309a051038
数据库与模型：新增多版迁移（章节证据快照、对话血缘、记忆事实/时间线 lineage 等），把「成稿 ↔ 对话/记忆」的溯源信息落到表结构里。 业务链路：会话与 WS、回忆录/故事流水线、记忆写入与 enrichment 等跟着接上线索与快照；新增章节证据快照与评测侧 EvalTraceService 等模块，方便组评审用的证据包。 内部评测：自动化 run 与手工 memoir 评审共用可追溯证据；rubric/ judge 相关脚本与文档有配套调整。 app-eval-web：Memoir/实验详情里能展开看证据摘要与 evidence_trace（含对话轮次 id）；Vite 代理与 development.sh 注入的 API 端口与当前默认内部评测端口一致，避免改端口后页面连错服务。 工程杂项：GitHub Actions / 仓库说明有更新；各适配器与支付/配额/plan 等多处为小改动或跟随主改动的收尾；新增/扩充了?
141 lines
5.0 KiB
Python
141 lines
5.0 KiB
Python
"""
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NarrativeAgent：生成创意标题和叙事改写。
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叙事正文走 `get_narrative_json_prompt` / `get_narrative_merge_json_prompt`（传记作家式书面语 + 事实边界）。
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"""
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from __future__ import annotations
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from typing import Any, Dict, Optional
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from app.agents.memoir.prompts import (
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get_creative_title_json_prompt,
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get_narrative_json_prompt,
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get_narrative_merge_json_prompt,
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)
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from app.agents.memoir.schemas import MemoirTitleOutput
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from app.agents.stage_constants import CHAPTER_CATEGORIES
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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.llm_call import llm_json_call
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from app.core.logging import get_logger
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logger = get_logger(__name__)
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class NarrativeAgent:
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"""生成章节标题和叙事正文"""
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def generate_title(
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self,
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stage: str,
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emotion: str,
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slots: Dict[str, str],
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user_profile: str = "",
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birth_year: Optional[int] = None,
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llm: Any = None,
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) -> str:
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"""生成创意标题。若无 LLM 则返回默认标题"""
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if not llm:
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return f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
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try:
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prompt = get_creative_title_json_prompt(
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stage=stage,
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emotion=emotion,
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slots=slots,
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user_profile=user_profile,
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birth_year=birth_year,
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)
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default_title = f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
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def _title_fallback() -> MemoirTitleOutput:
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return MemoirTitleOutput(title=default_title)
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out = llm_json_call(
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llm,
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prompt,
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MemoirTitleOutput,
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max_tokens=settings.memoir_title_max_tokens,
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agent="NarrativeAgent.generate_title",
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fallback_factory=_title_fallback,
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)
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title = (out.title or "").strip()
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if title:
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return title.strip('"')
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return default_title
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except Exception as e:
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logger.warning("NarrativeAgent 生成标题失败: {}", e)
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return f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
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def generate_narrative(
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self,
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stage: str,
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slots: Dict[str, str],
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new_content: str,
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existing_content: str = "",
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user_profile: str = "",
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birth_year: Optional[int] = None,
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llm: Any = None,
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background_voice: str = "default",
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occupation: str = "",
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*,
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fallback_plain_oral: str = "",
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) -> str:
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"""将新对话改写为叙述。若无 LLM 则直接拼接。
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若 `existing_content` 非空（append 路径），使用整篇合并提示，输出覆盖全篇的有序段落。
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`fallback_plain_oral`：仅含本段口述（勿传含 evidence 的组装串）。LLM 异常时只回退到
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口述/旧正文拼接，避免把「本段用户口述+摘录」整包写入 story。
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"""
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oral_fb = (fallback_plain_oral or "").strip()
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if not llm:
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if existing_content:
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if oral_fb:
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return f"{existing_content}\n\n{oral_fb}"
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return f"{existing_content}\n\n{new_content}"
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return oral_fb or new_content
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try:
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merge_mode = bool((existing_content or "").strip())
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if merge_mode:
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prompt = get_narrative_merge_json_prompt(
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stage=stage,
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slots=slots,
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new_content=new_content,
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existing_content=existing_content,
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user_profile=user_profile,
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birth_year=birth_year,
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background_voice=background_voice,
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occupation=occupation,
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)
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max_tokens = int(settings.memoir_narrative_merge_max_tokens)
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agent_name = "NarrativeAgent.generate_narrative_merge"
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else:
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prompt = get_narrative_json_prompt(
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stage=stage,
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slots=slots,
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new_content=new_content,
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existing_content=existing_content,
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user_profile=user_profile,
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birth_year=birth_year,
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background_voice=background_voice,
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occupation=occupation,
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)
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max_tokens = int(settings.memoir_narrative_max_tokens)
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agent_name = "NarrativeAgent.generate_narrative"
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return invoke_json_object(
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llm,
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prompt,
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max_tokens=max_tokens,
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agent=agent_name,
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).strip()
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except Exception as e:
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logger.warning("NarrativeAgent 生成叙事失败: {}", e)
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ex = (existing_content or "").strip()
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if ex and oral_fb:
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return f"{existing_content}\n\n{oral_fb}"
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if oral_fb:
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return oral_fb
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if ex:
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return str(existing_content)
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return ""
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