semvec/pypi-packages huggingface dataset:
phoenix-agents 0.1.20
A custom GenAI agent framework
Requires Python: >=3.13

# Phoenix Agents

A framework for building AI agents with modular connectors and LLM integration.

## Installation

```bash
pip install phoenix-agents
```

## Features

- Modular connector system for agent interactions
- Built-in support for Azure AI and OpenAI
- Chat history management
- Extensible agent architecture
- Session management for multi-user scenarios

## Quick Start


Here's a simple example using Phoenix to create a simple AI agent:

```python
from dotenv import load_dotenv
import asyncio
import os

from phoenix.agent import Agent
from phoenix.user_session import UserSession
from phoenix.models.azure_ai_inference import AzureAIInferece
import phoenix.models.openai_history as openai_history

load_dotenv()

async def main():
    # Initialize chat history
    chat_history = openai_history.ChatHistory()

    # Setup LLM with Azure AI
    llm = AzureAIInferece(
        token=os.getenv("GITHUB_TOKEN"),
        history=chat_history
    )

    # Create agent
    agent = Agent(
        brain=llm,
        history=chat_history,
    )

    # Create user session and interact
    session = UserSession()
    response = await agent.call("Hello, how are you?", session)
    print(response)

if __name__ == "__main__":
    asyncio.run(main())
```

Here's a simple example using Phoenix to create a moody AI agent with MCP connectors (The full example can be found in [Moody AI Repository](https://github.com/Nie-Mand/moody-agent)):

```python
from dotenv import load_dotenv
import asyncio
import os

from phoenix.agent import Agent
from phoenix.user_session import UserSession
from phoenix.models.azure_ai_inference import AzureAIInferece
import phoenix.models.openai_history as openai_history
from phoenix.connectors.mcp import MCPClient, MCPServer

load_dotenv()

async def main():
    # Initialize chat history
    chat_history = openai_history.ChatHistory()

    # Setup LLM with Azure AI
    llm = AzureAIInferece(
        token=os.getenv("GITHUB_TOKEN"),
        history=chat_history
    )

    # Setup MCP connector with servers
    mcp = MCPClient([
        MCPServer(path="path/to/mood.py")
    ])

    try:
        await mcp.connect()

        # Create agent
        agent = Agent(
            brain=llm,
            history=chat_history,
            connector=mcp,
            system="You are a moody AI, you need to know your current mood to know how to respond.",
        )

        # Create user session and interact
        session = UserSession()
        response = await agent.call("Hello, how are you?", session)
        print(response)

    finally:
        await mcp.cleanup()

if __name__ == "__main__":
    asyncio.run(main())
```

## Configuration

The framework uses environment variables for configuration. Create a `.env` file with:

```
GITHUB_TOKEN=your_token_here
```

## Contributing

Contributions are welcome!

## License
MIT

## Documentation
TODO
neffy 0.1.1
A Python interface of NEFFy C++ tool: NEFF Calculator and MSA File Converter

# NEFFy: NEFF Calculator and MSA File Converter
NEFFy is a versatile and efficient tool for bioinformatics research, offering advanced features for calculating NEFF (Normalized Effective Number of Sequences) for Multiple Sequence Alignments (MSA)s of any biological sequences, including protein, RNA, and DNA across various MSA formats. <br>
Additionally, NEFFy includes built-in support for format conversion, allowing users to seamlessly convert between different MSA formats.

# Python Library

Neffy also provides a python library as an interface of the executable files.

## Library Usage
### An example of neff computation:
```bash
cd example
python compute_neff.py
```

You can find more examples of using the Python library's various methods for NEFF calculations in the examples directory. For method parameters and detailed explanations, please refer to the documentation [usage guide](https://maryam-haghani.github.io/NEFFy/usage_guide.html#python_neff_main).


### An example of MSA conversion:
```bash
cd example
python convert_msa.py
```

Additional examples of using NEFFy for MSA conversion can be found in the example directory. For further detailed explanations, please refer to the documentation [usage guide](https://maryam-haghani.github.io/NEFFy/usage_guide.html#python_converter).

<br>

# Supported File Formats
- __A2M__ (aligned FASTA-like format)
- __A3M__ (compressed aligned FASTA-like format with lowercase letters for insertions)
- __FASTA__, __AFA__, __FAS__, __FST__, __FSA__ (FASTA format)
- __STO__ (Stockholm format)
- __CLUSTAL__ (CLUSTAL format)
- __ALN__ (ALN format)
- __PFAM__ (format mostly used for nucleotides)

In the [documentation](https://maryam-haghani.github.io/NEFFy/msa_formats.html), you will find a brief explanation of each format, along with an illustrative alignment example for each one.

