---
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layout: documentation
id: overview
---

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<div class="col-md-8 second-column">
    <section id="overview">
        <h2 class="section-title">Overview</h2>
        <p>
            NLPCraft is a Java-based <a href="/legal.html">open source</a> library for adding a natural language
            interface to any applications. It can work with
            any private or public data source, and has no hardware or software lock-in.
            You can build models and intents for NLPCraft using any JVM-based languages like Java, Scala, Kotlin, Groovy, etc. NLPCraft
            exposes REST APIs for integration with user applications that can be written in any language or system.
        </p>
        <p>
            One of the key features of NLPCraft is its use of advance semantic modelling that is tailor made for
            domain-specific natural language interface. It doesn't require traditional ML
            approach involving model training or corpora development leading to much <em>simpler</em>
            implementation and shorter development time. 
        </p>
        <p>
            How does it work in a nutshell?
        </p>
        <p>
            When using NLPCraft you will be dealing with three main components:
        </p>
        <ul>
            <li><a href="#data-model">Data model</a></li>
            <li><a href="#data-probe">Data probe</a></li>
            <li><a href="#server">REST Server</a></li>
        </ul>
        <figure>
            <img class="img-fluid" src="/images/homepage-fig1.1.png" alt="">
            <figcaption><b>Fig 1.</b> NLPCraft Architecture</figcaption>
        </figure>
    </section>
    <section id="data-model">
        <h3 class="section-title">Data Model</h3>
        <p>
            NLPCraft employs model-as-a-code approach where entire data model is an implementation of
            <a target="javadoc" href="/apis/latest/org/apache/nlpcraft/model/NCModel.html">NCModel</a> Java interface that
            can be developed using any JVM programming language like Java, Scala, Kotlin or Groovy.
            Data model implementation defines how to interpret user input, and how to query or control a particular
            data source. Model-as-a-code natively supports any software lifecycle tools and frameworks in Java eco-system.
        </p>
        <p>
            Typically, declarative portion of the model will be stored in a separate JSON or YAML file
            for simpler modifications. There are no practical limitation on how complex or simple a model
            can be, or what other tools it can use. Most data models will also use <a href="/intent-matching.html">intent-based matching</a>
            provided by NLPCraft out-of-the-box.
        </p>
        <p>
            To use data model it has to be deployed into data probe.
        </p>
    </section>
    <section id="data-probe">
        <h3 class="section-title">Data Probe</h3>
        <p>
            Data probe is an application designed to securely deploy and manage data models.
            Each probe can deploy and manage multiple models and many probes can be connected to the REST server (or a cluster of REST servers).
            The main purpose of the data probe is to separate data model hosting from managing REST calls from the clients.
            While you would typically have just one REST server, you may have multiple data probes deployed
            in different geo-locations and configured differently.
        </p>
        <p>
            Data probes can be deployed and run anywhere as long as there is an outbound connectivity to the REST server, and are
            typically deployed in DMZ or close to your target data sources: on-premise, in the cloud, etc. Data
            probe uses strong 256-bit encryption and ingress only connectivity for communicating with the REST server.
        </p>
    </section>
    <section id="server">
        <h3 class="section-title">REST Server</h3>
        <p>
            REST server (or a cluster of REST servers behind a load balancer) provides URL endpoint for user applications
            to securely query data sources using NLI via data models deployed in data probes. Its main purpose is to
            accept REST-over-HTTP calls from user applications, manage connected data probes, and route user requests
            to and from requested data probes.
        </p>
        <p>
            Unlike data probe that gets restarted every time the model is changed, i.e. during development, the
            REST server is a "start-and-forget" component that can be launched once while various data probes can
            continuously reconnect to it. It can typically run as a Docker image locally on premise or on the cloud.
        </p>
        <p>
            Learn more about <a href="data-model.html">data model</a>,
            <a href="server-and-probe.html#probe">data probe</a> and <a href="server-and-probe.html#server">REST server</a>.
        </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="#data-model">Data Model</a></li>
        <li><a href="#data-probe">Data Probe</a></li>
        <li><a href="#server">REST Server</a></li>
        {% include quick-links.html %}
    </ul>
</div>