semvec/pypi-packages huggingface dataset:
iAppGone 0.1
Fully Functional Uninstaller for Mac OS
Keywords: uninstaller, macos, applications

# iAppGone

**iAppGone** is a command-line tool for macOS that allows you to list and uninstall applications from different sources like `/Applications`, Homebrew, and the App Store. It helps you reclaim disk space by completely removing applications and their associated artifacts.

## Features

- List all installed applications.
- Uninstall a specific application.
- Uninstall multiple applications by providing their index numbers.
- Supports applications installed via `/Applications`, Homebrew, and the App Store.
- Removes associated artifacts such as caches, preferences, and logs.

## Requirements

- Python 3

## Installation

```bash
pip install pandas tabulate colorama
git clone https://github.com/h4rithd/iAppGone.git
cd iAppGone
sudo python3 iAppGone.py -l
```

## Usage
### List all installed applications:
```bash
sudo python3 iAppGone.py -l
```

### Uninstall a specific application:
```bash
sudo python3 iAppGone.py -u AppName
```
Replace AppName with the name of the application you want to uninstall.

### Uninstall multiple applications by index numbers:
```bash
sudo python3 iAppGone.py -m 1,2,4
```
Replace 1,2,4 with the index numbers of the applications you want to uninstall, separated by commas.

### Options
```
-l, --list: List all installed applications.
-u, --app: Uninstall a specific application by name.
-m, --multi: Uninstall multiple applications by index numbers.
```

## Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or a pull request.

## License

This project is licensed under the MIT License. See the LICENSE file for details.
lute3-mandarin 0.0.3
Learning Using Texts - Chinese Parser
Requires Python: >=3.8

# `lute3-mandarin`

A Mandarin parser for Lute (`lute3`) using the `jieba` library, and
`pypinyin` for readings.

## Installation

See the [Lute manual](https://luteorg.github.io/lute-manual/install/plugins.html).

## Usage

When this parser is installed, you can add "Mandarin Chinese" as a
language to Lute, which comes with a simple story.

## Parsing exceptions

Sometimes `jieba` groups too many characters together when parsing.
For example, it returns "清华大学" as a single word of four
characters, which might not be correct.

You can specify how Lute should correct these cases by adding some
simple "rules" to the file
`plugins/lute_mandarin/parser_exceptions.txt` found in your Lute
`data` directory.  This file is automatically created when Lute
starts.  Each rule contains the characters of the word as parsed by
`jieba`, with regular commas added where the word should be split.

Some examples:

| File content | Results when parsing "清华大学" |
| --- | --- |
| (empty file) | "清华大学" |
| <pre><code>清华,大学</code></pre> | Two tokens, "清华" and "大学" (the single token is split in two) |
| <pre><code>清,华,大,学</code></pre> | Four tokens, "清", "华", "大", "学" |
| <pre><code>清华,大学<br>大,学</code></pre> | Three tokens, "清华", "大, "学" (results are recursively broken down if rules are found) |
py-dbchat 0.0.2rc126.post1
Chat with your existing database without using vector DB.
Requires Python: >=3.8.1

# DBChat
An agentic AI tool designed to seamlessly interact with existing databases using an agentic AI approach. This project aims to simplify the process of querying databases, making it more intuitive and accessible for everyone, regardless of their technical background.

# Features
- Uses an Agentic approach to answer to user's question
- Safe from user executing malicious DDL queries.
- Can add Data-safety by only exposing certain tables and columns in the schema.
- Doesn't expect the LLM to generate executable code, rather behaves as an reasoning engine
- reduced hallucinations and more deterministic in nature
- no need for vector DBs
- Supports all relational databases supported by sql alchemy. Refer https://docs.sqlalchemy.org/en/20/dialects/


# How to Run?
```
docker pull dhanilan/dbchat
docker run -p 5173:5173 -p 8000:8000 dhanilan/dbchat
```
default  database to save connections and conversation is mongo at `mongodb://localhost:27017/dbchat`

if you want to save them at specific mongo database pass it as an env
```
docker run  -p 5173:5173 -p 8000:8000 -e DB_URL=mongodb://host.docker.internal:27017/dbchat dhanilan/dbchat

```

if you want to run spider dataset from https://yale-lily.github.io/spider

```
docker run  -p 5173:5173 -p 8000:8000 -e DB_URL=mongodb://host.docker.internal:27017/dbchat -e ATTACH_SPIDER_DATASET=1 dhanilan/dbchat

```

## using docker-compose

The docker compose also comes with a chinook database for testing. You can use it by adding a connection to the chinook database in the UI
use  `postgresql+psycopg2://postgres:postgres@chinook:5143/chinook` as the connection string

update the docker-compose.yml with the required envs if neccessary and run
```
docker-compose up -d
```

# Architecture
Built with Autogen.

![alt text](https://github.com/dhanilan/dbchat/blob/main/architecture.png?raw=true)


# Development

Dev container contains all the necessary deps , mongodb storing the schema and chat history , conversations, settings etc
and also a chinook database in postgres for playing around


## Installing dependencies
### UI
`pip install -r requirements.txt`

## server
`cd src/ui && npm i`


## To run the Application in local

### UI
`cd src/ui &&  npm run dev`

### API
`cd src && uvicorn api.main:app`


### Open the UI
By default opens in http://localhost:5173/

Goto setting to save the OPEN AI API key

## connect to dev container chinook db

`postgresql+psycopg2://postgres:postgres@localhost:5432/chinook`



## Roadmap
- [ ] clean up some code for now
- [ ] Add more tests
- [ ] add feature to annotate the schema
- [ ] add few shots of the conversation to the schema
- [ ] add feature to add custom prompts
- [ ] add access control for tables and columns
`