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>
adminandClaude Sonnet 4.6