Near Neighbor Search

Near Neighbor Search (NNS) generally retrieves chunks from the storage substrate in the increasing order of Hamming distance (from a query).

As we mentioned earlier, this is equivalent to a strictly decreasing order of overlap (between query and candidate). If overlap encodes the semantic relevance, this translates to a list of semantically similar candidates.

It leverages an underlying Associative Index for efficient recovery of candidates. The Associative Index is a semantic index that enables fast similarity-based lookup over stored hypervectors. Conceptually it turns a key-value substrate (item memory) into an associative memory — one where retrieval is by content similarity, not by exact content or key match.

This NNS module has a constant time complexity, with help from associative index. This implies the query time remain bounded, independent of the number of entries in the storage system. The secret sauce is the efficient random-access to underlying associative index.

Unlike approximate nearest neighbor methods (LSH, HNSW, etc.), the NNS module can computes exact overlap counts via the associative index. There is no approximation error and no index-specific parameters to tune.

Jump to the API reference for Near-Neighbor Search.