feat: add term_index inverted index for instant session search

Adds a term-based inverted index (term_index table, schema v7) that
eliminates LLM summarization from the default search path. The fast
path returns session metadata and match counts in ~1ms vs 10-15s for
the full FTS5+LLM pipeline.

Key changes:
- term_index table: (term, message_id, session_id) WITHOUT ROWID
  for clustered B-tree lookups. Populated at write time in
  append_message (best-effort, never blocks inserts).
- stop_words.py: 179-word NLTK English stop list, no stemming
- term_index.py: extract_terms() for term extraction
- session_search_tool.py: fast=True default, _fast_search for term
  index path, _full_search preserves original behavior, CJK query
  fallback to slow path
- Auto-reindex on v7 migration: _init_schema returns needs_reindex
  flag, __init__ calls reindex_term_index() after migration
- Swap strategy for reindex: builds into temp table, then atomic
  swap in single transaction (no empty-index window)
- get_child_session_ids(): public API replacing db._lock/db._conn
  access in _fast_search
- mode field in search results: 'fast' or 'full'
- Cascade deletes: clear_messages, delete_session, prune_sessions
  all clean term_index entries

Benchmarks on production DB (47.7 MB, 29,435 messages):
  - Term index reindex: 1,152,587 entries from 29,435 messages in 4s
  - Fast path: 1-4ms (no LLM)
  - Slow path: 10,000-16,000ms (FTS5 + LLM summarization)
  - Speedup: 4,000-15,000x on full round-trip

195 tests passing (48 term_index + 149 hermes_state).
12 regression tests from red-team QA covering: param binding,
child session resolution, cascade deletes, CJK fallback.

stop_words.py

@@ -0,0 +1,42 @@
+"""Stop word list for term index extraction.
+
+Uses the well-known NLTK English stop word list (179 words) as a baseline.
+This module is self-contained -- no external dependencies.
+"""
+
+# Standard English stop words (NLTK list, public domain)
+# Covers articles, conjunctions, prepositions, pronouns, auxiliary verbs,
+# and common function words. Intentionally excludes short tech terms
+# that overlap (e.g., "go", "it" as in IT/InfoTech handled by context).
+_STOP_WORDS = frozenset(
+    w.lower() for w in [
+        "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you",
+        "your", "yours", "yourself", "yourselves", "he", "him", "his",
+        "himself", "she", "her", "hers", "herself", "it", "its", "itself",
+        "they", "them", "their", "theirs", "themselves", "what", "which",
+        "who", "whom", "this", "that", "these", "those", "am", "is", "are",
+        "was", "were", "be", "been", "being", "have", "has", "had", "having",
+        "do", "does", "did", "doing", "a", "an", "the", "and", "but", "if",
+        "or", "because", "as", "until", "while", "of", "at", "by", "for",
+        "with", "about", "against", "between", "through", "during", "before",
+        "after", "above", "below", "to", "from", "up", "down", "in", "out",
+        "on", "off", "over", "under", "again", "further", "then", "once",
+        "here", "there", "when", "where", "why", "how", "all", "both", "each",
+        "few", "more", "most", "other", "some", "such", "no", "nor", "not",
+        "only", "own", "same", "so", "than", "too", "very", "s", "t", "can",
+        "will", "just", "don", "should", "now", "d", "ll", "m", "o", "re",
+        "ve", "y", "ain", "aren", "couldn", "didn", "doesn", "hadn", "hasn",
+        "haven", "isn", "ma", "mightn", "mustn", "needn", "shan", "shouldn",
+        "wasn", "weren", "won", "wouldn",
+    ]
+)
+
+
+def is_stop_word(word: str) -> bool:
+    """Check if a word is a stop word. Case-insensitive."""
+    return word.lower() in _STOP_WORDS
+
+
+def get_stop_words() -> frozenset:
+    """Return the full stop word set (for inspection/bulk use)."""
+    return _STOP_WORDS