perf: cache PreparedQuery per stream, skip double-normalize in dedupe (#282)

Scoring hot path (_normalize_score_dedupe) re-tokenized the same
ranking_query ~240x per stream: once per item for local_relevance,
plus ~5x per item across snippet windows. Query tokens are immutable
within a stream, so compute them once as relevance.PreparedQuery and
thread through signals.annotate_stream and snippet.extract_best_snippet.

dedupe._PreparedText called normalize_text twice: once in __init__ and
again via get_ngrams. Factor out _ngrams_of_normalized so the prepared
path skips the redundant pass while get_ngrams keeps its public contract.

Behavior unchanged.

skills/last30days/scripts/lib/pipeline.py

@@ -30,6 +30,7 @@ from . import (
     query,
     reddit,
     reddit_public,
+    relevance,
     rerank,
     schema,
     signals,
@@ -500,11 +501,12 @@ def _normalize_score_dedupe(
         source, raw_items, from_date, to_date,
         freshness_mode=freshness_mode,
     )
-    normalized = signals.annotate_stream(normalized, ranking_query, freshness_mode)
+    prepared_query = relevance.PreparedQuery(ranking_query)
+    normalized = signals.annotate_stream(normalized, prepared_query, freshness_mode)
     normalized = signals.prune_low_relevance(normalized)
     normalized = dedupe.dedupe_items(normalized)
     for item in normalized:
-        item.snippet = snippet.extract_best_snippet(item, ranking_query)
+        item.snippet = snippet.extract_best_snippet(item, prepared_query)
     return normalized