📅 2024-12-06 — Session: Optimized Email and Job Processing Automation
🕒 07:10–08:20
🏷️ Labels: Automation, Email Processing, Job Advisory, Rabbitmq, Apscheduler, AI
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to enhance the efficiency of email processing and job advisory systems through automation.
Key Activities
- Developed a scheduling and architecture plan for email processing agents to ensure efficient background processing and caching.
- Integrated and optimized workflow execution with APScheduler, including logging enhancements and concurrency management.
- Configured a team of specialized agents for job market coaching and advisory services.
- Created a comprehensive framework for job advisor assistants to parse and analyze job opportunities from ATS sheets.
- Implemented a Job Advisor Agent using RabbitMQ and MongoDB for processing and storing job opportunities.
- Designed a system prompt for AI career advisory and refined it for efficient job analysis.
- Developed a user prompt for job posting analysis with structured JSON output.
- Planned an ATS schema for effective job application management.
- Executed queue management tasks in RabbitMQ and resolved errors related to queue existence.
Achievements
- Successfully outlined and implemented strategies for email and job processing automation.
- Enhanced the integration of APScheduler for workflow management.
- Improved job advisory systems with structured agent roles and communication.
Pending Tasks
- Further testing and monitoring of the implemented systems to ensure stability and efficiency.
- Continuous refinement of prompts and schemas to adapt to evolving job market needs.
Outcome
The session resulted in a comprehensive approach to optimizing email and job processing systems, with a focus on automation, efficiency, and effective data management.