Why might one avoid defining a specific size for the VECTOR column during development?

Defining a specific size for the VECTOR column during development can be avoided because embedding models produce vectors with varying dimensions. This variability is a crucial aspect of utilizing different embedding models, as they may generate vectors of differing sizes based on how they are structured and the data they are trained on.

By not imposing a specific size requirement, you allow for flexibility in using various models that may be more suited to different types of data or applications. This adaptability can enhance the performance of similarity searches and ensure that the database can effectively accommodate new models or techniques that may emerge over time, which might generate vector outputs that are not uniform in size.

In contrast, a defined size could lead to potential issues such as truncation of important information or the inability to utilize newer models that produce vectors of different lengths, undermining the effectiveness of the system overall.