Vector Database Checklist Point

Mark these points during VectorDB Development

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In the dynamic domain of artificial intelligence (AI), search and match capabilities are one of the crucial functionalities. At the forefront of addressing the escalating demand for efficient search mechanisms stands VectorDB—a transformative solution assured to revolutionize information retrieval. However, the integration of VectorDB as a cornerstone in AI applications is not without its challenges.

Introduction

VectorDB emerges as a pivotal player, promising to elevate the efficiency of search and match functionalities by transforming entities into embeddings or vectors. However, the power of VectorDB comes with a caveat—rashly crafting it without care can result in unintended consequences, such as generating an undesirable retrieval list. This article defines key considerations and essential points to ensure the meticulous construction of VectorDB.

Determine Goal

In any decision on stack or process to make a better product, we must include a Goal as the target. It will be easy to create evaluation metrics once we have the Goal. Also, since we have a goal, there won’t be any confusion in the creation of VectorDB which needs a clear context.

Data Quality

Garbage in, Garbage Out. As with other techniques or models that need data as a source, the VectorDB performance always depends on the data quality. The data should be cleaned from noise and preprocessed based on our goal before being stored in the VectorDB. Not only that, make sure the data have the context that we want for our VectorDB.

Choose the right VectorDB