The idea of its operation is quite elegant - it's one of the most interesting discoveries of recent years.
🟢 How it works?
HNSW builds a multi-level graph, where each upper layer contains exponentially fewer nodes than the layer below.
☞ All vectors are located in the lower layer (layer 0), which is well connected.
☞ Only some vectors appear in layer 1, even fewer in layer 2, etc.
☞ The upper layers work as "fast lanes", allowing you to skip a large number of irrelevant data.
During the search, the algorithm starts from the upper layer, finds the nearest node, descends to the lower layer and repeats the process. By the time you reach the lower layer, you have already narrowed the search to the most relevant environment - there's no need to sort through everything.
This explains why HNSW is so economical with memory. It can "jump over" large amounts of data without evaluating each element.
Key parameters that affect the balance of speed and quality:
☞ ef - the size of the candidate list during the search
☞ maxConnections - how many connections each node can have
☞ distance - a metric for comparing vectors (cosine, dot product, etc.)
Adding new elements works in a similar way: first, we search for the optimal location, then we create connections. Restructuring the graph is resource-intensive, but queries themselves are performed very quickly.
You can read more in detail here ✅
••••••••••••••••••••••••••••••••••••••••••••••
🤖 Data & ML | @DataXplore