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380# Shared Data Models for Equalify Reflow
This package provides type-safe Pydantic models and constants for all microservices in the Equalify Reflow system.
## Installation
The package is part of the main project and is installed when you install the project dependencies:
```bash
uv pip install -e .
```
## Package Structure
```
shared/
├── models/ # Pydantic data models
│ ├── job.py # Job tracking models (JobStatus, JobSubmission)
│ ├── pii.py # PII detection models (PIIFinding, PIIResult)
│ ├── approval.py # Approval workflow models
│ ├── processing.py # Processing result models
│ ├── queue.py # Queue payload models
│ └── redis_schema.py # Redis key generation functions
└── constants/ # Application constants
├── queues.py # Queue names and keys
├── statuses.py # Job status constants
└── redis_keys.py # Redis key utilities
```
## Usage Examples
### Job Submission
```python
from datetime import datetime
from shared.models import JobSubmission, JobStatus
# Create a new job submission
submission = JobSubmission(
job_id="550e8400-e29b-41d4-a716-446655440000",
s3_key="temp/550e8400-e29b-41d4-a716-446655440000/syllabus.pdf",
created_at=datetime.utcnow(),
file_size_bytes=2456789,
original_filename="CS101_Syllabus_Fall2024.pdf"
)
# Initialize job status
status = JobStatus(
job_id=submission.job_id,
status="pii_scanning",
created_at=submission.created_at,
updated_at=submission.created_at
)
# Serialize for Redis storage
json_data = status.model_dump_json()
```
### PII Detection
```python
from shared.models import PIIFinding, PIIResult
# Create PII findings
findings = [
PIIFinding(
entity_type="PERSON",
start=120,
end=132,
score=0.85,
text="John Student"
),
PIIFinding(
entity_type="EMAIL_ADDRESS",
start=200,
end=220,
score=0.95,
text="student@uic.edu"
)
]
# Create PII result
result = PIIResult(
job_id="550e8400-e29b-41d4-a716-446655440000",
findings=findings,
total_findings=len(findings)
)
```
### Queue Operations
```python
from datetime import datetime, timedelta
from shared.models import (
PIIQueuePayload,
ApprovalQueuePayload,
ProcessingQueuePayload
)
# Push to PII scanning queue
pii_payload = PIIQueuePayload(
job_id="550e8400-e29b-41d4-a716-446655440000",
s3_key="temp/550e8400-e29b-41d4-a716-446655440000/input.pdf",
created_at=datetime.utcnow()
)
# If PII detected, create approval payload
approval_payload = ApprovalQueuePayload(
job_id=pii_payload.job_id,
s3_key=pii_payload.s3_key,
pii_findings=findings,
approval_token="a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
expires_at=datetime.utcnow() + timedelta(days=1)
)
# After approval or clean PII scan, create processing payload
processing_payload = ProcessingQueuePayload(
job_id=pii_payload.job_id,
s3_key=pii_payload.s3_key,
approved_at=datetime.utcnow() if findings else None
)
```
### State Machine Validation
```python
from shared.models import JobStatus, VALID_TRANSITIONS
# Check valid transitions
status = JobStatus(
job_id="550e8400-e29b-41d4-a716-446655440000",
status="pii_scanning",
created_at=datetime.utcnow(),
updated_at=datetime.utcnow()
)
# Validate state transition
if status.can_transition_to("processing"):
status.status = "processing"
status.updated_at = datetime.utcnow()
# View all valid transitions
print(VALID_TRANSITIONS)
# {
# "pii_scanning": ["awaiting_approval", "processing", "failed"],
# "awaiting_approval": ["processing", "denied", "failed"],
# "processing": ["completed", "failed"],
# "completed": [],
# "failed": [],
# "denied": []
# }
```
### Redis Key Generation
```python
from shared.models import (
job_status_key,
queue_key,
timeout_key,
PII_QUEUE,
APPROVAL_QUEUE,
PROCESSING_QUEUE
)
# Generate job status key
job_id = "550e8400-e29b-41d4-a716-446655440000"
key = job_status_key(job_id)
# Returns: "eq-pdf:job:550e8400-e29b-41d4-a716-446655440000"
# Use predefined queue constants
redis.lpush(PII_QUEUE, pii_payload.model_dump_json())
redis.lpush(APPROVAL_QUEUE, approval_payload.model_dump_json())
redis.lpush(PROCESSING_QUEUE, processing_payload.model_dump_json())
```
### Constants Usage
```python
from shared.constants import (
STATUS_PII_SCANNING,
STATUS_AWAITING_APPROVAL,
STATUS_PROCESSING,
STATUS_COMPLETED,
STATUS_FAILED,
STATUS_DENIED,
TERMINAL_STATUSES,
ACTIVE_STATUSES
)
# Check if status is terminal
if status.status in TERMINAL_STATUSES:
print("Job is complete (success or failure)")
# Check if status is active
if status.status in ACTIVE_STATUSES:
print("Job is still processing")
```
