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new b7b145f Update nlpcraft-idl-intent-definition-language.html
b7b145f is described below
commit b7b145f662b656fbf89274874a1743dac3e2a761
Author: Aaron Radzinski <[email protected]>
AuthorDate: Fri Jun 4 21:40:30 2021 -0700
Update nlpcraft-idl-intent-definition-language.html
---
blogs/nlpcraft-idl-intent-definition-language.html | 126 +++++++++++++++++++--
1 file changed, 119 insertions(+), 7 deletions(-)
diff --git a/blogs/nlpcraft-idl-intent-definition-language.html
b/blogs/nlpcraft-idl-intent-definition-language.html
index fb832c3..3db798a 100644
--- a/blogs/nlpcraft-idl-intent-definition-language.html
+++ b/blogs/nlpcraft-idl-intent-definition-language.html
@@ -300,7 +300,17 @@ publish_date: June 3, 2021
term~/org.mypackage.MyClass#termMethod/? // Reference
to external code.
</pre>
<p>
- Note the special syntax for the last term.
+ Note the special syntax for the last term. The
existing set of built-in functions are typically
+ enough to define many terms of the intent. But, if
necessary, the programmer can create her
+ own predicates in Java, Scala, Kotlin, Groovy or any
other Java based language and write them
+ in the term body. That is, NLPCraft IDL can have
snippets of code written entirely in other languages:
+ </p>
+ <pre class="brush: idl">
+ term~/org.mypackage.MyClass#termMethod/? // Reference
to external code.
+ </pre>
+ <p>
+ This function <code>termMethod</code> receives an
argument that contains all the necessary
+ data on the user's request, and as the output it
should return the value of a specific type.
</p>
</li>
<li>
@@ -323,7 +333,6 @@ publish_date: June 3, 2021
</li>
</ul>
</li>
- <li><p><b>Term Variables</b></p></li>
<li>
<p><b>IDL Built-In Functions</b></p>
<p>
@@ -345,12 +354,115 @@ publish_date: June 3, 2021
More detailed information and a description of each function
can be found <a href="/intent-matching.html#idl_functions">here</a>.
</p>
</li>
- <li><p><b>Intent Fragments</b></p></li>
- <li><p><b>Intent Flow</b></p></li>
- <li><p><b>Intent Metadata</b></p></li>
- <li><p><b>Ordered Flag</b></p></li>
+ <li>
+ <p><b>Term Variables</b></p>
+ <p>
+ The term body written in IDL script is a predicate written
using IDL built-in functions. To avoid the
+ unnecessary repetitive computations when working with built-in
functions, local term variables can be used:
+ </p>
+ <pre class="brush: idl">
+ term(t2)={
+ @a = meta_model('a')
+ @list = list(1, 2, 3, 4)
+
+ (@a == 42 || @a == 44) && has_all(@list, list(3, 2))
+ }
+ </pre>
+ <p>
+ Local variables are defined and used with the special prefix
<code>@</code>. They used to avoid
+ repeated computations and shortening/simplifying of the IDL
term predicate expressions.
+ </p>
+ </li>
+ <li>
+ <p><b>Intent Fragments</b></p>
+ <p>
+ Fragment is a named set of terms that is created to be
reusable across intents.
+ See example by following this <a
href="/intent-matching.html#idl">link</a>.
+ </p>
+ </li>
+ <li>
+ <p><b>Intent Flow</b></p>
+ <p>
+ Here we define an additional intent selection rule based on
data from previous intent matches
+ within the current session. This rule can be defined using a
regex based on the IDs of
+ the previously matched intents:
+ </p>
+ <pre class="brush: idl">
+ flow="^(?:login)(^:logout)*$"
+ </pre>
+ <p>
+ This rule means that for the intent to be matched, it is
necessary that within the current session
+ the intent with the <code>login</code> identifier has already
been matched before, and
+ not with identifier <code>logout</code>.
+ </p>
+ <p>
+ If it is necessary to define more complex logic, it can also
be moved into user code
+ written in any Java-based language, like the term body:
+ </p>
+ <pre class="brush: scala">
+ @NCIntent("intent=x
+ flow=/com.company.dialog.Flow#customFlow/
+ term~{tok_id() == 'some_id'}"
+ )
+ def onX(): NCResult = { .. }
+ </pre>
+ <p>
+ The predicate defined in the method <code>customFlow()</code>
receives at the input a list with all
+ intents information, previously matched within the current
session, and returns a boolean value.
+ </p>
+ </li>
+ <li>
+ <p><b>Intent Metadata</b></p>
+ <p>
+ Optional element. A additional dataset that can be used by
term predicates presented in JSON format.
+ </p>
+ </li>
+ <li>
+ <p><b>Ordered Flag</b></p>
+ <p>
+ Optional element. The default is <code>false</code>. Defines
whether or not the order of the terns in the intent is important.
+ </p>
+ </li>
</ul>
</section>
-
+<section>
+ <h2 class="section-title">Why Do We Need NLPCraft IDL? <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ All the logic for creating intents, defined using NLPCraft IDL, can be
written in any Java based language.
+ Why, then, is this new language needed at all? Even if its syntax is
short, simple and straightforward, you
+ still have to spend some time studying it.
+ </p>
+ <p>
+ Below are some of the reasons for using NLPCraft IDL:
+ </p>
+ <ul>
+ <li>
+ The program terseness. A custom DSL code is always shorter than a
general programming language code with
+ the same logic. For intents with non-trivial rules, this can be
important.
+ </li>
+ <li>
+ If the NLPCraft IDL code is defined in a separate file, then
editing the IDL program, for example,
+ to change the intent matching logic, does not require the rebuild
of the code of the model and its callbacks.
+ </li>
+ <li>
+ Separating the logic of writing callbacks and the logic of
matching intents. Different people can
+ work with these tasks. Due to the deliberate limited language
resources, DSL is easier to learn by a non-programmer.
+ </li>
+ <li>
+ Currently, models can be created in any Java based language.
Apache NLPCraft plans to expand the list of
+ supported languages in the near future. Using NLPCraft IDL will
allow you to maintain one common language
+ for defining intents for different types of models within the same
project.
+ </li>
+ </ul>
+</section>
+<section>
+ <h2 class="section-title">Conclusion <a href="#"><i class="top-link fas
fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ I hope you were able to get a first impression of the capabilities of
the NLPCraft IDL language and the
+ types of tasks that can be solved using it. <a
href="/intent-matching.html">Here</a> you will find a detailed description of
the
+ language and its capabilities. Additional examples of models created
in Java, Kotlin, Groovy and Scala,
+ using NLPCraft IDL to define intents, are available in the <a
href="https://github.com/apache/incubator-nlpcraft/tree/master/nlpcraft-examples";>GitHub</a>
project.
+ </p>
+</section>
