"""
MemoryRecallInjector

Injects relevant memories into LLM system prompt before response generation.
Token budget: 800 by default (configurable).
Priority: pain_point > goal > preference > fact > event
"""

from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from sqlalchemy.ext.asyncio import AsyncSession
    from app.models.memory import UserMemory


MEMORY_TYPE_PRIORITY = {
    "pain_point": 1,
    "goal": 2,
    "preference": 3,
    "fact": 4,
    "event": 5,
}

DEFAULT_TOKEN_BUDGET = 800


class MemoryRecallInjector:
    """Inject relevant memories into system prompt with token budget control."""

    def __init__(self, token_budget: int = DEFAULT_TOKEN_BUDGET):
        self.token_budget = token_budget

    async def build_context(
        self,
        db: "AsyncSession",
        user_id: str,
        current_message: str,
        token_budget: int | None = None,
    ) -> str:
        """Build memory context string for injection into system prompt.

        1. Recall relevant memories (top_k=20)
        2. Filter out archived memories
        3. Rank by importance × relevance × type priority
        4. Select within token budget
        5. Format as system prompt fragment
        """
        budget = token_budget or self.token_budget

        # 1. Recall candidates using existing memory service
        candidates = await recall_user_memories_for_injection(
            db, user_id, current_message, top_k=20
        )

        # 2. Filter archived and non-UserMemory
        active = [
            m
            for m in candidates
            if isinstance(m, UserMemory) and not getattr(m, "is_archived", False)
        ]

        # 3. Rank
        ranked = self._rank(active, current_message)

        # 4. Budget select
        selected = self._budget_select(ranked, budget)

        # 5. Format
        return self._format(selected)

    def _rank(
        self,
        memories: list["UserMemory"],
        query: str,
    ) -> list["UserMemory"]:
        """Rank memories by: relevance * 0.6 + importance * 0.4 * type_boost.

        pain_point/goal get 1.0 type boost, others get 0.8.
        """

        def score(m: "UserMemory") -> float:
            relevance = (
                getattr(m, "similarity_score", 0.5) if hasattr(m, "similarity_score") else 0.5
            )
            importance = getattr(m, "importance_score", 0.5) or 0.5
            mem_type = getattr(m, "memory_type", None) or "fact"
            type_boost = 1.0 if mem_type in ("goal", "pain_point") else 0.8
            return relevance * 0.6 + importance * 0.4 * type_boost

        return sorted(memories, key=score, reverse=True)

    def _budget_select(
        self,
        memories: list["UserMemory"],
        token_budget: int,
    ) -> list["UserMemory"]:
        """Greedy selection until token budget runs out.

        Rough estimate: 1 token ≈ 2 characters, fixed overhead = 20 tokens.
        """
        selected = []
        used = 20  # "[关于你的记忆]\n"
        for m in memories:
            content = getattr(m, "content", "") or ""
            cost = len(content) // 2 + 10
            if used + cost > token_budget:
                break
            selected.append(m)
            used += cost
        return selected

    def _format(self, memories: list["UserMemory"]) -> str:
        """Format memories as system prompt fragment."""
        if not memories:
            return ""
        lines = ["[关于你的记忆]"]
        for m in memories:
            mem_type = getattr(m, "memory_type", None) or ""
            content = getattr(m, "content", "") or ""
            type_label = f"[{mem_type}]" if mem_type else ""
            lines.append(f"- {type_label} {content}".strip())
        return "\n".join(lines)


async def recall_user_memories_for_injection(
    db: "AsyncSession",
    user_id: str,
    query: str,
    top_k: int = 5,
) -> list:
    """Recall user memories for injection (used by MemoryRecallInjector).

    This is a simplified version of recall_user_memories that returns
    UserMemory objects directly instead of dicts.
    """
    import re
    from sqlalchemy import select
    from app.models.memory import UserMemory

    def _extract_query_tokens(q: str) -> list[str]:
        normalized = (q or "").lower()
        tokens = [token for token in re.findall(r"[a-z0-9]+", normalized) if len(token) >= 3]
        for chunk in re.findall(r"[\u4e00-\u9fff]+", q or ""):
            stripped = chunk.strip()
            if len(stripped) >= 4:
                tokens.append(stripped)
        return list(dict.fromkeys(tokens))

    query_tokens = _extract_query_tokens(query)

    result = await db.execute(
        select(UserMemory)
        .where(UserMemory.user_id == user_id)
        .order_by(UserMemory.importance_score.desc(), UserMemory.created_at.desc())
    )
    memories = list(result.scalars().all())

    if query_tokens:
        matched = [
            m
            for m in memories
            if any(token in ((getattr(m, "content", "") or "").lower()) for token in query_tokens)
        ]
        if matched:
            return matched[:top_k]
        return memories[:top_k]

    return memories[:top_k]