From 5844985ce5f1a0f5c50cb282ecee1e755b3415c2 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Tue, 6 Feb 2024 17:55:56 -0500
Subject: [PATCH 01/67] drafted blog for langchain4j example

---
 img/blog/langchain4j-example-chat-room.png    | Bin 0 -> 229501 bytes
 ...open-liberty-with-langchain4j-example.adoc | 129 ++++++++++++++++++
 2 files changed, 129 insertions(+)
 create mode 100644 img/blog/langchain4j-example-chat-room.png
 create mode 100644 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc

diff --git a/img/blog/langchain4j-example-chat-room.png b/img/blog/langchain4j-example-chat-room.png
new file mode 100644
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diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
new file mode 100644
index 000000000..50d650667
--- /dev/null
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -0,0 +1,129 @@
+---
+layout: post
+title: "Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty"
+# Do NOT change the categories section
+categories: blog
+author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
+author_github: https://github.com/gkwan-ibm
+seo-title: Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
+seo-description: Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs.
+blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs."
+open-graph-image: https://openliberty.io/img/twitter_card.jpg
+open-graph-image-alt: Open Liberty Logo
+---
+= Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
+Gilbert Kwan <https://github.com/gkwan-ibm>
+:imagesdir: /
+:url-prefix:
+:url-about: /
+
+
+
+"Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_
+
+https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the capablities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+
+If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an exemplar application that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
+
+
+
+image::/img/blog/cargo-tracker-ee10-home.png[Cargo Tracker Home Page,width=50%,align="left"]
+image::/img/blog/cargo-tracker-ee10-map.png[Cargo Tracker Map,width=50%,align="right"]
+
+
+== Try out the jakartaee-microprofile-example application
+
+Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` system variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
+
+The application uses Hugging Face. You need to get a Hugging Face API Key:
+
+* Sign up and login to https://huggingface.co
+* Go to Access Tokens by https://huggingface.co/settings/tokens
+* New a Read token
+
+To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. 
+
+You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
+
+[source]
+----
+git clone https://github.com/langchain4j/langchain4j-examples.git
+----
+
+=== Environment Set Up
+
+To run the application, navigate to the `jakartaee-microprofile-example`` directory:
+
+[source]
+----
+cd langchain4j-examples/jakartaee-microprofile-example
+----
+
+and set environment variables:
+
+[source]
+----
+export JAVA_HOME=<your Java 17 home path>
+export HUGGING_FACE_API_KEY=<your Hugging Face read token>
+----
+
+=== Start the application
+
+Use the provided Maven wrapper to start the application by using the https://openliberty.io/docs/latest/development-mode.html[Liberty dev mode]:
+
+
+[source]
+----
+./mvnw liberty:dev
+----
+
+After you see the following message, the application is ready:
+
+----
+**************************************************************
+*    Liberty is running in dev mode.
+----
+
+To ensure the application has started successfully, you can run the tests by pressing the `enter/return` key from the command-line session where you started dev mode. If the tests pass, you should see a similar output to the following example:
+
+----
+[INFO] -------------------------------------------------------
+[INFO]  T E S T S
+[INFO] -------------------------------------------------------
+[INFO] Running it.dev.langchan4j.example.ChatServiceIT
+[INFO] ...
+[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.439 s...
+[INFO] ...
+[INFO] Running it.dev.langchan4j.example.ModelResourceIT
+[INFO] Tests run: 3, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.733 s...
+[INFO] 
+[INFO] Results:
+[INFO] 
+[INFO] Tests run: 4, Failures: 0, Errors: 0, Skipped: 0
+----
+
+=== Access the application
+
+Once the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
+
+image::/img/blog/langchain4j-example-chat-room.png[Chat Room  of LangChain4J Jakarta EE and MicroProfile example,width=70%,align="center"]
+
+You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
+
+* `What is large language models?`
+* `tell me more`
+* `which are the top used models?`
+* `any documentation?`
+
+
+
+== Nice work! Where to next?
+
+Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 
+
+== Helpful links
+* link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket]
+* link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices]
+* link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice]
+
+

From a51546dc68ddb9dcdef51c0ed585e2cfc7e1da60 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Tue, 6 Feb 2024 18:05:56 -0500
Subject: [PATCH 02/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 50d650667..5ee4d5952 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -26,11 +26,6 @@ https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the capabli
 If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an exemplar application that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
 
 
-
-image::/img/blog/cargo-tracker-ee10-home.png[Cargo Tracker Home Page,width=50%,align="left"]
-image::/img/blog/cargo-tracker-ee10-map.png[Cargo Tracker Map,width=50%,align="right"]
-
-
 == Try out the jakartaee-microprofile-example application
 
 Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` system variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].

From 5c5cc2d596dfb7512e26cfa1c4c7c45da5e4a3c3 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Tue, 6 Feb 2024 18:41:31 -0500
Subject: [PATCH 03/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 52 ++++++++++++++++---
 1 file changed, 44 insertions(+), 8 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 5ee4d5952..39f32df5e 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -36,9 +36,7 @@ The application uses Hugging Face. You need to get a Hugging Face API Key:
 * Go to Access Tokens by https://huggingface.co/settings/tokens
 * New a Read token
 
-To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. 
-
-You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
+To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
 
 [source]
 ----
@@ -47,14 +45,14 @@ git clone https://github.com/langchain4j/langchain4j-examples.git
 
 === Environment Set Up
 
-To run the application, navigate to the `jakartaee-microprofile-example`` directory:
+To run the application, navigate to the `jakartaee-microprofile-example` directory:
 
 [source]
 ----
 cd langchain4j-examples/jakartaee-microprofile-example
 ----
 
-and set environment variables:
+and set the environment variables:
 
 [source]
 ----
@@ -64,7 +62,7 @@ export HUGGING_FACE_API_KEY=<your Hugging Face read token>
 
 === Start the application
 
-Use the provided Maven wrapper to start the application by using the https://openliberty.io/docs/latest/development-mode.html[Liberty dev mode]:
+To start the application, use the provided Maven wrapper to run the https://openliberty.io/docs/latest/development-mode.html[Liberty dev mode]:
 
 
 [source]
@@ -75,11 +73,20 @@ Use the provided Maven wrapper to start the application by using the https://ope
 After you see the following message, the application is ready:
 
 ----
-**************************************************************
+************************************************************************
 *    Liberty is running in dev mode.
+*        Automatic generation of features: [ Off ]
+*        h - see the help menu for available actions, type 'h' and press Enter.
+*        q - stop the server and quit dev mode, press Ctrl-C or type 'q' and press Enter.
+*        
+*    Liberty server port information:
+*        Liberty server HTTP port: [ 9080 ]
+*        Liberty server HTTPS port: [ 9443 ]
+*        Liberty debug port: [ 7777 ]
+************************************************************************
 ----
 
-To ensure the application has started successfully, you can run the tests by pressing the `enter/return` key from the command-line session where you started dev mode. If the tests pass, you should see a similar output to the following example:
+To ensure the application has started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you should see a similar output to the following example:
 
 ----
 [INFO] -------------------------------------------------------
@@ -110,7 +117,36 @@ You can type in any text that you want to chat with the AI agent. Here are some
 * `which are the top used models?`
 * `any documentation?`
 
+== What are Jakarta EE and MicroProfile features used?
+
+To connect to the AI provider, such as Hugging Face in the application, an API key is required. For security practice, it is not hard-coded in either the code or properties file. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps microservices to run in different environments without code changes.
+
+[java]
+----
+    @Inject
+    @ConfigProperty(name = "hugging.face.api.key")
+    private String HUGGING_FACE_API_KEY;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.id")
+    private String CHAT_MODEL_ID;
 
+    @Inject
+    @ConfigProperty(name = "chat.model.timeout")
+    private Integer TIMEOUT;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.max.token")
+    private Integer MAX_NEW_TOKEN;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.temperature")
+    private Double TEMPERATURE;
+
+    @Inject
+    @ConfigProperty(name = "chat.memory.max.messages")
+    private Integer MAX_MESSAGES;
+----
 
 == Nice work! Where to next?
 

From 57600b2336aa4a4a8146d22a22495a9687faf5ef Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Tue, 6 Feb 2024 18:57:54 -0500
Subject: [PATCH 04/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 28 +++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 39f32df5e..6310e34a8 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -21,7 +21,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 
 "Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_
 
-https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the capablities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the capabilities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
 
 If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an exemplar application that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
 
@@ -119,7 +119,19 @@ You can type in any text that you want to chat with the AI agent. Here are some
 
 == What are Jakarta EE and MicroProfile features used?
 
-To connect to the AI provider, such as Hugging Face in the application, an API key is required. For security practice, it is not hard-coded in either the code or properties file. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps microservices to run in different environments without code changes.
+To connect to the AI provider, such as Hugging Face used in the application, an API key is required.
+
+[java]
+----
+HuggingFaceChatModel model = HuggingFaceChatModel.builder()
+                                 .accessToken(HUGGING_FACE_API_KEY)
+                                 .modelId(CHAT_MODEL_ID)
+                                 .timeout(ofSeconds(TIMEOUT))
+                                 .temperature(TEMPERATURE)
+                                 .maxNewTokens(MAX_NEW_TOKEN)
+----
+
+For security practice, it is not hard-coded in either the code or properties file. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps microservices to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 [java]
 ----
@@ -148,6 +160,18 @@ To connect to the AI provider, such as Hugging Face in the application, an API k
     private Integer MAX_MESSAGES;
 ----
 
+To fine tune the langchain4j model or even try out another LLM, you simply update the values in the `langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties` file.
+
+[text]
+----
+hugging.face.api.key=set it by env variable
+chat.model.id=NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
+chat.model.timeout=120
+chat.model.max.token=200
+chat.model.temperature=1.0
+chat.memory.max.messages=20
+----
+
 == Nice work! Where to next?
 
 Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 

From 20aa5b369a0d34c5d668c7e1ec46e350a04ab83f Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 10:22:32 -0500
Subject: [PATCH 05/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 49 ++++++++++++++-----
 1 file changed, 36 insertions(+), 13 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 6310e34a8..d336bbe4f 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -21,20 +21,20 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 
 "Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_
 
-https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the capabilities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the abilities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
 
-If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an exemplar application that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
+If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an example that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
 
 
 == Try out the jakartaee-microprofile-example application
 
-Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` system variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
+Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
 
 The application uses Hugging Face. You need to get a Hugging Face API Key:
 
 * Sign up and login to https://huggingface.co
 * Go to Access Tokens by https://huggingface.co/settings/tokens
-* New a Read token
+* New a read token
 
 To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
 
@@ -117,24 +117,45 @@ You can type in any text that you want to chat with the AI agent. Here are some
 * `which are the top used models?`
 * `any documentation?`
 
-== What are Jakarta EE and MicroProfile features used?
 
-To connect to the AI provider, such as Hugging Face used in the application, an API key is required.
+== Jakarta EE and MicroProfile features used in the application
+
+The application demostrates how to use the LangChain4J APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
+
+=== Creating the LangChain4J AI service
+
+The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
 [java]
 ----
-HuggingFaceChatModel model = HuggingFaceChatModel.builder()
-                                 .accessToken(HUGGING_FACE_API_KEY)
-                                 .modelId(CHAT_MODEL_ID)
-                                 .timeout(ofSeconds(TIMEOUT))
-                                 .temperature(TEMPERATURE)
-                                 .maxNewTokens(MAX_NEW_TOKEN)
+public class AgentManager {
+    ...
+    private ChatAgent createAgent() {
+        HuggingFaceChatModel model = HuggingFaceChatModel.builder()
+                                         .accessToken(HUGGING_FACE_API_KEY)
+                                         .modelId(CHAT_MODEL_ID)
+                                         .timeout(ofSeconds(TIMEOUT))
+                                         .temperature(TEMPERATURE)
+                                         .maxNewTokens(MAX_NEW_TOKEN)
+                                         .waitForModel(true)
+                                         .build();
+        ChatAgent agent = AiServices.builder(ChatAgent.class)
+                                    .chatLanguageModel(model)
+                                    .chatMemory(MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
+                                    .build();
 ----
 
-For security practice, it is not hard-coded in either the code or properties file. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps microservices to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+
+=== Externalizing the configuration
+
+As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps microservices to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 [java]
 ----
+public class AgentManager {
+    
+    ...
+
     @Inject
     @ConfigProperty(name = "hugging.face.api.key")
     private String HUGGING_FACE_API_KEY;
@@ -158,6 +179,8 @@ For security practice, it is not hard-coded in either the code or properties fil
     @Inject
     @ConfigProperty(name = "chat.memory.max.messages")
     private Integer MAX_MESSAGES;
+
+    ...
 ----
 
 To fine tune the langchain4j model or even try out another LLM, you simply update the values in the `langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties` file.

From 8ce1c22890cac95cf5e9c946809d9e6aa29bcf48 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 11:20:14 -0500
Subject: [PATCH 06/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 56 ++++++++++++++++++-
 1 file changed, 53 insertions(+), 3 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index d336bbe4f..8a24fa194 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -118,7 +118,7 @@ You can type in any text that you want to chat with the AI agent. Here are some
 * `any documentation?`
 
 
-== Jakarta EE and MicroProfile features used in the application
+== How the application works?
 
 The application demostrates how to use the LangChain4J APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
 
@@ -148,7 +148,7 @@ public class AgentManager {
 
 === Externalizing the configuration
 
-As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps microservices to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 [java]
 ----
@@ -183,7 +183,7 @@ public class AgentManager {
     ...
 ----
 
-To fine tune the langchain4j model or even try out another LLM, you simply update the values in the `langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties` file.
+To fine tune the langchain4j model or even try out another LLM, you simply update the values in the `langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties` file or provide them through the enviroment variables.
 
 [text]
 ----
@@ -195,6 +195,56 @@ chat.model.temperature=1.0
 chat.memory.max.messages=20
 ----
 
+=== Communicating between the client and LLM
+
+The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the message by the `send` method.
+
+https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example/src/main/webapp/chatroom.js[`chatroom.js`]
+[java]
+----
+    const webSocket = new WebSocket('ws://localhost:9080/chat');
+    ...
+    function sendMessage() {
+        ...
+        var myMessage = document.getElementById('myMessage').value;
+        ...
+        webSocket.send(myMessage);
+        ...
+    }
+----
+
+The service recieves the user messeages, forward them to the LLM, and then boardcast the answers back to the client
+
+----
+public class ChatService {
+    ...
+    @OnMessage
+    public void onMessage(String message, Session session) {
+        ...
+        String answer = askAgent(session, message);
+        ...
+    }
+    ...
+    private String askAgent(Session session, String userMessage) {
+        ...
+        answer = agent.chat(userMessage).trim();
+        ...
+    }
+    ...
+    private void sendMessageToSession(Session session, String message) {
+        ...
+        manager.getSession(session.getId())
+               .getBasicRemote()
+               .sendObject(message);
+        ...
+    }
+----
+
+
+and then broadcast the LLM responsed messages back to the client.
+
+
+
 == Nice work! Where to next?
 
 Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 

From f32a985c1c0b3a2b233e3b71400c57dec5c5f806 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 11:57:59 -0500
Subject: [PATCH 07/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 8a24fa194..52fec53b6 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -10,6 +10,7 @@ seo-description: Experience AI technology in an Jakarta EE and MicroProfile appl
 blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
+example-location: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
 ---
 = Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
 Gilbert Kwan <https://github.com/gkwan-ibm>
@@ -118,7 +119,7 @@ You can type in any text that you want to chat with the AI agent. Here are some
 * `any documentation?`
 
 
-== How the application works?
+== How does the application work?
 
 The application demostrates how to use the LangChain4J APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
 
@@ -126,7 +127,8 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
-[java]
+{{example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+[source, java]
 ----
 public class AgentManager {
     ...
@@ -150,7 +152,8 @@ public class AgentManager {
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-[java]
+{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+[source, java]
 ----
 public class AgentManager {
     
@@ -183,9 +186,8 @@ public class AgentManager {
     ...
 ----
 
-To fine tune the langchain4j model or even try out another LLM, you simply update the values in the `langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties` file or provide them through the enviroment variables.
+To fine tune the langchain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the enviroment variables.
 
-[text]
 ----
 hugging.face.api.key=set it by env variable
 chat.model.id=NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
@@ -199,8 +201,8 @@ chat.memory.max.messages=20
 
 The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the message by the `send` method.
 
-https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example/src/main/webapp/chatroom.js[`chatroom.js`]
-[java]
+{example-location}/src/main/webapp/chatroom.js[`chatroom.js`]
+[source, java]
 ----
     const webSocket = new WebSocket('ws://localhost:9080/chat');
     ...
@@ -213,8 +215,10 @@ https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-
     }
 ----
 
-The service recieves the user messeages, forward them to the LLM, and then boardcast the answers back to the client
+The service recieves the user messeages, forward them to the LLM, and then boardcast the LLM responsed answers back to the client.
 
+{example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`ChatService` class]
+[source, java]
 ----
 public class ChatService {
     ...
@@ -241,10 +245,6 @@ public class ChatService {
 ----
 
 
-and then broadcast the LLM responsed messages back to the client.
-
-
-
 == Nice work! Where to next?
 
 Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 

From 4da9d0da759e1531e03ef56ff14dc16cd6c9c23b Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 12:01:32 -0500
Subject: [PATCH 08/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 52fec53b6..7c518dc9b 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -127,7 +127,7 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
-{{example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{{page.example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----
 public class AgentManager {
@@ -145,6 +145,8 @@ public class AgentManager {
                                     .chatLanguageModel(model)
                                     .chatMemory(MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
                                     .build();
+    ...
+}
 ----
 
 
@@ -152,7 +154,7 @@ public class AgentManager {
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{page.example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----
 public class AgentManager {

From 9920d076eb7083c7a9cd48068722c1b63aca35a8 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 12:15:50 -0500
Subject: [PATCH 09/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 7c518dc9b..af1c84fd0 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -127,6 +127,10 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
+{{page.example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+
+{page.example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+
 {{page.example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----
@@ -145,6 +149,8 @@ public class AgentManager {
                                     .chatLanguageModel(model)
                                     .chatMemory(MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
                                     .build();
+       ...
+    }
     ...
 }
 ----

From 5764e8b2e07daaa647b399cb211d254d6cbee0b0 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 12:34:56 -0500
Subject: [PATCH 10/67] drafted blog for langchain4j example

---
 .../2024-02-29-open-liberty-with-langchain4j-example.adoc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index af1c84fd0..1bfbd18a1 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -10,7 +10,7 @@ seo-description: Experience AI technology in an Jakarta EE and MicroProfile appl
 blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
-example-location: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
+examplelocation: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
 ---
 = Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
 Gilbert Kwan <https://github.com/gkwan-ibm>
@@ -127,11 +127,11 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
-{{page.example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+{{page.examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
 
-{page.example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+{page.examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
 
-{{page.example-location}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{{page.examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----
 public class AgentManager {

From 34e4b58ee7169cb2adb25f7c83243105657550f9 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 12:36:30 -0500
Subject: [PATCH 11/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 1bfbd18a1..fbde2c8c8 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -131,6 +131,10 @@ The application uses the `HuggingFaceChatModel` class to provide the model for b
 
 {page.examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
 
+{{examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+
+{examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+
 {{page.examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----

From a28e2445241eae2b08eb3f9f68ed7bb4f73704dd Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 12:40:09 -0500
Subject: [PATCH 12/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index fbde2c8c8..6c3b3cd50 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -131,9 +131,9 @@ The application uses the `HuggingFaceChatModel` class to provide the model for b
 
 {page.examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
 
-{{examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+"{{examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java"
 
-{examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+"{examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java"
 
 {{page.examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]

From 378f631c0ea2e8d238c630030cef143cbe9fe226 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 14:00:23 -0500
Subject: [PATCH 13/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 20 +++++--------------
 1 file changed, 5 insertions(+), 15 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 6c3b3cd50..36e0281c1 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -10,7 +10,6 @@ seo-description: Experience AI technology in an Jakarta EE and MicroProfile appl
 blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
-examplelocation: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
 ---
 = Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
 Gilbert Kwan <https://github.com/gkwan-ibm>
@@ -127,18 +126,11 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
-{{page.examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
+:example-location: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
 
-{page.examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java
-
-"{{examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java"
-
-"{examplelocation}/src/main/java/dev/langchain4j/example/chat/AgentManager.java"
-
-{{page.examplelocation}}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----
-public class AgentManager {
     ...
     private ChatAgent createAgent() {
         HuggingFaceChatModel model = HuggingFaceChatModel.builder()
@@ -156,7 +148,6 @@ public class AgentManager {
        ...
     }
     ...
-}
 ----
 
 
@@ -164,11 +155,9 @@ public class AgentManager {
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-{page.example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----
-public class AgentManager {
-    
     ...
 
     @Inject
@@ -232,7 +221,6 @@ The service recieves the user messeages, forward them to the LLM, and then board
 {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`ChatService` class]
 [source, java]
 ----
-public class ChatService {
     ...
     @OnMessage
     public void onMessage(String message, Session session) {
@@ -254,9 +242,11 @@ public class ChatService {
                .sendObject(message);
         ...
     }
+    ...
 ----
 
 
+
 == Nice work! Where to next?
 
 Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 

From 7f1641dd281e223c94b9438aa8c305e7963c8b61 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 14:10:08 -0500
Subject: [PATCH 14/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 36e0281c1..1a16c1fa3 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -17,6 +17,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 :url-prefix:
 :url-about: /
 
+:example-location: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
 
 
 "Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_
@@ -126,8 +127,6 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
-:example-location: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
-
 {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
 [source, java]
 ----

From 39ba8172f315fcd0eccc2dc036bbc342c307ed58 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 14:22:13 -0500
Subject: [PATCH 15/67] drafted blog for langchain4j example

---
 .../2024-02-29-open-liberty-with-langchain4j-example.adoc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 1a16c1fa3..51118e6f9 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -127,7 +127,7 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
 [source, java]
 ----
     ...
@@ -154,7 +154,7 @@ The application uses the `HuggingFaceChatModel` class to provide the model for b
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`AgentManager` class]
+{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
 [source, java]
 ----
     ...
@@ -201,7 +201,7 @@ chat.memory.max.messages=20
 
 The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the message by the `send` method.
 
-{example-location}/src/main/webapp/chatroom.js[`chatroom.js`]
+{example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom.js`]
 [source, java]
 ----
     const webSocket = new WebSocket('ws://localhost:9080/chat');
@@ -217,7 +217,7 @@ The application provides the interactive UI client for users to communicate with
 
 The service recieves the user messeages, forward them to the LLM, and then boardcast the LLM responsed answers back to the client.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`ChatService` class]
+{example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`]
 [source, java]
 ----
     ...

From d7f45544f0a14adc209a40030baeebd13b2661ff Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 14:36:52 -0500
Subject: [PATCH 16/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 51118e6f9..d4bed3e17 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -125,7 +125,7 @@ The application demostrates how to use the LangChain4J APIs, https://openliberty
 
 === Creating the LangChain4J AI service
 
-The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized `ChatAgent` interface, the application can send messages to the LLM by the `chat()` method.
+The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by the `chat()` method.
 
 {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
 [source, java]
@@ -199,7 +199,7 @@ chat.memory.max.messages=20
 
 === Communicating between the client and LLM
 
-The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the message by the `send` method.
+The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
 
 {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom.js`]
 [source, java]
@@ -215,7 +215,7 @@ The application provides the interactive UI client for users to communicate with
     }
 ----
 
-The service recieves the user messeages, forward them to the LLM, and then boardcast the LLM responsed answers back to the client.
+The service recieves the user messeages through the WebSocket `onMessage()` method, forward them to the LLM by calling the `ChatAgent.chat()` method, and then boardcast the LLM responsed answers back to the client session through the `sendObect()` method.
 
 {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`]
 [source, java]

From 8fc450829cf1a91976b34a833cd18c7ee873ffca Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 15:03:54 -0500
Subject: [PATCH 17/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 56 ++++++++++++++-----
 1 file changed, 43 insertions(+), 13 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index d4bed3e17..f17c36464 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -1,17 +1,17 @@
 ---
 layout: post
-title: "Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty"
+title: "Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty"
 # Do NOT change the categories section
 categories: blog
 author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
 author_github: https://github.com/gkwan-ibm
-seo-title: Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
-seo-description: Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs.
-blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4J APIs."
+seo-title: Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty
+seo-description: Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4j APIs.
+blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4j APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
 ---
-= Run LangChain4J in Jakarta EE and MicroProfile application on Open Liberty
+= Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty
 Gilbert Kwan <https://github.com/gkwan-ibm>
 :imagesdir: /
 :url-prefix:
@@ -22,7 +22,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 
 "Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_
 
-https://github.com/langchain4j/langchain4j[LangChain4J APIs] provide the abilities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+https://github.com/langchain4j/langchain4j[LangChain4j APIs] provide the abilities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
 
 If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an example that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
 
@@ -109,7 +109,7 @@ To ensure the application has started successfully, you can run the tests by pre
 
 Once the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
 
-image::/img/blog/langchain4j-example-chat-room.png[Chat Room  of LangChain4J Jakarta EE and MicroProfile example,width=70%,align="center"]
+image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jakarta EE and MicroProfile example,width=70%,align="center"]
 
 You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
 
@@ -121,11 +121,11 @@ You can type in any text that you want to chat with the AI agent. Here are some
 
 == How does the application work?
 
-The application demostrates how to use the LangChain4J APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
+The application demostrates how to use the LangChain4j APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
 
-=== Creating the LangChain4J AI service
+=== Creating the LangChain4j AI service
 
-The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by the `chat()` method.
+The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by its `chat()` method.
 
 {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
 [source, java]
@@ -152,7 +152,7 @@ The application uses the `HuggingFaceChatModel` class to provide the model for b
 
 === Externalizing the configuration
 
-As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the langchain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
 [source, java]
@@ -186,7 +186,7 @@ As the above code, to access the model, an API key is required. For security pra
     ...
 ----
 
-To fine tune the langchain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the enviroment variables.
+To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the enviroment variables.
 
 ----
 hugging.face.api.key=set it by env variable
@@ -244,13 +244,43 @@ The service recieves the user messeages through the WebSocket `onMessage()` meth
     ...
 ----
 
+=== Adding metrics
 
+To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
 
-== Nice work! Where to next?
+{example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`]
+[source, java]
+----
+    @OnMessage
+    @Timed(name = "chatProcessingTime",
+           absolute = true,
+           description = "Time needed chatting to the agent.")
+    public void onMessage(String message, Session session) {
+        ...
+----
+
+And, collect how many agents are created by applying the `@Counted` annotation to the `createAgent()` method of the `AgentManager` class.
+
+{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
+[source, java]
+----
+    @Counted(name = "createChatAgentCount",
+             absolute = true,
+             description = "Number of ChatAgent are created.")
+	public ChatAgent createAgent(Session session) {
+        ...
+----
+
+If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided on the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`].
+
+
+== Where to next?
 
 Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 
 
 == Helpful links
+* link:https://github.com/langchain4j[LangChain4j]
+* link:https://huggingface.co/models[Hugging Face LLMs]
 * link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket]
 * link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices]
 * link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice]

From d196f92e779ac8f1e0c289827e02fbebf5ec5917 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 15:13:33 -0500
Subject: [PATCH 18/67] drafted blog for langchain4j example

---
 .../2024-02-29-open-liberty-with-langchain4j-example.adoc  | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index f17c36464..68e5b5803 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -259,7 +259,7 @@ To determine the performance and health of the application, it uses the MicroPro
         ...
 ----
 
-And, collect how many agents are created by applying the `@Counted` annotation to the `createAgent()` method of the `AgentManager` class.
+And, collect how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
 
 {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
 [source, java]
@@ -267,7 +267,7 @@ And, collect how many agents are created by applying the `@Counted` annotation t
     @Counted(name = "createChatAgentCount",
              absolute = true,
              description = "Number of ChatAgent are created.")
-	public ChatAgent createAgent(Session session) {
+    public ChatAgent createAgent(Session session) {
         ...
 ----
 
@@ -282,7 +282,6 @@ Check out the https://openliberty.io/guides/[Open Liberty guides] for more infor
 * link:https://github.com/langchain4j[LangChain4j]
 * link:https://huggingface.co/models[Hugging Face LLMs]
 * link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket]
+* link:https://openliberty.io/guides/cdi-intro.html[Injecting dependencies into microservices]
 * link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices]
 * link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice]
-
-

From f7df2c69403d3f2371c29f38d99511e93cbc196e Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 15:21:58 -0500
Subject: [PATCH 19/67] drafted blog for langchain4j example

---
 ...open-liberty-with-langchain4j-example.adoc | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 68e5b5803..b908c2dc4 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -271,8 +271,30 @@ And, collect how many agents are created by applying the `@Counted` annotation t
         ...
 ----
 
+Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
+
+----
+# HELP createChatAgentCount_total Number of ChatAgent are created.
+# TYPE createChatAgentCount_total counter
+createChatAgentCount_total{mp_scope="application",} 5.0
+# HELP chatProcessingTime_seconds Time needed chatting to the agent.
+# TYPE chatProcessingTime_seconds summary
+chatProcessingTime_seconds{mp_scope="application",quantile="0.5",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.75",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.95",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.98",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.99",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.999",} 0.0
+chatProcessingTime_seconds_count{mp_scope="application",} 6.0
+chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
+# HELP chatProcessingTime_seconds_max Time needed chatting to the agent.
+# TYPE chatProcessingTime_seconds_max gauge
+chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
+
+
 If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided on the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`].
 
