Sqlite-vec: Work-in-progress vector search SQLite extension that runs anywhere

You can generate "text embeddings" (ie vectors) from your text with an embedding model. Once you have your text represented as vectors, then "closest vector" means "more semantically similar", as defined by the embedding model you use. This can allow you to build semantic search engines, recommendations, and classifiers on top of your text and embeddings. It's kindof like a fancier and fuzzier keyword search.

I wouldn't completely replace keyword search with vector search, but it's a great addition that essentially lets you perform calculations on text

I put together an extensive guide to understanding embeddings and vector search last year: https://simonwillison.net/2023/Oct/23/embeddings/

One use case is for retrieving similar documents, such as when recommending related content. Another use case is retrieving document segments that are similar to a user query and passing them along with the user query to a large language model for improvement in the generated response. Vector search is also better in some ways than keyword search since it can find documents that are semantically similar even when the user may not have used the exact keyword, or even partial keywords like “Postgres” instead of “PostgreSQL.”

will fix up those links this weekend, thanks for pointing it out!

the cvec0.all file can be ignored. i was debugging a issue and uploaded a mscv compiled version as that, but its not part of the official release. prefer the tar.gz version and let me know if it doesn't work

It can be statically compiled into the official SQLite Wasm builds:

https://alexgarcia.xyz/sqlite-vec/wasm.html

It can also be statically compiled into the Wasm used by my Go bindings.

What library do you use to compute embedding right now? I'm wondering if it's possible to do both embedding and vector search client-side for a local-first app.

>> In our product, we use duckdb-wasm to do vector searches on the client side.

Curious, what is your product?

Edit: Nevermind, your recent post explained it quite well: https://news.ycombinator.com/item?id=40520073

Not really sure what I can give you. The tools were basically just golang, gcc and docker. Everything else inspection wise just came while I was writing the code. The viewer I use is jellyfin, but I didn't get far enough in the project to have a functioning plugin for it. If you want more info feel free to email me or shoot me a friend request on discord (both visible on my profile).

In the past I mainly screwed around with [2] musig as it was fairly easy to modify, right off hand I don't know why I stopped using it. [1] seek-tune (not-shazam) was just post here on hn yesterday, I had to modify it quite a bit to actually get something working in a way I was happy with. Overall it was a little slow, but that's not something I'm gonna fault the author for as the project is still quite new. While browsing around yesterday after messing with [2], I found [3] cpuimage/shazam. It's a little easier for me to screw around with as it's in c, and would probably be the one I try and modify to use this project.

Everything is based on the shazam algorithm or something similar, so chunking the audio stream, and fingerprinting it. I was focused on that because it allows me to figure out where OP and ED are in some anime and allows me to add a skip button. Also since anime in the same season typically have the same OP/ED I can use that to try and auto categorize them after the first ones been identified.

TL;DR: It's all shazam look alikes, everything else was to annoying to use.

[1] https://github.com/cgzirim/seek-tune [2] https://github.com/sfluor/musig [3] https://github.com/cpuimage/shazam

Yes! There's a bug in the datasette-sqlite-vec plugin right now but expect a fix shortly.