3121d1384d
Interview/chat prompt layers, reply planner, style profiles, memory injection, interview meta store, and related tests. Work not finished. Made-with: Cursor
221 lines
8.3 KiB
Python
221 lines
8.3 KiB
Python
"""访谈 focus planner：规则 TurnPlan 之后的可选 LLM 细化（JSON），判断本轮承接重点并微调记忆引用与回复形状。"""
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from __future__ import annotations
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import json
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from dataclasses import replace
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from typing import Any
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from app.agents.chat.interview_turn_plan import (
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InterviewTurnPlan,
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apply_safe_mode_override,
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)
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from app.core.langchain_llm import ainvoke_json_object
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from app.core.logging import get_logger
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logger = get_logger(__name__)
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_VALID_FOCUS_PRIMARIES: frozenset[str] = frozenset(
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{"emotion", "relationship", "identity", "scene", "memoir_gap", "follow_user"}
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)
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def merge_reply_planner_json_into_turn_plan(
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plan: InterviewTurnPlan,
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raw_json: str,
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) -> InterviewTurnPlan:
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"""将 planner 返回的 JSON 合并进 TurnPlan；非法字段忽略，且不得突破安全边界。"""
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if not (raw_json or "").strip():
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return plan
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try:
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data = json.loads(raw_json)
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except json.JSONDecodeError:
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logger.warning("reply_planner json decode failed")
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return plan
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if not isinstance(data, dict):
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return plan
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kw: dict[str, Any] = {}
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touched_focus = False
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mu = data.get("memory_usage")
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if mu in ("none", "allowed_with_attribution"):
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if plan.memory_usage == "none" and mu == "allowed_with_attribution":
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pass
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else:
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kw["memory_usage"] = mu
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rs = data.get("reply_shape")
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if rs in ("flexible", "ack_only", "ack_then_question"):
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kw["reply_shape"] = rs
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mrs = data.get("memory_reference_style")
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if isinstance(mrs, str) and 2 <= len(mrs.strip()) <= 24:
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kw["memory_reference_style"] = mrs.strip()
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# forbid_first_person_experience：仅允许 true；模型若建议 false 一律忽略
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if data.get("forbid_first_person_experience") is False:
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logger.debug("reply_planner ignored forbid_first_person_experience=false")
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if "primary_focus" in data:
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pf = data.get("primary_focus")
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if isinstance(pf, str) and pf in _VALID_FOCUS_PRIMARIES:
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kw["primary_focus"] = pf # type: ignore[assignment]
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touched_focus = True
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if "secondary_focus" in data:
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sf = data.get("secondary_focus")
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if sf is None or (isinstance(sf, str) and not str(sf).strip()):
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kw["secondary_focus"] = None
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touched_focus = True
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elif isinstance(sf, str) and sf in _VALID_FOCUS_PRIMARIES:
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kw["secondary_focus"] = sf # type: ignore[assignment]
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touched_focus = True
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fsum = data.get("focus_summary")
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if isinstance(fsum, str) and fsum.strip():
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s = fsum.strip()
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if len(s) > 200:
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s = s[:199].rstrip() + "…"
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kw["focus_summary"] = s
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touched_focus = True
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mo = data.get("mode_override")
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if mo is not None and mo != "":
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merged_mode = apply_safe_mode_override(
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plan.mode,
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str(mo) if not isinstance(mo, str) else mo,
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primary_focus=str(kw.get("primary_focus", plan.primary_focus)),
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)
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if merged_mode is not None and merged_mode != plan.mode:
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kw["mode"] = merged_mode
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touched_focus = True
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if touched_focus:
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kw["focus_source"] = "llm"
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if not kw:
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return plan
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return replace(plan, **kw)
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def _build_reply_planner_prompt(
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*,
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plan: InterviewTurnPlan,
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user_message_preview: str,
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memory_evidence_preview: str,
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scene_cues_preview: str,
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recent_questions_preview: str,
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) -> str:
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mem_note = (
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(memory_evidence_preview or "").strip()[:1200]
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if (memory_evidence_preview or "").strip()
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else "（本轮无检索记忆预览）"
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)
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um = (user_message_preview or "").strip()[:800]
