---
active_crumb: Light Switch FR <code><sub>ex</sub></code>
layout: documentation
id: light_switch_fr
fa_icon: fa-cube
---

<!--
 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 class="col-md-8 second-column example">
    <section id="overview">
        <h2 class="section-title">Overview <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            This example provides a very simple French language implementation for NLI-powered light switch. You can say something like
            "Éteignez les lumières dans toute la maison" or "Allumez les lumières".
            By modifying intent callbacks using, for example, HomeKit or Arduino-based controllers you can provide the
            actual light switching.
        </p>
        <p>
            <b>Complexity:</b> <span class="complexity-two-star"><i class="fas fa-square"></i> <i class="fas fa-square"></i> <i class="far fa-square"></i></span><br/>
            <span class="ex-src">Source code: <a target="github" href="https://github.com/apache/incubator-nlpcraft/tree/master/nlpcraft-examples/lightswitch_fr">GitHub <i class="fab fa-fw fa-github"></i></a><br/></span>
            <span class="ex-review-all">Review: <a target="github" href="https://github.com/apache/incubator-nlpcraft/tree/master/nlpcraft-examples">All Examples at GitHub <i class="fab fa-fw fa-github"></i></a></span>
        </p>
    </section>
    <section id="new_project">
        <h2 class="section-title">Create New Project <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            You can create new Scala projects in many ways - we'll use SBT
            to accomplish this task. Make sure that <code>build.sbt</code> file has the following content:
        </p>
        <pre class="brush: js, highlight: [7, 8, 9, 10]">
            ThisBuild / version := "0.1.0-SNAPSHOT"
            ThisBuild / scalaVersion := "3.1.3"
            lazy val root = (project in file("."))
              .settings(
                name := "NLPCraft LightSwitch FR Example",
                version := "{{site.latest_version}}",
                libraryDependencies += "org.apache.nlpcraft" % "nlpcraft" % "{{site.latest_version}}",
                libraryDependencies += "org.apache.lucene" % "lucene-analyzers-common" % "8.11.2",
                libraryDependencies += "org.languagetool" % "languagetool-core" % "5.8",
                libraryDependencies += "org.languagetool" % "language-fr" % "5.8"
                libraryDependencies += "org.scalatest" %% "scalatest" % "3.2.14" % "test"
              )
        </pre>

        <p>
            <code>Lines 8, 9 and 10</code> add libraries which used for support base NLP operations with French language.
        </p>

        <p><b>NOTE: </b>use the latest versions of Scala and ScalaTest.</p>
        <p>Create the following files so that resulting project structure would look like the following:</p>
        <ul>
            <li><code>lightswitch_model_fr.yaml</code> - YAML configuration file which contains model description.</li>
            <li><code>LightSwitchFrModel.scala</code> - Model implementation.</li>
            <li><code>NCFrSemanticEntityParser.scala</code> - Semantic entity parser, custom implementation for French language.</li>
            <li><code>NCFrLemmaPosTokenEnricher.scala</code> - Lemma and point of speech token enricher, custom implementation for French language.</li>
            <li><code>NCFrStopWordsTokenEnricher.scala</code> - Stop-words token enricher, custom implementation for French language.</li>
            <li><code>NCFrTokenParser.scala</code> - Token parser, custom implementation for French language.</li>
            <li><code>LightSwitchFrModelSpec.scala</code> - Test that allows to test your model.</li>
        </ul>
        <pre class="brush: plain, highlight: [7, 10, 14, 17, 18, 20, 24]">
            |  build.sbt
            +--project
            |    build.properties
            \--src
               +--main
               |  +--resources
               |  |  lightswitch_model_fr.yaml
               |  \--scala
               |     \--demo
               |        |  LightSwitchFrModel.scala
               |        \--nlp
               |           +--entity
               |           |  \--parser
               |           |       NCFrSemanticEntityParser.scala
               |           \--token
               |              +--enricher
               |              |    NCFrLemmaPosTokenEnricher.scala
               |              |    NCFrStopWordsTokenEnricher.scala
               |              \--parser
               |                   NCFrTokenParser.scala
               \--test
                   \--scala
                       \--demo
                            LightSwitchFrModelSpec.scala
        </pre>
    </section>
    <section id="model">
        <h2 class="section-title">Data Model<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            We are going to start with declaring the static part of our model using YAML which we will later load using
            <code>NCModelAdapter</code> in our Scala-based model implementation.
            Open <code>src/main/resources/<b>light_switch_fr.yaml</b></code>
            file and replace its content with the following YAML:
        </p>
        <pre class="brush: js, highlight: [1, 10, 17, 25]">
            macros:
              "&lt;ACTION&gt;" : "{allumer|laisser|mettre}"
              "&lt;KILL&gt;" : "{éteindre|couper|tuer|arrêter|éliminer|baisser|no}"
              "&lt;ENTIRE_OPT&gt;" : "{entière|pleine|tout|total|_}"
              "&lt;FLOOR_OPT&gt;" : "{là-haut|à l'étage|en bas|{1er|premier|2ème|deuxième|3ème|troisième|4ème|quatrième|5ème|cinquième|dernier|haut|rez-de-chaussée|en bas} étage|_}"
              "&lt;TYPE&gt;" : "{chambre|salle|pièce|placard|mansardé|loft|mezzanine|rangement {chambre|salle|pièce|_}}"
              "&lt;LIGHT&gt;" : "{tout|_} {cela|lumière|éclairage|illumination|lampe}"

