Intent Matching

Overview

NCModel processing logic is defined as a pipeline and the collection of one or more intents to be matched on. The sections below explain what intent is, how to define it in your model, and how it works.

Intent

The goal of the data model implementation is to take the user input text, pass it through processing pipeline and match the resulting variants to a specific user-defined code that will execute for that input. The mechanism that provides this matching is called an intent.

The intent generally refers to the goal that the end-user had in mind when speaking or typing the input utterance. The intent has a declarative part or template written in IDL - Intent Definition Language that strictly defines a particular form the user input. Intent is also bound to a callback method that will be executed when that intent, i.e. its template, is detected as the best match for a given input. A typical data model will have multiple intents defined for each form of the expected user input that model wants to react to.

For example, a data model for banking chatbot or analytics application can have multiple intents for each domain-specific group of input such as opening an account, closing an account, transferring money, getting statements, etc.

Intents can be specific or generic in terms of what input they match. Multiple intents can overlap and NLPCraft will disambiguate such cases to select the intent with the overall best match. In general, the most specific intent match wins.

IDL Syntax

NLPCraft intents are written in Intent Definition Language (IDL). IDL is a relatively straightforward declarative language. For example, here's a simple intent x with two terms a and b:

            /* Intent 'x' definition. */
            intent=x
                term(a)~{# == 'my_elm'} // Term 'a'.
                term(b)={has(ent_groups, "my_group")} // Term 'b'.
        

IDL intent defines a match between the parsed user input represented as the collection of entities, and the user-define callback method. IDL intents are bound to their callbacks via Java annotation and can be located in the same Java annotations or in external *.idl files.

You can review the formal ANTLR4 grammar for IDL, but here are the general properties of IDL:

• IDL has context-free grammar. In simpler terms, all whitespaces outside of string literals are ignored.
• IDL supports Java-style comments, both single line // Comment. as well as multi-line /* Comment. */.
• String literals can use either single quotes ('text') or double quotes ("text") simplifying IDL usage in Scala - you don't have to escape double quotes. Both quotes can be escaped in string, i.e. "text with \" quote" or 'text with \' quote'
• Built-in literals true, false and null for boolean and null values.
• Algebraic and logical expression including operator precedence follow standard Java language conventions.
• Both integer and real numeric literals can use underscore '_' character for separation as in 200_000.
• Numeric literals use Java string conversions.
• IDL has only 10 reserved keywords: flow fragment import intent meta options term true false null
• Identifiers and literals can use the same Unicode space as Java.
• IDL provides over 100 built-in functions to aid in intent matching. IDL functions are pure immutable mathematical functions that work on a runtime stack. In other words, they look like Python functions: IDL length(trim(" text ")) vs. OOP-style " text ".trim().length().
• IDL is a lazily evaluated language, i.e. expressions are evaluated only when required during runtime. That means that evaluated left-to-right logical AND and OR operators, for example, skip their right-part expressions if the left expression result is determinative for the overall result - so-called short-circuit evaluation. Some IDL functions like if and or_else also provide the similar short-circuit evaluation.

IDL program consists of intent, fragment, or import statements in any order or combination:

• intent statement

  Intent is defined as one or more terms. Each term is a predicate over a instance of NCEntity trait. For an intent to match all of its terms have to evaluate to true. Intent definition can be informally explained using the following full-feature example:

                      intent=xa
                          flow="^(?:login)(^:logout)*$"
                          meta={'enabled': true}
                          term(a)={month >= 6 && !# != "z" && meta_intent('enabled') == true}[1,3]
                          term(b)~{
                              @usrTypes = meta_req('user_types')
  
                              (# == 'order' || # == 'order_cancel') && has_all(@usrTypes, list(1, 2, 3))
                          }
  
                      intent=xb
                          options={
                              'ordered': false,
                              'unused_free_words': true,
                              'unused_entities': false,
                              'allow_stm_only': false
                          }
                          term(a)={length("some text") > 0}
                          fragment(frag, {'p1': 25, 'p2': {'a': false}})
                  

