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Better threaded PDF pipeline

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Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Christoph Auer 2025-07-16 17:01:33 +02:00
parent f98c7e21dd
commit 8c905f3e70
11 changed files with 2759 additions and 1412 deletions
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40
docling/datamodel/pipeline_options.py
View File

@ -334,39 +334,29 @@ class ProcessingPipeline(str, Enum):
ASR = "asr" ASR = "asr"
class AsyncPdfPipelineOptions(PdfPipelineOptions): class ThreadedPdfPipelineOptions(PdfPipelineOptions):
"""Enhanced options for async pipeline with cross-document batching""" """Pipeline options for the threaded PDF pipeline with batching and backpressure control"""
# GPU batching configuration - larger than sync defaults # Batch sizes for different stages
layout_batch_size: int = 64 ocr_batch_size: int = 4
ocr_batch_size: int = 32 layout_batch_size: int = 4
table_batch_size: int = 16 table_batch_size: int = 4
# Async coordination # Timing control
batch_timeout_seconds: float = 2.0 batch_timeout_seconds: float = 2.0
max_concurrent_extractions: int = 16
# Queue sizes for backpressure # Backpressure and queue control
extraction_queue_size: int = 100 queue_max_size: int = 100
model_queue_size_multiplier: float = 2.0 # queue_size = batch_size * multiplier max_workers: Optional[int] = None # None uses ThreadPoolExecutor default
# Resource management # Pipeline coordination
max_gpu_memory_mb: Optional[int] = None stage_timeout_seconds: float = 10.0 # Timeout for feeding items to stages
enable_resource_monitoring: bool = True collection_timeout_seconds: float = 5.0 # Timeout for collecting results
# Safety settings
enable_exception_isolation: bool = True
cleanup_validation: bool = True
@classmethod @classmethod
def from_sync_options( def from_sync_options(
cls, sync_options: PdfPipelineOptions cls, sync_options: PdfPipelineOptions
) -> "AsyncPdfPipelineOptions": ) -> "ThreadedPdfPipelineOptions":
"""Convert sync options to async options""" """Convert sync options to threaded options"""
# Start with sync options and override with async defaults
data = sync_options.model_dump() data = sync_options.model_dump()
# Remove sync-specific fields if any
data.pop("page_batch_size", None) # We don't use fixed page batching
return cls(**data) return cls(**data)

123
docling/document_converter.py
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@ -1,9 +1,8 @@
import asyncio
import hashlib import hashlib
import logging import logging
import sys import sys
import time import time
from collections.abc import AsyncIterable, Iterable, Iterator from collections.abc import Iterable, Iterator
from functools import partial from functools import partial
from pathlib import Path from pathlib import Path
from typing import Dict, List, Optional, Tuple, Type, Union from typing import Dict, List, Optional, Tuple, Type, Union
@ -218,29 +217,29 @@ class DocumentConverter:
@validate_call(config=ConfigDict(strict=True)) @validate_call(config=ConfigDict(strict=True))
def convert( def convert(
self, self,
source: Union[Path, str, DocumentStream], source: Union[Path, str, DocumentStream], # TODO review naming
headers: Optional[Dict[str, str]] = None, headers: Optional[Dict[str, str]] = None,
raises_on_error: bool = True, raises_on_error: bool = True,
max_num_pages: int = sys.maxsize, max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize, max_file_size: int = sys.maxsize,
page_range: PageRange = DEFAULT_PAGE_RANGE, page_range: PageRange = DEFAULT_PAGE_RANGE,
) -> ConversionResult: ) -> ConversionResult:
for result in self.convert_all( all_res = self.convert_all(
source=[source], source=[source],
headers=headers,
raises_on_error=raises_on_error, raises_on_error=raises_on_error,
max_num_pages=max_num_pages, max_num_pages=max_num_pages,
max_file_size=max_file_size, max_file_size=max_file_size,
headers=headers,
page_range=page_range, page_range=page_range,
): )
return result return next(all_res)
@validate_call(config=ConfigDict(strict=True)) @validate_call(config=ConfigDict(strict=True))
def convert_all( def convert_all(
self, self,
source: Iterable[Union[Path, str, DocumentStream]], source: Iterable[Union[Path, str, DocumentStream]], # TODO review naming
headers: Optional[Dict[str, str]] = None, headers: Optional[Dict[str, str]] = None,
raises_on_error: bool = True, raises_on_error: bool = True, # True: raises on first conversion error; False: does not raise on conv error
max_num_pages: int = sys.maxsize, max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize, max_file_size: int = sys.maxsize,
page_range: PageRange = DEFAULT_PAGE_RANGE, page_range: PageRange = DEFAULT_PAGE_RANGE,
@ -251,10 +250,7 @@ class DocumentConverter:
page_range=page_range, page_range=page_range,
) )
conv_input = _DocumentConversionInput( conv_input = _DocumentConversionInput(
path_or_stream_iterator=source, path_or_stream_iterator=source, limits=limits, headers=headers
allowed_formats=self.allowed_formats,
limits=limits,
headers=headers,
) )
conv_res_iter = self._convert(conv_input, raises_on_error=raises_on_error) conv_res_iter = self._convert(conv_input, raises_on_error=raises_on_error)
@ -276,107 +272,6 @@ class DocumentConverter:
"Conversion failed because the provided file has no recognizable format or it wasn't in the list of allowed formats." "Conversion failed because the provided file has no recognizable format or it wasn't in the list of allowed formats."
) )
async def convert_all_async(
self,
source: Iterable[Union[Path, str, DocumentStream]],
headers: Optional[Dict[str, str]] = None,
raises_on_error: bool = True,
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
page_range: PageRange = DEFAULT_PAGE_RANGE,
) -> AsyncIterable[ConversionResult]:
"""
Async version of convert_all with cross-document batching.
Yields results as they complete, not necessarily in input order.
"""
limits = DocumentLimits(
max_num_pages=max_num_pages,
max_file_size=max_file_size,
page_range=page_range,
)
conv_input = _DocumentConversionInput(
path_or_stream_iterator=source, limits=limits, headers=headers
)
# Create async document stream
async def doc_stream():
for doc in conv_input.docs(self.format_to_options):
yield doc
# Check if we have async-capable pipelines
has_async = False
for format_opt in self.format_to_options.values():
if hasattr(format_opt.pipeline_cls, "execute_stream"):
has_async = True
break
if has_async:
# Use async pipeline for cross-document batching
# For now, assume PDF pipeline handles all async processing
pdf_format_opt = self.format_to_options.get(InputFormat.PDF)
if pdf_format_opt is None:
return
pipeline_cls = pdf_format_opt.pipeline_cls
if hasattr(pipeline_cls, "execute_stream"):
# Initialize async pipeline
pipeline_options = self.format_to_options[
InputFormat.PDF
].pipeline_options
# Convert to async options if needed
from docling.datamodel.pipeline_options import AsyncPdfPipelineOptions
if not isinstance(pipeline_options, AsyncPdfPipelineOptions):
pipeline_options = AsyncPdfPipelineOptions.from_sync_options(
pipeline_options
)
pipeline = pipeline_cls(pipeline_options)
# Process all documents through async pipeline
async for result in pipeline.execute_stream(doc_stream()):
yield result
else:
# Fallback to sequential async processing
async for doc in doc_stream():
result = await asyncio.to_thread(
self._process_document, doc, raises_on_error
)
yield result
else:
# All pipelines are sync, process sequentially with threading
async for doc in doc_stream():
result = await asyncio.to_thread(
self._process_document, doc, raises_on_error
)
yield result
async def convert_async(
self,
source: Union[Path, str, DocumentStream],
headers: Optional[Dict[str, str]] = None,
raises_on_error: bool = True,
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
page_range: PageRange = DEFAULT_PAGE_RANGE,
) -> ConversionResult:
"""Async convenience method for single document conversion."""
async for result in self.convert_all_async(
[source],
headers=headers,
raises_on_error=raises_on_error,
max_num_pages=max_num_pages,
max_file_size=max_file_size,
page_range=page_range,
):
return result
# If no results were yielded, raise an error
raise RuntimeError(f"No conversion result produced for source: {source}")
def _convert( def _convert(
self, conv_input: _DocumentConversionInput, raises_on_error: bool self, conv_input: _DocumentConversionInput, raises_on_error: bool
) -> Iterator[ConversionResult]: ) -> Iterator[ConversionResult]:

54
docling/models/layout_model.py
View File

@ -148,25 +148,48 @@ class LayoutModel(BasePageModel):
def __call__( def __call__(
self, conv_res: ConversionResult, page_batch: Iterable[Page] self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]: ) -> Iterable[Page]:
for page in page_batch: # Convert to list to allow multiple iterations
pages = list(page_batch)
# Separate valid and invalid pages
valid_pages = []
valid_page_images = []
for page in pages:
assert page._backend is not None assert page._backend is not None
if not page._backend.is_valid(): if not page._backend.is_valid():
yield page continue
else:
with TimeRecorder(conv_res, "layout"):
assert page.size is not None assert page.size is not None
page_image = page.get_image(scale=1.0) page_image = page.get_image(scale=1.0)
assert page_image is not None assert page_image is not None
valid_pages.append(page)
valid_page_images.append(page_image)
# Process all valid pages with batch prediction
batch_predictions = []
if valid_page_images:
with TimeRecorder(conv_res, "layout"):
batch_predictions = self.layout_predictor.predict_batch( # type: ignore[attr-defined]
valid_page_images
)
# Process each page with its predictions
valid_page_idx = 0
for page in pages:
assert page._backend is not None
if not page._backend.is_valid():
yield page
continue
page_predictions = batch_predictions[valid_page_idx]
valid_page_idx += 1
clusters = [] clusters = []
for ix, pred_item in enumerate( for ix, pred_item in enumerate(page_predictions):
self.layout_predictor.predict(page_image)
):
label = DocItemLabel( label = DocItemLabel(
pred_item["label"] pred_item["label"].lower().replace(" ", "_").replace("-", "_")
.lower()
.replace(" ", "_")
.replace("-", "_")
) # Temporary, until docling-ibm-model uses docling-core types ) # Temporary, until docling-ibm-model uses docling-core types
cluster = Cluster( cluster = Cluster(
id=ix, id=ix,
@ -183,7 +206,6 @@ class LayoutModel(BasePageModel):
) )
# Apply postprocessing # Apply postprocessing
processed_clusters, processed_cells = LayoutPostprocessor( processed_clusters, processed_cells = LayoutPostprocessor(
page, clusters, self.options page, clusters, self.options
).postprocess() ).postprocess()
@ -202,14 +224,10 @@ class LayoutModel(BasePageModel):
) )
conv_res.confidence.pages[page.page_no].ocr_score = float( conv_res.confidence.pages[page.page_no].ocr_score = float(
np.mean( np.mean([c.confidence for c in processed_cells if c.from_ocr])
[c.confidence for c in processed_cells if c.from_ocr]
)
) )
page.predictions.layout = LayoutPrediction( page.predictions.layout = LayoutPrediction(clusters=processed_clusters)
clusters=processed_clusters
)
if settings.debug.visualize_layout: if settings.debug.visualize_layout:
self.draw_clusters_and_cells_side_by_side( self.draw_clusters_and_cells_side_by_side(

54
docling/pipeline/async_base_pipeline.py
View File

@ -1,54 +0,0 @@
import asyncio
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import AsyncIterable, Dict, Optional, Set, Tuple
from docling.backend.pdf_backend import PdfPageBackend
from docling.datamodel.base_models import Page
from docling.datamodel.document import ConversionResult, InputDocument
from docling.datamodel.pipeline_options import PipelineOptions
_log = logging.getLogger(__name__)
@dataclass
class DocumentTracker:
"""Tracks document processing state for resource management"""
doc_id: str
total_pages: int
processed_pages: int = 0
page_backends: Dict[int, PdfPageBackend] = field(
default_factory=dict
) # page_no -> backend
conv_result: Optional[ConversionResult] = None
class AsyncPipeline(ABC):
"""Base class for async pipeline implementations"""
def __init__(self, pipeline_options: PipelineOptions):
self.pipeline_options = pipeline_options
self.keep_images = False
self.keep_backend = False
@abstractmethod
async def execute_stream(
self, input_docs: AsyncIterable[InputDocument]
) -> AsyncIterable[ConversionResult]:
"""Process multiple documents with cross-document batching"""
async def execute_single(
self, in_doc: InputDocument, raises_on_error: bool = True
) -> ConversionResult:
"""Process a single document - for backward compatibility"""
async def single_doc_stream():
yield in_doc
async for result in self.execute_stream(single_doc_stream()):
return result
# Should never reach here
raise RuntimeError("No result produced for document")

