cross-posted from: https://piefed.jeena.net/post/33729

Simple Python bindings for @ggerganov's llama.cpp library. This package provides:

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        Low-level access to C API via ctypes interface.
    High-level Python API for text completion
        OpenAI-like API
        LangChain compatibility
        LlamaIndex compatibility
    OpenAI compatible web server
        Local Copilot replacement
        Function Calling support
        Vision API support
        Multiple Models


  

Documentation is available at https://llama-cpp-python.readthedocs.io/en/latest.

cross-posted from: https://programming.dev/post/13395352

Would love to hear any suggestions, feedback or comments.

It runs!.. it's just a toy.

is there any win32 alternative that is multi platform and doesnt consume too much resources and are fast? thx

Had some fun with generators while practicing DRY.

Wondering if anyone here has some advise or a good place to learn about dealing with databases with Python. I know SQL fairly well for pulling data and simple updates, but running into potential performance issues the way I've been doing it. Here are 2 examples.

1. Dealing with Pandas dataframes. I'm doing some reconciliation between a couple of different datasources. I do not have a primary key to work with. I have some very specific matching criteria to determine a match. The matching process is all built within Python. Is there a good way to do the database commits with updates/inserts en masse vs. line by line? I've looked into upsert (or inserts with clause to update with existing data), but pretty much all examples I've seen rely on primary keys (which I don't have since the data has 4 columns I'm matching on).
2. Dealing with JSON files which have multiple layers of related data. My database is built in such a way that I have a table for header information, line level

So I made a small little command-line utility for myself just for practice, but I had a hard time figuring out how to actually turn it into something I can just use on the command line with no fuss. It uses a virtual environment as Python packages should, so it needs to be run in that environment and I was having trouble figuring out how to do it.

But then I remembered that pipx runs application in a virtual environment, and after checking the docs, I found out that it allows installing local packages by just pointing install at the package directory. So I did, and after setting up the command name as a project script that points to main it ended up working.

I haven't ever heard of anyone doing something like this for a personal program though. Is something like this a bad idea? Is it over engineering or error prone? Is there another way that most people do something like this?