pepsico-document-confidence 0.1.1

Proxy-based confidence scoring framework for extraction quality assessment

document-confidence

A proxy-based confidence scoring framework for evaluating extraction quality without requiring ground truth labels. This library provides a production-ready architecture for assessing document extraction results and routing them to appropriate recovery workflows.

Overview

document-confidence implements a multi-metric scoring system that evaluates extraction quality across multiple dimensions. The library operates deterministically without requiring external APIs, LLM calls, or cloud dependencies, making it suitable for high-throughput document processing pipelines.

Architecture

The library follows a modular architecture with:

• Metric-based Scoring - Independent metrics for different quality dimensions
• Weighted Aggregation - Configurable weights for metric prioritization
• Deficiency Classification - Automatic detection of specific failure types
• Recommendation Engine - Routing decisions for accept/recover/human review
• Protocol-based Interfaces - Type-safe contracts for extensibility

Installation

pip install document-confidence

Optional Dependencies

# For development
pip install document-confidence[dev]

Quick Start

from document_confidence import (
    ConfidenceConfig,
    ConfidenceScorer,
    RecommendationType,
)
from document_confidence.metrics import (
    TextCoverageMetric,
    TableCompletenessMetric,
    SchemaFillMetric,
    ConsistencyMetric,
    DensityMetric,
)

# Configure confidence scoring
config = ConfidenceConfig(
    acceptance_threshold=90.0,
    human_review_threshold=80.0,
    weights={
        "text_coverage": 0.25,
        "table_completeness": 0.20,
        "schema_fill": 0.25,
        "consistency": 0.15,
        "density": 0.15,
    },
)

# Create metrics
metrics = [
    TextCoverageMetric(),
    TableCompletenessMetric(),
    SchemaFillMetric(),
    ConsistencyMetric(),
    DensityMetric(),
]

# Initialize scorer
scorer = ConfidenceScorer(config, metrics)

# Score extraction
report = scorer.score(
    extraction=extracted_data,
    page_metadata=page_metadata,
    parse_results=parse_results,
)

# Get recommendation
if report.recommendation == RecommendationType.ACCEPT:
    print("Extraction accepted")
elif report.recommendation == RecommendationType.HUMAN_REVIEW:
    print("Requires human review")
else:
    print("Recovery needed")

Configuration

Confidence Configuration

from document_confidence import ConfidenceConfig

config = ConfidenceConfig(
    acceptance_threshold=90.0,
    human_review_threshold=80.0,
    weights={
        "text_coverage": 0.25,
        "table_completeness": 0.20,
        "schema_fill": 0.25,
        "consistency": 0.15,
        "density": 0.15,
    },
    enable_metric_explanations=True,
    normalize_weights=True,
    strict_schema_validation=True,
    minimum_metric_score=0.50,
)

Metrics

Text Coverage Metric

Detects OCR failures by measuring text coverage.

from document_confidence.metrics import TextCoverageMetric

metric = TextCoverageMetric(weight=0.25)
score = metric.compute(extraction, page_metadata, parse_results)

Formula: extracted_text_length / expected_text_length

Threshold: < 0.70 → OCR_GAP deficiency

Table Completeness Metric

Detects missing tables by measuring table completeness.

from document_confidence.metrics import TableCompletenessMetric

metric = TableCompletenessMetric(weight=0.20)
score = metric.compute(extraction, page_metadata, parse_results)

Formula: recovered_cells / expected_cells

Threshold: < 0.80 → TABLE_MISSING deficiency

Schema Fill Metric

Measures extraction completeness by checking schema fill.

from document_confidence.metrics import SchemaFillMetric

metric = SchemaFillMetric(weight=0.25)
score = metric.compute(extraction, page_metadata, parse_results)

Formula: required_fields_populated / required_fields_total

Consistency Metric

Detects internal contradictions in extraction.

from document_confidence.metrics import ConsistencyMetric

metric = ConsistencyMetric(weight=0.15)
score = metric.compute(extraction, page_metadata, parse_results)

Checks:

• Duplicate shelf numbers
• Same UPC with different names
• Missing cross-reference mappings
• Orphan products

Formula: 1 - error_rate

Threshold: < 0.70 → CROSSREF_BROKEN deficiency

Density Metric

Detects implausible shelf layouts by measuring density.

from document_confidence.metrics import DensityMetric

metric = DensityMetric(weight=0.15)
score = metric.compute(extraction, page_metadata, parse_results)

Checks:

• Products per shelf (too sparse or too dense)
• Facings distribution
• Section density

Threshold: < 0.60 → SPATIAL_FAILURE deficiency

Deficiency Classification

The library automatically classifies deficiencies based on metric scores:

from document_confidence.models import DeficiencyType

deficiency_types = [
    DeficiencyType.OCR_GAP,        # Text coverage < 0.70
    DeficiencyType.TABLE_MISSING,   # Table completeness < 0.80
    DeficiencyType.SPATIAL_FAILURE, # Density < 0.60
    DeficiencyType.CROSSREF_BROKEN, # Consistency < 0.70
]

Each deficiency includes:

• Type
• Severity (0.0 - 1.0)
• Affected pages
• Human-readable description

Confidence Bands

Overall scores are mapped to confidence bands:

95-100  → EXCELLENT
90-95   → GOOD
80-90   → FAIR
60-80   → POOR
0-60    → CRITICAL

Recommendations

Based on overall score and deficiencies:

score >= 90              → ACCEPT
80 <= score < 90         → HUMAN_REVIEW
score < 80               → RECOVER

Custom Metrics

Create custom metrics by extending BaseConfidenceMetric:

from document_confidence.metrics import BaseConfidenceMetric

class CustomMetric(BaseConfidenceMetric):
    def __init__(self, weight: float = 0.10):
        super().__init__(name="custom", weight=weight)
    
    def _compute(self, extraction, page_metadata, parse_results):
        # Custom computation logic
        return 0.9  # Return score between 0.0 and 1.0

Development

Running Tests

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=document_confidence

Code Style

# Format code
black document_confidence

# Lint code
ruff check document_confidence

# Type check
mypy document_confidence

Design Principles

1. Deterministic - No external APIs or LLM calls
2. Computationally Lightweight - O(n) complexity where possible
3. Extensible - Plugin architecture for custom metrics
4. Type-safe - Full type hints with Pydantic validation
5. Production-ready - Enterprise-scale performance

Dependencies

• document-core>=0.1.0 - Shared interfaces and models
• pydantic>=2.0 - Data validation
• typing_extensions>=4.0 - Type extensions

Performance

The library is designed for:

• 1000+ page documents
• O(n) metric calculations
• Minimal memory usage
• No repeated traversals

License

MIT

Support

For issues, questions, or contributions, please visit the project repository.