semvec/pypi-packages huggingface dataset:
aigent 0.0.1
Create, run and manage your intelligent LLM agents on dataframes.
Requires Python: >=3.9,<4.0
Keywords: nnext,agents,dataframes,engine,client

# <a href="https://nnext.ai/"><img src="https://d3g1vr8yw3euzd.cloudfront.net/nnext-ultra-wide-tingle.png" alt="NNext Python Client"></a>

## About

The NNext Python Client.

NNext is a

* ⚡ blazingly fast
* 🔍 nearest-neighbors vector search engine

<a href="https://twitter.com/intent/follow?screen_name=nnextai"><img src="https://img.shields.io/badge/Follow-nnextai-blue.svg?style=flat&logo=twitter"></a>

[Installation](#installation) |  [Quick Start](#quick-start) | [Documentation](#documentation)

## Installation

To install the pynnext client, activate a virtual environment, and install via pip:

### Supported Python Versions

```shell
Python >= 3.7, < 3.11
```

#### Mac/Linux

```shell
pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install nnext
```

#### Windows

```shell
pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install nnext
```

## Quick Start

In order start interacting with NNext, you need to obtain a client
here [https://console.nnext.ai/](https://console.nnext.ai/).

Here's a quick example showcasing how you can create an index, insert vectors/documents and search among them via NNext.

Let's begin by installing the Connecting to NNext.

```sql
SELECT images.uid,
       images.name,
       images.vector < - > 'VECTOR(0.19, 0.81, 0.75, 0.11)'::vector AS dist
FROM nnext-public-data.images.laion
ORDER BY
    dist
    LIMIT 100
```

```python
import nnext

nnclient = nnext.NNextClient(api_key="NNEXT_API_KEY")

# Perform a query.
QUERY = """
        SELECT images.uid, images.name,
          images.vector <-> 'VECTOR(0.19, 0.81, 0.75, 0.11)'::vector AS dist
        FROM nnext-public-data.images.laion
        ORDER BY
            dist
        LIMIT 100;
    """
query_job = nnclient.query(QUERY)  # API request
rows = query_job.result()  # Waits for query to finish

for row in rows:
    print(row.name)
```

## Documentation

More documentation is available here here [https://nnext.ai/docs](https://nnext.ai/docs).:

<a href="https://nnext.ai/docs" target="_blank" rel="noopener noreferrer"><img src="https://d3g1vr8yw3euzd.cloudfront.net/3.png" height="100"></a>
Ibp2py 1.0.4
SAP Data Retrieval and Processing Library for IBP
Keywords: SAP IBP API ODATA

# Ibp2py - Python library for SAP IBP

This is a Python library designed to make it easier to interact with the SAP Integrated Business Planning (IBP) API.

## Installation

You can install ibpy using pip:

```bash
`pip install ibpy
```
## Usage

To use ibpy, first create an instance of the `ibpy` class using your SAP IBP username and password:

```python
from ibpy import ibpy  
username = "YOUR_IBP_USERNAME" 
password = "YOUR_IBP_PASSWORD" 
host = 'YOUR_HOST_URL' 
connection = Ibp2py(username, password, host)
```

### Extracting Master Data

Here is an example of how to extract master data:

```python
PlanningAreaID = 'SAP01' 
VersionID = "BASE" 
MasterDataTypeID = "M3LOCATION" 
PlanningAreaDescr = "Planning" 
VersionName = "Base Line" 
select = '*' 
data = connection.masterdata(MasterDataTypeID, select=select, PlanningAreaID=PlanningAreaID, VersionID=VersionID, PlanningAreaDescr=PlanningAreaDescr, VersionName=VersionName)
```

### Extracting Telemetry Data

Here is an example of how to extract telemetry data:

```python
data = connection.telemetry('PlanningView')
```
### Extracting Key Figure Data

Here is an example of how to extract key figure data:

```python
filters="(PERIODID0_TSTAMP ge datetime'2023-04-01T00:00:00' and PERIODID0_TSTAMP lt datetime'2023-07-02T00:00:00')"+ " and CONSENSUSDEMAND gt 0" 
PlanningAreaID = 'SAP01' 
MasterData = 'PRDID,CUSTID' 
KeyFigures = 'CONSENSUSDEMAND' 
data = connection.keyfigure(PlanningAreaID, MasterData, KeyFigures,filters=filters)
```
## Contact Information

For any issues or queries related to ibpy, feel free to reach out:

- Author: Pedro Rastha
- Email: [pedrorastha@gmail.com](mailto:pedrorastha@gmail.com)
- LinkedIn: [@pedrorastha](https://www.linkedin.com/in/pedrorastha)


## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License

[Apache License 2.0](https://choosealicense.com/licenses/apache-2.0/)

**Note:** This library is open source and is not sponsored or supported by SAP.
vlclient 1.0.1
Valorant Client API Wrapper
Requires Python: >=3.11,<4.0

# vlclient

API Wrapper for VALORANT's client API

**NOTE**: This is a fork of the new inactive [valclient.py](https://github.com/colinhartigan/valclient.py)
with some edits of my taste.

## Fork changes

Skipped some configurations files I did not even understand and went straight
to using Poetry.

## Installation

Using `pip`:

```sh
pip install vlclient
```

Using [poetry](https://python-poetry.org):

```sh
poetry add vlclient
```

## Usage

```python
from vlclient.client import Client

client = Client(region="na")
client.activate()

# get MatchID of latest match
history = client.fetch_match_history(queue_id="unrated")
print(history["History"][0]["MatchID"])
```

## Documentation

Refer to [Techdoodle's documentation](https://github.com/techchrism/valorant-api-docs/tree/trunk/docs) for exact API details.

Most endpoints are implemented in this wrapper, but some are missing.
Please consider contributing.