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.yaml
samples/data/restaurant-en/intents.yaml
samples/data/restaurant-en/attributes.yaml
samples/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.

Name		Name	Last commit message	Last commit date
Latest commit History 223 Commits
.vscode		.vscode
documentation		documentation
samples		samples
src		src
test		test
.gitignore		.gitignore
.npmignore		.npmignore
.travis.yml		.travis.yml
LICENSE.txt		LICENSE.txt
README.md		README.md
TODO.md		TODO.md
package-lock.json		package-lock.json
package.json		package.json
script.txt		script.txt
tsconfig.json		tsconfig.json
tslint.json		tslint.json

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