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"""
Extraction Validation
Validates extracted data and provides confidence scoring.
"""
import logging
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple
from ..chunks.models import (
ExtractionResult,
FieldExtraction,
ConfidenceLevel,
)
from .schema import ExtractionSchema, FieldSpec, FieldType
logger = logging.getLogger(__name__)
@dataclass
class ValidationIssue:
"""A validation issue found during extraction validation."""
field_name: str
issue_type: str # "missing", "invalid", "low_confidence", "type_mismatch"
message: str
severity: str = "warning" # "error", "warning", "info"
suggested_action: Optional[str] = None
@dataclass
class ValidationResult:
"""Result of extraction validation."""
is_valid: bool
issues: List[ValidationIssue] = field(default_factory=list)
confidence_score: float = 0.0
field_scores: Dict[str, float] = field(default_factory=dict)
recommendations: List[str] = field(default_factory=list)
@property
def error_count(self) -> int:
return sum(1 for i in self.issues if i.severity == "error")
@property
def warning_count(self) -> int:
return sum(1 for i in self.issues if i.severity == "warning")
def get_issues_for_field(self, field_name: str) -> List[ValidationIssue]:
"""Get all issues for a specific field."""
return [i for i in self.issues if i.field_name == field_name]
class ExtractionValidator:
"""
Validates extraction results against schemas.
Checks for:
- Required field presence
- Type correctness
- Value constraints
- Confidence thresholds
"""
def __init__(
self,
min_confidence: float = 0.5,
strict_mode: bool = False,
):
self.min_confidence = min_confidence
self.strict_mode = strict_mode
def validate(
self,
extraction: ExtractionResult,
schema: ExtractionSchema,
) -> ValidationResult:
"""
Validate extraction result against schema.
Args:
extraction: Extraction result to validate
schema: Schema defining expected fields
Returns:
ValidationResult with issues and scores
"""
issues: List[ValidationIssue] = []
field_scores: Dict[str, float] = {}
# Check each field
for field_spec in schema.fields:
field_issues, score = self._validate_field(
field_spec=field_spec,
extraction=extraction,
)
issues.extend(field_issues)
field_scores[field_spec.name] = score
# Check for unexpected fields
expected_fields = {f.name for f in schema.fields}
for field_name in extraction.data.keys():
if field_name not in expected_fields:
issues.append(ValidationIssue(
field_name=field_name,
issue_type="unexpected",
message=f"Unexpected field: {field_name}",
severity="info",
))
# Calculate overall score
if field_scores:
confidence_score = sum(field_scores.values()) / len(field_scores)
else:
confidence_score = 0.0
# Determine validity
is_valid = (
all(i.severity != "error" for i in issues) and
confidence_score >= schema.min_overall_confidence
)
# Generate recommendations
recommendations = self._generate_recommendations(issues, extraction)
return ValidationResult(
is_valid=is_valid,
issues=issues,
confidence_score=confidence_score,
field_scores=field_scores,
recommendations=recommendations,
)
def _validate_field(
self,
field_spec: FieldSpec,
extraction: ExtractionResult,
) -> Tuple[List[ValidationIssue], float]:
"""Validate a single field."""
issues: List[ValidationIssue] = []
score = 1.0
value = extraction.data.get(field_spec.name)
field_extraction = self._get_field_extraction(field_spec.name, extraction)
# Check presence
if value is None:
if field_spec.required:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="missing",
message=f"Required field '{field_spec.name}' is missing",
severity="error",
suggested_action="Manual review required",
))
return issues, 0.0
else:
return issues, 1.0 # Optional field, OK to be missing
# Check abstention
if field_spec.name in extraction.abstained_fields:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="abstained",
message=f"Field '{field_spec.name}' was abstained due to low confidence",
severity="warning",
suggested_action="Manual verification recommended",
))
score *= 0.5
# Check confidence
if field_extraction:
if field_extraction.confidence < self.min_confidence:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="low_confidence",
message=f"Field '{field_spec.name}' has low confidence: {field_extraction.confidence:.2f}",
severity="warning",
suggested_action="Manual verification recommended",
))
score *= field_extraction.confidence
else:
score *= field_extraction.confidence
# Check type
type_issues = self._validate_type(field_spec, value)
issues.extend(type_issues)
if type_issues:
score *= 0.7
# Check constraints
constraint_issues = self._validate_constraints(field_spec, value)
issues.extend(constraint_issues)
if constraint_issues:
score *= 0.8
return issues, max(0.0, min(1.0, score))
def _validate_type(
self,
field_spec: FieldSpec,
value: Any,
) -> List[ValidationIssue]:
"""Validate field type."""