## Validation Rules
### Job ID
- Must be valid UUID format: `550e8400-e29b-41d4-a716-446655440000`
### S3 Keys
- Temporary uploads must use `temp/` prefix
- Example: `temp/550e8400-e29b-41d4-a716-446655440000/document.pdf`
### File Size
- Minimum: 1 byte
- Maximum: 100MB (100,000,000 bytes)
### Confidence Score
- Range: 0.0 to 1.0
- Example: 0.87 = 87% confidence
### Approval Token
- Length: 32-64 characters
- Example: `a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6`
## State Machine
The job workflow follows this state machine:
```
pii_scanning
├── awaiting_approval → processing → completed
├── processing → completed
└── failed (terminal)
awaiting_approval
├── processing → completed
├── denied (terminal)
└── failed (terminal)
processing
├── completed (terminal)
└── failed (terminal)
```
## Redis Schema
All Redis keys use the `eq-pdf:` prefix for namespacing:
- **Job Status**: `eq-pdf:job:{job_id}` (Hash)
- **PII Queue**: `eq-pdf:queue:pii` (List)
- **Approval Queue**: `eq-pdf:queue:approval` (List)
- **Processing Queue**: `eq-pdf:queue:processing` (List)
- **Approval Timeouts**: `eq-pdf:timeouts:approval` (Sorted Set)
- **Daily Metrics**: `eq-pdf:metrics:daily` (Hash)
## Testing
Run the test suite:
```bash
uv run pytest tests/models/ -v
```
All models include comprehensive tests for:
- Validation rules
- State machine transitions
- JSON serialization/deserialization
- Redis compatibility
- Field constraints
## Type Safety
All models are fully typed and compatible with:
- MyPy static type checking
- IDE autocomplete
- Pydantic validation
Example type checking:
```python
from shared.models import JobStatus
def update_job_status(status: JobStatus) -> None:
# IDE will provide autocomplete for all fields
print(status.job_id)
print(status.status)
# Type checker will catch errors
# status.status = "invalid" # Error: Literal type violation
```
## Shared Services (PRD-003 Extensions)
### StorageService - Cleanup Operations
Added in PRD-003 completion for timeout worker support:
```python
from src.services.storage_service import StorageService
storage = StorageService(s3_client, temp_bucket, results_bucket)
# Cleanup all temp files for a specific job
deleted_count = await storage.cleanup_temp_files_for_job("job-123")
print(f"Deleted {deleted_count} temp files")
# List temp files older than 24 hours
old_files = await storage.list_temp_files(older_than_hours=24)
for file in old_files:
print(f"{file['key']} - {file['age_hours']:.1f} hours old, {file['size']} bytes")
# Delete specific S3 object (idempotent)
success = await storage.delete_from_s3("bucket", "key")
```
### QueueService - Timeout Tracking
Added in PRD-003 completion for approval timeout management:
```python
from datetime import datetime, timedelta, timezone
from src.services.queue_service import QueueService
queue = QueueService(redis_client)
# Add job to timeout tracking (sorted set)
expires_at = datetime.now(timezone.utc) + timedelta(hours=4)
await queue.add_to_timeout_tracking("job-123", expires_at)
# Get all expired approvals
expired_jobs = await queue.get_expired_timeouts()
for job_id, timestamp in expired_jobs:
print(f"Job {job_id} expired at {datetime.fromtimestamp(timestamp)}")
# Remove job from timeout tracking (after approval/denial)
removed = await queue.remove_from_timeout_tracking("job-123")
# Get count of jobs awaiting approval
count = await queue.get_timeout_count()
print(f"{count} jobs awaiting approval")
```
### JobService - Job Cleanup
Added in PRD-003 completion for job retention management:
```python
from src.services.job_service import JobService
job_service = JobService(redis_client)
# Clean up old completed/failed job
deleted = await job_service.cleanup_old_job("job-123")
if deleted:
print("Job removed from Redis")
else:
print("Job didn't exist or cleanup failed")
```
### Configuration - Cleanup & Retention Policies
Added in PRD-003 completion:
```python
from src.config import settings
# Timeout worker schedules
settings.approval_check_interval_seconds # 30 - Check timeouts every 30s
settings.temp_cleanup_interval_hours # 1 - Clean temp files hourly
settings.orphan_cleanup_interval_hours # 4 - Check for orphans every 4 hours
# Retention policies
settings.approval_timeout_hours # 4 - Approval deadline
settings.temp_file_retention_hours # 24 - Delete temp files after 24h
settings.job_retention_days # 30 - Keep jobs for 30 days
settings.max_processing_hours # 2 - Mark stuck jobs after 2h
```
## Version
Current version: 0.1.0
## License
Part of the Equalify Reflow project.