📅 2024-12-06 — Session: Implemented and Optimized Email and Job Processing Agents
🕒 07:10–08:20
🏷️ Labels: Automation, Email Processing, Job Advisory, Rabbitmq, Apscheduler
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to implement and optimize various automation agents for email processing and job advisory tasks, ensuring efficient scheduling, execution, and error handling.
Key Activities
- Email Processing Architecture: Developed a detailed plan for scheduling and architecture of email processing agents, including components like the Email Ingestor and Gatekeeper.
- APScheduler Integration: Optimized the execution of workflows within an APScheduler setup, focusing on logging, task scheduling, and concurrency management.
- Specialized Agent Setup: Configured a team of specialized agents for job market coaching, defining roles and communication systems.
- Job Advisor Framework: Created a framework for job advisor assistants to parse job opportunities from ATS sheets, leveraging GPT for data enrichment.
- Job Advisor Agent Implementation: Integrated a Job Advisor Agent using RabbitMQ and MongoDB for processing job opportunities.
- System and User Prompts: Designed system and user prompts for AI job analysis, focusing on token efficiency and structured JSON output.
- ATS Schema Design: Planned a comprehensive schema for an Applicant Tracking System to manage job applications effectively.
- RabbitMQ Queue Management: Developed workflows for cleaning queues and re-running email distribution, and resolved a queue not found error.
Achievements
- Successfully outlined and implemented the architecture for email processing and job advisory agents.
- Enhanced the scheduling and execution of workflows using APScheduler.
- Developed a robust framework for job advisor assistants and integrated them with RabbitMQ and MongoDB.
- Designed effective system and user prompts for AI-driven job analysis.
- Created a comprehensive ATS schema for job application management.
Pending Tasks
- Further testing and refinement of the agent communication systems and error handling mechanisms.
- Continuous monitoring and optimization of the APScheduler setup for improved performance.