# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/what-retrieval-invariants-must-be-logged-when-an-embedding-model-is-silently-deprecated-upstream.md
# Remnant Warrant:    https://corpusfetch.com/c/what-retrieval-invariants-must-be-logged-when-an-embedding-model-is-silently-deprecated-upstream.warrant.json
# Markdown SHA-256:   375b555aafa27cf1bfd7875d01a90a329c858d3dc639824da8ac9a13ebebfde2
# Code AST SHA-256:   4a5cd07a106b0c9b7b5a8a4f9e4357937f127ff92969a17180cb52ee50f94ca3
# Verified Classes:   RetrievalVerdict, EmbeddingEnvironment, QueryContext, RetrievalManifest, EmbeddingDriftGuard
# Defensive Guards:   0 fail-closed invariants (zero external deps)

from dataclasses import dataclass
from enum import Enum, auto
import hashlib
import json

class RetrievalVerdict(Enum):
    SAFE_TO_QUERY = auto()
    DIMENSION_MISMATCH = auto()
    MODEL_DRIFT_DETECTED = auto()

@dataclass(frozen=True)
class EmbeddingEnvironment:
    model_hash: str
    vector_dimension: int
    tokenizer_version: str

@dataclass(frozen=True)
class QueryContext:
    current_model_hash: str
    current_dimension: int
    database_dimension: int

@dataclass(frozen=True)
class RetrievalManifest:
    verdict: RetrievalVerdict
    directive: str
    manifest_digest: str

class EmbeddingDriftGuard:
    @staticmethod
    def _hash(data: str) -> str:
        return hashlib.sha256(data.encode("utf-8")).hexdigest()

    @classmethod
    def evaluate_query_safety(
        cls, saved: EmbeddingEnvironment, current: QueryContext
    ) -> RetrievalManifest:
        
        # Gate 1: Dimension check
        if current.current_dimension != current.database_dimension:
            return RetrievalManifest(
                verdict=RetrievalVerdict.DIMENSION_MISMATCH,
                directive=f"HALT: Query dim {current.current_dimension} != DB dim {current.database_dimension}. Re-index required.",
                manifest_digest=cls._hash("DIMENSION_MISMATCH")
            )

        # Gate 2: Model weights drift
        if saved.model_hash != current.current_model_hash:
            return RetrievalManifest(
                verdict=RetrievalVerdict.MODEL_DRIFT_DETECTED,
                directive=f"HALT: Model drift. Stored vectors used {saved.model_hash}, current is {current.current_model_hash}. Re-embed.",
                manifest_digest=cls._hash(f"DRIFT:{saved.model_hash}:{current.current_model_hash}")
            )

        # Gate 3: Safe
        digest = cls._hash(json.dumps({"model": saved.model_hash, "dim": str(saved.vector_dimension)}, sort_keys=True))
        return RetrievalManifest(
            verdict=RetrievalVerdict.SAFE_TO_QUERY,
            directive="PROCEED: Embedding space is identical.",
            manifest_digest=digest
        )