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"""
Evidence Builder for Document Grounding
Creates evidence references for extracted information.
Handles image cropping and base64 encoding.
"""
import base64
import io
from typing import List, Optional, Dict, Any, Tuple
from pydantic import BaseModel, Field
import numpy as np
from PIL import Image
from loguru import logger
from ..schemas.core import (
BoundingBox,
DocumentChunk,
EvidenceRef,
OCRRegion,
)
class GroundingConfig(BaseModel):
"""Configuration for grounding and evidence generation."""
# Image cropping
include_images: bool = Field(
default=True,
description="Include cropped images in evidence"
)
crop_padding: int = Field(
default=10,
ge=0,
description="Padding around crop regions in pixels"
)
max_image_size: int = Field(
default=512,
ge=64,
description="Maximum dimension for cropped images"
)
image_format: str = Field(
default="PNG",
description="Image format for encoding (PNG/JPEG)"
)
image_quality: int = Field(
default=85,
ge=1,
le=100,
description="JPEG quality if using JPEG format"
)
# Snippet settings
max_snippet_length: int = Field(
default=200,
ge=50,
description="Maximum length of text snippets"
)
include_context: bool = Field(
default=True,
description="Include surrounding context in snippets"
)
def crop_region_image(
image: np.ndarray,
bbox: BoundingBox,
padding: int = 10,
max_size: Optional[int] = None,
) -> np.ndarray:
"""
Crop a region from an image.
Args:
image: Source image (RGB, HWC format)
bbox: Bounding box to crop
padding: Padding around the crop
max_size: Maximum dimension (will resize if larger)
Returns:
Cropped image as numpy array
"""
height, width = image.shape[:2]
# Get coordinates with padding
x1 = max(0, int(bbox.x_min) - padding)
y1 = max(0, int(bbox.y_min) - padding)
x2 = min(width, int(bbox.x_max) + padding)
y2 = min(height, int(bbox.y_max) + padding)
# Crop
cropped = image[y1:y2, x1:x2]
# Resize if needed
if max_size and max(cropped.shape[:2]) > max_size:
pil_img = Image.fromarray(cropped)
pil_img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
cropped = np.array(pil_img)
return cropped
def encode_image_base64(
image: np.ndarray,
format: str = "PNG",
quality: int = 85,
) -> str:
"""
Encode image to base64 string.
Args:
image: Image as numpy array
format: Image format (PNG/JPEG)
quality: JPEG quality if applicable
Returns:
Base64-encoded string
"""
pil_img = Image.fromarray(image)
# Convert to RGB if needed
if pil_img.mode != "RGB":
pil_img = pil_img.convert("RGB")
# Encode
buffer = io.BytesIO()
if format.upper() == "JPEG":
pil_img.save(buffer, format="JPEG", quality=quality)
else:
pil_img.save(buffer, format="PNG")
buffer.seek(0)
encoded = base64.b64encode(buffer.read()).decode("utf-8")
return encoded
def create_evidence_ref(
chunk: DocumentChunk,
source_type: str = "text",
snippet: Optional[str] = None,
confidence: float = 1.0,
image: Optional[np.ndarray] = None,
config: Optional[GroundingConfig] = None,
) -> EvidenceRef:
"""
Create an evidence reference from a document chunk.
Args:
chunk: Source chunk
source_type: Type of source (text/table/figure)
snippet: Optional specific snippet (defaults to chunk text)
confidence: Confidence score
image: Optional page image for cropping
config: Grounding configuration
Returns:
EvidenceRef instance
"""
config = config or GroundingConfig()
# Create snippet
if snippet is None:
snippet = chunk.text[:config.max_snippet_length]
if len(chunk.text) > config.max_snippet_length:
snippet += "..."
# Create base evidence
evidence = EvidenceRef(
chunk_id=chunk.chunk_id,
page=chunk.page,
bbox=chunk.bbox,
source_type=source_type,
snippet=snippet,
confidence=confidence,
)
# Add image if available and configured
if image is not None and config.include_images:
try:
cropped = crop_region_image(
image,
chunk.bbox,
padding=config.crop_padding,
max_size=config.max_image_size,
)
evidence.image_base64 = encode_image_base64(
cropped,
format=config.image_format,
quality=config.image_quality,
)
except Exception as e:
logger.warning(f"Failed to crop evidence image: {e}")
return evidence
class EvidenceBuilder:
"""
Builder for creating evidence references.
