s2geometry
faiss
s2geometry | faiss | |
---|---|---|
26 | 71 | |
2,185 | 28,202 | |
1.3% | 1.9% | |
5.8 | 9.4 | |
2 days ago | 6 days ago | |
C++ | C++ | |
Apache License 2.0 | MIT License |
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.
s2geometry
-
Hexagons and Hilbert Curves – The Horrors of Distributed Spatial Indices
I experimented with geospatial Hilbert Curves as a Postgres extension [0] for PostGIS using the S2 [1] spherical geometry library. S2 uses a scale free cell coverage pattern that is numbered using six interlocking space filling Hilbert Curves [2].
By having both high level (cell) and low level (cell id) geometries it was a very powerful library which allowed projection from the hilbert space into a Postgres spatial index (spgist) including various trees, like noted in this article. It appears to be still quite active in development.
[0] https://github.com/michelp/pgs2
[1] https://s2geometry.io/
[2] https://s2geometry.io/devguide/s2cell_hierarchy
- Show HN: TG – Fast geometry library in C
- Unum: Vector Search engine in a single file
-
Understanding Geohashes
If you check the h3geo comparison page, you should see plenty of alternatives to geohash, such as s2 or even h3 itself.
- Evaluation of Location Encoding Systems
-
Inscribed angle theorem in 3D/higher dimension
See some discussion I started at https://github.com/google/s2geometry/issues/190
-
An Interactive Explanation of Quadtrees
> It was quite hard for me to find open-source implementations of linear quadtrees.
You probably know this, but the S2 library has one: https://github.com/google/s2geometry
-
Why doesn’t my pokèstop show up?
https://s2geometry.io shows how this works
-
Needing advice to improve geodesic calculation time
If your points are distributed globally, however, I'd suggest using something like s2geometry (calculates over a sphere instead of an ellipsoid which is much faster + already has something called S2ClosestPointQuery).
-
What is the best data structure for this problem?
Some alternative solutions are S2 from Google and H3 from Uber. These don't have the same issues as geohash because they work on a 3-d model of the geoid and not a 2-d cylindrical projection like Geohash.
faiss
-
Haystack DB – 10x faster than FAISS with binary embeddings by default
There are also FAISS binary indexes[0], so it'd be great to compare binary index vs binary index. Otherwise it seems a little misleading to say it is a FAISS vs not FAISS comparison, since really it would be a binary index vs not binary index comparison. I'm not too familiar with binary indexes, so if there's a significant difference between the types of binary index then it'd be great to explain what that is too.
[0] https://github.com/facebookresearch/faiss/wiki/Binary-indexe...
-
Show HN: Chromem-go – Embeddable vector database for Go
Or just use FAISS https://github.com/facebookresearch/faiss
- OpenAI: New embedding models and API updates
-
You Shouldn't Invest in Vector Databases?
You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.
This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).
For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.
faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}
-
Approximate Nearest Neighbors Oh Yeah
If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss
Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.
The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982
-
Can someone please help me with this problem?
According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.
-
Ask HN: Are there any unsolved problems with vector databases
Indexes for vector databases in high dimensions are nowhere near are effective as the 2-d indexes used in GIS or the 1-d B-tree indexes that are commonly used in databases.
Back around 2005 I was interested in similarity search and read a lot of conference proceedings on the top and was basically depressed at the state of vector database indexes and felt that at least for the systems I was prototyping I was OK with a full scan and later in 2013 I had the assignment of getting a search engine for patents using vector embeddings in front of customers and we got performance we found acceptable with full scan.
My impression today is that the scene is not too different than it was in 2005 but I can't say I haven't missed anything. That is, you have tradeoffs between faster algorithms that miss some results and slower algorithms that are more correct.
I think it's already a competitive business. You have Pinecone which had the good fortune of starting before the gold rush. Many established databases are adding vector extension. I know so many engineering managers who love postgresql and they're just going to load a vector extension and go. My RSS reader YOShInOn uses SBERT embeddings to cluster and classify text and certainly More Like This and semantic search are on the agenda, I'd expect it to take about an hour to get
https://github.com/facebookresearch/faiss
up and working, I could spend more time stuck on some "little" front end problem like getting something to look right in Bootstrap than it would take to get working.
I can totally believe somebody could make a better vector db than what's out there but will it be better enough? A startup going through YC now could spend 2-3 to get a really good product and find customers and that is forever in a world where everybody wants to build AI applications right now.
-
Code Search with Vector Embeddings: A Transformer's Approach
As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.
-
Unum: Vector Search engine in a single file
But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...
-
Introduction to Vector Similarity Search
https://github.com/facebookresearch/faiss
What are some alternatives?
h3 - Hexagonal hierarchical geospatial indexing system
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
S2 geometry - S2 geometry library in Go
Milvus - A cloud-native vector database, storage for next generation AI applications
0.30000000000000004 - Floating Point Math Examples
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
s2 - Node.js JavaScript / TypeScript bindings for Google S2
pgvector - Open-source vector similarity search for Postgres
Kyrix - Interactive details-on-demand data visualizations at scale
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.
sled - the champagne of beta embedded databases
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/