配置 SSOT(TOML + .env) 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis(DB/0)与 Celery broker/backend(DB/1)显式拆分;连接池、sync client 可观测性(OpenTelemetry + LGTM)
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""
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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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from typing import Any
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from app.core.logging import get_logger
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from app.core.text_normalize import apply_oral_rules, llm_normalize_text
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from app.features.conversation.constants import chat
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logger = get_logger(__name__)
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apply_conversation_input_rules = apply_oral_rules
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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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return llm_normalize_text(
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text,
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llm,
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max_input_chars=int(chat.input_normalize_llm_max_input_chars),
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max_tokens=int(chat.input_normalize_llm_max_tokens),
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agent_name="chat_input_normalize.llm",
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)
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def normalize_chat_input_for_agent(
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text: str,
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*,
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llm: Any | None = None,
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is_from_voice: bool = False,
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) -> 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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- chat_input_normalize_llm_voice_only:mode=llm 时仅 is_from_voice 为真才调用 LLM
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"""
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if not chat.input_normalize_enabled:
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return text or ""
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mode = (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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effective_llm = llm
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if chat.input_normalize_llm_voice_only and not is_from_voice:
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effective_llm = None
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refined = _llm_normalize_chat_input(base, effective_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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