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"""
Evidence Building and Management
Creates and manages evidence references for extracted data.
Links every extraction to its visual source.
"""
import hashlib
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from ..chunks.models import (
BoundingBox,
DocumentChunk,
EvidenceRef,
TableChunk,
ChartChunk,
)
@dataclass
class EvidenceConfig:
"""Configuration for evidence building."""
# Crop settings
crop_enabled: bool = True
crop_output_dir: Optional[Path] = None
crop_format: str = "png"
crop_padding_percent: float = 0.02 # 2% padding around bbox
# Evidence settings
include_snippet: bool = True
max_snippet_length: int = 200
include_context: bool = True
context_chars: int = 50
class EvidenceBuilder:
"""
Builds evidence references for extractions.
Creates links between extracted values and their
visual sources in the document.
"""
def __init__(self, config: Optional[EvidenceConfig] = None):
self.config = config or EvidenceConfig()
self._crop_counter = 0
def create_evidence(
self,
chunk: DocumentChunk,
value: Any,
field_name: Optional[str] = None,
crop_image: Optional[Any] = None,
) -> EvidenceRef:
"""
Create an evidence reference from a chunk.
Args:
chunk: Source chunk
value: Extracted value
field_name: Optional field name being extracted
crop_image: Optional cropped image for this evidence
Returns:
EvidenceRef linking to the source
"""
# Generate crop path if image provided
crop_path = None
if crop_image is not None and self.config.crop_enabled:
crop_path = self._save_crop(crop_image, chunk)
# Create snippet
snippet = self._create_snippet(chunk.text, str(value))
# Determine source type
if isinstance(chunk, TableChunk):
source_type = "table"
elif isinstance(chunk, ChartChunk):
source_type = "chart"
else:
source_type = chunk.chunk_type.value
return EvidenceRef(
chunk_id=chunk.chunk_id,
doc_id=chunk.doc_id,
page=chunk.page,
bbox=chunk.bbox,
source_type=source_type,
snippet=snippet,
confidence=chunk.confidence,
crop_path=crop_path,
)
def create_evidence_from_bbox(
self,
doc_id: str,
page: int,
bbox: BoundingBox,
source_text: str,
confidence: float = 1.0,
source_type: str = "region",
crop_image: Optional[Any] = None,
) -> EvidenceRef:
"""
Create evidence from a bounding box.
Args:
doc_id: Document ID
page: Page number
bbox: Bounding box of evidence
source_text: Text content
confidence: Confidence score
source_type: Type of source (text, table, chart, etc.)
crop_image: Optional cropped image
Returns:
EvidenceRef for the region
"""
# Generate chunk_id for the region
chunk_id = self._generate_region_id(doc_id, page, bbox)
# Generate crop path if image provided
crop_path = None
if crop_image is not None and self.config.crop_enabled:
crop_path = self._save_crop_direct(
crop_image,
doc_id,
page,
chunk_id,
)
return EvidenceRef(
chunk_id=chunk_id,
doc_id=doc_id,
page=page,
bbox=bbox,
source_type=source_type,
snippet=source_text[:self.config.max_snippet_length],
confidence=confidence,
crop_path=crop_path,
)
def create_table_cell_evidence(
self,
table_chunk: TableChunk,
row: int,
col: int,
crop_image: Optional[Any] = None,
) -> Optional[EvidenceRef]:
"""
Create evidence for a specific table cell.
Args:
table_chunk: Source table
row: Cell row (0-indexed)
col: Cell column (0-indexed)
crop_image: Optional cropped cell image
Returns:
EvidenceRef for the cell, or None if cell not found
"""
cell = table_chunk.get_cell(row, col)
if cell is None:
return None
cell_id = f"r{row}c{col}"
# Generate crop path
crop_path = None
if crop_image is not None and self.config.crop_enabled:
crop_path = self._save_crop_direct(
crop_image,
table_chunk.doc_id,
table_chunk.page,
f"{table_chunk.chunk_id}_{cell_id}",
)
return EvidenceRef(
chunk_id=table_chunk.chunk_id,
doc_id=table_chunk.doc_id,
page=table_chunk.page,
bbox=cell.bbox,
source_type="table_cell",
snippet=cell.text[:self.config.max_snippet_length],
confidence=cell.confidence,
cell_id=cell_id,
crop_path=crop_path,
)
def merge_evidence(
self,
evidence_list: List[EvidenceRef],
) -> List[EvidenceRef]:
"""
Merge overlapping evidence references.
Combines evidence that refers to the same region.
"""
if len(evidence_list) <= 1:
return evidence_list
merged = []
used = set()
for i, ev1 in enumerate(evidence_list):
if i in used:
continue
# Find overlapping evidence
group = [ev1]
for j, ev2 in enumerate(evidence_list[i + 1:], start=i + 1):
if j in used:
continue
if (ev1.doc_id == ev2.doc_id and
ev1.page == ev2.page and
ev1.bbox.iou(ev2.bbox) > 0.5):
group.append(ev2)
used.add(j)
# Merge group
if len(group) == 1:
merged.append(ev1)
else:
merged.append(self._merge_evidence_group(group))
used.add(i)
return merged
def _merge_evidence_group(
self,
group: List[EvidenceRef],
) -> EvidenceRef:
"""Merge a group of overlapping evidence."""
