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<div class="col-md-8 second-column">
    <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>
            Apache NLPCraft is an <a target=_blank href="https://www.apache.org/licenses/">open source</a> Scala library for adding a natural language interface to modern applications.
            It enables people to interact with your products using voice or text.
            Its design is based on advanced <a href="/intent-matching.html">Intent Definition Language</a> (IDL) for defining non-trivial intents and
            a fully deterministic intent matching algorithm for the input utterances.
        </p>
        <p>
            One of the key features of NLPCraft is its use of <a href="/intent-matching.html">IDL</a> coupled with deterministic intent matching that are tailor made for
            <em>domain-specific</em> natural language interface. This design doesn't force developers to use direct deep learning
            approach with time consuming corpora development and model training - resulting in much a
            <em>simpler <span class="amp">&</span> faster</em> implementation.
        </p>

        <p>
            NlpCraft library contains two base elements: <code>Model</code> and <code>Client</code>.
        </p>

        <ul>
            <li>
                <code>Model</code> is domain specific object which responsible for user input interpretation. Model contains intents, defined via NlpCraft IDL with related code callbacks. Intent is user defined callback and rule, according to which this callback should be called. Rule is most often some template, based on expected set of entities in user input, but it can be more flexible.
            </li>

            <li>
                <code>Client</code> is object, which allows to communicate with given model. Main methods are user input processing and control of communication session.
            </li>
        </ul>

        <p>Typical part of code:</p>

        <pre class="brush: scala, highlight: []">
              // Prepares domain model.
              val mdl = new CustomNlpModel()

              // Prepares client for given model.
              val client = new NCModelClient(mdl)

              // Sends text request to model by user ID "userId".
              val result = client.ask("Some user command", "userId")

              // Clears dialog session for user with ID "userId".
              client.clearDialog("userId")
        </pre>

        <p>
            Model definition includes two parts:
        </p>
        <ul>
            <li>
                <code>Configuration</code>. Static configuration parameters including name, version, etc.
            </li>
            <li>
                <code>Pipeline</code>. Most important component, which defines user input processing chain.
                <code>Pipeline</code> can be based on standard and custom user defined components.
            </li>
        </ul>

        <p>
             Before looking at pipeline elements more throughly, let's start with terminology.
        </p>

        <ul>
            <li>
                <code>Token</code>. It is simple string, part of user input, which split according to some rules, for instance by spaces and some additional conditions, which depends on language and some expectations.
                So user input "<b>Where is it?</b>" contains four tokens: "<b>Where</b>", "<b>is</b>", "<b>it</b>", "<b>?</b>".
            </li>
            <li>
                <code>Entity</code>. According to wikipedia, named entity is a real-world object, such as a person, location, organization, product, etc., that can be denoted with a proper name. It can be abstract or have a physical existence. Each entity can contain one or more tokens.
            </li>
            <li>
                <code>Variant</code>. List of entities. Potentially, each token can be recognized as different entities, so user input can be processed as set of variants. For example user input "Mercedes" can be processed as 2 variants, both of them contains single element list of entities: car brand or Spanish family name.
            </li>
        </ul>

        <p>
            Back to pipeline. Pipeline should be created based in following components:
        </p>
        <ul>
            <li>
                <code>Token parser</code>. Mandatory NLP component, it is required for parsing plain text, user input, and split this text into tokens  list. NlpCraft provides default EN implementation of token parser. Also, project contain various examples for FR and RU languages.
            </li>
            <li>
                <code>Tokens enrichers</code> optional list. Tokens enricher is component which allows to add additional properties to prepared tokens, like part of speech, quote, stop-words flags or any other. NlpCraft provides default set of EN tokens enrichers implementations.
            </li>
            <li>
                <code>Tokens validators</code> optional list. Tokens validator is user defined component, where tokens are inspected and exception can be thrown from user code to break user input processing.
            </li>
            <li>
                <code>Entity parsers</code> mandatory list. At least one entity parser must be defined. Having prepared tokens as input, each entity parser tries to find user defined named entities. NlpCraft provides wrappers for named-entity recognition components of OpenNLP and Stanford libraries.
            </li>
            <li>
                <code>Entity enrichers</code> optional list. Entity enricher is component which allows to add additional properties to prepared entities. Can be useful for extending existing entity enrichers functionality.
            </li>
            <li>
                <code>Entity mappers</code> optional list. Entity mapper is component which allows to map one set of entities into another after the entities were parsed and enriched. Can be useful for building complex parsers based on existed.
            </li>
            <li>
                <code>Entity validators</code> optional list. Entities validator is user defined component, where prepared entities are inspected and  exceptions can be thrown from user code to break user input processing.
            </li>
            <li>
                <code>Variant filter</code>. Optional component which allows filtering detected variants, rejecting undesirable.
            </li>
        </ul>

        <p>
            Below example if <code>Model</code> creation. <code>Pipeline</code> is prepared using <code>NCPipelineBuilder</code> class helper.
        </p>

        <pre class="brush: scala, highlight: []">
            val pipeline =
                new NCPipelineBuilder().
                    withTokenParser(new NCFrTokenParser()).
                    withTokenEnricher(new NCFrLemmaPosTokenEnricher()).
                    withTokenEnricher(new NCFrStopWordsTokenEnricher()).
                    withEntityParser(new NCFrSemanticEntityParser("lightswitch_model_fr.yaml")).
                    build
            val cfg = NCModelConfig("nlpcraft.lightswitch.fr.ex", "LightSwitch Example Model FR", "1.0")

            val mdl = new NCModelAdapter(cfg, pipeline)
        </pre>

        <p>
            This flexible system allows to create any pipelines on any language. You can collect NlpCraft predefined components, write your own and easy reuse custom components.
        </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>
        {% include quick-links.html %}
    </ul>
</div>