How do you use pgvector with Python and psycopg2?

# Install dependencies
# pip install pgvector psycopg2-binary openai

import psycopg2
from pgvector.psycopg2 import register_vector
from openai import OpenAI

# Connect and register the vector type adapter
conn = psycopg2.connect("postgresql://user:pass@localhost/mydb")
register_vector(conn)  # allows Python lists <-> vector column
cur = conn.cursor()

# Create the schema
cur.execute("""
    CREATE TABLE IF NOT EXISTS documents (
        id        BIGSERIAL PRIMARY KEY,
        content   TEXT NOT NULL,
        embedding VECTOR(1536)
    )
""")
conn.commit()

# Generate embedding and insert
oai = OpenAI()
text = "How does pgvector work?"
response = oai.embeddings.create(model="text-embedding-3-small", input=text)
embedding = response.data[0].embedding  # list of 1536 floats

cur.execute(
    "INSERT INTO documents (content, embedding) VALUES (%s, %s)",
    (text, embedding)   # psycopg2 + register_vector handles the conversion
)
conn.commit()

# Query: find 5 most similar documents
query_text = "What is the pgvector extension?"
query_embedding = oai.embeddings.create(
    model="text-embedding-3-small", input=query_text
).data[0].embedding

cur.execute(
    "SELECT id, content, embedding <=> %s AS distance"
    " FROM documents ORDER BY distance LIMIT 5",
    (query_embedding,)
)
results = cur.fetchall()
for row in results:
    print(f"id={row[0]}, dist={row[2]:.4f}: {row[1]}")

cur.close()
conn.close()