Extension transforming PostgreSQL to a vector database: pgvector

In a digital era brimming with unstructured data - from the movies and songs we enjoy to complex documents and videos - the challenge is how to effectively compare, categorize, or unearth relationships within such diverse content. The traditional rows and columns approach falls short, paving the way for a solution that views data through a numerical lens, as vectors. Enter pgvector, a pioneering open-source PostgreSQL extension designed for vector similarity search.
In this video, we are going to explore about this with in-depth intuition.
Github Link: github.com/pgvector/pgvector
Code is available in pinned comment.
Prerequisite:
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SQL Queries: ================ CREATE EXTENSION vector; SELECT typname FROM pg_type WHERE typname = 'vector'; CREATE TABLE test_embeddings(product_id bigint, embeddings vector(3) ); INSERT INTO test_embeddings VALUES (1, '[1, 2, 3]'), (2, '[2,-3,-4]'); SELECT product_id, embeddings, embeddings '[3,1,2]' AS distance FROM test_embeddings ORDER BY embeddings '[3,1,2]'; Python Code: ================= def cosine_similarity(a,b): return 1-(a[0]*b[0]+a[1]*b[1]+a[2]*b[2])/(((a[0]**2+a[1]**2+a[2]**2)**0.5)*((b[0]**2+b[1]**2+b[2]**2))**0.5)