semvec/pypi-packages huggingface dataset:
lang-wrapper-for-rag 0.0.1
A modern and secure RAG chatbot using FastAPI and LangChain.
Requires Python: <4.0,>=3.10

# python-mini-chatbot-rag
preparing for interview
pip install poetry
poetry init
Rendben, ez egy fontos pontosítás\! A Poetry egy sokkal strukturáltabb `pyproject.toml` fájlt használ.

Ebben az esetben a `pyproject.toml` fájlod a `[tool.poetry]` szekciót fogja használni a függőségek definiálására, nem az általános `[project]` szekciót.

Íme a `pyproject.toml` fájl, ahogyan az egy Poetry projekt esetében kinézne.

-----

## Módosított `pyproject.toml` (Poetry-hez)

-----

## 🚀 Futtatási útmutató (Poetry-vel)

1.  **Hozd létre a `pyproject.toml` fájlt:** Mentsd el a fenti TOML tartalmat.

2.  **Mentsd el a kódot:** Mentsd a Python kódot `app.py` néven.

3.  **Függőségek telepítése (Poetry):**

    ```bash
    # Ez létrehozza a venv-et (ha kell) és telepíti az összes függőséget
    poetry install
    ```

    *Megjegyzés: Ha a `[tool.poetry.group.dev.dependencies]`-t is hozzáadtad, a `poetry install` alapból telepíti azt is. Ha nem, akkor a `poetry install --with dev` parancs teszi ezt meg.*

4.  **Töltsd le a Spacy modellt (Kritikus lépés\!):**
    Ezt a Poetry-n *kívül* vagy a Poetry környezetén *belül* is megteheted. A legegyszerűbb:

    ```bash
    poetry run python -m spacy download en_core_web_lg
    ```

5.  **Készítsd elő a dokumentumokat:**
    Helyezd a `hr_policies.pdf` fájlodat egy `docs` mappába (`docs/hr_policies.pdf`).

6.  **Indítsd el a szervert:**

    ```bash
    poetry run uvicorn app:app --reload
    ```

docker build -t rag-chatbot .

docker run -d -p 8000:8000 --name chatbot rag-chatbot

# Egyszeri beállítás: add hozzá a TestPyPI-t a Poetry-hez
poetry config repositories.testpypi https://test.pypi.org/legacy/

# Publikálás (kérni fogja a TestPyPI tokenedet)
poetry publish --repository testpypi
campo-estatico-mdf-DS 0.1.0
Un paquete para resolver el campo electrostático 2D con el Método de Diferencias Finitas.
Requires Python: >=3.8

# Proyecto: Campo Electrostático 2D con MDF

Este proyecto resuelve la ecuación de Laplace en 2D utilizando el Método de Diferencias Finitas (MDF) para calcular el potencial electrostático y el campo eléctrico en una región rectangular.

## Características

- **Backend:** Módulo en Python (`campo_estatico_mdf`) que implementa la solución numérica.
- **Frontend:** Interfaz interactiva con Streamlit (`app.py`) para visualizar los resultados.
- **Pruebas:** Pruebas unitarias con `pytest`.
- **Documentación:** Documentación técnica generada con `Sphinx`.

## Estructura del Proyecto

```
├── campo_estatico_mdf/
│   ├── __init__.py
│   └── solver.py
├── tests/
│   └── test_solver.py
├── docs/
│   └── ...
├── app.py
└── README.md
```
## Autores
- Sebastian Rodriguez
- Camilo Huertas
- Julian Avila
tensap 1.8
Tensor Approximation Package: a Python package for the                  approximation of functions and tensors.
Requires Python: >=3.6

[![Build Status](https://github.com/anthony-nouy/tensap/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/anthony-nouy/tensap/actions/workflows/build.yml)
[![codecov](https://codecov.io/gh/anthony-nouy/tensap/branch/master/graph/badge.svg)](https://codecov.io/gh/anthony-nouy/tensap)
[![Doc](https://readthedocs.org/projects/spack/badge/?version=latest)](https://anthony-nouy.github.io/sphinx/tensap/master/)


# tensap (Tensor Approximation Package)

A Python package for the approximation of functions and tensors.

tensap features low-rank tensors (including canonical, tensor-train and tree-based tensor formats or tree tensor networks), sparse tensors, polynomials, and allows the plug-in of other approximation tools. It provides different approximation methods based on interpolation, least-squares projection or statistical learning.

See the tutorials folder and its subfolders for examples of use.

Install from PyPi:

```
pip install tensap
```


Install from conda:

```
conda config --add channels conda-forge
conda config --set channel_priority strict
conda install tensap
```
llama-index-storage-chat-store-postgres 0.3.2
llama-index storage-chat-store postgres integration
Requires Python: <4.0,>=3.9

# LlamaIndex Chat_Store Integration: Postgres Chat Store

## Installation

`pip install llama-index-storage-chat-store-postgres`

## Usage

Using `PostgresChatStore`, you can store your chat history remotely, without having to worry about manually persisting and loading the chat history.

```python
from llama_index.storage.chat_store.postgres import PostgresChatStore
from llama_index.core.memory import ChatMemoryBuffer

chat_store = PostgresChatStore.from_uri(
    uri="postgresql+asyncpg://postgres:password@127.0.0.1:5432/database",
)

chat_memory = ChatMemoryBuffer.from_defaults(
    token_limit=3000,
    chat_store=chat_store,
    chat_store_key="user1",
)
```
offvsix 0.1.5
Visual Studio Code Extension Downloader for offline usage
Requires Python: <4.0,>=3.10

# offvsix: Offline Visual Studio Code Extension Downloader

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

`offvsix` is a Python CLI utility designed to streamline the process of downloading Visual Studio Code extensions for offline installations. Whether you're preparing for a coding session without internet access or simply want to keep your favorite extensions handy, `offvsix` has you covered!

## Features

- Download specific versions of extensions.
- Download extensions to a custom directory.
- Use a proxy server for downloading.
- Avoid redundant downloads with optional caching.
- Get detailed logs with verbose mode.
- **Bulk download**: Supply a text file with a list of extensions to download them all at once!

## Installation

You can install the package from PyPI:

```bash
pip install offvsix
```

Or for offline installation, download the wheel file and run:

```bash
pip install offvsix-<version>.whl
```

## Usage

### Basic usage:

```bash
offvsix <publisher.extension>
```

For example:

```bash
offvsix ms-python.python
```

### Using a Text File:

To download multiple extensions, you can use a text file where each line is an extension name:

```bash
offvsix --file extensions.txt
```

### Install downloaded extension
```bash
code --install-extension ./extensions/ms-python.python-2023.17.12561009.vsix
```

### Options:

- `--version` to specify the version.
- `--destination` to specify the destination folder.
- `--no-cache` to force re-download.
- `--no-print` without output.
- `--file` to specify a text file containing a list of extensions to download.
- `--proxy` to use a proxy server.
- `--json`  Output result as JSON.
- `--target-platform` VS Code target platform (e.g. win32-x64, linux-x64, darwin-arm64).

## Contributing

All contributions are welcome! Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

MIT Licensed. See [LICENSE](LICENSE) for full details.

## Author

- [Lucian BLETAN](https://github.com/gni)