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+# 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 ```markdown ## 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.
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				# 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 ```markdown ## 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.