Google Git
Sign in
apache / incubator-nlpcraft-website / ae1e40a0fbfc0403b40bbf30e3d1c5557fb48f4d / . / intent-matching.html
blob: 04f7cf051a8b9871ee1d34898a00efd9d310f5d2 [file] [log] [blame]
---
active_crumb: Intent Matching
layout: documentation
id: intent_matching
---
<!--
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
this work for additional information regarding copyright ownership.
The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<div id="intent-matching" class="col-md-8 second-column">
<section>
<h2 class="section-title">Overview <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
{% scaladoc NCModel 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.
</p>
</section>
<section id="intent">
<h2 class="section-title">Intent <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
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 <em>intent</em>.
</p>
<p>
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 <em>declarative part or template</em> written in <a href="#idl">IDL - Intent Definition Language</a> that strictly defines
a particular form the user input.
Intent is also <a href="#binding">bound</a> 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.
</p>
<p>
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.
</p>
<p>
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.
</p>
</section>
<section id="idl">
<h2 class="section-title">IDL Syntax <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
NLPCraft intents are written in Intent Definition Language (IDL).
IDL is a relatively straightforward declarative language. For example,
here's a simple intent <code>x</code> with two terms <code>a</code> and <code>b</code>:
</p>
<pre class="brush: idl">
/* Intent 'x' definition. */
intent=x
term(a)~{# == 'my_elm'} // Term 'a'.
term(b)={has(ent_groups, "my_group")} // Term 'b'.
</pre>
<p>
IDL intent defines a match between the parsed user input represented as the collection of
{% scaladoc2 NCEntity entities %},
and the user-define callback method. IDL intents are <a href="#binding">bound</a> to their callbacks via
<a href="#binding">Java annotation</a> and can be <a href="#idl_location">located</a> in the same Java annotations or
in external <code>*.idl</code> files.
</p>
<p>
You can review the formal
<a target="github" href="https://github.com/apache/incubator-nlpcraft/blob/master/nlpcraft/src/main/scala/org/apache/nlpcraft/internal/intent/compiler/antlr4/NCIDL.g4">ANTLR4 grammar</a>
for IDL,
but here are the general properties of IDL:
</p>
<ul>
<li>
IDL has
<a target="wiki" href="https://en.wikipedia.org/wiki/Context-free_grammar">context-free grammar</a>. In simpler terms,
all whitespaces outside of string literals are ignored.
</li>
<li>
IDL supports Java-style comments, both single line <code>// Comment.</code> as well as multi-line <code>/* Comment. */</code>.
</li>
<li>
String literals can use either single quotes (<code>'text'</code>) or double quotes (<code>"text"</code>) simplifying IDL usage in Scala - you
don't have to escape double quotes. Both quotes can be escaped in string, i.e. <code>"text with \" quote"</code> or
<code>'text with \' quote'</code>
</li>
<li>
Built-in literals <code>true</code>, <code>false</code> and <code>null</code> for boolean and null values.
</li>
<li>
Algebraic and logical expression including operator precedence follow standard Java language conventions.
</li>
<li>
Both integer and real numeric literals can use underscore <code>'_'</code> character for separation as in <code>200_000</code>.
</li>
<li>
Numeric literals use Java string conversions.
</li>
<li>
IDL has only 10 reserved keywords: <code>flow fragment import intent meta options term true false null</code>
</li>
<li>
Identifiers and literals can use the same Unicode space as Java.
</li>
<li>
IDL provides over 100 <a href="#idl_functions">built-in functions</a> 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 <code>length(trim(" text "))</code> vs.
OOP-style <code>" text ".trim().length()</code>.
</li>
<li>
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
<code>if</code> and <code>or_else</code> also provide the similar short-circuit evaluation.
