short-order 
short-order is an exerimental natural language conversational agent intended for domains with a fixed vocabulary of entities and a small number of intents. Uses might include ordering food from a restaurant or organizing your song collection.
short-order is based on a pattern-driven tokenizer from the companion token-flow project. For more information on configuring short-order, please see our concepts explainer.
Here's a sample dialog involving ordering at the drive through of ficticious restaurant called Mike's American Grill:
% node build/samples/parser_demo.js
14 items contributed 200 aliases.
5 items contributed 28 aliases.
16 items contributed 31 aliases.
60 items contributed 212 aliases.
-----------------------------------------
SHORT-ORDER "Welcome to Mike's American Grill. What can I get started for you?"
-----------------------------------------
CUSTOMER: "hi there give me uh a coffee with two creams":
QTY ITEM TOTAL
1 Medium Coffee 1.29
ADD 2 Cream
Subtotal 1.29
Tax 0.12
Total 1.41
SHORT-ORDER: "Ok. Can I get you anything else?"
-----------------------------------------
CUSTOMER: "let's start over":
QTY ITEM TOTAL
Subtotal
Tax
Total
SHORT-ORDER: "Welcome to Mike's American Grill. What can I get started for you?"
-----------------------------------------
CUSTOMER: "can I get a cheeseburger well done with no pickles double onion double lettuce and a coffee two cream two sugar":
QTY ITEM TOTAL
1 Cheeseburger 1.99
Well Done
NO Pickles
XTRA Sliced Red Onion
XTRA Leaf Lettuce
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
Subtotal 3.28
Tax 0.30
Total 3.58
SHORT-ORDER: "Got it. Is that everything?"
-----------------------------------------
CUSTOMER: "blah blah blah":
QTY ITEM TOTAL
1 Cheeseburger 1.99
Well Done
NO Pickles
XTRA Sliced Red Onion
XTRA Leaf Lettuce
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
Subtotal 3.28
Tax 0.30
Total 3.58
SHORT-ORDER: "I didn't understand that. What else would you like?"
-----------------------------------------
CUSTOMER: "also get me a hamburger with swiss please":
QTY ITEM TOTAL
1 Cheeseburger 1.99
Well Done
NO Pickles
XTRA Sliced Red Onion
XTRA Leaf Lettuce
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
1 Hamburger 1.69
ADD Swiss Cheese Slice 0.30
Subtotal 5.27
Tax 0.47
Total 5.74
SHORT-ORDER: "Got it. What else?"
-----------------------------------------
CUSTOMER: "lose the cheeseburger and get me a couple pet chicken":
QTY ITEM TOTAL
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
1 Hamburger 1.69
ADD Swiss Cheese Slice 0.30
2 Grilled Petaluma Chicken Sandwich 7.98
Subtotal 11.26
Tax 1.01
Total 12.27
SHORT-ORDER: "Got it. Anything else?"
-----------------------------------------
CUSTOMER: "just a sec":
QTY ITEM TOTAL
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
1 Hamburger 1.69
ADD Swiss Cheese Slice 0.30
2 Grilled Petaluma Chicken Sandwich 7.98
Subtotal 11.26
Tax 1.01
Total 12.27
SHORT-ORDER: "Take your time."
-----------------------------------------
CUSTOMER: "i'll also take I don't know a surf n turf":
QTY ITEM TOTAL
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
1 Hamburger 1.69
ADD Swiss Cheese Slice 0.30
2 Grilled Petaluma Chicken Sandwich 7.98
1 Surf N Turf 7.99
Subtotal 19.25
Tax 1.73
Total 20.98
SHORT-ORDER: "Ok. What beverage would you like with your Surf N Turf?"
-----------------------------------------
CUSTOMER: "make that with a small diet coke":
QTY ITEM TOTAL
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
1 Hamburger 1.69
ADD Swiss Cheese Slice 0.30
2 Grilled Petaluma Chicken Sandwich 7.98
1 Surf N Turf 7.99
1 Small Diet Coke
Subtotal 19.25
Tax 1.73
Total 20.98
SHORT-ORDER: "Ok. Is that all?"
