Traditional filtering is typically based on tabular (rows in a database) or tree-like (JSON documents) data formats. The landscape changes significantly when the data itself is structured as a graph. When employing HNSW in a graph-based setup, both continuous vectors and discrete facets become vertices in the same graph. This allows for more nuanced relationships and more efficient alignment. Furthermore, the upper layers within HNSW represent a form of compression. With your data in a graph, you can move beyond the classic HNSW node-degree compression algorithms to consider more semantic forms of compression, which take domain-specific ontologies into account.

Vectors need Graphs! Embedding vectors are a pivotal tool when using Generative AI. While vectors might initially seem an unlikely partner to graphs, their relationship is more intricate than it first appears.