  NOTES:

  intent=xa ^{line 1}
  intent=xb ^{line 11}

  xa and xb are the mandatory intent IDs. Intent ID is any arbitrary unique string matching the following lexer template: (UNI_CHAR|UNDERSCORE|LETTER|DOLLAR)+(UNI_CHAR|DOLLAR|LETTER|[0-9]|COLON|MINUS|UNDERSCORE)*

  options={...} ^{line 12}

  Optional. Matching options specified as JSON object. Entire JSON object as well as each individual JSON field is optional. Allows to customize the matching algorithm for this intent:

  Option	Type	Description	Default Value
  ordered	Boolean	Whether or not this intent is ordered. For ordered intent the specified order of terms is important for matching this intent. If intent is unordered its terms can be found in any order in the input text. Note that ordered intent significantly limits the user input it can match. In most cases the ordered intent is only applicable to processing of a formal grammar (like a programming language) and mostly unsuitable for the natural language processing. Note that while the ordered flag affect entire intent and all its terms, you can define the individual term that depends on the position of the entity. This, in fact, allows you have a subset of terms that order dependant. See the following IDL functions for details: ent_index() ent_all() ent_count() ent_is_last() ent_is_first() ent_is_before_type() ent_is_before_group() ent_is_between_types() ent_is_between_groups() ent_is_after_type() ent_is_after_group()	false
  unused_free_words	Boolean	Whether or not free words - that are unused by intent matching - should be ignored (value true) or reject the intent match (value false). Free words are the words in the user input that were not recognized as any entity. Typically, for the natural language comprehension it is safe to ignore free words. For the formal grammar, however, this could make the matching logic too loose.	true
  unused_entities	Boolean	Whether or not unused entities should be ignored (value true) or reject the intent match (value false). By default, tne unused entities are not ignored since it is assumed that user would define pipeline entity parser on purpose and construct the intent logic appropriate.	false
  allow_stm_only	Boolean	Whether or not the intent can match when all of the matching entities came from STM. By default, this special case is disabled (value false). However, in specific intents designed for free-form language comprehension scenario, like, for example, SMS messaging - you may want to enable this option.	false

  flow="^(?:login)(^:logout)*$" ^{line 2}
  

  Optional. Dialog flow is a history of previously matched intents to match on. If provided, the intent will first match on the history of the previously matched intents before processing its terms by using regular expressions. Dialog flow specification is a string with the standard Java regular expression. The history of previously matched intents is presented as a space separated string of intent IDs that were selected as the best match during the current conversation, in the chronological order with the most recent matched intent ID being the first element in the string. Dialog flow regular expression will be matched against that string representing intent IDs.

  In the line 2, the ^(?:login)(^:logout)*$ dialog flow regular expression defines that intent should only match when the immediate previous intent was login and no logout intents are in the history. If the history is "login order order" - this intent will match. However, for "login logout" or "order login" history this dialog flow will not match.

  Note that if dialog flow is defined and it doesn't match the history the terms of the intent won't be tested at all.

  meta={'enabled': true} ^{line 3}

  Optional. Just like the most of the components in NLPCraft, the intent can have its own metadata. Intent metadata is defined as a standard JSON object which will be converted into java.util.Map instance and can be accessed in intent's terms via meta_intent() IDL function. The typical use case for declarative intent metadata is to parameterize its behavior, i.e. the behavior of its terms, with a clearly defined properties that are provided inside intent definition itself.

  term(a)={month >= 6 && !# != "z" && meta_intent('enabled') == true}[1,3] ^{line 4}
  term(b)~{ ^{line 5}
  @usrTypes = meta_req('user_types')
(# == 'order' || # == 'order_cancel') && has_all(@usrTypes, list(1, 2, 3))
}
term(a)={length("some text") > 0} ^{line 18}

  Term is a building block of the intent. Intent must have at least one term. Term has optional ID, an entity predicate and optional quantifiers. It supports conversation context if it uses '~' symbol or not if it uses '=' symbol in its definition. For the conversational term the system will search for a match using entities from the current request as well as the entities from conversation STM (short-term-memory). For a non-conversational term - only entities from the current request will be considered.

