lantern
usearch
lantern | usearch | |
---|---|---|
5 | 21 | |
651 | 1,647 | |
6.9% | 6.5% | |
9.6 | 9.8 | |
4 days ago | 6 days ago | |
C | C++ | |
GNU General Public License v3.0 or later | 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.
lantern
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Are we at peak vector database?
Traditional DBs already kinda support vector DBs via pg_vector extensions and such.
There is a YC startup, latnern, that also built their own extension for postgres that is open source and is better for vector DB use cases: https://github.com/lanterndata/lantern
But yeah! Traditional DBs already support this, if you consider this extension to be part of Postgres.
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90x Faster Than Pgvector β Lantern's HNSW Index Creation Time
This extension is licensed under the Business Source License[0], which makes it incompatible with most DBaaS offerings. The BSL is a closed-source license. Good choice for Lantern, but unusable for everyone else.
Some Postgres offerings allow you to bring your own extensions, for instance Neon[1], where I work. I tried to look at AWS docs for you, but couldn't find anything about that. I did find Trusted Language Extensions[2], but that seems to be more about writing your own extension. Couldn't find a way to upload arbitrary extensions.
[0]: https://github.com/lanterndata/lantern/commit/dda7f064ca80af...
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Show HN: Lantern β a PostgreSQL vector database for building AI applications
Install and use our extension here` https://github.com/lanterndata/lantern
Features today + Coming soon
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 β Lantern's HNSW Index Creation Time
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Python, C, Assembly β 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 π
- 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?
vector-search-class-notes - Class notes for the course "Long Term Memory in AI - Vector Search and Databases" COS 597A @ Princeton Fall 2023
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 π¦
frameless - Expressive types for Spark.
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 ποΈ
lantern_extras - Routines for generating, manipulating, parsing, importing vector embeddings into Postgres tables
uform - Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and π video, up to 5x faster than OpenAI CLIP and LLaVA πΌοΈ & ποΈ
react-semantic-search
faiss - A library for efficient similarity search and clustering of dense vectors.
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity β 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 π
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
voy - πΈοΈπ¦ A WASM vector similarity search written in Rust
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python