WIP
diff --git a/_layouts/default.html b/_layouts/default.html
index 620b0b8..cfe9da2 100644
--- a/_layouts/default.html
+++ b/_layouts/default.html
@@ -130,7 +130,7 @@
<div id="footer">
<div class="container">
<div class="text-muted text-center">
- <span>Copyright © 2021 Apache Software Foundation</span>
+ <span>Copyright © 2023 Apache Software Foundation</span>
<span>
<a target=_new href="https://apache.org"><img alt="asf" src="/images/asf_logo-h24.png"></a>
</span>
diff --git a/getting-started.html b/getting-started.html
deleted file mode 100644
index e88aa3b..0000000
--- a/getting-started.html
+++ /dev/null
@@ -1,163 +0,0 @@
----
-active_crumb: Docs
-layout: documentation
-id: getting_started
----
-
-<!--
- 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">
- <section id="getting-started">
- <h2 class="section-title">Getting Started <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
- <p>
- Lets review how to run NLPCraft <a href="https://github.com/apache/incubator-nlpcraft/tree/master/nlpcraft-examples" target="github">examples</a>.
- We will use an example shipped with NLPCraft along with demonstrating the main components of NLPCraft -
- <a href="/server-and-probe.html#probe">data probe</a>,
- <a href="/server-and-probe.html#server">REST server</a>, and
- <a href="/tools/script.html"><code>nlpcraft.{sh|cmd}</code></a> script.
- </p>
- <p>
- We assume the following:
- </p>
- <ul>
- <li>
- You <a href="/download.html#zip">downloaded</a> NLPCraft {{site.latest_version}} as <b>binary release</b>.
- <ul>
- <li>
- If you downloaded the source release - run <code class="script">mvn clean package -P examples</code> to build binaries before proceeding.
- </li>
- </ul>
- </li>
- <li>You followed <a href="/installation.html">installation</a> instructions.</li>
- <li>You are using MacOS/Linux environment.</li>
- </ul>
- </section>
- <section>
- <h2 id="probe-server" class="section-sub-title">Data Probe <span class="amp">&</span> REST Server <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
- <p>
- <a href="/server-and-probe.html">Data probes</a> are used to deploy and host data model, while <a href="/server-and-probe.html">REST server</a> (or a
- cluster of servers) is used to accept client REST calls and route them to the data model deployed on data probes.
- </p>
- <p>
- Data probe and REST server are both Java applications.
- In this tutorial, we'll use <a href="/tools/script.html"><code>nlpcraft.{sh|cmd}</code></a> script that comes with NLPCraft
- to manage them. To start REST server and data probe - run the following two commands:
- </p>
- <pre class="brush: bash">
- $ bin/nlpcraft.sh start-server
- $ bin/nlpcraft.sh start-probe --cp=./build/nlpcraft-examples/weather/nlpcraft-example-weather-{{site.latest_version}}.jar --mdls=org.apache.nlpcraft.examples.weather.NCWeatherModel
- </pre>
- <p>
- At this point data probe and the REST server are started and connected.
- </p>
- <h2 id="querying" class="section-sub-title">Using REST API <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
- <p>
- We will be using NLPCraft CLI script to issue REST calls. Let's start NLPCraft CLI in interactive REPL mode
- by running <a href="/tools/script.html"><code>nlpcraft.{sh|cmd}</code></a> script with no argument:
- </p>
- <pre class="brush: bash">
- $ bin/nlpcraft.sh
- </pre>
- <p>
- <b>NOTES:</b>
- </p>
- <ul>
- <li>
- NLPCraft CLI automatically detects the REST server and data probe we have started in
- the previous step. You can see the status of the REST server and data probe in the REPL prompt or you can
- use <code>info</code> command to see a status information.
- </li>
- <li>
- When NLPCraft CLI detects running REST server it automatically signs in with the <a href="/using-rest.html#users">default user account</a>
- to obtain access token that is required for all REST calls. It will automatically use this access token
- while in REPL mode whenever required.
- </li>
- <li>
- Use <span class="keyboard">Tab</span> key anytime for auto-completion for commands, parameters,
- file systems paths, and model class names.
- Use <span class="keyboard">↑</span> and <span class="keyboard">↓</span> keys to scroll
- through command history.
- </li>
- </ul>
- <p>
- While in REPL mode lets use <code>ask</code> command to issue REST requests to our data model.
