feat : convert from get to post endpoint (#27)

* feat : convert from get to post endpoint

* rename : project name

* add request and response, convert endpoint to post

.github/workflows/llm-rag-with-langchain4j-spring-boot.yml → .github/workflows/rag-langchain4j-AllMiniLmL6V2-llm.yml

@@ -1,13 +1,13 @@
-name: llm-rag-with-langchain4j-spring-boot CI Build
+name: rag-langchain4j-AllMiniLmL6V2-llm CI Build
 
 on:
   push:
     paths:
-      - "llm-rag-with-langchain4j-spring-boot/**"
+      - "rag-langchain4j-AllMiniLmL6V2-llm/**"
     branches: [main]
   pull_request:
     paths:
-      - "llm-rag-with-langchain4j-spring-boot/**"
+      - "rag-langchain4j-AllMiniLmL6V2-llm/**"
     types:
       - opened
       - synchronize
@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     defaults:
       run:
-        working-directory: llm-rag-with-langchain4j-spring-boot
+        working-directory: rag-langchain4j-AllMiniLmL6V2-llm
     strategy:
       matrix:
         distribution: [ 'temurin' ]

llm-rag-with-langchain4j-spring-boot/pom.xml → rag-langchain4j-AllMiniLmL6V2-llm/pom.xml

@@ -9,9 +9,9 @@
         <relativePath/> <!-- lookup parent from repository -->
     </parent>
     <groupId>org.example.ai</groupId>
-    <artifactId>llm-rag-with-langchain4j-spring-boot</artifactId>
+    <artifactId>rag-langchain4j-AllMiniLmL6V2-llm</artifactId>
     <version>0.0.1-SNAPSHOT</version>
-    <name>llm-rag-with-langchain4j-spring-boot</name>
+    <name>rag-langchain4j-AllMiniLmL6V2-llm</name>
     <description>Demo project for Spring Boot</description>
 
     <properties>

llm-rag-with-langchain4j-spring-boot/src/main/java/com/learning/ai/config/AIConfig.java → rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/config/AIConfig.java

@@ -2,7 +2,6 @@
 
 import static dev.langchain4j.data.document.loader.FileSystemDocumentLoader.loadDocument;
 
-import com.zaxxer.hikari.HikariDataSource;
 import dev.langchain4j.data.document.Document;
 import dev.langchain4j.data.document.DocumentSplitter;
 import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser;
@@ -22,7 +21,7 @@
 import dev.langchain4j.store.embedding.pgvector.PgVectorEmbeddingStore;
 import java.io.IOException;
 import java.net.URI;
-import javax.sql.DataSource;
+import org.springframework.boot.autoconfigure.jdbc.JdbcConnectionDetails;
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
 import org.springframework.core.io.Resource;
@@ -62,13 +61,13 @@ EmbeddingModel embeddingModel() {
 
     @Bean
     EmbeddingStore<TextSegment> embeddingStore(
-            EmbeddingModel embeddingModel, ResourceLoader resourceLoader, DataSource dataSource) throws IOException {
+            EmbeddingModel embeddingModel, ResourceLoader resourceLoader, JdbcConnectionDetails jdbcConnectionDetails)
+            throws IOException {
 
         // Normally, you would already have your embedding store filled with your data.
         // However, for the purpose of this demonstration, we will:
 
-        HikariDataSource hikariDataSource = (HikariDataSource) dataSource;
-        String jdbcUrl = hikariDataSource.getJdbcUrl();
+        String jdbcUrl = jdbcConnectionDetails.getJdbcUrl();
         URI uri = URI.create(jdbcUrl.substring(5));
         String host = uri.getHost();
         int dbPort = uri.getPort();
@@ -78,8 +77,8 @@ EmbeddingStore<TextSegment> embeddingStore(
         EmbeddingStore<TextSegment> embeddingStore = PgVectorEmbeddingStore.builder()
                 .host(host)
                 .port(dbPort != -1 ? dbPort : 5432)
-                .user(hikariDataSource.getUsername())
-                .password(hikariDataSource.getPassword())
+                .user(jdbcConnectionDetails.getUsername())
+                .password(jdbcConnectionDetails.getPassword())
                 .database(path.substring(1))
                 .table("ai_vector_store")
                 .dimension(384)

llm-rag-with-langchain4j-spring-boot/src/main/java/com/learning/ai/config/AICustomerSupportAgent.java → rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/config/AICustomerSupportAgent.java

