Announcing autoscaling in feature-preview!Learn More

The pgvector extension

Learn how to use the pgvector for vector similarity search and storing embeddings

The pgvector extension enables vector similarity search and storing embeddings in PostgreSQL. It is particularly useful for applications involving natural language processing, such as those built on top of OpenAI's GPT models. This topic covers the concepts of vector similarity and embeddings, explains how to enable the pgvector extension in Neon, and demonstrates how to create, store, and query vectors.

Vector similarity

Vector similarity is a method used to measure how similar two items are by representing them as vectors, which are series of numbers. This approach can be applied to various types of data, such as words, images, or other elements. By using a mathematical model, each item is converted into a vector, and then these vectors are compared to determine their similarity. The closer the vectors are in terms of distance, the more alike the items.


An embedding is a technique that transforms data, such as words, into vectors, enabling machine learning algorithms to efficiently process and analyze them. This transformation captures the relationships and similarities between data, allowing algorithms to identify patterns and make accurate predictions.

A widely used example of embeddings is in natural language processing, where words are represented as vectors. For instance, consider the words "apple", "orange", and "car". By representing each word as a vector in a 2-dimensional space, you can visually observe their relationships:

Apple: (1.2, 0.8) Orange: (1.1, 0.9) Car: (0.3, 1.5)

In this space, the vectors for "apple" and "orange" are closer together than either is to "car", indicating that they are more similar to each other than to "car". This relationship is captured by vectors in a way that machine learning algorithms can easily understand and utilize for a variety of tasks.

Enable the pgvector extension

You can enable the pgvector extension by running the following CREATE EXTENSION statement in the Neon SQL Editor or from a client such as psql that is connected to Neon.


For information about using the Neon SQL Editor, see Query with Neon's SQL Editor. For information about using the psql client with Neon, see Connect with psql.

Create a table to store vectors

To create a table for storing vectors, use the following SQL command, adjusting the dimensions as needed.

  embedding VECTOR(3)

The command generates a table named items with an embedding column capable of storing vectors with 3 dimensions. OpenAI's text-embedding-ada-002 model supports 1536 dimensions for each piece of text, which creates more accurate embeddings for natural language processing tasks. For more information about embeddings, see Embeddings, in the OpenAI documentation.

Storing vectors and embeddings

Once you have generated an embedding using a service like the OpenAI API, you can store the resulting vector in your database. Using a PostgreSQL client library in your preferred programming language, you can execute an INSERT statement similar to the following to store embeddings:

INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');

This command inserts two new rows into the items table with the provided embeddings.

Querying vectors

To retrieve vectors and calculate similarity, use SELECT statements and the built-in vector operators. For instance, you can find the top 5 most similar items to a given embedding using the following query:

SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

This query computes the Euclidean distance (L2 distance) between the given vector and the vectors stored in the items table, sorts the results by the calculated distance, and returns the top 5 most similar items.

pgvector also supports inner product (<#>) and cosine distance (<=>).

For more information about querying vectors, refer to the pgvector README.

Indexing vectors

Using an index on the vector column can improve query performance with a minor cost in recall.

You can add an index for each distance function you want to use. For example, the following query adds an index to the embedding column for the L2 distance distance function:

CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);

For additional indexing guidance and examples, see Indexing, in the pgvector README.


pgvector source code:

Need help?

Send a request to, or join the Neon community forum.

Edit this page
Was this page helpful?