  A term is matched if its entity predicate returns true. The matched term represents one or more entities, sequential or not, that were detected in the user input. Intent has a list of terms (always at least one) that all have to be matched in the user input for the intent to match. Note that term can be optional if its min quantifier is zero. Whether the order of the terms is important for matching is governed by intent's ordered parameter.

  Term ID (a and b) is optional. It is only required by @NCIntentTerm annotation to link term's entities to a formal parameter of the callback method. Note that term ID follows the same lexical rules as intent ID.

  Inside of curly brackets { } you can have an optional list of term variables and the mandatory term expression that must evaluate to a boolean value. Term variable name must start with @ symbol and be unique within the scope of the current term. All term variables must be defined and initialized before term expression which must be the last statement in the term:

                                      term(b)~{
                                          @a = meta_req('a')
                                          @lst = list(1, 2, 3, 4)
  
                                          has_all(@lst, list(@a, 2))
                                      }
                                  

  Term variable initialization expression as well as term's expression follow Java-like expression grammar including precedence rules, brackets and logical combinators, as well as built-in IDL functions calls:

                                      term={true} // Special case of 'constant' term.
                                      term={
                                          // Variable declarations.
                                          @a = round(1.25)
                                          @b = meta_req('my_prop')
  
                                          // Last expression must evaluate to boolean.
                                          (@a + 2) * @b > 0
                                      }
                                      term={
                                          // Variable declarations.
                                          @c = meta_ent('prop')
                                          @lst = list(1, 2, 3)
  
                                          // Last expression must evaluate to boolean.
                                          abs(@c) > 1 && size(@lst) != 5
                                      }
                                  

  NOTE: while term variable initialization expressions can have any type - the term's expression itself, i.e. the last expression in the term's body, must evaluate to a boolean result only. Failure to do so will result in a runtime exception during intent evaluation. Note also that such errors cannot be detected during intent compilation phase.

  ? and [1,3] define an inclusive quantifier for that term, i.e. how many times the match for this term should found. You can use the following quick abbreviations:

  • * is equal to [0,∞]
  • + is equal to [1,∞]
  • ? is equal to [0,1]
  • No quantifier defaults to [1,1]

  As mentioned above the quantifier is inclusive, i.e. the [1,3] means that the term should appear once, two times or three times.

  fragment(frag, {'p1': 25, 'p2': {'a': false}}) ^{line 19}
  

  Fragment reference allows to insert the terms defined by that fragment in place of this fragment reference. Fragment reference has mandatory fragment ID parameter and optional JSON second parameter. Optional JSON parameter allows to parameterize the inserted terms' behavior and it is available to the terms via meta_frag() IDL function.

• fragment statement

  Fragments allow to group and name a set of reusable terms. Such groups can be further parameterized at the place of reference and enable the reuse of one or more terms by multiple intents. For example:

                      // Fragments.
                      fragment=buzz term~{# == meta_frag('id')}
                      fragment=when
                          term(nums)~{
                              // Term variable.
                              @type = meta_ent('num:unittype')
                              @iseq = meta_ent('num:isequalcondition')
  
                              # == 'num' && @type == 'datetime' && @iseq == true
                          }[0,7]
  
                      // Intents.
                      intent=alarm
                          // Insert parameterized terms from fragment 'buzz'.
                          fragment(buzz, {"id": "x:alarm"})
  
                          // Insert terms from fragment 'when'.
                          fragment(when)
                  

  NOTES:

  • Fragment statements (line 2 and 3) have a name (buzz and when) and a list of terms.
  • Terms follow the same syntax as in intent definition.
  • When a fragment is referenced in intent (lines 15 and 18) it is replaced with its terms.

• import statement

  Import statement allows to import IDL declarations from either local file, classpath resource or URL:

                      // Import using absolute path.
                      import('/opt/globals.idl')
  
                      // Import using classpath resource.
                      import('org/apache/nlpcraft/examples/alarm/intents.idl')
  
                      // Import using URL.
                      import('ftp://user:password@myhost:22/opt/globals.idl')
                  

  NOTES:

  • The effect of importing is the same as if the imported declarations were inserted in place of import statement.
  • Recursive and cyclic imports are detected and safely ignored.
  • Import statement starts with import keyword and has a string parameter that indicates the location of the resource to import.
  • For the classpath resource you don't need to specify leading forward slash.

