Trackify

Trackify is an AI-powered job application tracker for organizing job leads, scoring fit, syncing to Airtable, and storing interview notes.

Features

• Job tracking with status, AI fit score, and talking points
• Resume embedding pipeline using OpenAI embeddings
• MongoDB notes storage for interview rounds and job notes
• Redis caching for match results and resume status
• Airtable sync for tracking job records externally
• Lambda scraper for automated job board ingestion
• Local development with Docker Compose

Architecture

• api service: FastAPI backend with PostgreSQL, Redis, MongoDB
• postgres: job metadata and resume embedding vectors in PGVector
• redis: caching for job match results and resume state
• mongo: notes storage for interview notes
• lambda: EventBridge-driven scraper that posts jobs to the API

Text-based architecture:

[Resume PDF] --> S3 --> Trackify API --> PostgreSQL resume_chunks
               \                        |
                --> RAG scoring pipeline -> Redis cache
                                         \-> Airtable sync

[Job Board URLs] --> Lambda scraper --> Trackify API /jobs

[MongoDB] <-- job notes

Local setup

1. Copy the example env file:

   cp .env.example .env

2. Update .env with your credentials.

3. Build and start services:

   docker-compose up --build

4. Access the API at http://localhost:8000.

API Endpoints

Method	Path	Description
GET	/health	Health check
POST	/jobs	Create a new job and trigger RAG scoring
GET	/jobs	List jobs with optional status/min_score filters
GET	/jobs/{id}	Get job details
PATCH	/jobs/{id}/status	Update job status and sync Airtable
POST	/jobs/{id}/notes	Add a note for a job
GET	/jobs/{id}/notes	Retrieve notes for a job
POST	/resume/upload	Upload a resume PDF to S3 and start embedding
GET	/resume/status	Check resume embedding status
GET	/matches	Get top 10 jobs by AI fit score

All requests must include header X-API-Key: <API_KEY> except /health.

Lambda deployment

1. Package lambda/scraper_handler.py with dependencies.
2. Create an AWS Lambda function using Python 3.11.
3. Set environment variables: JOB_URLS, TRACKIFY_API_URL, API_KEY.
4. Configure EventBridge rule with cron expression cron(0 0 */1 * ? *) or similar for daily runs.
5. Use the lambda handler scraper_handler.lambda_handler.

n8n workflow setup

To automate job tracking and alerts with n8n:

1. Create an HTTP Request node for Trackify API endpoints.
2. Add header X-API-Key using the same key from .env.
3. Use Webhook or Schedule trigger for periodic polling.
4. Add nodes to:
   • Create or update jobs in Trackify
   • Fetch /matches and send Slack or email alerts
   • Create MongoDB notes via /jobs/{id}/notes

Environment variables

Variable	Description
DATABASE_URL	Async PostgreSQL connection string
MONGO_URI	MongoDB connection URI
REDIS_URL	Redis connection URL
AWS_ACCESS_KEY_ID	AWS API key ID
AWS_SECRET_ACCESS_KEY	AWS secret key
AWS_REGION	AWS region for S3
AWS_S3_BUCKET	S3 bucket name for resume uploads
OPENAI_API_KEY	OpenAI API key for embeddings and generation
AIRTABLE_API_KEY	Airtable API key
AIRTABLE_BASE_ID	Airtable base ID
AIRTABLE_TABLE_NAME	Airtable table name
API_KEY	API authentication key for X-API-Key header
JOB_URLS	Comma-separated job board URLs for Lambda scraper
TRACKIFY_API_URL	Trackify API base URL for Lambda scraper