issues = []
expected_type = self._get_expected_python_type(field_spec.field_type)
if expected_type and not isinstance(value, expected_type):
# Try conversion
try:
expected_type(value)
except (ValueError, TypeError):
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="type_mismatch",
message=f"Field '{field_spec.name}' expected {field_spec.field_type.value}, got {type(value).__name__}",
severity="warning" if not self.strict_mode else "error",
))
return issues
def _validate_constraints(
self,
field_spec: FieldSpec,
value: Any,
) -> List[ValidationIssue]:
"""Validate field constraints."""
issues = []
# Pattern
if field_spec.pattern:
import re
if not re.match(field_spec.pattern, str(value)):
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="pattern_mismatch",
message=f"Field '{field_spec.name}' does not match pattern: {field_spec.pattern}",
severity="warning",
))
# Range
try:
num_value = float(value)
if field_spec.min_value is not None and num_value < field_spec.min_value:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="below_minimum",
message=f"Field '{field_spec.name}' value {num_value} is below minimum {field_spec.min_value}",
severity="warning",
))
if field_spec.max_value is not None and num_value > field_spec.max_value:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="above_maximum",
message=f"Field '{field_spec.name}' value {num_value} is above maximum {field_spec.max_value}",
severity="warning",
))
except (ValueError, TypeError):
pass
# Length
str_value = str(value)
if field_spec.min_length is not None and len(str_value) < field_spec.min_length:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="too_short",
message=f"Field '{field_spec.name}' is too short: {len(str_value)} < {field_spec.min_length}",
severity="warning",
))
if field_spec.max_length is not None and len(str_value) > field_spec.max_length:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="too_long",
message=f"Field '{field_spec.name}' is too long: {len(str_value)} > {field_spec.max_length}",
severity="warning",
))
# Allowed values
if field_spec.allowed_values and value not in field_spec.allowed_values:
issues.append(ValidationIssue(
field_name=field_spec.name,
issue_type="not_in_allowed",
message=f"Field '{field_spec.name}' value '{value}' not in allowed values",
severity="warning",
))
return issues
def _get_field_extraction(
self,
field_name: str,
extraction: ExtractionResult,
) -> Optional[FieldExtraction]:
"""Get field extraction by name."""
for fe in extraction.fields:
if fe.field_name == field_name:
return fe
return None
def _get_expected_python_type(self, field_type: FieldType) -> Optional[type]:
"""Get expected Python type for field type."""
type_map = {
FieldType.INTEGER: int,
FieldType.FLOAT: float,
FieldType.BOOLEAN: bool,
FieldType.LIST: list,
FieldType.OBJECT: dict,
}
return type_map.get(field_type)
def _generate_recommendations(
self,
issues: List[ValidationIssue],
extraction: ExtractionResult,
) -> List[str]:
"""Generate recommendations based on issues."""
recommendations = []
# Count issue types
missing_count = sum(1 for i in issues if i.issue_type == "missing")
low_conf_count = sum(1 for i in issues if i.issue_type == "low_confidence")
type_count = sum(1 for i in issues if i.issue_type == "type_mismatch")
if missing_count > 0:
recommendations.append(
f"Review document for {missing_count} missing required field(s)"
)
if low_conf_count > 0:
recommendations.append(
f"Manual verification recommended for {low_conf_count} low-confidence field(s)"
)
if type_count > 0:
recommendations.append(
f"Check data types for {type_count} field(s) with type mismatches"
)
if extraction.overall_confidence < 0.5:
recommendations.append(
"Overall extraction confidence is low - consider manual review"
)
if len(extraction.abstained_fields) > 0:
recommendations.append(
f"System abstained on {len(extraction.abstained_fields)} field(s) due to uncertainty"
)
return recommendations
class CrossFieldValidator:
"""
Validates relationships between fields.
