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tune(learning): lesson-synthesis cluster threshold 0.82 → 0.68 (#158)
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כיול אמפירי (dry-run על 81 לקחים): הלקחים מגוונים (חציון-דמיון ~0.48, מקס' ~0.72), כך
ש-0.82 מיזג 0 אשכולות. 0.68 תופס 5 קונסולידציות אמיתיות (רישוי+השבחה+מבנה), כולן עברו את
שער-ה-drift 0.80 (0.848–0.916) → נאמנות נשמרת. שער-ה-drift נשאר רשת-הביטחון מתחת לסף.

Invariants: INV-LRN8 (כיול-סף בלבד; המנגנון/השערים ללא שינוי). depends-on #158.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Chaim
2026-06-28 22:44:55 +00:00
parent 7a7db6933d
commit 6c3c6a16c2
1 changed files with 6 additions and 1 deletions
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7
mcp-server/src/legal_mcp/config.py
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@@ -286,7 +286,12 @@ LESSON_SYNTH_MODEL = os.environ.get("LESSON_SYNTH_MODEL", HALACHA_EXTRACT_MODEL)
LESSON_SYNTH_EFFORT = os.environ.get("LESSON_SYNTH_EFFORT", "high") LESSON_SYNTH_EFFORT = os.environ.get("LESSON_SYNTH_EFFORT", "high")
LESSON_SYNTH_DRIFT_FLOOR = float(os.environ.get("LESSON_SYNTH_DRIFT_FLOOR", "0.80")) LESSON_SYNTH_DRIFT_FLOOR = float(os.environ.get("LESSON_SYNTH_DRIFT_FLOOR", "0.80"))
# Cosine floor for two lessons to land in the same cluster (greedy, within shard). # Cosine floor for two lessons to land in the same cluster (greedy, within shard).
LESSON_SYNTH_CLUSTER_THRESHOLD = float(os.environ.get("LESSON_SYNTH_CLUSTER_THRESHOLD", "0.82")) # Tuned empirically (#158 dry-run, 81 lessons): the lessons are diverse (median
# pairwise cosine ~0.48, max ~0.72), so 0.82 merged nothing. 0.68 captures the real
# near-duplicates (5 faithful clusters across rishuy+betterment, all clearing the
# 0.80 drift gate) without merging weakly-related lessons; the drift gate is the
# quality backstop below this.
LESSON_SYNTH_CLUSTER_THRESHOLD = float(os.environ.get("LESSON_SYNTH_CLUSTER_THRESHOLD", "0.68"))
# Mistral OCR (fallback for scanned PDFs — replaces Google Cloud Vision) # Mistral OCR (fallback for scanned PDFs — replaces Google Cloud Vision)
MISTRAL_API_KEY = os.environ.get("MISTRAL_API_KEY", "") MISTRAL_API_KEY = os.environ.get("MISTRAL_API_KEY", "")