</li>
</ul>
<p>
IDL program consists of
<a href="#intent_statement">intent</a>,
<a href="#fragment_statement">fragment</a>, or
<a href="#import_statement">import</a> statements in any order or combination:
</p>
<ul>
<li>
<p id="intent_statement">
<b><code>intent</code> statement</b>
</p>
<p>
Intent is defined as one or more terms. Each term is a predicate over a instance of
{% scaladoc NCEntity 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:
</p>
<pre class="brush: idl">
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}})
</pre>
<p><b>NOTES:</b></p>
<dl>
<dt>
<code>intent=xa</code> <sup><small>line 1</small></sup><br/>
<code>intent=xb</code> <sup><small>line 11</small></sup>
</dt>
<dd>
<code>xa</code> and <code>xb</code> are the mandatory intent IDs. Intent ID is any arbitrary unique string matching the following lexer template:
<code>(UNI_CHAR|UNDERSCORE|LETTER|DOLLAR)+(UNI_CHAR|DOLLAR|LETTER|[0-9]|COLON|MINUS|UNDERSCORE)*</code>
</dd>
<dt><code>options={...}</code> <sup><small>line 12</small></sup></dt>
<dd>
<em>Optional.</em>
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:
<table class="gradient-table">
<thead>
<tr>
<td>Option</td>
<td>Type</td>
<td>Description</td>
<td>Default Value</td>
</tr>
</thead>
<tbody>
<tr>
<td><code>ordered</code></td>
<td><code>Boolean</code></td>
<td>
<p>
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.
</p>
<p>
Note that while the <code>ordered</code> 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 <a href="#idl_functions">IDL functions</a> for details:
</p>
<ul>
<li><code>ent_index()</code></li>
<li><code>ent_all()</code></li>
<li><code>ent_count()</code></li>
<li><code>ent_is_last()</code></li>
<li><code>ent_is_first()</code></li>
<li><code>ent_is_before_type()</code></li>
<li><code>ent_is_before_group()</code></li>
<li><code>ent_is_between_types()</code></li>
<li><code>ent_is_between_groups()</code></li>
<li><code>ent_is_after_type()</code></li>
<li><code>ent_is_after_group()</code></li>
</ul>
</td>
<td><code>false</code></td>
</tr>
<tr>
<td><code>unused_free_words</code></td>
<td><code>Boolean</code></td>
<td>
Whether or not free words - that are unused by intent matching - should be
ignored (value <code>true</code>) or reject the intent match (value <code>false</code>).
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.
</td>
<td><code>true</code></td>
</tr>
<tr>
<td><code>unused_entities</code></td>
<td><code>Boolean</code></td>
<td>
Whether or not unused entities should be
ignored (value <code>true</code>) or reject the intent match (value <code>false</code>).
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.
</td>
<td><code>false</code></td>
</tr>
<tr>
<td><code>allow_stm_only</code></td>
<td><code>Boolean</code></td>
<td>
Whether or not the intent can match when all of the matching entities came from STM.
By default, this special case is disabled (value <code>false</code>). However, in specific intents
designed for free-form language comprehension scenario, like, for example, SMS messaging - you
may want to enable this option.
</td>
<td><code>false</code></td>
</tr>
</tbody>
</table>
</dd>
<dt>
<code>flow="^(?:login)(^:logout)*$"</code> <sup><small>line 2</small></sup><br/>
</dt>
<dd>
<p>
<em>Optional.</em> 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 <b>regular expressions</b>.
Dialog flow specification is a string with the standard
<a target=_blank href="https://docs.oracle.com/en/java/javase/11/docs/api/java.base/java/util/regex/Pattern.html">Java regular expression</a>.
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.
</p>
<p>
In the line 2, the <code>^(?:login)(^:logout)*$</code> dialog flow regular expression defines that intent
should only match when the immediate previous intent was <code>login</code> and no <code>logout</code> intents
are in the history. If the history is <code>"login order order"</code> - this intent will match. However, for
<code>"login logout"</code> or <code>"order login"</code> history this dialog flow will not match.
</p>
<p>
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.
</p>
</dd>
<dt>
<code>meta={'enabled': true}</code> <sup><small>line 3</small></sup>
</dt>
<dd>
<p>
<em>Optional.</em>
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 <code>java.util.Map</code>
instance and can be accessed in intent's terms via
<a href="#idl_functions"><code>meta_intent()</code></a> 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.
</p>
</dd>
<dt>
<code>term(a)={month >= 6 && !# != "z" && meta_intent('enabled') == true}[1,3]</code> <sup><small>line 4</small></sup><br>
<code>term(b)~{</code> <sup><small>line 5</small></sup><br>
<code style="padding-left: 20px">@usrTypes = meta_req('user_types')</code><br>
<code style="padding-left: 20px">(# == 'order' || # == 'order_cancel') && has_all(@usrTypes, list(1, 2, 3))</code><br>
<code>}</code><br>
<code>term(a)={length("some text") > 0}</code> <sup><small>line 18</small></sup>
</dt>
<dd>
<p>
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 <code>'~'</code> symbol or not if it uses <code>'='</code>
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.