433
docling/pipeline/async_standard_pdf_pipeline.py
View File

@ -1,433 +0,0 @@
import asyncio
import logging
from typing import Any, AsyncIterable, Dict, List, Optional, Tuple
from docling.datamodel.base_models import ConversionStatus, Page
from docling.datamodel.document import ConversionResult, InputDocument
from docling.datamodel.pipeline_options import AsyncPdfPipelineOptions
from docling.datamodel.settings import settings
from docling.models.base_ocr_model import BaseOcrModel
from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
from docling.models.document_picture_classifier import (
DocumentPictureClassifier,
DocumentPictureClassifierOptions,
)
from docling.models.factories import get_ocr_factory, get_picture_description_factory
from docling.models.layout_model import LayoutModel
from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
from docling.models.page_preprocessing_model import (
PagePreprocessingModel,
PagePreprocessingOptions,
)
from docling.models.readingorder_model import ReadingOrderModel, ReadingOrderOptions
from docling.models.table_structure_model import TableStructureModel
from docling.pipeline.async_base_pipeline import AsyncPipeline
from docling.pipeline.graph import GraphRunner, get_pipeline_thread_pool
from docling.pipeline.resource_manager import AsyncPageTracker
from docling.pipeline.stages import (
AggregationStage,
BatchProcessorStage,
ExtractionStage,
PageProcessorStage,
SinkStage,
SourceStage,
)
from docling.utils.profiling import ProfilingScope, TimeRecorder
_log = logging.getLogger(__name__)
class AsyncStandardPdfPipeline(AsyncPipeline):
"""
An async, graph-based pipeline for processing PDFs with cross-document batching.
"""
def __init__(self, pipeline_options: AsyncPdfPipelineOptions):
super().__init__(pipeline_options)
self.pipeline_options: AsyncPdfPipelineOptions = pipeline_options
self.page_tracker = AsyncPageTracker(
keep_images=self._should_keep_images(),
keep_backend=self._should_keep_backend(),
)
# Get shared thread pool for enrichment operations
self._thread_pool = get_pipeline_thread_pool()
self._initialize_models()
def _should_keep_images(self) -> bool:
return (
self.pipeline_options.generate_page_images
or self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
)
def _should_keep_backend(self) -> bool:
return (
self.pipeline_options.do_formula_enrichment
or self.pipeline_options.do_code_enrichment
or self.pipeline_options.do_picture_classification
or self.pipeline_options.do_picture_description
)
def _initialize_models(self):
artifacts_path = self._get_artifacts_path()
self.reading_order_model = ReadingOrderModel(options=ReadingOrderOptions())
self.preprocessing_model = PagePreprocessingModel(
options=PagePreprocessingOptions(
images_scale=self.pipeline_options.images_scale,
)
)
self.ocr_model = self._get_ocr_model(artifacts_path)
self.layout_model = LayoutModel(
artifacts_path=artifacts_path,
accelerator_options=self.pipeline_options.accelerator_options,
options=self.pipeline_options.layout_options,
)
self.table_model = TableStructureModel(
enabled=self.pipeline_options.do_table_structure,
artifacts_path=artifacts_path,
options=self.pipeline_options.table_structure_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
self.assemble_model = PageAssembleModel(options=PageAssembleOptions())
self.code_formula_model = CodeFormulaModel(
enabled=self.pipeline_options.do_code_enrichment
or self.pipeline_options.do_formula_enrichment,
artifacts_path=artifacts_path,
options=CodeFormulaModelOptions(
do_code_enrichment=self.pipeline_options.do_code_enrichment,
do_formula_enrichment=self.pipeline_options.do_formula_enrichment,
),
accelerator_options=self.pipeline_options.accelerator_options,
)
self.picture_classifier = DocumentPictureClassifier(
enabled=self.pipeline_options.do_picture_classification,
artifacts_path=artifacts_path,
options=DocumentPictureClassifierOptions(),
accelerator_options=self.pipeline_options.accelerator_options,
)
self.picture_description_model = self._get_picture_description_model(
artifacts_path
)
def _get_artifacts_path(self) -> Optional[str]:
from pathlib import Path
artifacts_path = None
if self.pipeline_options.artifacts_path is not None:
artifacts_path = Path(self.pipeline_options.artifacts_path).expanduser()
elif settings.artifacts_path is not None:
artifacts_path = Path(settings.artifacts_path).expanduser()
if artifacts_path is not None and not artifacts_path.is_dir():
raise RuntimeError(
f"The value of {artifacts_path=} is not valid. "
"When defined, it must point to a folder containing all models required by the pipeline."
)
return artifacts_path
def _get_ocr_model(self, artifacts_path: Optional[str] = None) -> BaseOcrModel:
factory = get_ocr_factory(
allow_external_plugins=self.pipeline_options.allow_external_plugins
)
return factory.create_instance(
options=self.pipeline_options.ocr_options,
enabled=self.pipeline_options.do_ocr,
artifacts_path=artifacts_path,
accelerator_options=self.pipeline_options.accelerator_options,
)
def _get_picture_description_model(self, artifacts_path: Optional[str] = None):
factory = get_picture_description_factory(
allow_external_plugins=self.pipeline_options.allow_external_plugins
)
return factory.create_instance(
options=self.pipeline_options.picture_description_options,
enabled=self.pipeline_options.do_picture_description,
enable_remote_services=self.pipeline_options.enable_remote_services,
artifacts_path=artifacts_path,
accelerator_options=self.pipeline_options.accelerator_options,
)
async def execute_stream(
self, input_docs: AsyncIterable[InputDocument]
) -> AsyncIterable[ConversionResult]:
"""Main async processing driven by a pipeline graph."""
stages = [
SourceStage("source"),
ExtractionStage(
"extractor",
self.page_tracker,
self.pipeline_options.max_concurrent_extractions,
),
PageProcessorStage("preprocessor", self.preprocessing_model),
BatchProcessorStage(
"ocr",
self.ocr_model,
self.pipeline_options.ocr_batch_size,
self.pipeline_options.batch_timeout_seconds,
),
BatchProcessorStage(
"layout",
self.layout_model,
self.pipeline_options.layout_batch_size,
self.pipeline_options.batch_timeout_seconds,
),
BatchProcessorStage(
"table",
self.table_model,
self.pipeline_options.table_batch_size,
self.pipeline_options.batch_timeout_seconds,
),
PageProcessorStage("assembler", self.assemble_model),
AggregationStage("aggregator", self.page_tracker, self._finalize_document),
SinkStage("sink"),
]
edges = [
# Main processing path
{
"from_stage": "source",
"from_output": "out",
"to_stage": "extractor",
"to_input": "in",
},
{
"from_stage": "extractor",
"from_output": "out",
"to_stage": "preprocessor",
"to_input": "in",
},
{
"from_stage": "preprocessor",
"from_output": "out",
"to_stage": "ocr",
"to_input": "in",
},
{
"from_stage": "ocr",
"from_output": "out",
"to_stage": "layout",
"to_input": "in",
},
{
"from_stage": "layout",
"from_output": "out",
"to_stage": "table",
"to_input": "in",
},
{
"from_stage": "table",
"from_output": "out",
"to_stage": "assembler",
"to_input": "in",
},
{
"from_stage": "assembler",
"from_output": "out",
"to_stage": "aggregator",
"to_input": "in",
},
# Failure path
{
"from_stage": "extractor",
"from_output": "fail",
"to_stage": "aggregator",
"to_input": "fail",
},
# Final output
{
"from_stage": "aggregator",
"from_output": "out",
"to_stage": "sink",
"to_input": "in",
},
]
runner = GraphRunner(stages, edges)
source_config = {"stage": "source", "channel": "out"}
sink_config = {"stage": "sink", "channel": "in"}
try:
async for result in runner.run(
input_docs,
source_config,
sink_config,
self.pipeline_options.extraction_queue_size,
):
yield result
except* Exception as eg:
_log.error(f"Pipeline failed with exceptions: {eg.exceptions}")
raise (eg.exceptions[0] if eg.exceptions else RuntimeError("Unknown error"))
finally:
await self.page_tracker.cleanup_all()
async def _finalize_document(self, conv_res: ConversionResult) -> None:
"""Finalize a complete document (same as StandardPdfPipeline._assemble_document)"""
# This matches the logic from StandardPdfPipeline
import warnings
import numpy as np
from docling.datamodel.base_models import AssembledUnit
all_elements = []
all_headers = []
all_body = []
with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT):
for p in conv_res.pages:
if p.assembled is not None:
for el in p.assembled.body:
all_body.append(el)
for el in p.assembled.headers:
all_headers.append(el)
for el in p.assembled.elements:
all_elements.append(el)
conv_res.assembled = AssembledUnit(
elements=all_elements, headers=all_headers, body=all_body
)
conv_res.document = self.reading_order_model(conv_res)
# Generate page images in the output
if self.pipeline_options.generate_page_images:
for page in conv_res.pages:
if page.image is not None:
page_no = page.page_no + 1
from docling_core.types.doc import ImageRef
conv_res.document.pages[page_no].image = ImageRef.from_pil(
page.image, dpi=int(72 * self.pipeline_options.images_scale)
)
# Handle picture/table images (same as StandardPdfPipeline)
self._generate_element_images(conv_res)
# Aggregate confidence values
self._aggregate_confidence(conv_res)
# Run enrichment pipeline
await self._enrich_document(conv_res)
# Set final status
conv_res.status = self._determine_status(conv_res)
def _generate_element_images(self, conv_res: ConversionResult) -> None:
"""Generate images for elements (same as StandardPdfPipeline)"""
import warnings
from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
if (
self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
):
scale = self.pipeline_options.images_scale
for element, _level in conv_res.document.iterate_items():
if not isinstance(element, DocItem) or len(element.prov) == 0:
continue
if (
isinstance(element, PictureItem)
and self.pipeline_options.generate_picture_images
) or (
isinstance(element, TableItem)
and self.pipeline_options.generate_table_images
):
page_ix = element.prov[0].page_no - 1
page = next(
(p for p in conv_res.pages if p.page_no == page_ix), None
)
if (
page is not None
and page.size is not None
and page.image is not None
):
crop_bbox = (
element.prov[0]
.bbox.scaled(scale=scale)
.to_top_left_origin(
page_height=page.size.height * scale
)
)
cropped_im = page.image.crop(crop_bbox.as_tuple())
element.image = ImageRef.from_pil(
cropped_im, dpi=int(72 * scale)
)
def _aggregate_confidence(self, conv_res: ConversionResult) -> None:
"""Aggregate confidence scores (same as StandardPdfPipeline)"""
import warnings
import numpy as np
if len(conv_res.pages) > 0:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
category=RuntimeWarning,
message="Mean of empty slice|All-NaN slice encountered",
)
conv_res.confidence.layout_score = float(
np.nanmean(
[c.layout_score for c in conv_res.confidence.pages.values()]
)
)
conv_res.confidence.parse_score = float(
np.nanquantile(
[c.parse_score for c in conv_res.confidence.pages.values()],
q=0.1,
)
)
conv_res.confidence.table_score = float(
np.nanmean(
[c.table_score for c in conv_res.confidence.pages.values()]
)
)
conv_res.confidence.ocr_score = float(
np.nanmean(
[c.ocr_score for c in conv_res.confidence.pages.values()]
)
)
async def _enrich_document(self, conv_res: ConversionResult) -> None:
"""Run enrichment models on document"""
# Run enrichment models (same as base pipeline but async)
from docling.utils.utils import chunkify
enrichment_models = [
self.code_formula_model,
self.picture_classifier,
self.picture_description_model,
]
for model in enrichment_models:
if model is None or not getattr(model, "enabled", True):
continue
# Prepare elements
elements_to_process = []
for doc_element, _level in conv_res.document.iterate_items():
prepared = model.prepare_element(conv_res=conv_res, element=doc_element)
if prepared is not None:
elements_to_process.append(prepared)
# Process in batches
for element_batch in chunkify(
elements_to_process, model.elements_batch_size
):
# Run model in shared thread pool to avoid blocking
await asyncio.get_running_loop().run_in_executor(
self._thread_pool,
lambda: list(model(conv_res.document, element_batch)),
)
def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus:
"""Determine conversion status"""
# Simple implementation - could be enhanced
if conv_res.pages and conv_res.document:
return ConversionStatus.SUCCESS
else:
return ConversionStatus.FAILURE