            elements:
              - id: "ls:loc"
                description: "Location of lights."
                synonyms:
                  - "&lt;ENTIRE_OPT&gt; &lt;FLOOR_OPT&gt; {cuisine|bibliothèque|placard|garage|bureau|salle de jeux|{salle à manger|buanderie|jeu} &lt;TYPE&gt;}"
                  - "&lt;ENTIRE_OPT&gt; &lt;FLOOR_OPT&gt; {maître|gamin|bébé|enfant|hôte|client|_} {coucher|bains|toilette|rangement} {&lt;TYPE&gt;|_}"
                  - "&lt;ENTIRE_OPT&gt; {maison|foyer|bâtiment|{1er|premier} étage|chaussée|{2ème|deuxième} étage}"

              - id: "ls:on"
                groups:
                  - "act"
                description: "Light switch ON action."
                synonyms:
                  - "{&lt;ACTION&gt;|_} &lt;LIGHT&gt;"
                  - "{&lt;LIGHT&gt;|_} &lt;ACTION&gt;"

              - id: "ls:off"
                groups:
                  - "act"
                description: "Light switch OFF action."
                synonyms:
                  - "&lt;KILL&gt; &lt;LIGHT&gt;"
                  - "&lt;LIGHT&gt; &lt;KILL&gt;"
        </pre>

        <ul>
            <li>
                <code>Line 1</code> defines several macros that are used later on throughout the model's elements
                to shorten the synonym declarations. Note how macros coupled with option groups  
                shorten overall synonym declarations 1000:1 vs. manually listing all possible word permutations.
            </li>
            <li>
                <code>Lines 10, 17, 25</code> define three model elements: the location of the light, and actions to turn
                the light on and off. Action elements belong to the same group <code>act</code> which
                will be used in our intent, defined in <code>LightSwitchFrModel</code> class. Note that these model
                elements are defined mostly through macros we have defined above.

            </li>
        </ul>
        <div class="bq info">
            <p><b>YAML vs. API</b></p>
            <p>
                As usual, this YAML-based static model definition is convenient but totally optional. All elements definitions
                can be provided programmatically inside Scala model <code>LightSwitchFrModel</code> class as well.
            </p>
        </div>
    </section>
    <section id="code">
        <h2 class="section-title">Model Class <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            Open <code>src/main/scala/demo/<b>LightSwitchFrModel.scala</b></code> file and replace its content with the following code:
        </p>
        <pre class="brush: scala, highlight: [11, 12, 13, 20, 21, 24, 25, 32]">
            package demo

            import com.google.gson.Gson
            import org.apache.nlpcraft.*
            import org.apache.nlpcraft.annotations.*
            import demo.nlp.entity.parser.NCFrSemanticEntityParser
            import demo.nlp.token.enricher.*
            import demo.nlp.token.parser.NCFrTokenParser
            import scala.jdk.CollectionConverters.*

