vectors are similarity, graphs are constraint. similarity will happily retrieve garbage that looks like the query. that’s the noise bottleneck — add more vectors, get more confident wrong answers. graphs and memory policies are basically the immune-system layer. question: when you evaluate a retrieval pipeline, how do you actually separate signal documents from the ones quietly adding noise?
We built graph-native embedding and reranking models that can plugin on top of your vector database. At query time we rotate embedding space to filter noise and sharpen the signal. So for this to work you need both graph-native embeddings / ranking + graph policies (this article)
vectors are similarity, graphs are constraint. similarity will happily retrieve garbage that looks like the query. that’s the noise bottleneck — add more vectors, get more confident wrong answers. graphs and memory policies are basically the immune-system layer. question: when you evaluate a retrieval pipeline, how do you actually separate signal documents from the ones quietly adding noise?
We built graph-native embedding and reranking models that can plugin on top of your vector database. At query time we rotate embedding space to filter noise and sharpen the signal. So for this to work you need both graph-native embeddings / ranking + graph policies (this article)