docling-pipelines

External Operator Integration Guide

This guide explains how external applications can integrate their own operators with the docling-pipelines framework when it’s installed as a wheel package.

Overview

The docling-pipelines package provides a plugin hook pattern that allows host applications to inject their own operators into the operator registry. This enables seamless integration of custom operators without modifying the docpipe codebase.

Architecture

External Application
├── docling-pipelines (installed wheel)
│   └── operator_registry.py (provides hooks)
└── custom_operators/
    ├── my_operator.py
    └── __init__.py (registers operators)

Quick Start

1. Create Your Custom Operators

# external_app/operators/my_custom_operator.py
from docpipe.core.operators.abstract_operator import AbstractOperator
import pyarrow as pa

class MyCustomOperator(AbstractOperator):
    """Custom operator for external application."""

    short_name = "my_custom_op"

    def __init__(self, *, config: dict):
        super().__init__(config=config)
        # Your initialization

    def transform(self, table: pa.Table) -> pa.Table:
        """Transform logic."""
        # Your transformation logic
        return table

    @classmethod
    def is_available(cls) -> bool:
        """Check if operator dependencies are available."""
        return True

2. Create Operator Registry

# external_app/operators/__init__.py
from external_app.operators.my_custom_operator import MyCustomOperator

# Define your application's operator frozenset
APP_OPERATORS = frozenset({
    MyCustomOperator,
    # Add more operators here
})

3. Register Operators at Application Startup

# external_app/__init__.py or main.py
from docpipe.core.operators.operator_registry import register_operator_provider
from external_app.operators import APP_OPERATORS

def get_app_operators(orchestrator=None):
    """
    Provider function that returns application operators.

    Args:
        orchestrator: Optional orchestrator type ("python", "spark")

    Returns:
        frozenset: Set of operator classes
    """
    # Optional: Filter by orchestrator
    if orchestrator == "spark":
        return frozenset({
            op for op in APP_OPERATORS
            if hasattr(op, 'supports_spark') and op.supports_spark
        })

    return APP_OPERATORS

# Register at application startup (before using docpipe)
register_operator_provider(get_app_operators)

4. Use Docling Pipelines with Your Operators

# external_app/pipeline.py
from docpipe.lib.docpipe_flow_manager import DocpipeFlowManager

# Your custom operator is now available in flows
flow_def = {
    "flow_name": "My Pipeline",
    "flow": [
        {
            "type": "ingest_source",
            "name": "ingest",
            "config": {"provider": "filesystem", "connection_params": {"paths": ["./data"]}}
        },
        {
            "type": "my_custom_op",  # Your custom operator!
            "name": "custom_processing",
            "config": {"param1": "value1"},
            "depends_on": ["ingest"]
        }
    ]
}

manager = DocpipeFlowManager(flow_def=flow_def)
result = manager.execute()

Advanced Usage

Orchestrator-Specific Operators

Filter operators based on orchestrator type:

def get_app_operators(orchestrator=None):
    """Return operators filtered by orchestrator."""

    if orchestrator == "python":
        return frozenset({
            PythonOnlyOperator,
            SharedOperator,
        })
    elif orchestrator == "spark":
        return frozenset({
            SparkOnlyOperator,
            SharedOperator,
        })

    # Return all if orchestrator not specified
    return APP_OPERATORS

Multiple Provider Registration

Register multiple operator sources:

from docpipe.core.operators.operator_registry import register_operator_provider

# Register core application operators
register_operator_provider(get_core_operators)

# Register plugin operators
register_operator_provider(get_plugin_operators)

# Register environment-specific operators
register_operator_provider(get_env_specific_operators)

Operator Priority and Override

Docling Pipelines uses a priority-based resolution system to handle operators with the same short_name. Operators are assigned priorities based on their owner attribute:

Built-in Priority Levels (lower number = higher priority):

• Custom operators (priority 100): Can override inbuilt operators
• Docling Pipelines operators (priority 200): Lowest precedence

Additional tiers can be registered at runtime — see Registering a Custom Priority Tier below.

Setting Operator Owner

from docpipe.core.constants.constants import DocpipeConstants

class CustomExtractOperator(AbstractOperator):
    """Custom extract operator with priority."""

    short_name = "extract"  # Same as docpipe's ExtractOperator
    owner = DocpipeConstants.OWNER_CUSTOM  # Priority 100

    def transform(self, table: pa.Table) -> pa.Table:
        # Custom extraction logic
        return table

Priority Rules

• Custom operators CAN override inbuilt operators (priority 100 < priority 200)
• Inbuilt operators CANNOT override Custom operators
• Same priority: Last registered wins

Example: Custom Operator Overriding OSS

# This custom operator will override docpipe's built-in extract operator
class MyExtractOperator(AbstractOperator):
    short_name = "extract"
    owner = DocpipeConstants.OWNER_CUSTOM  # Priority 100 beats inbuilt priority 200

    def transform(self, table: pa.Table) -> pa.Table:
        # Your custom logic replaces docpipe's extract
        return table

Registering a Custom Priority Tier

If you need an operator tier with higher precedence than OWNER_CUSTOM, register it at startup before operators are loaded. Priorities below 100 outrank all built-in tiers.

from docpipe.core.orchestration.operator_factory import OperatorFactory

# Call once at application startup, before operators are loaded
OperatorFactory.register_owner_priority(owner="my_app", priority=10)

class MyAppOperator(AbstractOperator):
    short_name = "extract"
    owner = "my_app"  # Priority 10 — overrides custom (100) and inbuilt (200)

    def transform(self, table: pa.Table) -> pa.Table:
        return table

Priority ranges:

Range	Purpose
0-99	Consumer tiers above all built-ins
100	OWNER_CUSTOM
101-199	Consumer tiers between custom and Docling Pipelines built-ins
200	OWNER_DOCPIPE

Note: If you don’t set the owner attribute, it defaults to OWNER_CUSTOM (priority 100).

