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## Problem `raptor.py` computes `n_neighbors = int((len(embeddings) - 1) ** 0.8)` and passes it to `umap.UMAP(...)`. In a dataset-scope RAPTOR build the first layer's `embeddings` is the entire KB's chunk set, so this is effectively unbounded: ~93k chunks → n_neighbors ≈ 9,446. UMAP's k-NN graph is `N × n_neighbors`; at these values the raw neighbor arrays alone are ~14 GB (93k × 9446 × 16 B), and the symmetrized fuzzy simplicial set + spectral init push peak well past 30 GB. The task executor is OOM-killed inside `fit_transform` before any clustering runs — the log shows "Task has been received" with no "Cluster one layer" line — after which the unacked task re-queues and OOMs again in a loop. The line above already flags this: `# Degrade too much ??`. ## Fix Cap `n_neighbors` at 100. UMAP's neighborhood size has strongly diminishing returns well below this (default 15; a few dozen already captures global structure), so the ceiling preserves — likely improves — cluster quality while bounding memory to O(N). Mirrors the existing `n_components=min(12, len(embeddings) - 2)` clamp two lines down. ```diff - n_neighbors = int((len(embeddings) - 1) ** 0.8) + n_neighbors = min(int((len(embeddings) - 1) ** 0.8), 100) ``` ## Repro Dataset-scope RAPTOR over a KB with ~90k+ chunks on a box with <~64 GB available: executor OOM-killed in the first-layer UMAP `fit_transform`. With the cap, first-layer UMAP peaks in low single-digit GB and the build proceeds to completion. ## Scope Only affects large dataset-scope builds; file-scope RAPTOR already had n_neighbors well under 100. No behavior change beyond the ceiling.