-----------------------------------------
CUSTOMER: "that'll do it":
QTY ITEM TOTAL
1 Medium Coffee 1.29
ADD 2 Cream
ADD 2 Sugar
1 Hamburger 1.69
ADD Swiss Cheese Slice 0.30
2 Grilled Petaluma Chicken Sandwich 7.98
1 Surf N Turf 7.99
1 Small Diet Coke
Subtotal 19.25
Tax 1.73
Total 20.98
SHORT-ORDER: "Thank you. Your total is $20.98. Please pull forward."
How Short-Order Worrks
As an example, consider the following utterance, which would typically come from a speech-to-text system:
I would like a Dakota burger with no onions extra pickles fries and a coke
In this example, the utterance has no commas, since they were not provided by the speech-to-text process.
Using token-flow, this text might be tokenized as
[ADD_TO_ORDER] [QUANTITY(1)] [DAKOTA_BURGER(pid=4)] [QUANTITY(0)]
[SLICED_RED_ONION(pid=5201)] [QUANTITY(1)] [PICKLES(pid=5200)]
[MEDIUM_FRENCH_FRIES(pid=401)] [CONJUNCTION] [QUANTITY(1)]
[MEDIUM_COKE(1001)]
After tokenization, the short-order parser groups the tokens into a tree that reflects the speaker's intent:
[ADD_TO_ORDER]
[QUANTITY(1)] [DAKOTA_BURGER(pid=4)] // Burger, standalone menu item.
[QUANTITY(0)] [SLICED_RED_ONION(pid=5201)] // Remove onion modification
[QUANTITY(1)] [PICKLES(pid=5200)] // Add pickles modification
[QUANTITY(1)] [MEDIUM_FRENCH_FRIES(pid=401)] // French Fries, standalone menu item
[QUANTITY(1)] [MEDIUM_COKE(1001)] // Coke, standalone
Try It Out
short-order is currently in the earliest stages of development, so documentation is sparse or nonexistant, and the code stability is uneven.
If you are interested in taking a look, you can clone the repo on GitHub or install short-order with npm.
npm install shortorder
short-order includes a number of working samples, based on a ficticious restaurant and an imaginary car dealership.
These samples are not included in the short-order npm package. To use them, you must clone the repo from GitHub.
You can find the definition files for the menu, intents, attributes, and quantifiers at
samples/data/restaurant-en/menu.yamlsamples/data/restaurant-en/intents.yamlsamples/data/restaurant-en/attributes.yamlsamples/data/restaurant-en/quantifiers.yaml
Drive Through Conversation Sample
This example generated the conversation at the beginning of this README. If you've cloned the repo, you can build and run the sample as follows:
npm install
npm run compile
node build/samples/parser_demo.js
Printing the menu
It is often helpful to be able to inspect the menu. The menu_demo sample prints out the menu.
