import numpy as np
from geometry_audit import GeometryAuditLog
from rrf_safe_similarity import stabilize_vector


class SavantWrapper:
    def __init__(
        self,
        embedder,
        r_min: float = 1e-4,
        r_max: float = 1e4,
        enable_audit: bool = True,
    ):
        """
        embedder: callable(text) -> np.ndarray
        """
        self.embedder = embedder
        self.r_min = r_min
        self.r_max = r_max
        self.audit_log = GeometryAuditLog() if enable_audit else None

        print("✅ SavantWrapper Initialized (Logarithmic Regularization Active).")

    def encode(self, text: str) -> np.ndarray:
        vec = self.embedder(text).astype(np.float64)

        stabilized_vec, audit_event = stabilize_vector(
            vec,
            r_min=self.r_min,
            r_max=self.r_max,
        )

        if audit_event and self.audit_log is not None:
            self.audit_log.record(audit_event)

        return stabilized_vec

    def batch_encode(self, texts):
        return np.vstack([self.encode(t) for t in texts])

    def get_audit_log(self):
        if self.audit_log is None:
            return []
        return self.audit_log.events