Building a Vector Search Engine from Scratch with HNSW in Python

Automated excerpt

Layer 0 contains every inserted vector, each connected to its M nearest neighbors. Layer 1 contains a random ~1/e subset of layer 0, similarly connected. Higher = better graph quality, slower inserts. return sum((x - y) ** 2 for x, y in zip(a, b)) ** 0. 5 for lyr in range(self. max_layer, level, -1): results = self. _search_layer(vector, ep, 1, lyr) for lyr in range(min(level, self. max_layer), -1, -1): neighbors = self. _search_layer(vector, ep, self. ef_construction, lyr) M_max = self. M_max0 if lyr == 0 else self. M self.

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