Vector Databases Explained: The Brain Behind AI Search

We all have that moment when we want to listen to a song, but we just can’t remember the name. Only a few words from the lyrics and the tune hum in your mind. Happens, right? You wish your friend could help you find that song. So the vector database is just like that friend.  

Traditional searches require exact words or the right keyword to get useful results. Whereas vector data can give you results with only fragments of information, just like trying to search for a song with a few words from the lyrics. Vectorization is redefining modern-day search intelligence and moving searches from keywords to intention & understanding the meaning. 

Keep reading for in-depth knowledge about vector databases, why we need them, how it differs from traditional databases, and many other aspects of them. 

Key Terms 

Vector: In maths & physics, a vector is a quantity that has both magnitude and direction. A common example given is an arrow travelling from A to B; the arrow is a vector that represents something that needs to ‘carry’ from point A to B. It helps inform quantities that can’t be expressed in a single number.

In this database, a vector represents a list of numbers that display data, including text, images, or audio in numeric form. It records the meaning or features of something in a format that computers can compare.

Dimension: It tells how many intersection points or sides an object has; a square is flat or 2D, but when it gets more sides, it becomes a cube and is called a 3D object with length, breadth, and height. 

Embedding: Text, images, or audio are simple representations of heavy numericals that show the high-dimensional space.

Similarity search: It is a method of finding vectors that are close to each other to give the most relevant results to a query. 

Semantic search: A type of search that is based on meaning rather than exact keywords. For instance, makeup and foundation can be treated as related. 

Cosine similarity: A method of measuring closeness between two vectors. The smaller the distance, the higher the similarity between items. 

ANN: Stands for Approximate Nearest Neighbor, is a quick search algorithm that helps vector databases to find the closest vectors instead of checking everything. It works very fast. 

RAG: Retrieval Augmented Generation is a system that gets relevant information from a vector store, then uses it to give more accurate results. 

Traditional Database vs Vector Database

What is a Vector Database?

A vector database is a type of database that stores different types of unstructured data, including images, text, audio, and video, in a numerical form called a vector. Vector has information about a data object in various dimensions. 

Every dimension carries a particular aspect of the object, so complex data is displayed in a numerical format.

Example: A vector that stores audio will have dimensions for pitch, tone, accent, background sounds, semantic meaning of speech, and speaking speed. 

Core Components of a Vector Database

A vector database is composed of several elements fitting together to make it work precisely. 

Embedding models

It helps turn data into vectors. For example, ‘I am writing’ will convert as a series of numbers. The database is dependent on vectors; if there are no embeddings, nothing in that data is searchable. 

Metadata

It is the additional information for each vector. Metadata tells about the document name, author, date, ID, product category, etc. It is helpful for vectors to find similar content, while metadata helps filter the results.

Vector index

It is one of the most important components of this database. It is a place to keep vectors in a sorted way to find them quickly. Without an index, the database will compare your query with every single vector one by one. 

Similarity search engine

After finding the candidate vector, this component measures the similarity between them. It finds the closest vectors and ranks them from the most to least relevant. 

Storage layer

All the data is kept safely in a storage layer. It contains everything from embeddings, metadata, and indexes. When a user makes a query, the database retrieves relevant vectors and their connected attributes from the top layer. The storage of the data depends on the size of the system to store it in memory, on disk, or across servers. 

Query processing layer

It is the layer that handles incoming queries and orchestrates the search process to retrieve relevant vector data. The query processing layer works with indexing & storage layers to process queries, filter, and rank candidate vectors based on the criteria of the query. It all happens before returning the results to the user or application. 

How does a Vector Database Work?

The semantic search database has a step-by-step process for generating fast and accurate answers. 

1. Data Collection 

This is the initial step to add the data to the system. The data is raw and can be in various formats such as PDFs, images, audio, videos, or web pages. The system can’t understand this data, and you can’t search it. Data ingestion is another term. 

2. Chunking

People split large datasets, such as big documents or books, into small pieces of information called data chunks. It is important to do so because each chunk is like one separate idea. Also, smaller pieces give better search results, and embedding works better on shorter text. 

3. Embedding generation

The embedding model processes each piece of data and converts it into a vector (embedding), making this the core step of the process. So now the data can represent its meaning in a high-dimensional space. 

4. Storage

Now the vector database stores all three major components, namely the vector, the original data chunk, and metadata. All this is done in the storage layer I mentioned above. 

5. Vector indexing

It is the step that is the speed gear of the process; to avoid checking each vector one by one, the database builds a vector index. Methods like HNSW and IVF arrange vectors by connecting or grouping the most similar ones, making it easier to find relevant matches.

6. User query arrives

Now the system gets a user query input. For example, someone searches ‘how to build a sand house’. This is a normal text input. 

