semvec/pypi-packages huggingface dataset:
flatmate 0.0.0
flatmate - Your Friendly Data Flattener. Converted Nested JSON from an API into a List of Single-Depth Dictionaries for Writing to a CSV
Keywords: csv, data, flat, flatten

# flatmate
Your Friendly Data Flattener.  Convert Nested JSON from an API into a List of Single-Depth Dictionaries for Writing to a CSV.

## install
```sh
pip install flatmate
```

## basic usage
```python
# in JSON-compatible format
data = {
  "agency": "GSA",
  "measurementType": {
    "method": "modules"
  },
  "version": "2.0.0",
  "releases": [
    {
      "name": "usasearch",
      "description": "System now maintained in open repo https://github.com/GSA/search-gov.",
      "permissions": {
        "licenses": None,
        "usageType": "governmentWideReuse"
      },
      "tags": [
        "GSA"
      ]
    },
    # ...
  ]
}
```

```python
from flatmate import flatten

# flatten data into a list of flat single-depth dictionaries
flatten(data, start="releases")
[
  {
    "releases.name": "usasearch",
    "releases.description": "System now maintained in open repo https://github.com/GSA/search-gov.",
    "releases.permissions.licenses": "null",
    "releases.permissions.usageType": "governmentWideReuse",
    "releases.tags": "GSA",
    # ...
  },
  # ...
]
```

# advanced usage
coming soon
duckducksearch 0.1.0
A Python library for searching using DuckDuckGo.
Requires Python: >=3.7

# DuckDuck Search Library

DuckDuck Search Library is a Python library that allows you to perform searches using [DuckDuckGo](https://duckduckgo.com/). With a simple and clean API, you can quickly and easily retrieve relevant documents and links from the internet. The library supports features like rate limiting, domain filtering, and optional answer retrieval directly from DuckDuckGo.

---

## Features

- **Easy Integration:**
  Provides a simple and intuitive API for performing searches.

- **Rate Limiting:**
  Built-in rate limiting helps to avoid making requests too frequently.

- **Domain Filtering:**
  Restrict search results to a specific domain if required.

- **Optional Answer Retrieval:**
  Optionally fetch direct answers from DuckDuckGo.

- **Flexible Configuration:**
  Customizable parameters such as region, safe search settings, time limits, and more.

- **Python 3.7+ Compatible:**
  Designed to work with modern versions of Python.

---

## Installation

You can install the library via pip:

### From PIP

```bash
pip install duckducksearch
```

### From Source

```bash
pip install git+https://github.com/bes-dev/duckducksearch.git
```

## Usage

```python
from duckduck_search_lib import DuckDuckSearch

# Initialize the searcher with your desired configuration.
searcher = DuckDuckSearch(
    top_k=10,
    max_results=20,
    region="wt-wt",
    safesearch="moderate",
    allowed_domain="",  # Leave empty for no domain restriction
    use_answers=False   # Set to True if you want to retrieve direct answers from DuckDuckGo
)

# Define your search query and number of results to retrieve.
query = "What is Python?"
num_results = 5

# Perform the search.
results = searcher.search(query, num_results)

# Output the results.
print("Documents:")
for doc in results["documents"]:
    print(f"Title: {doc.title}")
    print(f"Content: {doc.content}")
    print(f"Link: {doc.link}")
    print("------------")

print("Links:")
for link in results["links"]:
    print(link)

```
sendero 0.2.0
sendero - data filtering for humans
Keywords: data, filter, path