@@ -1,6 +1,6 @@
 package com.learning.ai.config;
 
-import com.learning.ai.domain.AICustomerSupportResponse;
+import com.learning.ai.domain.response.AICustomerSupportResponse;
 import dev.langchain4j.service.SystemMessage;
 import dev.langchain4j.service.V;
 

llm-rag-with-langchain4j-spring-boot/src/main/java/com/learning/ai/config/ChatTools.java → rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/config/ChatTools.java

@@ -12,7 +12,7 @@ public class ChatTools {
     /**
      * This tool is available to {@link AICustomerSupportAgent}
      */
-    @Tool
+    @Tool("chatAssistantTools")
     String currentTime() {
         log.info("Inside ChatTools");
         return LocalTime.now().toString();

llm-rag-with-langchain4j-spring-boot/src/main/java/com/learning/ai/config/SwaggerConfig.java → rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/config/SwaggerConfig.java

@@ -6,5 +6,7 @@
 import org.springframework.context.annotation.Configuration;
 
 @Configuration(proxyBeanMethods = false)
-@OpenAPIDefinition(info = @Info(title = "llm-rag-with-langchain4j", version = "v1.0.0"), servers = @Server(url = "/"))
+@OpenAPIDefinition(
+        info = @Info(title = "rag-langchain4j-AllMiniLmL6V2-llm", version = "v1.0.0"),
+        servers = @Server(url = "/"))
 public class SwaggerConfig {}

rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/controller/CustomerSupportController.java

@@ -0,0 +1,28 @@
+package com.learning.ai.controller;
+
+import com.learning.ai.config.AICustomerSupportAgent;
+import com.learning.ai.domain.request.AIChatRequest;
+import com.learning.ai.domain.response.AICustomerSupportResponse;
+import jakarta.validation.Valid;
+import org.springframework.validation.annotation.Validated;
+import org.springframework.web.bind.annotation.PostMapping;
+import org.springframework.web.bind.annotation.RequestBody;
+import org.springframework.web.bind.annotation.RequestMapping;
+import org.springframework.web.bind.annotation.RestController;
+
+@RestController
+@RequestMapping("/api/ai")
+@Validated
+public class CustomerSupportController {
+
+    private final AICustomerSupportAgent aiCustomerSupportAgent;
+
+    public CustomerSupportController(AICustomerSupportAgent aiCustomerSupportAgent) {
+        this.aiCustomerSupportAgent = aiCustomerSupportAgent;
+    }
+
+    @PostMapping("/chat")
+    public AICustomerSupportResponse customerSupportChat(@RequestBody @Valid AIChatRequest aiChatRequest) {
+        return aiCustomerSupportAgent.chat(aiChatRequest.question());
+    }
+}

rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/domain/request/AIChatRequest.java

@@ -0,0 +1,13 @@
+package com.learning.ai.domain.request;
+
+import jakarta.validation.constraints.NotBlank;
+import jakarta.validation.constraints.Pattern;
+import jakarta.validation.constraints.Size;
+import java.io.Serializable;
+
+public record AIChatRequest(
+        @NotBlank(message = "Query cannot be empty")
+                @Size(max = 800, message = "Query exceeds maximum length")
+                @Pattern(regexp = "^[a-zA-Z0-9 ?]*$", message = "Invalid characters in query")
+                String question)
+        implements Serializable {}

llm-rag-with-langchain4j-spring-boot/src/main/java/com/learning/ai/domain/AICustomerSupportResponse.java → rag-langchain4j-AllMiniLmL6V2-llm/src/main/java/com/learning/ai/domain/response/AICustomerSupportResponse.java

