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#wasm#oxirs#https#search#tantivy#github#com#documents#bindings#insane

Discussion (15 Comments)Read Original on HackerNews

ghm2199about 2 hours 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!
ghm2199about 2 hours ago
Also the removal latency is on a log scale. Which is quite insane.
nharadaabout 2 hours 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
badatnamesabout 1 hour ago
Anthropic employee. This is what your brain on kool aid looks like
deeviantabout 1 hour ago
Then again, if the only thing the human doing is bitching about AI use, it's not really that comparatively useful.
anishvargheseabout 2 hours 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?
westurner40 minutes 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.

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...

cpursleyabout 1 hour ago
Also interested.
sp1982about 2 hours ago
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...
spoaceman777735 minutes ago
Well. That is insane. O_O Fantastic job!
burgerboiiabout 2 hours ago
Who is this co-author called t <t@t>?
refulgentis12 minutes 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.
zuzululuabout 2 hours ago
what could i use this for as part of my agentic workflow? codebase indexing? docs ?
kyxscabout 2 hours ago
notes/docs/wiki is a great use case
esafakabout 2 hours ago
lancedb and duckdb integrations would be great...