# Take the one with highest confidence
best = max(group, key=lambda e: e.confidence)
# Merge bounding boxes
merged_bbox = BoundingBox(
x_min=min(e.bbox.x_min for e in group),
y_min=min(e.bbox.y_min for e in group),
x_max=max(e.bbox.x_max for e in group),
y_max=max(e.bbox.y_max for e in group),
normalized=best.bbox.normalized,
)
# Combine snippets
snippets = list(set(e.snippet for e in group if e.snippet))
combined_snippet = " | ".join(snippets)[:self.config.max_snippet_length]
return EvidenceRef(
chunk_id=best.chunk_id,
doc_id=best.doc_id,
page=best.page,
bbox=merged_bbox,
source_type=best.source_type,
snippet=combined_snippet,
confidence=max(e.confidence for e in group),
cell_id=best.cell_id,
crop_path=best.crop_path,
)
def _create_snippet(
self,
full_text: str,
value: str,
) -> str:
"""Create a text snippet highlighting the value."""
if not self.config.include_snippet:
return ""
# Try to find value in text
value_lower = value.lower()
text_lower = full_text.lower()
idx = text_lower.find(value_lower)
if idx >= 0 and self.config.include_context:
# Add context around value
start = max(0, idx - self.config.context_chars)
end = min(len(full_text), idx + len(value) + self.config.context_chars)
snippet = full_text[start:end]
if start > 0:
snippet = "..." + snippet
if end < len(full_text):
snippet = snippet + "..."
return snippet[:self.config.max_snippet_length]
# Return start of text
return full_text[:self.config.max_snippet_length]
def _generate_region_id(
self,
doc_id: str,
page: int,
bbox: BoundingBox,
) -> str:
"""Generate a stable ID for a region."""
content = f"{doc_id}_{page}_{bbox.xyxy}"
return hashlib.md5(content.encode()).hexdigest()[:16]
def _save_crop(
self,
image: Any,
chunk: DocumentChunk,
) -> Optional[str]:
"""Save a crop image for a chunk."""
return self._save_crop_direct(
image,
chunk.doc_id,
chunk.page,
chunk.chunk_id,
)
def _save_crop_direct(
self,
image: Any,
doc_id: str,
page: int,
identifier: str,
) -> Optional[str]:
"""Save a crop image directly."""
if self.config.crop_output_dir is None:
return None
try:
from PIL import Image
import numpy as np
# Convert to PIL if needed
if isinstance(image, np.ndarray):
pil_image = Image.fromarray(image)
elif isinstance(image, Image.Image):
pil_image = image
else:
return None
# Create output path
output_dir = Path(self.config.crop_output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
filename = f"{doc_id}_{page}_{identifier}.{self.config.crop_format}"
output_path = output_dir / filename
pil_image.save(output_path)
return str(output_path)
except Exception:
return None
class EvidenceTracker:
"""
Tracks evidence references during extraction.
Maintains a collection of evidence and provides
methods for querying and validation.
"""
def __init__(self):
self._evidence: List[EvidenceRef] = []
self._by_field: Dict[str, List[EvidenceRef]] = {}
self._by_chunk: Dict[str, List[EvidenceRef]] = {}
def add(
self,
evidence: EvidenceRef,
field_name: Optional[str] = None,
) -> None:
"""Add an evidence reference."""
self._evidence.append(evidence)
# Index by chunk
if evidence.chunk_id not in self._by_chunk:
self._by_chunk[evidence.chunk_id] = []
self._by_chunk[evidence.chunk_id].append(evidence)
# Index by field
if field_name:
if field_name not in self._by_field:
self._by_field[field_name] = []
self._by_field[field_name].append(evidence)
def get_all(self) -> List[EvidenceRef]:
"""Get all evidence references."""
return self._evidence.copy()
def get_for_field(self, field_name: str) -> List[EvidenceRef]:
"""Get evidence for a specific field."""
return self._by_field.get(field_name, []).copy()
def get_for_chunk(self, chunk_id: str) -> List[EvidenceRef]:
"""Get evidence from a specific chunk."""
return self._by_chunk.get(chunk_id, []).copy()
def get_by_page(self, page: int) -> List[EvidenceRef]:
"""Get evidence from a specific page."""
return [e for e in self._evidence if e.page == page]
def get_high_confidence(self, threshold: float = 0.8) -> List[EvidenceRef]:
"""Get evidence above confidence threshold."""
return [e for e in self._evidence if e.confidence >= threshold]
def validate_field(
self,
field_name: str,
min_evidence: int = 1,
min_confidence: float = 0.5,
) -> bool:
"""
Validate that a field has sufficient evidence.
Args:
field_name: Field to validate
min_evidence: Minimum number of evidence references
min_confidence: Minimum confidence score
Returns:
True if field has sufficient evidence
"""
field_evidence = self.get_for_field(field_name)
if len(field_evidence) < min_evidence:
return False
# Check confidence
max_confidence = max((e.confidence for e in field_evidence), default=0)
return max_confidence >= min_confidence
def clear(self) -> None:
"""Clear all evidence."""
self._evidence = []
self._by_field = {}
self._by_chunk = {}
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