</p>
<p>
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 <code>ordered</code> parameter.
</p>
<p>
Term ID (<code>a</code> and <code>b</code>) is optional. It is only required by
<a href="#binding"><code>@NCIntentTerm</code></a>
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.
</p>
<p>
Inside of curly brackets <code>{</code> <code>}</code> 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
<code>@</code> 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:
</p>
<pre class="brush: idl">
term(b)~{
@a = meta_req('a')
@lst = list(1, 2, 3, 4)
has_all(@lst, list(@a, 2))
}
</pre>
<p>
Term variable initialization expression as well as term's expression follow
<em>Java-like expression grammar</em> including precedence rules, brackets and logical combinators, as well as
built-in <a href="#idl_functions">IDL functions</a> calls:
</p>
<pre class="brush: idl">
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
}
</pre>
<div class="bq info">
<p>
<b>NOTE:</b> while term variable initialization expressions can have any type - the
term's expression itself, i.e. the last expression in the term's body, <em>must evaluate to a boolean result only.</em>
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.
</p>
</div>
<p>
<code>?</code> and <code>[1,3]</code> 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:
</p>
<ul class="recover-bottom-margin">
<li><code>*</code> is equal to <code>[0,∞]</code></li>
<li><code>+</code> is equal to <code>[1,∞]</code></li>
<li><code>?</code> is equal to <code>[0,1]</code></li>
<li>No quantifier defaults to <code>[1,1]</code></li>
</ul>
<p>
As mentioned above the quantifier is inclusive, i.e. the <code>[1,3]</code> means that
the term should appear once, two times or three times.
</p>
</dd>
<dt>
<code>fragment(frag, {'p1': 25, 'p2': {'a': false}})</code> <sup><small>line 19 </small></sup><br>
</dt>
<dd>
<p>
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 <code>meta_frag()</code> <a href="#idl_functions">IDL function.</a>
</p>
</dd>
</dl>
</li>
<li>
<p id="fragment_statement">
<b><code>fragment</code> statement</b>
</p>
<p>
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:
</p>
<pre class="brush: idl, highlight: [2, 3, 15, 18]">
// 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)
</pre>
<p><b>NOTES:</b></p>
<ul class="recover-bottom-margin">
<li>
Fragment statements (line 2 and 3) have a name (<code>buzz</code> and <code>when</code>) and a list of terms.
</li>
<li>
Terms follow the same syntax as in intent definition.
</li>
<li>
When a fragment is referenced in intent (lines 15 and 18) it is replaced with its terms.
</li>
</ul>
</li>
<li>
<p id="import_statement">
<b><code>import</code> statement</b>
</p>
<p>
Import statement allows to import IDL declarations from either local file, classpath resource or URL:
</p>
<pre class="brush: idl">
// 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')
</pre>
<p>
<b>NOTES:</b>
</p>
<ul class="recover-bottom-margin">
<li>
The effect of importing is the same as if the imported declarations were inserted in place of import
statement.
</li>
<li>
Recursive and cyclic imports are detected and safely ignored.
</li>
<li>
Import statement starts with <code>import</code> keyword and has a string parameter that indicates
the location of the resource to import.
</li>
<li>
For the classpath resource you don't need to specify leading forward slash.
</li>
</ul>
<p></p>
</li>
</ul>
<h2 id="intent-lifecycle" class="section-sub-title">Intent Lifecycle <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
During {% scaladoc NCModelClient NCModelClient %} initialization it scans the provided model class for the intents.
All found intents are compiled into an internal representation.
</p>
<p>
Note that not all intent-related problems can be detected at the compilation phase,
and {% scaladoc NCModelClient 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.
</p>
<p>
Intents are compiled only once during the {% scaladoc NCModelClient NCModelClient %} initialization and
cannot be re-compiled. Model logic, however, can affect the intent behavior through
{% scaladoc NCModel NCModel %} callback methods and metadata all of which can change at runtime and
are accessible through <a href="#idl_functions">IDL functions.</a>
</p>
<h2 id="intent-examples" class="section-sub-title">Intent Examples <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
Here's few of intent examples with explanations:
</p>
<p>
<b>Example 1:</b>
</p>
<pre class="brush: idl">
intent=a
term~{# == 'x:type'}
term(nums)~{# == 'num' && lowercase(meta_ent('num:unittype')) == 'datetime'}[0,2]
</pre>
<p><b>NOTES:</b></p>
<ul>
<li>
Intent has ID <code>a</code>.
