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new 109f427 minor changes and publish
109f427 is described below
commit 109f427637e6190161f18fc14529d1b2476f1813
Author: Paul King <[email protected]>
AuthorDate: Wed Oct 15 06:27:04 2025 +1000
minor changes and publish
---
site/src/site/blog/groovy-ai.adoc | 31 ++++++++++++++++++++++---------
1 file changed, 22 insertions(+), 9 deletions(-)
diff --git a/site/src/site/blog/groovy-ai.adoc
b/site/src/site/blog/groovy-ai.adoc
index eab549d..86d1ba1 100644
--- a/site/src/site/blog/groovy-ai.adoc
+++ b/site/src/site/blog/groovy-ai.adoc
@@ -1,31 +1,34 @@
= Exploring AI with Groovy™
Paul King <paulk-asert|PMC_Member>
-:revdate: 2025-10-14T19:27:25+00:00
+:revdate: 2025-10-15T07:06:20+00:00
:keywords: groovy, ai, ollama4j, langchain4j, spring-ai, embabel
:description: A tour of using Groovy with modern AI libraries including
ollama4j, LangChain4j, Spring AI, and Embabel.
-:draft: true
== Introduction
> [blue]#In this post, we’ll look at several ways to integrate Groovy with AI
> tools, including `ollama4j`, `langchain4j`, `Spring AI`, and `Embabel`.#
-image:img/bulcock_beach_sunset.jpg[Bulcock Beach, 350, float="right"]
+image:img/bulcock_beach_sunset.jpg[Bulcock Beach at Sunset looking towards
Pumicestone Passage, 320, float="right"]
We'll use a simple chat example, perhaps similar to what you might have done
yourself when trying out your favourite LLM.
We'll ask for activities to do while on vacation.
To make the examples as accessible as possible, we'll use Ollama's open-source
models,
-which can be run locally. The libraries we use here can also connect to remote
models and services.
+which can be run locally. So, no need to get keys, or use up your token limits.
+The libraries we use here can also connect to remote models and services.
+
The examples mostly use the `mistral:7b` model, which you'll need to download
to run the examples
unchanged, but feel free to try other models and see what results you get.
We also used Groovy 5 and JDK 25, but the examples should work on other Groovy
and Java versions.
== Using ollama4j
+Since we are using Ollama, we'll start with a library aimed directly at that
tool.
https://github.com/ollama/ollama4j[_Ollama4j_] provides a client for
interacting with local or remote https://ollama.com/[Ollama] models.
We first create an instance of the `Ollama` class.
-We set a generous timeout to allow for longer-running models.
+We set a generous timeout to allow for longer-running models
+but otherwise leave the defaults as is.
While not strictly necessary, we can call the `ping` method to verify that the
Ollama server is reachable.
[source,groovy]
@@ -92,7 +95,8 @@ Best thing:
== Using LangChain4j
-https://github.com/langchain4j/langchain4j[_LangChain4j_] brings LangChain’s
composable AI approach to the JVM.
+https://github.com/langchain4j/langchain4j[_LangChain4j_] brings LangChain’s
composable AI approach to the JVM. Its unified API might be a good option if
you want to experiment with different models and providers later.
+
Here’s the same initial prompt using its `OllamaChatModel` interface:
[source,groovy]
@@ -122,8 +126,11 @@ Response:
== Using Spring AI
-https://docs.spring.io/spring-ai/reference/[_Spring AI_] provides first-class
integration with the Spring ecosystem.
-In Groovy, this makes it simple to embed AI capabilities into a Spring Boot
app.
+https://docs.spring.io/spring-ai/reference/[_Spring AI_] provides first-class
integration with the Spring ecosystem. This would be a good option if you are
already using Spring Boot or need
+its integration and deployment capabilities.
+
+In Groovy, it's simple to embed AI capabilities into a Spring Boot app.
+Our entire script is shown below:
[source,groovy]
----
@@ -139,6 +146,9 @@ void main() {
}
----
+We also need to set up a few properties, e.g. in `application.properties`,
+to tell Spring AI to use Ollama and our chosen model.
+
The output might look something like:
----
@@ -290,7 +300,7 @@ Activity(Explore the Eumundi Markets, Eumundi Markets,
Eumundi, Day 4, Morning t
As a final example, let's look at how Embabel can orchestrate multiple AI
calls using its agent model.
-Let's first extend our domain model to be able to have a bunch of alternative
itineraries
+Let's first extend our domain model to be able to support a set of alternative
itineraries
and allow them to be rated.
[source,groovy]
@@ -347,6 +357,9 @@ void main() {
}
----
+Here we are using two different models for different tasks — `mistral:7b` for
generating itineraries and `qwen3:8b` for rating them. Embabel provides richer
ways to control and configure the models, but this simple approach works well
for our example.
+The `@AchievesGoal` annotation makes use of Embabel's goal oriented action
planning (GOAP) capabilities.
+
The output might look something like (slightly formatted here for readability):
----
