First: what are these fancy terms? Well, a vector database is essentially just a way to store data as vectors instead of traditional rows and columns like you might see in a regular old SQL database. And an embedding is basically a mathematical representation of that data as a set of numbers or coordinates within some sort of multidimensional space.

So why do we care about this for coding assistants? Well, let’s say you have a bunch of code snippets stored in your favorite text editor (or IDE) and you want to be able to search through them quickly and easily using natural language queries. Instead of having to manually sift through thousands or millions of lines of code looking for that one specific function call, you can just type something like “find me all the instances where we use the ‘sort’ method in our Python scripts” and your coding assistant will do the heavy lifting for you!

But how does it actually work? Well, first the LLM (which is essentially a fancy neural network) takes in some input text (like that query I just mentioned) and generates an embedding based on its meaning. Then, using some sort of vector database like Elasticsearch or MongoDB, we can search through our code snippets for any instances where that same embedding appears. We’ve got a list of all the relevant code snippets right at our fingertips!

Now, you might be wondering: why bother with this fancy vector stuff instead of just using regular old SQL queries? Well, there are a few reasons. First of all, it allows us to perform much more complex searches than we could with traditional databases. For example, let’s say you want to find all the instances where we use the ‘sort’ method in our Python scripts that also involve sorting by date or time. With vector embeddings, this is a piece of cake! Just type something like “find me all the instances where we use the ‘sort’ method and sort by date” and your coding assistant will do the rest!

But there are some downsides to using vector databases as well. For one thing, they can be much more resource-intensive than traditional SQL databases (especially when it comes to indexing and query optimization). And because they’re still a relatively new technology, there aren’t always great tools or libraries available for working with them in coding assistants.

So what does the future hold for vector databases and embeddings in LLMs? Well, according to some experts in the field (like those over at Google Research), we can expect to see much more sophisticated natural language processing capabilities as these technologies continue to evolve and mature. And who knows maybe one day our coding assistants will be able to understand not just what we’re saying, but also what we mean!