Initial project setup

backend/recommender.py

@@ -0,0 +1,8 @@
+from backend.embeddings import get_embeddings
+from backend.vector_store import VectorDB
+
+db = VectorDB()
+
+def recommend_similar(article_text, top_k=3):
+    query_embed = get_embeddings([article_text])[0]
+    return db.search(query_embed, k=top_k)