Redis Vector Store & RAG: The Most Asked Spring AI Interview Topic

Understanding Retrieval Augmented Generation — simply, clearly, with real code

Introduction

If someone asks you one Spring AI question in an interview, it will almost certainly be this:

“What is RAG?”

In this post, we’ll cover Redis Vector Store and RAG (Retrieval Augmented Generation) from scratch — what they are, why they exist, and how to implement them with Spring AI.

Part 1: Redis Vector Store

Redis — What You Already Know

Redis is a fast key-value store. Normally you use it like this:

name → Ajeet
age  → 22

Simple. Fast. In-memory.

Redis for AI — Storing Vectors

But Redis can do something much more powerful for AI applications. Instead of storing plain values, it can store embedding vectors:

Java   → [0.45, 0.78, 0.23...]
Spring → [0.46, 0.79, 0.22...]

These vectors represent the semantic meaning of text. Words that are similar in meaning will have vectors that are close to each other — and Redis can search through millions of them in milliseconds.

Dependency

xml

<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter-vector-store-redis</artifactId>
</dependency>

Configuration

properties

spring.data.redis.host=localhost
spring.data.redis.port=6379

Flow

Application
     ↓
Redis Vector Store
     ↓
Store Embeddings

Interview Answer

“Redis Vector Store is used for fast storage and retrieval of embeddings for similarity search.”

Part 2: Redis Vector Store Implementation

Create the Vector Store Bean

java

@Bean
VectorStore vectorStore(EmbeddingModel model) {
    return RedisVectorStore.builder(redisTemplate, model)
                           .build();
}

Store Documents

java

vectorStore.add(
    List.of(
        new Document("Spring Boot tutorial")
    )
);

Search for Similar Documents

java

List<Document> docs = vectorStore.similaritySearch("Spring");

What Happens Behind the Scenes

Document
   ↓
Embedding generated
   ↓
Redis stores vector
   ↓
User query vector generated
   ↓
Cosine similarity search
   ↓
Results returned

Every document you store gets converted to a vector. Every search query also becomes a vector. Redis then finds the stored vectors that are directionally closest to the query vector — that’s cosine similarity at work.

Part 3: What is RAG?

RAG stands for Retrieval Augmented Generation.

The Problem RAG Solves

Imagine you have a 200-page company PDF. You want to ask an AI questions about it. With a normal LLM:

User: "What is in my company PDF?"
AI:   "I don't know."

The LLM has no idea — it was never trained on your PDF. Its knowledge is frozen at training time.

This is the core limitation RAG fixes.

RAG to the Rescue

PDF
 ↓
Split text into chunks
 ↓
Generate embeddings
 ↓
Store in Vector Database
 ↓
User asks a question
 ↓
Similarity Search
 ↓
Relevant chunks retrieved
 ↓
Sent to LLM as context
 ↓
Final accurate answer

Now the AI has access to your external knowledge — your PDFs, your docs, your database — at query time.

Real-Life Analogy

Think of an exam hall.

Without RAG — the student answers purely from memory. If they never studied that topic, they fail.

With RAG — the student has the textbook open. They find the relevant page, read it, and answer accurately.

RAG gives your LLM the textbook.

Interview Answer

“RAG combines retrieval from external knowledge sources with LLM generation to provide accurate and context-aware responses.”

Short version: “RAG lets LLM use external data.”

Part 4: RAG Implementation with Spring AI

Step 1 — Store Your Documents

java

vectorStore.add(
    List.of(
        new Document("Spring Boot simplifies Java development")
    )
);

Step 2 — Search for Relevant Documents

java

List<Document> docs = vectorStore.similaritySearch("What is Spring?");

Step 3 — Send to AI with Context

java

String response = chatClient.prompt()
    .user("What is Spring?")
    .call()
    .content();

The Complete RAG Flow

User Query
      ↓
Generate Query Embedding
      ↓
Vector Store Search
      ↓
Find Similar Documents
      ↓
Add Documents to Prompt
      ↓
LLM
      ↓
Final Response

End-to-End Example

You stored this document:

"Spring Boot simplifies Java development"

User asks:

"What is Spring Boot?"

Internally:

Query → Embedding → Redis similarity search
      → Document found → Send to LLM → Answer generated

Output:

"Spring Boot is a framework that simplifies Java application development."

The LLM didn’t guess. It retrieved the relevant context and generated an accurate answer.

30-Second Interview Revision

ConceptOne LineRedis Vector StoreStores vectors for fast similarity retrievalRAGRetrieval + LLM generation = context-aware answersCosine SimilarityFinds semantically closest vectorsEmbeddingText converted to numerical vector

RAG Flow (memorize this):

Document → Split → Embedding → Vector DB
→ Similarity Search → LLM → Response

Why RAG is High-Value in Interviews

RAG is not just a Spring AI concept — it’s the foundation of almost every production AI application being built today. Customer support bots, document Q&A systems, internal knowledge bases — they all run on RAG.

Understanding it end-to-end, from chunking documents to similarity search to LLM generation, puts you ahead of most candidates.

Happy building! 🚀