We all love the convenience of a search engine. However, if you’re a developer, or someone who works with data on a regular basis, it can be frustrating when you have to jump through hoops just to get your results.
Search engines are very good at finding things based on text queries and keywords, but what if there was another way? What if we could organize our data more like how human beings organize their thoughts?
Vector search is a type of search that returns the closest results to a given query. It’s different from traditional search because, in vector space, every item has an associated list of attributes (vectors) and each vector represents some aspect of an object. This allows you to find similar objects based on their attributes rather than just their names or text contents. Vector space has many uses, including finding the closest matches to a query when searching for images or videos on YouTube or Amazon Mechanical Turk tasks such as tagging images with keywords for machine learning systems like Google Photos or Cloud Vision API
Understanding vector search and how it works?
Vector search is a new way to search, but it’s not so different from traditional searching. In vector search, you’re looking for objects that match a set of criteria. This could be anything from “find me all the books written by author X” or “I want to see all the restaurants within 10 miles of my house.”
In traditional searching, we use words as our first line of attack–and often those words don’t tell us enough about what we actually want. For example: If I type into Google “what’s the best restaurant in San Francisco?” I’ll get back results that are good but not necessarily great (because they weren’t designed specifically for me). With vector search, however, we can use additional information like location data or personal preferences when building our results list–and this makes them much more accurate!
The difference between traditional vs vector search
Vector search is a new way of searching. It’s more efficient, accurate and uses less power than traditional search.
The benefits of a vector database
Vector search is more flexible than traditional search. In a vector database, you can use the same query to find similar items and items that are similar to a given query item. This means that you don’t have to make multiple queries if you want both kinds of results; if your data is organized into vectors and stored in a vector database, then all of these searches can be done with one query!
This flexibility comes at a cost: in order for vector databases to work well with their added complexity, they need more memory than regular databases do–but this shouldn’t be too much of an issue as long as you’re working with relatively small datasets (which should be true for most applications).
The challenges of traditional databases
The traditional database model is not well suited to high-dimensional datasets. As we mentioned in the previous section, it’s difficult to index and search across all dimensions at once.
Traditional databases also have trouble with efficient retrieval of data when you need it. When you read a book, you don’t want to know every single word in it; you just want the ones that are relevant to your query! A traditional database would have no way of knowing how many words there were in total and thus could not intelligently filter out those irrelevant words before sending them back as results–it would simply return all documents containing any given word or phrase (even if only one).
Vector search in action
Vector search is a powerful form of data retrieval that can be used for a variety of applications, including information retrieval and text analysis. It’s especially useful when you want to find the closest match to a given vector. A good example is finding someone’s phone number from their name: if you know “John Smith” has the phone number 555-5555, then you can ask for all individuals whose names are closest to “John Smith” (or which least differ from it) and get their corresponding phone numbers as well.
Vector search is also useful for finding similar documents within large collections of documents or images; this process is known as document retrieval or image retrieval respectively. In this case we’re not interested in exact matches but rather documents/images that look like they came from the same source(s).
Picking the right number of dimensions
The number of dimensions you need depends on the problem you are trying to solve. A good rule of thumb is to pick a number that captures the underlying structure of your data while not being overly large. For example, if you have a dataset with 100 items in it and each item contains three attributes (e.g., x1, x2, x3), then having one dimension for each attribute would be too many because there aren’t enough dimensions for all 100 items. On the other hand, if you were working with even more information about each item–such as its location on Earth–then adding another dimension might help capture those relationships better than two or three would do alone!
When to use vector search vs. traditional searching
Vector search is a powerful tool that can be used in a variety of situations. It’s especially useful when you need to find similar objects or groups of objects within your dataset, but it’s also useful for finding exact matches when you don’t have enough data to warrant using full-text search.
A good rule of thumb is to use vector search whenever possible–it will save you time and money!
Vector search can unlock a whole new range of possibilities for developers.
Vector search is a new type of database that can be used to find things that are similar, but not identical. This makes it much more flexible than traditional databases which are optimized for exact matches.
In this article, I’ll explain how vector search works and provide some examples of how it might be used in your own applications.
We hope that this article has given you a better understanding of vector search and helped you decide whether it’s right for your project. If so, we encourage you to try out our open-source library for vector search on GitHub!