Semantic search over Hacker News, built on pgvector

Details

External ID

47111800

Source

HN

Company

—

Product

Semantic search over Hacker News, built on pgvector

Website domain

rivestack.io

Launched

Feb. 22, 2026

Cohort

—

Upvotes

5

Upvotes percentile

0.10512129380053908

Tags

—

Fetched at

Sept. 7, 2026, 9:25 p.m.

Updated at

Sept. 7, 2026, 9:25 p.m.

Description

I built https://ask.rivestack.io — a semantic search engine over Hacker News posts. Instead of keyword matching, it finds results by meaning, so you can search things like "best way to handle authentication in microservices" and get relevant threads even if they don't contain those exact words. How it works:Indexed HN posts and comments into PostgreSQL with pgvector (HNSW index) Embeddings generated with OpenAI's embedding model Queries run as nearest-neighbor vector searches — typical response under 50ms The whole thing runs on a single Postgres instance, no separate vector DBI built this partly because I wanted a better way to search HN, and partly to dogfood my own project — Rivestack (https://rivestack.io), a managed PostgreSQL service with pgvector baked in. I wanted to see how pgvector holds up with a real dataset at a reasonable scale. A few things I learned along the way:HNSW vs IVFFlat matters a lot at this scale. HNSW gave me much better recall with acceptable index build times. Storing embeddings alongside relational data in the same DB simplifies things enormously — no syncing between a vector store and your main DB. pgvector has gotten surprisingly fast in recent versions. For most use cases, you really don't need a dedicated vector database.The search is free to use. Rivestack has a free tier too if anyone wants to try something similar. Happy to answer questions about the architecture, pgvector tuning, or anything else.

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