️ PostgreSQL + pgvector and SQL Server 2025 as Vector Stores for RAG - A Practitioner's Guide

The provided content outlines a comprehensive guide on setting up and utilizing both PostgreSQL with the pgvector extension (referred to as pgvector) and SQL Server 2025 for storing, querying, and managing embeddings in a Retrieval-Augmented Generation (RAG) system. Below is a summary of key points and steps involved:

Part 1: PostgreSQL with pgVector

Setup

• Database Creation: Create a database in PostgreSQL.
• Table Creation: Define tables such as rag_chunks, rag_staleness, etc., to store document chunks, metadata, and embeddings.

sql
 
1CREATE TABLE rag_chunks (
2    id SERIAL PRIMARY KEY,
3    source TEXT NOT NULL,
4    chunk_index INT NOT NULL,
5    content TEXT NOT NULL,
6    file_hash VARCHAR(64),
7    ingested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
8    embedding VECTOR(768)
9);
10
11-- Additional tables and indexes can be created similarly.

• Vector Index: Create a vector index on the embedding column to enable efficient similarity searches.

sql
 
1CREATE INDEX idx_rag_embedding ON rag_chunks USING ivfflat (embedding);

Python Integration

• Dependencies: Install necessary Python packages (`

Read the full article at Towards AI - Medium

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