diff --git a/.env b/.env
new file mode 100644
index 0000000..335f306
--- /dev/null
+++ b/.env
@@ -0,0 +1,2 @@
+# Postgres database address for cocoindex
+COCOINDEX_DATABASE_URL=postgres://cocoindex:cocoindex@localhost/cocoindex
diff --git a/README.md b/README.md
index 36cf6ae..02fa1b0 100644
--- a/README.md
+++ b/README.md
@@ -28,13 +28,13 @@ export COCOINDEX_DATABASE_URL="postgresql://cocoindex:cocoindex@localhost:5432/c
 Setup index:
 
 ```bash
-python quickstart.py cocoindex setup
+cocoindex setup quickstart.py
 ```
 
 Update index:
 
 ```bash
-python quickstart.py cocoindex update
+cocoindex update quickstart.py
 ```
 
 Run query:
diff --git a/quickstart.py b/quickstart.py
index 911f101..579082c 100644
--- a/quickstart.py
+++ b/quickstart.py
@@ -1,4 +1,22 @@
 import cocoindex
+from dotenv import load_dotenv
+from psycopg_pool import ConnectionPool
+import os
+
+@cocoindex.transform_flow()
+def text_to_embedding(
+    text: cocoindex.DataSlice[str],
+) -> cocoindex.DataSlice[list[float]]:
+    """
+    Embed the text using a SentenceTransformer model.
+    This is a shared logic between indexing and querying, so extract it as a function.
+    """
+    return text.transform(
+        cocoindex.functions.SentenceTransformerEmbed(
+            model="sentence-transformers/all-MiniLM-L6-v2"
+        )
+    )
+
 @cocoindex.flow_def(name="TextEmbedding")
 def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
     # Add a data source to read files from a directory
@@ -18,9 +36,7 @@ def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoind
         # Transform data of each chunk
         with doc["chunks"].row() as chunk:
             # Embed the chunk, put into `embedding` field
-            chunk["embedding"] = chunk["text"].transform(
-                cocoindex.functions.SentenceTransformerEmbed(
-                    model="sentence-transformers/all-MiniLM-L6-v2"))
+            chunk["embedding"] = text_to_embedding(chunk["text"])
 
             # Collect the chunk into the collector.
             doc_embeddings.collect(filename=doc["filename"], location=chunk["location"],
@@ -31,35 +47,54 @@ def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoind
         "doc_embeddings",
         cocoindex.storages.Postgres(),
         primary_key_fields=["filename", "location"],
-        vector_index=[("embedding", cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
+        vector_indexes=[
+            cocoindex.VectorIndexDef(
+                field_name="embedding",
+                metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
+            )
+        ],
+    )
 
-query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
-    name="SemanticsSearch",
-    flow=text_embedding_flow,
-    target_name="doc_embeddings",
-    query_transform_flow=lambda text: text.transform(
-        cocoindex.functions.SentenceTransformerEmbed(
-            model="sentence-transformers/all-MiniLM-L6-v2")),
-    default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)
+def search(pool: ConnectionPool, query: str, top_k: int = 5):
+    # Get the table name, for the export target in the text_embedding_flow above.
+    table_name = cocoindex.utils.get_target_storage_default_name(
+        text_embedding_flow, "doc_embeddings"
+    )
+    # Evaluate the transform flow defined above with the input query, to get the embedding.
+    query_vector = text_to_embedding.eval(query)
+    # Run the query and get the results.
+    with pool.connection() as conn:
+        with conn.cursor() as cur:
+            cur.execute(
+                f"""
+                SELECT filename, text, embedding <=> %s::vector AS distance
+                FROM {table_name} ORDER BY distance LIMIT %s
+            """,
+                (query_vector, top_k),
+            )
+            return [
+                {"filename": row[0], "text": row[1], "score": 1.0 - row[2]}
+                for row in cur.fetchall()
+            ]
 
-@cocoindex.main_fn()
 def _main():
-    # Run queries to demonstrate the query capabilities.
+    # Initialize the database connection pool.
+    pool = ConnectionPool(os.getenv("COCOINDEX_DATABASE_URL"))
+    # Run queries in a loop to demonstrate the query capabilities.
     while True:
-        try:
-            query = input("Enter search query (or Enter to quit): ")
-            if query == '':
-                break
-            results, _ = query_handler.search(query, 10)
-            print("\nSearch results:")
-            for result in results:
-                print(f"[{result.score:.3f}] {result.data['filename']}")
-                print(f"    {result.data['text']}")
-                print("---")
-            print()
-        except KeyboardInterrupt:
+        query = input("Enter search query (or Enter to quit): ")
+        if query == "":
             break
-
+        # Run the query function with the database connection pool and the query.
+        results = search(pool, query)
+        print("\nSearch results:")
+        for result in results:
+            print(f"[{result['score']:.3f}] {result['filename']}")
+            print(f"    {result['text']}")
+            print("---")
+        print()
 
 if __name__ == "__main__":
+    load_dotenv()
+    cocoindex.init()
     _main()
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