"""Self-refusal detection (regex-based).

Used as the post-gate in approach E: after the LLM produces a response, scan
the first ~300 chars for explicit-refusal patterns. If found, the model is
admitting it doesn't have the requested fact -> we either retrieve from
external corpus or surface the refusal honestly.

The patterns target what R1-distill / Qwen-family chat models actually emit
when they encounter post-cutoff or fictional-entity questions under a strict
system prompt:
  "I am sorry, I cannot answer that"
  "I don't have information on..."
  "That is after my training cutoff"
  "As of my last update..."
  "I'm not sure..."

Tested refusal-trigger rate (4 out-of-corpus unknowns):
  V0 loose prompt:               25%
  V1 strict prompt:              50%
  V2 cutoff-aware prompt:        75%   <- recommended (see RECOMMENDED_SYSTEM)
  V3 combined (strict + cutoff): 50%   (over-specified, worse)

Residual failure: topic-hijack cases (e.g. "Who won the 2024 Nobel Physics?"
gets rewritten to a fabricated 1986/1998 Nobel answer). Prompt engineering
alone cannot fully suppress this. Mitigations: short-latency heuristic,
topic-blocklist, answer-verification pass.
"""

import re


_REFUSAL_RE = re.compile(
    r"\b(I (am sorry|don'?t|do not|am unable|cannot|can'?t)\s+"
    r"(know|have|recall|remember|answer|provide|find|see))"
    r"|\b(I'?m not (sure|aware|certain))"
    r"|\b(no (information|details|data) (on|about|regarding))"
    r"|\b(as of my (training|last update|knowledge)|my training data|training cutoff)"
    r"|\bcannot answer that\b"
    r"|\bdon'?t have (information|details|data)\b"
    r"|\bafter my (training|knowledge) cutoff\b"
    r"|\bI lack (information|data)\b",
    re.IGNORECASE,
)


def detect_refusal(text: str, scan_chars: int = 400) -> bool:
    """Return True if `text[:scan_chars]` contains an explicit self-refusal phrase."""
    if not text:
        return False
    return _REFUSAL_RE.search(text[:scan_chars]) is not None


# The recommended system prompt for production: maximizes refusal rate on
# out-of-corpus unknowns while staying short and not over-specified.
RECOMMENDED_SYSTEM = (
    "You are a helpful assistant. Your knowledge cutoff is March 2024. "
    "For any question about events, people, or facts after March 2024, "
    "you MUST explicitly say 'That is after my training cutoff.' "
    "Do not guess. Do not fabricate. "
    "If a Context is provided, use it to answer."
)