- </p>
- <div class="accordion" id="questions">
- <div class="card">
- <div class="card-header" id="q1">
- <h2 class="mb-0">
- <button class="btn btn-link btn-block text-left" type="button" data-toggle="collapse" data-target="#a1">
- <b>Q:</b> What is the current forecast for Chicago?
- </button>
- </h2>
- </div>
- <div id="a1" class="collapse" data-parent="#questions">
- <div class="card-body">
- <p>
- <img class="img-fluid" src="/images/getting_started_fig4.png" alt="">
- </p>
- <p>
- <b>A:</b> we get a full 5-day forecast for Chicago.
- </p>
- </div>
- </div>
- </div>
- <div class="card">
- <div class="card-header" id="q2">
- <h2 class="mb-0">
- <button class="btn btn-link btn-block text-left collapsed" type="button" data-toggle="collapse" data-target="#a2">
- <b>Q:</b> Any chance of snow today in Moscow?
- </button>
- </h2>
- </div>
- <div id="a2" class="collapse" data-parent="#questions">
- <div class="card-body">
- <p>
- <img class="img-fluid" src="/images/getting_started_fig5.png" alt="">
- </p>
- <p>
- <b>A:</b> we get today's Moscow weather report.
- </p>
- </div>
- </div>
- </div>
- </div>
- </section>
- <section>
- <h2 class="section-title">Done! 👌 <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
- <p>
- Use <code>stop</code> command to stop REST server and the data probe and <code>quit</code>
- command to quit from REPL mode:
- </p>
- <p>
- <img class="img-fluid" src="/images/getting_started_fig6.png" alt="">
- </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="#getting-started">Getting Started</a></li>
- <li><a href="#probe-server">Data Probe <span class="amp">&</span> Server</a></li>
- <li><a href="#querying">Using REST API</a></li>
- {% include quick-links.html %}
- </ul>
-</div>
-
-
-
diff --git a/key-concepts-old.html b/key-concepts-old.html
deleted file mode 100644
index 7510169..0000000
--- a/key-concepts-old.html
+++ /dev/null
@@ -1,587 +0,0 @@
----
-active_crumb: Docs
-layout: documentation
-id: key_concepts
----
-
-<!--
- 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" xmlns="http://www.w3.org/1999/html">
- <section id="overview">
- <h2 class="section-title">Key Concepts<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
-
- <p>
- NLPCraft is based on three main concepts:
- </p>
- <ul>
- <li>
- {% scaladoc NCModel NCModel %} is a user-configured object responsible for input interpretation.
- </li>
- <li>
- {% scaladoc NCPipeline NCPipeline %} is a part of the model configuration that defines
- specifics of the user input processing.
- </li>
- <li>
- {% scaladoc NCModelClient NCModelClient %} is responsible for interaction with the data model.
- </li>
- </ul>
-
- <p>Here's the typical code structure when working with NLPCraft:</p>
-
- <pre class="brush: scala, highlight: []">
- // Init data model.
- val mdl = new CustomNlpModel()
-
- // Creates client for given model.
- val cli = new NCModelClient(mdl)
-
- // Sends text request to model by user ID "user01".
- val result = client.ask("Some user command", "user01")
- </pre>
- </section>
-
- <section id="terminology">
- <h2 class="section-title">Terminology<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
- <p>
- Let's start with the nomenclature of the main NLPCraft types:
- </p>
-
- <table class="gradient-table">
- <thead>
- <tr>
- <th>Type</th>
- <th>Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><b>{% scaladoc NCModel NCModel %}</b></td>
- <td>
- <code>Model</code> is the main component in NLPCraft. User-define data model contains its {% scaladoc NCModelConfig NCModelConfig %},
- input processing {% scaladoc NCPipeline NCPipeline %} and life-cycle callbacks.
- NLPCraft employs model-as-a-code approach where entire data model is an implementation of just
- this interface. The instance of this interface is passed to {% scaladoc NCModelClient NCModelClient %} class.
- Note that the model-as-a-code approach natively supports any software life cycle tools and frameworks
- like various build tools, CI/SCM tools, IDEs, etc. You don't need any additional tools to manage some
- aspects of your data models - your entire model and all of its components are part of your project's source code.
- Note that in most cases, one would use a convenient {% scaladoc NCModelAdapter NCModelAdapter %} adapter to implement this interface.
