An explanation of vector search
In this video, TechTarget editor Natasha Carter talks about vector search.
Billions of people search the internet globally, but sometimes, they might not get their desired results due to spelling errors or the wrong keyword. Vector search can help users get more accurate search results.
Search engines typically only provide results on keyword matching and ranking the relevant results based on algorithms, so some results might be lost with different spellings or the wrong keyword for the user intent.
Vector search uses multidimensional data that often leads to more relevant, contextual and diverse results than a typical search platform.
This video will explain what vector search is and how it works.
Samantha Poutre is an editorial assistant at TechTarget and a student at Roger Williams University. She studies creative writing at Roger Williams with a minor in global communications. She has served as an editor for two of her university's newspapers and enjoys participating in clubs involving writing and the arts.
Tired of typing in the same keywords in hundreds of ways to find what you're looking for? Enter vector search.
For the most part, exactly what you type into a search box is exactly what you get, as far as traditional searching goes. So, you might miss results that are related or spelled differently.
With vector search, data -- including words, sentences, images and audio -- is represented with numbers across multiple dimensions; we'll get into that in a moment, but it's this multidimensional data that enables more accurate search results that account for context and relevance.
Vector search isn't a new technique, but it is having a resurgence because of its importance in generative AI development. Dig into the details of its role in GenAI by clicking the link above, or in the description below. And subscribe for more videos on all things business tech.
While we say vectors are "numbers" that represent data, they're actually complex mathematical representations. In order to search vectors, they first have to be arranged in what's called a vector embedding, a multidimensional view of data generated by machine learning techniques such as neural networks. The more similar vectors are, the closer together they are in the embedding.
For instance, an embedding could represent that cat and feline are more related (therefore closer together in the vector space) than cat and car -- even though their spelling is more similar.
Overall, vector search improves the depth and breadth of searching different types of data, offering benefits like the ability to:
- Browse unstructured data, which is crucial for AI model development.
- Recognize context and provide more relevant results.
- Understand the semantics of a search query.
- Integrate easily with AI and ML frameworks.
However, there are limitations and challenges for vector search, like:
- Resource requirements, i.e., high computing power.
- Less accuracy when compared to targeted keyword search.
- Explainability challenges when compared to keyword search.
Vector search can be applied to a wide range of use cases, the most common today including:
- Recommendation engines.
- Natural language processing, or NLP.
- Anomaly detection.
- Drug discovery.
- Genomics.
- Image search.
- Retrieval augmented generation, or RAG -- among others.
What questions do you have about vector search? How do you see it impacting generative AI development? Share your thoughts in the comments, and remember to like and subscribe, too.