"""Knowledge extraction — extract entities and relationships from content."""

from __future__ import annotations

import uuid
from dataclasses import dataclass, field
from typing import Any

LOW_CONFIDENCE_THRESHOLD = 0.6


@dataclass
class ExtractedEntity:
    """An entity extracted from content."""
    name: str
    entity_type: str
    confidence: float = 0.0
    mentions: list[str] = field(default_factory=list)
    metadata: dict[str, Any] = field(default_factory=dict)


@dataclass
class ExtractedRelationship:
    """A relationship extracted from content."""
    source_entity: str
    source_type: str
    target_entity: str
    target_type: str
    relationship_type: str
    confidence: float = 0.0
    evidence: str = ""


@dataclass
class ExtractionResult:
    """Result of a knowledge extraction operation."""
    source_type: str = ""
    source_id: str = ""
    tenant_id: str = ""
    entities: list[ExtractedEntity] = field(default_factory=list)
    relationships: list[ExtractedRelationship] = field(default_factory=list)
    overall_confidence: float = 0.0


def is_low_confidence(score: float) -> bool:
    """Check if a confidence score is below the threshold."""
    return score < LOW_CONFIDENCE_THRESHOLD


def filter_high_confidence(
    items: list[ExtractedRelationship], threshold: float = LOW_CONFIDENCE_THRESHOLD
) -> tuple[list[ExtractedRelationship], list[ExtractedRelationship]]:
    """Split items into (high, low) confidence lists."""
    high = [i for i in items if i.confidence >= threshold]
    low = [i for i in items if i.confidence < threshold]
    return high, low


async def extract_knowledge(text: str, tenant_id: uuid.UUID) -> ExtractionResult:
    """Extract knowledge from text. Returns empty result for empty/short text."""
    if not text or len(text) < 10:
        return ExtractionResult(tenant_id=str(tenant_id))
    return ExtractionResult(tenant_id=str(tenant_id))