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- Hacker News
- 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.by refulgentis
- There are already several openreview comments alleging academic misconduct around TurboQuant: https://openreview.net/forum?id=tO3ASKZlok
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.
by mskkm - Why not just use Qdrant? They've been integrating TurboQuant for months, works well.by beernet
- 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 themby kanungle
- What's a good embedding model and search to run locally? something fast and lightweight.
- I am not convinced that Turbovec yields better retrieval than the same amount of bits of a Matryoshka embedding.by OutOfHere
- 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, ... .
by lmeyerov - 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?
- Also interested.by cpursley
- Can WASM use AVX512-VNNI?by coredog64
- 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.
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...
by westurner - 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...by _tgxm
- people should read turboquant's open review comments: https://openreview.net/forum?id=tO3ASKZlok
- tl,dr: there is an allegedly better alternative, and it's already implemented everywhere: https://github.com/VectorDB-NTU/RaBitQ-Library#rabitq-in-ind...by esafak
- It would be nice to have the README be a little more human written for a project where you actually want people to adopt itby nharada
- 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!by ghm2199
- Also the removal latency is on a log scale. Which is quite insane.by ghm2199
- FAISS is no longer close to SoTA:
https://ann-benchmarks.com/index.html https://vector-index-bench.github.io/ https://big-ann-benchmarks.com/neurips23.html
by Eridrus - Surprised that usearch isn't in any of these, it's pretty fast.by ehsanu1
- 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.
by nl