ir/__init__.py

"""
Libra IR - Entity Information Retrieval Module

A standalone IR module for entity retrieval with sensitive topic detection.
Optimized for Precision, Recall, and Latency.

Architecture Levels:
- Level 3 (Current): Alias Lookup + BM25 + Reranking + Sensitive Detection
- Level 4 (Planned): + Dense Retrieval (Embeddings + FAISS)
- Level 5 (Planned): + Hybrid Fusion (BM25 + Dense + RRF)
- Level 6 (Planned): + LLM Verification

Usage:
    from libra_shield.ir import EntityRetriever
    
    retriever = EntityRetriever()
    result = retriever.retrieve("Tell me about MBZ")
    
    # Check for sensitive topics
    if result.is_sensitive:
        print(f"Sensitive: {result.reframe_guidance}")
    
    for r in result.entities:
        print(f"{r.entity.name}: {r.score:.2f}")
"""

from .knowledge_base import KnowledgeBase
from .models import (
    BenchmarkResult,
    Entity,
    RetrievalConfig,
    RetrievalResult,
)
from .normalizer import ArabicNameNormalizer, get_normalizer
from .reranker import EntityReranker, rerank_results
from .retriever import EntityRetriever, EnhancedAliasRetriever, RetrievalOutput
from .retrievers.alias import AliasRetriever
from .retrievers.bm25 import BM25Retriever
from .sensitive_detector import SensitiveMatch, SensitiveTopicDetector


__all__ = [
    # Main entry point
    "EntityRetriever",
    "RetrievalOutput",
    
    # Knowledge Base
    "KnowledgeBase",
    
    # Sensitive Detection
    "SensitiveTopicDetector",
    "SensitiveMatch",
    
    # Core components
    "ArabicNameNormalizer",
    "get_normalizer",
    
    # Retrievers (internal, for advanced usage)
    "EnhancedAliasRetriever",
    "AliasRetriever", 
    "BM25Retriever",
    
    # Reranker
    "EntityReranker",
    "rerank_results",
    
    # Models
    "Entity",
    "RetrievalResult",
    "RetrievalConfig",
    "BenchmarkResult",
]