import logging
from abc import ABC, abstractmethod
from typing import Any

from .schemas import TeamRole
from .state import AgentState

logger = logging.getLogger("agents")

class BaseAgent(ABC):
    """
    Abstract Base Class for all agents (Specialists and Auxiliary).
    Enforces a strict `.process()` interface for LangGraph nodes.
    """

    role: TeamRole | None = None

    @abstractmethod
    async def process(self, state: AgentState) -> dict[str, Any]:
        """
        Process the given state and return updates to be merged into the AgentState.
        Should handle its own LLM interactions, prompts, and schema validation.
        """
        pass

    def get_context_for_prompt(self, state: AgentState) -> str:
        """Utility for extracting the combined readable context for prompts."""
        parts = []
        if state.get("context"):
            parts.append(state["context"])

        if state.get("prd_context"):
            prd = state["prd_context"]
            # Formatting logic to render parts of the PRD context if needed
            # E.g., user stories, constraints.
            if isinstance(prd, dict) and "full_text" in prd:
                parts.append(prd["full_text"])

        # Could also include RAG retrieval_context if applicable
        if state.get("retrieval_context"):
            parts.append("--- REFERENCE DOCUMENTS ---")
            parts.append(state["retrieval_context"])

        return "\n\n".join(parts)