</li>
<li>
Intent uses default conversational support (<code>true</code>) and default order (<code>false</code>).
</li>
<li>
Intent has two conversational terms (<code>~</code>) that have to be found for the intent to match. Note that second
term is optional as it has <code>[0,2]</code> quantifier.
</li>
<li>
Both terms have to be found in the user input for the intent to match.
</li>
<li>
First term matches any single entity with type <code>x:type</code>.
</li>
<li>
Second term can appear zero, once or two times and it matches entity with ID <code>num</code> with
<code>num:unittype</code> metadata property equal to <code>'datetime'</code> string.
</li>
<li>
IDL function <code>lowercase</code> used on <code>num:unittype</code> metadata property value.
</li>
<li>
Note that since second term has ID (<code>nums</code>) it can be references by <code>@NCIntentTerm</code>
annotation by the callback formal parameter.
</li>
</ul>
<br/>
<p>
<b>Example 2:</b>
</p>
<pre class="brush: idl">
intent=id2
flow='id1 id2'
term={# == 'myent' && signum(get(meta_ent('score'), 'best')) != -1}
term={has_any(ent_groups, list('actors', 'owners'))}
</pre>
<p><b>NOTES:</b></p>
<ul>
<li>
Intent has ID <code>id2</code>.
</li>
<li>
Intent has dialog flow pattern: <code>'id1 id2'</code>. It expects the sequence of intents <code>id1</code> and
<code>id2</code> somewhere in the history of previously matched intents in the course of the current conversation.
</li>
<li>
Intent has two non-conversational terms (<code>=</code>). Both terms have to be present only once (their implicit quantifiers are <code>[1,1]</code>).
</li>
<li>
Both terms have to be found in the user input for the intent to match.
</li>
<li>
First term should be a entity with ID <code>myent</code> and have metadata property <code>score</code> of type
map. This map should have a value with the string key <code>'best'</code>. <code>signum</code> of this map value
should not equal <code>-1</code>. Note that <code>meta_ent()</code>, <code>get()</code> and
<code>signum()</code> are all built-in <a href="#idl_functions">IDL functions</a>.
</li>
<li>
Second term should be an entity that belongs to either <code>actors</code> or <code>owners</code> group.
</li>
</ul>
</section>
<section id="idl_functions" >
<h2 class="section-title">IDL Functions <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
IDL provides over 100 built-in functions that can be used in IDL intent definitions.
IDL function call takes on traditional
<code><b>fun_name</b>(p1, p2, ... pk)</code> 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.
</p>
<div class="bq info">
<p><b>Special Shorthand <code>#</code></b></p>
<p>
The frequently used IDL function <code>ent_type()</code> has a special shorthand <code>#</code>. For example,
the following expressions are all equal:
</p>
<pre class="brush: idl">
ent_type() == 'type'
ent_type == 'type' // Remember - empty parens are optional.
# == 'type'
</pre>
</div>
<p>
When chaining the function
calls IDL uses mathematical notation (a-la Python) rather than object-oriented one: IDL <code>length(trim(" text "))</code> vs. OOP-style <code>" text ".trim().length()</code>.
</p>
<p>
IDL functions operate with the following types:
</p>
<table class="gradient-table">
<thead>
<tr>
<th>JVM Type</th>
<th>IDL Name</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr><td><code>java.lang.String</code></td><td><code>String</code></td><td></td></tr>
<tr>
<td>
<code>java.lang.Long</code><br/>
<code>java.lang.Integer</code><br/>
<code>java.lang.Short</code><br/>
<code>java.lang.Byte</code>
</td>
<td><code>Long</code></td>
<td>
Smaller numerical types will be converted to <code>java.lang.Long</code>.
</td>
</tr>
<tr>
<td>
<code>java.lang.Double</code><br/>
<code>java.lang.Float</code>
</td>
<td><code>Double</code></td>
<td>
<code>java.lang.Float</code> will be converted to <code>java.lang.Double</code>.
