semvec/pypi-packages huggingface dataset:
licofage 0.9.2
Generate deterministic finite automata associated to linear equations expressed in the numeration system associated to a given substitution.
Requires Python: >=3.9

# licofage

licofage is a tool designed to enhance the accessiblity of the scientific paper *Addition in Dumont-Thomas Numeration Systems in Theory and Practice*. The authors of the paper are O. Carton, JM. Couvreur, M. Delacourt and N. Ollinger.

The implentation is a prototype of a method for generating deterministic finite automata associated with linear equations expressed in the numeration system associated with a given substitution.

Once the installation of the package is complete, the `licofage` command-line tool may be invoked.

The command `licofage -h` provides comprehensive invocation coverage.
ho-rag 1.0.0
Add your description here
Requires Python: >=3.12

# 🏥 Patient Query Assistant with RAG + OpenAI API

A Python-based MCP Server that answers patient-specific medical queries using a Retrieval-Augmented Generation (RAG) system integrated with the OpenAI Chat Completions API.  
The function retrieves context-specific medical data about a patient from an Azure AI Search (a Vector Database), and uses an LLM to generate precise, context-aware responses.

---

## 📖 Overview

This utility allows you to query a patient's medical information by providing:
- **`patient_name`**: The name of the patient (as a string).
- **`query`**: The natural language question you want to ask (as a string).

The system normalizes the patient’s name by removing spaces and converting it to lowercase, retrieves relevant data using an indexer, and sends this context along with your query to an OpenAI model to generate a smart, human-readable response.

---

## 🛠️ Features

✅ Patient name normalization (removes spaces and lowercases for consistent lookups)  
✅ Retrieval-Augmented Generation (RAG) powered by Azure AI Search and Storage Accounts 
✅ Natural language interaction via OpenAI Chat Completions API  
✅ Designed for secure and context-aware medical information handling  

---
linear-operator-learning 0.2.5
A package to learn linear operators
Requires Python: >=3.10

<p align = "left">
  <img src="logo.png" alt="SVG Image" style="width:50%;"/>
</p>

## Install

To install this package as a dependency, run:

```bash
pip install linear-operator-learning
```

## Development

To develop this project, please setup the [`uv` project manager](https://astral.sh/uv) by running the following commands:

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
git clone git@github.com:CSML-IIT-UCL/linear_operator_learning.git 
cd linear_operator_learning
uv sync --dev
uv run pre-commit install
```

### Optional
Set up your IDE to automatically apply the `ruff` styling.
- [VS Code](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff)
- [PyCharm](https://plugins.jetbrains.com/plugin/20574-ruff)

## Development principles

Please adhere to the following principles while contributing to the project:

1. Adopt a functional style of programming. Avoid abstractions (classes) at all cost.
2. To add a new feature, create a branch and when done open a Pull Request. You should _**not**_ approve your own PRs.
3. The package contains both `numpy` and `torch` based algorithms. Let's keep them separated.
4. The functions shouldn't change the `dtype` or device of the inputs (that is, keep a functional approach).
5. Try to complement your contributions with simple examples to be added in the `examples` folder. If you need some additional dependency add it to the `examples` dependency group as `uv add --group examples _your_dependency_`.