feat : convert endpoint to post

README.md

@@ -12,6 +12,6 @@ AI implementations using java, stores and either of Langchain4j or springai fram
 | [pgvector springai](./pgvector-springai)                             | Embeddings implementation using springai and pgvector                                 |
 | [playground](./playground)                                           | AI playground using Langchain4j                                                       |
 | [rag langchain4j AllMiniLmL6V2](./rag-langchain4j-AllMiniLmL6V2-llm) | RAG Implementation using Langchain4j, PGVector store and openai LLM                   |
-| [rag springai ollama llm](./rag-springai-ollama-llm)                 | RAG Implementation using springai, PGVector store and ollama LLM with orca-mini model |
+| [rag springai ollama llm](./rag-springai-ollama-llm)                 | RAG Implementation using springai, Redis store and ollama LLM with llama2 model |
 | [rag springai openai llm](./rag-springai-openai-llm)                 | RAG Implementation using springai, PGVector store and openai LLM                      |
 

rag-springai-ollama-llm/docker/docker-compose.yml

@@ -1,7 +1,7 @@
 version: '3.7'
 services:
     ollama:
-        image: langchain4j/ollama-orca-mini:latest
+        image: langchain4j/ollama-llama2:latest
         ports:
           - '11434:11434'
     redis-stack:

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

@@ -1,17 +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.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.validation.annotation.Validated;
-import org.springframework.web.bind.annotation.GetMapping;
-import org.springframework.web.bind.annotation.RequestMapping;
-import org.springframework.web.bind.annotation.RequestParam;
-import org.springframework.web.bind.annotation.RestController;
+import org.springframework.web.bind.annotation.*;
 
 @RestController
 @RequestMapping("/api/ai")
@@ -26,15 +22,10 @@ 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);
+    @PostMapping("/chat")
+    AIChatResponse ragService(@RequestBody @Valid AIChatRequest aiChatRequest) {
+        String chatResponse = aiChatService.chat(aiChatRequest.question());
         LOGGER.info("chatResponse :{}", chatResponse);
-        return Map.of("response", chatResponse);
+        return new AIChatResponse(chatResponse);
     }
 }

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

@@ -0,0 +1,3 @@
+package com.learning.ai.llmragwithspringai.model.response;
+
+public record AIChatResponse(String queryResponse) {}

rag-springai-ollama-llm/src/main/resources/application.properties

@@ -3,10 +3,9 @@ spring.application.name=rag-springai-ollama-llm
 spring.threads.virtual.enabled=true
 spring.mvc.problemdetails.enabled=true
 
-spring.ai.ollama.chat.options.model=orca-mini
-spring.ai.ollama.chat.options.responseFormat=json_object
+spring.ai.ollama.chat.options.model=llama2
 
-spring.ai.ollama.embedding.options.model=orca-mini
+spring.ai.ollama.embedding.options.model=llama2
 
 spring.ai.vectorstore.redis.index=vector_store
 spring.ai.vectorstore.redis.prefix=ai

rag-springai-ollama-llm/src/test/java/com/learning/ai/llmragwithspringai/LlmRagWithSpringAiApplicationIntTest.java

@@ -6,7 +6,9 @@
 import static org.hamcrest.Matchers.is;
 
 import com.learning.ai.llmragwithspringai.config.AbstractIntegrationTest;
+import com.learning.ai.llmragwithspringai.model.request.AIChatRequest;
 import io.restassured.RestAssured;
+import io.restassured.http.ContentType;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.TestInstance;
@@ -25,20 +27,23 @@ void setUp() {
 
     @Test
     void testRag() {
-        given().param("question", "Is Rohit Sharma batsman")
+        given().contentType(ContentType.JSON)
+                .body(new AIChatRequest("Is Rohit Sharma batsman?"))
                 .when()
-                .get("/api/ai/chat")
+                .post("/api/ai/chat")
                 .then()
                 .statusCode(200)
-                .body("response", containsString("Yes"))
-                .log();
+                .body("queryResponse", containsString("Yes"))
+                .log()
+                .all();
     }
 
     @Test
     void testEmptyQuery() {
-        given().param("question", "")
+        given().contentType(ContentType.JSON)
+                .body(new AIChatRequest(""))
                 .when()
-                .get("/api/ai/chat")
+                .post("/api/ai/chat")
                 .then()
                 .statusCode(400)
                 .header("Content-Type", is("application/problem+json"))
@@ -53,9 +58,10 @@ void testEmptyQuery() {
     @Test
     void testLongQueryString() {
         String longQuery = "a".repeat(1000); // Example of a very long query string
-        given().param("question", longQuery)
+        given().contentType(ContentType.JSON)
+                .body(new AIChatRequest(longQuery))
                 .when()
-                .get("/api/ai/chat")
+                .post("/api/ai/chat")
                 .then()
                 .statusCode(400)
                 .header("Content-Type", is("application/problem+json"))
@@ -69,9 +75,10 @@ void testLongQueryString() {
 
     @Test
     void testSpecialCharactersInQuery() {
-        given().param("question", "@#$%^&*()")
+        given().contentType(ContentType.JSON)
+                .body(new AIChatRequest("@#$%^&*()"))
                 .when()
-                .get("/api/ai/chat")
+                .post("/api/ai/chat")
                 .then()
                 .statusCode(400)
                 .header("Content-Type", is("application/problem+json"))

rag-springai-ollama-llm/src/test/java/com/learning/ai/llmragwithspringai/TestLlmRagWithSpringAiApplication.java

@@ -16,8 +16,8 @@ public class TestLlmRagWithSpringAiApplication {
 
     @Bean
     OllamaContainer ollama(DynamicPropertyRegistry properties) {
-        OllamaContainer ollama = new OllamaContainer(DockerImageName.parse("langchain4j/ollama-orca-mini:latest")
-                .asCompatibleSubstituteFor("ollama/ollama"));
+        OllamaContainer ollama = new OllamaContainer(
+                DockerImageName.parse("langchain4j/ollama-llama2:latest").asCompatibleSubstituteFor("ollama/ollama"));
         properties.add("spring.ai.ollama.base-url", ollama::getEndpoint);
         return ollama;
     }