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dff25f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | """
LLM output parsing utilities for the persona engine.
Parses structured output from the single-pass Actor:
【内心独白】 → monologue
【最终回复】 → reply
【表达方式】 → modality (语音/文字/照片/…)
Supports both Chinese and English section headers.
"""
from __future__ import annotations
import re
# -- Modality Parsing --
# No hardcoded map — registered SKILLs are the source of truth.
# Parser only extracts the raw modality keyword from LLM output.
def _parse_modality(raw: str) -> str:
"""Extract primary modality keyword from Actor output.
Returns the first token before any punctuation/space.
Skill engine decides if it maps to a registered skill.
"""
import re
cleaned = raw.strip().lstrip("\uff1a: \n")
# Take first token before punctuation (。.，,、/ or whitespace)
match = re.match(r'[\w\u4e00-\u9fff]+', cleaned)
return match.group(0) if match else "文字"
# -- Section header regex: Chinese 【】 and English [] formats --
_SECTION_RE = re.compile(
r'(?:【(?P<zh>内心独白|最终回复|表达方式)】'
r'|\[(?P<en>Inner Monologue|Final Reply|Expression Mode)\])'
)
_TAG_MAP = {
'内心独白': 'monologue', 'Inner Monologue': 'monologue',
'最终回复': 'reply', 'Final Reply': 'reply',
'表达方式': 'modality', 'Expression Mode': 'modality',
}
def extract_reply(raw: str) -> tuple[str, str, str]:
"""Extract monologue, reply, and modality from Actor output.
Supports both Chinese (【最终回复】) and English ([Final Reply]) section headers.
Returns canonical Chinese modality key for internal consistency.
"""
# Strip <think>...</think> blocks from reasoning models (e.g. MiniMax M2.7)
raw = re.sub(r'<think>.*?</think>', '', raw, flags=re.DOTALL).strip()
sections: dict[str, str] = {}
matches = list(_SECTION_RE.finditer(raw))
for i, m in enumerate(matches):
tag = m.group('zh') or m.group('en')
key = _TAG_MAP[tag]
start = m.end()
end = matches[i + 1].start() if i + 1 < len(matches) else len(raw)
sections[key] = raw[start:end].strip()
monologue = sections.get('monologue', '')
reply = sections.get('reply', '')
modality_raw = sections.get('modality', '')
# Parse modality with bilingual map
modality = _parse_modality(modality_raw) if modality_raw else "文字"
# Silence short-circuit: Actor chose not to speak
if modality in ("静默", "silence", "Silence"):
return monologue, "", "静默"
if not reply:
# Fallback: strip action descriptions
reply = re.sub(r'[(（(][^)）)]*[)）)]', '', raw).strip()
reply = re.sub(r'\*[^*]+\*', '', reply).strip()
if not reply:
reply = "..."
else:
# Normal path: strip action tags like *顿了顿* / ＊顿了顿＊ and （沉默） from reply
reply = re.sub(r'[*＊][^*＊]+[*＊]', '', reply).strip()
reply = re.sub(r'[（(][^（(）)]{1,40}[）)]', '', reply).strip()
if not reply:
reply = "..."
return monologue, reply, modality
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