fast_vector_similarity VS swiss_army_llama

Yes, check out my library for vector similarity that has various other measures which are more discriminative:

https://github.com/Dicklesworthstone/fast_vector_similarity

pip install fast_vector_similarity

Got it. I'd encourage you to expose more of that functionality at the level of your application if possible. I think there is a lot of potential in using more than just cosine similarity, especially when there are lots of candidates and you really want to sharpen up the top few recommendations to the best ones. You might find this open-source library I made recently useful for that:

https://github.com/Dicklesworthstone/fast_vector_similarity

I've had good results from starting with cosine similarity (using FAISS) and then "enriching" the top results from that with more sophisticated measures of similarity from my library to get the final ranking.

You can see how I did something similar in my library here:

https://github.com/Dicklesworthstone/fast_vector_similarity/...

Basically you use ndarray instead of numpy, try to vectorize anything you can, and for the for loops that can’t be vectorized, you can use rayon to do them in parallel.

Yeah, like the other commenter said, everything is in this file here:

https://github.com/Dicklesworthstone/fast_vector_similarity/...

If you also make your project using Rust and Maturin, you can literally just copy and paste that into your project because it's totally generic, and if the repo is public, GitHub will just run it all for you for free.

The only thing is you need to create an account on PyPi (pip) and add 2-Factor Auth so you can generate an API key. Then you go into the repo settings and go to secrets, and create a Github Actions secret with the name PYPI_API_TOKEN and make the value your PyPi token. That's it! It will not only compile all the wheels for you but even upload the project to PyPi for you using the settings found in your pyproject.toml file, like this:

https://github.com/Dicklesworthstone/fast_vector_similarity/...

Depends what you mean by "local". If you mean in your own home, then there isn't a particularly cheap way unless you have a decent spare machine. If you mean "I get to control everything myself" then you can rent a cheap VPS on a value host like Contabo (you can get 8cores, 30gb of ram, and 1tb SSD on Ubuntu 22.04 for something like $35/month-- just stick the to US data centers).

Then if you want something that is extremely quick and easy to set up and provides a convenient REST api for completions/embeddings with some other nice features, you might want to check out my project here:

https://github.com/Dicklesworthstone/swiss_army_llama

Especially if you use Docker to set it up, you can go from a brand new box to a working setup in under 20 minutes and then access it via the Swagger page from any browser.

I found all of them to be quite bloated and annoying to use directly, which is why I made my own FastAPI based one, Swiss Army Llama. I’m obviously biased, but I far prefer it:

https://github.com/Dicklesworthstone/swiss_army_llama

I just added a very cool feature that lets you supply a sample JSON file and it will automatically generate a BNF grammar for it. You can also supply a pydantic data model description and it will generate the corresponding JSON BNF for you:

https://github.com/Dicklesworthstone/swiss_army_llama/blob/m...

And then you can add that grammar file and it will validate it with this:

https://github.com/Dicklesworthstone/swiss_army_llama/blob/5...

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...

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.