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+# Nested Index Query Performance Example with Memgraph
+
+This example demonstrates how to use GQLAlchemy to showcase the performance impact of nested indexes in Memgraph.
+
+## 🧠 What This Example Does
+
+The script performs the following actions:
+
+1. **Connects to a running Memgraph instance** using `gqlalchemy`.
+2. **Creates 2000 `Person` nodes** with a nested `address` property.
+3. **Executes a query** to find people by a nested property (country) and times the query execution.
+4. **Creates a nested index** on `:Person(address.country)`.
+5. **Executes the same query again** and times the execution to show the performance improvement.
+
+## 🚀 How to Run Memgraph with Docker
+
+To run Memgraph Community using Docker:
+
+```bash
+docker run -it --rm -p 7687:7687 memgraph/memgraph:3.3
+```
+
+## 🛠 Requirements
+
+Install dependencies with:
+
+```bash
+pip install -r requirements.txt
+```
+
+Your `requirements.txt` should include:
+
+```
+GQLAlchemy==1.7.0
+```
+
+## 🧪 How to Run the Script
+
+Once Memgraph is running:
+
+```bash
+python3 nested_index_example.py
+```
+
+## 🔖 Version Compatibility
+
+This example was built and tested with:
+
+- **Memgraph v3.3**
+
+If you run into any issues or have questions, feel free to reach out on the [Memgraph Discord server](https://discord.gg/memgraph). We're happy to help!
+
+## 🏢 Enterprise or Community?
+
+This example works with **Memgraph Community Edition** 
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diff --git a/python/querying/nested_index_example/nested_index_example.py b/python/querying/nested_index_example/nested_index_example.py
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+from gqlalchemy import Memgraph
+import random
+import time
+
+# Establish a connection to Memgraph
+memgraph = Memgraph(host='127.0.0.1', port=7687)
+
+# Clean up any existing data
+memgraph.execute("MATCH (n) DETACH DELETE n;")
+memgraph.execute("DROP INDEX ON :Person(address.country);")
+
+# Sample data for addresses
+countries = ["USA", "Canada", "UK", "Germany", "France", "Italy", "Spain", "Australia", "Japan", "Brazil"]
+cities = ["New York", "Toronto", "London", "Berlin", "Paris", "Rome", "Madrid", "Sydney", "Tokyo", "Rio de Janeiro"]
+streets = ["Main St", "Oak Ave", "Pine Rd", "Maple Dr", "Elm St", "Cedar Ave", "Birch Rd", "Spruce Dr", "Willow St", "Ash Ave"]
+
+# Create 2000 Person nodes with nested address
+for i in range(2000):
+    name = f"Person_{i}"
+    address = {
+        'street': f"{random.randint(1,999)} {random.choice(streets)}",
+        'city': random.choice(cities),
+        'country': random.choice(countries)
+    }
+    query = f"""
+    CREATE (:Person {{name: '{name}', address: {{street: '{address['street']}', city: '{address['city']}', country: '{address['country']}'}}}})
+    """
+    memgraph.execute(query)
+
+# Query to time (e.g., find all people in the UK)
+country_to_query = "UK"
+query = f"""
+MATCH (p:Person)
+WHERE p.address.country = '{country_to_query}'
+RETURN p
+"""
+
+start = time.time()
+results = list(memgraph.execute_and_fetch(query))
+time_no_index = time.time() - start
+print(f"Query time without index: {time_no_index:.4f} seconds. Results: {len(results)}")
+
+# Create nested index
+memgraph.execute("CREATE INDEX ON :Person(address.country);")
+
+# Wait a moment for index to be created
+import time as t
+t.sleep(2)
+
+start = time.time()
+results = list(memgraph.execute_and_fetch(query))
+time_with_index = time.time() - start
+print(f"Query time with index: {time_with_index:.4f} seconds. Results: {len(results)}") 
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diff --git a/python/querying/nested_index_example/requirements.txt b/python/querying/nested_index_example/requirements.txt
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+GQLAlchemy==1.7.0 
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