Add 'Classification' example and update 'Question Answering (RAG)'

00-use-cases/classification/README.md

@@ -0,0 +1,59 @@
+# Classification
+
+Classification with LLMs via Ollama.
+
+## Running the application
+
+The application relies on Ollama for providing LLMs. You can either run Ollama locally on your laptop, or rely on the Testcontainers support in Spring Boot to spin up an Ollama service automatically.
+
+### Ollama as a native application
+
+First, make sure you have [Ollama](https://ollama.ai) installed on your laptop.
+Then, use Ollama to run the _mistral_ large language model.
+
+```shell
+ollama run mistral
+```
+
+Finally, run the Spring Boot application.
+
+```shell
+./gradlew bootRun
+```
+
+### Ollama as a dev service with Testcontainers
+
+The application relies on the native Testcontainers support in Spring Boot to spin up an Ollama service with a _mistral_ model at startup time.
+
+```shell
+./gradlew bootTestRun
+```
+
+## Calling the application
+
+You can now call the application that will use Ollama and _mistral_ to classify your text.
+This example uses [httpie](https://httpie.io) to send HTTP requests.
+
+Simple prompt:
+
+```shell
+http --raw "The piano is an instrument that evokes warmth and introspection, creating a sense of intimacy and drama." :8080/classify/simple
+```
+
+Structured prompt:
+
+```shell
+http --raw "The piano is an instrument that evokes warmth and introspection, creating a sense of intimacy and drama." :8080/classify/structured
+```
+
+Few shots simple prompt:
+
+```shell
+http --raw "The piano is an instrument that evokes warmth and introspection, creating a sense of intimacy and drama." :8080/classify/few-shots-prompt
+```
+
+Few shots with history prompt:
+
+```shell
+http --raw "The piano is an instrument that evokes warmth and introspection, creating a sense of intimacy and drama." :8080/classify/few-shots-history
+```

00-use-cases/classification/src/main/java/com/thomasvitale/ai/spring/Classification.java

@@ -0,0 +1,13 @@
+package com.thomasvitale.ai.spring;
+
+import org.springframework.boot.SpringApplication;
+import org.springframework.boot.autoconfigure.SpringBootApplication;
+
+@SpringBootApplication
+public class Classification {
+
+    public static void main(String[] args) {
+        SpringApplication.run(Classification.class, args);
+    }
+
+}

00-use-cases/classification/src/main/java/com/thomasvitale/ai/spring/ClassificationController.java

@@ -0,0 +1,36 @@
+package com.thomasvitale.ai.spring;
+
+import org.springframework.web.bind.annotation.PostMapping;
+import org.springframework.web.bind.annotation.RequestBody;
+import org.springframework.web.bind.annotation.RestController;
+
+@RestController
+class ClassificationController {
+
+    private final ClassificationService classificationService;
+
+    ClassificationController(ClassificationService classificationService) {
+        this.classificationService = classificationService;
+    }
+
+    @PostMapping("/classify/simple")
+    String classifySimple(@RequestBody String input) {
+        return classificationService.classifySimple(input);
+    }
+
+    @PostMapping("/classify/structured")
+    ClassificationType classifyStructured(@RequestBody String input) {
+        return classificationService.classifyStructured(input);
+    }
+
+    @PostMapping("/classify/few-shots-prompt")
+    ClassificationType classifyFewShotsPrompt(@RequestBody String input) {
+        return classificationService.classifyFewShotsPrompt(input);
+    }
+
+    @PostMapping("/classify/few-shots-history")
+    ClassificationType classifyFewShotsHistory(@RequestBody String input) {
+        return classificationService.classifyFewShotsHistory(input);
+    }
+
+}

00-use-cases/classification/src/main/java/com/thomasvitale/ai/spring/ClassificationService.java

