build(spring-ai): Spring AI 1.0.0-M3

pom.xml

@@ -21,7 +21,7 @@
         <maven.compiler.target>21</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
 
-        <spring-ai.version>1.0.0-M2</spring-ai.version>
+        <spring-ai.version>1.0.0-M3</spring-ai.version>
         <spring-shell.version>3.3.3</spring-shell.version>
         <lombok.version>1.18.34</lombok.version>
     </properties>

workshop/exercise-1.md

@@ -17,6 +17,7 @@ In the LLMService constructor, set chatClient with the result of calling `build(
 
 ```java
 private final ChatClient chatClient;
+private final SystemMessage systemMessage;
 
 public LLMService(ChatClient.Builder builder, @Value("classpath:/prompt-system.md") Resource promptSystem) {
     this.systemMessage = new SystemMessage(promptSystem);
@@ -29,12 +30,12 @@ public LLMService(ChatClient.Builder builder, @Value("classpath:/prompt-system.m
 Update the `prompt-system.md` file in `src/main/resources` folder with the following content:
 
 ```markdown
-Please answer the question asked and provide the shortest possible response without extra text, using formal English language.
+Please answer the question asked and provide the shortest possible response without extra text nor line-breaks, using formal English language.
 ```
 
 ### Part 3 - Create query options object
 
-Create a `OllamaOptions` attribute and initialize it in the constructor by using `OllamaOptions.create()` method and set model to `mistral:7b` and temperature to `0.8f`.
+Create a `OllamaOptions` attribute and initialize it in the constructor by using `OllamaOptions.create()` method and set model to `mistral:7b` and temperature to `0.8`.
 
 ### Part 4 - Implement the model query in streaming mode
 

workshop/exercise-4.md

@@ -127,7 +127,7 @@ Message message = promptTemplate.createMessage(Map.of("context", context, "quest
 Prompt prompt = new Prompt(List.of(systemMessage, message), 
                 OllamaOptions.create()
                 .withModel("mistral:7b")
-                .withTemperature(0.9f));
+                .withTemperature(0.9));
 
 System.out.println("Preparing the answer...");
 

workshop/solution/exercise-1/LLMService.java

@@ -23,15 +23,15 @@
 public class LLMService {
 
     private final ChatClient chatClient;
-    private final OllamaOptions options;
     private final SystemMessage systemMessage;
+    private final OllamaOptions options;
 
     public LLMService(ChatClient.Builder builder, @Value("classpath:/prompt-system.md") Resource promptSystem) {
         this.systemMessage = new SystemMessage(promptSystem);
         this.chatClient = builder.build();
         this.options = OllamaOptions.create()
                 .withModel("mistral:7b")
-                .withTemperature(0.8f);
+                .withTemperature(0.8);
     }
 
     private Stream<String> getResponse(final Message userMessage) {

workshop/solution/exercise-2/LLMService.java

@@ -23,16 +23,16 @@
 public class LLMService {
 
     private final ChatClient chatClient;
-    private final OllamaOptions options;
     private final SystemMessage systemMessage;
+    private final OllamaOptions options;
     private final List<Message> history;
 
     public LLMService(ChatClient.Builder builder, @Value("classpath:/prompt-system.md") Resource promptSystem) {
         this.systemMessage = new SystemMessage(promptSystem);
         this.chatClient = builder.build();
         this.options = OllamaOptions.create()
                 .withModel("mistral:7b")
-                .withTemperature(0.8f);
+                .withTemperature(0.8);
         this.history = new ArrayList<>();
     }
 

workshop/solution/exercise-3/LLMService.java

@@ -23,8 +23,8 @@
 public class LLMService {
 
     private final ChatClient chatClient;
-    private final OllamaOptions options;
     private final SystemMessage systemMessage;
+    private final OllamaOptions options;
     private final List<Message> history;
     private final DataService dataService;
     private final PromptTemplate userPromptTemplate;
@@ -34,7 +34,7 @@ public LLMService(ChatClient.Builder builder, @Value("classpath:/prompt-system.m
         this.chatClient = builder.build();
         this.options = OllamaOptions.create()
                 .withModel("mistral:7b")
-                .withTemperature(0.8f);
+                .withTemperature(0.8);
         this.history = new ArrayList<>();
         this.dataService = dataService;
         this.userPromptTemplate = new PromptTemplate("""

workshop/solution/exercise-4/RAGService.java

@@ -47,7 +47,7 @@ public Stream<String> getResponse(final String question) {
         Prompt prompt = new Prompt(List.of(systemMessage, message),
                 OllamaOptions.create()
                         .withModel("mistral:7b")
-                        .withTemperature(0.9f));
+                        .withTemperature(0.9));
 
         System.out.println("Preparing the answer...");