</td>
</tr>
<tr><td><code>java.lang.Boolean</code></td><td><code>Boolean</code></td><td>You can use <code><b>true</b></code> or <code><b>false</b></code> literals.</td></tr>
<tr><td><code>java.util.List&lt;T&gt;</code></td><td><code>List[T]</code></td><td>Use <code>list(...)</code> IDL function to create new list.</td></tr>
<tr><td><code>java.util.Map&lt;K,V&gt;</code></td><td><code>Map[K,V]</code></td><td></td></tr>
<tr><td><code>{% scaladoc NCEntity NCEntity %}</code></td><td><code>Entity</code></td><td></td></tr>
<tr><td><code>java.lang.Object</code></td><td><code>Any</code></td><td>Any of the supported types above. Use <code><b>null</b></code> literal for null value.</td></tr>
</tbody>
</table>
<p>
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.
</p>
<div class="bq warn">
<p><b>Unsupported Types</b></p>
<p>
Detection of the unsupported types by IDL functions cannot be done during IDL compilation and
can <em>only be done during runtime execution</em>.
This means that even though the model compiles IDL
intents and {% scaladoc NCModelClient NCModelClient %} starts successfully - it does not guarantee that intents will operate correctly.
</p>
</div>
<p id="fn-list">
All IDL functions are organized into the following groups:
</p>
<nav>
<div class="nav nav-tabs" role="tablist">
<a class="nav-item nav-link active" data-toggle="tab" id="fn_ent_tab" href="#fn_ent" role="tab">Entity</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_text_tab" href="#fn_text" role="tab">Text</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_math_tab" href="#fn_math" role="tab">Math</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_collection_tab" href="#fn_collection" role="tab">Collection</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_metadata_tab" href="#fn_metadata" role="tab">Metadata</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_datetime_tab" href="#fn_datetime" role="tab">Date <span class="amp">&amp;</span> Time</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_request_tab" href="#fn_req" role="tab">Request</a>
<a class="nav-item nav-link" data-toggle="tab" id="fn_other_tab" href="#fn_other" role="tab">Other</a>
</div>
</nav>
<div class="tab-content">
<div class="tab-pane fade show active" id="fn_ent" role="tabpanel">
<div class="accordion" id="ent_fns">
{% for fn in site.data.idl-fns.fn-ent %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#ent_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_text" role="tabpanel">
<div class="accordion" id="text_fns">
{% for fn in site.data.idl-fns.fn-text %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#text_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_math" role="tabpanel">
<div class="accordion" id="math_fns">
{% for fn in site.data.idl-fns.fn-math %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#math_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_collection" role="tabpanel">
<div class="accordion" id="collections_fns">
{% for fn in site.data.idl-fns.fn-collections %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#collections_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_metadata" role="tabpanel">
<div class="accordion" id="metadata_fns">
{% for fn in site.data.idl-fns.fn-metadata %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#metadata_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_datetime" role="tabpanel">
<div class="accordion" id="datetime_fns">
{% for fn in site.data.idl-fns.fn-datetime %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#datetime_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_req" role="tabpanel">
<div class="accordion" id="req_fns">
{% for fn in site.data.idl-fns.fn-req %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#req_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
<div class="tab-pane fade show" id="fn_other" role="tabpanel">
<div class="accordion" id="other_fns">
{% for fn in site.data.idl-fns.fn-other %}
<div class="card">
<div class="card-header">
<h2 class="mb-0">
<button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#fn_{{fn.name}}">
<span><code>{{fn.sig}}</code></span>
<span class="fn-short-desc">{{fn.synopsis}}</span>
</button>
</h2>
</div>
<div id="fn_{{fn.name}}" class="collapse" data-parent="#other_fns">
<div class="card-body">
<p class="fn-desc">
<em>Description:</em><br>
{{fn.desc}}
</p>
<p class="fn-usage">
<em>Usage:</em><br>
</p>
<pre class="brush:idl">{{fn.usage}}</pre>
</div>
</div>
</div>
{% endfor %}
</div>
</div>
</div>
</section>
<section id="idl_location" >
<h2 class="section-title">IDL Location <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
IDL declarations can be placed in different locations based on user preferences:
</p>
<ul>
<li>
<p>
<code>NCIntent</code> java annotation
takes a string as its parameter that should be a valid IDL declaration. For example, Scala code snippet:
</p>
<pre class="brush: scala, highlight: [1, 2]">
&#64;NCIntent("import('/opt/myproj/global_fragments.idl')") // Importing.
