2 Home

Miguel edited this page 2025-02-05 08:17:05 -03:00

Table of Contents

• Email to Chronological Markdown Converter
• Overview
• Purpose
• Core Functionality
• Input Processing
• Content Management
• Format Structure
• Key Features
• LLM Processing Considerations

Email to Chronological Markdown Converter

Overview

This script processes email files (.eml) into a chronological narrative in Markdown format, optimized for processing with Large Language Models (LLMs). It extracts essential information from emails while removing unnecessary metadata, creating a clean, temporal narrative that can be easily analyzed.

Purpose

The main goal is to convert email threads and nested communications into a simple, chronological text format that LLMs can effectively process to:

• Analyze communication patterns
• Extract key information
• Track project development
• Identify important decisions and their timeline
• Understand relationships and interactions between participants

Core Functionality

Input Processing

• Reads .eml files from the current directory
• Handles nested .eml files found as attachments
• Supports multiple languages (English, Italian)
• Extracts sender name and timestamp as primary identifiers

Content Management

• Maintains a single chronological file (cronologia.md)
• Removes redundant metadata and formatting
• Preserves essential content in plain text
• Converts tables to markdown format
• Links to non-email attachments
• Eliminates duplicate entries

Format Structure

## YYYYMMDDhhmmss|Sender Name

Message content in plain text...
Tables converted to markdown format...

### Attachments
- [[document1.pdf]]
- [[image1.jpg]]

---

Key Features

1. Chronological Organization

   • All messages sorted by date
   • Consistent timestamp format
   • Clear sender identification

2. Content Cleaning

   • Removes email headers
   • Eliminates signatures
   • Strips formatting
   • Preserves table structure

3. Attachment Handling

   • Creates 'attachments' folder
   • Maintains file references
   • Processes nested emails
   • Prevents duplicates

LLM Processing Considerations

The output is structured to facilitate:

1. Temporal analysis
2. Relationship mapping
3. Topic tracking
4. Decision point identification
5. Project timeline reconstruction

The consistent formatting and cleaned content allow LLMs to focus on:

• Message content analysis
• Temporal relationships
• Communication patterns
• Project development tracking
• Key information extraction

This format enables LLMs to effectively process email communications while maintaining the contextual and temporal relationships essential for understanding the narrative flow of information.