# reasoning.py
import os
from llm.llm_router import call_llm


# ============================================================
# Select which backend this module should use
# ============================================================
CORTEX_LLM = os.getenv("CORTEX_LLM", "PRIMARY").upper()
GLOBAL_TEMP = float(os.getenv("LLM_TEMPERATURE", "0.7"))


async def reason_check(
    user_prompt: str,
    identity_block: dict | None,
    rag_block: dict | None,
    reflection_notes: list[str]
) -> str:
    """
    Build the *draft answer* for Lyra Cortex.
    This is the first-pass reasoning stage (no refinement yet).
    """

    # --------------------------------------------------------
    # Build Reflection Notes block
    # --------------------------------------------------------
    notes_section = ""
    if reflection_notes:
        notes_section = "Reflection Notes (internal, never show to user):\n"
        for note in reflection_notes:
            notes_section += f"- {note}\n"
        notes_section += "\n"

    # --------------------------------------------------------
    # Identity block (constraints, boundaries, rules)
    # --------------------------------------------------------
    identity_txt = ""
    if identity_block:
        try:
            identity_txt = f"Identity Rules:\n{identity_block}\n\n"
        except Exception:
            identity_txt = f"Identity Rules:\n{str(identity_block)}\n\n"

    # --------------------------------------------------------
    # RAG block (optional factual grounding)
    # --------------------------------------------------------
    rag_txt = ""
    if rag_block:
        try:
            rag_txt = f"Relevant Info (RAG):\n{rag_block}\n\n"
        except Exception:
            rag_txt = f"Relevant Info (RAG):\n{str(rag_block)}\n\n"

    # --------------------------------------------------------
    # Final assembled prompt
    # --------------------------------------------------------
    prompt = (
        f"{notes_section}"
        f"{identity_txt}"
        f"{rag_txt}"
        f"User message:\n{user_prompt}\n\n"
        "Write the best possible *internal draft answer*.\n"
        "This draft is NOT shown to the user.\n"
        "Be factual, concise, and focused.\n"
    )

    # --------------------------------------------------------
    # Call the LLM using the module-specific backend
    # --------------------------------------------------------
    draft = await call_llm(
        prompt,
        backend=CORTEX_LLM,
        temperature=GLOBAL_TEMP,
    )

    return draft