simsimd
llama_embeddings_fastap
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| 1 | 2 | |
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simsimd
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Show HN: Fast Vector Similarity Using Rust and Python
It’s a good start, but you can’t generally get even remotely close to hardware potential in Rust, let alone Python.
I had to implement a separate C99 library to always trigger the newest SIMD intrinsics, occasionally leveraging SVE on more recent ARM CPUs, that compilers don’t know how to generate.
That library is in turn used in USearch, which is designed for Approximate Search, but some users recently reported that they use it for brute force as well… where it performed 20x faster than FAISS.
https://github.com/unum-cloud/simsimd
llama_embeddings_fastap
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Show HN: Fast Vector Similarity Using Rust and Python
Cool, I also made a similar kind of tool recently that I also shared on HN a couple weeks ago. You might find it useful for generating and managing LLM embeddings locally:
https://github.com/Dicklesworthstone/llama_embeddings_fastap...
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Show Show HN: Llama2 Embeddings FastAPI Server
Thanks for pointing out those models. I see from a quick Huggingface search that the bge model is available in GGML format. You can trivially add new GGML format models to the code by simply adding the direct download link to this line:
https://github.com/Dicklesworthstone/llama_embeddings_fastap...
So to add the base bge model, you could just add this URL to the list:
https://huggingface.co/maikaarda/bge-base-en-ggml/resolve/ma...
I will add that as an additional default.
What are some alternatives?
swiss_army_llama - A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
DoctorGPT - 💻📚💡 DoctorGPT provides advanced LLM prompting for PDFs and webpages.
openembeddings - Self-hostable pay for what you use embedding server for bge-large-en and arbitrary embedding models using crypto
fast_vector_similarity - The Fast Vector Similarity Library is designed to provide efficient computation of various similarity measures between vectors.
serve - Serve, optimize and scale PyTorch models in production
np-sims - numpy ufuncs for vector similarity
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/