generated from kgod/ai-review-template
hypandClaude
ae2d9b128d
自内部仓库剥离深度优化与 RAG 知识库后的交付版本： - Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强） - 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护 - 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏 - PostgreSQL 运行时 + Alembic 迁移链 Co-Authored-By: Claude <noreply@anthropic.com>
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
"""P0 shadow rollout: run the LLM intent classifier beside rule routing, log only.
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Observation-only — the LLM result never influences routing. Disagreements are
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logged as `chat_intent_shadow` events to build the P1 golden dataset.
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"""
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from __future__ import annotations
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import dataclasses
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import logging
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from typing import Any
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from .chat_intent_classifier import (
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ChatIntentClassifier,
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LLMChatIntentClassifier,
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RuleBasedChatIntentClassifier,
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)
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from .chat_intents import CHAT_INTENT_REGISTRY_VERSION
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from .llm_services import OpenAICompatibleStructuredClient, log_ai_event
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from .settings import Settings
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class ChatIntentShadowLogger:
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"""Compares LLM vs rule classification per message. Never used for routing."""
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def __init__(self, primary: ChatIntentClassifier, rules: ChatIntentClassifier) -> None:
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self.primary = primary
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self.rules = rules
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def observe(self, message: str, *, state_summary: dict[str, Any]) -> None:
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rule_result = self.rules.classify(message, state_summary=state_summary)
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try:
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llm_result = self.primary.classify(message, state_summary=state_summary)
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except Exception as exc:
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log_ai_event(
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"chat_intent_shadow_error",
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level=logging.WARNING,
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exception=type(exc).__name__,
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trace_id=getattr(exc, "trace_id", None),
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)
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return
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log_ai_event(
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"chat_intent_shadow",
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registry_version=CHAT_INTENT_REGISTRY_VERSION,
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rule_intent=rule_result.intent.value,
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llm_intent=llm_result.intent.value,
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llm_confidence=llm_result.confidence,
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disagreement=llm_result.intent != rule_result.intent,
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)
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def build_chat_intent_shadow(settings: Settings, client: Any | None = None) -> ChatIntentShadowLogger | None:
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"""Shadow only when explicitly enabled *and* an LLM provider is configured."""
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if settings.intent_router_mode != "shadow" or not settings.use_openai:
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return None
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llm_settings = settings
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if settings.intent_model:
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llm_settings = dataclasses.replace(settings, openai_model=settings.intent_model)
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completion = OpenAICompatibleStructuredClient(llm_settings, client)
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return ChatIntentShadowLogger(LLMChatIntentClassifier(completion), RuleBasedChatIntentClassifier())
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