Relying solely on vector search carries several risks compared to traditional keyword search. Firstly, it often incurs significantly higher computational costs for generating and storing embeddings, as well as for executing queries, especially with large datasets. Secondly, vector search is highly dependent on the quality and representativeness of the embeddings themselves; poor embeddings can lead to irrelevant or biased results, amplifying issues present in the training data. Furthermore, understanding *why* certain results are returned can be challenging due to its "black box" nature, making interpretability and explainability difficult for users or developers. Unlike keyword search which provides exact matches, vector search offers approximate similarity, potentially missing precise results when exactness is critical. Therefore, while powerful for semantic understanding, it requires careful implementation and evaluation to mitigate these inherent drawbacks. More details: https://www.gtb-hd.de/url?q=https://infoguide.com.ua