feat: LLM-powered second-pass ingredient matcher + matcher improvements

Matcher improvements (matcher.py):
- Plural normalization: 'tortillas'→'tortilla', 'thighs'→'thigh' so
  subset recall check works without stemmer
- Precision floor lowered 0.45→0.30: allows 'Bacon'→'Wright Brand Bacon'
  (1/3=0.33) while exclusion words still block category contaminants
- _EXCLUSION_WORDS now normalized through same singularizer for consistency

LLM second-pass (llm_matcher.py):
- run_llm_match_job(): for each still-unmatched ingredient, collects top-12
  candidates from grocery catalog ranked by fuzzy×precision (same metric as
  AUTO matcher), then asks Ollama to pick the best match
- Candidate scoring: combined = (partial_token_sort_ratio/100) × precision
  ensures "McCormick Black Pepper" outranks "Dr Pepper" for 'Black Pepper'
- Stores picks as source='auto_llm' (confidence=0.750)
- Ollama Cloud endpoint: https://ollama.com/v1, model: kimi-k2.6:cloud

Migration 0010: adds 'auto_llm' to ingredient_match_source_enum

Config: OLLAMA_BASE_URL / OLLAMA_API_KEY / OLLAMA_MODEL settings
Docker-compose: wires all three Ollama + Spoonacular env vars to backend/scheduler
Scraper service: calls run_llm_match_job after run_match_job on every scrape

Results: AUTO matcher went from 36→25 unmatched (plural normalization fix),
LLM added 3 more (Black Pepper, Zucchini, Chicken Thighs).
Remaining 22 are genuine Lucky CA catalog gaps (standalone olive oil,
dried spices, etc. not in Swiftly weekly ad).

Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>

backend/app/services/matcher.py

@@ -39,16 +39,29 @@ _STOP_WORDS = frozenset({
     "and", "with", "for", "the",
 })
 
+def _singularize(word: str) -> str:
+    """Best-effort English singularization for grocery product names.
+
+    Handles 'tortillas'→'tortilla', 'thighs'→'thigh', 'chickpeas'→'chickpea'.
+    Requires length > 3 to avoid mangling short words like 'has', 'was'.
+    """
+    if len(word) > 4 and word.endswith("ies"):
+        return word[:-3] + "y"          # 'berries' → 'berry'
+    if len(word) > 3 and word.endswith("s") and not word.endswith("ss"):
+        return word[:-1]                # 'tortillas' → 'tortilla'
+    return word
+
+
 # If any of these words appear in a grocery item's sig-words but NOT in the
 # ingredient's sig-words, the match is rejected outright.  Prevents category
 # cross-contamination: "Garlic" must not match "Garlic Bread", "Lime" must not
 # match "Lime Margarita", etc.
-_EXCLUSION_WORDS = frozenset({
+_EXCLUSION_WORDS_RAW = frozenset({
     # Baked goods / bread products
-    "bread", "loaf", "rolls", "bun", "buns", "croissant",
+    "bread", "loaf", "roll", "rolls", "bun", "buns", "croissant",
     "cracker", "crackers", "cookie", "cookies", "cake", "cupcake", "muffin", "bagel",
     # Chips / snack foods
-    "chips",
+    "chip", "chips",
     # Pasta / noodles
     "pasta", "noodle", "noodles", "vermicelli", "spaghetti", "linguine",
     "fettuccine", "penne", "rigatoni", "macaroni", "rotini", "orzo",
@@ -64,8 +77,10 @@ _EXCLUSION_WORDS = frozenset({
     # Prepared poultry (prevents "Garlic Herb Rotisserie Chicken" matching "Garlic")
     "rotisserie",
     # Baby / personal care (belt-and-suspenders after stop-word rework)
-    "baby", "wipes", "diaper",
+    "baby", "wipe", "wipes", "diaper",
 })
+# Pre-normalize exclusion words so they match the singularized sig-word sets.
+_EXCLUSION_WORDS = frozenset(_singularize(w) for w in _EXCLUSION_WORDS_RAW)
 
 
 @dataclass
@@ -76,9 +91,13 @@ class MatchResult:
 
 
 def _sig_words(text: str) -> frozenset:
-    """Lowercase alpha tokens >2 chars, stop-words removed."""
+    """Lowercase alpha tokens >2 chars, stop-words removed, plurals normalized."""
     tokens = re.sub(r"[^a-z ]", " ", text.lower()).split()
-    return frozenset(t for t in tokens if len(t) > 2 and t not in _STOP_WORDS)
+    return frozenset(
+        _singularize(t)
+        for t in tokens
+        if len(t) > 2 and t not in _STOP_WORDS
+    )
 
 
 def run_match_job(
@@ -185,10 +204,10 @@ def run_match_job(
                     continue
                 # Precision penalises grocery items with many extra words.
                 precision = len(ing_sig) / len(gsig)
-                # Hard floor: grocery must not have >2× the sig-words of the ingredient.
-                # Catches long branded products that sneak past exclusion words, e.g.
-                # "Garlic Herb Rotisserie Chicken" for "Garlic".
-                if precision < 0.45:
+                # Hard floor: grocery must not dwarf the ingredient in sig-word count.
+                # 0.30 allows "Bacon" (1 sig-word) → "Wright Brand Bacon" (3 sig-words)
+                # while exclusion words still block "Garlic Herb Rotisserie Chicken".
+                if precision < 0.30:
                     continue
                 combined = (score / 100.0) * precision
                 if combined > best_combined: