semvec/pypi-packages huggingface dataset:
langdash 1.20.3
A simple library for interfacing with language models.
Requires Python: >=3.8

# langdash

A simple library for interfacing with language models.

**Currently in beta!**

**Features:**

  * Support for guided text generation, text classification (through prompting) and vector-based document searching.
  * Lightweight, build-it-yourself-style prompt wrappers in pure Python, with no domain-specific language involved.
  * Token healing and transformers/RNN state reuse for fast inference, like in [Microsoft's guidance](https://github.com/microsoft/guidance).
  * First-class support for ggml backends.

**Documentation:** [Read on readthedocs.io](https://langdash.readthedocs.io/en/latest/)

**Repository:** [main](https://git.mysymphony.jp.net/nana/langdash/) / [Gitlab mirror](https://gitlab.com/nanamochizuki77/langdash)

## Installation

Use [pip](https://pip.pypa.io/en/stable/) to install. By default, langdash does not come preinstalled with any additional modules. You will have to specify what you need like in the following command:

```
pip install --user langdash[embeddings,sentence_transformers]
```

List of modules:
  
  * **core:**
    * *embeddings:* required for running searching through embeddings
  * **backends:**
    * Generation backends: *rwkv_cpp*, *llama_cpp*, *ctransformers*, *transformers*
    * Embedding backends: *sentence_transformers*

**Note:** If running from source, initialize the git submodules in the `langdash/extern` folder to compile foreign backends.
    
## Usage

Examples:

  * [Text generation](https://git.mysymphony.jp.net/nana/langdash/src/branch/master/docs/examples/text-generation.md)
  * [Generating TV shows](https://git.mysymphony.jp.net/nana/langdash/src/branch/master/docs/examples/generating-tv-shows.md)
  * [Embedding search](https://git.mysymphony.jp.net/nana/langdash/src/branch/master/docs/examples/embedding-search.md)

See [examples folder](https://git.mysymphony.jp.net/nana/langdash/src/branch/master/examples) for full examples.

## Running the Examples

All examples can be ran with the following command:

```
python examples/instruct.py [model type] [model name or path]
```

You can specify additional model parameters using the `-ae` CLI argument, and passing a valid Python literal. For example, to run the chat example using the WizardLM model with context length of 4096, do:

```
python examples/chat.py llama_cpp /path/to/ggml-wizardlm.bin -ae n_ctx 4096
```

Some examples require you to specify the prompt format. Formats include: `wizardlm` (shortened Alpaca format without the first prompt line and `# Instruction:`), and `alpaca` (the full format). You will need to specify it for most of the examples:

```
python examples/instruct.py llama_cpp /path/to/ggml-wizardlm.bin -ae n_ctx 4096 --prompt-format wizardlm
```

For a full list, see the `examples/_instruct_format.py` file.


## License

Apache 2.0
archivy 1.7.7
Minimalist knowledge base focused on digital preservation and building your second brain.
Requires Python: >=3.6

![logo](docs/img/logo.png)

# Archivy

Archivy is a self-hostable knowledge repository that allows you to learn and retain information in your own personal and extensible wiki.

Features:

- If you add bookmarks, their web-pages contents' will be saved to ensure that you will **always** have access to it, following the idea of [digital preservation](https://jeffhuang.com/designed_to_last/). Archivy is also easily integrated with other services and your online accounts.
- Knowledge base organization with bidirectional links between notes, and embedded tags.
- Everything is a file! For ease of access and editing, all the content is stored in extended markdown files with yaml front matter. This format supports footnotes, LaTeX math rendering, syntax highlighting and more. 
- Extensible plugin system and API for power users to take control of their knowledge process
- [syncing options](https://github.com/archivy/archivy-git)
- Powerful and advanced search. 
- Image upload


[demo video](https://www.uzpg.me/assets/images/archivy.mov)

[Roadmap](https://github.com/archivy/archivy/issues/74#issuecomment-764828063)

Upcoming:

- Annotations
- Multi User System with permission setup.

## Quickstart


Install archivy with `pip install archivy`. Other installations methods are listed [here](https://archivy.github.io/install), including Docker.

Run the `archivy init` command to setup you installation.

Then run this and enter a password to create a new user:

```bash
$ archivy create-admin <username>
```

Finally, execute `archivy run` to serve the app. You can open it at https://localhost:5000 and login with the credentials you entered before.

You can then use archivy to create notes, bookmarks and then organize and store information.

See the [official docs](https://archivy.github.io) for information on other installation methods.

## Community

Archivy is dedicated to building **open and quality knowledge base software** through collaboration and community discussion.

To get news and updates on Archivy and its development, you can [watch the archivy repository](https://github.com/archivy/archivy) or follow [@uzpg_ on Twitter](https://twitter.com/uzpg_).

You can interact with us through the [issue board](https://github.com/archivy/archivy/issues) and the more casual [discord server](https://discord.gg/uQsqyxB).

Note: If you're interested in the applications of AI to knowledge management, we're also working on this with [Espial](https://github.com/Uzay-G/espial).
dsakit 2.0.1
A package aimed at providing various data structures and their functions to solve dsa problems
Requires Python: >=3.7

# DSA Kit (Data Structures and Algorithms Kit)
[![PyPI version](https://badge.fury.io/py/dsakit.svg)](https://badge.fury.io/py/dsakit)
[![GitHub license](https://img.shields.io/github/license/Aayush65/DSAKit)](https://github.com/Aayush65/DSAKit/blob/main/LICENSE)

## Overview

DSA Kit is a comprehensive collection of Python modules that implement various data structures and algorithms from scratch. The primary goal of this project is to provide efficient and well-optimized implementations of fundamental data structures and algorithms.

## Features

- Developed all data structures as Python modules using best-known algorithms.
- Ensured efficient Time and Space complexities for all implemented algorithms.
- Comprehensive modules for Linked Lists, Binary Trees, and Tries with essential functions not available in Python's standard library.
- Implemented default data structures like HashSet, HashMap, Circular Deque, and Min/Max Heap.

## Index of Modules

Click on the following link for their respective documentations

1. [Linked List](https://github.com/Aayush65/DSAKit/blob/main/src/LinkedList.md)
2. [Binary Trees](https://github.com/Aayush65/DSAKit/blob/main/src/BinaryTree.md)
3. [Tries](https://github.com/Aayush65/DSAKit/blob/main/src/Trie.md)
4. [UnionFind](https://github.com/Aayush65/DSAKit/blob/main/src/UnionFind.md)

## Installation

You can install DSA Kit using pip:

```bash
pip install dsakit
```
OR
```bash
pip3 install dsakit
```

## Usage

After installation, you can use any of the 4 modules of this package:
- LinkedList
- BinaryTree
- Trie
- UnionFind,
using the following import on python

```python
from LinkedList import *
from BinaryTree import *
from Trie import *
from UnionFind import *
```

## License

DSA Kit is open-source software released under the [MIT License](https://github.com/Aayush65/DSAKit/blob/main/LICENSE). Feel free to use it in your projects.
react-flask 0.1.0
Fully typed Flask and React applications
Requires Python: >=3.11.2,<4.0.0

# 💫 React Flask (RF)

### Fully typed Flask and React applications

![maze](/docs/banner.jpg)

Welcome to RF, a _strongly-linked_ Flask and React application template.

RF combines a [Flask](https://flask.palletsprojects.com/en/2.3.x/) server, with a [React TypeScript](https://www.typescriptlang.org/docs/handbook/react.html) UI into a comprehensive full-stack framework for building modern web applications.

RF features a sophisticated types manager that automatically synchronizes [Python Type hints](https://docs.python.org/3/library/typing.html) and [TypeScript interfaces](https://www.typescriptlang.org/docs/handbook/interfaces.html). This means that as you make changes to your API code in the server, RF diligently keeps the API Client up to date.

## Built on popular tools

We use cutting edge Python tools including:

* [Pydantic 2.0](https://docs.pydantic.dev/latest/)
* [Flask 2.3](https://flask.palletsprojects.com/en/2.3.x/)

And our TypeScript is modern too:

* [Node 18.17.0](https://nodejs.org/en)
* [React 18](https://react.dev/)
* [TypeScript 5.1](https://www.typescriptlang.org/)

Both TypeScript and Python hold their positions as two of the [most widely used programming languages globally](https://www.statista.com/statistics/793628/worldwide-developer-survey-most-used-languages/). As a result, they are frequently combined in various projects.

However, setting up a smooth and efficient development environment that integrates these languages can be a cumbersome and time-consuming process, often leading to a subpar developer experience. Thankfully, RF steps in to solve this challenge by through it's sophisticated types manager.

## Type-safe productivity boost

RF provides:

* A types manager which keeps the API interface type-safe.
* React: A collection of type-safe API hooks for querying the HTTP API.
* React: No single page app, so bundled JS for the client loads faster.
* Flask: A manager for generating new React pages without any manual effort.
* Flask: View-based routing to reduce the bundle size of JS on page loads.

## Currently in development

Before this becomes a fully fledged package, it exists as a decent template that can be forked when you start a new project and worked on from there.

## Set up

Fork the code, then run the build and dev command to get started:

```bash
git clone git@github.com:beckett-software/react-flask.git
cd react-flask
make build
make dev
```
data-to-object 0.0.1
This is a simple package that deserializes json to your custom model include custom attributes and child objects.
Requires Python: >=3.7

This is a simple package that deserializes json to your custom model include custom attributes and child objects. It can return a single model or a list of the models. This will allow you to access through the model's attributes. 

The deserializer can read list of dictionaries, dictionary or json string.

For more details on how to use, please visit https://github.com/dsun615/json-2-object/wiki
dearprudence 1.1.0
Internal Python library filled with sugar for swallowing downscalingCMIP6 parameter files
Requires Python: >=3.9

[![Test](https://github.com/brews/dearprudence/actions/workflows/test.yaml/badge.svg)](https://github.com/brews/dearprudence/actions/workflows/test.yaml)
[![codecov](https://codecov.io/gh/brews/dearprudence/branch/main/graph/badge.svg?token=FZD8KX4RC1)](https://codecov.io/gh/brews/dearprudence)

# dearprudence
Internal Python library filled with sugar for swallowing downscalingCMIP6 parameter files.

## Examples

```python
import dearprudence


tasmax_spec = dearprudence.read_params("GFDL-ESM4-tasmax.yaml")

print(tasmax_spec[0].ssp)
# Cmip6Record(activity_id='ScenarioMIP',
#             experiment_id='ssp370', 
#             table_id='day', 
#             variable_id='tasmax', 
#             source_id='GFDL-ESM4', 
#             institution_id='NOAA-GFDL', 
#             member_id='r1i1p1f1',
#             grid_label='gr1',
#             version='20180701')

tasmax_spec[0].variable_id = "foobar"
dearprudence.write_params("pointlessly_modified.yaml", tasmax_spec)

if dearprudence.cmip6_catalog_has(tasmax_spec[0].ssp):
    print("Exists in CMIP6 In The Cloud!")
```

## Installation

Install with `pip` using:
```shell
pip install dearprudence
```

`dearprudence` requires Python > 3.9. No external packages are required. The `intake_esm` package needs to be installed to use `dearprudence.check_cmip6_catalog()`.

Install the unreleased bleeding-edge version of the package with:
```shell
pip install git+https://github.com/brews/dearprudence
```

## Support
Source code is available online at https://github.com/brews/dearprudence/. This software is Open Source and available under the Apache License, Version 2.0.

## Development

Please file bugs in the [bug tracker](https://github.com/brews/dearprudence/issues).

Want to contribute? Great! Fork the main branch and file a pull request when you're ready. Please be sure to write unit tests and follow [pep8](https://www.python.org/dev/peps/pep-0008/). Fork away!