Intent Lifecycle

During NCModelClient initialization it scans the provided model class for the intents. All found intents are compiled into an internal representation.

Note that not all intent-related problems can be detected at the compilation phase, and NCModelClient can be initialized with intents not being completely validated. For example, each term in the intent must evaluate to a boolean result. This can only be checked at runtime. Another example is the number and the types of parameters passed into IDL function which is only checked at runtime as well.

Intents are compiled only once during the NCModelClient initialization and cannot be re-compiled. Model logic, however, can affect the intent behavior through NCModel callback methods and metadata all of which can change at runtime and are accessible through IDL functions.

Intent Examples

Here's few of intent examples with explanations:

Example 1:

        intent=a
            term~{# == 'x:type'}
            term(nums)~{# == 'num' && lowercase(meta_ent('num:unittype')) == 'datetime'}[0,2]
        

NOTES:

• Intent has ID a.
• Intent uses default conversational support (true) and default order (false).
• Intent has two conversational terms (~) that have to be found for the intent to match. Note that second term is optional as it has [0,2] quantifier.
• Both terms have to be found in the user input for the intent to match.
• First term matches any single entity with type x:type.
• Second term can appear zero, once or two times and it matches entity with ID num with num:unittype metadata property equal to 'datetime' string.
• IDL function lowercase used on num:unittype metadata property value.
• Note that since second term has ID (nums) it can be references by @NCIntentTerm annotation by the callback formal parameter.

Example 2:

        intent=id2
            flow='id1 id2'
            term={# == 'myent' && signum(get(meta_ent('score'), 'best')) != -1}
            term={has_any(ent_groups, list('actors', 'owners'))}
        

NOTES:

• Intent has ID id2.
• Intent has dialog flow pattern: 'id1 id2'. It expects the sequence of intents id1 and id2 somewhere in the history of previously matched intents in the course of the current conversation.
• Intent has two non-conversational terms (=). Both terms have to be present only once (their implicit quantifiers are [1,1]).
• Both terms have to be found in the user input for the intent to match.
• First term should be a entity with ID myent and have metadata property score of type map. This map should have a value with the string key 'best'. signum of this map value should not equal -1. Note that meta_ent(), get() and signum() are all built-in IDL functions.
• Second term should be an entity that belongs to either actors or owners group.

IDL Functions

IDL provides over 100 built-in functions that can be used in IDL intent definitions. IDL function call takes on traditional fun_name(p1, p2, ... pk) syntax form. If function has no parameters, the brackets are optional. IDL function operates on stack - its parameters are taken from the stack and its result is put back onto stack which in turn can become a parameter for the next function call and so on. IDL functions can have zero or more parameters and always have one result value. Some IDL functions support variable number of parameters.

                ent_type() == 'type'
                ent_type == 'type' // Remember - empty parens are optional.
                # == 'type'
            

When chaining the function calls IDL uses mathematical notation (a-la Python) rather than object-oriented one: IDL length(trim(" text ")) vs. OOP-style " text ".trim().length().

IDL functions operate with the following types:

Some IDL functions are polymorphic, i.e. they can accept arguments and return result of multiple types. Encountering unsupported types will result in a runtime error during intent matching. It is especially important to watch out for the types when adding objects to various metadata containers and using that metadata in the IDL expressions.

