"""float32 embedding (de)serialization and cosine similarity."""

from __future__ import annotations

import logging

import numpy as np

logger = logging.getLogger(__name__)


def vector_to_bytes(vector: list[float]) -> bytes:
    """Convert a vector to bytes for SQLite storage."""
    logger.debug("Converting vector (length: %d) to bytes", len(vector))
    result = np.array(vector, dtype=np.float32).tobytes()
    logger.debug("Vector converted to %d bytes", len(result))
    return result


def bytes_to_vector(blob: bytes) -> np.ndarray:
    """Convert bytes back to a vector."""
    logger.debug("Converting %d bytes back to vector", len(blob))
    result = np.frombuffer(blob, dtype=np.float32)
    logger.debug("Vector reconstructed with %d dimensions", len(result))
    return result


def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
    """Calculate cosine similarity between two vectors."""
    vec1 = vec1.flatten()
    vec2 = vec2.flatten()
    logger.debug(
        "Calculating cosine similarity between vectors of dimension %d",
        len(vec1),
    )
    norm1 = np.linalg.norm(vec1)
    norm2 = np.linalg.norm(vec2)
    if norm1 == 0 or norm2 == 0:
        return 0.0
    result = float(np.dot(vec1, vec2) / (norm1 * norm2))
    logger.debug("Similarity calculated: %.4f", result)
    return result