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docling/pipeline/graph.py
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from __future__ import annotations
import asyncio
import weakref
from abc import ABC, abstractmethod
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from typing import Any, AsyncIterable, Dict, List, Literal, Optional
# Sentinel to signal stream completion
STOP_SENTINEL = object()
# Global thread pool for pipeline operations - shared across all stages
_PIPELINE_THREAD_POOL: Optional[ThreadPoolExecutor] = None
_THREAD_POOL_REFS = weakref.WeakSet()
def get_pipeline_thread_pool(max_workers: Optional[int] = None) -> ThreadPoolExecutor:
"""Get or create the shared pipeline thread pool."""
global _PIPELINE_THREAD_POOL
if _PIPELINE_THREAD_POOL is None or _PIPELINE_THREAD_POOL._shutdown:
_PIPELINE_THREAD_POOL = ThreadPoolExecutor(
max_workers=max_workers, thread_name_prefix="docling_pipeline"
)
_THREAD_POOL_REFS.add(_PIPELINE_THREAD_POOL)
return _PIPELINE_THREAD_POOL
def shutdown_pipeline_thread_pool(wait: bool = True) -> None:
"""Shutdown the shared thread pool."""
global _PIPELINE_THREAD_POOL
if _PIPELINE_THREAD_POOL is not None:
_PIPELINE_THREAD_POOL.shutdown(wait=wait)
_PIPELINE_THREAD_POOL = None
@dataclass(slots=True)
class StreamItem:
"""
A wrapper for data flowing through the pipeline, maintaining a link
to the original conversion result context.
"""
payload: Any
conv_res_id: int
conv_res: Any # Opaque reference to ConversionResult
class PipelineStage(ABC):
"""A single, encapsulated step in a processing pipeline graph."""
def __init__(self, name: str, max_workers: Optional[int] = None):
self.name = name
self.input_queues: Dict[str, asyncio.Queue] = {}
self.output_queues: Dict[str, List[asyncio.Queue]] = {}
self._loop: Optional[asyncio.AbstractEventLoop] = None
self._thread_pool = get_pipeline_thread_pool(max_workers)
@abstractmethod
async def run(self) -> None:
"""
The core execution logic for the stage. This method is responsible for
consuming from input queues, processing data, and putting results into
output queues.
"""
async def _send_to_outputs(self, channel: str, items: List[StreamItem] | List[Any]):
"""Helper to send processed items to all connected output queues."""
if channel in self.output_queues:
for queue in self.output_queues[channel]:
for item in items:
await queue.put(item)
async def _signal_downstream_completion(self):
"""Signal that this stage is done processing to all output channels."""
for channel_queues in self.output_queues.values():
for queue in channel_queues:
await queue.put(STOP_SENTINEL)
@property
def loop(self) -> asyncio.AbstractEventLoop:
if self._loop is None:
self._loop = asyncio.get_running_loop()
return self._loop
@property
def thread_pool(self) -> ThreadPoolExecutor:
"""Get the shared thread pool for this stage."""
return self._thread_pool
class GraphRunner:
"""Connects stages and runs the pipeline graph."""
def __init__(self, stages: List[PipelineStage], edges: List[Dict[str, str]]):
self._stages = {s.name: s for s in stages}
self._edges = edges
def _wire_graph(self, queue_max_size: int):
"""Create queues for edges and connect them to stage inputs and outputs."""
for edge in self._edges:
from_stage, from_output = edge["from_stage"], edge["from_output"]
to_stage, to_input = edge["to_stage"], edge["to_input"]
queue = asyncio.Queue(maxsize=queue_max_size)
# Connect to source stage's output
self._stages[from_stage].output_queues.setdefault(from_output, []).append(
queue
)
# Connect to destination stage's input
self._stages[to_stage].input_queues[to_input] = queue
async def _run_source(
self,
source_stream: AsyncIterable[Any],
source_stage: str,
source_channel: str,
):
"""Feed the graph from an external async iterable."""
output_queues = self._stages[source_stage].output_queues.get(source_channel, [])
async for item in source_stream:
for queue in output_queues:
await queue.put(item)
# Signal completion to all downstream queues
for queue in output_queues:
await queue.put(STOP_SENTINEL)
async def _run_sink(self, sink_stage: str, sink_channel: str) -> AsyncIterable[Any]:
"""Yield results from the graph's final output queue."""
queue = self._stages[sink_stage].input_queues[sink_channel]
while True:
item = await queue.get()
if item is STOP_SENTINEL:
break
yield item
await queue.put(STOP_SENTINEL) # Allow other sinks to terminate
async def run(
self,
source_stream: AsyncIterable,
source_config: Dict[str, str],
sink_config: Dict[str, str],
queue_max_size: int = 32,
) -> AsyncIterable:
"""
Executes the entire pipeline graph.
Args:
source_stream: The initial async iterable to feed the graph.
source_config: Dictionary with "stage" and "channel" for the entry point.
sink_config: Dictionary with "stage" and "channel" for the exit point.
queue_max_size: The max size for the internal asyncio.Queues.
"""
self._wire_graph(queue_max_size)
try:
async with asyncio.TaskGroup() as tg:
# Create a task for the source feeder
tg.create_task(
self._run_source(
source_stream, source_config["stage"], source_config["channel"]
)
)
# Create tasks for all pipeline stages
for stage in self._stages.values():
tg.create_task(stage.run())
# Yield results from the sink
async for result in self._run_sink(
sink_config["stage"], sink_config["channel"]
):
yield result
finally:
# Ensure thread pool cleanup on pipeline completion
# Note: We don't shutdown here as other pipelines might be using it
pass

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docling/pipeline/resource_manager.py
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@ -1,101 +0,0 @@
import asyncio
import logging
import uuid
from dataclasses import dataclass, field
from typing import Dict, Optional
from docling.datamodel.base_models import Page
from docling.datamodel.document import ConversionResult
from docling.pipeline.async_base_pipeline import DocumentTracker
from docling.pipeline.graph import get_pipeline_thread_pool
_log = logging.getLogger(__name__)
@dataclass
class AsyncPageTracker:
"""Manages page backend lifecycle across documents"""
_doc_trackers: Dict[str, DocumentTracker] = field(default_factory=dict)
_lock: asyncio.Lock = field(default_factory=asyncio.Lock)
keep_images: bool = False
keep_backend: bool = False
def __post_init__(self):
"""Initialize shared thread pool reference after dataclass creation"""
self._thread_pool = get_pipeline_thread_pool()
async def register_document(
self, conv_res: ConversionResult, total_pages: int
) -> str:
"""Register a new document for tracking"""
async with self._lock:
# Use UUID for better collision resistance than str(id())
doc_id = str(uuid.uuid4())
self._doc_trackers[doc_id] = DocumentTracker(
doc_id=doc_id, total_pages=total_pages, conv_result=conv_res
)
# Store the doc_id in the conv_res for later lookup
conv_res._async_doc_id = doc_id
return doc_id
async def track_page_loaded(self, page: Page, conv_res: ConversionResult) -> None:
"""Track when a page backend is loaded"""
async with self._lock:
doc_id = getattr(conv_res, "_async_doc_id", None)
if doc_id and doc_id in self._doc_trackers and page._backend is not None:
self._doc_trackers[doc_id].page_backends[page.page_no] = page._backend
async def track_page_completion(
self, page: Page, conv_res: ConversionResult
) -> bool:
"""Track page completion and cleanup when all pages done"""
async with self._lock:
doc_id = getattr(conv_res, "_async_doc_id", None)
if not doc_id or doc_id not in self._doc_trackers:
_log.warning(f"Document {doc_id} not registered for tracking")
return False
tracker = self._doc_trackers[doc_id]
tracker.processed_pages += 1
# Clear this page's image cache if needed
if not self.keep_images:
page._image_cache = {}
# If all pages from this document are processed, cleanup
if tracker.processed_pages == tracker.total_pages:
await self._cleanup_document_resources(tracker)
del self._doc_trackers[doc_id]
# Clean up the doc_id from conv_res
if hasattr(conv_res, "_async_doc_id"):
delattr(conv_res, "_async_doc_id")
return True # Document is complete
return False # Document is not yet complete
async def _cleanup_document_resources(self, tracker: DocumentTracker) -> None:
"""Cleanup all resources for a completed document"""
if not self.keep_backend:
# Unload all page backends for this document
for page_no, backend in tracker.page_backends.items():
if backend is not None:
try:
# Run unload in shared thread pool to avoid blocking
await asyncio.get_running_loop().run_in_executor(
self._thread_pool, backend.unload
)
except Exception as e:
_log.warning(
f"Failed to unload backend for page {page_no}: {e}"
)
tracker.page_backends.clear()
_log.debug(f"Cleaned up resources for document {tracker.doc_id}")
async def cleanup_all(self) -> None:
"""Cleanup all tracked documents - for shutdown"""
async with self._lock:
for tracker in self._doc_trackers.values():
await self._cleanup_document_resources(tracker)
self._doc_trackers.clear()

