semvec/pypi-packages huggingface dataset:
databaseautomation-sunny 0.0.4
A python package for connecting with database.
License: MIT
Requires Python: >=3.7

# Gemstone-Price-Prediction-End-to-End-Pipeline


# requirements_dev.txt we use for the testing

It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini

We use if for the testing in the python package testing against different version of the python

## how tox works tox enviornment creation

1. Install depedencies and packages
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox

# pyproject.toml

it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg

In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application

*types of testing*

1. Automated testing
2. Manual testing

*Mode of testing*

1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle
sfisop-dataspace-sdk 1.0.1
SDK for the SmartOcean Platform Data Space Service
License: MIT
Requires Python: <4.0.0,>=3.9

# Data Space Service Client SDK

Basic client library for accessing the SmartOcean data space service to retrieve historical time series data from data sources.

## Data Space Service REST API

The SmartOcean dataspace service provides a REST API. 

# Sample Client Code

A sample implementation of the client-side code is provided in the `main.py` file. 

```python
import datetime

from sfisop.dataspace_sdk.client import DataSpaceClient
from sfisop.dataspace_sdk.config import get_dataspace_config, get_dataspace_configfile
from sfisop.dataspace_sdk import sop_datasources as data_sources

if __name__ == '__main__':

    config_file = get_dataspace_configfile("DataSpace Configuration")
    dataspace_config = get_dataspace_config(config_file)

    client = DataSpaceClient(dataspace_config)

    response_latest = client.get_latest(data_sources.HVLVIRTUAL)
    print(response_latest.status_code)
    print(response_latest.text)

    start_time = datetime.datetime(2024, 6, 1, 0, 0, 0)
    end_time = datetime.datetime(2024, 6, 7, 0, 0, 0)

    response_period = client.get_period(data_sources.HVLVIRTUAL, start_time, end_time)
    print(response_period.status_code)
    print(response_period.text)
```

It can be executed as:

```bash
python client.py <path-to-configuration-file>
```

The code is to be used as a starting point for the implementation of the client applications.

A configuration file that can be used for the SmartOcean data space service can be found in the configs folder.

The data sources are currently available on the SmartOcean platform for demonstration purposes can be found in the `sop_datasources.py` file.

# Configuration and credentials        

The configuration file provided as on the command-line must contain the following information:

```
DATASPACE_BASE_URL: <base-url-for-dataspace-service>
DATASPACE_API_KEY_ID: DATASPACE_API_KEY
```

where the `DATASPACE_API_KEY` is the API key identifier that is contained in an `.env` file that must contain the following information:

```
DATASPACE_API_KEY = '<api-key>'
```

This indirect configuration of the API key makes it possible to share a single `.env` file for credentials across different deployments of the data space service.

The API key must be obtained from the SmartOcean data space service.
ensembleai 1.2.7
A Python package for AI-MultiModal MultiAgents methods and tools.
License: MIT
Requires Python: >=3.6

# EnsembleAI Agents

## Overview
EnsembleAI is a framework that allows multiple AI agents to collaborate and perform various tasks using different tools and models. This example demonstrates how to initialize agents with different roles, equip them with tools, and link them together in a processing pipeline using an environment.

## Installation
To use the `ensembleai` library, ensure you have it installed:
```sh
pip install ensembleai
```

## Usage
### Import Necessary Modules
```python
from ensembleai.agents import Agent
from ensembleai.environments import Environment
from ensembleai.tools import WikipediaTool, ImageAnalysisTool, WebScrapingTool, RAGTool, YouTubeTranscriptTool
from ensembleai.models import LLMModel
```

### Initialize Models and Tools
```python
# Initialize the model
model = LLMModel(name="test-model", api_key="your-api-key")

# Initialize tools
youtube_tool = YouTubeTranscriptTool(api_key="your-youtube-api-key", keyword="your-keyword")  # Add 'channel_name' for specific channel search.
wikipedia_tool = WikipediaTool(topic="your-topic")
image_tool = ImageAnalysisTool(text="your-text", urls=["your-image-urls"])
webscraper_tool = WebScrapingTool(urls=["your-urls"])
rag_tool = RAGTool(file_paths=["path/to/your/file.pdf"])
```

### Create Agents and Assign Tools
```python
agent1 = Agent(name="Agent1", model_instance=model, role="role1", work="work1")
agent1.add_tool("youtube_transcript", youtube_tool)

agent2 = Agent(name="Agent2", model_instance=model, role="role2", work="work2")
agent2.add_tool("wikipedia", wikipedia_tool)

agent3 = Agent(name="Agent3", model_instance=model, role="role3", work="work3")
agent3.add_tool("image_analysis", image_tool)

agent4 = Agent(name="Agent4", model_instance=model, role="role4", work="work4")
agent4.add_tool("web_scraping", webscraper_tool)

agent5 = Agent(name="Agent5", model_instance=model, role="role5", work="work5")
agent5.add_tool("RAG", rag_tool)
```

### Define Agent Workflow
```python
agent1.give_to(agent2)
agent2.give_to(agent3)
agent3.give_to(agent4)
agent4.give_to(agent5)
```

### Create and Start the Environment
```python
env = Environment(agents=[agent1, agent2, agent3, agent4, agent5])
env.start()
```

## How It Works
- **Agent1** uses `YouTubeTranscriptTool` to fetch transcripts based on a keyword.
- **Agent2** uses `WikipediaTool` to gather topic-related information.
- **Agent3** utilizes `ImageAnalysisTool` to analyze images.
- **Agent4** employs `WebScrapingTool` to extract web-based information.
- **Agent5** leverages `RAGTool` for retrieval-augmented generation from PDFs.
- Agents communicate sequentially, passing results to the next agent in the pipeline.

## License
This project is licensed under the MIT License.

## Contributing
Contributions are welcome! Feel free to open issues and submit pull requests.