# sendero
Data Filtering for Humans

## why sendero?
"sendero" means footpath in Spanish

## install
```bash
pip install sendero
```

## basic usage
```python
# in JSON-compatible format
data = {
  "agency": "GSA",
  "measurementType": {
    "method": "modules"
  },
  "version": "2.0.0",
  "releases": [
    {
      "name": "usasearch",
      "description": "System now maintained in open repo https://github.com/GSA/search-gov.",
      "permissions": {
        "licenses": None,
        "usageType": "governmentWideReuse"
      },
      "tags": [
        "GSA"
      ]
    },
    # ...
  ]
}
```

```python
from sendero import list_paths, get

list_paths(data, 'agency')
[
    "agency",
    "measurementType.method",
    "releases.contact.email",
    "releases.description",
    # ...
]

get(data, 'agency')
["GSA"]

# get nested properties using dot syntax
get(data, 'releases.permissions.licenses.name')
["CC0 1.0 Universal", "PD", "agpl-3.0", ... ]

# get nested properties using double underscore syntax
get(data, 'releases__permissions__licenses__name')
["CC0 1.0 Universal", "PD", "agpl-3.0", ... ]
```

# advanced usage
## clean
Filter out None and empty strings.
```python
from sendero import get

# dirty
get(data, "releases.license", clean=False)
[ null, 'https://creativecommons.org/publicdomain/zero/1.0', None, None, ... ]

# clean
get(data, "releases.license", clean=True)
[ 'https://creativecommons.org/publicdomain/zero/1.0', 'https://github.com/GSA/open.gsa.gov/blob/gh-pages/TERMS.md', ... ]
```

## unique
If you only want unique results returned:
```python
# default
get(data, 'releases.tags')
["GSA", "GSA", "GSA", ...]

# uniques only
get(data, 'releases.tags', unique=True)
["GSA","gsa","socialmedia", "mobileapps", ...]
```

## sort
If you want your results sorted
```python
get(data, 'releases.tags', sort=True)
["508", "API", "Bing", "DigitalGovSearch", ...]
```

## delimiter
By default, sendero tries syntax where the steps are separted by
`"."` or `"__"`.  If you'd like to restrict the syntax or use a custom delimiter:
```python
get(data, 'releases--tags', delimiter='--')

# accepts releases--tags and releases__tags
get(data, 'releases--tags', delimiter=['--', '__'])
```

## stringify
```python
# converts numbers to strings
get(data, "releases.laborHours", stringify=True, unique=True)
["0", "200", "12345" ]

# convert objects to JSON strings
get(data, "releases.permissions", stringify=True, unique=True)
[
  '{"licenses":null,"usageType":"governmentWideReuse"}',
  '{"licenses":[{"URL":"http://choosealicense.com/licenses/mit/","name":"mit"}],"usageType":"openSource"}',
  '{"licenses":[{"URL":"http://choosealicense.com/licenses/gpl-3.0/","name":"gpl-3.0"}],"usageType":"openSource"}',
  '{"licenses":null,"usageType":"openSource"}',
  # ...
]
```
kxchat 0.1.1
Add your description here
Requires Python: >=3.10

# kxchat

`kxchat` is a conversational AI application that leverages state-of-the-art natural language processing models to facilitate interactive chat experiences. It allows users to load models from specified repositories and engage in dynamic conversations.

## Table of Contents

- [kxchat](#kxchat)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Quick Start](#quick-start)
  - [Features](#features)
  - [Development](#development)

## Installation

kxchat requires Python 3.11 or higher.

Use `pip`/`uv`

```bash
pip install kxchat
```

```bash
uv add kxchat
```

## Quick Start

To start a chat room using a model from a specific repository, run the following command:

```bash
kxchat room <repo> [--revision <revision>]
```

- `<repo>`: The URL or path to the repository containing the model.
- `--revision <revision>`: The specific revision of the model to use (default is "main").

Example:

```bash
kxchat room meta-llama/Meta-Llama-3.1-8B-Instruct
```


## Features

- **Dynamic Model Loading:** Load models from any specified repository and revision.
- **Interactive Chat:** Engage in real-time conversations with the AI.
- **Command Line Interface:** Simple and intuitive CLI for easy interaction.

## Development

To set up a development environment, install the development dependencies:

```bash
uv sync --dev
```