            class LightSwitchFrModel extends NCModelAdapter(
                NCModelConfig("nlpcraft.lightswitch.fr.ex", "LightSwitch Example Model FR", "1.0"),
                new NCPipelineBuilder().
                    withTokenParser(new NCFrTokenParser()).
                    withTokenEnricher(new NCFrLemmaPosTokenEnricher()).
                    withTokenEnricher(new NCFrStopWordsTokenEnricher()).
                    withEntityParser(new NCFrSemanticEntityParser("lightswitch_model_fr.yaml")).
                    build
            ):
                @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 =
                    val action = if actEnt.getId == "ls:on" then "allumer" else "éteindre"
                    val locations = if locEnts.isEmpty then "toute la maison" else locEnts.map(_.mkText).mkString(", ")

                    // Add HomeKit, Arduino or other integration here.
                    // By default - just return a descriptive action string.
                    NCResult(new Gson().toJson(Map("locations" -> locations, "action" -> action).asJava))
        </pre>
        <p>
            The intent callback logic is very simple - we return a descriptive confirmation message
            back (explaining what lights were changed). With action and location detected, you can add
            the actual light switching using HomeKit or Arduino devices. Let's review this implementation step by step:
        </p>
        <ul>
            <li>
                On <code>line 11</code> our class extends <code>NCModelAdapter</code> that allows us to pass
                prepared configuration and pipeline into model.
            </li>
            <li>
                <code>Line 12</code> creates model configuration with most default parameters.
            </li>
            <li>
                <code>Line 13</code> creates pipeline based on custom French language components:
                <ul>
                    <li><code>NCFrTokenParser</code> - Token parser.</li>
                    <li><code>NCFrLemmaPosTokenEnricher</code> - Lemma and point of speech token enricher.</li>
                    <li><code>NCFrStopWordsTokenEnricher</code> - Stop-words token enricher.</li>
                    <li><code>NCFrSemanticEntityParser</code> - Semantic entity parser extending.</li>
                </ul>
                Note that <code>NCFrSemanticEntityParser</code> is based on semantic model definition
                described in <code>lightswitch_model_fr.yaml</code> file.
            </li>
            <li>
                <code>Lines 20 and 21</code> annotate intents <code>ls</code> and its callback method <code>onMatch()</code>.
                Intent <code>ls</code> requires one action (a token belonging to the group <code>act</code>) and optional list of light locations
                (zero or more tokens with ID <code>ls:loc</code>) - by default we assume the entire house as a default location.
            </li>
            <li>
                <code>Lines 24 and 25</code> map terms from detected intent to the formal method parameters of the
                <code>onMatch()</code> method.
            </li>
            <li>
                On the <code>line 32</code> the intent callback simply returns a confirmation message.
            </li>
        </ul>
    </section>
    <section id="custom">
        <h2 class="section-title">Custom Components <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            Open <code>src/main/scala/demo/nlp/token/parser/<b>NCFrTokenParser.scala</b></code> file and replace its content with the following code:
        </p>
        <pre class="brush: scala, highlight: [19]">
            package demo.nlp.token.parser

            import org.apache.nlpcraft.*
            import org.languagetool.tokenizers.fr.FrenchWordTokenizer
            import scala.jdk.CollectionConverters.*

            class NCFrTokenParser extends NCTokenParser:
                private val tokenizer = new FrenchWordTokenizer

                override def tokenize(text: String): List[NCToken] =
                    val toks = collection.mutable.ArrayBuffer.empty[NCToken]
                    var sumLen = 0

                    for ((word, idx) <- tokenizer.tokenize(text).asScala.zipWithIndex)
                        val start = sumLen
                        val end = sumLen + word.length

                        if word.strip.nonEmpty then
                            toks += new NCPropertyMapAdapter with NCToken:
                                override def getText: String = word
                                override def getIndex: Int = idx
                                override def getStartCharIndex: Int = start
                                override def getEndCharIndex: Int = end

