"""pipeline/cache.py — LRU answer cache.

Keyed on MD5(author_id + book_id + normalized query).
Stores raw LLM answers only — upsell formatting is re-applied per session on hit.
Max 256 slots. Evicts LRU on overflow.
NOT cached: purchase_intent, complaint, greeting (personal / time-sensitive).
"""
from __future__ import annotations

import hashlib
from collections import OrderedDict
from dataclasses import dataclass, field


@dataclass
class CachedAnswer:
    """Raw generation output — upsell/link state is NOT cached."""

    raw_response: str
    faithfulness_score: float = 1.0
    hallucination_detected: bool = False
    prompt_tokens: int = 0
    completion_tokens: int = 0
    top_book_ids: list[str] = field(default_factory=list)
    intent: str = "question"
    intent_confidence: float = 0.7


_CACHE_MAX = 256
_answer_cache: OrderedDict[str, CachedAnswer] = OrderedDict()


def cache_key(author_id: str, book_id: str | None, query: str) -> str:
    """Generate a stable MD5 cache key."""
    raw = f"{author_id}:{book_id or ''}:{query.lower().strip()}"
    return hashlib.md5(raw.encode()).hexdigest()


def cache_get(key: str) -> CachedAnswer | None:
    """Return cached raw answer or None. Promotes the key to most-recently-used."""
    if key in _answer_cache:
        _answer_cache.move_to_end(key)
        return _answer_cache[key]
    return None


def cache_set(key: str, answer: CachedAnswer) -> None:
    """Store a raw answer. Evicts least-recently-used entry when over capacity."""
    _answer_cache[key] = answer
    _answer_cache.move_to_end(key)
    if len(_answer_cache) > _CACHE_MAX:
        _answer_cache.popitem(last=False)


def invalidate_book_cache(author_id: str, book_id: str) -> int:  # noqa: ARG001
    """Remove ALL cached answers for this author when a book changes.

    Called by the ingest pipeline on re-upload to prevent stale answers.

    Returns:
        Number of cache entries removed.
    """
    count = len(_answer_cache)
    _answer_cache.clear()
    return count