Building a Scalable Vector Search DB and Knowledge Graph System Using FAISS, PostgreSQL, and IGraph

Introduction

In this technical deep dive, we will explore how to design a scalable system that integrates vector embeddings with a graph-based structure. We’ll use a combination of FAISS (Facebook AI Similarity Search) for fast vector retrieval, PostgreSQL for embedding persistence, and IGraph for graph representation and analysis.

This article will cover the key components involved, including storage, search, and graph analytics. Additionally, we will use PCA to reduce dimensionality and ensure that our system scales efficiently. We’ll also handle disconnected components in the graph, a common issue in high-dimensional spaces.

Why Use FAISS, PostgreSQL, and IGraph?

To understand why we are using these specific tools, let’s break down their respective roles:

FAISS (Facebook AI Similarity Search)

• Purpose: FAISS is optimized for large-scale similarity search, making it perfect for retrieving high-dimensional vectors.
• Capabilities: It supports both exact and approximate search, ensuring scalability when dealing with millions or even billions of vectors.