                        sumLen = end

                    toks.toList
        </pre>
        <ul>
            <li>
                <code>NCFrTokenParser</code> is a simple wrapper which implements <code>NCTokenParser</code> based on
                open source <a href="https://languagetool.org">Language Tool</a> library.
            </li>
            <li>
                <code>Line 19</code> creates the <code>NCToken</code> instance.
            </li>
        </ul>

        <p>
            Open <code>src/main/scala/demo/nlp/token/enricher/<b>NCFrLemmaPosTokenEnricher.scala</b></code> file and replace its content with the following code:
        </p>
        <pre class="brush: scala, highlight: [27, 28]">
            package demo.nlp.token.enricher

            import org.apache.nlpcraft.*
            import org.languagetool.AnalyzedToken
            import org.languagetool.tagging.fr.FrenchTagger
            import scala.jdk.CollectionConverters.*

            class NCFrLemmaPosTokenEnricher extends NCTokenEnricher:
                private def nvl(v: String, dflt : => String): String = if v != null then v else dflt

                override def enrich(req: NCRequest, cfg: NCModelConfig, toks: List[NCToken]): Unit =
                    val tags = FrenchTagger.INSTANCE.tag(toks.map(_.getText).asJava).asScala

                    require(toks.sizeIs == tags.size)

                    toks.zip(tags).foreach { case (tok, tag) =>
                        val readings = tag.getReadings.asScala

                        val (lemma, pos) = readings.size match
                            // No data. Lemma is word as is, POS is undefined.
                            case 0 => (tok.getText, "")
                            // Takes first. Other variants ignored.
                            case _ =>
                                val aTok: AnalyzedToken = readings.head
                                (nvl(aTok.getLemma, tok.getText), nvl(aTok.getPOSTag, ""))

                        tok.put("pos", pos)
                        tok.put("lemma", lemma)

                        () // Otherwise NPE.
                    }
        </pre>
        <ul>
            <li>
                <code>NCFrLemmaPosTokenEnricher</code> lemma and point of speech tokens enricher is based on
                open source <a href="https://languagetool.org">Language Tool</a> library.
            </li>
            <li>
                On <code>line 27 and 28</code> the tokens are enriched by <code>pos</code> and <code>lemma</code> data.
            </li>
        </ul>

        <p>
            Open <code>src/main/scala/demo/nlp/token/enricher/<b>NCFrStopWordsTokenEnricher.scala</b></code> file and replace its content with the following code:
        </p>

        <pre class="brush: scala, highlight: [17]">
            package demo.nlp.token.enricher

            import org.apache.lucene.analysis.fr.FrenchAnalyzer
            import org.apache.nlpcraft.*

            class NCFrStopWordsTokenEnricher extends NCTokenEnricher:
                private final val stops = FrenchAnalyzer.getDefaultStopSet

                private def getPos(t: NCToken): String = t.get("pos").getOrElse(throw new NCException("POS not found in token."))
                private def getLemma(t: NCToken): String = t.get("lemma").getOrElse(throw new NCException("Lemma not found in token."))

                override def enrich(req: NCRequest, cfg: NCModelConfig, toks: List[NCToken]): Unit =
                    for (t <- toks)
                        val lemma = getLemma(t)
                        lazy val pos = getPos(t)

                        t.put(
                            "stopword",
                            lemma.length == 1 && !Character.isLetter(lemma.head) && !Character.isDigit(lemma.head) ||
                            stops.contains(lemma.toLowerCase) ||
                            pos.startsWith("I") ||
                            pos.startsWith("O") ||
                            pos.startsWith("P") ||
                            pos.startsWith("D")
                        )
        </pre>
        <ul>
            <li>
                <code>NCFrStopWordsTokenEnricher</code> is a stop-words tokens enricher based on
                open source <a href="https://lucene.apache.org/">Apache Lucene</a> library.
            </li>
            <li>
                On <code>line 17</code> tokens are enriched by <code>stopword</code> flags data.
            </li>
        </ul>

        <p>
            Open <code>src/main/scala/demo/nlp/entity/parser/<b>NCFrSemanticEntityParser.scala</b></code> file and replace its content with the following code:
        </p>

