QuerySphere / ingestion /async_coordinator.py
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# DEPENDENCIES
import asyncio
from typing import Any
from typing import List
from typing import Dict
from pathlib import Path
import concurrent.futures
from typing import Optional
from datetime import datetime
from config.settings import get_settings
from config.logging_config import get_logger
from utils.error_handler import handle_errors
from utils.error_handler import ProcessingException
from ingestion.progress_tracker import get_progress_tracker
from document_parser.parser_factory import get_parser_factory
# Setup Settings and Logging
settings = get_settings()
logger = get_logger(__name__)
class AsyncCoordinator:
"""
Asynchronous document processing coordinator: Manages parallel processing of multiple documents with resource optimization
"""
def __init__(self, max_workers: Optional[int] = None):
"""
Initialize async coordinator
Arguments:
----------
max_workers { int } : Maximum parallel workers (default from settings)
"""
self.logger = logger
self.max_workers = max_workers or settings.MAX_WORKERS
self.parser_factory = get_parser_factory()
self.progress_tracker = get_progress_tracker()
# Processing statistics
self.total_processed = 0
self.total_failed = 0
self.avg_processing_time = 0.0
self.logger.info(f"Initialized AsyncCoordinator: max_workers={self.max_workers}")
@handle_errors(error_type = ProcessingException, log_error = True, reraise = True)
async def process_documents_async(self, file_paths: List[Path], progress_callback: Optional[callable] = None) -> Dict[str, Any]:
"""
Process multiple documents asynchronously with progress tracking
Arguments:
----------
file_paths { list } : List of file paths to process
progress_callback { callable } : Callback for progress updates
Returns:
--------
{ dict } : Processing results
"""
if not file_paths:
return {"processed" : 0,
"failed" : 0,
"results" : [],
}
self.logger.info(f"Starting async processing of {len(file_paths)} documents")
# Initialize progress tracking
task_id = self.progress_tracker.start_task(total_items = len(file_paths),
description = "Document processing",
)
try:
# Process files in parallel with semaphore for resource control
semaphore = asyncio.Semaphore(self.max_workers)
tasks = [self._process_single_file_async(file_path = file_path,
semaphore = semaphore,
task_id = task_id,
progress_callback = progress_callback,
) for file_path in file_paths]
results = await asyncio.gather(*tasks, return_exceptions = True)
# Process results
processed = list()
failed = list()
for file_path, result in zip(file_paths, results):
if isinstance(result, Exception):
self.logger.error(f"Failed to process {file_path}: {repr(result)}")
failed.append({"file_path": file_path, "error": str(result)})
self.total_failed += 1
else:
processed.append(result)
self.total_processed += 1
# Update statistics
self._update_statistics(processed_count = len(processed))
final_result = {"processed" : len(processed),
"failed" : len(failed),
"success_rate" : (len(processed) / len(file_paths)) * 100,
"results" : processed,
"failures" : failed,
"task_id" : task_id,
}
self.progress_tracker.complete_task(task_id)
self.logger.info(f"Async processing completed: {len(processed)} successful, {len(failed)} failed")
return final_result
except Exception as e:
self.progress_tracker.fail_task(task_id, str(e))
raise ProcessingException(f"Async processing failed: {repr(e)}")
async def _process_single_file_async(self, file_path: Path, semaphore: asyncio.Semaphore, task_id: str, progress_callback: Optional[callable] = None) -> Dict[str, Any]:
"""
Process single file asynchronously
Arguments:
----------
file_path { Path } : File to process
semaphore { Semaphore } : Resource semaphore
task_id { str } : Progress task ID
progress_callback { callable } : Progress callback
Returns:
--------
{ dict } : Processing result
"""
async with semaphore:
try:
self.logger.debug(f"Processing file: {file_path}")
# Update progress
self.progress_tracker.update_task(task_id = task_id,
current_item = file_path.name,
current_status = "parsing",
)
processed_so_far = len([t for t in self.progress_tracker.active_tasks.get(task_id, {}) if t])
self.progress_tracker.update_task(task_id = task_id, processed_items = processed_so_far)
if progress_callback:
progress_callback(self.progress_tracker.get_task_progress(task_id))
# Parse document
start_time = datetime.now()
text, metadata = await asyncio.to_thread(self.parser_factory.parse,
file_path = file_path,
extract_metadata = True,
clean_text = True,
)
processing_time = (datetime.now() - start_time).total_seconds()
# Update progress
self.progress_tracker.update_task(task_id = task_id,
current_item = file_path.name,
current_status = "completed",
)
# Handle metadata (could be Pydantic model or dict)
metadata_dict = metadata.dict() if hasattr(metadata, 'dict') else (metadata if isinstance(metadata, dict) else {})
result = {"file_path" : str(file_path),
"file_name" : file_path.name,
"text_length" : len(text),
"processing_time" : processing_time,
"metadata" : metadata_dict,
"success" : True,
}
# Increment processed items count
current_progress = self.progress_tracker.get_task_progress(task_id)
if current_progress:
self.progress_tracker.update_task(task_id = task_id,
processed_items = current_progress.processed_items + 1,
)
if progress_callback:
progress_callback(self.progress_tracker.get_task_progress(task_id))
return result
except Exception as e:
self.logger.error(f"Failed to process {file_path}: {repr(e)}")
self.progress_tracker.update_task(task_id = task_id,
current_item = file_path.name,
current_status = f"failed: {str(e)}",
)
raise ProcessingException(f"File processing failed: {repr(e)}")
def process_documents_threaded(self, file_paths: List[Path], progress_callback: Optional[callable] = None) -> Dict[str, Any]:
"""
Process documents using thread pool (alternative to async)
Arguments:
----------
file_paths { list } : List of file paths
progress_callback { callable } : Progress callback
Returns:
--------
{ dict } : Processing results
"""
self.logger.info(f"Starting threaded processing of {len(file_paths)} documents")
task_id = self.progress_tracker.start_task(total_items = len(file_paths),
description = "Threaded document processing",
)
try:
with concurrent.futures.ThreadPoolExecutor(max_workers = self.max_workers) as executor:
# Submit all tasks
future_to_file = {executor.submit(self._process_single_file_sync, file_path, task_id, progress_callback): file_path for file_path in file_paths}
results = list()
failed = list()
for future in concurrent.futures.as_completed(future_to_file):
file_path = future_to_file[future]
try:
result = future.result()
results.append(result)
self.total_processed += 1
except Exception as e:
self.logger.error(f"Failed to process {file_path}: {repr(e)}")
failed.append({"file_path": file_path, "error": str(e)})
self.total_failed += 1
final_result = {"processed" : len(results),
"failed" : len(failed),
"success_rate" : (len(results) / len(file_paths)) * 100,
"results" : results,
"failures" : failed,
"task_id" : task_id,
}
self.progress_tracker.complete_task(task_id)
self.logger.info(f"Threaded processing completed: {len(results)} successful, {len(failed)} failed")
return final_result
except Exception as e:
self.progress_tracker.fail_task(task_id, str(e))
raise ProcessingException(f"Threaded processing failed: {repr(e)}")
def _process_single_file_sync(self, file_path: Path, task_id: str, progress_callback: Optional[callable] = None) -> Dict[str, Any]:
"""
Process single file synchronously (for thread pool)
Arguments:
----------
file_path { Path } : File to process
task_id { str } : Progress task ID
progress_callback { callable } : Progress callback
Returns:
--------
{ dict } : Processing result
"""
self.logger.debug(f"Processing file (sync): {file_path}")
# Update progress
self.progress_tracker.update_task(task_id = task_id,
current_item = file_path.name,
current_status = "parsing",
)
if progress_callback:
progress_callback(self.progress_tracker.get_task_progress(task_id))
# Parse document
start_time = datetime.now()
text, metadata = self.parser_factory.parse(file_path = file_path,
extract_metadata = True,
clean_text = True,
)
processing_time = (datetime.now() - start_time).total_seconds()
# Update progress
self.progress_tracker.update_task(task_id = task_id,
current_item = file_path.name,
current_status = "completed",
)
# Handle metadata (could be Pydantic model or dict)
metadata_dict = metadata.dict() if hasattr(metadata, 'dict') else (metadata if isinstance(metadata, dict) else {})
result = {"file_path" : str(file_path),
"file_name" : file_path.name,
"text_length" : len(text),
"processing_time" : processing_time,
"metadata" : metadata_dict,
"success" : True,
}
# Increment processed items count
current_progress = self.progress_tracker.get_task_progress(task_id)
if current_progress:
self.progress_tracker.update_task(task_id = task_id,
processed_items = current_progress.processed_items + 1,
)
if progress_callback:
progress_callback(self.progress_tracker.get_task_progress(task_id))
return result
def _update_statistics(self, processed_count: int):
"""
Update processing statistics
Arguments:
----------
processed_count { int } : Number of documents processed in current batch
"""
# Update average processing time (simplified)
if (processed_count > 0):
self.avg_processing_time = (self.avg_processing_time + (processed_count * 1.0)) / 2
def get_coordinator_stats(self) -> Dict[str, Any]:
"""
Get coordinator statistics
Returns:
--------
{ dict } : Statistics dictionary
"""
return {"total_processed" : self.total_processed,
"total_failed" : self.total_failed,
"success_rate" : (self.total_processed / (self.total_processed + self.total_failed)) * 100 if (self.total_processed + self.total_failed) > 0 else 0,
"avg_processing_time" : self.avg_processing_time,
"max_workers" : self.max_workers,
"active_tasks" : self.progress_tracker.get_active_task_count(),
}
def cleanup(self):
"""
Cleanup resources
"""
self.progress_tracker.cleanup_completed_tasks()
self.logger.debug("AsyncCoordinator cleanup completed")
# Global async coordinator instance
_async_coordinator = None
def get_async_coordinator(max_workers: Optional[int] = None) -> AsyncCoordinator:
"""
Get global async coordinator instance
Arguments:
----------
max_workers { int } : Maximum workers
Returns:
--------
{ AsyncCoordinator } : AsyncCoordinator instance
"""
global _async_coordinator
if _async_coordinator is None:
_async_coordinator = AsyncCoordinator(max_workers)
return _async_coordinator
async def process_documents_async(file_paths: List[Path], **kwargs) -> Dict[str, Any]:
"""
Convenience function for async document processing
Arguments:
----------
file_paths { list } : List of file paths
**kwargs : Additional arguments
Returns:
--------
{ dict } : Processing results
"""
coordinator = get_async_coordinator()
return await coordinator.process_documents_async(file_paths, **kwargs)