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scene_block = (
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(scene_cues_preview or "").strip()[:600]
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if (scene_cues_preview or "").strip()
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else "（本轮无场景关键词触发的氛围线索）"
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)
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rq_block = (
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(recent_questions_preview or "").strip()[:400]
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if (recent_questions_preview or "").strip()
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else "（无）"
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)
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focus_hint = f"{plan.primary_focus}"
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if plan.secondary_focus:
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focus_hint += f" / 次:{plan.secondary_focus}"
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return f"""你是回忆录访谈的「本轮重点计划器」。只输出**一个 JSON 对象**，不要 markdown，不要解释。
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## 任务
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先判断：用户本轮**最该被接住、最不该被忽略**的是什么（情绪、关系与他人、身份与面子、现场感官、或叙述槽缺口）。再决定如何微调基线。
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## 当前规则基线（服务端已算好，须尊重安全边界）
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- mode: {plan.mode}
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- primary_focus（规则先验）: {focus_hint}
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- memory_usage: {plan.memory_usage}
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- reply_shape: {plan.reply_shape}
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- memory_reference_style: {plan.memory_reference_style}
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- forbid_first_person_experience: {plan.forbid_first_person_experience}
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## 用户本轮话（截断）
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{um}
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## 近期你已问过的问题（截断；避免重复角度）
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{rq_block}
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## 检索记忆预览（供规划追问角度；**非**正文提纲，勿复述成长摘要）
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{mem_note}
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## 场景氛围线索（仅关键词映射，**不是用户原话**；可作辅助意象，不得压过用户明确提到的人名、关系与面子）
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{scene_block}
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## 输出 JSON 字段（仅限下列键；未提及的键不要输出）
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- primary_focus: \"emotion\" | \"relationship\" | \"identity\" | \"scene\" | \"memoir_gap\" | \"follow_user\"
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- secondary_focus: 同上或 null
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- focus_summary: 字符串，≤80 字，用**中文**写清**追问角度 / 承接方向**（问什么、先接住哪条张力），**不要**写成回复正文提纲或旧记忆复述
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- mode_override: \"emotion_first\" | \"clarify_first\" | \"memoir_push\" | \"follow_user_only\" | null
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- memory_usage: \"none\" | \"allowed_with_attribution\"
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- reply_shape: \"flexible\" | \"ack_only\" | \"ack_then_question\"
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- memory_reference_style: 2–24 字，用于「你之前提过…」类归因起句
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- forbid_first_person_experience: 必须为 true
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## 约束
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1. **不要编造**用户未说的人、事、时地。
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2. 若基线 memory_usage 为 none，则输出 memory_usage 必须为 none。
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3. 若用户话里同时有**明确他人/称谓/观众/面子/身份自称**与**身体感受或环境**，通常应把 primary_focus 设为 relationship 或 identity，而不是 scene。
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4. mode_override 仅在确实需要时给出；与基线相同时填 null。不要为了改而改。
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5. 若用户在追问「讲讲她的故事/说说他」等，倾向 reply_shape=\"ack_then_question\"（仍最多一个问句）。
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6. focus_summary **不得**支配主回复措辞或诱导复述检索细节；若基线 memory_usage 为 none，**不得**输出 allowed_with_attribution。
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7. focus_summary 用于：**先接住本轮核心张力**、再决定追问槽位；若用户话里含说不清/不确定/暧昧羞涩，倾向 mode_override=\"clarify_first\"（勿强推问卷）。"""
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async def maybe_refine_turn_plan_with_llm(
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llm: Any,
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*,
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plan: InterviewTurnPlan,
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text_for_model: str,
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memory_evidence_text: str,
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max_tokens: int,
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temperature: float,
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scene_cues_for_planner: list[str] | None = None,
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recent_questions_preview: str = "",
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) -> tuple[InterviewTurnPlan, str]:
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"""可选：调用轻量 JSON focus planner；失败返回原 plan 与空 raw。"""
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if llm is None:
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return plan, ""
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scene_cues_preview = ""
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if scene_cues_for_planner:
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scene_cues_preview = "\n".join(
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f"- {c}" for c in scene_cues_for_planner[:8]
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)
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prompt = _build_reply_planner_prompt(
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plan=plan,
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user_message_preview=text_for_model,
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memory_evidence_preview=memory_evidence_text,
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scene_cues_preview=scene_cues_preview,
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recent_questions_preview=recent_questions_preview,
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)
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try:
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pl_llm = llm.bind(temperature=float(temperature))
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raw = await ainvoke_json_object(
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pl_llm,
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prompt,
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max_tokens=max_tokens,
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agent="ReplyPlanner.interview",
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)
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if not raw:
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return plan, ""
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merged = merge_reply_planner_json_into_turn_plan(plan, raw)
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return merged, raw
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except Exception as e:
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logger.warning("reply_planner llm failed: {}", e)
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return plan, ""
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__all__ = [
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"maybe_refine_turn_plan_with_llm",
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"merge_reply_planner_json_into_turn_plan",
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]
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