uform
usearch
| uform | usearch | |
|---|---|---|
| 8 | 21 | |
| 894 | 1,691 | |
| 9.3% | 8.9% | |
| 9.2 | 9.8 | |
| 10 days ago | 4 days ago | |
| Python | C++ | |
| 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.
uform
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CatLIP: Clip Vision Accuracy with 2.7x Faster Pre-Training on Web-Scale Data
question: any good on-device size image embedding models?
tried https://github.com/unum-cloud/uform which i do like, especially they also support languages other than English. Any recommendations on other alternatives?
- Multimodal Embeddings for JavaScript, Swift, and Python
- Show HN: UForm v2 Featuring Multimodal Matryoshka, Multimodal DPO, and ONNX
- UForm v1: Multimodal Chat in 1.5B Parameters
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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
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Show HN: U)Search Images demo in 200 lines of Python
[2]: https://github.com/unum-cloud/uform
- Show HN: UForm v2 โ tiny CLIP-like embeddings in 21 languages and Graphcore API
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Unum: Vector Search engine in a single file
Ouch! Thatโs fat! Which model is that?
We have built a few video-search system by now, using USearch and UForm for embedding. They are only 256 dims and you can concatenate a few from different parts of the video. Any chance it would help?
https://github.com/unum-cloud/uform
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 โ Lantern's HNSW Index Creation Time
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Python, C, Assembly โ 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 ๐
- 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 :)
What are some alternatives?
CogVLM - a state-of-the-art-level open visual language model | ๅคๆจกๆ้ข่ฎญ็ปๆจกๅ
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 ๐ฆ
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
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 ๐๏ธ
LinkBERT - [ACL 2022] LinkBERT: A Knowledgeable Language Model ๐ Pretrained with Document Links
faiss - A library for efficient similarity search and clustering of dense vectors.
neural-file-sorter - A neural network based file sorter. Trains an autoencoder to sort images or audio based on the similarity of their encodings, or uses the OpenAI CLIP model.
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity โ 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 ๐
ucall - Remote Procedure Calls - 50x lower latency and 70x higher bandwidth than FastAPI, implementing JSON-RPC & ๐ REST over io_uring and SIMDJSON โ๏ธ
semantic-search-app-template - Tutorial and template for a semantic search app powered by the Atlas Embedding Database, Langchain, OpenAI and FastAPI
voy - ๐ธ๏ธ๐ฆ A WASM vector similarity search written in Rust