7. Query embedding 

The system uses the same embedding model to convert the user’s query into a vector. As a result, both the stored data and the user query exist in the same vector space, making it possible to compare them accurately.

8. Nearest neighbor search 

The database performs something called ANN (approximate nearest neighbor search) to compare the query vector with stored vectors. Common similarity methods, such as cosine similarity and Euclidean distance, find the vectors that have the most similar meaning to each other, instead of exact words. 

9. Filtering 

If metadata filters are put to use, they are leveraged here to refine the results. 

10. Ranking & retrieval

In this step, the closest matching or candidate vectors rank high, and the top results are returned. The system returns the original piece of text, metadata, and sometimes similarity scores. Now these results can be directly shown to users or passed to an LLM. 

11. For RAG

In modern AI applications, developers often use vector databases with Retrieval-Augmented Generation (RAG). The vector database retrieves the most relevant information, and the large language model (LLM) uses that information to generate a final answer. This retrieval step is optional but commonly used to improve response accuracy and relevance.

Benefits of Vector Stores

Vector DBs are becoming increasingly advantageous in today’s growing AI search. Here are some of their benefits: 

• Speed and efficiency are two of the major advantages of vector DBs, as they use indexing techniques to support faster searching. This helps in getting relevant search results across datasets with millions and even billions of data points. 

•  They have high scalability because databases can store and manage very large amounts of unprocessed data in parallel using nodes. 

• They are flexible, and it becomes easy to display data in different formats; even complicated data like molecular structures can be shown. 

• Many vector databases are used for real-time or near-real-time querying. They have the ability to perform fast, similar searches, ensuring apps can deliver timely and relevant results. 

• Vector databases are specially made for managing high-dimensional data efficiently. This is why they are best suited for applications in NLP, computer vision, and genomics, where data is mostly in high-dimensional spaces. 

Use Cases of Vector DB

Being highly scalable, efficient, and flexible, these databases can be adjusted to meet specific business and AI applications. Though the use of vector stores is widespread, here are some key use cases:

Chatbots/Conversational AI

A vector database, when used to implement RAG, helps enhance virtual assistant interactions. They find information based on meaning and not just words. The system gives more accurate and relevant information in real-time and often with the source or document of the information. 

Recommendation systems

A vector DB stores user interactions and item features as embeddings, and it can find similarities with the help of them. It is becoming a cornerstone for modern-day searches. For instance, if you listen to romantic songs, the system can suggest similar songs with themes or moods, and not just the same genre tags. 

Genomics

A vector database can store and analyze genetic sequences, protein structures, and other molecular data. This is the ideal type of dataset needed in genomics. Because it makes it easier for researchers and scientists to find genetic flows with similar patterns. All this is important for discovering how genes are made and understanding tough biological processes. 

Types Of Vector Database

There are two main categories of vector databases, including:

Dedicated Vector Database

These are a special type of vector database made to store and query vector embeddings. The complete architecture of these is best leveraged for fast nearest neighbor search. They have built-in embedding indexing, horizontal scalability, and can be optimized for AI workloads with extremely fast similarity search. Popular examples: Pinecone, Milvus, Weaviate, Qdrant

Vector Additions for Traditional Databases

Here, some traditional databases have added vector capabilities as extensions. Rather than introducing a new infrastructure component, these databases allow developers to store vectors side-by-side with relational data. 

They can combine structured data and embeddings and offer easier integration for existing applications. 

Popular examples: PostgreSQL + pgvector, Redis with vector similarity search, Elasticsearch with vector search

Embedded Vector Databases

They run inside an application instead of a separate server. And this type of vector database is typically a library rather than a full database system. It offers features such as running locally in the application, being very fast for smaller datasets, and needing minimal infrastructure. 

Popular examples: FAISS (Facebook AI Similarity Search), Chroma, Annoy

Hybrid Vector Databases

These databases combine vector similarity search with traditional-style keyword search, allowing applications to retrieve results using both semantic meaning and exact keyword matching. It is ideal for enterprise knowledge systems, combines vector and lexical search, and offers more accurate retrieval to offer for many tasks. 

Popular examples: Weaviate hybrid search, Elastic vector search, Typesense hybrid search

Choosing the best vector databases depends on your business requirements and use case. 

Summing Up

Vector databases are the power behind modern-day AI search. Unlike traditional databases, they store data in vectors (a numerical representation given to each data point), and that is how they answer your search queries. So you don’t have to give exact keywords in the query, but even similar words or phrases can get you results. These database response the meaning or context of the search query. I have mentioned the types of vector data stores, how they work, and we also discussed their core components. There are several benefits of such a database that we learned here, and how they clearly differ from a normal database.