"""
Transcript ingest 之后的记忆富化：单次 LLM 调用产出会话摘要 + 结构化事实。

由 async MemoryService 调用；失败仅打日志，不阻断主流程。
不再维护 ingest 路径上的 rolling 摘要与 timeline 物化。
"""

from __future__ import annotations

from typing import Any

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from app.core.langchain_llm import ainvoke_json_object
from app.core.llm_gateway import LlmGateway, LlmUseCase
from app.core.logging import get_logger
from app.features.memory.enrichment_pipeline import (
    dedupe_key,
    normalize_object_json,
    normalize_subject,
)
from app.features.memory.llm_schemas import (
    EnrichmentPayload,
    enrichment_payload_to_fact_dicts,
    parse_json_payload,
)
from app.features.memory.models import MemoryChunk, MemorySource
from app.features.memory.repo import (
    create_memory_fact,
    create_memory_summary,
)
from app.features.user.models import User

logger = get_logger(__name__)


def _lineage_snapshot_from_source(source: MemorySource | None) -> dict | None:
    raw = getattr(source, "lineage_json", None) if source else None
    return raw if isinstance(raw, dict) and raw else None


def _resolve_llm() -> Any | None:
    try:
        return LlmGateway().langchain_llm_for(
            LlmUseCase("memory.enrichment", fast=True)
        )
    except Exception as e:
        logger.warning("memory enrichment 无法获取 LLM: {}", e)
        return None


def _max_enrichment_chars() -> int:
    from app.core.config import settings

    return settings.memory_enrichment_max_chars


def _enrichment_prompt(numbered_blocks: str, narrator_label: str) -> str:
    """合并会话摘要说明与事实抽取规则，供单次 JSON 输出。"""
    text = numbered_blocks.strip()[: _max_enrichment_chars()]
    return (
        "你是回忆录记忆分析助手。用户正在口述人生回忆，所有内容默认是**过去发生的事**，"
        "而非当前或未来计划（除非原文明确说「现在」「打算」「准备将要」等）。\n\n"
        "请从下列口述内容中完成两件事：\n"
        "1) 用 2～8 句中文概括要点（不编造、不评价）\n"
        "2) 抽取结构化事实列表（见下方规则）\n\n"
        "## 事实抽取规则\n"
        "1. subject 必须用明确的人名或固定称谓：\n"
        f"   - 叙述者本人统一用「{narrator_label}」\n"
        "   - 其他人用全名或稳定专名（如「王伟」），禁止用「他」「她」「我」「我们大伙」等代词作 subject；"
        "若代词在上下文中可唯一解析为某人，则 subject 写该人姓名/专名\n"
        "2. 事件、职务变动、地点迁移等一律按**过去回忆**理解；travel/调动/命令类表述勿写成「即将要做」"
        "除非原文明确为未来时态\n"
        "3. 若可推断大约年代或人生阶段，将 approximate_era 写入 object_json（与 value 等字段并存），"
        '例如 "1990年代"、"2001年"、"退休后"、"30岁前后"\n'
        "4. fact_type: person|event|relation|place|milestone\n"
        "5. predicate：简短中文谓语（如「出生地」「担任职务」「调往」）\n"
        "6. object_json：字符串或对象；可含 value、approximate_era 等\n"
        "7. confidence 0..1；source_chunk_id 必须等于某段 [chunk_id=...] 中的 id\n\n"
        '只输出 JSON：{"summary":"...","facts":[...]}，无事实则 "facts":[]。\n\n'
        f"{text}"
    )


async def _run_enrichment_llm_async(
    llm: Any, numbered: str, narrator_label: str
) -> EnrichmentPayload | None:
    if not llm or not (numbered or "").strip():
        return None
    prompt = _enrichment_prompt(numbered, narrator_label)
    try:
        raw = await ainvoke_json_object(
            llm,
            prompt,
            max_tokens=8192,
            agent="memory.enrichment",
        )
        return parse_json_payload(raw, EnrichmentPayload)
    except (TypeError, ValueError) as e:
        logger.warning("enrichment LLM async 解析失败: {}", e)
        return None


async def enrich_memory_after_ingest_async(
    db: AsyncSession,
    user_id: str,
    source_id: str,
    llm: Any | None = None,
) -> None:
    from app.core.config import settings

    if not settings.memory_enrichment_enabled:
        return
    if llm is None:
        llm = _resolve_llm()
    if not llm:
        return
    narrator_name: str | None = None
    u_row = await db.get(User, user_id)
    if u_row and (u_row.nickname or "").strip():
        narrator_name = (u_row.nickname or "").strip()
    stmt = (
        select(MemoryChunk)
        .where(MemoryChunk.source_id == source_id)
        .order_by(MemoryChunk.chunk_index.asc())
    )
    result = await db.execute(stmt)
    chunks = list(result.unique().scalars().all())
    if not chunks:
        return
    src_row = await db.get(MemorySource, source_id)
    lineage_snapshot = _lineage_snapshot_from_source(src_row)
    chunk_ids = [c.id for c in chunks]
    chunk_texts = [c.content for c in chunks]
    numbered = "\n\n".join(
        f"[chunk_id={cid}]\n{txt}"
        for cid, txt in zip(chunk_ids, chunk_texts, strict=False)
    )
    narrator_label = (narrator_name or "").strip() or "叙述者"

    payload = await _run_enrichment_llm_async(llm, numbered, narrator_label)
    if payload is None:
        return

    session_summary_text = str(payload.summary or "").strip()
    if session_summary_text:
        await create_memory_summary(
            db,
            user_id=user_id,
            summary_type="session",
            content=session_summary_text,
            source_chunk_ids=chunk_ids,
        )

    raw_facts = enrichment_payload_to_fact_dicts(payload)
    seen: set[tuple] = set()
    for f in raw_facts:
        key = dedupe_key(f, narrator_name=narrator_name)
        if key in seen:
            continue
        seen.add(key)
        scid = f.get("source_chunk_id")
        if scid and scid not in chunk_ids:
            scid = chunk_ids[0] if chunk_ids else None
        await create_memory_fact(
            db,
            user_id=user_id,
            fact_type=f.get("fact_type") or "event",
            subject=normalize_subject(f.get("subject"), narrator_name),
            predicate=f.get("predicate"),
            object_json=normalize_object_json(f.get("object_json")),
            confidence=float(f.get("confidence") or 0.75),
            source_chunk_id=scid,
            status="confirmed",
            lineage_json=lineage_snapshot,
        )