- </td>
- </tr>
- <tr>
- <td><b>{% scaladoc NCToken NCToken %}</b></td>
- <td>
- <code>Token</code> is simple string, part of user input, which is obtained by splitting user input
- according to some rules. For example, the user input "<b>Where is it?</b>" contains four tokens:
- "<code>Where</code>", "<code>is</code>", "<code>it</code>", "<code>?</code>".
- Usually <code>tokens</code> are words and punctuation symbols which also contain additional
- information like point of speech tags, relative position in the overall input text, stopword flag,
- stem and lemma forms, etc. List of parsed <code>tokens</code> serves as an input for parsing <code>entities</code>.
- </td>
- </tr>
- <tr>
- <td><b>{% scaladoc NCEntity NCEntity %}</b></td>
- <td>
- <code>Entity</code> typically represents a real-world object, such as a person, location, organization,
- or product that can often be denoted with a proper name. It can be abstract or have a physical existence.
- Each <code>entity</code> consists of zero or more <code>tokens</code>. Combination of entities form one or more parsing
- <code>variants</code>.
- </td>
- </tr>
- <tr>
- <td><b>{% scaladoc NCVariant NCVariant %}</b></td>
- <td>
- <code>Variant</code> is a unique set of <code>entities</code>. In many cases, a <code>token</code> or a group
- of <code>tokens</code> can be recognized as more than one <code>entity</code> - resulting in multiple possible
- interpretations of the original sequence of tokens. Each such interpretation is defined as a parsing <code>variant</code>.
- For example, user input <b>"Look at this crane."</b> can be interpreted as two <code>variants</code>,
- one of them containing <code>entity</code> <b>BIRD<sub>[crane]</sub></b> and another containing <code>entity</code> <b>MACHINE<sub>[crane]</sub></b>.
- Set of <code>variants</code> ultimately serves as an input to <a href="intent-matching.html">intent matching</a>.
- </td>
- </tr>
- <tr>
- <td><b>{% scaladoc NCPipeline NCPipeline %}</b></td>
- <td>
- <code>Pipeline</code> is the main configuration property of the model. Pipeline consists of an ordered sequence
- of pipeline components. User input starts at the first component of the
- pipeline as a simple text and exits the end of the pipeline as a one or more parsing <code>variants</code>.
- The output of the pipeline is further passed as an input to <a href="intent-matching.html">intent matching</a>.
- </td>
- </tr>
- <tr>
- <td><b>{% scaladoc NCModelCofig NCModelConfig %}</b></td>
- <td>
- <code>Pipeline</code> is the main configuration property of the model. Pipeline consists of an ordered sequence
- of pipeline components. User input starts at the first component of the
- pipeline as a simple text and exits the end of the pipeline as a one or more parsing <code>variants</code>.
- The output of the pipeline is further passed as an input to <a href="intent-matching.html">intent matching</a>.
- </td>
- </tr>
- <tr>
- <td><b><a target="scaladoc" href="/apis/latest/">@NCIntent</a></b></td>
- <td>
- <code>Variant</code> is a unique set of <code>entities</code>. In many cases, a <code>token</code> or a group
- of <code>tokens</code> can be recognized as more than one <code>entity</code> - resulting in multiple possible
- interpretations of the original sequence of tokens. Each such interpretation is defined as a parsing <code>variant</code>.
- For example, user input <b>"Look at this crane."</b> can be interpreted as two <code>variants</code>,
- one of them containing <code>entity</code> <b>BIRD<sub>[crane]</sub></b> and another containing <code>entity</code> <b>MACHINE<sub>[crane]</sub></b>.
- </td>
- </tr>
-
- </tbody>
- </table>
-
- <figure>
- <img alt="named entities" class="img-fluid" src="/images/text-tokens-entities2.png">
- <figcaption><b>Fig 1.</b> Text -> Tokens -> Entities -> Parsing Variants.</figcaption>
- </figure>
-
- <p>
- When <code>Variant</code> is prepared, the suitable <code>Intent</code> is trying to matched with it.
- </p>
-
- <table class="gradient-table">
- <thead>
- <tr>
- <th>Term</th>
- <th>Description</th>
- </tr>
- </thead>
- <tbody>
-
- <tr>
- <td><code>Intent</code></td>
- <td>
- <code>Intent</code> is user defined callback method and rule according to which this callback should be called.
- Most often rule is some template based on expected set of <code>entities</code> in user input,
- but it can be defined more flexible.