+----
 
 == Where to next?
 

From 5db26efe79c62bad9354a90e79ef6d1d4a9948df Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 15:35:59 -0500
Subject: [PATCH 20/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index b908c2dc4..02b15eb8e 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -290,11 +290,10 @@ chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
 # HELP chatProcessingTime_seconds_max Time needed chatting to the agent.
 # TYPE chatProcessingTime_seconds_max gauge
 chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
-
+----
 
 If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided on the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`].
 
-----
 
 == Where to next?
 

From 3f8e4c1db0fbe50b64a454f62474be873d6bc86f Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 15:53:16 -0500
Subject: [PATCH 21/67] drafted blog for langchain4j example

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 02b15eb8e..24d5ecce7 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -244,7 +244,7 @@ The service recieves the user messeages through the WebSocket `onMessage()` meth
     ...
 ----
 
-=== Adding metrics
+=== Enabling metrics
 
 To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
 

From d36bcb611c0ab9210d62d0634444539d9c5e2ee9 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Wed, 7 Feb 2024 16:06:07 -0500
Subject: [PATCH 22/67] drafted blog for langchain4j example

---
 ...-29-open-liberty-with-langchain4j-example.adoc | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 24d5ecce7..6ce20640a 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -127,7 +127,7 @@ The application demostrates how to use the LangChain4j APIs, https://openliberty
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by its `chat()` method.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
 [source, java]
 ----
     ...
@@ -154,7 +154,7 @@ The application uses the `HuggingFaceChatModel` class to provide the model for b
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
 [source, java]
 ----
     ...
@@ -201,7 +201,7 @@ chat.memory.max.messages=20
 
 The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
 
-{example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom.js`]
+See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom.js`] file.
 [source, java]
 ----
     const webSocket = new WebSocket('ws://localhost:9080/chat');
@@ -217,7 +217,7 @@ The application provides the interactive UI client for users to communicate with
 
 The service recieves the user messeages through the WebSocket `onMessage()` method, forward them to the LLM by calling the `ChatAgent.chat()` method, and then boardcast the LLM responsed answers back to the client session through the `sendObect()` method.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`]
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
 [source, java]
 ----
     ...
@@ -248,7 +248,7 @@ The service recieves the user messeages through the WebSocket `onMessage()` meth
 
 To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`]
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
 [source, java]
 ----
     @OnMessage
@@ -261,7 +261,7 @@ To determine the performance and health of the application, it uses the MicroPro
 
 And, collect how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
 
-{example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`]
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
 [source, java]
 ----
     @Counted(name = "createChatAgentCount",
@@ -272,7 +272,6 @@ And, collect how many agents are created by applying the `@Counted` annotation t
 ----
 
 Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
-
 ----
 # HELP createChatAgentCount_total Number of ChatAgent are created.
 # TYPE createChatAgentCount_total counter
@@ -292,7 +291,7 @@ chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
 chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
 ----
 
-If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided on the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`].
+If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`] file.
 
 
 == Where to next?

From 56a6b5e5ac3059d82b5d27cc8429d40d03d415e0 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Fri, 9 Feb 2024 12:32:58 -0500
Subject: [PATCH 23/67] add toc

---
 ...open-liberty-with-langchain4j-example.adoc | 25 +++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 6ce20640a..5cce5efde 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -26,7 +26,19 @@ https://github.com/langchain4j/langchain4j[LangChain4j APIs] provide the abiliti
 
 If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an example that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
 
-
+* <<tryout, Try out the jakartaee-microprofile-example application>>
+  - <<env_setup, Environment Set Up>>
+  - <<start_app, Start the application>>
+  - <<access_app, Access the application>>
+* <<how_app_work, How does the application work?>>
+  - <<create_service, Creating the LangChain4j AI service>>
+  - <<external_config, Externalizing the configuration>>
+  - <<communicate, Communicating between the client and LLM>>
+  - <<enable_metrics, Enabling metrics>>
+* <<where_to_next, Where to next?>>
+* <<help_links, Helpful links>>
+
+[#tryout]
 == Try out the jakartaee-microprofile-example application
 
 Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
@@ -44,6 +56,7 @@ To access the repository remotely you'll need to install https://git-scm.com/boo
 git clone https://github.com/langchain4j/langchain4j-examples.git
 ----
 
+[#env_setup]
 === Environment Set Up
 
 To run the application, navigate to the `jakartaee-microprofile-example` directory:
@@ -61,6 +74,7 @@ export JAVA_HOME=<your Java 17 home path>
 export HUGGING_FACE_API_KEY=<your Hugging Face read token>
 ----
 
+[#start_app]
 === Start the application
 
 To start the application, use the provided Maven wrapper to run the https://openliberty.io/docs/latest/development-mode.html[Liberty dev mode]:
@@ -105,6 +119,7 @@ To ensure the application has started successfully, you can run the tests by pre
 [INFO] Tests run: 4, Failures: 0, Errors: 0, Skipped: 0
 ----
 
+[#access_app]
 === Access the application
 
 Once the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
@@ -119,10 +134,12 @@ You can type in any text that you want to chat with the AI agent. Here are some
 * `any documentation?`
 
 
+[#how_app_work]
 == How does the application work?
 
 The application demostrates how to use the LangChain4j APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
 
+[#create_service]
 === Creating the LangChain4j AI service
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by its `chat()` method.
@@ -149,7 +166,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManag
     ...
 ----
 
-
+[#external_config]
 === Externalizing the configuration
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
@@ -197,6 +214,7 @@ chat.model.temperature=1.0
 chat.memory.max.messages=20
 ----
 
+[#communicate]
 === Communicating between the client and LLM
 
 The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
@@ -244,6 +262,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
     ...
 ----
 
+[#enable_metrics]
 === Enabling metrics
 
 To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
@@ -294,10 +313,12 @@ chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
 If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`] file.
 
 
+[#where_to_next]
 == Where to next?
 
 Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 
 
+[#help_links]
 == Helpful links
 * link:https://github.com/langchain4j[LangChain4j]
 * link:https://huggingface.co/models[Hugging Face LLMs]

From 1bed626217fb9f111f69c2cb99d29b43a46b7442 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Fri, 9 Feb 2024 13:28:30 -0500
Subject: [PATCH 24/67] add toc

---
 ...-02-29-open-liberty-with-langchain4j-example.adoc | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 5cce5efde..4912a6b04 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -145,7 +145,7 @@ The application demostrates how to use the LangChain4j APIs, https://openliberty
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by its `chat()` method.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
-[source, java]
+[source, java, role="no_copy"]
 ----
     ...
     private ChatAgent createAgent() {
@@ -172,7 +172,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManag
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
-[source, java]
+[source, java, role="no_copy"]
 ----
     ...
 
@@ -220,7 +220,7 @@ chat.memory.max.messages=20
 The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
 
 See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom.js`] file.
-[source, java]
+[source, java, role="no_copy"]
 ----
     const webSocket = new WebSocket('ws://localhost:9080/chat');
     ...
@@ -236,7 +236,7 @@ See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom
 The service recieves the user messeages through the WebSocket `onMessage()` method, forward them to the LLM by calling the `ChatAgent.chat()` method, and then boardcast the LLM responsed answers back to the client session through the `sendObect()` method.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
-[source, java]
+[source, java, role="no_copy"]
 ----
     ...
     @OnMessage
@@ -268,7 +268,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
 To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
-[source, java]
+[source, java, role="no_copy"]
 ----
     @OnMessage
     @Timed(name = "chatProcessingTime",
@@ -281,7 +281,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
 And, collect how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
-[source, java]
+[source, java, role="no_copy"]
 ----
     @Counted(name = "createChatAgentCount",
              absolute = true,

From a980e04f3fc247e76ea5eea555ec3a7b8aae4880 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Fri, 9 Feb 2024 13:32:21 -0500
Subject: [PATCH 25/67] update code blocks

---
 ...-02-29-open-liberty-with-langchain4j-example.adoc | 12 ++----------
 1 file changed, 2 insertions(+), 10 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 4912a6b04..db9a7ca6b 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -147,7 +147,6 @@ The application uses the `HuggingFaceChatModel` class to provide the model for b
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
 [source, java, role="no_copy"]
 ----
-    ...
     private ChatAgent createAgent() {
         HuggingFaceChatModel model = HuggingFaceChatModel.builder()
                                          .accessToken(HUGGING_FACE_API_KEY)
@@ -163,7 +162,6 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManag
                                     .build();
        ...
     }
-    ...
 ----
 
 [#external_config]
@@ -174,8 +172,6 @@ As the above code, to access the model, an API key is required. For security pra
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
 [source, java, role="no_copy"]
 ----
-    ...
-
     @Inject
     @ConfigProperty(name = "hugging.face.api.key")
     private String HUGGING_FACE_API_KEY;
@@ -199,8 +195,6 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManag
     @Inject
     @ConfigProperty(name = "chat.memory.max.messages")
     private Integer MAX_MESSAGES;
-
-    ...
 ----
 
 To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the enviroment variables.
@@ -238,20 +232,19 @@ The service recieves the user messeages through the WebSocket `onMessage()` meth
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
 [source, java, role="no_copy"]
 ----
-    ...
     @OnMessage
     public void onMessage(String message, Session session) {
         ...
         String answer = askAgent(session, message);
         ...
     }
-    ...
+ 
     private String askAgent(Session session, String userMessage) {
         ...
         answer = agent.chat(userMessage).trim();
         ...
     }
-    ...
+ 
     private void sendMessageToSession(Session session, String message) {
         ...
         manager.getSession(session.getId())
@@ -259,7 +252,6 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
                .sendObject(message);
         ...
     }
-    ...
 ----
 
 [#enable_metrics]

From 63d7dfc0e64ecc30f8067eacb8232a7b3bed5f31 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Fri, 23 Feb 2024 09:09:40 -0500
Subject: [PATCH 26/67] fix review comments

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index db9a7ca6b..53516c295 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -47,7 +47,7 @@ The application uses Hugging Face. You need to get a Hugging Face API Key:
 
 * Sign up and login to https://huggingface.co
 * Go to Access Tokens by https://huggingface.co/settings/tokens
-* New a read token
+* Create a new access token with "read" role
 
 To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
 
@@ -128,8 +128,7 @@ image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jaka
 
 You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
 
-* `What is large language models?`
-* `tell me more`
+* `What are large language models?`
 * `which are the top used models?`
 * `any documentation?`
 

From 2274fff9da7c8b1063c4da42e2ba27e2ff133eeb Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Fri, 23 Feb 2024 15:30:57 -0500
Subject: [PATCH 27/67] update a question

---
 posts/2024-02-29-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
index 53516c295..23f3a69ef 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
@@ -129,7 +129,7 @@ image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jaka
 You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
 
 * `What are large language models?`
-* `which are the top used models?`
+* `Which are the most used models?`
 * `any documentation?`
 
 

From 7de387834b3d22b3f4ace198cc6e652d67160f1f Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Thu, 29 Feb 2024 15:26:12 -0500
Subject: [PATCH 28/67] improve the suggested messages

---
 img/blog/langchain4j-example-chat-room.png    | Bin 229501 -> 219183 bytes
 ...pen-liberty-with-langchain4j-example.adoc} |   4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename posts/{2024-02-29-open-liberty-with-langchain4j-example.adoc => 2024-03-15-open-liberty-with-langchain4j-example.adoc} (99%)

diff --git a/img/blog/langchain4j-example-chat-room.png b/img/blog/langchain4j-example-chat-room.png
index ccba2133d35371a7b51fbda56251ee1459bcaafa..e5647d1f03d102fc8fd064a41576cbcaaec19148 100644
GIT binary patch
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diff --git a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
similarity index 99%
rename from posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
rename to posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
index 23f3a69ef..2c5eeb2de 100644
--- a/posts/2024-02-29-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
@@ -128,8 +128,8 @@ image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jaka
 
 You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
 
-* `What are large language models?`
-* `Which are the most used models?`
+* `What is MicroProfile?`
+* `Which companies contribute MicroProfile?`
 * `any documentation?`
 
 

From f1a0fad89dcc84c97d06ae96afd054ef555f4858 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Thu, 14 Mar 2024 12:24:14 -0400
Subject: [PATCH 29/67] update for code change

---
 ...open-liberty-with-langchain4j-example.adoc | 86 +++++++++----------
 1 file changed, 40 insertions(+), 46 deletions(-)

diff --git a/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
index 2c5eeb2de..9b4f55386 100644
--- a/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
@@ -129,7 +129,7 @@ image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jaka
 You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
 
 * `What is MicroProfile?`
-* `Which companies contribute MicroProfile?`
+* `Which top 10 companies contribute MicroProfile?`
 * `any documentation?`
 
 
@@ -141,34 +141,44 @@ The application demostrates how to use the LangChain4j APIs, https://openliberty
 [#create_service]
 === Creating the LangChain4j AI service
 
-The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`ChatAgent`] interface, the application can send messages to the LLM by its `chat()` method.
+The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. 
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
 [source, java, role="no_copy"]
 ----
-    private ChatAgent createAgent() {
-        HuggingFaceChatModel model = HuggingFaceChatModel.builder()
-                                         .accessToken(HUGGING_FACE_API_KEY)
-                                         .modelId(CHAT_MODEL_ID)
-                                         .timeout(ofSeconds(TIMEOUT))
-                                         .temperature(TEMPERATURE)
-                                         .maxNewTokens(MAX_NEW_TOKEN)
-                                         .waitForModel(true)
-                                         .build();
-        ChatAgent agent = AiServices.builder(ChatAgent.class)
-                                    .chatLanguageModel(model)
-                                    .chatMemory(MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
-                                    .build();
+    public Assistant getAssistant() {
+        ...
+            HuggingFaceChatModel model = HuggingFaceChatModel.builder()
+                .accessToken(HUGGING_FACE_API_KEY)
+                .modelId(CHAT_MODEL_ID)
+                .timeout(ofSeconds(TIMEOUT))
+                .temperature(TEMPERATURE)
+                .maxNewTokens(MAX_NEW_TOKEN)
+                .waitForModel(true)
+                .build();
+            assistant = AiServices.builder(Assistant.class)
+                .chatLanguageModel(model)
+                .chatMemoryProvider(
+                    sessionId -> MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
+                .build();
        ...
     }
 ----
 
+Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`Assistant`] interface, the application can send messages to the LLM by its `chat()` method.
+
+----
+    interface Assistant {
+       String chat(@MemoryId String sessionId, @UserMessage String userMessage);
+    }
+----
+
 [#external_config]
 === Externalizing the configuration
 
 As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
 [source, java, role="no_copy"]
 ----
     @Inject
@@ -234,22 +244,19 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
     @OnMessage
     public void onMessage(String message, Session session) {
         ...
-        String answer = askAgent(session, message);
-        ...
-    }
- 
-    private String askAgent(Session session, String userMessage) {
-        ...
-        answer = agent.chat(userMessage).trim();
-        ...
-    }
- 
-    private void sendMessageToSession(Session session, String message) {
-        ...
-        manager.getSession(session.getId())
-               .getBasicRemote()
-               .sendObject(message);
-        ...
+        try {
+            ...
+            answer = agent.chat(sessionId, message);
+        } catch (Exception e) {
+            ...
+        }
+
+        try {
+            session.getBasicRemote().sendObject(answer);
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+
     }
 ----
 
@@ -271,21 +278,8 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
 
 And, collect how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/AgentManager.java[`src/main/java/dev/langchain4j/example/chat/AgentManager.java`] file.
-[source, java, role="no_copy"]
-----
-    @Counted(name = "createChatAgentCount",
-             absolute = true,
-             description = "Number of ChatAgent are created.")
-    public ChatAgent createAgent(Session session) {
-        ...
-----
-
 Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
 ----
-# HELP createChatAgentCount_total Number of ChatAgent are created.
-# TYPE createChatAgentCount_total counter
-createChatAgentCount_total{mp_scope="application",} 5.0
 # HELP chatProcessingTime_seconds Time needed chatting to the agent.
 # TYPE chatProcessingTime_seconds summary
 chatProcessingTime_seconds{mp_scope="application",quantile="0.5",} 0.0

From 8288e8c3bb8e30d24b386d42152f66e50b917bf4 Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Tue, 26 Mar 2024 11:12:59 -0400
Subject: [PATCH 30/67] update the url to official GH repo

---
 ...oc => 2024-03-28-open-liberty-with-langchain4j-example.adoc} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename posts/{2024-03-15-open-liberty-with-langchain4j-example.adoc => 2024-03-28-open-liberty-with-langchain4j-example.adoc} (99%)

diff --git a/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
similarity index 99%
rename from posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
rename to posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 9b4f55386..3ea33f852 100644
--- a/posts/2024-03-15-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -17,7 +17,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 :url-prefix:
 :url-about: /
 
-:example-location: https://github.com/gkwan-ibm/langchain4j-examples/blob/jee-mp-example/jakartaee-microprofile-example
+:example-location: https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example
 
 
 "Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_

From f3fda0eb1030d90524ba02bc0ca3d0b12103bb9b Mon Sep 17 00:00:00 2001
From: Grace Jansen <gracejansen@graces-mbp.hursley.uk.ibm.com>
Date: Wed, 27 Mar 2024 11:28:52 +0000
Subject: [PATCH 31/67] edits to langchain4j blog draft and addition of image
 to this post

---
 img/blog/langchain4j.png                      | Bin 0 -> 92127 bytes
 ...open-liberty-with-langchain4j-example.adoc |  56 ++++++++++++++++--
 2 files changed, 50 insertions(+), 6 deletions(-)
 create mode 100644 img/blog/langchain4j.png

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diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 3ea33f852..439a4eeaa 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -20,12 +20,11 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 :example-location: https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example
 
 
-"Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks." ... _from https://www.ibm.com/topics/large-language-models_
-
-https://github.com/langchain4j/langchain4j[LangChain4j APIs] provide the abilities to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
-
-If you want to experience AI technologies, you can try out the `jakartaee-microprofile-example` project in the https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. It is an example that demostrates how to use LangChain4j APIs in Jakarta EE and MicroProfile application on Open Liberty.
+Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models, or LLMs, such as ChatGPT, over the last few years there has been a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
 
+* <<what_Are_LLMs, Find out what LLMs are>>
+* <<Java_LLMs, Explore frameworks that simplify how LLMs and generative AI can be applied to applications>>
+* <<using_Langchain4j, Discover how LangChain can be used in Java applications>>
 * <<tryout, Try out the jakartaee-microprofile-example application>>
   - <<env_setup, Environment Set Up>>
   - <<start_app, Start the application>>
@@ -38,9 +37,54 @@ If you want to experience AI technologies, you can try out the `jakartaee-microp
 * <<where_to_next, Where to next?>>
 * <<help_links, Helpful links>>
 
+[#what_Are_LLMs]
+== What are LLMs?
+
+A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of paramters, using self-supervised and semi-supervised learning techniques. This makes them capable of understanding and generating natural language and other types of content to perform a wide range of tasks, including:
+
+* *Text generation*: language generation abilities, such as writing emails, blog posts or other mid-to-long form content in response to prompts that can be refined and polished. An excellent example is retrieval-augmented generation (RAG). 
+
+* *Content summarization*: summarize long articles, news stories, research reports, corporate documentation and even customer history into thorough texts tailored in length to the output format.
+
+* *AI assistants*: chatbots that answer customer queries, perform backend tasks and provide detailed information in natural language as a part of an integrated, self-serve customer care solution. 
+
+* *Code generation*: assists developers in building applications, finding errors in code and uncovering security issues in multiple programming languages, even “translating” between them.
+
+* *Sentiment analysis*: analyze text to determine the customer’s tone in order understand customer feedback at scale and aid in brand reputation management. 
+
+* *Language translation*: provides wider coverage to organizations across languages and geographies with fluent translations and multilingual capabilities. 
+
+
+See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/5sLYAQS9sWQ?si=X9v5pPdzlK24bCsK" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+LLMs can be found in the service offerings of almost all of the major cloud service providers, e.g. IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
+
+[#Java_LLMs]
+== How can we take advantage of LLMs in Java applications?
+
+Developing applications powered by large language models isn't always an easy task. Integrating AI/LLM capabilities into an application can be challenging. To make this easier, LangChain, an open source framework was developed in 2022 to help streamline the process of creating generative AI apps. 
+
+LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease. 
+
+Although LangChain is primarily available in Python and JavaScript/TypeScript versions, there are options available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs provide the ability to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+
+image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=50%,align="center"]
+
+[#using_Langchain4j]
+== How to use LangChain4j in a Jakarta EE and MicroProfile application
+
+Langchain4j has a very useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repositry where it stores example applications. However, although this repo provided useful examples of using LangChain4j in general Java apps, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE/MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example` which can now be found in this https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application using Jakarta EE and MicroProfile on Open Liberty.
+
+
 [#tryout]
 == Try out the jakartaee-microprofile-example application
 
+To see how you could apply LangChain4j to your own Jakarta EE and/or MicroProfile application, check out this example project for yourself.
+
+[#pre-reqs]
+=== Pre-requisites
 Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
 
 The application uses Hugging Face. You need to get a Hugging Face API Key:
@@ -295,7 +339,7 @@ chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
 chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
 ----
 
-If you interest on other simply ways to use LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`] file.
+If you are interested in other ways to use the LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`] file.
 
 
 [#where_to_next]

From 88f86aae55d3781a01ac19bfc5a738fb2cfb1bea Mon Sep 17 00:00:00 2001
From: Grace Jansen <gracejansen@graces-mbp.hursley.uk.ibm.com>
Date: Wed, 27 Mar 2024 13:28:49 +0000
Subject: [PATCH 32/67] edits to langchain4j blog

---
 ...open-liberty-with-langchain4j-example.adoc | 32 ++++---------------
 1 file changed, 7 insertions(+), 25 deletions(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 439a4eeaa..9d60f3b06 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -10,6 +10,10 @@ seo-description: Experience AI technology in an Jakarta EE and MicroProfile appl
 blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4j APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
+additional_authors: 
+- name: Grace Jansen
+  github: https://github.com/GraceJansen
+  image: https://avatars0.githubusercontent.com/GraceJansen
 ---
 = Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty
 Gilbert Kwan <https://github.com/gkwan-ibm>
@@ -26,38 +30,16 @@ Artificial Intelligence (AI) is an exciting and disruptive field that is already
 * <<Java_LLMs, Explore frameworks that simplify how LLMs and generative AI can be applied to applications>>
 * <<using_Langchain4j, Discover how LangChain can be used in Java applications>>
 * <<tryout, Try out the jakartaee-microprofile-example application>>
-  - <<env_setup, Environment Set Up>>
-  - <<start_app, Start the application>>
-  - <<access_app, Access the application>>
 * <<how_app_work, How does the application work?>>
-  - <<create_service, Creating the LangChain4j AI service>>
-  - <<external_config, Externalizing the configuration>>
-  - <<communicate, Communicating between the client and LLM>>
-  - <<enable_metrics, Enabling metrics>>
-* <<where_to_next, Where to next?>>
-* <<help_links, Helpful links>>
 
 [#what_Are_LLMs]
 == What are LLMs?
 
-A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of paramters, using self-supervised and semi-supervised learning techniques. This makes them capable of understanding and generating natural language and other types of content to perform a wide range of tasks, including:
-
-* *Text generation*: language generation abilities, such as writing emails, blog posts or other mid-to-long form content in response to prompts that can be refined and polished. An excellent example is retrieval-augmented generation (RAG). 
-
-* *Content summarization*: summarize long articles, news stories, research reports, corporate documentation and even customer history into thorough texts tailored in length to the output format.
-
-* *AI assistants*: chatbots that answer customer queries, perform backend tasks and provide detailed information in natural language as a part of an integrated, self-serve customer care solution. 
-
-* *Code generation*: assists developers in building applications, finding errors in code and uncovering security issues in multiple programming languages, even “translating” between them.
-
-* *Sentiment analysis*: analyze text to determine the customer’s tone in order understand customer feedback at scale and aid in brand reputation management. 
-
-* *Language translation*: provides wider coverage to organizations across languages and geographies with fluent translations and multilingual capabilities. 
-
+A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of paramters, using self-supervised and semi-supervised learning techniques. This makes them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.
 
 See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/5sLYAQS9sWQ?si=X9v5pPdzlK24bCsK" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+video::5sLYAQS9sWQ[youtube]
 
 LLMs can be found in the service offerings of almost all of the major cloud service providers, e.g. IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
 
@@ -70,7 +52,7 @@ LangChain provides tools and abstractions to improve the customization, accuracy
 
 Although LangChain is primarily available in Python and JavaScript/TypeScript versions, there are options available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs provide the ability to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
 
-image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=50%,align="center"]
+image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=70%,align="center"]
 
 [#using_Langchain4j]
 == How to use LangChain4j in a Jakarta EE and MicroProfile application

From d93da1b2027f54b6ebd5b4baa9283910af631e24 Mon Sep 17 00:00:00 2001
From: Grace Jansen <gracejansen@graces-mbp.hursley.uk.ibm.com>
Date: Wed, 27 Mar 2024 14:31:16 +0000
Subject: [PATCH 33/67] editing embedded video in langchain4j blog

---
 .../2024-03-28-open-liberty-with-langchain4j-example.adoc  | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 9d60f3b06..40adfcf13 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -26,11 +26,12 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 
 Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models, or LLMs, such as ChatGPT, over the last few years there has been a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
 
+In this blog we'll explore what LLMs are, frameworks that can be used to simplify their use in Java applications and how to get started using them in a Jakarta EE/MicroProfile app:
 * <<what_Are_LLMs, Find out what LLMs are>>
-* <<Java_LLMs, Explore frameworks that simplify how LLMs and generative AI can be applied to applications>>
+* <<Java_LLMs, Explore app frameworks that simplify the use of LLMs>>
 * <<using_Langchain4j, Discover how LangChain can be used in Java applications>>
 * <<tryout, Try out the jakartaee-microprofile-example application>>
-* <<how_app_work, How does the application work?>>
+* <<how_app_work, Learn how this sample application works>>
 
 [#what_Are_LLMs]
 == What are LLMs?
@@ -39,7 +40,7 @@ A language model is a model of natural language based on probabilities. They are
 
 See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
 
-video::5sLYAQS9sWQ[youtube]
+video::5sLYAQS9sWQ[youtube,width=70%,align="center"]
 
 LLMs can be found in the service offerings of almost all of the major cloud service providers, e.g. IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
 

From 3f6e085a8cceecf5923852eca40fa6bc6330b237 Mon Sep 17 00:00:00 2001
From: Grace Jansen <gracejansen@graces-mbp.hursley.uk.ibm.com>
Date: Wed, 27 Mar 2024 15:58:35 +0000
Subject: [PATCH 34/67] edit video in langchain4j blog

---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 40adfcf13..b3f64f734 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -27,6 +27,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models, or LLMs, such as ChatGPT, over the last few years there has been a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
 
 In this blog we'll explore what LLMs are, frameworks that can be used to simplify their use in Java applications and how to get started using them in a Jakarta EE/MicroProfile app:
+
 * <<what_Are_LLMs, Find out what LLMs are>>
 * <<Java_LLMs, Explore app frameworks that simplify the use of LLMs>>
 * <<using_Langchain4j, Discover how LangChain can be used in Java applications>>
@@ -40,7 +41,7 @@ A language model is a model of natural language based on probabilities. They are
 
 See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
 
-video::5sLYAQS9sWQ[youtube,width=70%,align="center"]
+video::5sLYAQS9sWQ[youtube,width=100%,height=100%,align="center"]
 
 LLMs can be found in the service offerings of almost all of the major cloud service providers, e.g. IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
 

From 10b78ca6fea9bb4cb823f486a6547e93f17fbf88 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Thu, 28 Mar 2024 08:38:30 -0400
Subject: [PATCH 35/67] adjust video dimensions

---
 ...-open-liberty-with-langchain4j-example.adoc | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index b3f64f734..984771ca3 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -10,7 +10,7 @@ seo-description: Experience AI technology in an Jakarta EE and MicroProfile appl
 blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4j APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
-additional_authors: 
+additional_authors:
 - name: Grace Jansen
   github: https://github.com/GraceJansen
   image: https://avatars0.githubusercontent.com/GraceJansen
@@ -41,16 +41,16 @@ A language model is a model of natural language based on probabilities. They are
 
 See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
 
-video::5sLYAQS9sWQ[youtube,width=100%,height=100%,align="center"]
+video::5sLYAQS9sWQ[youtube, width="560", height="315", align="center"]
 
 LLMs can be found in the service offerings of almost all of the major cloud service providers, e.g. IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
 
 [#Java_LLMs]
 == How can we take advantage of LLMs in Java applications?
 
