"""
Transcript ingest 之后的记忆富化：摘要、事实、时间线。

由 Celery（sync）与 MemoryService.ingest（async）调用；失败仅打日志，不阻断主流程。
"""

from __future__ import annotations

from typing import Any

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

from app.core.logging import get_logger
from app.features.memory.extractor import (
    extract_facts_from_transcript_async,
    extract_facts_from_transcript_sync,
)
from app.features.memory.models import MemoryChunk, MemorySummary
from app.features.memory.repo import (
    create_memory_fact,
    create_memory_fact_sync,
    create_memory_summary,
    create_memory_summary_sync,
    create_timeline_event,
    create_timeline_event_sync,
    delete_timeline_events_by_memory_source,
    delete_timeline_events_by_memory_source_sync,
    list_chunks_for_source_sync,
    upsert_rolling_summary_sync,
)
from app.features.memory.summarizer import (
    generate_rolling_summary_async,
    generate_rolling_summary_sync,
    generate_session_summary_async,
    generate_session_summary_sync,
)
from app.features.memory.enrichment_pipeline import (
    dedupe_key,
    normalize_object_json,
    normalize_subject,
)
from app.features.user.models import User
from app.features.memory.timeline import (
    build_timeline_events_from_facts_async,
    build_timeline_events_from_facts_sync,
)

logger = get_logger(__name__)


def _resolve_llm_sync() -> Any | None:
    try:
        from app.core.dependencies import get_llm_provider_fast

        return get_llm_provider_fast().langchain_llm
    except Exception as e:
        logger.warning("memory enrichment 无法获取 LLM: {}", e)
        return None


def enrich_memory_after_ingest_sync(
    session: Session,
    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_sync()
    if not llm:
        return
    narrator_name: str | None = None
    u_row = session.get(User, user_id)
    if u_row and (u_row.nickname or "").strip():
        narrator_name = (u_row.nickname or "").strip()
    chunks = list_chunks_for_source_sync(session, source_id)
    if not chunks:
        return
    chunk_texts = [c.content for c in chunks]
    chunk_ids = [c.id for c in chunks]
    numbered = "\n\n".join(
        f"[chunk_id={cid}]\n{txt}" for cid, txt in zip(chunk_ids, chunk_texts)
    )

    session_summary_text = generate_session_summary_sync(llm, chunk_texts)
    if session_summary_text:
        create_memory_summary_sync(
            session,
            user_id=user_id,
            summary_type="session",
            content=session_summary_text,
            source_chunk_ids=chunk_ids,
        )

    existing_rolling = (
        session.execute(
            select(MemorySummary)
            .where(
                MemorySummary.user_id == user_id,
                MemorySummary.summary_type == "rolling",
            )
            .order_by(MemorySummary.updated_at.desc())
            .limit(1)
        )
        .unique()
        .scalar_one_or_none()
    )
    existing_text = existing_rolling.content if existing_rolling else None
    rolling_text = generate_rolling_summary_sync(llm, existing_text, chunk_texts)
    if rolling_text:
        upsert_rolling_summary_sync(
            session,
            user_id=user_id,
            content=rolling_text,
            source_chunk_ids=chunk_ids,
        )

    raw_facts = extract_facts_from_transcript_sync(
        llm, numbered, narrator_name=narrator_name
    )
    seen: set[tuple] = set()
    inserted: list[dict] = []
    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
        row = create_memory_fact_sync(
            session,
            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",
        )
        inserted.append(
            {
                "id": row.id,
                "fact_type": row.fact_type,
                "subject": row.subject,
                "predicate": row.predicate,
                "object_json": row.object_json,
            }
        )

    if inserted:
        delete_timeline_events_by_memory_source_sync(
            session, user_id=user_id, memory_source_id=source_id
        )
        events = build_timeline_events_from_facts_sync(llm, inserted)
        for ev in events:
            create_timeline_event_sync(
                session,
                user_id=user_id,
                event_year=ev.get("event_year"),
                event_date=ev.get("event_date"),
                title=ev["title"],
                description=ev.get("description"),
                source_fact_ids=ev.get("source_fact_ids") or None,
                memory_source_id=source_id,
            )


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_sync()
    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
    chunk_texts = [c.content for c in chunks]
    chunk_ids = [c.id for c in chunks]
    numbered = "\n\n".join(
        f"[chunk_id={cid}]\n{txt}" for cid, txt in zip(chunk_ids, chunk_texts)
    )

    session_summary_text = await generate_session_summary_async(llm, chunk_texts)
    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,
        )

    roll_stmt = (
        select(MemorySummary)
        .where(
            MemorySummary.user_id == user_id,
            MemorySummary.summary_type == "rolling",
        )
        .order_by(MemorySummary.updated_at.desc())
        .limit(1)
    )
    r_result = await db.execute(roll_stmt)
    existing_row = r_result.unique().scalar_one_or_none()
    existing_text = existing_row.content if existing_row else None

    rolling_text = await generate_rolling_summary_async(llm, existing_text, chunk_texts)
    if rolling_text:
        if existing_row:
            existing_row.content = rolling_text
            existing_row.source_chunk_ids = chunk_ids
        else:
            await create_memory_summary(
                db,
                user_id=user_id,
                summary_type="rolling",
                content=rolling_text,
                source_chunk_ids=chunk_ids,
            )

    raw_facts = await extract_facts_from_transcript_async(
        llm, numbered, narrator_name=narrator_name
    )
    seen: set[tuple] = set()
    inserted: list[dict] = []
    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
        row = 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",
        )
        inserted.append(
            {
                "id": row.id,
                "fact_type": row.fact_type,
                "subject": row.subject,
                "predicate": row.predicate,
                "object_json": row.object_json,
            }
        )

    if inserted:
        await delete_timeline_events_by_memory_source(
            db, user_id=user_id, memory_source_id=source_id
        )
        events = await build_timeline_events_from_facts_async(llm, inserted)
        for ev in events:
            await create_timeline_event(
                db,
                user_id=user_id,
                event_year=ev.get("event_year"),
                event_date=ev.get("event_date"),
                title=ev["title"],
                description=ev.get("description"),
                source_fact_ids=ev.get("source_fact_ids") or None,
                memory_source_id=source_id,
            )