Vector Database: The Game-Changing Tech Powering AI Search

If you’re trying to build an AI agent or search engine, you’ve likely heard some talk about vector databases.

Vector databases play an essential role in the interplay between data, resources, and queries, but tackling them can be daunting. I’ve been there: scrolling through esoteric terms like embeddings and fuzzy search, not sure whether I was over-engineering or just missing something basic.

Who determines which YouTube videos to recommend? How do search engines overcome typos? How does Instagram always seem to show me the perfect fluffy dog?

In this article, we'll unpack the world of vectors, similarity, and semantic search, and how you can build more personalized applications.

What is a Vector Database?

A vector database stores data as a collection of numerical representations (known as vectors) that capture the meaning of the data. This allows you to search based on similarity, rather than just specific keywords.

Vector databases are a key technology behind modern chat, search, and recommendation systems.

How Do Vector Databases Work?

Vector databases store text, images, and spreadsheets as a series of vectors, also called embeddings. Each of these vectors is a series of numbers that, on the surface, doesn’t look like much, but under the hood it captures the abstract meaning of the data.

This data – be it emails, meeting transcripts, product descriptions – isn’t replaced by becoming a series of numbers, it’s indexed.

These tiny, dense embeddings make information retrieval both efficient and meaningful. They allow us to compare items based on similarity.

Key Concepts

What is an Embedding Model?

Embedding models are machine learning models trained to convert data into embeddings.

These models are trained to compress data into a vector (our embedding), and then regenerate it. The compressed vector stores as much semantic information from the data as possible.

That means they don’t just store the words, but the ideas behind them. For example, an embedding might capture that:

• “puppy” and “dog” are closely related
• “How do I reset my password?” is similar in meaning to “Can’t log in to my account”
• “affordable laptop” and “budget-friendly computer” refer to the same thing
  

These kinds of patterns help AI agents and search engines compare inputs based on meaning, not just matching keywords.

What is Semantic Search?

So, how are embeddings compared for similarity?

As previously mentioned, an embedding vector is a series of numbers. These numbers are a representation of a point in high dimensional space. We can visualize things in 2D or 3D, but how about 384? Instead of X, Y, and Z, we have hundreds of values, all coming together to specify one unique point.

These vectors allow us to measure how “close” 2 pieces of content are – not in terms of words, but in terms of meaning.

Semantic search processes a query into a vector, and searches the database for the nearest vectors. These result vectors should, in principle, be the most similar to the user’s query.

Approximate Nearest Neighbor (ANN) Search

Semantic search is performed using an Approximate Nearest Neighbor (ANN) algorithm. The goal of ANN is to answer the question, “which vector in my database is most similar to my query?”

There are several ANN algorithms, each with its own strengths. For example:

Hierarchical Navigable Small World (HNSW)

HNSW is optimized for real-time, low-latency search. It’s great for personalized content feeds and recommendation systems – any scenario that requires searching quickly through frequently updating data.

Inverted File Index (IVF)

IVF is more suitable for large-scale, mostly unchanging data. Think e-commerce catalogs, or academic paper directories.

In practice, the algorithm will be hidden in the engine or platform used to implement the search.

Use Cases of Vector Databases

Now that we understand how vectors are created and matched, let’s take a look at the different ways we can use them to power applications.

RAG (Retrieval-Augmented Generation)

This LLM generation strategy seems to be the talk of the town, and for good reason: RAG is reliable, accurate, and provides specific responses, all made possible with Vector DBs.

With RAG, the user’s query is embedded and compared against the rest of the database for similar items. The model then references these items when generating a response.

RAG avoids relying on the model’s internal knowledge or the conversation’s history, both of which can tend to be false or irrelevant.

Say you ask for a summary of Napoleon’s childhood. The model’s response is plausible, but is it accurate? With RAG, documents relevant to your query will be used to steer the model’s response. That way, you can check the primary resource, keeping model outputs verifiable.‍

If you want to see what this looks like in practice, here's a guide for building a chatbot with RAG.

Product and Content Recommendations

Vector databases aren’t only used to respond to user queries. They can also be used to optimize a user’s experience.

Tracking users’ navigation history and clustering similar items lets businesses determine the best product or content to recommend to the user.

This is a great example of what we refer to as the algorithm: strategic content recommendations and targeted advertising.

Think of a video-sharing platform: every video has its own embedding stored in the database. When you watch one, the system can suggest others with nearby embeddings — meaning similar content, even if the titles or tags are completely different.

Over time, your watch history becomes a kind of personalized “cloud” of embeddings, helping the system understand your preferences and recommend what you'll want to see next.

The Benefits of Vector DBs Over Traditional Databases

Now that we have a sense for the hows and whats of vector databases, let’s talk whys: what advantages do they afford you in chatbots and search engines?

1. They Provide More Context to Chatbots

LLMs are prone to forgetting and hallucination in long conversations. Users and devs don’t have a clear sense of which information is retained.

With strategies like RAG, the model searches the database against your query to find whatever information is needed to give an accurate response.

