life-echo/api/app/features/memory/service.py

"""
MemoryService — conversation / memoir 的统一门面。

- ingest_transcript: transcript -> memory_sources, chunks, embedding
- ingest 成功后：向 ``memory_idle`` 队列派发 LLM 富化（见 ``schedule_memory_enrichment``），不阻塞请求
- retrieve: 委托 HybridRetriever 返回 evidence bundle（向量 chunks）

Celery 侧使用 `ingest_transcript_sync` + `retrieve_evidence_sync`，与异步路径对齐见
`api/docs/memory-retrieval.md`。
"""

from sqlalchemy.ext.asyncio import AsyncSession

from app.core.logging import get_logger
from app.features.conversation.lineage_schemas import (
    primary_user_message_id_from_lineage,
)
from app.features.memory.enrichment_scheduler import MemoryEnrichmentScheduler
from app.features.memory.ingest_service import MemoryIngestService
from app.features.memory.repo import (
    create_curation_action,
    set_chunk_excluded,
    set_memory_fact_status,
)
from app.features.memory.retrieval_service import MemoryRetrievalService
from app.features.memory.schemas import EvidenceBundle
from app.ports.embedding import EmbeddingProvider

logger = get_logger(__name__)


class MemoryService:
    def __init__(
        self,
        db: AsyncSession,
        *,
        embedding_provider: EmbeddingProvider | None = None,
    ):
        self._db = db
        self._embedding = embedding_provider

    async def ingest_transcript(
        self,
        user_id: str,
        conversation_id: str,
        transcript: str,
        *,
        lineage_json: dict | None = None,
    ) -> str:
        """
        Ingest conversation transcript into memory.
        Creates MemorySource, chunks, populates embedding.
        Returns source_id.
        """
        service = MemoryIngestService(self._db, embedding_provider=self._embedding)
        return await service.ingest_transcript(
            user_id,
            conversation_id,
            transcript,
            lineage_json=lineage_json,
        )

    async def retrieve(
        self, user_id: str, query: str, *, top_k: int = 10
    ) -> EvidenceBundle:
        """Retrieve relevant evidence. 委托 HybridRetriever。"""
        service = MemoryRetrievalService(self._db, embedding_provider=self._embedding)
        return await service.retrieve(user_id, query, top_k=top_k)

    async def exclude_chunk(
        self, user_id: str, chunk_id: str, *, reason: str = ""
    ) -> bool:
        ok = await set_chunk_excluded(self._db, chunk_id, user_id, True)
        if not ok:
            return False
        await create_curation_action(
            self._db,
            user_id=user_id,
            action_type="exclude",
            target_type="chunk",
            target_id=chunk_id,
            details={"reason": reason} if reason else None,
        )
        await self._db.commit()
        return True

    async def restore_chunk(self, user_id: str, chunk_id: str) -> bool:
        ok = await set_chunk_excluded(self._db, chunk_id, user_id, False)
        if not ok:
            return False
        await create_curation_action(
            self._db,
            user_id=user_id,
            action_type="restore",
            target_type="chunk",
            target_id=chunk_id,
            details=None,
        )
        await self._db.commit()
        return True

    async def confirm_fact(self, user_id: str, fact_id: str) -> bool:
        ok = await set_memory_fact_status(self._db, fact_id, user_id, "confirmed")
        if not ok:
            return False
        await create_curation_action(
            self._db,
            user_id=user_id,
            action_type="confirm",
            target_type="fact",
            target_id=fact_id,
            details=None,
        )
        await self._db.commit()
        return True

    async def reject_fact(
        self, user_id: str, fact_id: str, *, reason: str = ""
    ) -> bool:
        ok = await set_memory_fact_status(self._db, fact_id, user_id, "rejected")
        if not ok:
            return False
        await create_curation_action(
            self._db,
            user_id=user_id,
            action_type="reject",
            target_type="fact",
            target_id=fact_id,
            details={"reason": reason} if reason else None,
        )
        await self._db.commit()
        return ok


def ingest_transcript_sync(
    session,
    user_id: str,
    conversation_id: str,
    transcript: str,
    *,
    lineage_json: dict | None = None,
) -> str:
    """
    Sync transcript ingest for Celery tasks.
    Creates source + chunks, and best-effort populates embeddings.
    Returns source_id.
    """
    from app.core.dependencies import get_embedding_provider
    from app.features.memory.chunker import chunk_transcript
    from app.features.memory.repo import (
        create_chunk_sync,
        create_source_sync,
        update_chunk_embedding_sync,
    )

    if not transcript or not transcript.strip():
        raise ValueError("transcript cannot be empty")

    primary_mid = (
        primary_user_message_id_from_lineage(lineage_json) if lineage_json else None
    )
    source = create_source_sync(
        session,
        user_id=user_id,
        source_type="transcript",
        raw_text=transcript.strip(),
        conversation_id=conversation_id,
        lineage_json=lineage_json,
        primary_user_message_id=primary_mid,
    )
    session.flush()

    chunks_text = chunk_transcript(transcript.strip())
    chunk_records: list[tuple[str, str]] = []
    for i, content in enumerate(chunks_text):
        chunk = create_chunk_sync(
            session,
            source_id=source.id,
            user_id=user_id,
            content=content,
            chunk_index=i,
        )
        session.flush()
        chunk_records.append((chunk.id, content))

    from app.core.config import settings

    vectors_written = 0
    embedding_available = False

    try:
        embedding_provider = get_embedding_provider()
        if embedding_provider is not None:
            embedding_available = embedding_provider.is_available()
    except Exception as e:
        logger.warning(
            "memory embedding provider 不可用(sync): {} exc_type={}",
            e,
            type(e).__name__,
        )
        embedding_provider = None

