Vector Database Search Optimization | Dataford Interview Questions

Write a function to optimize a search operation over a large-scale vector database.

Implement top-k vector search using dot-product similarity. Given a query vector, a list of vectors, and k, return the indices of the k most similar vectors, ordered by descending score. Break ties by returning the smaller index first. Use a bounded heap rather than sorting every scored vector.

The input vectors are nonempty and all have the same dimension.

Constraints

1 <= len(vectors) <= 5000
1 <= len(query) <= 256
All vectors have the same dimension as query
1 <= k <= len(vectors)
-10^6 <= query[i], vectors[i][j] <= 10^6
Return indices in descending dot-product order, breaking ties by smaller index

Function Signature

def search_vectors(query, vectors, k):

Your question is Vector Database Search Optimization. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Write a function to optimize a search operation over a large-scale vector database.

The input vectors are nonempty and all have the same dimension.

Constraints

1 <= len(vectors) <= 5000
1 <= len(query) <= 256
All vectors have the same dimension as query
1 <= k <= len(vectors)
-10^6 <= query[i], vectors[i][j] <= 10^6
Return indices in descending dot-product order, breaking ties by smaller index

Function Signature

def search_vectors(query, vectors, k):

Your question is Vector Database Search Optimization. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.