perf: fix O(n^2) -> O(n) LSH neighbor lookup in dedup

Replaced O(n) linear scan `next((n for n in candidates if n["id"] == neighbor_id))` with O(1) dict lookup via pre-built `candidates_by_id`. Also pre-caches `_norm()` results in `norm_cache` to avoid recomputing per inner iteration.

For a 36k-file codebase (~100k high-entropy candidates) this reduces the pass-2 loop from O(n^2*B) (~30–100B iterations) to O(n*B), eliminating the multi-minute CPU hang after AST extraction completes.

graphify/dedup.py

@@ -213,19 +213,26 @@ def deduplicate_entities(
     if len(candidates) >= 2:
         lsh = MinHashLSH(threshold=_LSH_THRESHOLD, num_perm=_NUM_PERM)
         minhashes: dict[str, MinHash] = {}
+        # Pre-build O(1) lookup structures so the query loop below doesn't scan
+        # the candidates list linearly for every LSH neighbor (was O(n²×B)).
+        candidates_by_id: dict[str, dict] = {}
+        norm_cache: dict[str, str] = {}
 
         for node in candidates:
-            norm_label = _norm(node.get("label", node.get("id", "")))
-            m = _make_minhash(norm_label)
-            minhashes[node["id"]] = m
+            node_id = node["id"]
+            candidates_by_id[node_id] = node
+            nl = _norm(node.get("label", node.get("id", "")))
+            norm_cache[node_id] = nl
+            m = _make_minhash(nl)
+            minhashes[node_id] = m
             try:
-                lsh.insert(node["id"], m)
+                lsh.insert(node_id, m)
             except ValueError:
                 pass  # duplicate key in LSH — already inserted
 
         for node in candidates:
             node_id = node["id"]
-            norm_label = _norm(node.get("label", node.get("id", "")))
+            norm_label = norm_cache[node_id]
             neighbors = lsh.query(minhashes[node_id])
 
             for neighbor_id in neighbors:
@@ -234,11 +241,11 @@ def deduplicate_entities(
                 if uf.find(node_id) == uf.find(neighbor_id):
                     continue
 
-                neighbor = next((n for n in candidates if n["id"] == neighbor_id), None)
+                neighbor = candidates_by_id.get(neighbor_id)
                 if neighbor is None:
                     continue
 
-                neighbor_norm = _norm(neighbor.get("label", neighbor.get("id", "")))
+                neighbor_norm = norm_cache.get(neighbor_id) or _norm(neighbor.get("label", neighbor.get("id", "")))
                 score = JaroWinkler.normalized_similarity(norm_label, neighbor_norm) * 100
 
                 if _is_variant_pair(norm_label, neighbor_norm):