Website SUPER SEARCH with Vector Databases and OpenAI Embeddings

In this video, I'll be discussing the downsides of using keyword search and how to overcome those limitations by building a vector database and using cosine similarity to rank items by semantic match. Specifically, I'll be showing you how to use OpenAI embeddings in Wix to accomplish this.

Keyword search is the traditional way of finding information on the internet. However, it has some significant limitations. For instance, it is not always accurate, and you can miss out on relevant results if you don't use the right keywords. Additionally, it is not able to account for the context of a search query, which can lead to irrelevant results.

Building a vector database is an excellent way to overcome these limitations. It involves converting text into vectors, which can be used to represent the meaning of the text. By comparing these vectors, we can determine how similar two pieces of text are.

Cosine similarity is a technique used to calculate the similarity between two vectors. It involves taking the cosine of the angle between two vectors. The result is a value between 0 and 1, where 0 indicates no similarity, and 1 indicates perfect similarity.

Using OpenAI embeddings in Wix makes it easy to build a vector database and use cosine similarity to rank items by semantic match. By incorporating this technique into your website, you can provide a more accurate and relevant search experience for your users.

In conclusion, if you're looking to improve the search experience on your website, I highly recommend building a vector database and using cosine similarity to rank items by semantic match. It's a powerful technique that can make a significant difference in the accuracy and relevancy of search results.