- Parameters extracted from user text input are passed into callback method.
- This method execution result is provided to user as answer on his request.
- <code>Intent</code> callbacks are methods defined in <code>Data Model</code> class annotated by
- <code>intent</code> rules via <a href="intent-matching.html">IDL</a>.
- </td>
- </tr>
- <tr>
- <td><code>IDL</code></td>
- <td>
- IDL, Intent Definition Language, is a relatively straightforward declarative language which
- defines a match between the parsed user input represented as the collection of tokens,
- 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 placed in model YAML/JSON file as well as in external *.idl files.
- </td>
- </tr>
- <tr>
- <td><code>Callback</code></td>
- <td>
- The user defined Scala method which mapped to the <code>intent</code>.
- This method receives as its parameters normalized values from user input text according to
- IDL matched terms.
- </td>
- </tr>
- </tbody>
- </table>
-
- <p>
- So, <code>Data Model</code> must be able to do tree following things:
- </p>
-
- <ul>
- <li>
- Parse user input text as the <code>tokens</code>.
- They are input for searching <code>named entities</code>.
- <code>Tokens</code> parsing components should be included into <a href="#model-pipeline">Model pipeline</a>.
- </li>
- <li>
- Find <code>named entities</code> based on these parsed <code>tokens</code>.
- They are input for searching <code>intents</code>.
- <code>Entity</code> parsing components should be included into <a href="#model-pipeline">Model pipeline</a>.
- </li>
- <li>
- Prepare <code>intents</code> with their callbacks methods which contain business logic.
- These methods should be defined directly in the model class definition or the model should have references on them.
- It will be described below. Callback can de defined in model scala class directly or via references.
- Look at the chapter <a href="intent-matching.html">Intent Matching</a> content for get more details.
- </li>
- </ul>
-
- <p>
- As example, let's prepare the system which can call persons from your contact list.
- Typical commands are: "<b>Please call to John Smith</b>" or "<b>Connect me with Barbara Dillan</b>".
- For solving this task this model should be able to recognize in user text following entities:
- <code>command</code> and <code>person</code> to apply this command.
- </p>
-
- <p>
- So, when request "<b>Please call to John Smith</b>" received, our model should be able to:
- </p>
-
- <ul>
- <li>
- Parse tokens splitting user text input:
- "<code>please</code>", "<code>call</code>", "<code>to</code>", "<code>john</code>", "<code>smith</code>".
- </li>
- <li>
- Find two named entities:
- <ul>
- <li>
- <code>command</code> by token "<code>call</code>".
- </li>
- <li>
- <code>person</code> by tokens "<code>john</code>" and "<code>smith</code>".
- </li>
- </ul>
- </li>
- <li>
- Have prepared intent:
- <pre class="brush: scala, highlight: [1, 2, 5, 6]">
- @NCIntent("intent=call term(command)={# == 'command'} term(person)={# == 'person'}")
- def onCommand(
- ctx: NCContext,
- im: NCIntentMatch,
- @NCIntentTerm("command") command: NCEntity,
- @NCIntentTerm("person") person: NCEntity
- ): NCResult = ? // Implement business logic here.
- </pre>
-
- <ul>
- <li>
- <code>Line 1</code> defines intent <code>call</code> with two conditions
- which expects two named entities in user input text.
- </li>
- <li>
- <code>Line 2</code> defines related callback method <code>onCommand()</code>.
- </li>
- <li>
- <code>Lines 4 and 5</code> define two callback method's arguments which are corresponded to
- <code>call</code> intent terms conditions. You can extract normalized value
- <code>john smith</code> from the <code>person</code> parameter and use it in the method body
- for getting his phone number etc.
- </li>
- </ul>
- </li>
- </ul>
- </section>
-
- <section id="model-configuration">
- <h2 class="section-title">Model Configuration<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
-
- <p>
- <code>Data Model</code> configuration represented as
- {% scaladoc NCModelConfig NCModelConfig %}
- contains set of parameters which are described below.
- </p>
- <table class="gradient-table">
- <thead>
- <tr>
- <th>Name</th>
- <th>Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td><code>id</code>, <code>name</code> and <code>version</code></td>
- <td>
- Mandatory model properties.
- </td>
- </tr>
- <tr>
- <td><code>description</code>, <code>origin</code></td>
- <td>
- Optional model properties.
