"""PostgreSQL cache backend with pgvector.

Uses PostgreSQL with pgvector extension for efficient vector similarity
search at scale with ACID guarantees.

Note:
    This backend requires PostgreSQL with pgvector extension and the
    asyncpg Python client. Install with: pip install asyncpg

TODO: Implement in Phase 13.2
"""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING

from ai_infra.cache.base import CacheBackend, CacheEntry, CacheStats

if TYPE_CHECKING:
    pass

logger = logging.getLogger(__name__)


class PostgresCacheBackend(CacheBackend):
    """PostgreSQL cache backend with pgvector.

    Uses pgvector extension for efficient semantic similarity search.
    Suitable for production deployments requiring ACID guarantees.

    Example:
        >>> from ai_infra.cache.backends.postgres import PostgresCacheBackend
        >>>
        >>> backend = PostgresCacheBackend(
        ...     url="postgresql://user:pass@localhost/db"
        ... )
        >>> backend.set(CacheEntry(
        ...     key="test",
        ...     value="response",
        ...     embedding=[0.1, 0.2, 0.3],
        ... ))

    Note:
        Requires PostgreSQL with pgvector extension installed.
        Install client with: pip install asyncpg
    """

    def __init__(
        self,
        url: str | None = None,
        table_name: str = "ai_cache",
        max_entries: int | None = None,
    ) -> None:
        """Initialize PostgreSQL cache backend.

        Args:
            url: PostgreSQL connection URL.
            table_name: Name of the cache table.
            max_entries: Maximum entries (LRU eviction when exceeded).

        Raises:
            CacheError: If PostgreSQL connection fails.
        """
        raise NotImplementedError(
            "PostgresCacheBackend is not yet implemented. "
            "Use MemoryCacheBackend instead, or wait for Phase 13.2."
        )

    def get(
        self,
        embedding: list[float],
        threshold: float = 0.95,
    ) -> CacheEntry | None:
        """Find a cached entry with semantic similarity above threshold."""
        raise NotImplementedError

    async def aget(
        self,
        embedding: list[float],
        threshold: float = 0.95,
    ) -> CacheEntry | None:
        """Async version of get()."""
        raise NotImplementedError

    def set(self, entry: CacheEntry) -> None:
        """Store a cache entry."""
        raise NotImplementedError

    async def aset(self, entry: CacheEntry) -> None:
        """Async version of set()."""
        raise NotImplementedError

    def delete(self, key: str) -> bool:
        """Delete a cache entry by key."""
        raise NotImplementedError

    async def adelete(self, key: str) -> bool:
        """Async version of delete()."""
        raise NotImplementedError

    def clear(self) -> int:
        """Remove all entries from the cache."""
        raise NotImplementedError

    async def aclear(self) -> int:
        """Async version of clear()."""
        raise NotImplementedError

    def stats(self) -> CacheStats:
        """Get cache statistics."""
        raise NotImplementedError
