semvec/pypi-packages huggingface dataset:
llm-fragments-zohoticket 0.4
LLM Fragment Loader for pulling ticket conversations from Zoho Desk
Requires Python: >=3.9

# llm-fragments-zohoticket

[LLM plugin](https://llm.datasette.io/en/stable/plugins/index.html) for pulling ticket conversations from [Zoho Desk](https://www.zoho.com/desk/).

## Installation

```bash
llm install llm-fragments-zohoticket
```

### Setting Up Zoho Desk API Credentials

1. **Create a Self Client**  
   Go to the [Zoho API Console](https://api-console.zoho.com/) and create a Self Client to obtain your `client_id` and `client_secret`.

2. **Retrieve Your Organisation ID**  
   In the Zoho Desk interface, navigate to:  
   **Settings > Developer Space > APIs > API Authentication**  
   to find your `organisation_id`.

3. **Set Environment Variables**  
   Add the following to your environment:

   - `ZOHODESK_CLIENT_ID` – Your Zoho API `client_id`
   - `ZOHODESK_CLIENT_SECRET` – Your Zoho API `client_secret`
   - `ZOHODESK_ORG_ID` – Your Zoho Desk `organisation_id`

## Usage

```bash
llm -f zohoticket:12345678 "summarize this conversation."
```
django-genc 0.1.2
A django app for working with GENC country codes
Requires Python: >=3.11
Keywords: codes, country, django, genc, iso

# django-genc

A reusable Django app for working with GENC (Geopolitical Entities, Names, and Codes) country codes. Source data comes from the [NGA site](https://nsgreg.nga.mil/registries/browse/results.jsp?registryType=genc&registerField=IE4&itemTypeField=ggp&gce=all&field=name&show=all&status=all&sort=nameasc) and the [CIA World Fact Book](https://www.cia.gov/the-world-factbook/references/country-data-codes/).

## Features

- Store and manage GENC country codes with their ISO equivalents
- Custom `CountryField` that accepts both 2-character and 3-character ISO and 3-character GENC codes
- Admin interface integration

## Installation

1. Install the package:

```bash
pip install django-genc
```

2. Add 'django_genc' to your INSTALLED_APPS:

```python
INSTALLED_APPS = [
    ...
    'django_genc.apps.GencConfig',
    ...
]
```

3. Run migrations:

```bash
python manage.py migrate
```

## Usage

### Models

```python
from django.db import models
from django_genc.models import CountryField

class MyModel(models.Model):
    country = CountryField()
```

## API

### CountryField

A custom field that can handle both 2-digit ISO and 3-digit GENC codes.

```python
from django_genc.models import CountryField

class MyModel(models.Model):
    country = CountryField()
```

The field will:
- Store values as 3-digit GENC codes in the database
- Accept both 2-digit ISO and 3-digit GENC codes as input
- Automatically convert ISO codes to GENC codes
- Validate that the code exists in the database

### CountryCode Model

The base model for storing country codes:

```python
from django_genc.models import CountryCode

# Get a country by GENC code
country = CountryCode.objects.get(genc_code='USA')

# Get a country by ISO code
country = CountryCode.objects.get(iso_code='US')

# Look up a country by a code under either standard
country = CountryCode.objects.get_by_code('US')
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
any-to-md-cli 0.1.1
A CLI tool to convert various file formats to Markdown using AI
Requires Python: >=3.8

# Any-to-MD CLI

A command-line tool that uses AI (via `litellm`) to convert various file contents into Markdown format.

## Features

- Converts content from a specified input file.
- Outputs well-formatted Markdown to a specified output file.
- Leverages Large Language Models (LLMs) through `litellm` for versatile conversion.
- Supports various LLM providers and models configurable via `litellm`.
- Allows custom prompt templates for tailored conversions.

## Installation
```bash
pip install any-to-md-cli
```

## API Key Configuration

This tool relies on `litellm`, which requires API keys for the chosen LLM provider (e.g., OpenAI, Anthropic, Cohere, etc.).

You **MUST** set the appropriate environment variable for the LLM provider you intend to use. For example:

- For OpenAI models (like `gpt-3.5-turbo`, `gpt-4`): `export OPENAI_API_KEY="your_openai_api_key"`
- For Anthropic models (like `claude`): `export ANTHROPIC_API_KEY="your_anthropic_api_key"`

Refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/providers) for a comprehensive list of supported providers and their required environment variables. You can also pass the API key directly using the `--api-key` option.

## Usage
```bash
any-to-md-cli --input path/to/your/input.file --output path/to/your/output.md
```

**Options:**

-   `-i, --input FILE_PATH`: Path to the input file (Required).
-   `-o, --output FILE_PATH`: Path to the output Markdown file (Required).
-   `-m, --model MODEL_NAME`: Specify the LLM model to use (e.g., `gpt-4o`, `claude-3-opus-20240229`). Defaults to `gpt-3.5-turbo`.
-   `-p, --prompt PROMPT_TEMPLATE`: Custom prompt template. Must include `{content}` placeholder.
-   `--api-key YOUR_API_KEY`: Directly provide the API key for the LLM.
-   `--base-url YOUR_BASE_URL`: Specify a custom base URL for API calls.
-   `--version`: Show the version and exit.
-   `--help`: Show this message and exit.

**Example with default prompt:**
```bash
any-to-md-cli -i notes.txt -o notes.md -m gpt-4o
```

**Example with custom prompt:**
```bash
any-to-md-cli -i code.py -o code_explanation.md -m gpt-4o -p "Explain the following Python code and provide usage examples in Markdown format."
```
soak 40
Process aridity templates en masse, like Helm but much more DRY

# soak
Process aridity templates en masse, like Helm but much more DRY.

This README is auto-generated, see [project wiki](https://wikiwheel.net/s/foyono/w/soak) for details.

## Commands

### soak
Process aridity templates as per all soak.arid configs in directory tree.

## API

<a id="soak.context"></a>

### soak.context

<a id="soak.util"></a>

### soak.util