Postgres Vector (pgvector) is a PostgreSQL extension that adds vector similarity search to your database. It lets you store and query high-dimensional embeddings — for example those produced by OpenAI, Cohere, or Sentence Transformers — directly alongside your relational data. Available as an open web service in Eyevinn Open Source Cloud, pgvector is the standard choice for AI-powered search, semantic recommendations, and retrieval-augmented generation (RAG) applications. This tutorial walks you through the steps to get started.

• If you have not already done so, sign up for an Eyevinn OSC account

Navigate to the Postgres Vector service in the Eyevinn OSC web console. Click Create pgvector and fill in:

Click the instance card once the status turns green and running. Note the IP and port shown — you will need them to build the connection string.

Based on the IP and port, the connection URL for your database is:

postgres://<user>:<password>@<IP>:<PORT>/<db>

For example, using the defaults:

postgres://postgres:mypassword@<IP>:<PORT>/postgres

Test the connection with psql:

psql "postgres://postgres:mypassword@<IP>:<PORT>/postgres"

Once connected, enable the extension in each database where you want to use vector search:

CREATE EXTENSION IF NOT EXISTS vector;

-- Create a table with a vector column (384 dimensions for e.g. all-MiniLM-L6-v2)
CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  content TEXT,
  embedding vector(384)
);

-- Insert a document with its embedding (example with 3 dimensions)
INSERT INTO documents (content, embedding)
VALUES ('Hello world', '[0.1, 0.2, 0.3]');

-- Find the 5 most similar documents to a query vector (cosine distance)
SELECT id, content, 1 - (embedding <=> '[0.1, 0.15, 0.25]') AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.15, 0.25]'
LIMIT 5;

Supported distance operators:

For large datasets, create an HNSW or IVFFlat index to speed up queries:

-- HNSW index (recommended, no training required)
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

-- IVFFlat index (requires at least some rows to train)
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

import openai
import psycopg2

conn = psycopg2.connect("postgres://postgres:mypassword@<IP>:<PORT>/postgres")
cur = conn.cursor()

# Generate an embedding with OpenAI
response = openai.embeddings.create(input="What is OSC?", model="text-embedding-3-small")
embedding = response.data[0].embedding

# Query for similar documents
cur.execute(
    "SELECT content FROM documents ORDER BY embedding <=> %s::vector LIMIT 5",
    (str(embedding),)
)
results = cur.fetchall()

osc create pgvector-pgvector myvectordb \
  -o PostgresPassword="mypassword" \
  -o PostgresDb="vectors"

• pgvector on GitHub
• pgvector documentation
• OpenAI embeddings guide