usearch
voy
| usearch | voy | |
|---|---|---|
| 21 | 4 | |
| 1,691 | 715 | |
| 8.9% | 4.5% | |
| 9.8 | 7.4 | |
| 5 days ago | 8 months ago | |
| C++ | Rust | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
usearch
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I'm writing a new vector search SQLite Extension
Might have a look at this library:
https://github.com/unum-cloud/usearch
It does HNSW and there is a SQLite related project, though not quite the same thing.
- USearch SQLite Extensions for Vector and Text Search
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Ask HN: What is the state of art approximate k-NN search algorithm today?
Another worth mentioning in this thread is usearch, though not a separate algorithm, based on HNSW with a bunch of optimizations https://github.com/unum-cloud/usearch
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Vector Databases: A Technical Primer [pdf]
I've used usearch successfully for a small project: https://github.com/unum-cloud/usearch/
- 90x Faster Than Pgvector – Lantern's HNSW Index Creation Time
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Python, C, Assembly – Faster Cosine Similarity
The hardest (still missing) part of efficient cosine computation distance computation is picking a good epsilon for the `sqrt` calculation and avoiding "division by zero" problems.
We have an open issue about it in USearch and a related one in SimSIMD itself, so if you have any suggestions, please share your insights - they would impact millions of devices using the library (directly on servers and mobile, and through projects like ClickHouse and some of the Google repos): https://github.com/unum-cloud/usearch/issues/320
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Show HN: I scraped 25M Shopify products to build a search engine
As you scale, you may benefit from these two projects I maintain, and the Big Tech uses :)
https://github.com/unum-cloud/usearch - for faster search
https://github.com/unum-cloud/uform - for cheaper multi-lingual multi-modal embeddings
- [P] unum-cloud/usearch: Fastest Open-Source Similarity Search engine for Vectors in Python, JavaScript, C++, C, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
- USearch: SIMD-accelerated Vector Search Structure for 10 Programming Languages
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Stringzilla: Fastest string sort, search, split, and shuffle using SIMD
> It doesn't appear to query CPUID
Yes, I'm actually looking for a good way to do it for other projects as well. I've looked into a couple more libs, and here is the best I've come up with so far: https://github.com/unum-cloud/usearch/blob/f942b6f334b31716f...
> Your substring routines have multiplicative worst case
Yes, that is true. It's a very simple stupid trick, just happens to work well for me :)
> It seems quite likely that your confirmation step
We have a different library internally at Unum, that avoids this shortcoming. It has a few thousand lines of C++ templates with SIMD intrinsics... and it's definitely more efficient, but the margins aren't always high. So I kept the pure C version with inlined functions as minimal and simple as possible.
> It would actually be possible to hook Stringzilla up to `memchr`'s benchmark suite if you were interested. :-)
Yes, that would be a fun thing to do! I haven't had time to look into `memchr` yet, but would expect great perf from your lib as well. For me the State of the Art is Intel HyperScan. Probably the most advanced SIMD library overall, not just for strings. I was very impressed with their perf ~5 years ago. But the repo is 200 K LOC... So get ready to invest a weekend :)
That said, I'm a bit slammed with work right now, including open-source. Hoping to ship a new major release in UCall this week, and a minor one in USearch :)
voy
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Ask HN: Semantic Vector Searching in WASM?
Would this[1] library help you? It's a Rust vector similarity search engine, written to be compiled to Wasm. I discovered it through articles like these[2].
[1] https://github.com/tantaraio/voy - Voy – A WASM vector similarity search written in Rust
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FANN: Vector Search in 200 Lines of Rust
This is great! See also Voy, A WASM vector similarity search written in Rust:
https://github.com/tantaraio/voy
- Show HN: Client-Side WASM Vector Search
What are some alternatives?
StringZilla - Up to 10x faster strings for C, C++, Python, Rust, and Swift, leveraging SWAR and SIMD on Arm Neon and x86 AVX2 & AVX-512-capable chips to accelerate search, sort, edit distances, alignment scores, etc 🦖
fann - Approx nearest neighbor search in Rust
ustore - Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
uform - Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️
gollum - Production grade LLM-ops in Golang
faiss - A library for efficient similarity search and clustering of dense vectors.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
wasm-learning - Building Rust functions for Node.js to take advantage of Rust's performance, WebAssembly's security and portability, and JavaScript's ease-of-use. Demo code and recipes.
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.