melusine 3.3.2

Melusine is a high-level library for emails processing

Release 3.3 : Drop sklearn inheritance, update debug mode activation and automate backend selection

• Free software: Apache Software License 2.0
• Documentation: maif.github.io/melusine
• Installation: pip install melusine
• Tutorials: Discover melusine

Overview

Discover Melusine, a comprehensive email processing library designed to optimize your email workflow. Leverage Melusine's advanced features to achieve:

• Effortless Email Routing: Ensure emails reach their intended destinations with high accuracy.
• Smart Prioritization: Prioritize urgent emails for timely handling and efficient task management.
• Snippet Summaries: Extract relevant information from lengthy emails, saving you precious time and effort.
• Precision Filtering: Eliminate unwanted emails from your inbox, maintaining focus and reducing clutter.

Melusine facilitates the integration of deep learning frameworks (HuggingFace, Pytorch, Tensorflow, etc), deterministic rules (regex, keywords, heuristics) into a full email qualification workflow.

Why Choose Melusine ?

Features that make Melusine stand out:

• Pre-packaged Tools : Melusine comes with out-of-the-box features such as
  • Segmenting an email conversation into individual messages
  • Tagging message parts (Email body, signatures, footers, etc)
  • Transferred email handling
• Streamlined Execution : Focus on the core email qualification logic while Melusine handles the boilerplate code, providing debug mode, pipeline execution, code parallelization, and more.
• Flexible Integrations : Melusine's modular architecture enables integration with various AI frameworks, ensuring compatibility with your preferred tools.
• Production ready : Proven in the MAIF production environment, Melusine provides the robustness and stability you need.

Email Segmentation Exemple

In the following example, an email is divided into two distinct messages separated by a transition pattern. Each message is then tagged line by line. This email segmentation can later be leveraged to enhance the performance of machine learning models.

Getting started

Explore our comprehensive documentation and tested tutorials to get started. Or dive into our minimal example to experience Melusine's simplicity and power:

    from melusine.data import load_email_data
    from melusine.pipeline import MelusinePipeline

    # Load an email dataset
    df = load_email_data()

    # Load a pipeline
    pipeline = MelusinePipeline.from_config("demo_pipeline")

    # Run the pipeline
    df = pipeline.transform(df)

The code above executes a default pipeline and returns a qualified email dataset with columns such as:

• messages: List of individual messages present in each email.
• emergency_result: Flag to identify urgent emails.

With Melusine, you're well-equipped to transform your email handling, streamlining processes, maximizing efficiency, and enhancing overall productivity.