Checks for:
- Consistency (e.g., subtotal + tax = total)
- Logical relationships
- Date ordering
"""
def validate_consistency(
self,
extraction: ExtractionResult,
rules: List[Dict[str, Any]],
) -> List[ValidationIssue]:
"""
Validate cross-field consistency rules.
Rules format:
{
"type": "sum",
"fields": ["subtotal", "tax"],
"equals": "total",
"tolerance": 0.01
}
"""
issues = []
for rule in rules:
rule_type = rule.get("type")
if rule_type == "sum":
issue = self._validate_sum_rule(extraction, rule)
if issue:
issues.append(issue)
elif rule_type == "date_order":
issue = self._validate_date_order(extraction, rule)
if issue:
issues.append(issue)
elif rule_type == "required_if":
issue = self._validate_required_if(extraction, rule)
if issue:
issues.append(issue)
return issues
def _validate_sum_rule(
self,
extraction: ExtractionResult,
rule: Dict[str, Any],
) -> Optional[ValidationIssue]:
"""Validate that sum of fields equals another field."""
fields = rule.get("fields", [])
equals_field = rule.get("equals")
tolerance = rule.get("tolerance", 0.01)
try:
sum_value = sum(
float(extraction.data.get(f, 0) or 0)
for f in fields
)
expected = float(extraction.data.get(equals_field, 0) or 0)
if abs(sum_value - expected) > tolerance:
return ValidationIssue(
field_name=equals_field,
issue_type="sum_mismatch",
message=f"Sum of {fields} ({sum_value}) does not equal {equals_field} ({expected})",
severity="warning",
)
except (ValueError, TypeError):
pass
return None
def _validate_date_order(
self,
extraction: ExtractionResult,
rule: Dict[str, Any],
) -> Optional[ValidationIssue]:
"""Validate that dates are in correct order."""
from datetime import datetime
before_field = rule.get("before")
after_field = rule.get("after")
before_val = extraction.data.get(before_field)
after_val = extraction.data.get(after_field)
if not before_val or not after_val:
return None
try:
# Try common date formats
formats = ["%Y-%m-%d", "%m/%d/%Y", "%d/%m/%Y", "%B %d, %Y"]
before_date = None
after_date = None
for fmt in formats:
try:
before_date = datetime.strptime(str(before_val), fmt)
break
except ValueError:
continue
for fmt in formats:
try:
after_date = datetime.strptime(str(after_val), fmt)
break
except ValueError:
continue
if before_date and after_date and before_date > after_date:
return ValidationIssue(
field_name=after_field,
issue_type="date_order",
message=f"Date {before_field} ({before_val}) should be before {after_field} ({after_val})",
severity="warning",
)
except Exception:
pass
return None
def _validate_required_if(
self,
extraction: ExtractionResult,
rule: Dict[str, Any],
) -> Optional[ValidationIssue]:
"""Validate conditional required fields."""
field = rule.get("field")
required_if = rule.get("required_if") # Field that must exist
condition_value = rule.get("value") # Optional specific value
condition_field_value = extraction.data.get(required_if)
# Check if condition is met
condition_met = False
if condition_value is not None:
condition_met = condition_field_value == condition_value
else:
condition_met = condition_field_value is not None
if condition_met:
field_value = extraction.data.get(field)
if field_value is None:
return ValidationIssue(
field_name=field,
issue_type="conditional_required",
message=f"Field '{field}' is required when '{required_if}' is present",
severity="warning",
)
return None
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