Issue #117 - Scaling Vector Search: HNSW and Approximate Search

2026-01-03 · Source: Machine Learning Pills · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

This article details Approximate Nearest Neighbour (ANN) search, a critical technique for scaling vector search in real-time AI systems. It explains how ANN methods trade a small amount of precision for significant performance gains, achieving 95-99% recall with sub-linear search times. The core focus is on Hierarchical Navigable Small World (HNSW), a common ANN index structure that builds a layered network of shortcuts to enable fast, efficient searching. HNSW operates by creating multiple graph layers, with higher layers containing fewer vectors for rapid long-distance traversal, and a detailed bottom layer for precise local searches. The article also briefly covers alternative ANN methods like IVF, PQ, and DiskANN, and provides a practical implementation example of semantic search using ChromaDB with a local "all-MiniLM-L6-v2" embedding model.

Key takeaway

For AI Engineers building real-time vector search applications, understanding ANN algorithms like HNSW is crucial. You should consider implementing HNSW-based vector databases to achieve sub-linear search performance and maintain high recall (e.g., 95-99%) on large datasets, significantly reducing latency compared to brute-force methods. Experiment with ChromaDB and local embedding models to quickly prototype and test semantic search capabilities without external API dependencies.

Key insights

ANN search, particularly HNSW, enables scalable, high-performance vector retrieval by trading exactness for speed.

Principles

• Exactness hinders speed in high-dimensional vector search.
• ANN methods offer a precision-vs-speed dial.
• HNSW uses layered graphs for efficient navigation.

Method

HNSW builds a multi-layered graph where top layers provide fast, coarse navigation and bottom layers enable precise local search, mimicking a "small world" network for efficient traversal.

In practice

• Use ChromaDB for local semantic search.
• Employ "all-MiniLM-L6-v2" for local embeddings.

Topics

• Approximate Nearest Neighbor
• HNSW Algorithm
• Vector Search
• Vector Databases
• Semantic Search

Best for: Machine Learning Engineer, AI Engineer, Data Scientist

Related on AIssential

• A Fun & Absurd Introduction to Vector Databases • Alexander Chatzizacharias • GOTO 2025 — Vector databases enable rapid semantic search by storing and indexing high-dimensional vector representations of data.
• Agent Memory with Vector Stores: HNSW, Forgetting, and Budgets — HNSW significantly improves vector store search latency for agent memory with minimal practical recall loss.
• How Vector Databases Use Specialized Indexes (HNSW, IVF, PQ, and Annoy) — Vector databases use specialized Approximate Nearest Neighbor (ANN) indexes to achieve millisecond similarity search across millions of high-dimensional vectors.
• Vector Search at Scale: The Production Engineer's Guide — Efficient vector search at scale requires sub-linear time, memory compression, and approximate results.
• Aperon Technical Report: Hierarchical No-Pointer Tangent-Local Search for High-Dimensional Approximate Nearest Neighbors — HNTL improves ANN search performance by eliminating pointers and using local tangent spaces for efficient, cache-friendly vector indexing.
• Building a 7× Faster Vector Search Engine in Go — Systematic profiling and algorithmic optimization are crucial for high-performance Go applications.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning Pills.