@@ -1,3 +1,3 @@
-package com.learning.ai.domain;
+package com.learning.ai.domain.response;
 
 public record AICustomerSupportResponse(String response) {}

llm-rag-with-langchain4j-spring-boot/src/main/resources/application.properties → rag-langchain4j-AllMiniLmL6V2-llm/src/main/resources/application.properties

@@ -1,3 +1,5 @@
+spring.application.name=rag-langchain4j-AllMiniLmL6V2-llm
+
 langchain4j.open-ai.chat-model.api-key=demo
 langchain4j.open-ai.chat-model.model-name=gpt-3.5-turbo
 langchain4j.open-ai.chat-model.temperature=0.7

llm-rag-with-langchain4j-spring-boot/src/test/java/com/learning/ai/LLMRagWithSpringBootTest.java → rag-langchain4j-AllMiniLmL6V2-llm/src/test/java/com/learning/ai/LLMRagWithSpringBootTest.java

@@ -1,10 +1,11 @@
 package com.learning.ai;
 
 import static io.restassured.RestAssured.given;
-import static io.restassured.RestAssured.when;
 import static org.hamcrest.Matchers.notNullValue;
 
+import com.learning.ai.domain.request.AIChatRequest;
 import io.restassured.RestAssured;
+import io.restassured.http.ContentType;
 import io.restassured.http.Method;
 import org.apache.http.HttpStatus;
 import org.junit.jupiter.api.BeforeAll;
@@ -26,15 +27,22 @@ public void setUp() {
     }
 
     @Test
-    void whenRequestGet_thenOK() {
-        when().request(Method.GET, "/api/chat").then().statusCode(HttpStatus.SC_OK);
+    void whenRequestPost_thenOK() {
+        given().contentType(ContentType.JSON)
+                .body(new AIChatRequest(
+                        "what should I know about the transition to consumer direct care network washington?"))
+                .when()
+                .request(Method.POST, "/api/ai/chat")
+                .then()
+                .statusCode(HttpStatus.SC_OK);
     }
 
     @Test
     void whenRequestGetTime_thenOK() {
-        given().param("message", "What is the time now?")
+        given().contentType(ContentType.JSON)
+                .body(new AIChatRequest("What is the time now?"))
                 .when()
-                .request(Method.GET, "/api/chat")
+                .request(Method.POST, "/api/ai/chat")
                 .then()
                 .statusCode(HttpStatus.SC_OK)
                 .body("response", notNullValue());

rag-springai-openai-llm/src/main/java/com/learning/ai/llmragwithspringai/controller/AiController.java

@@ -1,14 +1,13 @@
 package com.learning.ai.llmragwithspringai.controller;
 
+import com.learning.ai.llmragwithspringai.model.request.AIChatRequest;
+import com.learning.ai.llmragwithspringai.model.response.AIChatResponse;
 import com.learning.ai.llmragwithspringai.service.AIChatService;
-import jakarta.validation.constraints.NotBlank;
-import jakarta.validation.constraints.Pattern;
-import jakarta.validation.constraints.Size;
-import java.util.Map;
+import jakarta.validation.Valid;
 import org.springframework.validation.annotation.Validated;
-import org.springframework.web.bind.annotation.GetMapping;
+import org.springframework.web.bind.annotation.PostMapping;
+import org.springframework.web.bind.annotation.RequestBody;
 import org.springframework.web.bind.annotation.RequestMapping;
-import org.springframework.web.bind.annotation.RequestParam;
 import org.springframework.web.bind.annotation.RestController;
 
 @RestController
@@ -22,14 +21,9 @@ public AiController(AIChatService aiChatService) {
         this.aiChatService = aiChatService;
     }
 
