"""
Entity IR Models
Data structures for entity retrieval system
"""

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional


@dataclass
class Entity:
    """
    Represents an entity from the knowledge base.
    Designed to work with both wiki_summary.json and dhow_summary.json formats.
    """
    name: str                           # Primary name (first in variants list)
    id: str = ""                        # Entity ID from knowledge base
    variants: List[str] = field(default_factory=list)  # All name variations
    source: str = ""                    # "wiki" or "dhow"

    # Core facts
    title: str = ""
    url: str = ""
    raw_text: str = ""
    summary: str = ""

    # Dhow-specific fields
    primary_position: str = ""
    primary_organization: str = ""
    family_name: str = ""
    city: str = ""
    country: str = ""

    # Full original data for RAG
    facts: Dict[str, Any] = field(default_factory=dict)
    
    def __post_init__(self):
        """Ensure name is always in variants"""
        if self.name and self.name not in self.variants:
            self.variants.insert(0, self.name)
    
    def get_searchable_text(self) -> str:
        """
        Returns combined text for BM25 indexing.
        Includes name variants + position/organization for disambiguation.
        """
        parts = list(self.variants)
        if self.primary_position:
            parts.append(self.primary_position)
        if self.primary_organization:
            parts.append(self.primary_organization)
        if self.family_name:
            parts.append(self.family_name)
        return " ".join(parts)


@dataclass
class RetrievalResult:
    """
    Result from entity retrieval with scoring information.
    """
    entity: Entity
    score: float
    match_type: str  # "alias", "exact", "bm25", "hybrid"
    
    # Debug info
    matched_variant: Optional[str] = None  # Which variant was matched
    normalized_query: Optional[str] = None  # Query after normalization


@dataclass
class RetrievalConfig:
    """
    Configuration for retrieval system.
    """
    # BM25 parameters (optimized for short Arabic names)
    bm25_k1: float = 1.8   # Term frequency saturation
    bm25_b: float = 0.4    # Document length normalization
    
    # Retrieval settings
    top_k: int = 5
    alias_boost: float = 10.0     # Score multiplier for alias matches
    exact_match_boost: float = 5.0  # Score multiplier for exact matches
    
    # Thresholds
    min_score_threshold: float = 0.1  # Minimum score to return


@dataclass 
class BenchmarkResult:
    """
    Results from running benchmark evaluation.
    """
    total_queries: int
    precision_at_1: float
    recall_at_5: float
    avg_latency_ms: float
    p95_latency_ms: float
    
    # Detailed breakdown
    alias_hits: int = 0
    bm25_hits: int = 0
    misses: int = 0
    
    def __str__(self) -> str:
        return f"""
Benchmark Results:
  Total Queries: {self.total_queries}
  Precision@1:   {self.precision_at_1:.2%}
  Recall@5:      {self.recall_at_5:.2%}
  Avg Latency:   {self.avg_latency_ms:.2f}ms
  P95 Latency:   {self.p95_latency_ms:.2f}ms
  
  Breakdown:
    Alias Hits:  {self.alias_hits}
    BM25 Hits:   {self.bm25_hits}
    Misses:      {self.misses}
""".strip()