E0e0·Apr 18, 2024
Perhaps sqlite-vss? It adds vector searches to sqlite.
https://github.com/asg017/sqlite-vss
In Embeddings are a good starting point for the AI curious app developerasg017
sqlite-vss
C++2023–2024
asg017 / sqlite-vss
sqlite-vss
A SQLite extension for efficient vector search, based on Faiss!
14Distinct people
17Mentions
0Explicit recommenders
2KGitHub stars
What to know before exploring
What it is
A SQLite extension for efficient vector search, based on Faiss!
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 7:02:26 AM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month17 dated mentions
Observed mentions: 2023-03-26 03:40:12 UTC to 2024-05-03 05:58:17 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 2023 | 2023-03-01 00:00:00 UTC | 2023-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2023 | 2023-04-01 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 3 | 2 | 1 |
| May 2023 | 2023-05-01 00:00:00 UTC | 2023-06-01 00:00:00 UTC | 2 | 1 | 1 |
| Jun 2023 | 2023-06-01 00:00:00 UTC | 2023-07-01 00:00:00 UTC | 2 | 1 | 1 |
| Jul 2023 | 2023-07-01 00:00:00 UTC | 2023-08-01 00:00:00 UTC | 1 | 0 | 1 |
| Aug 2023 | 2023-08-01 00:00:00 UTC | 2023-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2023 | 2023-09-01 00:00:00 UTC | 2023-10-01 00:00:00 UTC | 1 | 1 | 0 |
| Oct 2023 | 2023-10-01 00:00:00 UTC | 2023-11-01 00:00:00 UTC | 1 | 0 | 1 |
| Nov 2023 | 2023-11-01 00:00:00 UTC | 2023-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2023 | 2023-12-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2024 | 2024-01-01 00:00:00 UTC | 2024-02-01 00:00:00 UTC | 2 | 0 | 2 |
| Feb 2024 | 2024-02-01 00:00:00 UTC | 2024-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2024 | 2024-03-01 00:00:00 UTC | 2024-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2024 | 2024-04-01 00:00:00 UTC | 2024-05-01 00:00:00 UTC | 2 | 0 | 2 |
| May 2024 | 2024-05-01 00:00:00 UTC | 2024-06-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
2 sourcesRe storing vectors in BLOB columns: ya, if it's not a lot of data and it's fast enough for you, then there's no problem doing it like that. I'd even just store then in JSON/npy files first and see how long you can get away with it. Once that gets too slow, then try SQLite/redis/valkey, and when that gets too slow, look into pgvector or other vector database solutions.
For SQLite specifically, very large BLOB columns might effect query performance, especially for large embeddings. For example, a 1536-dimension vector from OpenAI would take 1536 * 4 = 6144 bytes of space, if stored in a compact BLOB format. That's larger than SQLite default page size of 4096, so that extra data will overflow into overflow pages. Which again, isn't too big of a deal, but if the original table had small values before, then table scans can be slower.
One solu
Read the rest
tion is to move it to a separate table, ex on an original `users` table, you can make a new `CREATE TABLE users_embeddings(user_id, embedding)` table and just LEFT JOIN that when you need it. Or you can use new techniques like Matryoshka embeddings[0] or scalar/binary quantization[1] to reduce the size of individual vectors, at the cost of lower accuracy. Or you can bump the page size of your SQLite database with `PRAGMA page_size=8192`.
I also have a SQLite extension for vector search[2], but there's a number of usability/ergonomic issues with it. I'm making a new one that I hope to release soon, which will hopefully be a great middle ground between "store vectors in a .npy files" and "use pgvector".
Re "do embeddings ever expire": nope! As long as you have access to the same model, the same text input should give the same embedding output. It's not like LLMs that have temperatures/meta prompts/a million other dials that make outputs non-deterministic, most embedding models should be deterministic and should work forever.
[0] https://huggingface.co/blog/matryoshka
[1] https://huggingface.co/blog/embedding-quantization
[2] https://github.com/asg017/sqlite-vss