@@ -0,0 +1,106 @@
+package com.thomasvitale.ai.spring;
+
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.messages.AssistantMessage;
+import org.springframework.ai.chat.messages.Message;
+import org.springframework.ai.chat.messages.SystemMessage;
+import org.springframework.ai.chat.messages.UserMessage;
+import org.springframework.stereotype.Service;
+
+import java.util.List;
+
+@Service
+class ClassificationService {
+
+    private final ChatClient chatClient;
+
+    ClassificationService(ChatClient.Builder chatClientBuilder) {
+        this.chatClient = chatClientBuilder.build();
+    }
+
+    String classifySimple(String message) {
+        return chatClient
+                .prompt()
+                .user(userSpec -> userSpec.text("""
+                    Classify the type of the provided text and return the classification type as "BOOK" OR "MUSIC".
+            
+                    {text}
+                    """)
+                    .param("text", message))
+                .call()
+                .content();
+    }
+
+    ClassificationType classifyStructured(String message) {
+        return chatClient
+                .prompt()
+                .user(userSpec -> userSpec.text("""
+                    Classify the type of the provided text and return the classification type.
+                    ---------------------
+                    TEXT:
+                    {text}
+                    ---------------------
+                    """)
+                        .param("text", message))
+                .user(message)
+                .call()
+                .entity(ClassificationType.class);
+    }
+
+    ClassificationType classifyFewShotsPrompt(String message) {
+        return chatClient
+                .prompt()
+                .user(userSpec -> userSpec.text("""
+                    Classify the type of the provided text and return the classification type.
+
+                    For example:
+        
+                    Input: The celesta is a good instrument for fantasy or mystery compositions, creating a sense of mystical and supernatural.
+                    Output: "MUSIC"
+        
+                    Input: In "The Lord of The Rings", Tolkien wrote the famous words: "There’s some good in this world, Mr. Frodo… and it’s worth fighting for.".
+                    Output: "BOOK"
+        
+                    Input: They're taking the hobbits to Isengard! To Isengard! To Isengard!
+                    Output: "UNKNOWN"
+        
+                    ---------------------
+                    TEXT:
+                    {text}
+                    ---------------------
+                    """)
+                        .param("text", message))
+                .call()
+                .entity(ClassificationType.class);
+    }
+
+    ClassificationType classifyFewShotsHistory(String message) {
+        return chatClient
+                .prompt()
+                .messages(getPromptWithFewShotsHistory(message))
+                .call()
+                .entity(ClassificationType.class);
+    }
+
+    private List<Message> getPromptWithFewShotsHistory(String message) {
+        return List.of(
+                new SystemMessage("""
+                    Classify the type of the provided text and return the classification type.
+                    """),
+                new UserMessage("""
+                    The celesta is a good instrument for fantasy or mystery compositions, creating a sense of mystical and supernatural.
+                    """),
+                new AssistantMessage("MUSIC"),
+                new UserMessage(""" 
+                        In "The Lord of The Rings", Tolkien wrote the famous words: "There’s some good in this world, Mr. Frodo… and it’s worth fighting for.".
+                        """),
+                new AssistantMessage("BOOK"),
+                new UserMessage("""
+                        They're taking the hobbits to Isengard! To Isengard! To Isengard!
+                        """),
+                new AssistantMessage("UNKNOWN"),
+                new UserMessage(message)
+        );
+    }
+
+}

00-use-cases/classification/src/main/java/com/thomasvitale/ai/spring/ClassificationType.java

@@ -0,0 +1,5 @@
+package com.thomasvitale.ai.spring;
+
+public enum ClassificationType {
+    BOOK, MUSIC, UNKNOWN;
+}

00-use-cases/classification/src/main/resources/application.yml

@@ -0,0 +1,9 @@
+spring:
+  ai:
+    ollama:
+      chat:
+        options:
+          model: mistral
+  threads:
+    virtual:
+      enabled: true

00-use-cases/classification/src/test/java/com/thomasvitale/ai/spring/ClassificationTests.java

@@ -0,0 +1,13 @@
+package com.thomasvitale.ai.spring;
+
+import org.junit.jupiter.api.Test;
+import org.springframework.boot.test.context.SpringBootTest;
+
+@SpringBootTest
+class ClassificationTests {
+
+    @Test
+    void contextLoads() {
+    }
+
+}

00-use-cases/question-answering-with-documents/src/test/java/com/thomasvitale/ai/spring/TestQuestionAnsweringWithDocuments.java → 00-use-cases/classification/src/test/java/com/thomasvitale/ai/spring/TestClassification.java

@@ -9,18 +9,18 @@
 import org.testcontainers.utility.DockerImageName;
 
 @TestConfiguration(proxyBeanMethods = false)
-public class TestQuestionAnsweringWithDocuments {
+public class TestClassification {
 
     @Bean
     @RestartScope
     @ServiceConnection
     OllamaContainer ollama() {
-        return new OllamaContainer(DockerImageName.parse("ghcr.io/thomasvitale/ollama-llama3")
+        return new OllamaContainer(DockerImageName.parse("ghcr.io/thomasvitale/ollama-mistral")
                 .asCompatibleSubstituteFor("ollama/ollama"));
     }
 
     public static void main(String[] args) {
-        SpringApplication.from(QuestionAnsweringWithDocuments::main).with(TestQuestionAnsweringWithDocuments.class).run(args);
+        SpringApplication.from(Classification::main).with(TestClassification.class).run(args);
     }
 
 }

00-use-cases/question-answering-with-documents/README.md → 00-use-cases/question-answering/README.md

@@ -1,4 +1,4 @@
-# Question Answering With Documents
+# Question Answering (RAG)
 
 Ask questions about documents with LLMs via Ollama.
 
@@ -9,10 +9,10 @@ The application relies on Ollama for providing LLMs. You can either run Ollama l
 ### Ollama as a native application
 
 First, make sure you have [Ollama](https://ollama.ai) installed on your laptop.
-Then, use Ollama to run the _llama3_ large language model.
+Then, use Ollama to run the _mistral_ large language model.
 
 ```shell
-ollama run llama3
+ollama run mistral
 ```
 
 Finally, run the Spring Boot application.
@@ -23,15 +23,15 @@ Finally, run the Spring Boot application.
 