    # 向量写入在 SAVEPOINT 内；失败仅回滚本段，source/chunks 主体仍可由外层提交。
    # LLM enrichment 在 commit 后由 schedule_memory_enrichment 入 memory_idle 队列。
    try:
        with session.begin_nested():
            if chunk_records and embedding_provider is not None:
                texts = [content for _, content in chunk_records]
                embeddings = embedding_provider.embed_texts_sync(texts)
                for (chunk_id, _), emb in zip(
                    chunk_records, embeddings, strict=False
                ):
                    if emb:
                        vectors_written += 1
                        update_chunk_embedding_sync(session, chunk_id, emb)
    except Exception as e:
        logger.warning(
            "memory embedding 跳过(sync): {} exc_type={}",
            e,
            type(e).__name__,
        )

    session.commit()

    enrichment_task_id: str | None = None
    if settings.memory_enrichment_enabled:
        try:
            from app.tasks.memory_enrichment_tasks import schedule_memory_enrichment

            enrichment_task_id = schedule_memory_enrichment(
                user_id, source.id, memoir_correlation_id=None
            )
        except Exception as e:
            logger.warning(
                "memory enrichment 任务派发失败: {} exc_type={}",
                e,
                type(e).__name__,
            )

    logger.info(
        "event=memory_ingest_done user_id={} conversation_id={} source_id={} "
        "chunks={} vectors_written={} embedding_available={} enrichment_enabled={} enrichment_task_id={} sync=1",
        user_id,
        conversation_id,
        source.id,
        len(chunk_records),
        vectors_written,
        embedding_available,
        settings.memory_enrichment_enabled,
        enrichment_task_id,
    )
    return source.id


def ingest_transcripts_batch_sync(
    session,
    user_id: str,
    items: list[tuple[str, str, dict | None]],
) -> list[str]:
    """
    Phase1 批量：多段 transcript 在同一会话内建 source/chunks，并单次 embed_texts_sync（在适配器 batch 限制内）。

    不 commit；不派发 enrichment（由调用方 commit 后 ``schedule_enrichment_for_sources``）。
    items: (conversation_id, transcript, lineage_json)
    返回与有效 items 顺序一致的 source_id 列表。
    """
    from app.core.dependencies import get_embedding_provider
    from app.features.memory.chunker import chunk_transcript
    from app.features.memory.repo import (
        create_chunk_sync,
        create_source_sync,
        update_chunk_embedding_sync,
    )

    source_ids: list[str] = []
    all_chunk_records: list[tuple[str, str]] = []

    for conversation_id, transcript, lineage_json in items:
        text = (transcript or "").strip()
        if not text:
            continue
        primary_mid = (
            primary_user_message_id_from_lineage(lineage_json) if lineage_json else None
        )
        source = create_source_sync(
            session,
            user_id=user_id,
            source_type="transcript",
            raw_text=text,
            conversation_id=conversation_id or None,
            lineage_json=lineage_json,
            primary_user_message_id=primary_mid,
        )
        session.flush()

        chunks_text = chunk_transcript(text)
        for i, content in enumerate(chunks_text):
            chunk = create_chunk_sync(
                session,
                source_id=source.id,
                user_id=user_id,
                content=content,
                chunk_index=i,
            )
            session.flush()
            all_chunk_records.append((chunk.id, content))
        source_ids.append(source.id)

    embedding_provider = None
    try:
        embedding_provider = get_embedding_provider()
    except Exception as e:
        logger.warning(
            "memory embedding provider 不可用(batch sync): {} exc_type={}",
            e,
            type(e).__name__,
        )

    vectors_written = 0
    try:
        with session.begin_nested():
            if all_chunk_records and embedding_provider is not None:
                texts = [content for _, content in all_chunk_records]
                embeddings = embedding_provider.embed_texts_sync(texts)
                for (chunk_id, _), emb in zip(
                    all_chunk_records, embeddings, strict=False
                ):
                    if emb:
                        vectors_written += 1
                        update_chunk_embedding_sync(session, chunk_id, emb)
    except Exception as e:
        logger.warning(
            "memory embedding 跳过(batch sync): {} exc_type={}",
            e,
            type(e).__name__,
        )

    emb_ok = (
        embedding_provider.is_available() if embedding_provider is not None else False
    )
    logger.info(
        "event=memory_ingest_batch_done user_id={} sources={} chunks={} "
        "vectors_written={} embedding_available={}",
        user_id,
        len(source_ids),
        len(all_chunk_records),
        vectors_written,
        emb_ok,
    )
    return source_ids


def schedule_enrichment_for_sources(
    user_id: str,
    source_ids: list[str],
    *,
    memoir_correlation_id: str | None = None,
) -> None:
    """After successful ingest commit, enqueue LLM enrichment for each source (memory_idle queue)."""
    MemoryEnrichmentScheduler().schedule_many(
        user_id,
        source_ids,
        memoir_correlation_id=memoir_correlation_id,
    )