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config.py

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+"""
+Two-Tower Configuration
+
+Architecture constants for Isengard (User Tower) and Mordor (Wine Tower).
+"""
+
+# =============================================================================
+# EMBEDDING DIMENSIONS
+# =============================================================================
+
+# Input embedding dimension (google-text-embedding-004)
+EMBEDDING_DIM = 768
+
+# Output vector dimensions for both towers (must match for dot product)
+USER_VECTOR_DIM = 128
+WINE_VECTOR_DIM = 128
+
+# Hidden layer dimension
+HIDDEN_DIM = 256
+
+# =============================================================================
+# CATEGORICAL FEATURES
+# =============================================================================
+
+# Feature list matching constants.py CATEGORICAL_FEATURES
+CATEGORICAL_FEATURES = [
+    "color",
+    "type",
+    "style",
+    "climate_type",
+    "climate_band",
+    "vintage_band",
+]
+
+# Categorical feature vocabulary sizes (approximate, for one-hot encoding)
+CATEGORICAL_VOCAB_SIZES = {
+    "color": 5,  # red, white, rosé, orange, sparkling
+    "type": 4,  # still, sparkling, fortified, dessert
+    "style": 10,  # Natural, Organic, Biodynamic, etc.
+    "climate_type": 4,  # cool, moderate, warm, hot
+    "climate_band": 4,  # cool, moderate, warm, hot
+    "vintage_band": 4,  # young, developing, mature, non_vintage
+}
+
+# Total categorical encoding dimension
+CATEGORICAL_ENCODING_DIM = sum(CATEGORICAL_VOCAB_SIZES.values())  # ~31
+
+# =============================================================================
+# TRAINING PARAMETERS
+# =============================================================================
+
+TRIPLET_MARGIN = 0.2  # Margin for triplet loss
+LEARNING_RATE = 1e-4
+BATCH_SIZE = 32
+POSITIVE_RATING_THRESHOLD = 4.0  # Ratings >= 4 are positive samples
+
+# =============================================================================
+# HUGGINGFACE INFERENCE
+# =============================================================================
+
+# Model ID on HuggingFace Hub (for model upload/download)
+HF_MODEL_ID = "swirl/two-tower-recommender"
+
+# Inference Endpoint URL is read from settings.HF_TWO_TOWER_ENDPOINT_URL
+# API Token is read from settings.HF_API_TOKEN (same as CLIP)