Updates on Image search using vectors

app/infrastructure/settings.py

@@ -432,6 +432,21 @@ IMAGE_SEARCH_DEFAULT_MIN_SCORE = float(os.getenv("IMAGE_SEARCH_DEFAULT_MIN_SCORE
 # product share an image and tie, so more than top_k must come back), and
 # the floor for hnsw.ef_search on that query so the index does not drop them.
 IMAGE_SEARCH_MAX_FETCH_K = int(os.getenv("IMAGE_SEARCH_MAX_FETCH_K", "100"))
+# How far the best image match must lead the best DIFFERENT photo for the
+# answer to count as confirmed (`match_confidence`). A phone photo of a card on
+# a screen measured 0.507 against its own product and 0.419 against a
+# stranger; with glare that gap closes, and a flipped order is a wrong
+# product shown with confidence. Pack sizes sharing one photo tie exactly and
+# are never each other's competitor. Unmeasured start: tune with
+# scripts/eval_identify.py.
+IMAGE_SEARCH_MIN_MARGIN = float(os.getenv("IMAGE_SEARCH_MIN_MARGIN", "0.05"))
+# Diagnosis: when set, every image-search request (vector, text, brand and the
+# top matches) is written here as one JSON file, for
+# scripts/replay_image_query.py. Blank = off. At most IMAGE_SEARCH_CAPTURE_MAX
+# files are kept; the oldest go first. The routes are public, so leave it off
+# except while chasing a report.
+IMAGE_SEARCH_CAPTURE_DIR = os.getenv("IMAGE_SEARCH_CAPTURE_DIR", "").strip()
+IMAGE_SEARCH_CAPTURE_MAX = int(os.getenv("IMAGE_SEARCH_CAPTURE_MAX", "200"))
 
 # ---------------------------------------------------------------------------
 # Identify a product from a phone photo - POST /api/search/identify