semvec/pypi-packages huggingface dataset:
klarity-connector 0.1.5
Python connector to GraphQL API of Klarity
License: MIT

### Klarity Connector
Python connector for Klarity API

#### Installation
```bash
pip install klarity-connector
```

#### Usage
```python

from klarity_connector import KlarityConnector

# Create a connector
connector = KlarityConnector(
    region='eu'
    api_key='YOUR_API_KEY',
    billing_period='current' | 'previous' # Current is a default billing period
)

# Sending GraphQL query
query = """
    query applications{
        applicationsPaginated(limit: 100, page: 0){
            results{
                name,
                cost{
                    currentMonth
                }
            }
        }
    }
"""

response = connector.graphql_request(query)

# Sending GraphQL mutation
mutation = """
    mutation createApplication($name: String!, $description: String){
        createApplication(name: $name, description: $description){
            name
        }
    }
"""

variables = {
    'name': 'My new application',
    'description': 'My new application description'
}

response = connector.graphql_request(mutation, variables)

```

### Official product documentation
[docs](https://docs.klarity.nordcloudapp.com)
embedbase-qdrant 1.0.1
Embedbase + Qdrant - Advanced and high-performant vector similarity search technology in your AI applications.
License: MIT
Requires Python: >=3.8,<3.12
Keywords: embeddings,machine learning,artificial intelligence


<div align="center">
    <h3 align="center">embedbase-qdrant</h3>
    <p align="center">
        <p align="center">
            <a href="https://github.com/different-ai/embedbase">Embedbase</a> + <a href="https://qdrant.tech">Qdrant</a>
            Advanced and high-performant vector similarity search technology in your AI applications 
        </p>
    </p>
    <br>
    ⚠️ Status: Alpha release ⚠️
    <br>
    <br>
    <a href="https://discord.gg/pMNeuGrDky"><img alt="Discord" src="https://img.shields.io/discord/ 1066022656845025310?color=black&style=for-the-badge"></a>
    <a href="https://badge.fury.io/py/embedbase-qdrant"><img alt="PyPI" src="https://img.shields.io/pypi/v/embedbase-qdrant?color=black&style=for-the-badge"></a>
    <br>
    <div align="center">
        <p align="center">
            If you have any feedback or issues, please let us know by opening an issue or contacting us on <a href="https://discord.gg/pMNeuGrDky">discord</a>.
        </p>
        <p align="center">
            Please refer to the <a href="https://docs.embedbase.xyz">documentation</a>
        </p>
    </div>

</div>


## Getting started

To install the Embedbase Qdrant library, run the following command:

```bash
pip install embedbase-qdrant
```

## Quick tour

Let's try Embedbase + Qdrant with an OpenAI `embedder`:

```bash
pip install openai uvicorn
```

```python
import os
import uvicorn
from embedbase import get_app
from embedbase.embedding.openai import Openai
from embedbase_qdrant import Qdrant

# here we use openai to create embeddings and qdrant to store the data
app = get_app().use_embedder(Openai(os.environ["OPENAI_API_KEY"])).use_db(Qdrant()).run()

if __name__ == "__main__":
    uvicorn.run(app)
```

Start a local Qdrant:

```bash
docker-compose up -d
```

Run Embedbase:

```bash
python3 main.py
```

![pika-1683309528643-1x](https://user-images.githubusercontent.com/25003283/236533294-3cd481ac-6437-47b6-ae58-d5a9a6e0e4bf.png)

Check out other [examples](./examples/main.py) and [documentation](https://docs.embedbase.xyz) for more details.
configgy 0.1.1
A subtle config loader for python
License: MIT
Requires Python: >=3.8,<4.0
Keywords: ´config,yaml,json,toml,ini

# Configgy

A subtle Python package for loading configuration files without having to specify the file type.

## Installation

You can install the package from PyPI using pip:

```bash
pip install configgy
```

## Usage

Here's an example of how to use the package to load a configuration file:

```python
from configgy.loader import ConfigLoader

data = ConfigLoader().load_config_file("file.json")
```

The `load_config` function takes a single argument, the path to the configuration file. It will automatically detect the file type and use the appropriate loader.

Currently, the package supports the following file types:

- INI
- YAML
- TOML
- JSON