Modern AI applications are no longer just about generating text—they are about generating accurate, context-aware responses. This is where Retrieval Augmented Generation (RAG) becomes powerful.
In this guide, you will learn how to build a simple RAG application using Java and Spring Boot, in a way that is easy to understand and practical to implement.
🚀 What Are We Building?
We will create a system that:
- Accepts a user query
- Searches relevant data
- Sends context to AI
- Returns accurate response
Flow:
User → Backend → Embedding → Vector Search → Context → AI → Response
🧠 Why Build a RAG Application?
Without RAG:
- AI gives generic answers ❌
With RAG:
- AI uses your data ✅
- Responses are accurate ✅
👉 This is how real-world AI systems work.
⚙️ Step 1: Project Setup
Create a Spring Boot project with:
- Java 11+
- Spring Web
- Maven
📦 Maven Dependency
org.springframework.boot
spring-boot-starter-web
🧩 Step 2: Define Data Model
package com.kscodes.ai;
public class Document {
private String content;
public Document(String content) {
this.content = content;
}
public String getContent() {
return content;
}
}
🧠 Step 3: Simulate Embedding + Vector Search
For simplicity, we simulate vector search.
package com.kscodes.ai;
import org.springframework.stereotype.Service;
import java.util.ArrayList;
import java.util.List;
@Service
public class VectorService {
private final List documents = new ArrayList<>();
public VectorService() {
documents.add(new Document("Claim settlement takes 7 days."));
documents.add(new Document("Policy covers accident damage."));
}
public String search(String query) {
// Simple keyword match (simulate vector search)
return documents.stream()
.map(Document::getContent)
.filter(doc -> doc.toLowerCase().contains(query.toLowerCase()))
.findFirst()
.orElse("No relevant data found");
}
}
🤖 Step 4: AI Service
package com.kscodes.ai;
import org.springframework.stereotype.Service;
@Service
public class AIService {
public String generateResponse(String context, String query) {
String prompt = "Answer based on context: " + context +
"\nQuestion: " + query;
// Simulated AI response
return "Based on policy, " + context;
}
}
🌐 Step 5: REST Controller
package com.kscodes.ai;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;
@RestController
@RequestMapping("/rag")
public class RAGController {
@Autowired
private VectorService vectorService;
@Autowired
private AIService aiService;
@PostMapping("/ask")
public String ask(@RequestBody String query) {
String context = vectorService.search(query);
return aiService.generateResponse(context, query);
}
}
▶️ Step 6: Test the API
POST http://localhost:8080/rag/ask
Request Body:
"What is claim settlement time?"
💡 How This Works
- User sends query
- System searches relevant data
- Context is passed to AI
- AI generates accurate answer
👉 This is a simplified RAG pipeline.
⚠️ Important Improvements (Real World)
🔸 Replace Fake Search
Use:
- Vector database (Pinecone, Weaviate, FAISS)
🔸 Use Real Embeddings
Use embedding APIs instead of keyword matching.
🔸 Call Real AI API
Replace mock AI with actual OpenAI API.
🧩 Architecture Overview
User → Spring Boot → Embedding Model → Vector DB → Context → LLM → Response
Response
🎯 Real-World Use Cases
- Insurance document Q&A
- Customer support chatbot
- Policy explanation system
- Knowledge base search
👉 This is used in almost every enterprise AI system.
📝 Summary
- RAG combines search + AI
- Helps AI use real data
- Improves accuracy
- Easy to integrate in Spring Boot