Merge pull request #475 from superlinked/update-article

Update multi-attribute-semantic-search.md

docs/articles/multi-attribute-semantic-search.md

@@ -427,7 +427,7 @@ Great results again! Our two other retrieved monsters - Luminoth and Zephyr Danc
 
 ## Conclusion
 
-Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive approach (above) - storing and searching attribute vectors separately, *then* combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. (Moreover, [multiple kNN searches take more time than a single one with concatenated vectors](https://redis.io/blog/benchmarking-results-for-vector-databases/).)
+Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive approach (above) - storing and searching attribute vectors separately, *then* combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. (Moreover, [multiple kNN searches take more time than a single search with concatenated vectors](https://redis.io/blog/benchmarking-results-for-vector-databases/).)
 
 To handle scenarios like this, it's better to store all your attribute vectors in the same vector store and perform *a single search*, weighting your attributes at query time. The Superlinked approach is more accurate, efficient, and scalable than the naive approach for any application that requires fast, reliable, nuanced, multi-attribute vector retrieval - whether your use case is tackling real world data challenges in your e-commerce or recommendation system... or something entirely different, like battling monsters.