How should a machine learning team improve search accuracy when using IVF indexes and observing missing relevant images?

The choice to add the TARGET ACCURACY clause to the query with a higher value is correct because it directly affects how the search results are filtered and presented based on the desired level of precision. In the context of IVF (Inverted File) indexes, specifying a higher TARGET ACCURACY means the retrieval process will prioritize more accurate and relevant results. This is particularly useful when the current configuration leads to missing relevant images, as a higher accuracy setting can help ensure that the search algorithm focuses on returning the most pertinent items within the dataset.

Increasing the TARGET ACCURACY can lead to the inclusion of additional relevant items by refining the search process, rather than merely retrieving a larger volume of results that may contain less relevant data. This approach strategically enhances the relevance of search outcomes based on the requirements of the task at hand, effectively addressing the concern of missing pertinent images in the search results.

The other options, while they may have their own merits, do not target the immediate issue of improving search relevance to tackle the missing images. For instance, changing the index type to HNSW may offer better performance in some contexts, but it involves a different indexing method rather than simply refining the accuracy of an existing search process. Increasing the VECTOR MEMORY SIZE and re-