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

- ingest_transcript: transcript -> memory_sources, chunks, embedding, FTS
- retrieve: 委托 HybridRetriever 返回 evidence bundle（FTS + 可选向量 RRF）

Celery 侧使用 `ingest_transcript_sync` + `retrieve_evidence_sync`，与异步路径差异见
`api/docs/memory-retrieval.md`。
"""

from sqlalchemy.ext.asyncio import AsyncSession

from app.features.memory.chunker import chunk_transcript
from app.features.memory.repo import (
    create_chunk,
    create_source,
    update_chunk_embedding,
    update_chunk_fts,
)
from app.ports.embedding import EmbeddingProvider


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
    ) -> str:
        """
        Ingest conversation transcript into memory.
        Creates MemorySource, chunks, populates embedding + FTS.
        Returns source_id.
        """
        if not transcript or not transcript.strip():
            raise ValueError("transcript cannot be empty")

        source = await create_source(
            self._db,
            user_id=user_id,
            source_type="transcript",
            raw_text=transcript.strip(),
            conversation_id=conversation_id,
        )

        chunks_text = chunk_transcript(transcript.strip())
        chunk_records = []
        for i, content in enumerate(chunks_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()

        # FTS: populate content_tsv
        for chunk_id, _ in chunk_records:
            await update_chunk_fts(self._db, chunk_id)

        # Embedding: 若有 provider 则写入
        if self._embedding and chunk_records:
            texts = [c for _, c in chunk_records]
            embeddings = await self._embedding.embed_texts(texts)
            for (chunk_id, _), emb in zip(chunk_records, embeddings):
                if emb:
                    await update_chunk_embedding(self._db, chunk_id, emb)

        await self._db.commit()
        return source.id

    async def retrieve(self, user_id: str, query: str, *, top_k: int = 10) -> dict:
        """Retrieve relevant evidence. 委托 HybridRetriever。"""
        from app.features.memory.retriever import HybridRetriever

        retriever = HybridRetriever(self._db, embedding_provider=self._embedding)
        return await retriever.retrieve(user_id=user_id, query=query, top_k=top_k)


def ingest_transcript_sync(
    session,
    user_id: str,
    conversation_id: str,
    transcript: str,
) -> str:
    """
    Sync transcript ingest for Celery tasks.
    Creates source + chunks + FTS. Skips embedding (async).
    Returns source_id.
    """
    from app.features.memory.chunker import chunk_transcript
    from app.features.memory.repo import (
        create_chunk_sync,
        create_source_sync,
        update_chunk_fts_sync,
    )

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

    source = create_source_sync(
        session,
        user_id=user_id,
        source_type="transcript",
        raw_text=transcript.strip(),
        conversation_id=conversation_id,
    )
    session.flush()

    chunks_text = chunk_transcript(transcript.strip())
    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()
        update_chunk_fts_sync(session, chunk.id)

    session.commit()
    return source.id