Document Processing for RAG

Document processing for RAG (Retrieval-Augmented Generation) is about getting information ready for use. It's the first step in making RAG work well. RAG uses both old and new info to give better answers.

When we process documents, we take big chunks of text and make them easy for computers to understand. This is like taking a big book and turning it into small, easy-to-read parts. We do this because computers can't read like humans. They need the info in a special format.

Breaking it down

First, we collect all the documents we want to use. These could be books, articles, or web pages. Then, we clean up the text. This means removing things that don't help, like extra spaces or weird symbols. We also might change all the words to lowercase to make them easier to work with.

Next, we break the text into smaller pieces. This is called tokenization. It's like cutting a sentence into words. But sometimes, we cut it into even smaller pieces called subwords. This helps the machine learning model understand better.

After that, we turn these words or subwords into numbers. This is where vectors come in. Each word gets turned into a list of numbers. This list is called a vector. The computer uses these numbers to understand what the words mean and how they relate to each other.

Metadata provides a map

We also need to keep track of where each piece of info came from. This is important because when RAG gives an answer, it needs to know where it got the info. It's like keeping a list of which books you used to write a report.

Sometimes, we also add extra info to help the computer understand better. This could be things like what type of document it is, when it was written, or who wrote it. This extra info is called metadata.

The last step is to store all this processed info in a special kind of database. This is where vector stores are relevant. They're like super-fast filing cabinets for all our processed documents. When we need to find info later, these stores help us find it quickly.

By processing documents this way, we make it easy for RAG to quickly find and use the right info when answering questions. This helps RAG give better, more accurate answers.