"""
SPARKNET Document Intelligence Subsystem

A comprehensive document processing pipeline for:
- OCR with PaddleOCR and Tesseract
- Layout detection and reading order reconstruction
- Semantic chunking with grounding evidence
- Document classification and field extraction
- Extraction validation with Critic/Verifier

Principles:
- Processing is not understanding: OCR alone is insufficient
- Every extraction includes evidence pointers (bbox, page, chunk_id)
- Modular, pluggable components with clean interfaces
- Abstain with evidence when confidence is low
"""

from .schemas.core import (
    BoundingBox,
    OCRRegion,
    LayoutRegion,
    LayoutType,
    DocumentChunk,
    ChunkType,
    EvidenceRef,
    ExtractionResult,
    DocumentMetadata,
    ProcessedDocument,
)

from .pipeline import (
    PipelineConfig,
    DocumentProcessor,
    get_document_processor,
    process_document,
)

from .validation import (
    CriticConfig,
    ValidationResult,
    ExtractionCritic,
    get_extraction_critic,
    VerifierConfig,
    VerificationResult,
    EvidenceVerifier,
    get_evidence_verifier,
)

__all__ = [
    # Core schemas
    "BoundingBox",
    "OCRRegion",
    "LayoutRegion",
    "LayoutType",
    "DocumentChunk",
    "ChunkType",
    "EvidenceRef",
    "ExtractionResult",
    "DocumentMetadata",
    "ProcessedDocument",
    # Pipeline
    "PipelineConfig",
    "DocumentProcessor",
    "get_document_processor",
    "process_document",
    # Validation
    "CriticConfig",
    "ValidationResult",
    "ExtractionCritic",
    "get_extraction_critic",
    "VerifierConfig",
    "VerificationResult",
    "EvidenceVerifier",
    "get_evidence_verifier",
]