What is the significance of splitting text into chunks during data loading into Oracle AI Vector Search?

Splitting text into chunks when loading data into Oracle AI Vector Search is important mainly because it minimizes token truncation. Each vector embedding model comes with its own maximum limit on the number of tokens it can process at once. If a text input exceeds this limit, it can lead to truncation, where the excess tokens are ignored. This not only results in a loss of valuable information but also leads to inaccurate or incomplete vector embeddings for the text. By dividing the text into manageable chunks, each within the token limit of the embedding model, all parts of the original text can be represented in the embedding process, ensuring that the resulting vectors capture the full context and details of the input data.

The other choices highlight important aspects of the data processing and embedding model, but they do not directly address the primary concern of managing token limits when preparing text data for vectorization.