# Exercise 2: Enterprise RAG System - Starter Code
# rag_system.py - Main RAG orchestration

import asyncio
from typing import List, Dict, Any, Optional
import logging
from datetime import datetime
import uuid

from config import RAGSettings
from query_engine import QueryEngine
from generator import ResponseGenerator
from document_processor import DocumentProcessor
from evaluation import RAGEvaluator

logger = logging.getLogger(__name__)

class RAGSystem:
    """
    Complete RAG system orchestrating retrieval and generation.
    """
    
    def __init__(self, settings: RAGSettings):
        """
        Initialize RAG system components.
        
        Args:
            settings: RAG configuration settings
        """
        self.settings = settings
        self.query_engine = None
        self.generator = None
        self.evaluator = None
        self.conversation_history = []
        
    async def initialize(self):
        """Initialize all RAG components."""
        # TODO: Initialize components
        # Copilot Prompt: Initialize query engine, generator, and evaluator
        # Set up vector store and embedding service
        pass
    
    async def query(
        self,
        query: str,
        mode: str = "balanced",  # concise, balanced, detailed
        filters: Optional[Dict[str, Any]] = None,
        include_sources: bool = True,
        use_history: bool = True
    ) -> Dict[str, Any]:
        """
        Execute RAG query pipeline.
        
        Args:
            query: User question
            mode: Response mode
            filters: Metadata filters for retrieval
            include_sources: Include source citations
            use_history: Use conversation history
            
        Returns:
            Response with sources and metadata
        """
        request_id = str(uuid.uuid4())
        start_time = datetime.utcnow()
        
        try:
            # TODO: Implement RAG pipeline
            # Copilot Prompt: 
            # 1. Retrieve relevant context using query engine
            # 2. Generate response using retrieved context
            # 3. Add citations if requested
            # 4. Update conversation history
            # 5. Evaluate response quality asynchronously
            
            # Placeholder implementation
            return {
                "request_id": request_id,
                "response": "RAG response placeholder",
                "sources": [],
                "confidence": 0.0,
                "metadata": {
                    "mode": mode,
                    "processing_time_ms": 0
                }
            }
            
        except Exception as e:
            logger.error(f"RAG query failed: {str(e)}")
            return self._error_response(request_id, str(e))
    
    async def query_stream(
        self,
        query: str,
        mode: str = "balanced",
        filters: Optional[Dict[str, Any]] = None
    ):
        """
        Stream RAG response.
        
        Args:
            query: User question
            mode: Response mode
            filters: Metadata filters
            
        Yields:
            Response chunks as they're generated
        """
        # TODO: Implement streaming RAG
        # Copilot Prompt: 
        # 1. Retrieve context first (non-streaming)
        # 2. Stream generation token by token
        # 3. Yield formatted chunks with metadata
        
        yield "Streaming not implemented yet"
    
    async def index_documents(
        self,
        documents: List[str],
        batch_size: int = 10
    ) -> Dict[str, Any]:
        """
        Index new documents into the RAG system.
        
        Args:
            documents: List of document paths
            batch_size: Batch size for processing
            
        Returns:
            Indexing results and statistics
        """
        # TODO: Implement document indexing
        # Copilot Prompt:
        # 1. Process documents using document processor
        # 2. Chunk documents intelligently
        # 3. Generate embeddings for chunks
        # 4. Store in vector database
        # 5. Return indexing statistics
        
        return {
            "documents_processed": 0,
            "chunks_created": 0,
            "errors": []
        }
    
    async def update_document(
        self,
        document_id: str,
        new_content: str
    ) -> bool:
        """
        Update an existing document in the system.
        
        Args:
            document_id: Document identifier
            new_content: Updated content
            
        Returns:
            Success status
        """
        # TODO: Implement document update
        # Copilot Prompt:
        # 1. Remove old document chunks
        # 2. Process new content
        # 3. Re-index updated chunks
        # 4. Maintain version history
        
        return False
    
    async def evaluate_response(
        self,
        query: str,
        response: str,
        context: List[Dict[str, Any]]
    ) -> Dict[str, float]:
        """
        Evaluate RAG response quality.
        
        Args:
            query: Original query
            response: Generated response
            context: Retrieved context
            
        Returns:
            Evaluation metrics
        """
        # TODO: Implement evaluation
        # Copilot Prompt:
        # 1. Evaluate relevance of response to query
        # 2. Check factual accuracy against context
        # 3. Assess response coherence
        # 4. Measure citation accuracy
        
        return {
            "relevance": 0.0,
            "factuality": 0.0,
            "coherence": 0.0,
            "citation_accuracy": 0.0
        }
    
    async def get_conversation_history(
        self,
        limit: int = 10
    ) -> List[Dict[str, Any]]:
        """Get recent conversation history."""
        return self.conversation_history[-limit:]
    
    async def clear_conversation_history(self):
        """Clear conversation history."""
        self.conversation_history = []
        logger.info("Conversation history cleared")
    
    async def health_check(self) -> Dict[str, Any]:
        """Check system health."""
        # TODO: Implement health check
        # Copilot Prompt:
        # 1. Check vector store connectivity
        # 2. Verify embedding service
        # 3. Test generator availability
        # 4. Return component statuses
        
        return {
            "status": "healthy",
            "components": {
                "query_engine": "unknown",
                "generator": "unknown",
                "vector_store": "unknown"
            }
        }
    
    def _error_response(
        self,
        request_id: str,
        error_message: str
    ) -> Dict[str, Any]:
        """Create error response."""
        return {
            "request_id": request_id,
            "response": "I encountered an error processing your request.",
            "sources": [],
            "confidence": 0.0,
            "metadata": {
                "error": error_message,
                "status": "error"
            }
        }
    
    async def record_feedback(
        self,
        request_id: str,
        rating: int,
        comment: Optional[str] = None
    ):
        """Record user feedback for a response."""
        # TODO: Implement feedback recording
        # Copilot Prompt: Store feedback for continuous improvement
        pass