"""
ToucanDB ML Integration Module

This module provides machine learning integration capabilities for ToucanDB,
including embedding providers, text processing, and semantic search functionality.
"""

import logging
from typing import Optional, Protocol, runtime_checkable

import numpy as np

logger = logging.getLogger(__name__)


@runtime_checkable
class EmbeddingProvider(Protocol):
    """
    Protocol for embedding providers.

    All embedding providers must implement this interface to be compatible
    with ToucanDB's ML-first features.
    """

    async def embed(self, texts: list[str]) -> list[list[float]]:
        """
        Generate embeddings for a list of texts.

        Args:
            texts: List of input texts to embed

        Returns:
            List of embedding vectors (one per input text)
        """
        ...


class SimpleEmbeddingProvider:
    """
    A simple embedding provider for testing and development.

    This provider generates random embeddings with the specified dimensions.
    For production use, replace with a real embedding model like sentence-transformers.
    """

    def __init__(self, dimensions: int = 384, seed: Optional[int] = None):
        """
        Initialize the simple embedding provider.

        Args:
            dimensions: Dimensionality of the embedding vectors
            seed: Random seed for reproducible embeddings
        """
        self.dimensions = dimensions
        self.seed = seed
        if seed is not None:
            np.random.seed(seed)

        logger.info(f"SimpleEmbeddingProvider initialized with {dimensions} dimensions")

    async def embed(self, texts: list[str]) -> list[list[float]]:
        """
        Generate simple hash-based embeddings for texts.

        Args:
            texts: List of input texts

        Returns:
            List of embedding vectors
        """
        embeddings = []

        for text in texts:
            # Create a simple hash-based embedding
            # This is just for testing - use real embeddings in production
            text_hash = hash(text)
            np.random.seed(abs(text_hash) % (2**32))

            # Generate normalized random vector
            embedding = np.random.normal(0, 1, self.dimensions)
            embedding = embedding / np.linalg.norm(embedding)

            embeddings.append(embedding.tolist())

        logger.debug(f"Generated embeddings for {len(texts)} texts")
        return embeddings


# Export public API
__all__ = [
    "EmbeddingProvider",
    "SimpleEmbeddingProvider",
]