-    @GetMapping("/chat")
-    Map<String, String> ragService(
-            @RequestParam
-                    @NotBlank(message = "Query cannot be empty")
-                    @Size(max = 255, message = "Query exceeds maximum length")
-                    @Pattern(regexp = "^[a-zA-Z0-9 ]*$", message = "Invalid characters in query")
-                    String question) {
-        String chatResponse = aiChatService.chat(question);
-        return Map.of("response", chatResponse);
+    @PostMapping("/chat")
+    AIChatResponse ragService(@Valid @RequestBody AIChatRequest aiChatRequest) {
+        String chatResponse = aiChatService.chat(aiChatRequest.question());
+        return new AIChatResponse(chatResponse);
     }
 }

rag-springai-openai-llm/src/main/java/com/learning/ai/llmragwithspringai/model/request/AIChatRequest.java

@@ -0,0 +1,13 @@
+package com.learning.ai.llmragwithspringai.model.request;
+
+import jakarta.validation.constraints.NotBlank;
+import jakarta.validation.constraints.Pattern;
+import jakarta.validation.constraints.Size;
+import java.io.Serializable;
+
+public record AIChatRequest(
+        @NotBlank(message = "Query cannot be empty")
+                @Size(max = 800, message = "Query exceeds maximum length")
+                @Pattern(regexp = "^[a-zA-Z0-9 ?]*$", message = "Invalid characters in query")
+                String question)
+        implements Serializable {}

rag-springai-openai-llm/src/main/java/com/learning/ai/llmragwithspringai/model/response/AIChatResponse.java

@@ -0,0 +1,5 @@
+package com.learning.ai.llmragwithspringai.model.response;
+
+import java.io.Serializable;
+
+public record AIChatResponse(String response) implements Serializable {}

rag-springai-openai-llm/src/main/java/com/learning/ai/llmragwithspringai/service/AIChatService.java

@@ -18,14 +18,13 @@ public class AIChatService {
 
     private static final String template =
             """
+            You're assisting with questions about cricket
 
-            You're assisting with questions about cricketers
             Cricket is a bat-and-ball game that is played between two teams of eleven players on a field at the centre of which is a 22-yard (20-metre) pitch with a wicket at each end,
             each comprising two bails balanced on three stumps.
             Two players from the batting team (the striker and nonstriker) stand in front of either wicket,
             with one player from the fielding team (the bowler) bowling the ball towards the striker's wicket from the opposite end of the pitch.
-            The striker's goal is to hit the bowled ball and then switch places with the nonstriker,
-            with the batting team scoring one run for each exchange.
+            The striker's goal is to hit the bowled ball and then switch places with the nonstriker, with the batting team scoring one run for each exchange.
             Runs are also scored when the ball reaches or crosses the boundary of the field or when the ball is bowled illegally.
 
             Use the information from the DOCUMENTS section to provide accurate answers but act as if you knew this information innately.
@@ -44,16 +43,16 @@ public AIChatService(ChatClient aiClient, VectorStore vectorStore) {
         this.vectorStore = vectorStore;
     }
 
-    public String chat(String message) {
+    public String chat(String searchQuery) {
         // Querying the VectorStore using natural language looking for the information about info asked.
-        List<Document> listOfSimilarDocuments = this.vectorStore.similaritySearch(message);
+        List<Document> listOfSimilarDocuments = this.vectorStore.similaritySearch(searchQuery);
         String documents = listOfSimilarDocuments.stream()
                 .map(Document::getContent)
                 .collect(Collectors.joining(System.lineSeparator()));
         // Constructing the systemMessage to indicate the AI model to use the passed information
         // to answer the question.
         Message systemMessage = new SystemPromptTemplate(template).createMessage(Map.of("documents", documents));
-        UserMessage userMessage = new UserMessage(message);
+        UserMessage userMessage = new UserMessage(searchQuery);
         Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
         ChatResponse aiResponse = aiClient.call(prompt);
         return aiResponse.getResult().getOutput().getContent();