life-echo/api/app/tasks/memory_enrichment_tasks.py

"""
Memory pipeline Celery tasks — retry embedding and enrichment after durable ingest.

Tasks are routed to ``settings.celery_memory_enrichment_queue`` (default ``memory_idle``);
run workers with ``-Q celery,memory_idle`` or a dedicated low-priority worker for that queue.
"""

import asyncio
import time
from typing import Any, cast

from celery import shared_task

from app.core.config import settings
from app.core.db import AsyncSessionLocal
from app.core.dependencies import get_embedding_provider
from app.core.logging import get_logger
from app.core.memoir_pipeline_progress import merge_fanout_item
from app.features.memory.service import MemoryService

logger = get_logger(__name__)


async def _enrich_memory_source_async(
    user_id: str,
    source_id: str,
) -> None:
    async with AsyncSessionLocal() as db:
        service = MemoryService(db)
        await service.enrich_source(user_id, source_id, llm=None)
        await db.commit()


async def _embed_memory_source_async(
    user_id: str,
    source_id: str,
) -> dict:
    async with AsyncSessionLocal() as db:
        service = MemoryService(db, embedding_provider=get_embedding_provider())
        result = await service.embed_source(
            user_id,
            source_id,
            raise_on_failure=True,
        )
        await db.commit()
        return result


def schedule_memory_embedding(
    user_id: str,
    source_id: str,
    *,
    memoir_correlation_id: str | None = None,
) -> str | None:
    """Enqueue embedding retry for a persisted memory source."""
    uid = (user_id or "").strip()
    sid = (source_id or "").strip()
    if not uid or not sid:
        return None
    q = (settings.celery_memory_enrichment_queue or "").strip() or "memory_idle"
    try:
        task = cast(Any, embed_memory_source)
        ar = task.apply_async(
            args=[uid, sid],
            kwargs={"memoir_correlation_id": memoir_correlation_id},
            queue=q,
        )
        emb_id = getattr(ar, "id", None)
        if not emb_id:
            return None
        cid = (memoir_correlation_id or "").strip()
        if cid:
            merge_fanout_item(
                cid,
                list_name="memory_embedding",
                id_field="source_id",
                item_id=sid,
                task_id=str(emb_id),
                status="enqueued",
            )
        return str(emb_id)
    except Exception as e:
        logger.warning(
            "event=memory_embedding_schedule_failed user_id={} source_id={} exc={} exc_type={}",
            uid,
            sid,
            e,
            type(e).__name__,
        )
        return None


def schedule_memory_enrichment(
    user_id: str,
    source_id: str,
    *,
    memoir_correlation_id: str | None = None,
) -> str | None:
    """
    Enqueue post-ingest LLM enrichment on the memory idle queue.

    When ``memoir_correlation_id`` is set, records ``fanout.memory_enrichment`` as enqueued
    for eval / pipeline progress (same as the former Phase1 loop).
    """
    if not settings.memory_enrichment_enabled:
        return None
    uid = (user_id or "").strip()
    sid = (source_id or "").strip()
    if not uid or not sid:
        return None
    q = (settings.celery_memory_enrichment_queue or "").strip() or "memory_idle"
    try:
        task = cast(Any, enrich_memory_source)
        ar = task.apply_async(
            args=[uid, sid],
            kwargs={"memoir_correlation_id": memoir_correlation_id},
            queue=q,
        )
        enr_id = getattr(ar, "id", None)
        if not enr_id:
            return None
        cid = (memoir_correlation_id or "").strip()
        if cid:
            merge_fanout_item(
                cid,
                list_name="memory_enrichment",
                id_field="source_id",
                item_id=sid,
                task_id=str(enr_id),
                status="enqueued",
            )
        return str(enr_id)
    except Exception as e:
        logger.warning(
            "event=memory_enrichment_schedule_failed user_id={} source_id={} exc={} exc_type={}",
            uid,
            sid,
            e,
            type(e).__name__,
        )
        return None


