Introduction to pgvector Extension in PostgreSQL
Explore the pgvector extension in PostgreSQL to understand vector storage and similarity search. Learn about cosine similarity, L2 distance, inner product, and indexing techniques like HNSW and IVFFlat to optimize high-dimensional data queries in AI applications.
pgvector is an open-source vector similarity search extension for PostgreSQL that enables efficient storage and querying of high-dimensional vectors. It allows neighbor search on vector data, making it suitable for a variety of applications such as recommendation systems, image and text search, and clustering analysis.
By leveraging PostgreSQL's capabilities, pgvector inherits features like ACID compliance, point-in-time recovery, JOINs, and scalability. Additionally, pgvector supports multiple programming languages (Java, Python, Go, C#, etc.), allowing us to generate and store vectors in one language and query them in another. pgvector offers both exact and approximate nearest-neighbor search algorithms, enabling users to strike a balance between accuracy and performance based on their specific requirements.
Basics
We will first need to enable the extension:
CREATE EXTENSION vector;
Create a table and insert data:
CREATE TABLE products (id bigserial PRIMARY KEY, embedding vector(3));INSERT INTO products (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
The first command creates a table named products with columns id as a bigserial primary key and embedding as a vector of size 3. The second command inserts two rows into the products table, each containing an embedding vector of size 3.
Query using distance functions
pgvector provides the ...