% node build/samples/menu_demo.js
1 Hamburger
Ingredients: Seasame Bun, Pickles, Sliced Red Onion, Leaf Lettuce, Tomato Slice, Ketchup, Yellow Mustard
Options: American Cheese Slice, Cheddar Cheese Slice, Swiss Cheese Slice, Monterey Jack Cheese Slice, Dijon Mustard, Tartar Sauce, Mayonnaise, Sriracha Mayonnaise, Well Done
2 Cheeseburger
Ingredients: Seasame Bun, American Cheese Slice, Pickles, Sliced Red Onion, Leaf Lettuce, Tomato Slice, Ketchup, Yellow Mustard
Options: Well Done
3 Big Apple Burger
Ingredients: Seasame Bun, American Cheese Slice, Pickles, Sliced Red Onion, Leaf Lettuce, Tomato Slice, Ketchup, Yellow Mustard
4 Dakota Burger
Ingredients: Seasame Bun, American Cheese Slice, Pickles, Sliced Red Onion, Leaf Lettuce, Tomato Slice, Ketchup, Yellow Mustard
100 Grilled Petaluma Chicken Sandwich
Ingredients: Whole Wheat Bun, Grilled Chicken Breast, Pickles, Leaf Lettuce, Tomato Slice, Tartar Sauce
101 Fried Petaluma Chicken Sandwich
Ingredients: Whole Wheat Bun, Fried Chicken Breast, Pickles, Leaf Lettuce, Tomato Slice, Mayonnaise
200 Down East Fish Sandwich
Ingredients: Seasame Bun, Fried Cod Fillet, American Cheese Slice, Tartar Sauce
201 Northwest Sockeye Sandwich
Ingredients: Ancient Grains Bun, Grilled Sockeye Fillet, Sliced Red Onion, Leaf Lettuce, Tomato Slice, Tartar Sauce
400 Small French Fries
401 Medium French Fries
402 Large French Fries
410 6 Wings
411 12 Wings
1000 Small Coke
Ingredients: Ice
1001 Medium Coke
Ingredients: Ice
1002 Large Coke
Ingredients: Ice
1003 Small Diet Coke
Ingredients: Ice
1004 Medium Diet Coke
Ingredients: Ice
1005 Large Diet Coke
Ingredients: Ice
1070 Small Unsweet Tea
Ingredients: Ice
1071 Medium Unsweet Tea
Ingredients: Ice
1072 Large Unsweet Tea
Ingredients: Ice
1073 Small Sweet Tea
Ingredients: Ice
1074 Medium Sweet Tea
Ingredients: Ice
1075 Large Sweet Tea
Ingredients: Ice
1100 Small Coffee
Options: Sleeve, Sugar, Sweet N Low, Equal, Stevia, Cream, Half And Half
1101 Medium Coffee
Options: Sleeve, Sugar, Sweet N Low, Equal, Stevia, Cream, Half And Half
1102 Large Coffee
Options: Sleeve, Sugar, Sweet N Low, Equal, Stevia, Cream, Half And Half
6000 Surf N Turf
Ingredients: Cheeseburger, Down East Fish Sandwich, Large Coke
Choices: beverage
Relevance Test Sample
This sample runs a suite of test utterances through the tokenization pipeline. The test utterances can be found at samples/data/restaurant-en/tests.yaml.
If you've cloned the repo, you can build and run the sample as follows:
npm install
npm run compile
node build/samples/relevance_demo.js
The output is the sequence of tokens extracted for each test utterance:
% node build/samples/relevance_demo.js
14 items contributed 143 aliases.
5 items contributed 22 aliases.
16 items contributed 31 aliases.
60 items contributed 210 aliases.
All tests passed.
0 general - PASSED
input "Hamburger with extra pickles"
output "[ENTITY:HAMBURGER,1] [QUANTITY:1] [QUANTITY:1] [ENTITY:PICKLES,5200]"
expected "[ENTITY:HAMBURGER,1] [QUANTITY:1] [QUANTITY:1] [ENTITY:PICKLES,5200]"
1 general - PASSED
input "Uh yeah I'd like a pet chicken fries and a coke"
output "[UNKNOWN:Uh] [INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:MEDIUM_FRENCH_FRIES,401] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
expected "[UNKNOWN:Uh] [INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:MEDIUM_FRENCH_FRIES,401] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
2 general - PASSED
input "Uh yeah I'd like a pet chicken french fries and a coke"
output "[UNKNOWN:Uh] [INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:MEDIUM_FRENCH_FRIES,401] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
expected "[UNKNOWN:Uh] [INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:MEDIUM_FRENCH_FRIES,401] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
3 general - PASSED
input "Can I get a cheeseburger with swiss"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:CHEESEBURGER,2] [QUANTITY:1] [ENTITY:SWISS_CHEESE_SLICE,5102]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:CHEESEBURGER,2] [QUANTITY:1] [ENTITY:SWISS_CHEESE_SLICE,5102]"
4 general - PASSED
input "I'll have two six piece wings"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:6_WINGS,410]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:6_WINGS,410]"
5 general - PASSED
input "I'll have five dozen wings"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:5] [ENTITY:12_WINGS,411]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:5] [ENTITY:12_WINGS,411]"
6 general - PASSED
input "Get me a coffee with two creams and one sugar"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [QUANTITY:1] [QUANTITY:2] [ENTITY:CREAM,1194] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:SUGAR,1190]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [QUANTITY:1] [QUANTITY:2] [ENTITY:CREAM,1194] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:SUGAR,1190]"
7 general - PASSED
input "Large iced tea unsweet"
output "[ENTITY:LARGE_UNSWEET_TEA,1072]"
expected "[ENTITY:LARGE_UNSWEET_TEA,1072]"
8 bugreport - PASSED
input "can I have two hamburgers"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
9 bugreport - PASSED
input "Can I get a coffee I'd also like two hamburgers"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
Suites:
general: 8/8
bugreport: 2/2
Priorities:
1: 10/10
Overall: 10/10
You can run the Spanish version as follows:
npm install
npm run compile
node build/samples/relevance_demo_spanish.js
It will produce output like
% node build/samples/relevance_demo.js
14 items contributed 133 aliases.
