---
title: "Understanding vector search and HNSW index with pgvector"
url: https://daily.dev/posts/understanding-vector-search-and-hnsw-index-with-pgvector-wnyx4sabu
source_url: https://neon.com/blog/understanding-vector-search-and-hnsw-index-with-pgvector
type: article
source: "Neon"
published: 2026-07-09T06:57:52.299Z
updated: 2026-07-09T07:01:51.825Z
tags: ["nlp", "postgresql", "vector-search", "pgvector"]
reading_time: 11
upvotes: 0
comments: 0
language: en
---

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# Understanding vector search and HNSW index with pgvector

**[Neon](https://daily.dev/sources/neontech)** · 11 min read · 0 upvotes · 0 comments

## Summary

Vector embeddings are central to Generative AI applications, enabling semantic similarity search. pgvector is the Postgres extension that supports vector search, and its 0.5.0 release introduced HNSW (Hierarchical Navigable Small Worlds), a graph-based approximate nearest neighbor (ANN) index. The post explains what vectors and embeddings are, how distance metrics like L2 and cosine work, and how to perform vector similarity search with pgvector SQL queries. It then dives into HNSW index creation, covering the key parameters m (graph degree), ef_construction (candidate list size during build), and ef_search (candidate list size during query). Trade-offs between exact and approximate search are discussed, along with HNSW pros (speed, high recall, scalability) and cons (approximate results, resource-intensive indexing, parameter complexity).

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://neon.com/blog/understanding-vector-search-and-hnsw-index-with-pgvector>

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---

Tags: [#nlp](https://daily.dev/tags/nlp), [#postgresql](https://daily.dev/tags/postgresql), [#vector-search](https://daily.dev/tags/vector-search), [#pgvector](https://daily.dev/tags/pgvector)

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