Summary: Google Colab integration with Neon lets you run Python notebooks in a browser-based Jupyter environment that connects to Lakebase Postgres and uses the pgvector extension to store and query vector embeddings. Use this guide when you want to prototype vector similarity search without local setup, using psycopg2 to connect, CREATE EXTENSION vector to enable pgvector, and the <-> distance operator to run nearest-neighbor queries. A pre-built Colab notebook is available for one-click setup.

Use Google Colab with Neon for vector similarity search

Google Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. You can use Google Colab to run python code through the browser.

This guide shows how to create a notebook in Colab, connect to a Neon database, install the pgvector extension to enable Neon as a vector store, and run a vector search query.

To perform the steps in this guide, you require a Neon database for storing vectors. You can use the ready-to-use neondb database or create your own. See Create a database for instructions.

Click Connect in the Console nav to open the Connect to your branch modal, and select a branch, a user, and the database you want to connect to. A connection string is constructed for you.

In your browser, navigate to Google Colab, and click New notebook.

Alternatively, you can open a predefined Google Colab notebook for this guide by clicking the Open in Colab button below.

https://colab.research.google.com/github/neondatabase/neon-google-colab-notebooks/blob/main/neon_pgvector_quickstart.ipynb

1. In your Colab notebook, create a code block to define your database connection and create a cursor object. Replace postgresql://[user]:[password]@[neon_hostname]/[dbname] with the database connection string you retrieved in the previous step.

   import os
   import psycopg2
   
   # Provide your Neon connection string
   connection_string = "postgresql://[user]:[password]@[neon_hostname]/[dbname]"
   
   # Connect using the connection string
   connection = psycopg2.connect(connection_string)
   
   # Create a new cursor object
   cursor = connection.cursor()

2. Execute the code block (Ctrl + Enter).

3. Add a code block for testing the database connection.

   # Execute this query to test the database connection
   cursor.execute("SELECT 1;")
   result = cursor.fetchone()
   
   # Check the query result
   if result == (1,):
       print("Your database connection was successful!")
   else:
       print("Your connection failed.")

4. Execute the code block (Ctrl + Enter).

1. Create a codeblock to install the pgvector extension to enable your Neon database as a vector store:

   # Execute this query to install the pgvector extension
   cursor.execute("CREATE EXTENSION IF NOT EXISTS vector;")

2. Execute the code block (Ctrl + Enter).

1. Add a code block to create a table and insert data:

   create_table_sql = '''
   CREATE TABLE items (
   id BIGSERIAL PRIMARY KEY,
   embedding VECTOR(3)
   );
   '''
   
   # Insert data
   insert_data_sql = '''
   INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]'), ('[7,8,9]');
   '''
   
   # Execute the SQL statements
   cursor.execute(create_table_sql)
   cursor.execute(insert_data_sql)
   
   # Commit the changes
   connection.commit()

2. Execute the code block (Ctrl + Enter).

1. Add a codeblock to 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)

2. Execute the code block (Ctrl + Enter).

For more information about using Neon with pgvector, see The pgvector extension.

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