I think their point is the size/performance tradeoff rather than outright performance. The point of TurboQuant is the size savings, while still giving high accuracy.
It's been a while, but I do recall some high-performing vector matching indexes being very large.
ehsanu1 1 days ago [-]
Surprised that usearch isn't in any of these, it's pretty fast.
ghm2199 2 days ago [-]
Wow! 4GB for 10 million documents. This means one could build a reverse index much faster than before and devx processes like debugging, performance testing would become much smoother. Can't wait for the sqlite bindings to come out!
ghm2199 2 days ago [-]
Also the removal latency is on a log scale. Which is quite insane.
nharada 2 days ago [-]
It would be nice to have the README be a little more human written for a project where you actually want people to adopt it
badatnames 2 days ago [-]
Anthropic employee. This is what your brain on kool aid looks like
deeviant 2 days ago [-]
Then again, if the only thing the human doing is bitching about AI use, it's not really that comparatively useful.
righthand 1 days ago [-]
Sure it is useful, the bitching is canary in the shit software mine. How do you know the software isnt shit if the Readme is shit?
If anyone is looking to retrofit to an existing pipeline, I use similar ideas to compress vectors for job search, getting roughly 8x compression with about a 3.5% drop in quality. My experiment: https://corvi.careers/blog/vector-search-embedding-compressi...
anishvarghese 2 days ago [-]
This looks perfect for local, privacy first search, but since it's built in Rust, has anyone tried compiling it to WASM to run directly inside a browser extension?
westurner 2 days ago [-]
oxirs does embeddings and GraphRAG, and full text search with Tantivy; oxirs-vec, oxirs-graphrag
There's an oxirs-wasm with RDF and SPARQL bindings with a query budget. Tantivy-wasm says that the release WASM bundle is 1.5 MB.
Interestingly, while we don't fine-tune generative models for Louie.ai, we found fine-tuning embedding models to be a major $ saver. Instead of 1K-2K wide frontier embedding vector lens... Just 64. Huge savings on vector DB $$$.
I'm curious how that works with something like turboquant. Not needed any more, still dominant, better together, ... .
cat-whisperer 1 days ago [-]
What's a good embedding model and search to run locally? something fast and lightweight.
beernet 2 days ago [-]
Why not just use Qdrant? They've been integrating TurboQuant for months, works well.
kanungle 1 days ago [-]
Integrated in 5 weeks and just expanded data types for turbo4 in last release. No longer need to store fp32 vectors if you don't need them
As it is heavily vibe coded, I think member of technical staff at antropic has no clue....
Next Prompt: remove t@t and force commit.
OutOfHere 1 days ago [-]
I am not convinced that Turbovec yields better retrieval than the same amount of bits of a Matryoshka embedding.
refulgentis 2 days ago [-]
Bloviating nonsense, 3rd time I’ve seen something like this in HN since TurboQuant came out. You don’t need float32, never did. Source: I’ve been writing on device embedding code for 4 years.
cute_boi 2 days ago [-]
Another vibe coded slop where they can't even spend time on Readme or documentation around code...
spoaceman7777 2 days ago [-]
Well. That is insane. O_O Fantastic job!
esafak 2 days ago [-]
lancedb and duckdb integrations would be great...
zuzululu 2 days ago [-]
what could i use this for as part of my agentic workflow? codebase indexing? docs ?
kyxsc 2 days ago [-]
notes/docs/wiki is a great use case
spread2009 21 hours ago [-]
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stlahxm 23 hours ago [-]
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Rendered at 11:34:22 GMT+0000 (Coordinated Universal Time) with Vercel.
https://ann-benchmarks.com/index.html https://vector-index-bench.github.io/ https://big-ann-benchmarks.com/neurips23.html
It's been a while, but I do recall some high-performing vector matching indexes being very large.
There's an oxirs-wasm with RDF and SPARQL bindings with a query budget. Tantivy-wasm says that the release WASM bundle is 1.5 MB.
cool-japan/oxirs: https://github.com/cool-japan/oxirs
oxirs-wasm: https://crates.io/crates/oxirs-wasm
tantivy-wasm: https://github.com/phiresky/tantivy-wasm
Is there an advantage to adding an MCP local memory interface over agent instructions on how to use a rust CLI?
And then write Markdown documents with Google OKF-like frontmatter YAML metadata for agents that work with tokens not linked data graphs; https://github.com/GoogleCloudPlatform/knowledge-catalog/blo...
wasmtime-mte implements ARM64 Memory Tagging Extensions in a fork of the wasmtime WASM runtime.
Memory Tagging Extensions for RISC-V would be a great project too
I'm curious how that works with something like turboquant. Not needed any more, still dominant, better together, ... .
Some write-ups argue that this was deliberate rather than a good-faith mistake: https://dev.to/gaoj0017/turboquant-and-rabitq-what-the-publi...
And now this. Pretty bold AI slop.
Next Prompt: remove t@t and force commit.