Dynamic Operator Loading

Load operators dynamically based on configuration:

def get_app_operators(orchestrator=None):
    """Dynamically load operators based on config."""
    import os
    from importlib import import_module

    operators = set()

    # Load from environment variable
    operator_modules = os.getenv("APP_OPERATOR_MODULES", "").split(",")

    for module_name in operator_modules:
        if module_name.strip():
            try:
                module = import_module(module_name)
                if hasattr(module, "OPERATORS"):
                    operators.update(module.OPERATORS)
            except ImportError as e:
                print(f"Failed to load operators from {module_name}: {e}")

    return frozenset(operators)

API Reference

register_operator_provider(provider_func)

Register an external operator provider function.

Parameters:

• provider_func (callable): Function that returns frozenset of operator classes
  • Signature: provider_func(orchestrator: str | None = None) -> frozenset

Raises:

• TypeError: If provider_func is not callable

Example:

from docpipe.core.operators.operator_registry import register_operator_provider

def my_provider(orchestrator=None):
    return frozenset({MyOperator1, MyOperator2})

register_operator_provider(my_provider)

clear_operator_providers()

Clear all registered operator providers. Useful for testing.

Example:

from docpipe.core.operators.operator_registry import clear_operator_providers

# Clear all providers
clear_operator_providers()

get_registered_provider_count()

Get the number of registered external operator providers.

Returns:

• int: Number of registered providers

Example:

from docpipe.core.operators.operator_registry import get_registered_provider_count

count = get_registered_provider_count()
print(f"Registered providers: {count}")

get_docpipe_operators(orchestrator=None)

Get all operators (Docling Pipelines built-ins + external).

Parameters:

• orchestrator (str, optional): Orchestrator type for filtering

Returns:

• frozenset: Combined set of operator classes

Example:

from docpipe.core.operators.operator_registry import get_docpipe_operators

# Get all operators
all_ops = get_docpipe_operators()

# Get Python-specific operators
python_ops = get_docpipe_operators(orchestrator="python")

Testing

Unit Testing with Custom Operators

import pytest
from docpipe.core.operators.operator_registry import (
    register_operator_provider,
    clear_operator_providers,
    get_docpipe_operators
)

@pytest.fixture(autouse=True)
def reset_providers():
    """Reset operator providers before each test."""
    clear_operator_providers()
    yield
    clear_operator_providers()

def test_custom_operator_registration():
    """Test that custom operators are registered correctly."""

    def test_provider(orchestrator=None):
        return frozenset({MyTestOperator})

    register_operator_provider(test_provider)

    operators = get_docpipe_operators()
    short_names = {op.short_name for op in operators}

    assert "my_test_op" in short_names

Best Practices

1. Register Early: Register operators at application startup, before any Docling Pipelines operations
2. Use Descriptive Names: Choose unique short_name values to avoid conflicts
3. Implement is_available(): Check dependencies in the is_available() method
4. Handle Errors Gracefully: Provider functions should handle errors without crashing
5. Document Dependencies: Clearly document any external dependencies your operators require
6. Test Thoroughly: Test operator registration and execution in your application context

Troubleshooting

Operators Not Found

Problem: Custom operators not appearing in flows

Solution:

1. Verify registration happens before docpipe usage
2. Check provider function returns frozenset
3. Ensure operators have short_name attribute
4. Check logs for registration errors

import logging
logging.basicConfig(level=logging.DEBUG)
# Check logs for operator registration messages

Operator Override Not Working

Problem: Custom operator not overriding docpipe operator

Solution:

1. Ensure short_name matches exactly
2. Set owner = DocpipeConstants.OWNER_CUSTOM on your operator
3. Verify priority: Custom (1) can override inbuilt (2)
4. Check logs for priority resolution messages
5. Ensure operator is registered before factory initialization

Example Debug:

import logging
logging.basicConfig(level=logging.INFO)

# Look for messages like:
# "Operator 'extract': MyExtractOperator (priority=1) overrides ExtractOperator (priority=2)"
# or
# "Operator 'extract': MyExtractOperator (priority=1) cannot override EnterpriseOp (priority=0)"

Import Errors

Problem: Cannot import docpipe modules

Solution:

1. Verify docling-pipelines wheel is installed
2. Check Python path includes docpipe package
3. Ensure compatible Python version (>=3.12)

Example Application Structure

external_app/
├── pyproject.toml
├── external_app/
│   ├── __init__.py          # Register operators here
│   ├── main.py              # Application entry point
│   ├── operators/
│   │   ├── __init__.py      # Define APP_OPERATORS
│   │   ├── custom_op1.py
│   │   └── custom_op2.py
│   └── flows/
│       └── pipeline.json    # Flow using custom operators
└── tests/
    └── test_operators.py

See Also

• Operator Reference
• Custom Operators Guide
• Architecture Documentation

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