2fded6fbd9
- Drop interview_reply_length and utterance_substance; always run stage LLM and memory retrieval when enabled; trim Settings fields and .env.example. - Replace guided/opening prompts with compact fact blocks plus unified behavior guidance; slim background_voice and persona to tone hints. - InterviewAgent uses fixed chat_interview max_tokens/chars/segments. Also includes stacked work: profile followup/extract path, evaluation rubric and judge schema updates, transcript SPLIT handling in execution service, user export markdown split tests, and golden case fixture.
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
"""Bundled parameters for chat system prompts (InterviewAgent)."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, List, Optional
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@dataclass
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class ChatPromptContext:
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"""访谈轮次构建 `get_guided_conversation_prompt` 所需的字段集合。"""
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current_stage: str
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empty_slots: List[str]
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filled_slots: Dict[str, str]
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all_stages_coverage: Optional[Dict[str, Dict]] = None
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detected_user_stage: str = ""
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user_profile_context: str = ""
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persona: str = "default"
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memory_evidence_text: str = ""
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background_voice: str = "default"
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occupation: str = ""
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def guided_system_prompt(self) -> str:
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"""用户原话仅以对话历史 + HumanMessage 注入模型。"""
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from app.agents.chat.prompts_conversation import get_guided_conversation_prompt
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return get_guided_conversation_prompt(
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current_stage=self.current_stage,
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empty_slots=self.empty_slots,
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filled_slots=self.filled_slots,
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all_stages_coverage=self.all_stages_coverage,
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detected_user_stage=self.detected_user_stage,
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user_profile_context=self.user_profile_context,
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persona=self.persona,
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memory_evidence_text=self.memory_evidence_text,
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background_voice=self.background_voice,
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occupation=self.occupation,
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)
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