diff --git a/generate_dua_tags_embedding.py b/generate_dua_tags_embedding.py
new file mode 100644
index 0000000..40cacdf
--- /dev/null
+++ b/generate_dua_tags_embedding.py
@@ -0,0 +1,69 @@
+import json
+from langchain_openai import OpenAIEmbeddings
+# from langchain.vectorstores.pgvector import PGVector
+from langchain_postgres import PGVector
+# from langchain.schema import Document
+from langchain_core.documents import Document
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Database configuration
+CONNECTION_STRING = 'postgresql+psycopg2://postgres:test@localhost:5433/vector_db'
+COLLECTION_NAME = 'duas_tags_vectors'
+
+# Load JSON data
+with open('duas_directus_published.json', 'r', encoding='utf-8') as f:
+    duas_data = json.load(f)
+
+print(f"Loaded {len(duas_data)} duas records")
+
+# Create documents with ONLY tags for embedding
+documents = []
+for dua in duas_data:
+    # Extract tags - handle both list and string formats
+    tags = dua.get('tags', [])
+    
+    if isinstance(tags, list):
+        tags_text = ', '.join(tags)
+    else:
+        tags_text = str(tags)
+    
+    # Skip if no tags
+    if not tags_text or tags_text.strip() == '':
+        continue
+    
+    # Create document with ONLY tags as content
+    # Store all original data in metadata for retrieval
+    doc = Document(
+        page_content=tags_text,  # Only tags will be embedded
+        metadata={
+            'id': dua.get('id'),
+            'arabic': dua.get('arabic', ''),
+            'transliteration': dua.get('transliteration', ''),
+            'translation': dua.get('translation', ''),
+            'urdu': dua.get('urdu', ''),
+            'romanUrdu': dua.get('romanUrdu', ''),
+            'category': dua.get('category', ''),
+            'occasion': dua.get('occasion', ''),
+            'source': dua.get('source', ''),
+            'tags': tags  # Keep original tags for reference
+        }
+    )
+    documents.append(doc)
+
+print(f"Created {len(documents)} documents from tags")
+
+# Initialize embeddings
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+# Create vector store - embeddings will be created only from tags (page_content)
+print("Creating embeddings and storing in pgvector...")
+db = PGVector.from_documents(
+    embedding=embeddings,
+    documents=documents,
+    collection_name=COLLECTION_NAME,
+    connection=CONNECTION_STRING
+)
+
+print("✓ Vector store created successfully!")