All IDL functions are organized into the following groups:

// Result: 'true' if the current entity type is equal to 'my_type'.
ent_type == 'my_type'
# == 'my_type'
ent_type(ent_this) == 'my_type'
#(ent_this) == 'my_type'

// Result: list of groups this entity belongs to.
ent_groups
ent_groups(ent_this)

// Result: current entity.
ent_this

// Result: entity original input text.
ent_text

// Result: 'true' if index of this entity in the original input is equal to 1.
ent_index == 1
ent_index(ent_this) == 1

// Result: 'true' if this entity is the first entity in the original input.
ent_is_first
ent_is_first(ent_this)

// Result: 'true' if this entity is the last entity in the original input.
ent_is_last
ent_is_last(ent_this)

// Result: 'true' if there is a entity with type 'a' after this entity.
ent_is_before_type('a')

// Result: 'true' if there is a entity with type 'a' before this entity.
ent_is_after_type('a')

// Result: 'true' if this entity is located after entity with type 'before' and before the entity with type 'after'.
ent_is_between_types('before', 'after')

// Result: 'true' if this entity is located after entity belonging to the group 'before' and before the entity belonging to the group 'after'.
ent_is_between_groups('before', 'after')

// Result: 'true' if there is a entity that belongs to the group 'grp' after this entity.
ent_is_before_group('grp')

// Result: 'true' if there is a entity that belongs to the group 'grp' before this entity.
ent_is_after_group('grp')

// Result: list of all entities for the original input.
ent_all

// Result: number of all entities for the original input.
ent_count

// Result: list of entities for the original input that have type 'type'.
ent_all_for_type('type')

// Result: list of entities for the original input that belong to th group 'grp'.
ent_all_for_group('grp')

// Result: 9
length("some text")

// Result: 3
@lst = list(1, 2, 3)
size(@lst)
count(@lst)

regex('textabc', '^text.*$') // Returns 'true'.
regex('_textabc', '^text.*$') // Returns 'false'.

// Result: "text"
trim(" text ")
strip(" text ")

// Result: "TEXT"
uppercase("text")

// Result: "text"
lowercase("TeXt")

// Result: true
is_alpha("text")

// Result: true
is_alphanum("text123")

// Result: false
is_whitespace("text123")
// Result: true
is_whitespace("   ")

// Result: true
is_num("123")

// Result: true
is_numspace("  123")

// Result: true
is_alphaspace("  text  ")

// Result: true
is_alphanumspace(" 123 text  ")

// Result: [ "a", "b", "c" ]
split("a|b|c", "|")

// Result: ["a", "b", "c"]
split_trim("a | b | c", "|")

// Result: true
starts_width("abc", "ab")

// Result: true
ends_width("abc", "bc")

// Result: true
contains("abc", "bc")

// Result: 1
index_of("abc", "bc")

// Result: "bc"
substr("abc", 1, 3)

// Result: "aBC"
replace("abc", "bc", "BC")

// Result: 1.2
to_double("1.2")
// Result: 1.0
to_double(1)

// Result: 1
to_int("1.2")
to_int(1.2)

// Result: 1
abs(-1)
// Result: 1.5
abs(-1.5)

// Result: 2.0
ceil(1.5)

// Result: 1.0
floor(1.5)

// Result: 1.0
rint(1.2)

// Result: 1
round(1.2)

// Result: 1.0
signum(1.2)

// Result: 4.0
sqrt(2.0)

// Result: -3.0
cbrt(-27.0)

acos(1.0)

asin(1.0)

atan(1.0)

tan(1.0)

sin(1.0)

cos(1.0)

tanh(1.0)

sinh(1.0)

cosh(1.0)

atan2(1.0, 1.0)

degrees(1.0)

radians(1.0)

exp(1.0)

expm1(1.0)

log(1.0)

log10(1.0)

// Result: 4
square(2)

log1p(1.0)

// Result: 3.14159265359
pi

// Result: 0.5772156649
euler

// Result: 3
max(list(1, 2, 3))

// Result: 1
min(list(1, 2, 3))

// Result: 2.0
avg(list(1, 2, 3))
avg(list("1.0", 2, "3"))

stdev(list(1, 2, 3))
stdev(list("1.0", 2, "3"))

// Result: 4.0
pow(2.0, 2.0)

hypot(2.0, 2.0)

// Result: []
list
// Result: [1, 2, 3]
list(1, 2, 3)
// Result: ["1", true, 1.25]
list("1", 2 == 2, to_double('1.25'))