&#64;NCIntent("intent=act term(act)={has(ent_groups, 'act')} fragment(f1)") // Defining in place.
def onMatch(
&#64;NCIntentTerm("act") actEnt: NCEntity,
&#64;NCIntentTerm("loc") locEnts: List[NCEntity]
): NCResult = {
...
}
</pre>
</li>
<li>
External <code>*.idl</code> files contain IDL declarations and can be imported in any other places where
IDL declarations are allowed. See <code>import()</code> statement explanation below. For example:
<pre class="brush: idl">
/*
* 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)
</pre>
</li>
</ul>
</section>
<section id="binding">
<h2 class="section-title">Binding Intent <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
IDL intents must be bound to their callback methods. This binding is accomplished using the
following Java annotations:
</p>
<table class="gradient-table">
<thead>
<tr>
<th>Annotation</th>
<th>Target</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>@NCIntent</code></td>
<td>Callback method or model class</td>
<td>
<p>
When applied to a method this annotation allows to define IDL intent in-place on the method
serving as its callback.
This annotation can also be applied to a model's class in which case it will just declare the intent
without binding it and the
callback method will need to use <code>@NCIntentRef</code> annotation to actually bind it to the
declared intent above. Note that multiple intents can be bound to the same callback method, but only
one callback method can be bound with a given intent.
</p>
<p>
This method is ideal for simple intents and quick declaration right in the source code and has
all the benefits of having IDL to be part of the source code. However, multi-line IDL declaration can be awkward
to add and maintain depending on Scala language, i.e. multi-line string literal support. In such
cases it is advisable to move IDL declarations into separate <code>*.idl</code> file or files
and import them at the model class level.
</p>
</td>
</tr>
<tr>
<td><code>@NCIntentRef</code></td>
<td>Callback method</td>
<td>
This annotation allows to reference an intent defined elsewhere like an external <code>*.idl</code> file,
or other <code>@NCIntent</code> annotations. In real
applications, this is a most common way to bound an externally defined intent to its callback method.
</td>
</tr>
<tr>
<td><code>@NCIntentObject</code></td>
<td>Model class field</td>
<td>
Marker annotation that can be applied to class member of main model.
The fields objects annotated with this annotation are scanned the same way as main model.
</td>
</tr>
<tr>
<td><code>@NCIntentTerm</code></td>
<td>Callback method parameter</td>
<td>
This annotation marks a formal callback method parameter to receive term's entities when the intent
to which this term belongs is selected as the best match.
</td>
</tr>
</tbody>
</table>
<p>
Here's a couple of examples of intent declarations to illustrate the basics of intent declaration and usage.
</p>
<p>
An intent from
<a href="examples/light_switch.html">Light Switch</a> example:
</p>
<pre class="brush: scala">
&#64;NCIntent("intent=ls term(act)={has(ent_groups, 'act')} term(loc)={# == 'ls:loc'}*")
def onMatch(
&#64;ctx: NCContext,
&#64;im: NCIntentMatch,
&#64;NCIntentTerm("act") actEnt: NCEntity,
&#64;NCIntentTerm("loc") locEnts: List[NCEntity]
): NCResult = {
...
}
</pre>
<p>
<b>NOTES:</b>
</p>
<ul>
<li>
The intent is defined in-place using <code>@NCIntent</code> annotation.
</li>
<li>
A term match is defined as one or more entities. Term can be optional if its min quantifier is zero.
</li>
<li>
An intent <code>act</code> has two non-conversational terms: one mandatory term and another
that can match zero or more entities with method <code>onMatch(...)</code> as its callback.
</li>
<li>
Terms is conversational if it uses <code>'~'</code> and non-conversational if it uses <code>'='</code>
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 <em>less specific.</em>
Non-conversational terms will be matched using entities found only in the current user input without looking at the conversation context.
</li>
<li>
Method <code>onMatch(...)</code> will be called if and when this intent is selected as the best match.
</li>
<li>
Note that terms have <code>min=1, max=1</code> quantifiers by default, i.e. one and only one.
</li>
<li>
First term defines any single entity that belongs to the group <code>act</code>. Note that model elements
can belong to multiple groups.
</li>
<li>
Note that both terms have IDs (<code>act</code> and <code>loc</code>) that are used in <code>onMatch(...)</code>
method parameters to automatically assign terms' entities to the formal method parameters using <code>@NCIntentTerm</code>
annotations.
