HNSW Graph Proximity Matching for Unstructured Vector Sets
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Solution Overview
Problem
Existing proximity-based searching techniques are not well suited for comparing two sets of unstructured records, as they scale poorly in computational complexity, making them inefficient for large datasets, such as those with hundreds of millions of items, and fail to find the closest pair of vectors between two sets effectively.
Innovation Solution
The creation of hierarchical navigable small world (HNSW) graphs for each set of vectors, allowing for layer-by-layer analysis to find the closest pair of points, reducing computational complexity and improving efficiency in determining the closest pair of vectors between two sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a naive approach iterating through every point in both sets is used, then the closest pair can be found, but the computational complexity scales poorly as O(nm)
Solution Approach 1:
The patent divides the large sets of vectors into multiple smaller clusters or groups. By segmenting the data, the algorithm can perform proximity searches within smaller subsets rather than comparing every vector against every other vector, thereby reducing the computational complexity from O(nm) to something more manageable while still ensuring the closest pair is found across the entire sets.
Solution Approach 2:
The patent introduces hierarchical levels or additional dimensions to the search space. Instead of a flat O(nm) comparison, it creates a multi-level structure where vectors are organized hierarchically, allowing the search to proceed through layers from coarse to fine granularity. This dimensional transformation enables efficient pruning of the search space while maintaining accuracy.
2Ease of operation
If traditional nearest-neighbor search algorithms are used, then a single closest point can be found, but they are not suited for comparing two sets with many points
Solution Approach 1:
The patent designs an algorithm that serves multiple functions: it can perform traditional nearest-neighbor search within a single set while also handling the more complex task of finding the closest pair between two different sets. This multi-functional approach maintains the simplicity of basic nearest-neighbor methods while extending their applicability to set-to-set comparisons through hierarchical organization and iterative refinement.
Data Source
AI summary
Provided is a process, including obtaining, with a computer system, a first set of vectors in an embedding space and a second set of vectors in the embedding space; determining, with the computer system, which pair of vectors in the first and second sets are closest to one another in the embedding space, the pair having one vector from each set; and storing, with the computer system, in memory, the pair of vectors in the first and second sets that are determined to be closest to one another.