A Vector Database is a specialized database that stores and searches data using vectors (numerical representations of information) rather than only traditional text, numbers, or exact values.

It is widely used in AI applications, ChatGPT-like systems, RAG (Retrieval-Augmented Generation), semantic search, and AI agents.

1. How does a Vector Database work?

The process generally works in 3 steps:

1. Convert data into embeddings: An AI embedding model converts text, documents, images, or other information into numerical vectors.
2. Store vectors: These vectors are stored in a vector database along with the original data or its reference.
3. Search by similarity: When a user asks a question, the question is converted into a vector, and the database finds the most similar stored vectors.

Example

Suppose you store these documents:

Document	Text
D1	How to reset my banking password
D2	How to transfer money
D3	How to recover account login credentials

Now a user searches:

“What should I do if I forgot my password?”

A traditional database may require matching keywords.

A vector database can find D1 and D3 because their meanings are similar, even if the exact words differ.

This is called Semantic Search.

2. Vector Database vs Traditional Database

Feature	Traditional DB	Vector DB
Search	Exact match, SQL conditions	Similarity search
Data	Rows and columns	Numerical vectors + metadata
Search example	WHERE name='John'	Find semantically similar content
Query	SQL	Vector similarity
Use case	Banking, transactions	AI, semantic search, RAG

Note: Vector databases can also store metadata and support filtering.

3. Popular Vector Databases

1.Pinecone –Managed cloud vector DB

2. Chroma – Simple AI / RAG development

3. Weaviate – Open-source vector DB

4. Milvus – Large-scale vector search

Other options include Qdrant and PostgreSQL with pgvector.

4. How is it used in AI applications (RAG)?

For example, imagine building a Backbase banking AI assistant:

1. Upload Backbase documentation, configuration files, and architecture documents.
2. Convert documents into embeddings.
3. Store embeddings in a vector database.
4. A developer asks: “How does Dashboard Service load customer tiles?”
5. The vector DB retrieves relevant documentation.
6. The LLM uses the retrieved information to generate an accurate answer.

This architecture is called RAG (Retrieval-Augmented Generation).

5. Simple Java Spring Boot architecture

User Question
|
Spring Boot AI Application
|
Embedding Model
|
Vector Database
|
Relevant Documents
|
LLM (GPT / Claude / Local LLM)
|
AI Response

Important: A vector database is not an AI model. It stores and retrieves information that AI models can use.