imag vector generation with dimentionality reduction
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@@ -417,6 +417,22 @@ IMAGE_EMBED_MODEL_PATH = _dir(
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# serialised by a lock (a TFLite interpreter is not thread-safe).
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IMAGE_EMBED_NUM_THREADS = int(os.getenv("IMAGE_EMBED_NUM_THREADS", "2"))
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# ---------------------------------------------------------------------------
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# Image search - POST /api/search/image-vector and /api/search/image
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# (app/services/image_search.py). Public, read-only.
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# ---------------------------------------------------------------------------
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IMAGE_SEARCH_DEFAULT_TOP_K = int(os.getenv("IMAGE_SEARCH_DEFAULT_TOP_K", "10"))
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IMAGE_SEARCH_MAX_TOP_K = int(os.getenv("IMAGE_SEARCH_MAX_TOP_K", "50"))
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# 0.0 on purpose. A simulated phone photo of Marie Gold against its catalog
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# render scored 0.63; the app team's "0.7 means the same product" is a
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# client-side rule of thumb for phone-vs-phone, so the server does not
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# impose it - callers pass min_score when they want a floor.
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IMAGE_SEARCH_DEFAULT_MIN_SCORE = float(os.getenv("IMAGE_SEARCH_DEFAULT_MIN_SCORE", "0.0"))
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# Candidates fetched PER brand table before re-ranking (pack sizes of one
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# product share an image and tie, so more than top_k must come back), and
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# the floor for hnsw.ef_search on that query so the index does not drop them.
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IMAGE_SEARCH_MAX_FETCH_K = int(os.getenv("IMAGE_SEARCH_MAX_FETCH_K", "100"))
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# ---------------------------------------------------------------------------
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# USDA FoodData Central - nutrition for loose, unbranded commodities
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# ---------------------------------------------------------------------------
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