Show HN: Chrome didn't have a native vector db so I built one:3

I haven't played with it yet, but very impressive :) a full vector DB built on top of IndexedDB. That means it should run on all modern browsers, and should be able to handle large volumes of data (at least, proportional to disk space).

That's a pretty well written bug report: https://github.com/wizenheimer/tinkerbird/issues/5

I hope you get the help you need for that!

Is there something that makes this specific to Chrome or is this a web compatible library?

What the hell does “chrome native” mean? Is it an extension? Who uses it?

So one could retain the contents of every website every visited and then run local, in-browser searches against that content? Is that the idea? This library would serve as the vector DB?

> TinkerBird uses IndexedDB as it's storage layer, which in turn builds upon Blobs and LevelDB storage systems. By using Indexeddb, it benefits from IndexedDB's adoption, stability and familiarity as a native choice for offline first workflows. Super impressive. Looking forward to use it!

Great project! Thanks for sharing, I'm looking for something like this.

One thing I'm curious about tho is the evolution of the project. Github says the last update in the code was 7 months ago. Is it because it is ready/done?

Avoiding the server call seems to only be time-efficient if calls to embed are time-efficient. The nice thing about server side inference is that you can use embedding models that can handle web-scale data independent of hardware

So one could retain the contents of every website every visited and then run local, in-browser searches against that content? Is that the idea? This li

This looks really promising and thanks for sharing! I've actually been looking into this exact type of functionality lately, for anyone else I'm just going to drop some other projects in this area (also if anyone, including OP, want's to chime in on how they compare- i know there's all sorts of trade-offs ie. the similarity algo chosen, one of these relies on wasm, etc):

https://github.com/tantaraio/voy

https://github.com/nitaiaharoni1/vector-storage

https://github.com/danielivanovz/indexed-vector-store

https://github.com/yusufhilmi/client-vector-search

Another area I've looked into briefly is also trying to generate vector embeddings fully in-browser. I know there will likely be tradeoffs with dimensionality / overall accuracy/performance but having a semi-decent way to do this in-browser would be awesome.

This project only runs in node (but there's an issue that talks about how it might be modified to run in-browser):

https://github.com/Anush008/fastembed-js

Looks like transformers.js supports embeddings in-browser:

https://github.com/xenova/transformers.js/releases/tag/2.1.0

https://github.com/pinecone-io/semantic-search-example/blob/...

Also it appears the client-vector-search supports both embeddings & vector indexing

https://github.com/yusufhilmi/client-vector-search

Hope this helps others looking into this stuff!

Cool! Keep it up!

how would you use this within chrome?

It’s a vector db on top of IndexedDB. I fail to see what’s “Chrome” (or “native”) about it. Is there anything Chrome-specific that prevents it from working in any other modern browser?

I wish you'd written the introduction in the readme yourself instead of letting a LLM do it for you. "utilizes", "significant challenge facing", "disrupts workflows" and then several repeats of the same information... all tell tale signs.