-Developing applications powered by large language models isn't always an easy task. Integrating AI/LLM capabilities into an application can be challenging. To make this easier, LangChain, an open source framework was developed in 2022 to help streamline the process of creating generative AI apps. 
+Developing applications powered by large language models isn't always an easy task. Integrating AI/LLM capabilities into an application can be challenging. To make this easier, LangChain, an open source framework was developed in 2022 to help streamline the process of creating generative AI apps.
 
-LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease. 
+LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease.
 
 Although LangChain is primarily available in Python and JavaScript/TypeScript versions, there are options available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs provide the ability to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
 
@@ -121,7 +121,7 @@ After you see the following message, the application is ready:
 *        Automatic generation of features: [ Off ]
 *        h - see the help menu for available actions, type 'h' and press Enter.
 *        q - stop the server and quit dev mode, press Ctrl-C or type 'q' and press Enter.
-*        
+*
 *    Liberty server port information:
 *        Liberty server HTTP port: [ 9080 ]
 *        Liberty server HTTPS port: [ 9443 ]
@@ -141,9 +141,9 @@ To ensure the application has started successfully, you can run the tests by pre
 [INFO] ...
 [INFO] Running it.dev.langchan4j.example.ModelResourceIT
 [INFO] Tests run: 3, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.733 s...
-[INFO] 
+[INFO]
 [INFO] Results:
-[INFO] 
+[INFO]
 [INFO] Tests run: 4, Failures: 0, Errors: 0, Skipped: 0
 ----
 
@@ -169,7 +169,7 @@ The application demostrates how to use the LangChain4j APIs, https://openliberty
 [#create_service]
 === Creating the LangChain4j AI service
 
-The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service. 
+The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
 [source, java, role="no_copy"]
@@ -329,7 +329,7 @@ If you are interested in other ways to use the LangChain4j APIs, you can study t
 [#where_to_next]
 == Where to next?
 
-Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty. 
+Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty.
 
 [#help_links]
 == Helpful links

From f48842a542696fefd151858d57e1fc99af218aff Mon Sep 17 00:00:00 2001
From: Gilbert Kwan <gkwan@ca.ibm.com>
Date: Thu, 28 Mar 2024 09:14:47 -0400
Subject: [PATCH 36/67] Update
 2024-03-28-open-liberty-with-langchain4j-example.adoc

---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 984771ca3..223d45e04 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -59,7 +59,7 @@ image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=70%,align
 [#using_Langchain4j]
 == How to use LangChain4j in a Jakarta EE and MicroProfile application
 
-Langchain4j has a very useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repositry where it stores example applications. However, although this repo provided useful examples of using LangChain4j in general Java apps, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE/MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example` which can now be found in this https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application using Jakarta EE and MicroProfile on Open Liberty.
+Langchain4j has a very useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repositry where it stores example applications. However, although this repo provided useful examples of using LangChain4j in general Java apps, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE/MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example` which can now be found in this https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application using Jakarta EE and MicroProfile on Open Liberty.
 
 
 [#tryout]

From a490dfed07fff5865b2dff33edeba0f6dce30c6d Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:07:49 +0000
Subject: [PATCH 37/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 223d45e04..8a2fea6f4 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -26,7 +26,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 
 Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models, or LLMs, such as ChatGPT, over the last few years there has been a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
 
-In this blog we'll explore what LLMs are, frameworks that can be used to simplify their use in Java applications and how to get started using them in a Jakarta EE/MicroProfile app:
+In this post, we'll explore what LLMs are and how to use them in Java applications. We'll also get started using them in an example Jakarta EE/MicroProfile app.
 
 * <<what_Are_LLMs, Find out what LLMs are>>
 * <<Java_LLMs, Explore app frameworks that simplify the use of LLMs>>

From 9df9b9d5c90d40d1e3988de0dbdf265e966247ac Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:08:05 +0000
Subject: [PATCH 38/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 8a2fea6f4..692f6c8db 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -30,7 +30,7 @@ In this post, we'll explore what LLMs are and how to use them in Java applicatio
 
 * <<what_Are_LLMs, Find out what LLMs are>>
 * <<Java_LLMs, Explore app frameworks that simplify the use of LLMs>>
-* <<using_Langchain4j, Discover how LangChain can be used in Java applications>>
+* <<using_Langchain4j, Discover how to use LangChain in Java applications>>
 * <<tryout, Try out the jakartaee-microprofile-example application>>
 * <<how_app_work, Learn how this sample application works>>
 

From 87dc4d83f04fc7fa11df7dd83cd7224b41f07f79 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:08:23 +0000
Subject: [PATCH 39/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 692f6c8db..3390925bd 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -37,7 +37,7 @@ In this post, we'll explore what LLMs are and how to use them in Java applicatio
 [#what_Are_LLMs]
 == What are LLMs?
 
-A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of paramters, using self-supervised and semi-supervised learning techniques. This makes them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.
+A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of parameters, using self-supervised and semi-supervised learning techniques. This training enables them to generate natural language and other types of content to perform a wide range of tasks.
 
 See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
 

From e3027e3a8c6631e56611b358fb266f9dfd716144 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:08:35 +0000
Subject: [PATCH 40/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 3390925bd..025601475 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -39,7 +39,7 @@ In this post, we'll explore what LLMs are and how to use them in Java applicatio
 
 A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of parameters, using self-supervised and semi-supervised learning techniques. This training enables them to generate natural language and other types of content to perform a wide range of tasks.
 
-See the introductory video below for more information on LLMs, including what they are, how they work and business applications.
+See the introductory video below for more information on LLMs, including what they are, how they work, and their business applications.
 
 video::5sLYAQS9sWQ[youtube, width="560", height="315", align="center"]
 

From cd52afc5a175081604973ce02275151d10dc0127 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:08:58 +0000
Subject: [PATCH 41/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 025601475..31df1c360 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -43,7 +43,7 @@ See the introductory video below for more information on LLMs, including what th
 
 video::5sLYAQS9sWQ[youtube, width="560", height="315", align="center"]
 
-LLMs can be found in the service offerings of almost all of the major cloud service providers, e.g. IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
+You can find LLMs in the service offerings of almost all of the major cloud service providers. For example, IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
 
 [#Java_LLMs]
 == How can we take advantage of LLMs in Java applications?

From d1886d74bc270179b7e554e3489c3587a03db127 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:09:19 +0000
Subject: [PATCH 42/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 31df1c360..e3a75c8c5 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -48,7 +48,7 @@ You can find LLMs in the service offerings of almost all of the major cloud serv
 [#Java_LLMs]
 == How can we take advantage of LLMs in Java applications?
 
-Developing applications powered by large language models isn't always an easy task. Integrating AI/LLM capabilities into an application can be challenging. To make this easier, LangChain, an open source framework was developed in 2022 to help streamline the process of creating generative AI apps.
+Integrating AI/LLM capabilities into an application can be challenging. The open source LangChain framework was developed in 2022 to help streamline the process of creating generative AI apps.
 
 LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease.
 

From 882d4cf5d9fc8bdc44214ba763017d8db0796e20 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:09:30 +0000
Subject: [PATCH 43/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index e3a75c8c5..170f290e3 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -129,7 +129,7 @@ After you see the following message, the application is ready:
 ************************************************************************
 ----
 
-To ensure the application has started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you should see a similar output to the following example:
+To ensure the application started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you should see output similar to the following example:
 
 ----
 [INFO] -------------------------------------------------------

From 22a085a4e2b29b5b632cd1403daf439402adb822 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:09:47 +0000
Subject: [PATCH 44/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 170f290e3..e0c47c0b9 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -158,7 +158,7 @@ You can type in any text that you want to chat with the AI agent. Here are some
 
 * `What is MicroProfile?`
 * `Which top 10 companies contribute MicroProfile?`
-* `any documentation?`
+* `Any documentation?`
 
 
 [#how_app_work]

From 7089089ceb9376829cc7ad80bbe123932af371e0 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:10:04 +0000
Subject: [PATCH 45/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index e0c47c0b9..301cc63db 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -204,7 +204,7 @@ Through the customized {example-location}/src/main/java/dev/langchain4j/example/
 [#external_config]
 === Externalizing the configuration
 
-As the above code, to access the model, an API key is required. For security practice, it is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+An API key is required to access the model. For security purposes, the key is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
 [source, java, role="no_copy"]

From eb4d4e9f28be4a3a5eb3b32a8d53168b4d03fb71 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:10:17 +0000
Subject: [PATCH 46/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 301cc63db..9cc89c6ac 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -264,7 +264,7 @@ See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom
     }
 ----
 
-The service recieves the user messeages through the WebSocket `onMessage()` method, forward them to the LLM by calling the `ChatAgent.chat()` method, and then boardcast the LLM responsed answers back to the client session through the `sendObect()` method.
+The service recieves the user messeages through the WebSocket `onMessage()` method, forwards them to the LLM by calling the `ChatAgent.chat()` method, and then broadcasts the LLM answers back to the client session through the `sendObect()` method.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
 [source, java, role="no_copy"]

From 532368dc8f95ef8f6ad39ed8dcb6c2d56aa910e1 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:10:29 +0000
Subject: [PATCH 47/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 9cc89c6ac..83aa76311 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -304,7 +304,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
         ...
 ----
 
-And, collect how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
+It also collects how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
 
 Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
 ----

From d629632a025a2b53b9c59ae2e6141bf6b2ae477f Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:10:44 +0000
Subject: [PATCH 48/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 83aa76311..fa2b7dc36 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -52,7 +52,7 @@ Integrating AI/LLM capabilities into an application can be challenging. The open
 
 LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease.
 
-Although LangChain is primarily available in Python and JavaScript/TypeScript versions, there are options available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs provide the ability to intergate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+Although LangChain is primarily available in Python and JavaScript/TypeScript versions, options are available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs provide the ability to integrate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
 
 image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=70%,align="center"]
 

From 3bbf89c996c77c155060f33eaa3a0971200831c7 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:10:54 +0000
Subject: [PATCH 49/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index fa2b7dc36..bba3b647c 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -65,7 +65,7 @@ Langchain4j has a very useful open source https://github.com/langchain4j/langcha
 [#tryout]
 == Try out the jakartaee-microprofile-example application
 
-To see how you could apply LangChain4j to your own Jakarta EE and/or MicroProfile application, check out this example project for yourself.
+To see how you could apply LangChain4j to your own Jakarta EE and MicroProfile applications, check out this example project for yourself.
 
 [#pre-reqs]
 === Pre-requisites

From b653a9b005b8474da675ceef8a81e02242aed174 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:11:18 +0000
Subject: [PATCH 50/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index bba3b647c..79745c22b 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -94,7 +94,7 @@ To run the application, navigate to the `jakartaee-microprofile-example` directo
 cd langchain4j-examples/jakartaee-microprofile-example
 ----
 
-and set the environment variables:
+Set the following environment variables:
 
 [source]
 ----

From 8227c3ae3b5fb61dfb3f4c872cc6890c876fd77b Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:11:30 +0000
Subject: [PATCH 51/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 79745c22b..ed04fbfa3 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -68,7 +68,7 @@ Langchain4j has a very useful open source https://github.com/langchain4j/langcha
 To see how you could apply LangChain4j to your own Jakarta EE and MicroProfile applications, check out this example project for yourself.
 
 [#pre-reqs]
-=== Pre-requisites
+=== Prerequisites
 Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
 
 The application uses Hugging Face. You need to get a Hugging Face API Key:

From cb000e189ac938d83a586dd8fc358a877524efbd Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:11:47 +0000
Subject: [PATCH 52/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index ed04fbfa3..2aab97f5b 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -69,7 +69,7 @@ To see how you could apply LangChain4j to your own Jakarta EE and MicroProfile a
 
 [#pre-reqs]
 === Prerequisites
-Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can make use of the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
+Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can use the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
 
 The application uses Hugging Face. You need to get a Hugging Face API Key:
 

From 9d4adc7b62b547dc9f9c867c3c614c795b4c73b6 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:12:03 +0000
Subject: [PATCH 53/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 2aab97f5b..6b9e073c4 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -74,7 +74,7 @@ Before you clone the application to your machine, install JDK 17 and ensure that
 The application uses Hugging Face. You need to get a Hugging Face API Key:
 
 * Sign up and login to https://huggingface.co
-* Go to Access Tokens by https://huggingface.co/settings/tokens
+* Go to the link:https://huggingface.co/settings/tokens[Access Tokens page] 
 * Create a new access token with "read" role
 
 To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running:

From 608fb17ba8bd16f87937aea30064efb4ac38f84b Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:12:11 +0000
Subject: [PATCH 54/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 6b9e073c4..34f31a3e8 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -75,7 +75,7 @@ The application uses Hugging Face. You need to get a Hugging Face API Key:
 
 * Sign up and login to https://huggingface.co
 * Go to the link:https://huggingface.co/settings/tokens[Access Tokens page] 
-* Create a new access token with "read" role
+* Create a new access token with `read` role
 
 To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
 

From 5382a301edb91010a91d1db4647dcaf9072caf81 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:12:23 +0000
Subject: [PATCH 55/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 34f31a3e8..3189fa185 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -77,7 +77,7 @@ The application uses Hugging Face. You need to get a Hugging Face API Key:
 * Go to the link:https://huggingface.co/settings/tokens[Access Tokens page] 
 * Create a new access token with `read` role
 
-To access the repository remotely you'll need to install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running:
+To access the repository remotely, install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running the following command:
 
 [source]
 ----

From 18b9b774ec29f6200df13dd0802501e816fca613 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:12:33 +0000
Subject: [PATCH 56/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 3189fa185..531d50324 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -85,7 +85,7 @@ git clone https://github.com/langchain4j/langchain4j-examples.git
 ----
 
 [#env_setup]
-=== Environment Set Up
+=== Environment setup
 
 To run the application, navigate to the `jakartaee-microprofile-example` directory:
 

From bebe94edbcc312f96ad96f404460d2c822bf4bc3 Mon Sep 17 00:00:00 2001
From: Grace Jansen <32962135+GraceJansen@users.noreply.github.com>
Date: Thu, 28 Mar 2024 21:13:05 +0000
Subject: [PATCH 57/67] Update
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc

Co-authored-by: David Mueller <48686014+dmuelle@users.noreply.github.com>
---
 posts/2024-03-28-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index 531d50324..baba3f014 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -105,7 +105,7 @@ export HUGGING_FACE_API_KEY=<your Hugging Face read token>
 [#start_app]
 === Start the application
 
-To start the application, use the provided Maven wrapper to run the https://openliberty.io/docs/latest/development-mode.html[Liberty dev mode]:
+To start the application, use the provided Maven wrapper to run Open Liberty in https://openliberty.io/docs/latest/development-mode.html[dev mode]:
 
 
 [source]

From ffb71924a9344911ed1c6556b4d757264db0bd7e Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 09:17:47 -0400
Subject: [PATCH 58/67] edits

---
 ...open-liberty-with-langchain4j-example.adoc | 34 +++++++++----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
index baba3f014..352c40fc5 100644
--- a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
@@ -24,7 +24,7 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 :example-location: https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example
 
 
-Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models, or LLMs, such as ChatGPT, over the last few years there has been a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
+Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models (LLMs) such as ChatGPT, there is a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
 
 In this post, we'll explore what LLMs are and how to use them in Java applications. We'll also get started using them in an example Jakarta EE/MicroProfile app.
 