300
docling/pipeline/stages.py
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@ -1,300 +0,0 @@
from __future__ import annotations
import asyncio
import logging
import time
from collections import defaultdict
from typing import Any, AsyncIterable, Callable, Coroutine, Dict, List
from docling.datamodel.document import ConversionResult, InputDocument, Page
from docling.pipeline.graph import STOP_SENTINEL, PipelineStage, StreamItem
_log = logging.getLogger(__name__)
class SourceStage(PipelineStage):
"""A placeholder stage to represent the entry point of the graph."""
async def run(self) -> None:
# This stage is driven by the GraphRunner's _run_source method
# and does not have its own execution loop.
pass
class SinkStage(PipelineStage):
"""A placeholder stage to represent the exit point of the graph."""
async def run(self) -> None:
# This stage is read by the GraphRunner's _run_sink method
# and does not have its own execution loop.
pass
class ExtractionStage(PipelineStage):
"""Extracts pages from documents and tracks them."""
def __init__(
self,
name: str,
page_tracker: Any,
max_concurrent_extractions: int,
):
super().__init__(name)
self.page_tracker = page_tracker
self.semaphore = asyncio.Semaphore(max_concurrent_extractions)
self.input_channel = "in"
self.output_channel = "out"
self.failure_channel = "fail"
async def _extract_page(
self, page_no: int, conv_res: ConversionResult
) -> StreamItem | None:
"""Coroutine to extract a single page."""
try:
async with self.semaphore:
page = Page(page_no=page_no)
conv_res.pages.append(page)
page._backend = await self.loop.run_in_executor(
self.thread_pool, conv_res.input._backend.load_page, page_no
)
if page._backend and page._backend.is_valid():
page.size = page._backend.get_size()
await self.page_tracker.track_page_loaded(page, conv_res)
return StreamItem(
payload=page, conv_res_id=id(conv_res), conv_res=conv_res
)
else:
_log.warning(
f"Failed to load or validate page {page_no} from document {conv_res.input.file.name}"
)
return None
except Exception as e:
_log.error(
f"Error extracting page {page_no} from document {conv_res.input.file.name}: {e}",
exc_info=True,
)
# Don't propagate individual page failures - document-level error handling will catch this
return None
async def _process_document(self, in_doc: InputDocument):
"""Processes a single document, extracting all its pages."""
conv_res = ConversionResult(input=in_doc)
try:
from docling.backend.pdf_backend import PdfDocumentBackend
if not isinstance(in_doc._backend, PdfDocumentBackend):
raise TypeError("Backend is not a valid PdfDocumentBackend")
total_pages = in_doc.page_count
await self.page_tracker.register_document(conv_res, total_pages)
start_page, end_page = conv_res.input.limits.page_range
page_indices_to_extract = [
i for i in range(total_pages) if (start_page - 1) <= i <= (end_page - 1)
]
tasks = [
self.loop.create_task(self._extract_page(i, conv_res))
for i in page_indices_to_extract
]
pages_extracted = await asyncio.gather(*tasks, return_exceptions=True)
# Filter out None results and exceptions, log any exceptions found
valid_pages = []
for i, result in enumerate(pages_extracted):
if isinstance(result, Exception):
_log.error(
f"Page extraction failed for page {page_indices_to_extract[i]} "
f"in document {in_doc.file.name}: {result}"
)
elif result is not None:
valid_pages.append(result)
await self._send_to_outputs(self.output_channel, valid_pages)
# If no pages were successfully extracted, mark as failure
if not valid_pages:
_log.error(
f"No pages could be extracted from document {in_doc.file.name}"
)
conv_res.status = "FAILURE"
await self._send_to_outputs(self.failure_channel, [conv_res])
except Exception as e:
_log.error(
f"Document-level extraction failed for {in_doc.file.name}: {e}",
exc_info=True,
)
conv_res.status = "FAILURE"
await self._send_to_outputs(self.failure_channel, [conv_res])
async def run(self) -> None:
"""Main loop to consume documents and launch extraction tasks."""
q_in = self.input_queues[self.input_channel]
while True:
doc = await q_in.get()
if doc is STOP_SENTINEL:
await self._signal_downstream_completion()
break
await self._process_document(doc)
class PageProcessorStage(PipelineStage):
"""Applies a synchronous, 1-to-1 processing function to each page."""
def __init__(self, name: str, model: Any):
super().__init__(name)
self.model = model
self.input_channel = "in"
self.output_channel = "out"
async def run(self) -> None:
q_in = self.input_queues[self.input_channel]
while True:
item = await q_in.get()
if item is STOP_SENTINEL:
await self._signal_downstream_completion()
break
# The model call is sync, run in thread to avoid blocking event loop
processed_page = await self.loop.run_in_executor(
self.thread_pool,
lambda: next(iter(self.model(item.conv_res, [item.payload]))),
)
item.payload = processed_page
await self._send_to_outputs(self.output_channel, [item])
class BatchProcessorStage(PipelineStage):
"""Batches items and applies a synchronous model to the batch."""
def __init__(
self,
name: str,
model: Any,
batch_size: int,
batch_timeout: float,
):
super().__init__(name)
self.model = model
self.batch_size = batch_size
self.batch_timeout = batch_timeout
self.input_channel = "in"
self.output_channel = "out"
async def _collect_batch(self, q_in: asyncio.Queue) -> List[StreamItem] | None:
"""Collects a batch of items from the input queue with a timeout."""
try:
# Wait for the first item without a timeout
first_item = await q_in.get()
if first_item is STOP_SENTINEL:
return None # End of stream
except asyncio.CancelledError:
return None
batch = [first_item]
start_time = self.loop.time()
while len(batch) < self.batch_size:
timeout = self.batch_timeout - (self.loop.time() - start_time)
if timeout <= 0:
break
try:
item = await asyncio.wait_for(q_in.get(), timeout)
if item is STOP_SENTINEL:
# Put sentinel back for other potential consumers or the main loop
await q_in.put(STOP_SENTINEL)
break
batch.append(item)
except asyncio.TimeoutError:
break # Batching timeout reached
return batch
async def run(self) -> None:
q_in = self.input_queues[self.input_channel]
while True:
batch = await self._collect_batch(q_in)
if not batch: # This can be None or an empty list.
await self._signal_downstream_completion()
break
# Group pages by their original ConversionResult
grouped_by_doc = defaultdict(list)
for item in batch:
grouped_by_doc[item.conv_res_id].append(item)
processed_items = []
for conv_res_id, items in grouped_by_doc.items():
conv_res = items[0].conv_res
pages = [item.payload for item in items]
# The model call is sync, run in thread
processed_pages = await self.loop.run_in_executor(
self.thread_pool, lambda: list(self.model(conv_res, pages))
)
# Re-wrap the processed pages into StreamItems
for i, page in enumerate(processed_pages):
processed_items.append(
StreamItem(
payload=page,
conv_res_id=items[i].conv_res_id,
conv_res=items[i].conv_res,
)
)
await self._send_to_outputs(self.output_channel, processed_items)
class AggregationStage(PipelineStage):
"""Aggregates processed pages back into completed documents."""
def __init__(
self,
name: str,
page_tracker: Any,
finalizer_func: Callable[[ConversionResult], Coroutine],
):
super().__init__(name)
self.page_tracker = page_tracker
self.finalizer_func = finalizer_func
self.success_channel = "in"
self.failure_channel = "fail"
self.output_channel = "out"
async def run(self) -> None:
success_q = self.input_queues[self.success_channel]
failure_q = self.input_queues.get(self.failure_channel)
async def handle_successes():
while True:
item = await success_q.get()
if item is STOP_SENTINEL:
break
is_doc_complete = await self.page_tracker.track_page_completion(
item.payload, item.conv_res
)
if is_doc_complete:
await self.finalizer_func(item.conv_res)
await self._send_to_outputs(self.output_channel, [item.conv_res])
async def handle_failures():
if failure_q is None:
return # No failure channel, nothing to do
while True:
failed_res = await failure_q.get()
if failed_res is STOP_SENTINEL:
break
await self._send_to_outputs(self.output_channel, [failed_res])
# Create tasks only for channels that exist
tasks = [handle_successes()]
if failure_q is not None:
tasks.append(handle_failures())
await asyncio.gather(*tasks)
await self._signal_downstream_completion()