<br>

# Error Handling
If any errors occur during the execution of the MSA Processor, an error message will be displayed, describing the issue encountered. <br/>
Please refer to the error message for troubleshooting or make necessary corrections to the input.

# License
This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE.txt) file for details.

# Citation

If you use **NEFFy** in your research, please cite the following:

**NEFFy: A Versatile Tool for Computing the Number of Effective Sequences**. Haghani, M., Bhattacharya, D., Murali, T. M. (2024).
*bioRxiv*. [https://www.biorxiv.org/content/10.1101/2024.12.01.625733v1](https://www.biorxiv.org/content/10.1101/2024.12.01.625733v1)


----------------
For further assistance, please see the [documentation](https://maryam-haghani.github.io/NEFFy/).
lib2secmail 0.6.1
Une librairie pour interagir avec l'API 2SecMail
Requires Python: >=3.6

# 📩 lib2secmail - Une bibliothèque Python pour utiliser l'API 2secmail.cloud

## 📌 Description
lib2secmail est une bibliothèque Python qui simplifie l'utilisation de l'API de 2secmail.cloud, permettant de créer et gérer des emails temporaires facilement.

## 🚀 Installation
```sh
pip install lib2secmail
```

## 🔧 Utilisation
```python
from lib2secmail import SecMail

# Récupérer les domaines disponibles
domains = SecMail.get_domains()
print(domains)

# Créer une adresse email aléatoire
random_email = SecMail.create_random_email()
print(random_email)

# Créer une adresse email personnalisée
custom_email = SecMail.create_custom_email("nomdumail")
print(custom_email)

# Récupérer les messages d'un email
messages = SecMail.get_messages(random_email)
print(messages)

# Récupérer un message spécifique
message = SecMail.get_message("id_du_message")
print(message)

# Supprimer un message
deleted = SecMail.delete_message("id_du_message")
print("Message supprimé:" if deleted else "Échec de la suppression")
```

## 📜 Méthodes disponibles
- `get_domains()`: Retourne la liste des domaines disponibles.
- `create_random_email()`: Génère un email aléatoire.
- `create_custom_email(name)`: Génère un email avec un nom personnalisé.
- `get_messages(email)`: Récupère les messages reçus pour une adresse donnée.
- `get_message(message_id)`: Récupère un message spécifique par son ID.
- `delete_message(message_id)`: Supprime un message par son ID.

## 💡 Remarque
L'API ne retourne pas toujours du JSON, donc les réponses sont traitées en texte brut.

## 📬 Contact
Si vous avez des suggestions ou des bugs à signaler, n'hésitez pas à ouvrir une issue sur GitHub !
kos-tests 0.0.1
The kos_tests project
Requires Python: >=3.11

# tests

Package of tests that run through KOS.

## Getting Started

Navigate into the root directory (the one that this README is in) and run:
```
pip install -e .
```

Now you can run any of the tests! 

To run the example waveform tests:
```
python -m kos_tests.run_tests --config configs/example_config.yaml
```
phi-masker-utils 0.1.1
Collection of Python modules for masking PHI in delimited files and Excel worksheets.
Requires Python: >=3.6
Keywords: phi_masker_utils

================
PHI Masker Utils
================


Collection of Python modules for masking PHI in delimited files and Excel worksheets.

Usage
-----

.. code-block:: python

    from phi_masker_utils import Masker

    config_file = "conf/config.yaml"
    config = yaml.safe_load(Path(config_file).read_text())

    # Tab-delimited file
    infile = "my.tsv"
    outfile = "my_masked.tsv"

    # Or comma-separated file
    infile = "my.csv"
    outfile = "my_masked.csv"

    masker = Masker(
        config=config,
        config_file=config_file,
        infile=infile,
        logfile=logfile,
        outdir=outdir,
        outfile=outfile,
        verbose=verbose,
    )

    masker.mask_phi_values()

Exported Script
---------------

.. code-block:: shell

    mask-file --infile ~/projects/phi-masker-utils/labguru_mockup.csv --outdir .
    --config_file was not specified and therefore was set to 
    '/tmp/phi-masker-utils/venv/lib/python3.10/site-packages/phi_masker_utils/conf/config.yaml'
    --logfile was not specified and therefore was set to './mask_file.log'
    --outfile was not specified and therefore was set to './labguru_mockup.csv'


=======
History
=======

0.1.0 (2024-09-15)
------------------

* First release on PyPI.