Handles:
- Evidence from chunks
- Evidence from OCR regions
- Evidence aggregation
- Image cropping and encoding
"""
def __init__(self, config: Optional[GroundingConfig] = None):
"""Initialize evidence builder."""
self.config = config or GroundingConfig()
def from_chunk(
self,
chunk: DocumentChunk,
image: Optional[np.ndarray] = None,
additional_context: Optional[str] = None,
) -> EvidenceRef:
"""
Create evidence reference from a chunk.
Args:
chunk: Source chunk
image: Optional page image for visual evidence
additional_context: Optional additional context
Returns:
EvidenceRef
"""
# Determine source type
source_type = chunk.chunk_type.value
# Build snippet with optional context
snippet = chunk.text[:self.config.max_snippet_length]
if additional_context:
snippet = f"{additional_context}\n{snippet}"
if len(chunk.text) > self.config.max_snippet_length:
snippet += "..."
return create_evidence_ref(
chunk=chunk,
source_type=source_type,
snippet=snippet,
confidence=chunk.confidence,
image=image,
config=self.config,
)
def from_ocr_region(
self,
region: OCRRegion,
chunk_id: str,
document_id: str,
image: Optional[np.ndarray] = None,
) -> EvidenceRef:
"""
Create evidence reference from an OCR region.
Args:
region: OCR region
chunk_id: ID to assign
document_id: Parent document ID
image: Optional page image
Returns:
EvidenceRef
"""
# Create a temporary chunk for the evidence
from ..schemas.core import DocumentChunk, ChunkType
chunk = DocumentChunk(
chunk_id=chunk_id,
chunk_type=ChunkType.TEXT,
text=region.text,
bbox=region.bbox,
page=region.page,
document_id=document_id,
source_path=None,
sequence_index=0,
confidence=region.confidence,
)
return self.from_chunk(chunk, image)
def aggregate_evidence(
self,
evidence_list: List[EvidenceRef],
combine_snippets: bool = True,
) -> List[EvidenceRef]:
"""
Aggregate and deduplicate evidence references.
Args:
evidence_list: List of evidence references
combine_snippets: Whether to combine snippets from same chunk
Returns:
Deduplicated evidence list
"""
if not evidence_list:
return []
# Group by chunk_id
by_chunk: Dict[str, List[EvidenceRef]] = {}
for ev in evidence_list:
if ev.chunk_id not in by_chunk:
by_chunk[ev.chunk_id] = []
by_chunk[ev.chunk_id].append(ev)
# Combine or select best
result = []
for chunk_id, evidences in by_chunk.items():
if len(evidences) == 1:
result.append(evidences[0])
else:
# Take highest confidence, combine snippets
best = max(evidences, key=lambda e: e.confidence)
if combine_snippets:
all_snippets = list(set(e.snippet for e in evidences))
combined = " ... ".join(all_snippets[:3])
best = EvidenceRef(
chunk_id=best.chunk_id,
page=best.page,
bbox=best.bbox,
source_type=best.source_type,
snippet=combined[:self.config.max_snippet_length],
confidence=best.confidence,
image_base64=best.image_base64,
)
result.append(best)
# Sort by page and position
result.sort(key=lambda e: (e.page, e.bbox.y_min, e.bbox.x_min))
return result
def create_grounding_context(
self,
evidence_list: List[EvidenceRef],
include_images: bool = False,
) -> str:
"""
Create a text context from evidence for LLM prompting.
Args:
evidence_list: Evidence references
include_images: Whether to include image markers
Returns:
Formatted context string
"""
if not evidence_list:
return ""
lines = ["Evidence from document:"]
for i, ev in enumerate(evidence_list, 1):
lines.append(
f"\n[{i}] Page {ev.page + 1}, {ev.source_type} "
f"(confidence: {ev.confidence:.2f}):"
)
lines.append(f' "{ev.snippet}"')
if include_images and ev.image_base64:
lines.append(" [Image available]")
return "\n".join(lines)
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