@@ -37,13 +37,13 @@ In this post, we'll explore what LLMs are and how to use them in Java applicatio
 [#what_Are_LLMs]
 == What are LLMs?
 
-A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply langauge models that are categorized by their large size.They are trained on immense amounts of data, possibly billions of parameters, using self-supervised and semi-supervised learning techniques. This training enables them to generate natural language and other types of content to perform a wide range of tasks.
+A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply language models that are categorized by their large size. They are trained on immense amounts of data, possibly billions of parameters, by using self-supervised and semi-supervised learning techniques. This training enables them to generate natural language and other types of content to perform a wide range of tasks.
 
-See the introductory video below for more information on LLMs, including what they are, how they work, and their business applications.
+See the following introductory video for more information on LLMs, including what they are, how they work, and their business applications.
 
 video::5sLYAQS9sWQ[youtube, width="560", height="315", align="center"]
 
-You can find LLMs in the service offerings of almost all of the major cloud service providers. For example, IBM offers models through it's https://www.ibm.com/watsonx[watsonx] services, https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
+You can find LLMs in the service offerings of almost all of the major cloud service providers. For example, IBM offers models through its link:https://www.ibm.com/watsonx[watsonx] services, link:https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  link:https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
 
 [#Java_LLMs]
 == How can we take advantage of LLMs in Java applications?
@@ -52,30 +52,30 @@ Integrating AI/LLM capabilities into an application can be challenging. The open
 
 LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease.
 
-Although LangChain is primarily available in Python and JavaScript/TypeScript versions, options are available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs provide the ability to integrate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face, etc.
+Although LangChain is primarily available in Python and JavaScript/TypeScript versions, options are available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs can help integrate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face.
 
 image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=70%,align="center"]
 
 [#using_Langchain4j]
 == How to use LangChain4j in a Jakarta EE and MicroProfile application
 
-Langchain4j has a very useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repositry where it stores example applications. However, although this repo provided useful examples of using LangChain4j in general Java apps, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE/MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example` which can now be found in this https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application using Jakarta EE and MicroProfile on Open Liberty.
+Langchain4j has a useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository where it stores example applications. However, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE or MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example`, which can now be found in this https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application by using Jakarta EE and MicroProfile on Open Liberty.
 
 
 [#tryout]
 == Try out the jakartaee-microprofile-example application
 
-To see how you could apply LangChain4j to your own Jakarta EE and MicroProfile applications, check out this example project for yourself.
+To see how you can apply LangChain4j to your own Jakarta EE and MicroProfile applications, check out this example project for yourself.
 
 [#pre-reqs]
 === Prerequisites
-Before you clone the application to your machine, install JDK 17 and ensure that your `JAVA_HOME` environment variable is set.  You can use the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
+Before you clone the application to your machine, install JDK 17, and ensure that your `JAVA_HOME` environment variable is set.  You can use the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
 
 The application uses Hugging Face. You need to get a Hugging Face API Key:
 
-* Sign up and login to https://huggingface.co
-* Go to the link:https://huggingface.co/settings/tokens[Access Tokens page] 
-* Create a new access token with `read` role
+* Sign up and log in to https://huggingface.co
+* Go to the link:https://huggingface.co/settings/tokens[Access Tokens page]
+* Create an access token with `read` role
 
 To access the repository remotely, install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running the following command:
 
@@ -129,7 +129,7 @@ After you see the following message, the application is ready:
 ************************************************************************
 ----
 
-To ensure the application started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you should see output similar to the following example:
+To ensure that the application started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you can see output similar to the following example:
 
 ----
 [INFO] -------------------------------------------------------
@@ -150,7 +150,7 @@ To ensure the application started successfully, you can run the tests by pressin
 [#access_app]
 === Access the application
 
-Once the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
+After the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
 
 image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jakarta EE and MicroProfile example,width=70%,align="center"]
 
@@ -164,7 +164,7 @@ You can type in any text that you want to chat with the AI agent. Here are some
 [#how_app_work]
 == How does the application work?
 
-The application demostrates how to use the LangChain4j APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
+The application demonstrates how to use the LangChain4j APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
 
 [#create_service]
 === Creating the LangChain4j AI service
@@ -204,7 +204,7 @@ Through the customized {example-location}/src/main/java/dev/langchain4j/example/
 [#external_config]
 === Externalizing the configuration
 
-An API key is required to access the model. For security purposes, the key is not hard-coded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+An API key is required to access the model. For security purposes, the key is not hardcoded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
 [source, java, role="no_copy"]
@@ -234,7 +234,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.
     private Integer MAX_MESSAGES;
 ----
 
-To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the enviroment variables.
+To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the environment variables.
 
 ----
 hugging.face.api.key=set it by env variable
@@ -264,7 +264,7 @@ See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom
     }
 ----
 
-The service recieves the user messeages through the WebSocket `onMessage()` method, forwards them to the LLM by calling the `ChatAgent.chat()` method, and then broadcasts the LLM answers back to the client session through the `sendObect()` method.
+The service receives the user messages through the WebSocket `onMessage()` method, forwards them to the LLM by calling the `ChatAgent.chat()` method, and then broadcasts the LLM answers back to the client session through the `sendObect()` method.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
 [source, java, role="no_copy"]

From 9c054740171f31500c97a8d58983245189cc9439 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 09:19:21 -0400
Subject: [PATCH 59/67] change filename for new pub date

#3557
---
 ...adoc => 2024-04-01-open-liberty-with-langchain4j-example.adoc} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename posts/{2024-03-28-open-liberty-with-langchain4j-example.adoc => 2024-04-01-open-liberty-with-langchain4j-example.adoc} (100%)

diff --git a/posts/2024-03-28-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
similarity index 100%
rename from posts/2024-03-28-open-liberty-with-langchain4j-example.adoc
rename to posts/2024-04-01-open-liberty-with-langchain4j-example.adoc

From dd829ba77de99667f25da091a594e7cfd72591ba Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 09:23:36 -0400
Subject: [PATCH 60/67] tags for langChain4j

#3557
---
 blog_tags.json | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/blog_tags.json b/blog_tags.json
index 233ee5cd0..6ba343d4f 100644
--- a/blog_tags.json
+++ b/blog_tags.json
@@ -76,7 +76,8 @@
             "featured": "true"
         },{
             "name": "microprofile",
-            "posts": ["DevNexus24", "24.0.0.2",
+            "posts": ["open-liberty-with-langchain4j-example",
+                      "DevNexus24", "24.0.0.2",
                       "23.0.0.12", "23.0.0.11",
                       "23.0.0.11-beta", "23.0.0.10-beta",
                       "liberty-ide-tools-maven-gradle-plugins-for-java-developers", "23.0.0.6",
@@ -335,7 +336,7 @@
         },
         {
             "name": "jakarta-ee",
-            "posts": ["DevNexus24",
+            "posts": ["open-liberty-with-langchain4j-example", "DevNexus24",
                       "24.0.0.1-beta", "23.0.0.12-beta",
                       "IvarJakartaData", "23.0.0.10-beta",
                       "23.0.0.9-beta",  "23.0.0.8-beta",
@@ -428,7 +429,8 @@
         },
         {
             "name": "developer-experience",
-            "posts": ["DevNexus24", "23.0.0.12",
+            "posts": ["open-liberty-with-langchain4j-example",
+                      "DevNexus24", "23.0.0.12",
                       "23.0.0.8", "23.0.0.7",
                       "liberty-ide-tools-maven-gradle-plugins-for-java-developers", "23.0.0.6",
                       "rapid-startup-instanton", "running-spring-boot-3",

From 6eec94e7f0b1642cc90fa53bc8c4414f6f308c2a Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 10:35:52 -0400
Subject: [PATCH 61/67] update title

#3557
---
 .../2024-04-01-open-liberty-with-langchain4j-example.adoc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
index 352c40fc5..c640c987d 100644
--- a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
@@ -1,13 +1,13 @@
 ---
 layout: post
-title: "Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty"
+title: "Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
 # Do NOT change the categories section
 categories: blog
 author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
 author_github: https://github.com/gkwan-ibm
 seo-title: Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty
-seo-description: Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4j APIs.
-blog_description: "Experience AI technology in an Jakarta EE and MicroProfile application running on Open Liberty by using LangChain4j APIs."
+seo-description: Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs.
+blog_description: "Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg
 open-graph-image-alt: Open Liberty Logo
 additional_authors:
@@ -15,7 +15,7 @@ additional_authors:
   github: https://github.com/GraceJansen
   image: https://avatars0.githubusercontent.com/GraceJansen
 ---
-= Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty
+= Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
 Gilbert Kwan <https://github.com/gkwan-ibm>
 :imagesdir: /
 :url-prefix:

From 5ff7fac98935fa7196f1b55359daf3dc0f849350 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 16:31:00 -0400
Subject: [PATCH 62/67] Update
 2024-04-01-open-liberty-with-langchain4j-example.adoc

---
 posts/2024-04-01-open-liberty-with-langchain4j-example.adoc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
index c640c987d..fbdbc6198 100644
--- a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
@@ -5,7 +5,7 @@ title: "Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
 categories: blog
 author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
 author_github: https://github.com/gkwan-ibm
-seo-title: Run LangChain4j in Jakarta EE and MicroProfile application on Open Liberty
+seo-title: Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
 seo-description: Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs.
 blog_description: "Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs."
 open-graph-image: https://openliberty.io/img/twitter_card.jpg

From 4b82702dbc7a3de4909ff82e72c5efe5951614e1 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 16:40:01 -0400
Subject: [PATCH 63/67] Update
 2024-04-01-open-liberty-with-langchain4j-example.adoc

---
 ...01-open-liberty-with-langchain4j-example.adoc | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
index fbdbc6198..ebf685559 100644
--- a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
@@ -171,7 +171,7 @@ The application demonstrates how to use the LangChain4j APIs, https://openlibert
 
 The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service.
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
 [source, java, role="no_copy"]
 ----
     public Assistant getAssistant() {
@@ -193,7 +193,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.
     }
 ----
 
-Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`Assistant`] interface, the application can send messages to the LLM by its `chat()` method.
+Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[Assistant] interface, the application can send messages to the LLM by its `chat()` method.
 
 ----
     interface Assistant {
@@ -206,7 +206,7 @@ Through the customized {example-location}/src/main/java/dev/langchain4j/example/
 
 An API key is required to access the model. For security purposes, the key is not hardcoded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[`src/main/java/dev/langchain4j/example/chat/ChatAgent.java`] file.
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
 [source, java, role="no_copy"]
 ----
     @Inject
@@ -234,7 +234,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.
     private Integer MAX_MESSAGES;
 ----
 
-To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[`langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties`] file or provide them through the environment variables.
+To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties] file or provide them through the environment variables.
 
 ----
 hugging.face.api.key=set it by env variable
@@ -250,7 +250,7 @@ chat.memory.max.messages=20
 
 The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
 
-See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom.js`] file.
+See the {example-location}/src/main/webapp/chatroom.js[src/main/webapp/chatroom.js] file.
 [source, java, role="no_copy"]
 ----
     const webSocket = new WebSocket('ws://localhost:9080/chat');
@@ -266,7 +266,7 @@ See the {example-location}/src/main/webapp/chatroom.js[`src/main/webapp/chatroom
 
 The service receives the user messages through the WebSocket `onMessage()` method, forwards them to the LLM by calling the `ChatAgent.chat()` method, and then broadcasts the LLM answers back to the client session through the `sendObect()` method.
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[src/main/java/dev/langchain4j/example/chat/ChatService.java] file.
 [source, java, role="no_copy"]
 ----
     @OnMessage
@@ -293,7 +293,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
 
 To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
 
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[`src/main/java/dev/langchain4j/example/chat/ChatService.java`] file.
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[src/main/java/dev/langchain4j/example/chat/ChatService.java] file.
 [source, java, role="no_copy"]
 ----
     @OnMessage
@@ -323,7 +323,7 @@ chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
 chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
 ----
 
-If you are interested in other ways to use the LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[`src/main/java/dev/langchain4j/example/rest/ModelResource.java`] file.
+If you are interested in other ways to use the LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[src/main/java/dev/langchain4j/example/rest/ModelResource.java] file.
 
