SQLite-Vector now with Google TurboQuant for a 38x speedup
SQLite-Vector is a new extension that enhances SQLite with vector search capabilities, offering significant speed improvements. It operates efficiently across various platforms and requires minimal memory. The extension is designed for ease of use, allowing integration into existing SQLite applications without complex setups.
- ▪SQLite-Vector provides a 38x speedup for vector searches using TurboQuant technology.
- ▪It supports multiple data types and operates seamlessly on mobile and desktop platforms.
- ▪The extension requires no preindexing and can be used offline, making it suitable for Edge AI applications.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/sqliteai/sqlite-vector |
| Publication time | Mon, 25 May 2026 14:23:52 +0000 |
| Retrieval time | 2026-05-25T14:37:38.029Z |
| Last seen | 2026-05-25T14:37:38.029Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | CzWCKOLreTO7 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
SQLite-Vector Production-grade vector search inside SQLite. Exact search, SIMD distance kernels, and SIMD 2/3/4-bit TurboQuant scans — runs anywhere SQLite runs: mobile, browser, edge, server. Free managed instance → · Docs · Website · Blog Data: Vector · Sync · Columnar · JS AI: AI · Agent · Memory · MCP Building RAG or semantic search? SQLite-Vector ships as an extension you can drop into any SQLite app. Need it managed with sync and auth? SQLite Cloud free tier gives you 512 MB and 20 connections, no credit card. SQLite Vector SQLite Vector is a cross-platform, ultra-efficient SQLite extension that brings vector search capabilities to your embedded database. It works seamlessly on iOS, Android, Windows, Linux, and macOS, using just 30MB of memory by default.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.