Day 2 HNSW Indexing Fundamentals At this point, you’ve learned how vector search retrieves the nearest vectors to a query using cosine similarity, dot product, or Euclidean distance. How does this work at scale? Why Vector Search Needs Indexing The Vector Search Challenge You might wonder if Qdrant calculates the distance to every single vector in your collection for each query. This method, known as brute force search, technically works but with millions or billions of vectors this is too ...
