"""
Implementation of the ContextualRAG class for the Scoras library.
This file provides the ContextualRAG class that extends SimpleRAG with contextual awareness.
"""

from typing import List, Dict, Any, Optional, Union
from .rag import SimpleRAG, Document

class ContextualRAG(SimpleRAG):
    """
    Extends SimpleRAG with contextual awareness.
    
    This class provides a more advanced RAG system that takes context into account
    when retrieving documents and generating responses.
    """
    
    def __init__(
        self,
        agent: Any,
        documents: Optional[List[Document]] = None,
        context_window: int = 5,
        enable_scoring: bool = True
    ):
        """
        Initialize a contextual RAG system.
        
        Args:
            agent: Agent to use for generation
            documents: Optional list of documents
            context_window: Size of the context window
            enable_scoring: Whether to track complexity scoring
        """
        super().__init__(agent=agent, documents=documents, enable_scoring=enable_scoring)
        self.context_window = context_window
        
        # Add additional complexity score for contextual awareness
        if self._enable_scoring:
            self._complexity_score.components["contextual"] = 1.0
            self._complexity_score.total_score += 1.0
            self._complexity_score.update()
    
    async def run(self, query: str, context: Optional[str] = None) -> str:
        """
        Run the contextual RAG system on the given query asynchronously.
        
        Args:
            query: Query to run
            context: Optional additional context
            
        Returns:
            Generated response
        """
        # In a real implementation, this would:
        # 1. Retrieve relevant documents based on the query and context
        # 2. Generate a prompt with the documents, query, and context
        # 3. Call the agent with the prompt
        # For now, we'll just return a placeholder response
        context_info = f" with context" if context else ""
        return f"Contextual RAG response for: {query}{context_info} (using {len(self.documents)} documents)"
    
    def run_sync(self, query: str, context: Optional[str] = None) -> str:
        """
        Run the contextual RAG system on the given query synchronously.
        
        Args:
            query: Query to run
            context: Optional additional context
            
        Returns:
            Generated response
        """
        # Synchronous version of run
        import asyncio
        return asyncio.run(self.run(query, context))
