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

"""Memory ingest service boundary."""

from __future__ import annotations

from sqlalchemy.ext.asyncio import AsyncSession

from app.core.config import settings
from app.core.logging import get_logger
from app.features.conversation.lineage_schemas import (
    primary_user_message_id_from_lineage,
)
from app.features.memory.chunker import chunk_transcript
from app.features.memory.embedding_scheduler import (
    MemoryEmbeddingRequest,
    MemoryEmbeddingScheduler,
)
from app.features.memory.embedding_service import MemoryEmbeddingService
from app.features.memory.enrichment_scheduler import (
    MemoryEnrichmentRequest,
    MemoryEnrichmentScheduler,
)
from app.features.memory.repo import (
    create_chunk,
    create_source,
)
from app.ports.embedding import EmbeddingProvider

logger = get_logger(__name__)


class MemoryIngestService:
    """Creates memory sources/chunks and schedules post-commit enrichment."""

    def __init__(
        self,
        db: AsyncSession,
        *,
        embedding_provider: EmbeddingProvider | None = None,
        embedding_scheduler: MemoryEmbeddingScheduler | None = None,
        enrichment_scheduler: MemoryEnrichmentScheduler | None = None,
    ) -> None:
        self._db = db
        self._embedding = embedding_provider
        self._embedding_scheduler = embedding_scheduler or MemoryEmbeddingScheduler()
        self._enrichment_scheduler = enrichment_scheduler or MemoryEnrichmentScheduler()

    async def ingest_transcript(
        self,
        user_id: str,
        conversation_id: str,
        transcript: str,
        *,
        lineage_json: dict | None = None,
    ) -> str:
        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 = await create_source(
            self._db,
            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,
        )

        chunk_records: list[tuple[str, str]] = []
        for i, content in enumerate(chunk_transcript(transcript.strip())):
            chunk = await create_chunk(
                self._db,
                source_id=source.id,
                user_id=user_id,
                content=content,
                chunk_index=i,
            )
            chunk_records.append((chunk.id, content))

        await self._db.flush()
        await self._db.commit()

        embedding_result = await MemoryEmbeddingService(
            self._db,
            embedding_provider=self._embedding,
        ).embed_source(user_id, source.id)
        embedding_task_id = self._schedule_embedding_retry_if_needed(
            user_id,
            source.id,
            embedding_result,
        )
        emb_ok = self._embedding.is_available() if self._embedding else False
        enrichment_task_id = self._enrichment_scheduler.schedule(
            MemoryEnrichmentRequest(user_id=user_id, source_id=source.id)
        )

        logger.info(
            "event=memory_ingest_done user_id={} conversation_id={} source_id={} "
            "chunks={} vectors_written={} embedding_status={} embedding_available={} "
            "embedding_task_id={} enrichment_enabled={} enrichment_task_id={}",
            user_id,
            conversation_id,
            source.id,
            len(chunk_records),
            embedding_result.get("vectors_written", 0),
            embedding_result.get("status"),
            emb_ok,
            embedding_task_id,
            settings.memory_enrichment_enabled,
            enrichment_task_id,
        )
        return source.id

    async def ingest_transcripts_batch(
        self,
        user_id: str,
        items: list[tuple[str, str, dict | None]],
        *,
        memoir_correlation_id: str | None = None,
    ) -> list[str]:
        """
        Batch ingest transcript items through the async memory path.

        items: (conversation_id, transcript, lineage_json). Empty transcripts are skipped.
        """
        source_ids: list[str] = []
        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 = await create_source(
                self._db,
                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,
            )
            source_ids.append(source.id)

            for i, content in enumerate(chunk_transcript(text)):
                chunk = await create_chunk(
                    self._db,
                    source_id=source.id,
                    user_id=user_id,
                    content=content,
                    chunk_index=i,
                )
                chunk_records.append((chunk.id, content))

        await self._db.flush()
        await self._db.commit()

        vectors_written = 0
        embedding_retry_task_ids: list[str] = []
        embedding_statuses: dict[str, int] = {}
        embedding_service = MemoryEmbeddingService(
            self._db,
            embedding_provider=self._embedding,
        )
        for source_id in source_ids:
            result = await embedding_service.embed_source(user_id, source_id)
            vectors_written += int(result.get("vectors_written") or 0)
            status = str(result.get("status") or "unknown")
            embedding_statuses[status] = embedding_statuses.get(status, 0) + 1
            task_id = self._schedule_embedding_retry_if_needed(
                user_id,
                source_id,
                result,
                memoir_correlation_id=memoir_correlation_id,
            )
            if task_id:
                embedding_retry_task_ids.append(task_id)

        emb_ok = self._embedding.is_available() if self._embedding else False
        task_ids = self._enrichment_scheduler.schedule_many(
            user_id,
            source_ids,
            memoir_correlation_id=memoir_correlation_id,
        )

        logger.info(
            "event=memory_ingest_batch_done user_id={} sources={} chunks={} "
            "vectors_written={} embedding_available={} embedding_statuses={} "
            "embedding_retry_tasks={} enrichment_enabled={} enrichment_tasks={}",
            user_id,
            len(source_ids),
            len(chunk_records),
            vectors_written,
            emb_ok,
            embedding_statuses,
            len(embedding_retry_task_ids),
            settings.memory_enrichment_enabled,
            len(task_ids),
        )
        return source_ids

    def _schedule_embedding_retry_if_needed(
        self,
        user_id: str,
        source_id: str,
        embedding_result: dict,
        *,
        memoir_correlation_id: str | None = None,
    ) -> str | None:
        status = str(embedding_result.get("status") or "")
        if status not in {"failed", "partial"}:
            return None
        return self._embedding_scheduler.schedule(
            MemoryEmbeddingRequest(
                user_id=user_id,
                source_id=source_id,
                memoir_correlation_id=memoir_correlation_id,
            )
        )


__all__ = ["MemoryIngestService"]