How does a vector database work? A quick tutorial

What’s a vector database?

And how different is it than a regular-old traditional relational database?

If you’re reading this, chances are good that you’ve already waded in to learn the basics on this cutting-edge new form of storing information. You’re keenly aware that with artificial intelligence (AI), everything is at a historic turning point, and vector databases appear to be one part of a fully amazing emerging picture.

And if you own or run an enterprise website, you’re undoubtedly wondering how you can harness this awe-generating technology to boost your ROI.

We’ve got you covered with this post. Here’s a rundown on how vector databases work their magic to do things like materially enhance user search and discovery.

What’s a vector database?

Along the same lines as how a traditional database works, a vector database stores, efficiently processes, and analyzes data sequences. It achieves this by representing information in a way that machines can easily understand: as vectors.

In mathematical space, vector data is represented as vector embeddings, numerical representations of words that are also known as vector representations and word embeddings. To store and retrieve unstructured data, embeddings are typically generated using machine-learning techniques such as neural networks, which map text input to vectors.

Vector DBs can thereby utilize embeddings to accurately inform indexing and search-engine functionality.

This type of system is ideal for tasks that involve natural language processing (NLP) and recognizing the content of images (such as with computer vision). What’s more, vector databases can accommodate especially large datasets, including ones containing time-series data. So as an emerging technological force, they’ve got a lot going for them. This explains why the field has already become populated with both closed- and open-source suppliers, including Milvus, Faiss, Qdrant, Weaviate, and Pinecone.

Generative AI and vector databases

Reading your mind…you’re wondering how vector databases are related to the recent hottest thing in data science, large language models (LLMs).

Are we right?

In a word, vector databases enable those large-scale models to be their best selves and not go off the rails. (Well, that’s a simplified analogy that rests on a human-oriented perspective, but it works.)

You’ve probably tried out ChatGPT and other generative AI interfaces. You know that gen AI facilitates the near-real-time creation of text in response to entered user prompts.

Amazingly promising, yes, but with a caveat.

Quick disclaimer

LLMs aren’t necessarily reliable in terms of telling the whole truth and nothing but the truth. They’re known to embellish the facts and flat out make stuff up; in short, they’re prone to the technological version of hallucination.

Plus, they’re limited to knowing the wisdom they’ve assimilated to date in training, so they may not be providing the most up-to-date information.

And, to top it off, LLMs haven’t been required to play by the same rules humans do. They don’t have to provide footnotes disclosing where they got their ideas; they aren’t required to show their work in order for a professor to give them a good grade. It’s patently unfair, but short of a mass rollout of explainable AI, it’s how much of the AI world works right now.

So without a savvy human fact checker doing a fair amount of research to pinpoint and correct AI-related issues, there’s a good chance that generative AI applications could get away with disseminating all manner of inaccurate information to large swaths of the human population.

Not good, you’d probably agree.

A check on runaway AI

Hold on now, this is where vector databases provide at least a small semblance of hope. This new generation of database is poised to help rectify the iffy generative AI situation by functioning as up-to-date, accurate, ground-base data storage for LLM querying, thereby keeping a fact-oriented eye on, and perhaps reigning in the brilliantly creative ramblings of, overzealous generative AI bots.

The result of this generative AI-vector power duo? First-rate search and other use cases where data must absolutely be accurate, as opposed to just entertaining, hilarious, gorgeously artistic, or passable as content that appears to make total sense, but who knows if it’s actually true?

How a vector database works

Here’s a look at what’s transpiring in the inner world of vector embeddings in a vector database.

The secret sauce of a successful vector database lies in its vector embeddings, broken-down bits of stored content.

First, embeddings are generated from content — text, images, audio, or video.

In this “vectorization” process, with words, for instance, the relationships between the words are captured. This ensures that the ones with similar meanings or contexts — similar vectors — will be placed physically near each other in the vector space.

As you might expect with a traditional database, the next step is vector indexing. Using algorithms (for example, product quantization or hierarchical navigable small world, HNSW), the embeddings are mapped to a data structure that facilitates quick search and duly stored in the database for easy retrieval.

Third is the querying stage. User queries are sent through the vector embedding model used to generate the data storage. When a query is submitted, the indexed query vector is compared with the indexed vectors, and the best retrieved information is pushed to the front.

The impact on search

Vector databases are being utilized as part and parcel of search providers’ tools. Why? The effectiveness of search functionality arises from a foundation of efficient vector storage. For example, Algolia NeuralSearch is utilizing AI to convert content into numerical values; relevancy is then determined based on proximity to the next nearest number.

To optimize search results, for instance with semantic search, a vector database relies on algorithms that participate in approximate nearest neighbor (ANN) search. To get the most accurate responses to a search query, a similarity metric is applied to find the nearest neighbors, and then the nearest vector in the space is retrieved.

The possible similarity measures used in this process include:

  • Cosine similarity, which measures the cosine of the angle between vectors. The vector orientation is typically used, rather than the magnitude.
  • Dot product, which determines the product of two vectors’ magnitudes and the cosine of the angle between them.
  • Euclidean distance, which determines the distance of a straight line running between vectors.

Approximate search results can be returned quickly, whereas more-accurate information may take a little longer to emerge. The ideal is obviously using a database system that achieves both objectives: be accurate and be quick about it.

The fourth step in vector database activity amounts to a version of mopping up: follow-up processing. The vector database might gather the nearest neighbors and produce final results, possibly also re-ranking the nearest neighbors.

So as you can see, in terms of the type of data and other aspects, a vector database is in some ways exactly like a traditional database but in other ways nothing like it. It facilitates vector similarity searches by utilizing the vector representation of the data. It can work with high-dimensional vectors, whereas a traditional database can’t scale effectively to achieve anywhere near the same effectiveness.

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Put vectors to work on your behalf

What’s the take-away on how vector databases so outstandingly do their jobs?

When it comes to leading-edge enterprise search frameworks, we’d honestly be lost without them. In the search industry, vector search powered by artificial intelligence is enabling more-accurate search, on-point recommendation systems, and prediction of desired content, even with the challenges inherent in extremely large datasets.

Vector databases have emerged as key to understanding intent — the precise content that someone needs or wants — whether the searcher is entering text in a search bar, doing an image search, looking for audio, conducting a video search, or innocently discovering and being guided by content as they browse an ecommerce website.

Want to tap the star power of vectors for fine-tuning your website search?

Algolia search is a proven performer thanks to a breakthrough algorithm that compresses vectors. Using our API, you can quickly upgrade your search to give your shoppers or customers the best information, while prospectively giving your site metrics a high-performance boost as well.

Let’s get your search on the road to real success! See a demo or contact us for all the data points on getting your business to thrive, and fast.

About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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