@shared_task(bind=True, max_retries=3, default_retry_delay=30)
def embed_memory_source(
    self,
    user_id: str,
    source_id: str,
    memoir_correlation_id: str | None = None,
):
    """Post-ingest embedding retry for persisted chunks."""
    tid = str(self.request.id)
    t0 = time.perf_counter()
    logger.info(
        "event=memory_embedding_start user_id={} source_id={} task_id={} msg=开始记忆向量化",
        user_id,
        source_id,
        tid,
    )
    merge_fanout_item(
        memoir_correlation_id,
        list_name="memory_embedding",
        id_field="source_id",
        item_id=source_id,
        task_id=tid,
        status="running",
    )
    try:
        result = asyncio.run(_embed_memory_source_async(user_id, source_id))
        ms = (time.perf_counter() - t0) * 1000
        logger.info(
            "event=memory_embedding_done user_id={} source_id={} duration_ms={:.1f} status={} vectors_written={} msg=记忆向量化完成",
            user_id,
            source_id,
            ms,
            result.get("status"),
            result.get("vectors_written", 0),
        )
        merge_fanout_item(
            memoir_correlation_id,
            list_name="memory_embedding",
            id_field="source_id",
            item_id=source_id,
            task_id=tid,
            status="success",
            extra=result,
        )
        return {"source_id": source_id, **result}
    except Exception as e:
        ms = (time.perf_counter() - t0) * 1000
        logger.warning(
            "event=memory_embedding_failed user_id={} source_id={} duration_ms={:.1f} "
            "exc={} exc_type={} msg=记忆向量化失败",
            user_id,
            source_id,
            ms,
            e,
            type(e).__name__,
        )
        merge_fanout_item(
            memoir_correlation_id,
            list_name="memory_embedding",
            id_field="source_id",
            item_id=source_id,
            task_id=tid,
            status="failure",
            extra={"error": str(e)},
        )
        raise self.retry(exc=e) from e


@shared_task(bind=True, max_retries=2, default_retry_delay=30)
def enrich_memory_source(
    self,
    user_id: str,
    source_id: str,
    memoir_correlation_id: str | None = None,
):
    """
    Post-ingest enrichment: one LLM call → session summary + structured facts.
    Runs outside the memoir Phase1 hot path so narrative generation isn't blocked.
    """
    if not settings.memory_enrichment_enabled:
        return {"status": "disabled"}

    tid = str(self.request.id)
    t0 = time.perf_counter()
    logger.info(
        "event=memory_enrichment_start user_id={} source_id={} task_id={} "
        "msg=开始记忆富化（会话摘要+事实）",
        user_id,
        source_id,
        tid,
    )
    merge_fanout_item(
        memoir_correlation_id,
        list_name="memory_enrichment",
        id_field="source_id",
        item_id=source_id,
        task_id=tid,
        status="running",
    )
    try:
        asyncio.run(_enrich_memory_source_async(user_id, source_id))
        ms = (time.perf_counter() - t0) * 1000
        logger.info(
            "event=memory_enrichment_done user_id={} source_id={} duration_ms={:.1f} "
            "msg=记忆富化完成",
            user_id,
            source_id,
            ms,
        )
        merge_fanout_item(
            memoir_correlation_id,
            list_name="memory_enrichment",
            id_field="source_id",
            item_id=source_id,
            task_id=tid,
            status="success",
        )
        return {"status": "success", "source_id": source_id}
    except Exception as e:
        ms = (time.perf_counter() - t0) * 1000
        logger.warning(
            "event=memory_enrichment_failed user_id={} source_id={} duration_ms={:.1f} "
            "exc={} exc_type={} msg=记忆富化失败",
            user_id,
            source_id,
            ms,
            e,
            type(e).__name__,
        )
        merge_fanout_item(
            memoir_correlation_id,
            list_name="memory_enrichment",
            id_field="source_id",
            item_id=source_id,
            task_id=tid,
            status="failure",
            extra={"error": str(e)},
        )
        raise self.retry(exc=e) from e