semvec/pypi-packages huggingface dataset:
teams-ai-azml-model 1.0.2
SDK focused on building AI based applications for Microsoft Teams.
Requires Python: <4.0,>=3.8
Keywords: microsoft, teams, ai, bot

# Teams AI Library

Welcome to the Teams AI Library Python package! 

This SDK is specifically designed to assist you in creating bots capable of interacting with Teams and Microsoft 365 applications. It is constructed using the [Bot Framework SDK](https://github.com/microsoft/botbuilder-python) as its foundation, simplifying the process of developing bots that interact with Teams' artificial intelligence capabilities. See the [Teams AI repo README.md](https://github.com/microsoft/teams-ai), for general information.

Requirements:
*   [Python](https://www.python.org/downloads/) (>=3.8, <4.0)
*   [Poetry](https://python-poetry.org/docs/)

## Getting Started

To get started, take a look at the [getting started docs](https://github.com/microsoft/teams-ai/blob/main/getting-started/README.md).

### Install Dependencies

```bash
$: poetry install
```

### Build

```bash
$: poetry build
```

### Test

```bash
$: poetry run test
```

### Lint

```bash
$: poetry run lint
```

## Format

```bash
$: poetry run fmt
```

## Clean

```bash
$: poetry run clean
```


## Migration

If you're migrating an existing project, switching to add on the Teams AI Library layer is quick and simple. See the [migration guide](https://github.com/microsoft/teams-ai/blob/main/getting-started/MIGRATION/PYTHON.md).
finally-data-logger 0.2.0
A simple data logging server and client.

# FinallyDataLogger

FinallyDataLogger is a simple, lightweight data logging server and client package. Log, modify, and fetch data with ease!

## Features

- Log data from any Python script easily.
- Store scalar, textual, and binary blob data (currently numpy arrays).
- Fetch and modify data based on criteria.
- Local server setup for easy data storage and retrieval.

## Installation

```bash
pip install FinallyDataLogger
```

## Quick Start

### Starting the server

From the command line:

```bash
finallydatalogger --port 5000 --dir /path/to/storage/directory
# to make it visible on the network
finallydatalogger --host 0.0.0.0 --port 5000 --dir /path/to/storage/directory
```

### Using the client

In your Python script:

```python
import numpy as np
from finally_data_logger import DataLogger

logger = DataLogger(port=5000) #  DataLogger(server_url="http://192.168.x.x", port=5000)

# Log some data
img = np.random.rand(10,10)
# a packet of data is stored in the form of a dictionary
data = {
    "name": "test1",
    "duration": 0.3,
    "image": img
}
response = logger.log_data(data)
print(response)

# Fetch data
criteria = {'name': 'test*'}
results = logger.get_data(criteria, fetch_blob=True)
print(results)


#To delete specific data based on criteria:
criteria = {'name': 'test*'}
response = logger.delete_data(criteria)
print(response)

#To reset the entire database and remove all logged data:
logger.reset_database()


```


## API

### Server Endpoints

- `POST /log_data`: Log data to the server.
- `PUT /modify_data/<entry_id>`: Modify a data entry by its ID.
- `POST /get_data`: Fetch data based on criteria.
- `POST /delete_data`: Delete data from the server based on given criteria.
- `POST /reset_database`: Reset the entire database and remove all blobs.

### Client Methods

- `log_data(data)`: Log data to the server.
- `modify_data(entry_id, modifications)`: Modify a data entry by its ID.
- `get_data(criteria)`: Fetch data based on criteria.
- `delete_data(criteria)`: Delete data from the server based on given criteria.
- `reset_database()`: Reset the entire database and remove all blobs on the server.


## Contributing

Contributions are welcome! Please read the [CONTRIBUTING](CONTRIBUTING.md) guide for more information.

## License

[MIT License](LICENSE)