        <pre class="brush: scala, highlight: [8, 12]">
            package demo.nlp.entity.parser

            import opennlp.tools.stemmer.snowball.SnowballStemmer
            import demo.nlp.token.parser.NCFrTokenParser
            import org.apache.nlpcraft.nlp.parsers.*

            class NCFrSemanticEntityParser(src: String) extends NCSemanticEntityParser(
                new NCSemanticStemmer:
                    private val stemmer = new SnowballStemmer(SnowballStemmer.ALGORITHM.FRENCH)
                    override def stem(txt: String): String = stemmer.synchronized { stemmer.stem(txt.toLowerCase).toString }
                ,
                new NCFrTokenParser(),
                mdlSrcOpt = Option(src)
            )
        </pre>
        <ul>
            <li>
                <code>NCFrSemanticEntityParser</code> extends <code>NCSemanticEntityParser</code>.
                It uses stemmer implementation from <a href="https://opennlp.apache.org/">Apache OpenNLP</a> project.
            </li>
        </ul>
    </section>

    <section id="testing">
        <h2 class="section-title">Testing <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            The test defined in <code>LightSwitchFrModelSpec</code> allows to check that all input test sentences are
            processed correctly and trigger the expected intent <code>ls</code>:
        </p>
        <pre class="brush: scala, highlight: [9, 11]">
            package demo

            import org.apache.nlpcraft.*
            import org.scalatest.funsuite.AnyFunSuite
            import scala.util.Using

            class LightSwitchFrModelSpec extends AnyFunSuite:
                test("test") {
                    Using.resource(new NCModelClient(new LightSwitchFrModel)) { client =>
                        def check(txt: String): Unit =
                            require(client.debugAsk(txt, "userId", true).getIntentId == "ls")

                        check("Éteignez les lumières dans toute la maison.")
                        check("Éteignez toutes les lumières maintenant.")
                        check("Allumez l'éclairage dans le placard de la chambre des maîtres.")
                        check("Éteindre les lumières au 1er étage.")
                        check("Allumez les lumières.")
                        check("Allumes dans la cuisine.")
                        check("S'il vous plait, éteignez la lumière dans la chambre à l'étage.")
                        check("Allumez les lumières dans toute la maison.")
                        check("Éteignez les lumières dans la chambre d'hôtes.")
                        check("Pourriez-vous éteindre toutes les lumières s'il vous plait?")
                        check("Désactivez l'éclairage au 2ème étage.")
                        check("Éteignez les lumières dans la chambre au 1er étage.")
                        check("Lumières allumées à la cuisine du deuxième étage.")
                        check("S'il te plaît, pas de lumières!")
                        check("Coupez toutes les lumières maintenant!")
                        check("Éteindre les lumières dans le garage.")
                        check("Lumières éteintes dans la cuisine!")
                        check("Augmentez l'éclairage dans le garage et la chambre des maîtres.")
                        check("Baissez toute la lumière maintenant!")
                        check("Pas de lumières dans la chambre, s'il vous plait.")
                        check("Allumez le garage, s'il vous plait.")
                        check("Tuez l'illumination maintenant.")
                    }
                }
        </pre>
        <ul>
            <li>
                <code>Line 9</code> creates the client for our model.
            </li>
            <li>
                <code>Line 11</code> calls a special method <code>debugAsk()</code>.
                It allows to check the winning intent and its callback parameters without actually
                calling the intent.
            </li>
            <li>
                <code>Lines 13-34</code> define all the test input sentences that should all
                trigger <code>ls</code> intent.
            </li>
        </ul>
        <p>
            You can run this test via SBT task <code>executeTests</code> or using IDE.
        </p>
        <pre class="brush: scala, highlight: []">
            $ sbt executeTests
        </pre>
    </section>
    <section>
        <h2 class="section-title">Done! 👌 <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            You've created light switch data model and tested it.
        </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="#overview">Overview</a></li>
        <li><a href="#new_project">New Project</a></li>
        <li><a href="#model">Data Model</a></li>
        <li><a href="#code">Model Class</a></li>
        <li><a href="#custom">Custom Components</a></li>
        <li><a href="#testing">Testing</a></li>
        {% include quick-links.html %}
    </ul>
</div>