Vector Databases: Pinecone, Weaviate & pgvector · Lesson

Storing Vectors in PostgreSQL

Learn to create tables with vector columns and insert embedding data directly into your PostgreSQL database.

Storing Vectors in PostgreSQL is a free Vector Databases: Pinecone, Weaviate & pgvector lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Vector Databases: Pinecone, Weaviate & pgvector learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Storing Vectors in PostgreSQL

Welcome back! In the previous lesson, we set up pgvector. Now, let's get practical and learn how to store actual vector embeddings in your PostgreSQL database.

This lesson covers creating tables with vector columns and inserting embedding data.

PostgreSQL Tables Reminder

Before storing vectors, let's quickly recall how tables work in PostgreSQL. You use the CREATE TABLE statement to define your table's structure, including column names and their data types.

With pgvector installed, we gain a new, powerful data type!

The `vector` Data Type

The core of storing embeddings with pgvector is the vector data type. When you define a column as vector(N), N represents the dimension of your embeddings.

vector(3): Stores 3-dimensional vectors.
vector(1536): Stores 1536-dimensional vectors (common for OpenAI embeddings).

It's crucial that N matches the dimension of the embeddings you're working with.

Creating a Table with Vectors

To store embeddings, you'll create a table just like any other, but specify one or more columns as type vector(N).

Here's an example creating a simple table to hold items and their 3-dimensional embeddings:

CREATE TABLE items (
  id SERIAL PRIMARY KEY,
  embedding vector(3)
);

Understanding Vector Dimensions

The number N in vector(N) is vital. It must precisely match the number of values (dimensions) in your actual embedding vectors. If your embedding model produces 768-dimensional vectors, your column must be vector(768).

Mismatched dimensions will result in errors when inserting data. Always check your embedding model's output dimension!

Inserting Single Vector Data

Once your table is set up, you can insert vector data using the standard INSERT INTO SQL command. Vector values are provided as a string representation of a list of numbers, e.g., '[1.2, 3.4, 5.6]'.

INSERT INTO items (embedding)
VALUES ('[1.0, 2.0, 3.0]');

Inserting Multiple Vectors

You can insert multiple vectors into your table efficiently by providing multiple sets of values in a single INSERT INTO statement.

Each set of values corresponds to a new row in your table.

INSERT INTO items (embedding) VALUES
  ('[4.0, 5.0, 6.0]'),
  ('[7.0, 8.0, 9.0]');

Storing Vectors with Metadata

In real-world applications, you'll almost always store additional information (metadata) alongside your vectors. This could be text content, a user ID, a timestamp, or any other relevant data.

Just add more columns to your table definition:

CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT,
  category VARCHAR(50),
  embedding vector(4)
);

Inserting Vectors & Metadata

When inserting data into a table with metadata, simply provide values for all the columns you wish to populate. Make sure the order of values matches the order of columns specified in your INSERT INTO statement.

INSERT INTO documents (content, category, embedding) VALUES
  ('The quick brown fox.', 'animal', '[0.1, 0.2, 0.3, 0.4]');

Quick Check: Table Creation

Which of the following SQL statements correctly creates a table named products with an auto-incrementing primary key id and a vector column product_embedding of 1536 dimensions?

Recap: Storing Your Vectors

Great job! You've learned the fundamental steps to store vector embeddings in PostgreSQL using pgvector:

Defining vector columns with vector(N), ensuring N matches your embedding dimensions.
Creating tables with both vector and metadata columns.
Inserting single and multiple vector data points, along with their associated metadata.

Next, we'll explore how to query this data to find similar vectors!

Frequently asked questions

Is the “Storing Vectors in PostgreSQL” lesson free?

Yes — the full text of “Storing Vectors in PostgreSQL” is free to read here on the web, and the Vector Databases: Pinecone, Weaviate & pgvector course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Vector Databases: Pinecone, Weaviate & pgvector course, upgrade to CoddyKit PRO.

What will I learn in “Storing Vectors in PostgreSQL”?

Learn to create tables with vector columns and insert embedding data directly into your PostgreSQL database. You practise Vector Databases: Pinecone, Weaviate & pgvector with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Vector Databases: Pinecone, Weaviate & pgvector?

No prior experience is required. Vector Databases: Pinecone, Weaviate & pgvector on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Storing Vectors in PostgreSQL” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Vector Databases: Pinecone, Weaviate & pgvector lesson?

Yes. Every Vector Databases: Pinecone, Weaviate & pgvector lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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