Automated Email Processing and Data Storage System

Here’s a detailed Product Requirements Document (PRD) based on discussion with ChatGPT. The write-up includes project objectives, architecture, and subproject details for the email parsing system using Python and PostgreSQL.

Next Steps

1. Access email via Python and fetch job posting emails.
2. Develop the Email Processing Service to parse relevant data.
3. Set up a local PostgreSQL instance and create the necessary tables.
4. Store parsed data in PostgreSQL.

1. Project Overview

1.1 Motivation

The primary goal of this project is to automate the processing of emails containing job postings, news links, or other relevant content, and store that data in a structured format. This is valuable since I can freely sign-up to many data sources (e.g. newsletters) that send to my email inbox. The system will eventually integrate with an LLM (Large Language Model) to provide summarization and insights from this data.

We aim to implement a scalable, modular architecture that can grow from a local development environment to a cloud-hosted, production-ready system.

1.2 Objectives

• Automate email parsing for specific categories like job postings.
• Store structured data (e.g., job title, company, posting date) and unstructured data (e.g., full email content, links) in a PostgreSQL database.
• Use design patterns that make the project scalable and easy to extend in the future.
• Lay the groundwork for integrating an LLM to summarize job postings or other content retrieved from emails.

3. High-Level System Design

3.1 Architecture Overview