5 items contributed 35 aliases.
16 items contributed 37 aliases.
60 items contributed 253 aliases.
Failing tests:
0 general - PASSED
input "Hamburguesa con Pickles Extra"
output "[ENTITY:HAMBURGER,1] [QUANTITY:1] [ENTITY:PICKLES,5200] [QUANTITY:1]"
expected "[ENTITY:HAMBURGER,1] [QUANTITY:1] [ENTITY:PICKLES,5200] [QUANTITY:1]"
1 general - PASSED
input "Si me gustaría unas pollo grillado petaluma papas fritas y una coca"
output "[INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:MEDIUM_FRENCH_FRIES,401] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
expected "[INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:MEDIUM_FRENCH_FRIES,401] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
2 general - PASSED
input "Si me gustaria unas pollo grillado petaluma papas pequeñas y una coca"
output "[INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:SMALL_FRENCH_FRIES,400] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
expected "[INTENT:SEPERATORS] [INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:GRILLED_PETALUMA_CHICKEN_SANDWICH,100] [ENTITY:SMALL_FRENCH_FRIES,400] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:MEDIUM_COKE,1001]"
3 general - PASSED
input "Puedo pedir una hamburguesa con queso suizo"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:CHEESEBURGER,2] [ENTITY:SWISS_CHEESE_SLICE,5102]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:CHEESEBURGER,2] [ENTITY:SWISS_CHEESE_SLICE,5102]"
4 general - PASSED
input "Quiero dos alitas de seis"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:6_WINGS,410]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:6_WINGS,410]"
5 general - FAILED
input "Quiero cinco alitas de doce"
output "[INTENT:ADD_TO_ORDER] [UNKNOWN:cinco] [ENTITY:12_WINGS,411]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:5] [ENTITY:12_WINGS,411]"
6 general - PASSED
input "Dame un cafe con dos cremas y un azucar"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [QUANTITY:1] [QUANTITY:2] [ENTITY:CREAM,1194] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:SUGAR,1190]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [QUANTITY:1] [QUANTITY:2] [ENTITY:CREAM,1194] [INTENT:CONJUNCTION] [QUANTITY:1] [ENTITY:SUGAR,1190]"
7 general - PASSED
input "Te sin edulcorante grande"
output "[ENTITY:LARGE_UNSWEET_TEA,1072]"
expected "[ENTITY:LARGE_UNSWEET_TEA,1072]"
8 bugreport - PASSED
input "Quiero dos hamburguesas"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
9 bugreport - PASSED
input "Puedo pedir un cafe también quiero dos hamburguesas"
output "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [INTENT:CONJUNCTION] [INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
expected "[INTENT:ADD_TO_ORDER] [QUANTITY:1] [ENTITY:MEDIUM_COFFEE,1101] [INTENT:CONJUNCTION] [INTENT:ADD_TO_ORDER] [QUANTITY:2] [ENTITY:HAMBURGER,1]"
Suites:
general: 7/8
bugreport: 2/2
Priorities:
1: 9/10
Overall: 9/10
REPL Sample
This sample provides a Read-Eval-Print-Loop that runs the tokenizer on each line entered.