 ### Ollama as a dev service with Testcontainers
 
-The application relies on the native Testcontainers support in Spring Boot to spin up an Ollama service with a _llama3_ model at startup time.
+The application relies on the native Testcontainers support in Spring Boot to spin up an Ollama service with a _mistral_ model at startup time.
 
 ```shell
 ./gradlew bootTestRun
 ```
 
 ## Calling the application
 
-You can now call the application that will use Ollama and llama3 to load text documents as embeddings and generate an answer to your questions based on those documents (RAG pattern).
+You can now call the application that will use Ollama and _mistral_ to load text documents as embeddings and generate an answer to your questions based on those documents (RAG pattern).
 This example uses [httpie](https://httpie.io) to send HTTP requests.
 
 ```shell

00-use-cases/question-answering/build.gradle

@@ -0,0 +1,40 @@
+plugins {
+    id 'java'
+    id 'org.springframework.boot'
+    id 'io.spring.dependency-management'
+}
+
+group = 'com.thomasvitale'
+version = '0.0.1-SNAPSHOT'
+
+java {
+    toolchain {
+        languageVersion = JavaLanguageVersion.of(21)
+    }
+}
+
+repositories {
+    mavenCentral()
+    maven { url 'https://repo.spring.io/milestone' }
+    maven { url 'https://repo.spring.io/snapshot' }
+}
+
+dependencies {
+    implementation platform("org.springframework.ai:spring-ai-bom:${springAiVersion}")
+
+    implementation 'org.springframework.boot:spring-boot-starter-web'
+    implementation 'org.springframework.ai:spring-ai-ollama-spring-boot-starter'
+
+    testAndDevelopmentOnly 'org.springframework.boot:spring-boot-devtools'
+
+    testImplementation 'org.springframework.boot:spring-boot-starter-test'
+    testImplementation 'org.springframework.boot:spring-boot-starter-webflux'
+    testImplementation 'org.springframework.boot:spring-boot-testcontainers'
+    testImplementation 'org.springframework.ai:spring-ai-spring-boot-testcontainers'
+    testImplementation 'org.testcontainers:junit-jupiter'
+    testImplementation 'org.testcontainers:ollama'
+}
+
+tasks.named('test') {
+    useJUnitPlatform()
+}

00-use-cases/question-answering-with-documents/src/main/java/com/thomasvitale/ai/spring/ChatService.java → 00-use-cases/question-answering/src/main/java/com/thomasvitale/ai/spring/ChatService.java

@@ -19,7 +19,7 @@ class ChatService {
 
     String chatWithDocument(String message) {
         return chatClient.prompt()
-                .advisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults().withTopK(3)))
+                .advisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults().withTopK(5)))
                 .user(message)
                 .call()
                 .content();

00-use-cases/question-answering-with-documents/src/main/java/com/thomasvitale/ai/spring/DocumentEtlPipeline.java → 00-use-cases/question-answering/src/main/java/com/thomasvitale/ai/spring/IngestionPipeline.java

@@ -16,9 +16,9 @@
 import java.util.List;
 
 @Component
-public class DocumentEtlPipeline {
+public class IngestionPipeline {
 
-    private static final Logger logger = LoggerFactory.getLogger(DocumentEtlPipeline.class);
+    private static final Logger logger = LoggerFactory.getLogger(IngestionPipeline.class);
     private final VectorStore vectorStore;
 
     @Value("classpath:documents/story1.md")
@@ -27,7 +27,7 @@ public class DocumentEtlPipeline {
     @Value("classpath:documents/story2.txt")
     Resource textFile2;
 
-    public DocumentEtlPipeline(VectorStore vectorStore) {
+    public IngestionPipeline(VectorStore vectorStore) {
         this.vectorStore = vectorStore;
     }
 
@@ -48,7 +48,7 @@ public void run() {
         documents.addAll(textReader2.get());
 
         logger.info("Creating and storing Embeddings from Documents");
-        vectorStore.add(new TokenTextSplitter().split(documents));
+        vectorStore.add(new TokenTextSplitter(300, 300, 5, 1000, true).split(documents));
     }
 
 }

00-use-cases/question-answering-with-documents/src/main/java/com/thomasvitale/ai/spring/QuestionAnsweringWithDocuments.java → 00-use-cases/question-answering/src/main/java/com/thomasvitale/ai/spring/QuestionAnswering.java

@@ -8,10 +8,10 @@
 import org.springframework.context.annotation.Bean;
 
 @SpringBootApplication
-public class QuestionAnsweringWithDocuments {
+public class QuestionAnswering {
 
     public static void main(String[] args) {
-        SpringApplication.run(QuestionAnsweringWithDocuments.class, args);
+        SpringApplication.run(QuestionAnswering.class, args);
     }
 
     @Bean