Python bindings for llama.cpp. Contribute to abetlen/llama-cpp-python development by creating an account on GitHub.
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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
In this tutorial, you'll learn a few Python naming conventions involving single and double underscores (_). You'll learn how to use this character to differentiate between public and non-public names in APIs, write safe classes for subclassing purposes, avoid name clashes, and more.
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.
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).
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