 
 [#where_to_next]

From bc144f75dd95681a39fae93b8d2d64364e30d855 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 16:50:31 -0400
Subject: [PATCH 64/67] Delete
 2024-04-01-open-liberty-with-langchain4j-example.adoc

---
 ...open-liberty-with-langchain4j-example.adoc | 341 ------------------
 1 file changed, 341 deletions(-)
 delete mode 100644 posts/2024-04-01-open-liberty-with-langchain4j-example.adoc

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
deleted file mode 100644
index ebf685559..000000000
--- a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
+++ /dev/null
@@ -1,341 +0,0 @@
----
-layout: post
-title: "Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
-# Do NOT change the categories section
-categories: blog
-author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
-author_github: https://github.com/gkwan-ibm
-seo-title: Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
-seo-description: Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs.
-blog_description: "Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs."
-open-graph-image: https://openliberty.io/img/twitter_card.jpg
-open-graph-image-alt: Open Liberty Logo
-additional_authors:
-- name: Grace Jansen
-  github: https://github.com/GraceJansen
-  image: https://avatars0.githubusercontent.com/GraceJansen
----
-= Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
-Gilbert Kwan <https://github.com/gkwan-ibm>
-:imagesdir: /
-:url-prefix:
-:url-about: /
-
-:example-location: https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example
-
-
-Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models (LLMs) such as ChatGPT, there is a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
-
-In this post, we'll explore what LLMs are and how to use them in Java applications. We'll also get started using them in an example Jakarta EE/MicroProfile app.
-
-* <<what_Are_LLMs, Find out what LLMs are>>
-* <<Java_LLMs, Explore app frameworks that simplify the use of LLMs>>
-* <<using_Langchain4j, Discover how to use LangChain in Java applications>>
-* <<tryout, Try out the jakartaee-microprofile-example application>>
-* <<how_app_work, Learn how this sample application works>>
-
-[#what_Are_LLMs]
-== What are LLMs?
-
-A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply language models that are categorized by their large size. They are trained on immense amounts of data, possibly billions of parameters, by using self-supervised and semi-supervised learning techniques. This training enables them to generate natural language and other types of content to perform a wide range of tasks.
-
-See the following introductory video for more information on LLMs, including what they are, how they work, and their business applications.
-
-video::5sLYAQS9sWQ[youtube, width="560", height="315", align="center"]
-
-You can find LLMs in the service offerings of almost all of the major cloud service providers. For example, IBM offers models through its link:https://www.ibm.com/watsonx[watsonx] services, link:https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  link:https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
-
-[#Java_LLMs]
-== How can we take advantage of LLMs in Java applications?
-
-Integrating AI/LLM capabilities into an application can be challenging. The open source LangChain framework was developed in 2022 to help streamline the process of creating generative AI apps.
-
-LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease.
-
-Although LangChain is primarily available in Python and JavaScript/TypeScript versions, options are available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs can help integrate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face.
-
-image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=70%,align="center"]
-
-[#using_Langchain4j]
-== How to use LangChain4j in a Jakarta EE and MicroProfile application
-
-Langchain4j has a useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository where it stores example applications. However, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE or MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example`, which can now be found in this https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application by using Jakarta EE and MicroProfile on Open Liberty.
-
-
-[#tryout]
-== Try out the jakartaee-microprofile-example application
-
-To see how you can apply LangChain4j to your own Jakarta EE and MicroProfile applications, check out this example project for yourself.
-
-[#pre-reqs]
-=== Prerequisites
-Before you clone the application to your machine, install JDK 17, and ensure that your `JAVA_HOME` environment variable is set.  You can use the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
-
-The application uses Hugging Face. You need to get a Hugging Face API Key:
-
-* Sign up and log in to https://huggingface.co
-* Go to the link:https://huggingface.co/settings/tokens[Access Tokens page]
-* Create an access token with `read` role
-
-To access the repository remotely, install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running the following command:
-
-[source]
-----
-git clone https://github.com/langchain4j/langchain4j-examples.git
-----
-
-[#env_setup]
-=== Environment setup
-
-To run the application, navigate to the `jakartaee-microprofile-example` directory:
-
-[source]
-----
-cd langchain4j-examples/jakartaee-microprofile-example
-----
-
-Set the following environment variables:
-
-[source]
-----
-export JAVA_HOME=<your Java 17 home path>
-export HUGGING_FACE_API_KEY=<your Hugging Face read token>
-----
-
-[#start_app]
-=== Start the application
-
-To start the application, use the provided Maven wrapper to run Open Liberty in https://openliberty.io/docs/latest/development-mode.html[dev mode]:
-
-
-[source]
-----
-./mvnw liberty:dev
-----
-
-After you see the following message, the application is ready:
-
-----
-************************************************************************
-*    Liberty is running in dev mode.
-*        Automatic generation of features: [ Off ]
-*        h - see the help menu for available actions, type 'h' and press Enter.
-*        q - stop the server and quit dev mode, press Ctrl-C or type 'q' and press Enter.
-*
-*    Liberty server port information:
-*        Liberty server HTTP port: [ 9080 ]
-*        Liberty server HTTPS port: [ 9443 ]
-*        Liberty debug port: [ 7777 ]
-************************************************************************
-----
-
-To ensure that the application started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you can see output similar to the following example:
-
-----
-[INFO] -------------------------------------------------------
-[INFO]  T E S T S
-[INFO] -------------------------------------------------------
-[INFO] Running it.dev.langchan4j.example.ChatServiceIT
-[INFO] ...
-[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.439 s...
-[INFO] ...
-[INFO] Running it.dev.langchan4j.example.ModelResourceIT
-[INFO] Tests run: 3, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.733 s...
-[INFO]
-[INFO] Results:
-[INFO]
-[INFO] Tests run: 4, Failures: 0, Errors: 0, Skipped: 0
-----
-
-[#access_app]
-=== Access the application
-
-After the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
-
-image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jakarta EE and MicroProfile example,width=70%,align="center"]
-
-You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
-
-* `What is MicroProfile?`
-* `Which top 10 companies contribute MicroProfile?`
-* `Any documentation?`
-
-
-[#how_app_work]
-== How does the application work?
-
-The application demonstrates how to use the LangChain4j APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
-
-[#create_service]
-=== Creating the LangChain4j AI service
-
-The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service.
-
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
-[source, java, role="no_copy"]
-----
-    public Assistant getAssistant() {
-        ...
-            HuggingFaceChatModel model = HuggingFaceChatModel.builder()
-                .accessToken(HUGGING_FACE_API_KEY)
-                .modelId(CHAT_MODEL_ID)
-                .timeout(ofSeconds(TIMEOUT))
-                .temperature(TEMPERATURE)
-                .maxNewTokens(MAX_NEW_TOKEN)
-                .waitForModel(true)
-                .build();
-            assistant = AiServices.builder(Assistant.class)
-                .chatLanguageModel(model)
-                .chatMemoryProvider(
-                    sessionId -> MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
-                .build();
-       ...
-    }
-----
-
-Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[Assistant] interface, the application can send messages to the LLM by its `chat()` method.
-
-----
-    interface Assistant {
-       String chat(@MemoryId String sessionId, @UserMessage String userMessage);
-    }
-----
-
-[#external_config]
-=== Externalizing the configuration
-
-An API key is required to access the model. For security purposes, the key is not hardcoded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
-
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
-[source, java, role="no_copy"]
-----
-    @Inject
-    @ConfigProperty(name = "hugging.face.api.key")
-    private String HUGGING_FACE_API_KEY;
-
-    @Inject
-    @ConfigProperty(name = "chat.model.id")
-    private String CHAT_MODEL_ID;
-
-    @Inject
-    @ConfigProperty(name = "chat.model.timeout")
-    private Integer TIMEOUT;
-
-    @Inject
-    @ConfigProperty(name = "chat.model.max.token")
-    private Integer MAX_NEW_TOKEN;
-
-    @Inject
-    @ConfigProperty(name = "chat.model.temperature")
-    private Double TEMPERATURE;
-
-    @Inject
-    @ConfigProperty(name = "chat.memory.max.messages")
-    private Integer MAX_MESSAGES;
-----
-
-To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties] file or provide them through the environment variables.
-
-----
-hugging.face.api.key=set it by env variable
-chat.model.id=NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
-chat.model.timeout=120
-chat.model.max.token=200
-chat.model.temperature=1.0
-chat.memory.max.messages=20
-----
-
-[#communicate]
-=== Communicating between the client and LLM
-
-The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
-
-See the {example-location}/src/main/webapp/chatroom.js[src/main/webapp/chatroom.js] file.
-[source, java, role="no_copy"]
-----
-    const webSocket = new WebSocket('ws://localhost:9080/chat');
-    ...
-    function sendMessage() {
-        ...
-        var myMessage = document.getElementById('myMessage').value;
-        ...
-        webSocket.send(myMessage);
-        ...
-    }
-----
-
-The service receives the user messages through the WebSocket `onMessage()` method, forwards them to the LLM by calling the `ChatAgent.chat()` method, and then broadcasts the LLM answers back to the client session through the `sendObect()` method.
-
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[src/main/java/dev/langchain4j/example/chat/ChatService.java] file.
-[source, java, role="no_copy"]
-----
-    @OnMessage
-    public void onMessage(String message, Session session) {
-        ...
-        try {
-            ...
-            answer = agent.chat(sessionId, message);
-        } catch (Exception e) {
-            ...
-        }
-
-        try {
-            session.getBasicRemote().sendObject(answer);
-        } catch (Exception e) {
-            e.printStackTrace();
-        }
-
-    }
-----
-
-[#enable_metrics]
-=== Enabling metrics
-
-To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
-
-See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[src/main/java/dev/langchain4j/example/chat/ChatService.java] file.
-[source, java, role="no_copy"]
-----
-    @OnMessage
-    @Timed(name = "chatProcessingTime",
-           absolute = true,
-           description = "Time needed chatting to the agent.")
-    public void onMessage(String message, Session session) {
-        ...
-----
-
-It also collects how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
-
-Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
-----
-# HELP chatProcessingTime_seconds Time needed chatting to the agent.
-# TYPE chatProcessingTime_seconds summary
-chatProcessingTime_seconds{mp_scope="application",quantile="0.5",} 0.0
-chatProcessingTime_seconds{mp_scope="application",quantile="0.75",} 0.0
-chatProcessingTime_seconds{mp_scope="application",quantile="0.95",} 0.0
-chatProcessingTime_seconds{mp_scope="application",quantile="0.98",} 0.0
-chatProcessingTime_seconds{mp_scope="application",quantile="0.99",} 0.0
-chatProcessingTime_seconds{mp_scope="application",quantile="0.999",} 0.0
-chatProcessingTime_seconds_count{mp_scope="application",} 6.0
-chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
-# HELP chatProcessingTime_seconds_max Time needed chatting to the agent.
-# TYPE chatProcessingTime_seconds_max gauge
-chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
-----
-
-If you are interested in other ways to use the LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[src/main/java/dev/langchain4j/example/rest/ModelResource.java] file.
-
-
-[#where_to_next]
-== Where to next?
-
-Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty.
-
-[#help_links]
-== Helpful links
-* link:https://github.com/langchain4j[LangChain4j]
-* link:https://huggingface.co/models[Hugging Face LLMs]
-* link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket]
-* link:https://openliberty.io/guides/cdi-intro.html[Injecting dependencies into microservices]
-* link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices]
-* link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice]

From 0fdb9cbb41bb997e906f78f45c5f241f76d2e8f5 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Fri, 29 Mar 2024 19:26:08 -0400
Subject: [PATCH 65/67] Create
 2024-04-01-open-liberty-with-langchain4j-example.adoc

---
 ...open-liberty-with-langchain4j-example.adoc | 341 ++++++++++++++++++
 1 file changed, 341 insertions(+)
 create mode 100644 posts/2024-04-01-open-liberty-with-langchain4j-example.adoc

