Use consolidated embeddings

backend/runner/inference.py

@@ -211,8 +211,14 @@ def _initialize_pipeline():
 
     model = model.eval()
 
-    # Load pre-computed embeddings
-    embeddings, sentence_ids = _load_embeddings(config["embeddings_dir"])
+    # Load pre-computed embeddings - USE CONSOLIDATED LOADING
+    if MODEL_TYPE == "clip":
+        embeddings, sentence_ids = load_embeddings_for_model("clip")
+    else:
+        embeddings, sentence_ids = load_embeddings_for_model("paintingclip")
+
+    if embeddings is None or sentence_ids is None:
+        raise ValueError(f"Failed to load embeddings for model type: {MODEL_TYPE}")
 
     # Load sentence metadata
     sentences_data = _load_sentences_metadata(SENTENCES_JSON)