- </td>
- </tr>
- <tr>
- <td><code>conversationTimeout</code></td>
- <td>
- Timeout of the user's conversation.
- If user doesn't communicate with the model this time period STM is going to be cleared.
- Loot at <a href="short-term-memory.html">Conversation</a> chapter to get more details.
- It is the mandatory parameter with default value.
- </td>
- </tr>
- <tr>
- <td><code>conversationDepth</code></td>
- <td>
- Maximum supported depth the user's conversation.
- Loot at <a href="short-term-memory.html">Conversation</a> chapter to get more details.
- It is the mandatory parameter with default value.
- </td>
- </tr>
- </tbody>
- </table>
- </section>
-
- <section id="model-pipeline">
- <h2 class="section-title">Model Pipeline<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
-
- <p>
- Model <code>Pipeline</code> is represented as {% scaladoc NCPipeline NCPipeline %} and
- contains following components:
- </p>
-
- <table class="gradient-table">
- <thead>
- <tr>
- <th>Component</th>
- <th>Mandatory</th>
- <th>Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td>{% scaladoc NCTokenParser NCTokenParser %}</td>
- <td>Mandatory single</td>
- <td>
- <code>Token parser</code> should be able to parse user input plain text and split this text
- into <code>tokens</code> list.
- NLPCraft provides two default English language implementations of token parser.
- Also, project contains examples for <a href="examples/light_switch_fr.html">French</a> and
- <a href="examples/light_switch_ru.html">Russia</a> languages token parser implementations.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCTokenEnricher NCTokenEnricher %}</td>
- <td>Optional list</td>
- <td>
- <code>Tokens enricher</code> is a component which allow to add additional properties for prepared tokens,
- like part of speech, quote, stop-words flags or any other.
- NLPCraft provides built-in English language set of token enrichers implementations.
- Here is an <a href="custom-components.html#token-enrichers">example</a>.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCTokenValidator NCTokenValidator %}</td>
- <td>Optional list</td>
- <td>
- <code>Token validator</code> is a component which allow to inspect prepared tokens and
- throw an exception to break user input processing.
- Here is an <a href="custom-components.html#token-validators">example</a>.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCEntityParser NCEntityParser %}</td>
- <td>Mandatory list</td>
- <td>
- <code>Entity parser</code> is a component which allow to find user defined named entities
- based on prepared tokens as input.
- NLPCraft provides wrappers for named-entity recognition components of
- <a href="https://opennlp.apache.org/">Apache OpenNLP</a> and
- <a href="https://nlp.stanford.edu/">Stanford NLP</a> and its own implementations.
- Note that at least one entity parser must be defined.
- Here is an <a href="custom-components.html#entity-parsers">example</a>.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCEntityEnricher NCEntityEnricher %}</td>
- <td>Optional list</td>
- <td>
- <code>Entity enricher</code> is component which allows to add additional properties for prepared entities.
- Can be useful for extending existing entity enrichers functionality.
- Here is an <a href="custom-components.html#entity-enrichers">example</a>.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCEntityMapper NCEntityMapper %}</td>
- <td>Optional list</td>
- <td>
- <code>Entity mappers</code> is component which allows to map one set of entities to another after the entities
- were parsed and enriched. Can be useful for building complex parsers based on existing.
- Here is an <a href="custom-components.html#entity-mappers">example</a>.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCEntityValidator NCEntityValidator %}</td>
- <td>Optional list</td>
- <td>
- <code>Entity validator</code> is a component which allow to inspect prepared entities and
- throw an exception to break user input processing.
- Here is an <a href="custom-components.html#entity-validators">example</a>.
- </td>
- </tr>
- <tr>
- <td> {% scaladoc NCVariantFilter NCVariantFilter %}</td>
- <td>Optional single</td>
- <td>
- <code>Variant filter</code> is a component which allows filtering detected variants and
- rejecting undesirable.
- Here is an <a href="custom-components.html#variant-filters">example</a>.
- </td>
- </tr>
- </tbody>
- </table>
-
- <figure>
- <img alt="pipeline" class="img-fluid" src="/images/pipeline.png">
- <figcaption><b>Fig 2.</b> Pipeline</figcaption>
- </figure>
-
- <p>
- Below {% scaladoc NCModel NCModel %} creation example.
- {% scaladoc NCPipeline NCPipeline %} is prepared using
- {% scaladoc NCPipelineBuilder NCPipelineBuilder %} 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 NCModel(cfg, pipeline):
- // Add your callbacks definition or references on them here.
