Vector Databases & RAG

Vector Databases and RAG (Retrieval Augmented Generation) form the core intelligence layer of modern AI applications.

They allow AI systems to understand, search, and generate responses using real and relevant data instead of relying only on pre-trained knowledge.

? Without this layer, AI gives generic answers.

? With this layer, AI becomes context-aware, accurate, and reliable.

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What is a Vector Database?

A vector database stores data in the form of numerical representations called vectors, which are created using embeddings.

In simple words: it stores the meaning of data, not just the raw text.

This enables fast and intelligent similarity-based search instead of traditional keyword matching.

Popular vector databases:

• Pinecone.
• Weaviate.
• Chroma.
• FAISS.

? These databases are designed to handle large-scale semantic search efficiently.

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What are Embeddings?

Embeddings convert text into numerical vectors that capture meaning and context.

In simple words: they transform human language into numbers so machines can understand relationships between words and sentences.

Example:

• “Java is a programming language.”
• “Python is used for coding.”

? Both sentences will have similar embeddings because their meaning is related.

Embeddings are the foundation of semantic search and RAG systems.

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Semantic Search (Basics)

Semantic search finds results based on meaning rather than exact keyword matching.

Instead of matching exact words, it understands the intent behind the query.

Example:

• Search → “How to store data in DB?”
• Result → “Database storage techniques.”

? Even if the words are different, the system understands the context and returns relevant results.

Example (Embedding + Search)

// Convert text to embedding (pseudo example)
List<Double> queryVector = embeddingService.embed("How to store data in DB?");

// Search similar vectors
List<String> results = vectorStore.similaritySearch(queryVector);

// Print results
results.forEach(System.out::println);

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What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that combines vector databases with Large Language Models (LLMs) to generate accurate and context-aware responses.

It enhances AI by allowing it to use external data instead of relying only on pre-trained knowledge.

In simple words: RAG = Search + AI Answer.

? Instead of guessing, the system first retrieves relevant data and then generates a response based on that context.

? This approach makes responses more accurate, reliable, and up-to-date.

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RAG Pipeline (Step-by-Step)

A complete RAG system works in structured stages to ensure accurate and context-aware responses.

1. Data Ingestion: Collect data from PDFs, documents, or databases.
2. Chunking Strategy: Break large data into smaller chunks for better search accuracy.
3. Embedding Generation: Convert each chunk into vector embeddings using embedding models.
4. Vector Storage: Store these embeddings in a vector database.
5. Query Retrieval: Convert the user query into an embedding and search for similar data.
6. LLM Response Generation: Send the retrieved context to the LLM to generate the final answer.

? Result → Accurate and context-aware response.

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Practical Example (RAG Flow in Code)

public class RAGExample {

    private final DocumentService documentService;
    private final OllamaService ollamaService;

    public RAGExample(DocumentService documentService, OllamaService ollamaService) {
        this.documentService = documentService;
        this.ollamaService = ollamaService;
    }

    public String askQuestion(String question) {

        String context = documentService.searchRelevantData(question);

        String prompt = "Answer based on this context: " + context +
                        " Question: " + question;

        return ollamaService.generateResponse(prompt);
    }
}

• First, relevant data is retrieved from documents.
• Then the LLM generates a response using that context.

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Integrating Vector DB with Spring AI

Spring AI simplifies the integration of vector databases and LLMs, making it easier to build RAG-based applications.

It handles embedding, retrieval, and response generation in a more structured and developer-friendly way.

Basic flow:

• Load documents.
• Convert them into embeddings.
• Store embeddings in a vector database.
• Perform similarity search.
• Send retrieved results to the LLM.

vectorStore.similaritySearch("Explain Docker");

chatClient.prompt()
    .user(query)
    .call()
    .content();

? This flow creates a complete RAG-based AI system that can answer queries using custom data.

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Similarity Search Techniques

Vector databases use mathematical techniques to find the closest and most relevant matches.

These techniques compare vectors based on their meaning and similarity.

• Cosine similarity.
• Euclidean distance.
• Dot product.

? These methods help identify the most relevant data quickly and efficiently.

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Use Cases

Vector Databases and RAG are widely used in real-world AI applications.

• Chat with documents (PDFs, files).
• AI-powered knowledge base systems.
• Customer support automation.
• Enterprise search and recommendation systems.

Example:

User asks → “What is our leave policy?”

? System retrieves relevant data from stored documents.

? AI generates an accurate and context-based answer.

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Conclusion

Vector Databases and RAG are the backbone of intelligent AI systems that provide real, context-aware responses instead of generic outputs. They allow AI to search real data before generating answers, making results more useful, accurate, and reliable.

Start with small datasets, test the RAG pipeline, and gradually scale to build production-ready AI applications.