835
docling/pipeline/threaded_standard_pdf_pipeline.py Normal file
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import logging
import threading
import time
import warnings
import weakref
from collections import defaultdict, deque
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Protocol, Tuple, Union, cast
import numpy as np
from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem
from docling.backend.pdf_backend import PdfDocumentBackend
from docling.datamodel.base_models import AssembledUnit, ConversionStatus, Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import ThreadedPdfPipelineOptions
from docling.datamodel.settings import settings
from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
from docling.models.document_picture_classifier import (
DocumentPictureClassifier,
DocumentPictureClassifierOptions,
)
from docling.models.factories import get_ocr_factory, get_picture_description_factory
from docling.models.layout_model import LayoutModel
from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
from docling.models.page_preprocessing_model import (
PagePreprocessingModel,
PagePreprocessingOptions,
)
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
from docling.models.readingorder_model import ReadingOrderModel, ReadingOrderOptions
from docling.models.table_structure_model import TableStructureModel
from docling.pipeline.base_pipeline import BasePipeline
from docling.utils.profiling import ProfilingScope, TimeRecorder
from docling.utils.utils import chunkify
_log = logging.getLogger(__name__)
@dataclass
class ThreadedItem:
"""Item flowing through the threaded pipeline with document context"""
payload: Page
conv_res_id: int
conv_res: ConversionResult
page_no: int = -1
error: Optional[Exception] = None
is_failed: bool = False
def __post_init__(self):
"""Ensure proper initialization of page number"""
if self.page_no == -1 and isinstance(self.payload, Page):
self.page_no = self.payload.page_no
@dataclass
class ProcessingResult:
"""Result of processing with error tracking for partial results"""
pages: List[Page] = field(default_factory=list)
failed_pages: List[Tuple[int, Exception]] = field(default_factory=list)
total_expected: int = 0
@property
def success_count(self) -> int:
return len(self.pages)
@property
def failure_count(self) -> int:
return len(self.failed_pages)
@property
def is_partial_success(self) -> bool:
return self.success_count > 0 and self.failure_count > 0
@property
def is_complete_failure(self) -> bool:
return self.success_count == 0 and self.failure_count > 0
@dataclass
class ThreadedQueue:
"""Thread-safe queue with backpressure control and memory management"""
max_size: int = 100
items: deque = field(default_factory=deque)
lock: threading.Lock = field(default_factory=threading.Lock)
not_full: threading.Condition = field(init=False)
not_empty: threading.Condition = field(init=False)
closed: bool = False
def __post_init__(self):
self.not_full = threading.Condition(self.lock)
self.not_empty = threading.Condition(self.lock)
def put(self, item: ThreadedItem, timeout: Optional[float] = None) -> bool:
"""Put item with backpressure control"""
with self.not_full:
if self.closed:
return False
start_time = time.time()
while len(self.items) >= self.max_size and not self.closed:
if timeout is not None:
remaining = timeout - (time.time() - start_time)
if remaining <= 0:
return False
self.not_full.wait(remaining)
else:
self.not_full.wait()
if self.closed:
return False
self.items.append(item)
self.not_empty.notify()
return True
def get_batch(
self, batch_size: int, timeout: Optional[float] = None
) -> List[ThreadedItem]:
"""Get a batch of items"""
with self.not_empty:
start_time = time.time()
# Wait for at least one item
while len(self.items) == 0 and not self.closed:
if timeout is not None:
remaining = timeout - (time.time() - start_time)
if remaining <= 0:
return []
self.not_empty.wait(remaining)
else:
self.not_empty.wait()
# Collect batch
batch: List[ThreadedItem] = []
while len(batch) < batch_size and len(self.items) > 0:
batch.append(self.items.popleft())
if batch:
self.not_full.notify_all()
return batch
def close(self):
"""Close the queue and wake up waiting threads"""
with self.lock:
self.closed = True
self.not_empty.notify_all()
self.not_full.notify_all()
def is_empty(self) -> bool:
with self.lock:
return len(self.items) == 0
def size(self) -> int:
with self.lock:
return len(self.items)
def cleanup(self):
"""Clean up resources and clear items"""
with self.lock:
self.items.clear()
self.closed = True
class ThreadedPipelineStage:
"""A pipeline stage that processes items using dedicated threads"""
def __init__(
self,
name: str,
model: Any,
batch_size: int,
batch_timeout: float,
queue_max_size: int,
):
self.name = name
self.model = model
self.batch_size = batch_size
self.batch_timeout = batch_timeout
self.input_queue = ThreadedQueue(max_size=queue_max_size)
self.output_queues: List[ThreadedQueue] = []
self.running = False
self.thread: Optional[threading.Thread] = None
def add_output_queue(self, queue: ThreadedQueue):
"""Connect this stage to an output queue"""
self.output_queues.append(queue)
def start(self):
"""Start the stage processing thread"""
if not self.running:
self.running = True
self.thread = threading.Thread(target=self._run, name=f"Stage-{self.name}")
self.thread.daemon = False # Ensure proper shutdown
self.thread.start()
def stop(self):
"""Stop the stage processing"""
self.running = False
self.input_queue.close()
if self.thread:
self.thread.join(timeout=30.0) # Reasonable timeout for shutdown
if self.thread.is_alive():
_log.warning(f"Stage {self.name} thread did not shutdown gracefully")
def _run(self):
"""Main processing loop for the stage"""
try:
while self.running:
batch = self.input_queue.get_batch(
self.batch_size, timeout=self.batch_timeout
)
if not batch and self.input_queue.closed:
break
if batch:
try:
processed_items = self._process_batch(batch)
self._send_to_outputs(processed_items)
except Exception as e:
_log.error(f"Error in stage {self.name}: {e}", exc_info=True)
# Send failed items downstream for partial processing
failed_items = []
for item in batch:
item.is_failed = True
item.error = e
failed_items.append(item)
self._send_to_outputs(failed_items)
except Exception as e:
_log.error(f"Fatal error in stage {self.name}: {e}", exc_info=True)
finally:
# Close output queues when done
for queue in self.output_queues:
queue.close()
def _process_batch(self, batch: List[ThreadedItem]) -> List[ThreadedItem]:
"""Process a batch through the model with error handling"""
# Group by document to maintain document integrity
grouped_by_doc = defaultdict(list)
for item in batch:
grouped_by_doc[item.conv_res_id].append(item)
processed_items = []
for conv_res_id, items in grouped_by_doc.items():
try:
# Filter out already failed items
valid_items = [item for item in items if not item.is_failed]
failed_items = [item for item in items if item.is_failed]
if valid_items:
conv_res = valid_items[0].conv_res
pages = [item.payload for item in valid_items]
# Process through model
processed_pages = list(self.model(conv_res, pages))
# Re-wrap processed pages
for i, page in enumerate(processed_pages):
processed_items.append(
ThreadedItem(
payload=page,
conv_res_id=valid_items[i].conv_res_id,
conv_res=valid_items[i].conv_res,
page_no=valid_items[i].page_no,
)
)
# Pass through failed items for downstream handling
processed_items.extend(failed_items)
except Exception as e:
_log.error(f"Model {self.name} failed for document {conv_res_id}: {e}")
# Mark all items as failed but continue processing
for item in items:
item.is_failed = True
item.error = e
processed_items.append(item)
return processed_items
def _send_to_outputs(self, items: List[ThreadedItem]):
"""Send processed items to output queues"""
for item in items:
for queue in self.output_queues:
# Use timeout to prevent blocking indefinitely
if not queue.put(item, timeout=5.0):
_log.warning(
f"Failed to send item from {self.name} due to backpressure"
)
def cleanup(self):
"""Clean up stage resources"""
if self.input_queue:
self.input_queue.cleanup()
for queue in self.output_queues:
queue.cleanup()
class ThreadedStandardPdfPipeline(BasePipeline):
"""
A threaded pipeline implementation that processes pages through
dedicated stage threads with batching and backpressure control.
"""
def __init__(self, pipeline_options: ThreadedPdfPipelineOptions):
super().__init__(pipeline_options)
self.pipeline_options: ThreadedPdfPipelineOptions = pipeline_options
# Initialize attributes with proper type annotations
self.keep_backend: bool = False
self.keep_images: bool = False
# Model attributes - will be initialized in _initialize_models
self.preprocessing_model: PagePreprocessingModel
self.ocr_model: Any # OCR models have different base types from factory
self.layout_model: LayoutModel
self.table_model: TableStructureModel
self.assemble_model: PageAssembleModel
self.reading_order_model: ReadingOrderModel
self._initialize_models()
self._setup_pipeline()
# Use weak references for memory management
self._document_tracker: weakref.WeakValueDictionary[int, ConversionResult] = (
weakref.WeakValueDictionary()
)
self._document_lock = threading.Lock()
def _get_artifacts_path(self) -> Optional[Path]:
"""Get artifacts path from options or settings"""
artifacts_path = None
if self.pipeline_options.artifacts_path is not None:
artifacts_path = Path(self.pipeline_options.artifacts_path).expanduser()
elif settings.artifacts_path is not None:
artifacts_path = Path(settings.artifacts_path).expanduser()
if artifacts_path is not None and not artifacts_path.is_dir():
raise RuntimeError(
f"The value of {artifacts_path=} is not valid. "
"When defined, it must point to a folder containing all models required by the pipeline."
)
return artifacts_path
def _get_ocr_model(self, artifacts_path: Optional[Path] = None):
"""Get OCR model instance"""
factory = get_ocr_factory(
allow_external_plugins=self.pipeline_options.allow_external_plugins
)
return factory.create_instance(
options=self.pipeline_options.ocr_options,
enabled=self.pipeline_options.do_ocr,
artifacts_path=artifacts_path,
accelerator_options=self.pipeline_options.accelerator_options,
)
def _get_picture_description_model(self, artifacts_path: Optional[Path] = None):
"""Get picture description model instance"""
factory = get_picture_description_factory(
allow_external_plugins=self.pipeline_options.allow_external_plugins
)
return factory.create_instance(
options=self.pipeline_options.picture_description_options,
enabled=self.pipeline_options.do_picture_description,
enable_remote_services=self.pipeline_options.enable_remote_services,
artifacts_path=artifacts_path,
accelerator_options=self.pipeline_options.accelerator_options,
)
def _initialize_models(self):
"""Initialize all pipeline models"""
artifacts_path = self._get_artifacts_path()
# Check if we need to keep images for processing
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
self.keep_images = (
self.pipeline_options.generate_page_images
or self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
)
self.preprocessing_model = PagePreprocessingModel(
options=PagePreprocessingOptions(
images_scale=self.pipeline_options.images_scale,
)
)
self.ocr_model = self._get_ocr_model(artifacts_path)
self.layout_model = LayoutModel(
artifacts_path=artifacts_path,
accelerator_options=self.pipeline_options.accelerator_options,
options=self.pipeline_options.layout_options,
)
self.table_model = TableStructureModel(
enabled=self.pipeline_options.do_table_structure,
artifacts_path=artifacts_path,
options=self.pipeline_options.table_structure_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
self.assemble_model = PageAssembleModel(options=PageAssembleOptions())
# Reading order and enrichment models
self.reading_order_model = ReadingOrderModel(options=ReadingOrderOptions())
# Initialize enrichment models and add only enabled ones to enrichment_pipe
self.enrichment_pipe = []
# Code Formula Enrichment Model
code_formula_model = CodeFormulaModel(
enabled=self.pipeline_options.do_code_enrichment
or self.pipeline_options.do_formula_enrichment,
artifacts_path=artifacts_path,
options=CodeFormulaModelOptions(
do_code_enrichment=self.pipeline_options.do_code_enrichment,
do_formula_enrichment=self.pipeline_options.do_formula_enrichment,
),
accelerator_options=self.pipeline_options.accelerator_options,
)
if code_formula_model.enabled:
self.enrichment_pipe.append(code_formula_model)
# Document Picture Classifier
picture_classifier = DocumentPictureClassifier(
enabled=self.pipeline_options.do_picture_classification,
artifacts_path=artifacts_path,
options=DocumentPictureClassifierOptions(),
accelerator_options=self.pipeline_options.accelerator_options,
)
if picture_classifier.enabled:
self.enrichment_pipe.append(picture_classifier)
# Picture description model
picture_description_model = self._get_picture_description_model(artifacts_path)
if picture_description_model is not None and picture_description_model.enabled:
self.enrichment_pipe.append(picture_description_model)
# Determine if we need to keep backend for enrichment
if (
self.pipeline_options.do_formula_enrichment
or self.pipeline_options.do_code_enrichment
or self.pipeline_options.do_picture_classification
or self.pipeline_options.do_picture_description
):
self.keep_backend = True
def _setup_pipeline(self):
"""Setup the pipeline stages and connections with proper typing"""
# Use pipeline options directly - they have proper defaults
opts = self.pipeline_options
# Create pipeline stages
self.preprocess_stage = ThreadedPipelineStage(
"preprocess",
self.preprocessing_model,
1,
opts.batch_timeout_seconds,
opts.queue_max_size,
)
self.ocr_stage = ThreadedPipelineStage(
"ocr",
self.ocr_model,
opts.ocr_batch_size,
opts.batch_timeout_seconds,
opts.queue_max_size,
)
self.layout_stage = ThreadedPipelineStage(
"layout",
self.layout_model,
opts.layout_batch_size,
opts.batch_timeout_seconds,
opts.queue_max_size,
)
self.table_stage = ThreadedPipelineStage(
"table",
self.table_model,
opts.table_batch_size,
opts.batch_timeout_seconds,
opts.queue_max_size,
)
self.assemble_stage = ThreadedPipelineStage(
"assemble",
self.assemble_model,
1,
opts.batch_timeout_seconds,
opts.queue_max_size,
)
# Create output queue for final results
self.output_queue = ThreadedQueue(max_size=opts.queue_max_size)
# Connect stages in pipeline order
self.preprocess_stage.add_output_queue(self.ocr_stage.input_queue)
self.ocr_stage.add_output_queue(self.layout_stage.input_queue)
self.layout_stage.add_output_queue(self.table_stage.input_queue)
self.table_stage.add_output_queue(self.assemble_stage.input_queue)
self.assemble_stage.add_output_queue(self.output_queue)
self.stages = [
self.preprocess_stage,
self.ocr_stage,
self.layout_stage,
self.table_stage,
self.assemble_stage,
]
def _build_document(self, conv_res: ConversionResult) -> ConversionResult:
"""Build document by processing pages through threaded pipeline"""
if not isinstance(conv_res.input._backend, PdfDocumentBackend):
raise RuntimeError(
f"The selected backend {type(conv_res.input._backend).__name__} for {conv_res.input.file} is not a PDF backend."
)
with TimeRecorder(conv_res, "doc_build", scope=ProfilingScope.DOCUMENT):
# Initialize pages
start_page, end_page = conv_res.input.limits.page_range
pages_to_process = []
for i in range(conv_res.input.page_count):
if (start_page - 1) <= i <= (end_page - 1):
page = Page(page_no=i)
conv_res.pages.append(page)
# Initialize page backend
page._backend = conv_res.input._backend.load_page(i)
if page._backend and page._backend.is_valid():
page.size = page._backend.get_size()
pages_to_process.append(page)
if not pages_to_process:
conv_res.status = ConversionStatus.FAILURE
return conv_res
# Register document for tracking with weak reference
doc_id = id(conv_res)
with self._document_lock:
self._document_tracker[doc_id] = conv_res
# Start pipeline stages
for stage in self.stages:
stage.start()
try:
# Feed pages into pipeline
self._feed_pipeline(pages_to_process, conv_res)
# Collect results from pipeline with partial processing support
result = self._collect_results_with_recovery(
