Files
gh-christianlouis-docuelevate/app/utils/step_manager.py
T

384 lines
13 KiB
Python
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
"""
Utility functions for managing file processing step status.
This module provides functions to initialize, update, and query the status
of file processing steps using the FileProcessingStep model.
"""
from datetime import datetime
from typing import Dict, List, Optional
from sqlalchemy.orm import Session
from app.config import settings
from app.models import FileProcessingStep, FileRecord
# Define the expected processing steps for a standard file workflow
# The "check_for_duplicates" step is conditionally included based on enable_deduplication setting
BASE_MAIN_PROCESSING_STEPS = [
"create_file_record",
"check_text",
"extract_text", # Or "process_with_azure_document_intelligence"
"extract_metadata_with_gpt",
"embed_metadata_into_pdf",
"finalize_document_storage",
"send_to_all_destinations",
]
OPTIONAL_PROCESSING_STEPS = {
"check_for_duplicates": settings.enable_deduplication, # Only if deduplication is enabled
}
# The terminal step is the last mandatory step in the processing pipeline.
# A file is only considered "completed" once this step has been recorded.
TERMINAL_STEP = "send_to_all_destinations"
# Combine steps based on configuration
MAIN_PROCESSING_STEPS = []
if settings.enable_deduplication:
MAIN_PROCESSING_STEPS.append("check_for_duplicates")
MAIN_PROCESSING_STEPS.extend(BASE_MAIN_PROCESSING_STEPS)
def initialize_file_steps(db: Session, file_id: int, include_uploads: bool = False) -> None:
"""
Initialize processing steps for a file with 'pending' status.
Args:
db: Database session
file_id: ID of the file
include_uploads: Whether to include upload destination steps (set after destinations are known)
"""
# Initialize main processing steps
for step_name in MAIN_PROCESSING_STEPS:
step = FileProcessingStep(file_id=file_id, step_name=step_name, status="pending")
db.add(step)
db.commit()
def add_upload_steps(db: Session, file_id: int, upload_destinations: List[str]) -> None:
"""
Add upload destination steps for a file.
Args:
db: Database session
file_id: ID of the file
upload_destinations: List of upload destination names (e.g., ["dropbox", "s3", "nextcloud"])
"""
for destination in upload_destinations:
# Add both queue and upload steps for each destination
for prefix in ["queue_", "upload_to_"]:
step_name = f"{prefix}{destination}"
# Check if step already exists
existing = (
db.query(FileProcessingStep)
.filter(FileProcessingStep.file_id == file_id, FileProcessingStep.step_name == step_name)
.first()
)
if not existing:
step = FileProcessingStep(file_id=file_id, step_name=step_name, status="pending")
db.add(step)
db.commit()
def update_step_status(
db: Session,
file_id: int,
step_name: str,
status: str,
error_message: Optional[str] = None,
started_at: Optional[datetime] = None,
completed_at: Optional[datetime] = None,
) -> None:
"""
Update the status of a processing step.
Args:
db: Database session
file_id: ID of the file
step_name: Name of the processing step
status: New status ("pending", "in_progress", "success", "failure", "skipped")
error_message: Error message if status is "failure"
started_at: When the step started (for "in_progress")
completed_at: When the step completed (for "success" or "failure")
"""
step = (
db.query(FileProcessingStep)
.filter(FileProcessingStep.file_id == file_id, FileProcessingStep.step_name == step_name)
.first()
)
if not step:
# Create the step if it doesn't exist
step = FileProcessingStep(
file_id=file_id,
step_name=step_name,
status=status,
started_at=started_at,
completed_at=completed_at,
error_message=error_message,
)
db.add(step)
else:
# Update existing step
step.status = status
if error_message is not None:
step.error_message = error_message
if started_at is not None:
step.started_at = started_at
if completed_at is not None:
step.completed_at = completed_at
db.commit()
def get_file_step_status(db: Session, file_id: int) -> Dict[str, Dict]:
"""
Get the current status of all processing steps for a file.
Args:
db: Database session
file_id: ID of the file
Returns:
Dictionary mapping step_name to status info:
{
"step_name": {
"status": "success",
"started_at": datetime,
"completed_at": datetime,
"error_message": None
},
...
}
"""
steps = db.query(FileProcessingStep).filter(FileProcessingStep.file_id == file_id).all()
result = {}
for step in steps:
result[step.step_name] = {
"status": step.status,
"started_at": step.started_at,
"completed_at": step.completed_at,
"error_message": step.error_message,
}
return result
def get_file_overall_status(db: Session, file_id: int) -> Dict:
"""
Get the overall processing status for a file based on its steps.
Only considers "real" processing steps that represent user-facing status.
Ignores diagnostic/internal steps like poll_task, upload_file, etc.
Args:
db: Database session
file_id: ID of the file
Returns:
Dictionary with overall status info:
{
"status": "completed", # "pending", "processing", "completed", "failed"
"has_errors": False,
"total_steps": 10,
"completed_steps": 8,
"failed_steps": 0,
"in_progress_steps": 2
}
"""
# If file is marked as duplicate, return duplicate status immediately
file_record = db.query(FileRecord).filter(FileRecord.id == file_id).one_or_none()
if file_record and file_record.is_duplicate:
