99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
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"""
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聊天输入归一：供访谈 Agent / 编排层对 ASR 与键盘输入做可控预处理（规则 / 可选 LLM）。
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不改变 segment 落库原文；仅作为模型侧派生净稿。
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与 memoir 共用同一套确定性规则，避免聊天与回忆录对同一句理解割裂。
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"""
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from __future__ import annotations
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import json
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import re
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from typing import Any
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from app.core.config import settings
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from app.core.langchain_llm import invoke_json_object
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from app.core.logging import get_logger
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from app.features.memoir.memoir_images.json_payload import extract_json_payload
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logger = get_logger(__name__)
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# 口语/ASR 常见同音：「没」误为「美」且与「看上」搭配（避免误伤「美容」「选美」等）
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_MEI_KANSHANG_RE = re.compile(r"美(?=看上[我你他她它])")
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def apply_conversation_input_rules(text: str) -> str:
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"""确定性规则；保守替换，仅覆盖高频误听误打模式。与 memoir 共用。"""
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s = text or ""
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if not s:
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return s
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return _MEI_KANSHANG_RE.sub("没", s)
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def _llm_normalize_chat_input(text: str, llm: Any) -> str | None:
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"""仅修正明显错字与同音字，不增事实；失败返回 None。"""
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if not llm or not (text or "").strip():
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return None
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max_in = int(settings.chat_input_normalize_llm_max_input_chars)
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t = (text or "").strip()
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if len(t) > max_in:
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logger.debug(
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"event=chat_input_normalize_llm_skip reason=input_too_long len={} max={}",
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len(t),
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max_in,
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)
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return None
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prompt = f"""你是口述转写纠错助手。只修正明显的同音错别字、别字与标点，使句子通顺可读。
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禁止增加事实、不补充细节、不摘要、不改写句式风格；不得新增人名、地名、数字、事件。
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若原文已通顺或无法确定错误，则照抄输入。
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【用户口述】
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{t}
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**JSON 输出**：只输出一个合法 JSON 对象。
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{{"normalized_text": "纠错后的完整文本（与输入等意，仅修错字与标点）"}}
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只输出 JSON，不要其它文字。"""
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try:
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raw = invoke_json_object(
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llm,
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prompt,
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max_tokens=int(settings.chat_input_normalize_llm_max_tokens),
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agent="chat_input_normalize.llm",
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)
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data = json.loads(extract_json_payload(raw))
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if not isinstance(data, dict):
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return None
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out = (data.get("normalized_text") or "").strip()
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if not out:
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return None
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return out
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except Exception as e:
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logger.warning("chat_input_normalize LLM 失败，回退规则结果: {}", e)
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return None
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def normalize_chat_input_for_agent(text: str, *, llm: Any | None = None) -> str:
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"""
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聊天侧单一出口：编排层与 InterviewAgent 共用。
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- 全局关闭：原文
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- off：原文
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- rules：仅规则
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- llm：先规则，再（可选）LLM；无 llm 或失败则保留规则结果
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"""
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if not settings.chat_input_normalize_enabled:
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return text or ""
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mode = (settings.chat_input_normalize_mode or "rules").strip().lower()
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if mode == "off":
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return text or ""
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base = apply_conversation_input_rules(text or "")
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if mode != "llm":
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return base
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refined = _llm_normalize_chat_input(base, llm)
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if refined is not None:
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return refined
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return base
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