conv_res, len(pages_to_process)
)
# Update conv_res with processed pages and handle partial results
self._update_document_with_results(conv_res, result)
finally:
# Stop pipeline stages
for stage in self.stages:
stage.stop()
# Cleanup stage resources
for stage in self.stages:
stage.cleanup()
# Cleanup output queue
self.output_queue.cleanup()
# Cleanup document tracking
with self._document_lock:
self._document_tracker.pop(doc_id, None)
return conv_res
def _feed_pipeline(self, pages: List[Page], conv_res: ConversionResult):
"""Feed pages into the pipeline"""
for page in pages:
item = ThreadedItem(
payload=page,
conv_res_id=id(conv_res),
conv_res=conv_res,
page_no=page.page_no,
)
# Feed into first stage with timeout
if not self.preprocess_stage.input_queue.put(
item, timeout=self.pipeline_options.stage_timeout_seconds
):
_log.warning(f"Failed to feed page {page.page_no} due to backpressure")
def _collect_results_with_recovery(
self, conv_res: ConversionResult, expected_count: int
) -> ProcessingResult:
"""Collect processed pages from the pipeline with partial result support"""
result = ProcessingResult(total_expected=expected_count)
doc_id = id(conv_res)
# Collect from output queue
while len(result.pages) + len(result.failed_pages) < expected_count:
batch = self.output_queue.get_batch(
batch_size=expected_count
- len(result.pages)
- len(result.failed_pages),
timeout=self.pipeline_options.collection_timeout_seconds,
)
if not batch:
# Timeout reached, log missing pages
missing_count = (
expected_count - len(result.pages) - len(result.failed_pages)
)
if missing_count > 0:
_log.warning(f"Pipeline timeout: missing {missing_count} pages")
break
for item in batch:
if item.conv_res_id == doc_id:
if item.is_failed or item.error is not None:
result.failed_pages.append(
(item.page_no, item.error or Exception("Unknown error"))
)
_log.warning(
f"Page {item.page_no} failed processing: {item.error}"
)
else:
result.pages.append(item.payload)
return result
def _update_document_with_results(
self, conv_res: ConversionResult, result: ProcessingResult
):
"""Update document with processing results and handle partial success"""
# Update conv_res with successfully processed pages
page_map = {p.page_no: p for p in result.pages}
valid_pages = []
for page in conv_res.pages:
if page.page_no in page_map:
valid_pages.append(page_map[page.page_no])
elif not any(
failed_page_no == page.page_no
for failed_page_no, _ in result.failed_pages
):
# Page wasn't processed but also didn't explicitly fail - keep original
valid_pages.append(page)
conv_res.pages = valid_pages
# Handle partial results
if result.is_partial_success:
_log.warning(
f"Partial processing success: {result.success_count} pages succeeded, "
f"{result.failure_count} pages failed"
)
conv_res.status = ConversionStatus.PARTIAL_SUCCESS
elif result.is_complete_failure:
_log.error("Complete processing failure: all pages failed")
conv_res.status = ConversionStatus.FAILURE
elif result.success_count > 0:
# All expected pages processed successfully
conv_res.status = ConversionStatus.SUCCESS
# Clean up page resources if not keeping images
if not self.keep_images:
for page in conv_res.pages:
# _image_cache is always present on Page objects, no need for hasattr
page._image_cache = {}
# Clean up page backends if not keeping them
if not self.keep_backend:
for page in conv_res.pages:
if page._backend is not None:
page._backend.unload()
def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult:
"""Assemble the final document from processed pages"""
all_elements = []
all_headers = []
all_body = []
with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT):
for p in conv_res.pages:
if p.assembled is not None:
for el in p.assembled.body:
all_body.append(el)
for el in p.assembled.headers:
all_headers.append(el)
for el in p.assembled.elements:
all_elements.append(el)
conv_res.assembled = AssembledUnit(
elements=all_elements, headers=all_headers, body=all_body
)
conv_res.document = self.reading_order_model(conv_res)
# Generate page images
if self.pipeline_options.generate_page_images:
for page in conv_res.pages:
if page.image is not None:
page_no = page.page_no + 1
conv_res.document.pages[page_no].image = ImageRef.from_pil(
page.image, dpi=int(72 * self.pipeline_options.images_scale)
)
# Generate element images
self._generate_element_images(conv_res)
# Aggregate confidence scores
self._aggregate_confidence(conv_res)
return conv_res
def _generate_element_images(self, conv_res: ConversionResult):
"""Generate images for picture and table elements"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
if (
self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
):
scale = self.pipeline_options.images_scale
for element, _level in conv_res.document.iterate_items():
if not isinstance(element, DocItem) or len(element.prov) == 0:
continue
if (
isinstance(element, PictureItem)
and self.pipeline_options.generate_picture_images
) or (
isinstance(element, TableItem)
and self.pipeline_options.generate_table_images
):
page_ix = element.prov[0].page_no - 1
page = next(
(p for p in conv_res.pages if p.page_no == page_ix), None
)
if (
page is not None
and page.size is not None
and page.image is not None
):
crop_bbox = (
element.prov[0]
.bbox.scaled(scale=scale)
.to_top_left_origin(
page_height=page.size.height * scale
)
)
cropped_im = page.image.crop(crop_bbox.as_tuple())
element.image = ImageRef.from_pil(
cropped_im, dpi=int(72 * scale)
)
def _aggregate_confidence(self, conv_res: ConversionResult):
"""Aggregate confidence scores across pages"""
if len(conv_res.pages) > 0:
import warnings
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
category=RuntimeWarning,
message="Mean of empty slice|All-NaN slice encountered",
)
conv_res.confidence.layout_score = float(
np.nanmean(
[c.layout_score for c in conv_res.confidence.pages.values()]
)
)
conv_res.confidence.parse_score = float(
np.nanquantile(
[c.parse_score for c in conv_res.confidence.pages.values()],
q=0.1,
)
)
conv_res.confidence.table_score = float(
np.nanmean(
[c.table_score for c in conv_res.confidence.pages.values()]
)
)
conv_res.confidence.ocr_score = float(
np.nanmean(
[c.ocr_score for c in conv_res.confidence.pages.values()]
)
)
def _enrich_document(self, conv_res: ConversionResult) -> ConversionResult:
"""Run enrichment models on the document"""
def _prepare_elements(conv_res: ConversionResult, model: Any) -> Iterable[Any]:
for doc_element, _level in conv_res.document.iterate_items():
prepared_element = model.prepare_element(
conv_res=conv_res, element=doc_element
)
if prepared_element is not None:
yield prepared_element
with TimeRecorder(conv_res, "doc_enrich", scope=ProfilingScope.DOCUMENT):
for model in self.enrichment_pipe:
for element_batch in chunkify(
_prepare_elements(conv_res, model),
model.elements_batch_size,
):
for element in model(
doc=conv_res.document, element_batch=element_batch
): # Must exhaust!
pass
return conv_res
def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus:
"""Determine the final conversion status"""
if conv_res.status == ConversionStatus.PARTIAL_SUCCESS:
return ConversionStatus.PARTIAL_SUCCESS
elif conv_res.pages and conv_res.document:
return ConversionStatus.SUCCESS
else:
return ConversionStatus.FAILURE
@classmethod
def get_default_options(cls) -> ThreadedPdfPipelineOptions:
return ThreadedPdfPipelineOptions()
@classmethod
def is_backend_supported(cls, backend):
return isinstance(backend, PdfDocumentBackend)

2
pyproject.toml
View File

@ -41,7 +41,7 @@ authors = [
{ name = "Panos Vagenas", email = "pva@zurich.ibm.com" }, { name = "Panos Vagenas", email = "pva@zurich.ibm.com" },
{ name = "Peter Staar", email = "taa@zurich.ibm.com" }, { name = "Peter Staar", email = "taa@zurich.ibm.com" },
] ]
requires-python = '>=3.11,<4.0' requires-python = '>=3.9,<4.0'
dependencies = [ dependencies = [
'pydantic (>=2.0.0,<3.0.0)', 'pydantic (>=2.0.0,<3.0.0)',
'docling-core[chunking] (>=2.42.0,<3.0.0)', 'docling-core[chunking] (>=2.42.0,<3.0.0)',

1973
uv.lock generated
View File

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