1 files changed, 65 insertions, 0 deletions
diff --git a/scripts/recompute_embeddings.py b/scripts/recompute_embeddings.py
new file mode 100644
index 0000000..884cc7b
--- /dev/null
+++ b/scripts/recompute_embeddings.py
@@ -0,0 +1,65 @@
+import json
+import os
+import sys
+import numpy as np
+import torch
+from tqdm import tqdm
+
+# Add src to sys.path
+sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))
+
+from personalization.config.settings import load_local_models_config
+from personalization.models.embedding.qwen3_8b import Qwen3Embedding8B
+from personalization.retrieval.preference_store.schemas import MemoryCard
+
+CARDS_FILE = "data/corpora/memory_cards.jsonl"
+EMBEDDINGS_FILE = "data/corpora/memory_embeddings.npy"
+
+def recompute_embeddings():
+    if not os.path.exists(CARDS_FILE):
+        print(f"Error: {CARDS_FILE} not found.")
+        return
+
+    print("Loading configuration and model...")
+    cfg = load_local_models_config()
+    embed_model = Qwen3Embedding8B.from_config(cfg)
+    
+    print(f"Reading memory cards from {CARDS_FILE}...")
+    cards = []
+    texts = []
+    with open(CARDS_FILE, "r", encoding="utf-8") as f:
+        for line in f:
+            if not line.strip(): continue
+            card = MemoryCard.model_validate_json(line)
+            cards.append(card)
+            # Embedding source: note_text (preference) or raw_query?
+            # Usually we embed the note_text for retrieval.
+            texts.append(card.note_text)
+            
+    print(f"Total cards: {len(cards)}")
+    
+    if not cards:
+        print("No cards found.")
+        return
+
+    print("Computing embeddings...")
+    # Batch processing
+    batch_size = 32
+    all_embs = []
+    
+    for i in tqdm(range(0, len(texts), batch_size)):
+        batch_texts = texts[i : i + batch_size]
+        # Qwen3Embedding8B.encode returns list of lists (if return_tensor=False)
+        embs = embed_model.encode(batch_texts, return_tensor=False)
+        all_embs.extend(embs)
+        
+    emb_array = np.array(all_embs, dtype=np.float32)
+    print(f"Embeddings shape: {emb_array.shape}")
+    
+    print(f"Saving to {EMBEDDINGS_FILE}...")
+    np.save(EMBEDDINGS_FILE, emb_array)
+    print("Done!")
+
+if __name__ == "__main__":
+    recompute_embeddings()
+