</li>
</ul>
<p>
In the following <a href="examples/time.html">Time</a> example
the intent is defined model class and referenced in code using <code>@NCIntentRef</code>
annotation:
</p>
<pre class="brush: js, highlight: [2, 5]">
@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 =
...
</pre>
<p>
<b>NOTES:</b>
</p>
<ul>
<li>
Intent is defined in the model, <code>line 2</code>.
</li>
<li>
This intent is referenced by annotation <code>@NCIntentRef("intent2")</code> with method <code>onMatch(...)</code>
as its callback, <code>line 5</code>.
</li>
<li>
This example defines an intent with two conversational terms both of which have to found for the
intent to match.
</li>
<li>
Terms is conversational if it uses <code>'~'</code> and non-conversational if it uses <code>'='</code>
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 <em>less specific.</em>
Non-conversational terms will be matched using entities found only in the current user input without looking at the conversation context.
</li>
<li>
Method <code>onMatch(...)</code> will be called when this intent is the best match detected.
</li>
<li>
Note that terms have <code>min=1, max=1</code> quantifiers by default.
</li>
<li>
First term is defined as a single mandatory (<code>min=1, max=1</code>) entity with ID <code>x:time</code>
whose element is defined in the model.
</li>
<li>
Second term is defined as a single mandatory (<code>min=1, max=1</code>) entity with entity<code>opennlp:location</code>.
</li>
<li>
Given data model definition above the following sentences will be matched by this intent:
<ul>
<li><code>What time is it now in New York City?</code></li>
<li><code>Show me time of the day in London.</code></li>
<li><code>Can you please give me the Tokyo's current date and time.</code></li>
</ul>
</li>
</ul>
</section>
<section id="logic">
<h2 class="section-title">Intent Matching Logic <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
{% scaladoc NCPipeline NCPipeline %} processing result is collection of {% scaladoc NCVariant NCVariant %} instances.
As example let's consider the input text <code>'A B C D'</code> and the following elements defined in our model:
</p>
<pre class="brush: js">
"elements": [
{
"id": "elm1",
"synonyms": ["A B"]
},
{
"id": "elm2",
"synonyms": ["B C"]
},
{
"id": "elm3",
"synonyms": ["D"]
}
],
</pre>
<p>
All of these elements will be detected but since two of them are overlapping (<code>elm1</code> and
<code>elm2</code>) there should be <b>two</b> parsing variants at the output of this step:
</p>
<ol>
<li><code>elm1</code>('A', 'B') <code>freeword</code>('C') <code>elm3</code>('D')</li>
<li><code>freeword</code>('A') <code>elm2</code>('B', 'C') <code>elm3</code>('D')</li>
</ol>
<p></p>
<p>
Note that initially the <em>system cannot determine which of these variants is the best one
for matching - there's simply not enough information at this stage</em>. 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
<em>certain weight</em>. 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.
</p>
<p>
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 <b>more specific match always wins</b>:
</p>
<ul>
<li>
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.
</li>
<li>
Entities for user-defined elements are more important than built-in entities.
</li>
<li>
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.
</li>
</ul>
</section>
<section id="intent_callback">
<h2 class="section-title">Intent Callback <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
Whether the intent is defined directly in <code>@NCIntent</code> annotation or indirectly via <code>@NCIntentRef</code>
annotation - it is always bound to a callback method:
</p>
<ul>
<li>
Callback can only be an instance method on the class implementing {% scaladoc NCModel NCModel %}
trait.
</li>
<li>
Method must have return type of {% scaladoc NCResult NCResult %}.
</li>
<li>
Method should have at least two parameters:
<ul>
<li>
Parameter of type {% scaladoc NCContext NCContext %} must be first.
</li>
<li>
Parameter of type {% scaladoc NCIntentMatch NCIntentMatch %} must be second.
</li>
<li>
Any other parameters must have <code>@NCIntentTerm</code> annotation.
</li>
</ul>
</li>
<li>
Method must support reflection-based invocation.
</li>
</ul>
<p>
<code>@NCIntentTerm</code> 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 <code>@NCIntnet</code> or
<code>@NCIntentRef</code>. <code>@NCIntentTerm</code> 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).
