Turning PostgreSQL Into a Vector Database with Docker

To store and query embeddings, we need a database capable of handling vector similarity search.

A practical solution is using PostgreSQL with the pgvector extension.

This allows PostgreSQL to store vectors and perform similarity queries efficiently.

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Docker Compose Setup

Create a docker-compose.yml.

services:
  postgres:
    image: pgvector/pgvector:pg16
    container_name: vector-db
    environment:
      POSTGRES_DB: vectordb
      POSTGRES_USER: admin
      POSTGRES_PASSWORD: admin
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data:

Run:

docker compose up -d

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Enable the Extension

Inside PostgreSQL:

CREATE EXTENSION vector;

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Creating a Vector Table

CREATE TABLE document_embedding (
    id BIGSERIAL PRIMARY KEY,
    document_id BIGINT,
    chunk_text TEXT,
    embedding VECTOR(1536)
);

1536 dimensions are common for many embedding models.

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Similarity Search

Example query:

SELECT chunk_text
FROM document_embedding
ORDER BY embedding <-> '[0.123, 0.443, ...]'
LIMIT 5;

The <-> operator calculates vector distance.

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Why Use PostgreSQL?

Advantages:

• production-ready database
• familiar SQL
• easy Docker setup
• integrates well with Java
• avoids introducing new infrastructure

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Sequence

1. Meaning: How Data Vectorization Powers AI
2. Turning PostgreSQL Into a Vector Database with Docker
3. Indexing Knowledge Base Content with Spring Boot and pgvector
4. Building Semantic Search with Spring Boot, PostgreSQL, and pgvector (RAG Retrieval)

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