"""
MCAL LangChain Integration

Provides memory components for LangChain chains and agents.

Installation:
    pip install mcal[langchain]

Usage:
    from mcal.integrations.langchain import MCALChatMemory
    
    memory = MCALChatMemory(llm_provider="anthropic")
    chain = ConversationChain(llm=llm, memory=memory)

Status: Coming Soon
"""

from typing import Any, Dict, List, Optional

# Check for LangChain availability
try:
    from langchain_core.memory import BaseMemory
    from langchain_core.messages import BaseMessage
    LANGCHAIN_AVAILABLE = True
except ImportError:
    LANGCHAIN_AVAILABLE = False
    BaseMemory = object  # Placeholder for type hints


def _check_langchain():
    """Raise helpful error if LangChain not installed."""
    if not LANGCHAIN_AVAILABLE:
        raise ImportError(
            "LangChain integration requires langchain-core package.\n"
            "Install with: pip install mcal[langchain]"
        )


class MCALChatMemory(BaseMemory if LANGCHAIN_AVAILABLE else object):
    """
    MCAL Memory for LangChain conversation chains.
    
    Provides goal-aware memory that preserves reasoning context
    across LangChain conversations.
    
    Status: Coming Soon - Basic structure implemented.
    
    Usage:
        from mcal.integrations.langchain import MCALChatMemory
        from langchain.chains import ConversationChain
        
        memory = MCALChatMemory(llm_provider="anthropic")
        
        chain = ConversationChain(
            llm=llm,
            memory=memory,
        )
    """
    
    # LangChain memory interface
    memory_key: str = "history"
    return_messages: bool = True
    
    def __init__(
        self,
        mcal: Optional[Any] = None,
        llm_provider: str = "anthropic",
        embedding_provider: str = "openai",
        storage_path: Optional[str] = None,
        user_id: str = "default",
        **mcal_kwargs
    ):
        _check_langchain()
        
        if LANGCHAIN_AVAILABLE:
            super().__init__()
        
        self.user_id = user_id
        
        if mcal is not None:
            # Use provided MCAL instance (Issue #106)
            self._mcal = mcal
        else:
            # Use singleton factory (Issue #106)
            from mcal import get_mcal
            self._mcal = get_mcal(
                storage_path=storage_path,
                llm_provider=llm_provider,
                embedding_provider=embedding_provider,
                **mcal_kwargs
            )
        self._messages: List[Any] = []
    
    @property
    def memory_variables(self) -> List[str]:
        """Memory variables provided to prompts."""
        return [self.memory_key]
    
    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Load memory variables for chain."""
        # TODO: Implement LangChain-specific memory loading
        if self.return_messages:
            return {self.memory_key: self._messages}
        return {self.memory_key: ""}
    
    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
        """Save context from chain run."""
        # TODO: Implement LangChain-specific context saving
        pass
    
    def clear(self) -> None:
        """Clear memory."""
        self._messages = []


__all__ = ["MCALChatMemory", "LANGCHAIN_AVAILABLE"]
