"""
Advanced RAG Processor - Modular Version
Orchestrates all RAG components for document question answering.
Version: 3.0 - Modular Architecture
"""

import time
from typing import Dict, Tuple
from pathlib import Path

# Import all modular components
from RAG.rag_modules.query_expansion import QueryExpansionManager
from RAG.rag_modules.embedding_manager import EmbeddingManager
from RAG.rag_modules.search_manager import SearchManager
from RAG.rag_modules.reranking_manager import RerankingManager
from RAG.rag_modules.context_manager import ContextManager
from RAG.rag_modules.answer_generator import AnswerGenerator

from LLM.llm_handler import llm_handler
from config.config import OUTPUT_DIR, TOP_K


class AdvancedRAGProcessor:
    """
    Advanced RAG processor with modular architecture for better maintainability.
    Orchestrates query expansion, hybrid search, reranking, and answer generation.
    """
    
    def __init__(self):
        """Initialize the advanced RAG processor with all modules."""
        self.base_db_path = Path(OUTPUT_DIR)
        
        # Initialize all managers
        print("🚀 Initializing Advanced RAG Processor (Modular)...")
        
        # Core components
        self.embedding_manager = EmbeddingManager()
        self.query_expansion_manager = QueryExpansionManager()
        self.search_manager = SearchManager(self.embedding_manager)
        self.reranking_manager = RerankingManager()
        self.context_manager = ContextManager()
        self.answer_generator = AnswerGenerator()
        
        # Keep reference to LLM handler for info
        self.llm_handler = llm_handler
        
        print(f"✅ Advanced RAG Processor initialized with {self.llm_handler.provider.upper()} LLM")
        print("📦 All modules loaded successfully:")
        print("   🔄 Query Expansion Manager")
        print("   🧠 Embedding Manager")
        print("   🔍 Search Manager (Hybrid)")
        print("   🎯 Reranking Manager")
        print("   📝 Context Manager")
        print("   💬 Answer Generator")
    
    async def answer_question(self, question: str, doc_id: str, logger=None, request_id: str = None) -> Tuple[str, Dict[str, float]]:
        """
        Answer a question using advanced RAG techniques with detailed timing.
        
        Args:
            question: The question to answer
            doc_id: Document ID to search in
            logger: Optional logger for tracking
            request_id: Optional request ID for logging
            
        Returns:
            Tuple of (answer, timing_breakdown)
        """
        timings = {}
        overall_start = time.time()
        
        try:
            # Check if collection exists
            collection_name = f"{doc_id}_collection"
            try:
                client = self.search_manager.get_qdrant_client(doc_id)
                collection_info = client.get_collection(collection_name)
            except Exception:
                return "I don't have information about this document. Please ensure the document has been processed.", timings
            
            print(f"🚀 Advanced RAG processing for: {question[:100]}...")
            
            # Step 1: Query Expansion
            step_start = time.time()
            expanded_queries = await self.query_expansion_manager.expand_query(question)
            expansion_time = time.time() - step_start
            timings['query_expansion'] = expansion_time
            if logger and request_id:
                logger.log_pipeline_stage(request_id, "query_expansion", expansion_time)
            
            # Step 2: Hybrid Search with Fusion
            step_start = time.time()
            search_results = await self.search_manager.hybrid_search(expanded_queries, doc_id, TOP_K)
            search_time = time.time() - step_start
            timings['hybrid_search'] = search_time
            if logger and request_id:
                logger.log_pipeline_stage(request_id, "hybrid_search", search_time)
            
            if not search_results:
                return "I couldn't find relevant information to answer your question.", timings
            
            # Step 3: Reranking
            step_start = time.time()
            reranked_results = await self.reranking_manager.rerank_results(question, search_results)
            rerank_time = time.time() - step_start
            timings['reranking'] = rerank_time
            if logger and request_id:
                logger.log_pipeline_stage(request_id, "reranking", rerank_time)
            
            # Step 4: Multi-perspective Context Creation
            step_start = time.time()
            context = self.context_manager.create_enhanced_context(question, reranked_results)
            context_time = time.time() - step_start
            timings['context_creation'] = context_time
            if logger and request_id:
                logger.log_pipeline_stage(request_id, "context_creation", context_time)

            # Step 5: Enhanced Answer Generation
            step_start = time.time()
            answer = await self.answer_generator.generate_enhanced_answer(question, context, expanded_queries)
            generation_time = time.time() - step_start
            timings['llm_generation'] = generation_time
            if logger and request_id:
                logger.log_pipeline_stage(request_id, "llm_generation", generation_time)

            # Calculate total time
            total_time = time.time() - overall_start
            timings['total_pipeline'] = total_time

            print(f"\n✅ Advanced RAG processing completed in {total_time:.4f}s")
            print(f"   🔍 Query expansion: {expansion_time:.4f}s")
            print(f"   🔎 Hybrid search: {search_time:.4f}s") 
            print(f"   🎯 Reranking: {rerank_time:.4f}s")
            print(f"   📝 Context creation: {context_time:.4f}s")
            print(f"   💬 LLM generation: {generation_time:.4f}s")

            return answer, timings
            
        except Exception as e:
            error_time = time.time() - overall_start
            timings['error_time'] = error_time
            print(f"❌ Error in advanced RAG processing: {str(e)}")
            return f"I encountered an error while processing your question: {str(e)}", timings
    
    def cleanup(self):
        """Cleanup all manager resources."""
        print("🧹 Cleaning up Advanced RAG processor resources...")
        
        # Cleanup search manager (which has the most resources)
        self.search_manager.cleanup()
        
        print("✅ Advanced RAG cleanup completed")
    
    def get_system_info(self) -> Dict:
        """Get information about the RAG system."""
        return {
            "version": "3.0 - Modular",
            "llm_provider": self.llm_handler.provider,
            "llm_model": self.llm_handler.model_name,
            "modules": [
                "QueryExpansionManager",
                "EmbeddingManager", 
                "SearchManager",
                "RerankingManager",
                "ContextManager",
                "AnswerGenerator"
            ],
            "base_db_path": str(self.base_db_path)
        }