simdjson-feedstock
awesome-vector-search
simdjson-feedstock | awesome-vector-search | |
---|---|---|
1 | 20 | |
0 | 1,284 | |
- | 3.2% | |
7.3 | 5.7 | |
about 1 month ago | 28 days ago | |
CMake | ||
BSD 3-clause "New" or "Revised" License | MIT License |
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simdjson-feedstock
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
numpy-feedstock: https://github.com/conda-forge/numpy-feedstock/blob/main/rec...
scipy-feedstock: https://github.com/conda-forge/scipy-feedstock/blob/main/rec...
pysimdjson-feedstock: https://github.com/conda-forge/pysimdjson-feedstock/blob/mai...
simdjson-feedstock: https://github.com/conda-forge/simdjson-feedstock/blob/main/...
mkl_random-feedstock: https://github.com/conda-forge/mkl_random-feedstock https://github.com/google/paranoid_crypto/tree/main/paranoid... :
> NumPy-based implementation of random number generation sampling using Intel (R) Math Kernel Library, mirroring numpy.random, but exposing all choices of sampling algorithms available in MKL
blas: https://github.com/conda-forge/blas-feedstock/blob/main/reci...
xtensor-blas-feedstock: https://github.com/conda-forge/xtensor-blas-feedstock
xtensor-fftw (FFT with xtensor (c++)) could probably be AVX-512 and SVE -optimized as well?
awesome-vector-search
- Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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Reality check on good embedding model (and this idea in general)
Probably. But there are a number of free open source ones. For example, I've got a document that I'm doing embedding-keys for that has about 8000 sentences. Here's a list of some [ https://github.com/currentslab/awesome-vector-search ]
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Rye, meet GPT3 ... and vice versa :)
note: search for vector databases not written in Go but with Go clients, in case there is anything more local/lightweight: https://github.com/currentslab/awesome-vector-search
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Vector database built for scalable similarity search
https://github.com/currentslab/awesome-vector-search
I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Supporting sequence of vectors does seems like a fresh air to the vector search service. I have added marqo to the list of awesome vector search (disclosure: I am the maintainer of the list) to increase your exposure.
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What are vector search engines?
If you want a proper curated list of various libraries and standalone services of vector search engines, refer to this awesome GitHub repository by Currents API.
- List of vector search libraries
- List of curated vector search libraries
- A GitHub repository that collects awesome vector search framework/engine, library, cloud service, and research papers
- Find anything fast with Google's vector search technology
What are some alternatives?
scipy-feedstock - A conda-smithy repository for scipy.
pgvector - Open-source vector similarity search for Postgres
numpy-feedstock - A conda-smithy repository for numpy.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
xtensor-fftw - FFTW bindings for the xtensor C++14 multi-dimensional array library
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
mkl_random-feedstock - A conda-smithy repository for mkl_random.
Milvus - A cloud-native vector database, storage for next generation AI applications
blas-feedstock - A conda-smithy repository for blas.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
xtensor-blas-feedstock - A conda-smithy repository for xtensor-blas.
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.