- </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>
-
- <section id="model-behavior">
- <h2 class="section-title">Model Behavior Overriding<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
-
- <p>
- There are also several {% scaladoc NCModel NCModel %}
- callbacks that you can override to affect model behavior during
- <a href="/intent-matching.html#model_callbacks">intent matching</a>
- to perform logging, debugging, statistic or usage collection, explicit update or initialization of
- conversation context, security audit or validation:
- </p>
- <table class="gradient-table">
- <thead>
- <tr>
- <th>Method</th>
- <th>Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td>{% scaladoc NCModel#onContext-38d onContext() %}</td>
- <td>
- Overriding this method allows to prepare result before intent matching.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModel#onMatchedIntent-946 onMatchedIntent() %}</td>
- <td>
- Overriding this method allows to reject matched intent and continue matching process.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModel#onResult-fffffaf3 onResult() %}</td>
- <td>
- Overriding this method allows to replace callback method execution result.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModel#onRejection-4fa onRejection() %}</td>
- <td>
- Overriding this method allows to change operation result when rejection occurs.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModel#onError-fffff759 onError() %}</td>
- <td>
- Overriding this method allows to change operation result when any error occurs.
- </td>
- </tr>
- </tbody>
- </table>
- </section>
-
- <section id="client">
- <h2 class="section-title">Client Responsibility<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
-
- <p>
- <code>Client</code> represented as {% scaladoc NCModelClient NCModelClient %}
- is necessary for communication with the <code>Data Model</code>. Base client methods are described below.
- </p>
-
- <table class="gradient-table">
- <thead>
- <tr>
- <th>Method</th>
- <th>Description</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td>{% scaladoc NCModelClient#ask-fffff9ce ask() %}</td>
- <td>
- Passes user text input to the model and receives back execution
- {% scaladoc NCResult NCResult %} or
- rejection exception if there isn't any triggered intents.
- {% scaladoc NCResult NCResult %} is wrapper on
- callback method execution result with additional information.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModelClient#debugAsk-fffff96c debugAsk() %}</td>
- <td>
- Passes user text input to the model and receives back callback and its parameters or
- rejection exception if there isn't any triggered intents.
- Main difference from <code>ask</code> that triggered intent callback method is not called.
- This method and this parameter can be useful in tests scenarios.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModelClient#clearStm-571 clearStm() %}</td>
- <td>
- Clears STM state. Memory is cleared wholly or with some predicate.
- Loot at <a href="short-term-memory.html">Conversation</a> chapter to get more details.
- Second variant of given method with another parameters is here - {% scaladoc NCModelClient#clearStm-1d8 clearStm() %}.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModelClient#clearDialog-571 clearDialog() %}</td>
- <td>
- Clears dialog state. Dialog is cleared wholly or with some predicate.
- Loot at <a href="short-term-memory.html">Conversation</a> chapter to get more details.
- Second variant of given method with another parameters is here - {% scaladoc NCModelClient#clearDialog-1d8 clearDialog() %}.
- </td>
- </tr>
- <tr>
- <td>{% scaladoc NCModelClient#close-94c close() %}</td>
- <td>
- Closes client. You can't call another client's methods after this method was closed.
- </td>
- </tr>
- </tbody>
- </table>
- </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">Key Concepts</a></li>
- <li><a href="#terminology">Terminology</a></li>
-<!-- <li><a href="#model-configuration">Model Configuration</a></li> -->
-<!-- <li><a href="#model-pipeline">Model Pipeline</a></li> -->
-<!-- <li><a href="#model-behavior">Model Behavior Overriding</a></li> -->
-<!-- <li><a href="#client">Client Responsibility</a></li> -->
- {% include quick-links.html %}
- </ul>
-</div>
-
-
-
-
diff --git a/use-cases.html b/use-cases.html
index da34d34..3f51023 100644
--- a/use-cases.html
+++ b/use-cases.html
@@ -30,7 +30,7 @@
<li class="side-nav-title">Documentation</li>
<li><a href="/docs.html">Overview</a></li>
<li><a href="/installation.html">Installation</a></li>
- <li><a href="/getting-started.html">Getting Started</a></li>
+ <li><a href="/first-example.html">First Example</a></li>
</ul>
</div>
<div class="col-md-8 second-column">
