Serverless PostgreSQL & AI: NeonDB with pgvector

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Introduction

In the world of AI-driven applications, database performance and scalability are crucial. Neon is an open-source, serverless PostgreSQL database that introduces cutting-edge features like autoscaling, branching, and vector support (pgvector). This makes it an ideal choice for AI workloads, real-time analytics, and scalable cloud applications.

In this blog, we’ll explore how to leverage Neon’s features to create scalable applications. We’ll walk through:

• Setting up a serverless PostgreSQL database.
• Running vector similarity searches with pgvector.
• Creating an interactive to-do list application.
• Implementing an AI chatbot with OpenAI and NeonDB.

Getting Started with Neon PostgreSQL

Before diving into code, let's take a quick look at Neon’s key features:

• Open Source: Transparent and free to use.
• Serverless: No infrastructure management required.
• Autoscaling: Automatically adjusts resources based on demand.
• Branching: Clone databases instantly for testing and development.
• Vector Search (pgvector): Optimized for AI and embedding-based searches.
• Fully PostgreSQL-Compatible: Works with all PostgreSQL tools and extensions.

Step 1: Connect to Neon PostgreSQL

First, let’s connect to a Neon database using Python and psycopg2.

import os
import psycopg2
from google.colab import userdata

connection_string = userdata.get('NEON_URL')
connection = psycopg2.connect(connection_string)
cursor = connection.cursor()

Explanation:

• We retrieve the database URL from Google Colab’s userdata.
• Establish a connection to the database.
• Create a cursor to execute SQL queries.

Step 2: Test the Database Connection

Before proceeding, let’s ensure our database connection is successful:

cursor.execute("SELECT 1;")
result = cursor.fetchone()

if result == (1,):
    print("Your database connection was successful!")
else:
    print("Your connection failed.")

Explanation:

• Executes a simple SELECT 1; query to test the connection.
• If the query returns (1,), the connection is successful.

Expected Output:

Your database connection was successful!

Implementing Vector Search with pgvector

Step 3: Install and Set Up pgvector

To perform vector similarity searches, we first need to install the pgvector extension in Neon.

cursor.execute("CREATE EXTENSION IF NOT EXISTS vector;")

Explanation:

• Ensures that the pgvector extension is installed and available in the database.

Step 4: Create a Table and Insert Vector Data

Let’s create a table that stores vector embeddings:

create_table_sql = '''
CREATE TABLE items (
  id BIGSERIAL PRIMARY KEY,
  embedding VECTOR(3)
);
'''

insert_data_sql = '''
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]'), ('[7,8,9]');
'''

cursor.execute(create_table_sql)
cursor.execute(insert_data_sql)
connection.commit()

Explanation:

• Creates a table items with a BIGSERIAL primary key and a 3-dimensional vector column.
• Inserts vector data to be used for similarity searches.

Step 5: Perform a Vector Similarity Search

cursor.execute("SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 3;")
all_data = cursor.fetchall()
print(all_data)

Explanation:

• Uses the <-> operator to compute similarity between vectors.
• Retrieves the top 3 nearest vectors to [3,1,2].

Expected Output:

[(1, '[1,2,3]'), (2, '[4,5,6]'), (3, '[7,8,9]')]

Building an Interactive To-Do List App

Step 6: Create the To-Do List Table

cursor.execute("""
    CREATE TABLE IF NOT EXISTS tasks (
        id SERIAL PRIMARY KEY,
        task TEXT NOT NULL,
        completed BOOLEAN DEFAULT FALSE
    )
""")
conn.commit()

Explanation:

• Creates a tasks table with an id, task description, and a completed status.

Step 7: Add a New Task

def add_task(task):
    cursor.execute("INSERT INTO tasks (task) VALUES (%s)", (task,))
    conn.commit()
    print("✅ Task added!")

Explanation:

• Inserts a new task into the tasks table.
• Commits the transaction to save the task.

Step 8: View All Tasks

def view_tasks():
    cursor.execute("SELECT id, task, completed FROM tasks")
    tasks = cursor.fetchall()
    print("\n📋 To-Do List:")
    for task in tasks:
        status = "✅" if task[2] else "❌"
        print(f"{task[0]}. {task[1]} {status}")

Explanation:

• Retrieves all tasks from the tasks table.
• Displays each task along with its completion status.

Step 9: Delete a Task

def delete_task(task_id):
    cursor.execute("DELETE FROM tasks WHERE id = %s", (task_id,))
    conn.commit()
    print("🗑️ Task deleted!")

Explanation:

• Deletes a task by its id from the database.
• Commits the deletion to update the database.

OpenAI Chatbot with NeonDB

Step 10: Store Chat History in NeonDB

cursor.execute("""
    CREATE TABLE IF NOT EXISTS chat_history (
        id SERIAL PRIMARY KEY,
        user_message TEXT NOT NULL,
        bot_response TEXT NOT NULL,
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    )
""")

Explanation:

• Creates a chat_history table to store user queries and chatbot responses.

Conclusion

NeonDB provides a powerful, scalable, and serverless PostgreSQL experience, making it perfect for AI applications. Whether you’re implementing vector search, task management, or AI chatbots, Neon simplifies the process.

Resources

• Neon Official Documentation
• pgvector Extension Guide
• PostgreSQL Official Site
• Neon Experiment Notebook

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