Rather than reminding and correcting the model for the umpteenth time, vector databases store relevant information and reference it explicitly.

2. They Make Search Results Typo-Tolerant

Even if we know the exact keywords, searching is messy. 

golfen retriever ≠ golden retriever, but your search engine should know better.

If we’re matching queries literally, a typo or misspelled word would disqualify a relevant option. 

When we abstract the meaning of the search query, the specific spelling or wording doesn’t matter nearly as much.

3. They Allow Users to Perform Fuzzy Search

Searching is less about keywords than it is about ✨vibes✨. 

Abstracting text into an embedding vector lets you store it in ineffable vibe space. So, on the surface, 

"Where can I get a killer flat white around here?" 

doesn’t look like 

"Best spots for a caffeine fix nearby", 

but your search engine will match them all the same. This is possible because the embeddings of the two phrases are very close, even though their wording is different.

4. Vector DBs can Compare Across Modalities

Data comes in all shapes, sizes, and types. We often need to compare data across different types. For instance, using text to search and filter product images.

Multimodal models are trained to compare different types of data, such as text, images, audio, and video.

This makes it easier to talk about your content. Find a product by describing its image, or ask about charts using plain language.

How to Build an AI Agent with Smart Search Capabilities

If you’re new to semantic search, you’re probably flooded with questions:

How do I prep my data?

Which data should I include?

Which embedding model should I use… and how do I know it’s working?

Fortunately, you don’t have to figure it all out up front. Here’s how to get started in a few easy steps:

1. Define Your Use Case

Start with something simple and useful. Here’s a few examples to get the gears turning:

• A retail chatbot that helps customers find the right products based on their needs and preferences. Ask it, “What’s a good winter jacket for hiking that's under $150?”

• A ticketing bot that triages employee IT requests in real-time. Ask, “Are there any high-priority tickets related to VPN access still unassigned?”

• A business process automation agent that manages order fulfillment from start to finish. Ask it, “Has the Smith order shipped yet, and did we send the confirmation email?”

All of these are quick to build, easy to test, and immediately valuable.

2. Choose Your Platform

If vector databases feel confusing or abstract, there are plenty of chatbot platforms that deal with embeddings and clustering for you behind the scenes.

3. Gather Your Data

Start with what you already have—text files, PDFs, spreadsheets. A good platform handles the formatting for you. Just upload your content, and it’ll take care of embedding and indexing behind the scenes.

Some specifics will depend on what platform you’re using. Here are some tips for getting the most out of your data.

4. Add a Description

Write a short, plain-language description of what your bot is for.

This helps set the tone and expectations: how the bot should talk to users, what kinds of questions it can expect, and what data it can reference.

For example:
“You are a support assistant for the HR team. Help employees find policies and answer questions about PTO and benefits. Use information from the employee handbook and HR documents. Be clear and polite. If you don’t know something, ask the user to contact HR.”

5. Test and Tweak

Test your setup with real queries. Ask what your customers would ask. Are the results relevant? Accurate?

Tweak your bot as needed:

• Incomplete results? Raise the chunk count for fuller responses.
• Slow response? Pick a faster model.
• Incorrect responses? Try a more accurate model, or add relevant data.

Platforms are highly customizable, so solving issues is usually just a matter of configuring, like playing with available models or changing the descriptions.

Build Smarter Search Capabilities

With recent advances in AI, searchable data isn’t just a nice-to-have—it’s becoming the default expectation.

You don’t have to master ANN or embeddings to build smarter search engines. Our platform gives you plug-and-play tools for semantic search and retrieval-augmented generation. No data prep needed.

Start building today. It’s free.

FAQs

1. How do I evaluate the performance of a vector database?

To evaluate the performance of a vector database, measure its query latency (how quickly it returns results), recall or precision (how relevant those results are), and scalability (how well it handles growth in data and queries). You should test with real queries to ensure it meets speed and accuracy expectations under load.

2. What are the storage requirements for large-scale vector data?

The storage requirements for large-scale vector data depend on the number of vectors and their dimensionality – for example, 1 million vectors at 768 dimensions using 32-bit floats would require over 3 GB of raw storage. At scale (millions to billions of vectors), expect requirements in the tens or hundreds of GBs, and use options like compression or approximate indexing to reduce storage costs.

3. What happens if two very different documents have similar embeddings due to noise or model bias?

If two unrelated documents generate similar embeddings, the search system may return incorrect results. To address this, you can fine-tune your embedding model on domain-specific data or use hybrid search techniques that combine vectors with metadata or keyword filters for disambiguation.

4. How is vector data versioned and managed over time?

Vector data is versioned by tracking the input data and the embedding model used to generate vectors. Common practices include storing timestamped snapshots and tagging index versions.

5. Is it possible to combine traditional keyword search with vector search?

Yes, combining traditional keyword search with vector search is called hybrid search, and it's supported by many platforms like Elasticsearch or Vespa. This method improves relevance by using lexical matching for precise queries and semantic vector similarity for understanding context.