"""Semantic cache for retrieval results (Qdrant + query embeddings)."""

from __future__ import annotations

import json
import logging
import time
import uuid
from typing import TYPE_CHECKING

from app.async_executor import run_sync_in_executor
from app.config import settings
from app.models.schemas import SearchResult

if TYPE_CHECKING:
    from qdrant_client import AsyncQdrantClient
    from qdrant_client.http import models as qmodels

logger = logging.getLogger(__name__)

_cache_instance: "SemanticCache | None" = None


def reset_semantic_cache() -> None:
    """Reset semantic cache singleton (tests)."""
    global _cache_instance
    _cache_instance = None


def is_semantic_cache_active() -> bool:
    """True when Qdrant-backed semantic cache is enabled."""
    return _use_semantic_cache()


def _use_semantic_cache() -> bool:
    return bool(
        settings.semantic_cache_enabled
        and (settings.vectorstore_backend or "faiss").strip().lower() == "qdrant"
    )


class SemanticCache:
    """Cache retrieval results keyed by tenant + query embedding similarity."""

    def __init__(
        self,
        client: "AsyncQdrantClient",
        collection: str,
        *,
        embed_query,
        ttl_seconds: int,
        threshold: float,
    ) -> None:
        self._client = client
        self._collection = collection
        self._embed_query = embed_query
        self._ttl_seconds = ttl_seconds
        self._threshold = threshold
        self._ready = False

    async def _embed_async(self, query: str) -> list[float]:
        """Embed without blocking the event loop (sentence-transformers / OpenAI)."""
        return await run_sync_in_executor(self._embed_query, query)

    async def ensure_collection(self, vector_size: int) -> None:
        if self._ready:
            return
        from qdrant_client.http import models as qmodels

        names = {c.name for c in (await self._client.get_collections()).collections}
        if self._collection not in names:
            await self._client.create_collection(
                collection_name=self._collection,
                vectors_config=qmodels.VectorParams(
                    size=vector_size,
                    distance=qmodels.Distance.COSINE,
                ),
                hnsw_config=qmodels.HnswConfigDiff(m=16, ef_construct=100),
                on_disk_payload=True,
            )
        self._ready = True

    async def get(self, query: str, tenant_id: str) -> list[SearchResult] | None:
        from qdrant_client.http import models as qmodels

        vector = await self._embed_async(query)
        await self.ensure_collection(len(vector))
        cutoff = time.time() - self._ttl_seconds
        hits = await self._client.search(
            collection_name=self._collection,
            query_vector=vector,
            limit=3,
            query_filter=qmodels.Filter(
                must=[
                    qmodels.FieldCondition(
                        key="tenant_id",
                        match=qmodels.MatchValue(value=tenant_id),
                    ),
                ]
            ),
        )
        for hit in hits:
            if float(hit.score) < self._threshold:
                continue
            created = float((hit.payload or {}).get("created_at", 0))
            if created < cutoff:
                continue
            raw = (hit.payload or {}).get("results_json", "[]")
            try:
                rows = json.loads(raw)
                return [SearchResult.model_validate(r) for r in rows]
            except Exception as exc:  # noqa: BLE001
                logger.warning("Semantic cache payload decode failed: %s", exc)
        return None

    async def put(
        self,
        query: str,
        tenant_id: str,
        results: list[SearchResult],
    ) -> None:
        from qdrant_client.http import models as qmodels

        if not results:
            return
        vector = await self._embed_async(query)
        await self.ensure_collection(len(vector))
        payload = {
            "tenant_id": tenant_id,
            "query": query[:500],
            "created_at": time.time(),
            "results_json": json.dumps([r.model_dump() for r in results]),
        }
        await self._client.upsert(
            collection_name=self._collection,
            points=[
                qmodels.PointStruct(
                    id=str(uuid.uuid4()),
                    vector=vector,
                    payload=payload,
                )
            ],
        )

    async def invalidate_tenant(self, tenant_id: str) -> None:
        """Drop cached retrieval rows for a tenant (after ingest/delete)."""
        from qdrant_client.http import models as qmodels

        if not tenant_id:
            return
        vector = await self._embed_async("cache dimension probe")
        await self.ensure_collection(len(vector))
        await self._client.delete(
            collection_name=self._collection,
            points_selector=qmodels.FilterSelector(
                filter=qmodels.Filter(
                    must=[
                        qmodels.FieldCondition(
                            key="tenant_id",
                            match=qmodels.MatchValue(value=tenant_id),
                        )
                    ]
                )
            ),
        )
        logger.debug("Semantic cache invalidated for tenant=%s", tenant_id)


async def invalidate_semantic_cache_for_tenant(tenant_id: str) -> None:
    """Best-effort semantic cache invalidation (no-op when cache disabled)."""
    cache = get_semantic_cache()
    if cache is None:
        return
    try:
        await cache.invalidate_tenant(tenant_id)
    except Exception as exc:  # noqa: BLE001
        logger.warning("Semantic cache invalidation failed tenant=%s: %s", tenant_id, exc)


def get_semantic_cache() -> SemanticCache | None:
    """Return a semantic cache singleton when enabled; otherwise ``None``."""
    global _cache_instance
    if not _use_semantic_cache():
        return None
    if _cache_instance is not None:
        return _cache_instance

    from app.embeddings.factory import get_embedding_client
    from app.vectorstore.qdrant_async import get_async_qdrant_client

    embedding = get_embedding_client()
    client = get_async_qdrant_client()
    _cache_instance = SemanticCache(
        client=client,
        collection=settings.qdrant_cache_collection,
        embed_query=embedding.embed_query,
        ttl_seconds=int(settings.semantic_cache_ttl_hours) * 3600,
        threshold=float(settings.semantic_cache_similarity_threshold),
    )
    return _cache_instance