// Result: 1
get(list(1, 2, 3), 0)
// Result: true
get(json('{"a": true}'), "a")

// Result: true
has(list("a", "b"), "a")

// Result: false
has_all(list("a", "b"), "a")

// Result: true
has_any(list("a", "b"), list("a"))

// Result: "a"
first(list("a", "b"))

// Result: "b"
last(list("a", "b"))

// Result: ["a", "b"]
keys(json('{"a": true, "b": 1}'))

// Result: [true, 1]
values(json('{"a": true, "b": 1}'))

// Result: [3, 2, 1]
reverse(list(1, 2, 3))

// Result: [1, 2, 3]
sort(list(2, 1, 3))

// Result: false
is_empty("text")
is_empty(list(1))
is_empty(json('{"a": 1}'))

// Result: true
non_empty("text")
non_empty(list(1))
non_empty(json('{"a": 1}'))

// Result: [1, 2, 3]
distinct(list(1, 2, 2, 3, 1))

// Result: [1, 2, 3, 4]
concat(list(1, 2), list(3, 4))

// Result: 'nlp:token:text' entity metadata property.
meta_ent('nlp:token:text')

// Result: 'my:prop' user request data property.
meta_req('my:prop')

// Result: 'my:prop' intent metadata property.
meta_intent('my:prop')

// Result: 'my:prop' conversation metadata property.
meta_conv('my:prop')

// Result: 'my:prop' fragment metadata property.
meta_frag('my:prop')

// Result: 'java.home' system property.
meta_sys('java.home')
// Result: 'HOME' environment variable.
meta_sys('HOME')

// Result: 'my:prop' configuration property.
meta_cfg('my:prop')

// Result: 2021
year

// Result: 5
month

// Result: 5
day_of_month

// Result: 5
day_of_week

// Result: 51
day_of_year

// Result: 11
hour

// Result: 11
minute

// Result: 11
second

// Result: 2
week_of_month

// Result: 21
week_of_year

// Result: 2
quarter

// Result: 122312341212
now

// Result: request ID.
req_id

// Result: request text.
req_text

// Result: input receive timsstamp in ms.
req_tstamp    

// Result: user ID.
user_id      

// Result:
//  - 'list(1, 2, 3)' if 1st parameter is 'true'.
//  - 'null' if 1st parameter is 'false'.
if(meta_model('my_prop') == true, list(1, 2, 3), null)

// Result: Map.
json('{"a": 2, "b": [1, 2, 3]}')

// Result: "1.25"
to_string(1.25)
// Result: list("1", "2", "3")
to_string(list(1, 2, 3))

// Result: 'some_prop' model metadata or 'text' if one is 'null'.
@dflt = 'text'
or_else(meta_model('some_prop'), @dflt)

IDL Location

IDL declarations can be placed in different locations based on user preferences:

• NCIntent java annotation takes a string as its parameter that should be a valid IDL declaration. For example, Scala code snippet:

                  @NCIntent("import('/opt/myproj/global_fragments.idl')") // Importing.
                  @NCIntent("intent=act term(act)={has(ent_groups, 'act')} fragment(f1)") // Defining in place.
                  def onMatch(
                      @NCIntentTerm("act") actEnt: NCEntity,
                      @NCIntentTerm("loc") locEnts: List[NCEntity]
                  ): NCResult = {
                      ...
                  }
              

• External *.idl files contain IDL declarations and can be imported in any other places where IDL declarations are allowed. See import() statement explanation below. For example:

                      /*
                       * File 'my_intents.idl'.
                       * ======================
                       */
  
                      import('/opt/globals.idl') // Import global intents and fragments.
  
                      // Fragments.
                      // ----------
                      fragment=buzz term~{# == 'x:alarm'}
                      fragment=when
                          term(nums)~{
                              // Term variables.
                              @type = meta_ent('num:unittype')
                              @iseq = meta_ent('num:isequalcondition')
  
                              # == 'num' && @type != 'datetime' && @iseq == true
                          }[0,7]
  
                      // Intents.
                      // --------
                      intent=alarm
                          fragment(buzz)
                          fragment(when)
                  

Binding Intent

IDL intents must be bound to their callback methods. This binding is accomplished using the following Java annotations:

Here's a couple of examples of intent declarations to illustrate the basics of intent declaration and usage.

