On-the-fly filtering for KNN search

[sanikolaev]

This PR implements on-the-fly filtering for KNN search, allowing the search algorithm to continue exploring until k filtered candidates are found, rather than finding k total candidates and then filtering them.

Key changes:

• Added k parameter to searchBaseLayerST() method to specify the target number of filtered results when filtering is enabled
• Added use_filter flag to determine when filtering mode is active
• Modified termination condition to continue searching until k filtered candidates are found when use_filter is true, instead of stopping after ef total candidates
• Updated exploration logic (should_explore) to continue exploring neighbors when filtering is enabled and fewer than k filtered candidates have been found, even if ef candidates have already been explored
• Added filtering check (is_filtered) before adding candidates to top_candidates priority queue, ensuring only filtered candidates are stored
• The filter callback (BaseFilterFunctor* isIdAllowed) is invoked for each candidate node via (*isIdAllowed)(label) to determine if it passes the filter
• Updated searchKnn() to pass the k parameter to searchBaseLayerST() when a filter is provided

The changes ensure that when filtering is enabled:

1. The search continues until k filtered candidates are found (if they exist)
2. Only candidates that pass the filter are added to top_candidates
3. The exploration continues even if ef candidates have been explored, as long as fewer than k filtered candidates have been found

This enables more accurate KNN search results when combined with attribute filters, as the algorithm actively searches for filtered candidates rather than relying on post-filtering which may return fewer than k results.