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
new file mode 100644
index 000000000..ebf685559
--- /dev/null
+++ b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
@@ -0,0 +1,341 @@
+---
+layout: post
+title: "Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
+# Do NOT change the categories section
+categories: blog
+author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
+author_github: https://github.com/gkwan-ibm
+seo-title: Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
+seo-description: Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs.
+blog_description: "Experience AI technology in Jakarta EE and MicroProfile applications that run on Open Liberty by using LangChain4j APIs."
+open-graph-image: https://openliberty.io/img/twitter_card.jpg
+open-graph-image-alt: Open Liberty Logo
+additional_authors:
+- name: Grace Jansen
+  github: https://github.com/GraceJansen
+  image: https://avatars0.githubusercontent.com/GraceJansen
+---
+= Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
+Gilbert Kwan <https://github.com/gkwan-ibm>
+:imagesdir: /
+:url-prefix:
+:url-about: /
+
+:example-location: https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example
+
+
+Artificial Intelligence (AI) is an exciting and disruptive field that is already transforming businesses, and even entire industries, by enabling automation, improving decision-making and unlocking new insights from data. With the rise in large language models (LLMs) such as ChatGPT, there is a significant shift in the performance of AI and its potential to drive enterprise value. So, how will this impact on software development and the creation of cloud-native Java applications for enterprises?
+
+In this post, we'll explore what LLMs are and how to use them in Java applications. We'll also get started using them in an example Jakarta EE/MicroProfile app.
+
+* <<what_Are_LLMs, Find out what LLMs are>>
+* <<Java_LLMs, Explore app frameworks that simplify the use of LLMs>>
+* <<using_Langchain4j, Discover how to use LangChain in Java applications>>
+* <<tryout, Try out the jakartaee-microprofile-example application>>
+* <<how_app_work, Learn how this sample application works>>
+
+[#what_Are_LLMs]
+== What are LLMs?
+
+A language model is a model of natural language based on probabilities. They are able to generate probabilities of a series of words together. https://www.ibm.com/topics/large-language-models[Large language models (LLMs)] are simply language models that are categorized by their large size. They are trained on immense amounts of data, possibly billions of parameters, by using self-supervised and semi-supervised learning techniques. This training enables them to generate natural language and other types of content to perform a wide range of tasks.
+
+See the following introductory video for more information on LLMs, including what they are, how they work, and their business applications.
+
+video::5sLYAQS9sWQ[youtube, width="560", height="315", align="center"]
+
+You can find LLMs in the service offerings of almost all of the major cloud service providers. For example, IBM offers models through its link:https://www.ibm.com/watsonx[watsonx] services, link:https://azure.microsoft.com/en-us/solutions/ai[Microsoft Azure] offers LLMs like Llama 2 and OpenAI GPT-4, and  link:https://aws.amazon.com/bedrock/[Amazon Bedrock] offers models from a range of AI companies.
+
+[#Java_LLMs]
+== How can we take advantage of LLMs in Java applications?
+
+Integrating AI/LLM capabilities into an application can be challenging. The open source LangChain framework was developed in 2022 to help streamline the process of creating generative AI apps.
+
+LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining and organizes the large quantities of data these models require so that they can be accessed with ease.
+
+Although LangChain is primarily available in Python and JavaScript/TypeScript versions, options are available to use LangChain in Java applications through community projects like https://github.com/langchain4j/langchain4j[LangChain4j]. LangChain4j APIs can help integrate LLMs into your Java application for different AI platforms, such as OpenAI and Hugging Face.
+
+image::/img/blog/langchain4j.png[langchain4j GitHub README image,width=70%,align="center"]
+
+[#using_Langchain4j]
+== How to use LangChain4j in a Jakarta EE and MicroProfile application
+
+Langchain4j has a useful open source https://github.com/langchain4j/langchain4j-examples[langchain4j-examples] GitHub repository where it stores example applications. However, we could not find any examples showcasing how you could experience these AI technologies in a Jakarta EE or MicroProfile based application. So, we decided to build one ourselves called `jakartaee-microprofile-example`, which can now be found in this https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example[langchain4j-examples] GitHub repository. This demo application highlights how to use LangChain4j APIs in an application by using Jakarta EE and MicroProfile on Open Liberty.
+
+
+[#tryout]
+== Try out the jakartaee-microprofile-example application
+
+To see how you can apply LangChain4j to your own Jakarta EE and MicroProfile applications, check out this example project for yourself.
+
+[#pre-reqs]
+=== Prerequisites
+Before you clone the application to your machine, install JDK 17, and ensure that your `JAVA_HOME` environment variable is set.  You can use the https://developer.ibm.com/languages/java/semeru-runtimes/downloads[IBM Semeru Runtime] as your chosen Java runtime. This runtime offers performance benefits from deep technology investment in projects such as Eclipse OpenJ9 and is available across a wide variety of hardware and software platforms. To find out more about IBM Semeru Runtime, see https://openliberty.io/blog/2022/08/19/open-liberty-semeru-performance.html[Open Liberty and Semeru Runtimes, cloud-native performance that matters].
+
+The application uses Hugging Face. You need to get a Hugging Face API Key:
+
+* Sign up and log in to https://huggingface.co
+* Go to the link:https://huggingface.co/settings/tokens[Access Tokens page]
+* Create an access token with `read` role
+
+To access the repository remotely, install https://git-scm.com/book/en/v2/Getting-Started-Installing-Git[Git] if you haven't already. You can clone the `langchain4j-examples` GitHub repository to your local machine by running the following command:
+
+[source]
+----
+git clone https://github.com/langchain4j/langchain4j-examples.git
+----
+
+[#env_setup]
+=== Environment setup
+
+To run the application, navigate to the `jakartaee-microprofile-example` directory:
+
+[source]
+----
+cd langchain4j-examples/jakartaee-microprofile-example
+----
+
+Set the following environment variables:
+
+[source]
+----
+export JAVA_HOME=<your Java 17 home path>
+export HUGGING_FACE_API_KEY=<your Hugging Face read token>
+----
+
+[#start_app]
+=== Start the application
+
+To start the application, use the provided Maven wrapper to run Open Liberty in https://openliberty.io/docs/latest/development-mode.html[dev mode]:
+
+
+[source]
+----
+./mvnw liberty:dev
+----
+
+After you see the following message, the application is ready:
+
+----
+************************************************************************
+*    Liberty is running in dev mode.
+*        Automatic generation of features: [ Off ]
+*        h - see the help menu for available actions, type 'h' and press Enter.
+*        q - stop the server and quit dev mode, press Ctrl-C or type 'q' and press Enter.
+*
+*    Liberty server port information:
+*        Liberty server HTTP port: [ 9080 ]
+*        Liberty server HTTPS port: [ 9443 ]
+*        Liberty debug port: [ 7777 ]
+************************************************************************
+----
+
+To ensure that the application started successfully, you can run the tests by pressing the `enter/return` key from the command-line session. If the tests pass, you can see output similar to the following example:
+
+----
+[INFO] -------------------------------------------------------
+[INFO]  T E S T S
+[INFO] -------------------------------------------------------
+[INFO] Running it.dev.langchan4j.example.ChatServiceIT
+[INFO] ...
+[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.439 s...
+[INFO] ...
+[INFO] Running it.dev.langchan4j.example.ModelResourceIT
+[INFO] Tests run: 3, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.733 s...
+[INFO]
+[INFO] Results:
+[INFO]
+[INFO] Tests run: 4, Failures: 0, Errors: 0, Skipped: 0
+----
+
+[#access_app]
+=== Access the application
+
+After the application is running, you can access it through a browser of your choice at http://localhost:9080/ and start experimenting with it.
+
+image::/img/blog/langchain4j-example-chat-room.png[Chat Room of LangChain4j Jakarta EE and MicroProfile example,width=70%,align="center"]
+
+You can type in any text that you want to chat with the AI agent. Here are some suggested messages:
+
+* `What is MicroProfile?`
+* `Which top 10 companies contribute MicroProfile?`
+* `Any documentation?`
+
+
+[#how_app_work]
+== How does the application work?
+
+The application demonstrates how to use the LangChain4j APIs, https://openliberty.io/docs/ref/feature/#cdi-4.0.html[Jakarta Contexts and Dependency Injection], https://openliberty.io/docs/latest/reference/feature/websocket-2.1.html[Jakarta WebSocket], https://openliberty.io/docs/ref/feature/#mpConfig-3.0.html[MicroProfile Config], and https://openliberty.io/docs/latest/reference/feature/mpMetrics-5.1.html[MicroProfile Metrics] features.
+
+[#create_service]
+=== Creating the LangChain4j AI service
+
+The application uses the `HuggingFaceChatModel` class to provide the model for building the AI service.
+
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
+[source, java, role="no_copy"]
+----
+    public Assistant getAssistant() {
+        ...
+            HuggingFaceChatModel model = HuggingFaceChatModel.builder()
+                .accessToken(HUGGING_FACE_API_KEY)
+                .modelId(CHAT_MODEL_ID)
+                .timeout(ofSeconds(TIMEOUT))
+                .temperature(TEMPERATURE)
+                .maxNewTokens(MAX_NEW_TOKEN)
+                .waitForModel(true)
+                .build();
+            assistant = AiServices.builder(Assistant.class)
+                .chatLanguageModel(model)
+                .chatMemoryProvider(
+                    sessionId -> MessageWindowChatMemory.withMaxMessages(MAX_MESSAGES))
+                .build();
+       ...
+    }
+----
+
+Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[Assistant] interface, the application can send messages to the LLM by its `chat()` method.
+
+----
+    interface Assistant {
+       String chat(@MemoryId String sessionId, @UserMessage String userMessage);
+    }
+----
+
+[#external_config]
+=== Externalizing the configuration
+
+An API key is required to access the model. For security purposes, the key is not hardcoded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
+[source, java, role="no_copy"]
+----
+    @Inject
+    @ConfigProperty(name = "hugging.face.api.key")
+    private String HUGGING_FACE_API_KEY;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.id")
+    private String CHAT_MODEL_ID;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.timeout")
+    private Integer TIMEOUT;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.max.token")
+    private Integer MAX_NEW_TOKEN;
+
+    @Inject
+    @ConfigProperty(name = "chat.model.temperature")
+    private Double TEMPERATURE;
+
+    @Inject
+    @ConfigProperty(name = "chat.memory.max.messages")
+    private Integer MAX_MESSAGES;
+----
+
+To fine tune the LangChain4j model or even try out another LLM, you simply update the values in the {example-location}/src/main/resources/META-INF/microprofile-config.properties[langchain4j-examples/jakartaee-microprofile-example/src/main/resources/META-INF/microprofile-config.properties] file or provide them through the environment variables.
+
+----
+hugging.face.api.key=set it by env variable
+chat.model.id=NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
+chat.model.timeout=120
+chat.model.max.token=200
+chat.model.temperature=1.0
+chat.memory.max.messages=20
+----
+
+[#communicate]
+=== Communicating between the client and LLM
+
+The application provides the interactive UI client for users to communicate with the LLM. Jakarta WebSocket enables two-way communication between the client and the `ChatService` service. Each client makes an HTTP connection to the service and send out the messages by the `send()` method.
+
+See the {example-location}/src/main/webapp/chatroom.js[src/main/webapp/chatroom.js] file.
+[source, java, role="no_copy"]
+----
+    const webSocket = new WebSocket('ws://localhost:9080/chat');
+    ...
+    function sendMessage() {
+        ...
+        var myMessage = document.getElementById('myMessage').value;
+        ...
+        webSocket.send(myMessage);
+        ...
+    }
+----
+
+The service receives the user messages through the WebSocket `onMessage()` method, forwards them to the LLM by calling the `ChatAgent.chat()` method, and then broadcasts the LLM answers back to the client session through the `sendObect()` method.
+
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[src/main/java/dev/langchain4j/example/chat/ChatService.java] file.
+[source, java, role="no_copy"]
+----
+    @OnMessage
+    public void onMessage(String message, Session session) {
+        ...
+        try {
+            ...
+            answer = agent.chat(sessionId, message);
+        } catch (Exception e) {
+            ...
+        }
+
+        try {
+            session.getBasicRemote().sendObject(answer);
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+
+    }
+----
+
+[#enable_metrics]
+=== Enabling metrics
+
+To determine the performance and health of the application, it uses the MicroProfile Metrics feature to collect how much processing time is needed for a chat by applying the `@Timed` annotation to the `onMessage()` method.
+
+See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatService.java[src/main/java/dev/langchain4j/example/chat/ChatService.java] file.
+[source, java, role="no_copy"]
+----
+    @OnMessage
+    @Timed(name = "chatProcessingTime",
+           absolute = true,
+           description = "Time needed chatting to the agent.")
+    public void onMessage(String message, Session session) {
+        ...
+----
+
+It also collects how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
+
+Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
+----
+# HELP chatProcessingTime_seconds Time needed chatting to the agent.
+# TYPE chatProcessingTime_seconds summary
+chatProcessingTime_seconds{mp_scope="application",quantile="0.5",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.75",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.95",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.98",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.99",} 0.0
+chatProcessingTime_seconds{mp_scope="application",quantile="0.999",} 0.0
+chatProcessingTime_seconds_count{mp_scope="application",} 6.0
+chatProcessingTime_seconds_sum{mp_scope="application",} 31.674357666
+# HELP chatProcessingTime_seconds_max Time needed chatting to the agent.
+# TYPE chatProcessingTime_seconds_max gauge
+chatProcessingTime_seconds_max{mp_scope="application",} 13.191547042
+----
+
+If you are interested in other ways to use the LangChain4j APIs, you can study the REST APIs that are provided by the {example-location}/src/main/java/dev/langchain4j/example/rest/ModelResource.java[src/main/java/dev/langchain4j/example/rest/ModelResource.java] file.
+
+
+[#where_to_next]
+== Where to next?
+
+Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty.
+
+[#help_links]
+== Helpful links
+* link:https://github.com/langchain4j[LangChain4j]
+* link:https://huggingface.co/models[Hugging Face LLMs]
+* link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket]
+* link:https://openliberty.io/guides/cdi-intro.html[Injecting dependencies into microservices]
+* link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices]
+* link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice]

From cf4898d17c6fdf683c45308b4c0cbc4210707552 Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Mon, 1 Apr 2024 08:21:36 -0400
Subject: [PATCH 66/67] Update
 2024-04-01-open-liberty-with-langchain4j-example.adoc

---
 posts/2024-04-01-open-liberty-with-langchain4j-example.adoc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
index ebf685559..011c43a6a 100644
--- a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
@@ -1,6 +1,6 @@
 ---
 layout: post
-title: "Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty
+title: "Run AI-enabled Jakarta EE and MicroProfile applications on Open Liberty"
 # Do NOT change the categories section
 categories: blog
 author_picture: https://avatars3.githubusercontent.com/gkwan-ibm
@@ -20,7 +20,6 @@ Gilbert Kwan <https://github.com/gkwan-ibm>
 :imagesdir: /
 :url-prefix:
 :url-about: /
-
 :example-location: https://github.com/langchain4j/langchain4j-examples/tree/main/jakartaee-microprofile-example
 
 

From c6a848a742cf5cdb258f127fbfa6505d24e5d17c Mon Sep 17 00:00:00 2001
From: David Mueller <David.Mueller1@ibm.com>
Date: Mon, 1 Apr 2024 09:37:45 -0400
Subject: [PATCH 67/67] edits per review

---
 ...-open-liberty-with-langchain4j-example.adoc | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
index 011c43a6a..c389e33bf 100644
--- a/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
+++ b/posts/2024-04-01-open-liberty-with-langchain4j-example.adoc
@@ -192,7 +192,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.
     }
 ----
 
-Through the customized {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[Assistant] interface, the application can send messages to the LLM by its `chat()` method.
+Through the customized `Assistant` interface, the application can send messages to the LLM by its `chat()` method.
 
 ----
     interface Assistant {
@@ -203,7 +203,7 @@ Through the customized {example-location}/src/main/java/dev/langchain4j/example/
 [#external_config]
 === Externalizing the configuration
 
-An API key is required to access the model. For security purposes, the key is not hardcoded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices] document.
+An API key is required to access the model. For security purposes, the key is not hardcoded in the code. The application externalizes the API key and the LangChain4j model properties with the MicroProfile Config feature that helps the application to run in different environments without code changes. You can learn more from the https://openliberty.io/docs/latest/external-configuration.html[External configuration of microservices^] document.
 
 See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatAgent.java[src/main/java/dev/langchain4j/example/chat/ChatAgent.java] file.
 [source, java, role="no_copy"]
@@ -303,9 +303,7 @@ See the {example-location}/src/main/java/dev/langchain4j/example/chat/ChatServic
         ...
 ----
 
-It also collects how many agents are created by applying the `@Counted` annotation to the `AgentManager.createAgent()` method.
-
-Visit the url http://localhost:9080/metrics?scope=application to check out the metrics.
+Visit the http://localhost:9080/metrics?scope=application URL to check out the metrics.
 ----
 # HELP chatProcessingTime_seconds Time needed chatting to the agent.
 # TYPE chatProcessingTime_seconds summary
@@ -328,13 +326,13 @@ If you are interested in other ways to use the LangChain4j APIs, you can study t
 [#where_to_next]
 == Where to next?
 
-Check out the https://openliberty.io/guides/[Open Liberty guides] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty.
+Check out the link:https://openliberty.io/guides/[Open Liberty guides^] for more information and interactive tutorials that walk you through using more Jakarta EE and MicroProfile APIs with Open Liberty.
 
 [#help_links]
 == Helpful links
 * link:https://github.com/langchain4j[LangChain4j]
 * link:https://huggingface.co/models[Hugging Face LLMs]
-* link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket]
-* link:https://openliberty.io/guides/cdi-intro.html[Injecting dependencies into microservices]
-* link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices]
-* link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice]
+* link:https://openliberty.io/guides/jakarta-websocket.html[Bidirectional communication between services using Jakarta WebSocket^]
+* link:https://openliberty.io/guides/cdi-intro.html[Injecting dependencies into microservices^]
+* link:https://openliberty.io/guides/microprofile-config.html[Configuring microservices^]
+* link:https://openliberty.io/guides/microprofile-metrics.html[Providing metrics from a microservice^]