An intent from Light Switch example:

            @NCIntent("intent=ls term(act)={has(ent_groups, 'act')} term(loc)={# == 'ls:loc'}*")
            def onMatch(
                @ctx: NCContext,
                @im: NCIntentMatch,
                @NCIntentTerm("act") actEnt: NCEntity,
                @NCIntentTerm("loc") locEnts: List[NCEntity]
            ): NCResult = {
                ...
            }
        

NOTES:

• The intent is defined in-place using @NCIntent annotation.
• A term match is defined as one or more entities. Term can be optional if its min quantifier is zero.
• An intent act has two non-conversational terms: one mandatory term and another that can match zero or more entities with method onMatch(...) as its callback.
• Terms is conversational if it uses '~' and non-conversational if it uses '=' symbol in its definition. If term is conversational, the matching algorithm will look into the conversation context short-term-memory (STM) to seek the matching entities for this term. Note that the terms that were fully or partially matched using entities from the conversation context will contribute a smaller weight to the overall intent matching weight since these terms are less specific. Non-conversational terms will be matched using entities found only in the current user input without looking at the conversation context.
• Method onMatch(...) will be called if and when this intent is selected as the best match.
• Note that terms have min=1, max=1 quantifiers by default, i.e. one and only one.
• First term defines any single entity that belongs to the group act. Note that model elements can belong to multiple groups.
• Note that both terms have IDs (act and loc) that are used in onMatch(...) method parameters to automatically assign terms' entities to the formal method parameters using @NCIntentTerm annotations.

In the following Time example the intent is defined model class and referenced in code using @NCIntentRef annotation:

            @NCIntent("fragment=city term(city)~{# == 'opennlp:location'}")
            @NCIntent("intent=intent2 term~{# == 'x:time'} fragment(city)")
            class TimeModel extends NCModel(
                ...
                @NCIntentRef("intent2")
                private def onRemoteMatch(
                    ctx: NCContext, im: NCIntentMatch, @NCIntentTerm("city") cityEnt: NCEntity
                ): NCResult =
                    ...
        

NOTES:

• Intent is defined in the model, line 2.
• This intent is referenced by annotation @NCIntentRef("intent2") with method onMatch(...) as its callback, line 5.
• This example defines an intent with two conversational terms both of which have to found for the intent to match.
• Terms is conversational if it uses '~' and non-conversational if it uses '=' symbol in its definition. If term is conversational, the matching algorithm will look into the conversation context short-term-memory (STM) to seek the matching entities for this term. Note that the terms that were fully or partially matched using entities from the conversation context will contribute a smaller weight to the overall intent matching weight since these terms are less specific. Non-conversational terms will be matched using entities found only in the current user input without looking at the conversation context.
• Method onMatch(...) will be called when this intent is the best match detected.
• Note that terms have min=1, max=1 quantifiers by default.
• First term is defined as a single mandatory (min=1, max=1) entity with ID x:time whose element is defined in the model.
• Second term is defined as a single mandatory (min=1, max=1) entity with entityopennlp:location.
• Given data model definition above the following sentences will be matched by this intent:
  • What time is it now in New York City?
  • Show me time of the day in London.
  • Can you please give me the Tokyo's current date and time.

Intent Matching Logic

NCPipeline processing result is collection of NCVariant instances. NCSemanticEntityParser is used for following example configured via JSON file. Let's consider the input text 'A B C D' and the following elements defined in our model:

            "elements": [
                {
                    "id": "elm1",
                    "synonyms": ["A B"]
                },
                {
                    "id": "elm2",
                    "synonyms": ["B C"]
                },
                {
                    "id": "elm3",
                    "synonyms": ["D"]
                }
            ],
            

All of these elements will be detected but since two of them are overlapping (elm1 and elm2) there should be two parsing variants at the output of this step:

1. elm1('A', 'B') freeword('C') elm3('D')
2. freeword('A') elm2('B', 'C') elm3('D')

Note that initially the system cannot determine which of these variants is the best one for matching - there's simply not enough information at this stage. It can only be determined when each variant is matched against model's intents. So, each parsing variant is matched against each intent. Each matching pair of a variant and an intent produce a match with a certain weight. If there are no matches at all - an error is returned. If matches were found, the match with the biggest weight is selected as a winning match. If multiple matches have the same weight, their respective variants' weights will be used to further sort them out. Finally, the intent's callback from the winning match is called.