return {
"status": "duplicate",
"has_errors": False,
"total_steps": 0,
"completed_steps": 0,
"failed_steps": 0,
"in_progress_steps": 0,
"skipped_steps": 0,
}
# Define which steps are "real" status-determining steps
# Only high-level logical steps, not implementation sub-steps
# Both process_with_ocr (current) and process_with_azure_document_intelligence (legacy)
# are included to correctly count steps for files processed before the OCR abstraction.
REAL_MAIN_STEPS = {
"create_file_record",
"check_text",
"extract_text",
"process_with_ocr",
"process_with_azure_document_intelligence",
"extract_metadata_with_gpt",
"embed_metadata_into_pdf",
"finalize_document_storage",
"send_to_all_destinations",
}
# Add check_for_duplicates if deduplication is enabled
if settings.enable_deduplication:
REAL_MAIN_STEPS.add("check_for_duplicates")
all_steps = db.query(FileProcessingStep).filter(FileProcessingStep.file_id == file_id).all()
# Filter to only real steps
steps = [
s
for s in all_steps
if s.step_name in REAL_MAIN_STEPS or s.step_name.startswith("queue_") or s.step_name.startswith("upload_to_")
]
if not steps:
return {
"status": "pending",
"has_errors": False,
"total_steps": 0,
"completed_steps": 0,
"failed_steps": 0,
"in_progress_steps": 0,
}
total_steps = len(steps)
completed_steps = sum(1 for s in steps if s.status == "success")
failed_steps = sum(1 for s in steps if s.status == "failure")
in_progress_steps = sum(1 for s in steps if s.status == "in_progress")
skipped_steps = sum(1 for s in steps if s.status == "skipped")
has_errors = failed_steps > 0
# Determine overall status
#
# The processing pipeline is not strictly linear: steps may be skipped,
# repeated, or dynamically added depending on the file (e.g. OCR is
# skipped when embedded text is found, local extraction is skipped for
# non-PDF files, dedup check may not record its result). Because of
# this, we use the terminal step as the authoritative signal that the
# pipeline finished successfully, rather than requiring every single
# intermediate step to be explicitly marked as success/skipped.
terminal_step_obj = next((s for s in steps if s.step_name == TERMINAL_STEP), None)
if has_errors:
status = "failed"
elif in_progress_steps > 0:
status = "processing"
elif completed_steps + skipped_steps == total_steps:
# All steps resolved completed only if terminal step was recorded.
if terminal_step_obj is not None:
status = "completed"
else:
status = "pending"
elif terminal_step_obj is not None and terminal_step_obj.status == "success":
# The terminal step succeeded but some intermediate steps are still
# "pending" (e.g. check_for_duplicates logged without file_id, or
# extract_text not marked when OCR path was taken). The pipeline
# is effectively complete.
status = "completed"
else:
status = "pending"
return {
"status": status,
"has_errors": has_errors,
"total_steps": total_steps,
"completed_steps": completed_steps,
"failed_steps": failed_steps,
"in_progress_steps": in_progress_steps,
"skipped_steps": skipped_steps,
}
def get_step_summary(db: Session, file_id: int) -> Dict:
"""
Get a summary of main steps vs upload steps with status counts.
Only counts "real" processing steps that represent user-facing status.
Ignores diagnostic/internal steps like poll_task, upload_file, etc.
Args:
db: Database session
file_id: ID of the file
Returns:
Dictionary with step counts:
{
"main": {"queued": 0, "in_progress": 1, "success": 7, "failure": 0, "skipped": 0},
"uploads": {"queued": 2, "in_progress": 0, "success": 4, "failure": 0, "skipped": 0},
"total_main_steps": 8,
"total_upload_tasks": 6
}
"""
# Define which steps are "real" status-determining steps
# Only high-level logical steps, not implementation sub-steps
# Both process_with_ocr (current) and process_with_azure_document_intelligence (legacy)
# are included to correctly count steps for files processed before the OCR abstraction.
REAL_MAIN_STEPS = {
"create_file_record",
"check_text",
"extract_text",
"process_with_ocr",
"process_with_azure_document_intelligence",
"extract_metadata_with_gpt",
"embed_metadata_into_pdf",
"finalize_document_storage",
"send_to_all_destinations",
}
# Add check_for_duplicates if deduplication is enabled
if settings.enable_deduplication:
REAL_MAIN_STEPS.add("check_for_duplicates")
steps = db.query(FileProcessingStep).filter(FileProcessingStep.file_id == file_id).all()
main_counts = {"queued": 0, "in_progress": 0, "success": 0, "failure": 0, "skipped": 0}
upload_counts = {"queued": 0, "in_progress": 0, "success": 0, "failure": 0, "skipped": 0}
main_steps_count = 0
upload_steps_count = 0
for step in steps:
# Normalize status
status = step.status.lower()
if status == "pending":
status = "queued"
# Check if it's an upload task (only count actual upload_to_* steps, not queue_* steps)
is_upload = step.step_name.startswith("upload_to_")
# Only count "real" steps
is_real_step = step.step_name in REAL_MAIN_STEPS or is_upload or step.step_name.startswith("queue_")
if not is_real_step:
# Skip diagnostic/internal steps like poll_task, upload_file, set_custom_fields, etc.
continue
if is_upload:
if status in upload_counts:
upload_counts[status] += 1
upload_steps_count += 1
elif step.step_name in REAL_MAIN_STEPS:
if status in main_counts:
main_counts[status] += 1
main_steps_count += 1
# Ensure the terminal step is always counted in total_main_steps.
# If the terminal step has not been recorded yet, the pipeline is not
# complete; counting it as "queued" prevents the status banner from
# showing "Completed" before the full pipeline has run.
# Note: if TERMINAL_STEP already appears in `steps`, the loop above has
# already incremented `main_steps_count` for it, so we only add here when
# the step is absent from the DB entirely.
if not any(step.step_name == TERMINAL_STEP for step in steps):
main_steps_count += 1
main_counts["queued"] += 1
return {
"main": main_counts,
"uploads": upload_counts,
"total_main_steps": main_steps_count,
"total_upload_tasks": upload_steps_count,
}