If you've cloned the repo, you can build and run the sample as follows:
npm run compile
node build/samples/repl_demo.js
% node build/samples/repl_demo.js
Welcome to the ShortOrder REPL.
Type your order below.
A blank line exits.
14 items contributed 143 aliases.
5 items contributed 22 aliases.
16 items contributed 31 aliases.
60 items contributed 210 aliases.
% i'd like a dakota burger fries and a coke
INTENT: ADD_TO_ORDER: "i'd like"
QUANTITY: 1: "a"
ENTITY: DAKOTA_BURGER(4): "dakota burger"
ENTITY: MEDIUM_FRENCH_FRIES(401): "fries"
INTENT: CONJUNCTION: "and"
QUANTITY: 1: "a"
ENTITY: MEDIUM_COKE(1001): "coke"
% actually make that a pet chicken with extra pickles
INTENT: CANCEL_LAST_ITEM: "actually"
INTENT: RESTATE: "make that"
QUANTITY: 1: "a"
ENTITY: GRILLED_PETALUMA_CHICKEN_SANDWICH(100): "pet chicken"
QUANTITY: 1: "with"
QUANTITY: 1: "extra"
ENTITY: PICKLES(5200): "pickles"
%
bye
Stemmer Confusion Matrix Sample
In some cases, the stemmer can stem words with different meanings to the same term.
One can check for these problems in their attributes.yaml, menu.yaml, quantifiers.yaml, stopwords.yaml, units.yaml and intents.yaml files by producing a stemmer confusion matrix.
node build/samples/stemmer_confusion_demo.js
Stemmer Confusion Matrix
"and": [and,And]
"small": [small,Small]
"medium": [medium,Medium]
"larg": [large,Large]
"half": [half,Half]
"hot": [hot,Hot]
"ice": [iced,Iced,Ice,ice]
"whole": [whole,Whole]
"low": [low,Low]
"dog": [Dog,dog]
"fri": [Fried,Fries]
"french": [French,french]
"onion": [Onion,Onions]
"wing": [Wings,wings,Wing]
"dozen": [dozen,Dozen]
"sweet": [Sweet,sweet]
"cream": [Cream,cream]
"pickl": [Pickles,Pickle]
"slice": [Slices,Sliced]
"salt": [Salt,Salted]
"done": [Done,done]
"that": [that,that's]
"thank": [thank,thanks]
In the example above, we see that the words fries and fried are treated as the same term, causing the phrase, "I'll have a pet chicken fries and a coke" to be interpreted as "pet chicken fried", instead of a "pet chicken" and "French fries".
% I'll have a pet chicken fries and a coke
INTENT: ADD_TO_ORDER: "I'll have"
QUANTITY: 1: "a"
ENTITY: FRIED_PETALUMA_CHICKEN_SANDWICH(101): "pet chicken fries"
INTENT: CONJUNCTION: "and"
QUANTITY: 1: "a"
ENTITY: MEDIUM_COKE(1001): "coke"
One can address this problem with a different stemmer or lemmatizer. One simple work-around is to wrap the default stemmer in a function that has special handling
for certain words like fried and fries:
function hackedStemmer(term: string): string {
const lowercase = term.toLowerCase();
if (lowercase === 'fries' || lowercase === 'fried') {
return lowercase;
}
return Tokenizer.defaultStemTerm(lowercase);
}
Conversational Agent Design Notes
Here's a very brief roadmap for the project.
- Write a the tokenizer. Code currently resides in the token-flow project.
- Implement a menu/catalog data structure with rules for the hierarchical composition of menu items, default ingrediants, optional ingrediants, substitutions, combos, specials, etc.
- Implement a general menu item attribute system, so that one can ask for a
"small latte"and then say"make it a double". - Implement an intent parser for adding items, customizing items, making substitutions, removing items, etc.
- Integrate intent parser into a conversational agent that takes the order, while asking clarifying questions and offering to upsell.
- Implement a sample bot that uses the conversational agent.