Although details on exact algorithm on weight calculation are too complex, here's the general guidelines on what determines the weight of the match between a parsing variant and the intent. Note that these rules coalesce around the principle idea that the more specific match always wins:

• A match that captures more entities has more weight than a match with less entities. As a corollary, the match with less free words (i.e. unused words) has bigger weight than a match with more free words.
• Entities for user-defined elements are more important than built-in entities.
• A more specific match has bigger weight. In other words, a match that uses an entity from the conversation context (i.e short-term-memory) has less weight than a match that only uses entities from the current request. In the same way older entities from the conversation give less weight than the more recent ones.

Intent Callback

Whether the intent is defined directly in @NCIntent annotation or indirectly via @NCIntentRef annotation - it is always bound to a callback method:

• Callback can only be an instance method on the class implementing NCModel trait.
• Method must have return type of NCResult.
• Method should have at least two parameters:
  • Parameter of type NCContext must be first.
  • Parameter of type NCIntentMatch must be second.
  • Any other parameters must have @NCIntentTerm annotation.
• Method must support reflection-based invocation.

@NCIntentTerm annotation marks callback parameter to receive term's entities. This annotations can only be used for the parameters of the callbacks, i.e. methods that are annotated with @NCIntnet or @NCIntentRef. @NCIntentTerm takes a term ID as its only mandatory parameter and should be applied to callback method parameters to get the entities associated with that term (if and when the intent was matched and that callback was invoked).

Depending on the term quantifier the method parameter type can only be one of the following types:

For example:

            NCIntent("intent=id term(termId)~{# == 'my_ent'}?")
            private def onMatch(
                ctx: NCContext,
                im: NCIntentMatch,
                @NCIntentTerm("termId") myEnt: Option[NCEntity]
            ): NCResult = {
               ...
            }
        

NOTES:

• Conversational term termId has [0,1] quantifier (it's optional).
• The formal parameter on the callback has a type of Option[NCEntity] because the term's quantifier is [0,1].

NCRejection and NCIntentSkip Exceptions

There are two exceptions that can be used by intent callback logic to control intent matching process.

When NCRejection exception is thrown by the callback it indicates that user input cannot be processed as is. This exception typically indicates that user has not provided enough information in the input string to have it processed automatically. In most cases this means that the user's input is either too short or too simple, too long or too complex, missing required context, or is unrelated to the requested data model.

NCIntentSkip is a control flow exception to skip current intent. This exception can be thrown by the intent callback to indicate that current intent should be skipped (even though it was matched and its callback was called). If there's more than one intent matched the next best matching intent will be selected and its callback will be called.

This exception becomes useful when it is hard or impossible to encode the entire matching logic using only declarative IDL. In these cases the intent definition can be relaxed and the "last mile" of intent matching can happen inside of the intent callback's user logic. If it is determined that intent in fact does not match then throwing this exception allows to try next best matching intent, if any.

Note that there's a significant difference between NCIntentSkip exception and model's NCModel#onMatchedIntent callback. Unlike this callback, the exception does not force re-matching of all intents, it simply picks the next best intent from the list of already matched ones. The model's callback can force a full reevaluation of all intents against the user input.

NCContext Trait

NCContext trait passed into intent callback as its first parameter. This trait provide runtime information about the model configuration, request, extracted tokens and all entities variants, conversation control trait NCConversation.

NCIntentMatch Trait

NCIntentMatch trait passed into intent callback as its second parameter. This trait provide runtime information about the intent that was matched (i.e. the intent with which this callback was annotated with).