slot.
  4. Enter a slot name. The slot name is later used to refer to and access the data that the slot contains. We recommend using standard naming conventions for slots.
  5. For the value field, look for a pre-configured entity (which you should have set up for your domain previously) by entering the "@" character and then the name of your desired entity.
  6. Decide how long you'd like the slot's data to be kept for, i.e., the duration.

{$botContext.slot.slotName} is how you can access values in slots and use them in other ways. For example, if you've stored a "shoes" entity in a shoe_type slot, you can have the bot respond with the consumer's stored answer with a text interaction like so:

"You answered: {$botContext.slot.shoe_type}!"

If the bot asked the consumer, "What type of shoes are you looking for?" and the consumer answered "boots,” the slot for the entity “shoes” would be populated with their answer. The bot could then respond with "You answered: boots!" populating the code above with the consumer’s answer.

Example 1: Fill a slot with a value for a single entity

Consider the following bot flow:

In the multiple choice question, the Pet Type rule's configuration looks like this:

The result is that:

  • If the consumer starts off with, “I want to buy a pet,” the multiple choice interaction is sent to the consumer, and the consumer can choose Dog, Cat, or Bird. This fills the slot.
  • If the consumer starts off with, “I want to buy a dog” (or any other recognized pet entity – it does not have to be dog, cat, or bird), then the multiple choice question is not sent to the consumer. But the slot is still filled.

Either way, when the consumer reaches the text interaction, the slot will have been filled, and the text interaction sends, "So you're looking for a dog. A fine choice!" Or, "So you're looking for a tortoise. A fine choice!"

The key point is that if the multiple choice question detects that the entity already exists, it performs the slot-filling step, but it does not send its content to the consumer. The bot flow simply continues to the next action.

Example 2: Fill a slot with a value for one of several entities

Sometimes an intent can relate to multiple entities: For example, a consumer can make a reservation at a hotel or a restaurant.

To handle this, a slot-filling question can have more than one slot-filling rule. This means you can set up the question so that it captures whichever of multiple entities a consumer is interested in. Importantly, the rules can all fill the same slot, so you can easily use the slot later on. Here is an example that illustrates the concept:

Note the two rules in the question:

  • Capture Hotel
  • Capture Restaurant

There are two different entity types involved: hotel and restaurant. Each rule captures one of those. Here is Capture Hotel:

And here is Capture Restaurant:

As we've discussed in Example 1 above, if either of the question's rules are met by the consumer's initial intent, the question fills the slot, and it does not send its content to the consumer.

In this case, both rules fill the slot establishment_name. So, regardless of whether the consumer starts off with, “I want to eat dinner at the Olive Garden” and Capture Restaurant is triggered, or “I want to stay at the Holiday Inn” and Capture Hotel is triggered, establishment_name will contain the restaurant or hotel name. So, it can be used in the subsequent text statement: “So that’s a reservation for Olive Garden” or “So that’s a reservation for Holiday Inn.”

Importantly, if the consumer were to start off more generally with, “I need a reservation,” then neither rule in the question would be triggered, so the question would send, “Where would you like to make your reservation?” The consumer's response would then be used to fill the establishment_name slot according to the rules. And again, the text statement sent in response would make use of the value.

Example 3: Fill multiple slots with the values for multiple entities

Slot-filling becomes especially useful when mining the entities that make up a consumer's intent to pre-populate your list of questions, and streamline the data collection process. Consider the following example:

  1. Create a new dialog and associate an intent from your domain as the dialog starter. For this example, we'll create the dialog ordering with the domain intent order item.
  2. Create a few entities that will be captured in the intent. For this example, we are going to create an entity for color with the values blue, white, and red; one for item with the values pants, shoes, shirt, underwear; and finally, one for size with the values small, medium, and large. Before moving on, we'll update and train the order item intent with some representative training phrases that contain these entities.
  3. Next, we create the questions that the dialog will ask. We'll add one question interaction per slot that we are looking to fill. Using Assist, we'll assign the entities to the relevant questions:

    Once completed, we'll have a list of questions that looks like the following:

    When you assign an entity to a question, this automatically creates a rule for each question. Each rule creates a slot that contains our slot variable (e.g., item) and whose value is the entity value (e.g., @item).

  4. Test the bot using an intent with slot choices as part of the query. When you enter the dialog, if a consumer has supplied an entity that is known to the domain, it will automatically populate the slot, skip the interaction, and move on to the next interaction's question.

If a consumer expresses all the slots as part of their intent query, it will skip to our confirmation step.