Vector Search with Embeddings and Cosine Similarity
This article is about understanding vector embeddings and how they can assist in AI. The article explains the importance of embeddings, which are like precursors to AI, helping us to understand information in a more human-friendly way. By converting a search string into a specific vector of numbers, we create a more efficient method of scanning a large database for the exact item we are looking for. This is pretty much how search engines like Google work.
The main benefit of vector embeddings is that it allows us to search using natural language, making the process more human-like and less machine-like. With the proper use of an embedding API, we can feed any information into an embedding. The output would be an array of floating point numbers, essentially a vector.
This is a simple but effective way to improve the search experience. The article goes on to explain how you can optimize your searches using tools like pgVector, an indexing system providing higher performance with large amounts of vectors. This system slices your data into partitions that make searches faster.
Finally, the article provides examples of JavaScript functions to create embedding strings and initialize the extensions for vector search. This article emphasizes the importance of vector embeddings and how they can vastly improve the search experience, enabling better understanding and more accurate results.