</p>
<p>
Depending on the term quantifier the method parameter type can only be one of the following types:
</p>
<table class="gradient-table">
<thead>
<tr>
<th>Quantifier</th>
<th>Scala Type</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>[1,1]</code></td>
<td><code>NCEntity</code></td>
</tr>
<tr>
<td><code>[0,1]</code></td>
<td><code>Option[NCEntity]</code></td>
</tr>
<tr>
<td><code>[1,∞]</code> or <code>[0,∞]</code></td>
<td><code>List[NCEntity]</code></td>
</tr>
</tbody>
</table>
<p>
For example:
</p>
<pre class="brush: scala">
NCIntent("intent=id term(termId)~{# == 'my_ent'}?")
private def onMatch(
ctx: NCContext,
im: NCIntentMatch,
@NCIntentTerm("termId") myEnt: Option[NCEntity]
): NCResult = {
...
}
</pre>
<p><b>NOTES:</b></p>
<ul>
<li>
Conversational term <code>termId</code> has <code>[0,1]</code> quantifier (it's optional).
</li>
<li>
The formal parameter on the callback has a type of <code>Option[NCEntity]</code> because the
term's quantifier is <code>[0,1]</code>.
</li>
</ul>
<h2 class="section-sub-title"><code>NCRejection</code> and <code>NCIntentSkip</code> Exceptions <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
There are two exceptions that can be used by intent callback logic to control intent matching process.
</p>
<p>
When {% scaladoc NCRejection 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.
</p>
<p>
{% scaladoc NCIntentSkip 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.
<p>
<p>
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.
</p>
<p>
Note that there's a significant difference between {% scaladoc NCIntentSkip NCIntentSkip %}
exception and model's {% scaladoc NCModel#onMatchedIntent-946 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.
</p>
<div class="bq info">
<p>
<b>IDL Expressiveness</b>
</p>
<p>
Note that usage of <code>NCIntentSkip</code> exception (as well as model's life-cycle callbacks) is a
required technique when you cannot express the desired matching logic with only IDL alone.
IDL is a high-level declarative language and it does
not support a complex programmable logic or other types of sophisticated matching algorithms. In such cases, you can
define a broad intent that would <em>broadly match</em> and then define the rest of the more complex matching logic in the callback
using <code>NCIntentSkip</code> exception to effectively indicate when intent doesn't match and other
intents, if any, have to be tried.
</p>
<p>
There are many use cases where IDL is not expressive enough. For example, if your intent matching depends
on financial market conditions, weather, state from external systems or details of the current user geographical
location or social network status - you will need to use <code>NCIntentSkip</code>-based logic or model's callbacks to support
that type of matching.
</p>
</div>
<h2 class="section-sub-title"><code>NCContext</code> Trait <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
{% scaladoc NCContext 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 {% scaladoc NCConversation NCConversation %}.
</p>
<h2 class="section-sub-title"><code>NCIntentMatch</code> Trait <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
{% scaladoc NCIntentMatch 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).
</p>
</section>
</div>
<div class="col-md-2 third-column">
<ul class="side-nav">
<li class="side-nav-title">On This Page</li>
<li><a href="#intent">Overview</a></li>
<li><a href="#idl">IDL Syntax</a></li>
<li><a class="toc2" href="#intent-lifecycle">Intent Lifecycle</a></li>
<li><a class="toc2" href="#intent-examples">Intent Examples</a></li>
<li><a href="#idl_functions">IDL Functions</a></li>
<li><a class="toc2" onclick="$('#fn_ent_tab').trigger('click')" href="#fn-list"><code><b>Entity</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_text_tab').trigger('click')" href="#fn-list"><code><b>Text</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_math_tab').trigger('click')" href="#fn-list"><code><b>Math</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_collection_tab').trigger('click')" href="#fn-list"><code><b>Collection</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_metadata_tab').trigger('click')" href="#fn-list"><code><b>Metadata</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_datetime_tab').trigger('click')" href="#fn-list"><code><b>Datetime</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_request_tab').trigger('click')" href="#fn-list"><code><b>Request</b></code> Functions</a></li>
<li><a class="toc2" onclick="$('#fn_other_tab').trigger('click')" href="#fn-list"><code><b>Other</b></code> Functions</a></li>
<li><a href="#idl_location">IDL Location</a></li>
<li><a href="#binding">Intent Binding</a></li>
<li><a href="#logic">Intent Matching</a></li>
<li><a href="#intent_callback">Intent Callback</a></li>
{% include quick-links.html %}
</ul>
</div>