RAG systems convert documents into vectors stored in a database. When you ask a question, it gets converted to a vector, matched against stored ones, and the most similar results come back as text. A simple pipeline can pull files from Google Drive into the vector store and let an AI agent retrieve info like company hours.
RAG systems explained in 30 seconds. The most simple way to think about it is documents are broken down into a random jumble of numbers and letters called vectors. These vectors are stored in something called a vector store, which essentially can just store all of that information. And we can connect that vector store up to an AI agent, which can query it to get the data that it needs. Let's say it's a vector store full of company information and it needs to know the opening times. The AI agent can input the vector store with a query saying opening times. That query is then also broken up into a random jumble of numbers and letters, and then those random numbers and letters are matched up with the vector store's random numbers and letters. And then the vector store can just return the numbers and letters that match up to that query's numbers and letters. And before it gets to us, it's converted back into text. And that might sound a little complicated, but you can literally build a system that does all of this in NaN that looks something like this, where it just takes information from your Google Drive, downloads it, uploads it to the vector store, And then this AI agent is connected up to the vector store and can retrieve information.
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