Advanced chunking for RAC. Let's say we have a paragraph of n-sentences delimited by a new line character. We start by calculating the semantic coefficient a little fancy view of saying semantic similarity between the consecutive sentences and plotted on a chart where x-axis is the sentence index and y being the similarity. We then create a hypothetical topical boundary where the similarity between the consecutive sentences has a high deviation than the previous pair of sentences. This way we get three coherent chunks that have high introsimilarity but low introsimilarity. We can now index them in a vector database and let the remaining RAC pipeline be the same. Follow for more.