a3f61fcc0f
- DB: segments 用户输入文本（Alembic 0002） - Chat: 阶段检测/阶段提示/回复限制，编排与访谈/画像 prompts 调整 - Memoir: 忠实度检查 agent，叙事与分类等链路更新 - Core: agent 日志、Alembic 启动、LangChain/日志/配置等 - Story: time_hints；Memory 检索与相关测试 - Expo: 助手头像、会话页与消息拆分、实时会话与文案/i18n - Docs/scripts/tests: 迁移脚本、LLM JSON/记忆检索文档、新增单测
158 lines
5.5 KiB
Python
158 lines
5.5 KiB
Python
"""
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ClassificationAgent：将内容分类到 8 个章节类别，或判定无价值返回 None。
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对应现有逻辑：_classify_chapter_category
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返回 None 表示本段不进入回忆录 Story/章节流水线；与 StoryRoute 中「可独立讲述的一段人生经历」
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（见 prompts.get_story_route_prompt）在标准上对齐：零散档案点不进 Story，记忆与 slot 抽取仍由上游完成。
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"""
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from __future__ import annotations
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import json
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import re
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from typing import Any, Optional
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from app.agents.memoir.prompts import (
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CHAPTER_CATEGORIES,
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get_chapter_classification_json_prompt,
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)
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from app.core.langchain_llm import invoke_json_object
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from app.core.logging import get_logger
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from app.features.memoir.memoir_images.json_payload import extract_json_payload
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logger = get_logger(__name__)
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# 与「仅档案句式」组合使用；过短但明显为叙事句的仍交 LLM 判断
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_FRAGMENT_SHORT_MAX_LEN = 48
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# 整段仅为出生年份/年份声明（零散档案，不成故事）
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_BIRTH_YEAR_LINE = re.compile(
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r"^[\s\u200b]*(?:我)?\d{4}\s*年\s*(出生|生的|生)?\s*[。.!！]?[\s\u200b]*$",
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re.UNICODE,
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)
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# 极短且为「我是某地人」式籍贯标签，无过程描写
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_SHORT_HUKOU_STYLE = re.compile(
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r"^[\s\u200b]*(?:我)?是[\u4e00-\u9fff]{1,10}(人|籍)\s*[。.!！]?[\s\u200b]*$",
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re.UNICODE,
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)
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# 5-stage 关键词（用于 LLM 失败时的兜底）；注意勿含易与「仅年份句」共现的泛词，以免误推类别
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STAGE_KEYWORDS = {
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"childhood": ["童年", "小时候", "家乡", "小镇"],
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"education": ["上学", "学校", "老师", "同学", "教育", "大学"],
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"career": ["工作", "职业", "事业", "公司", "同事", "创业"],
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"family": ["伴侣", "孩子", "家庭", "家人", "结婚", "父母"],
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"belief": ["信念", "价值观", "座右铭", "坚持", "原则"],
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}
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# 5-stage → 默认 8-category 映射（LLM 分类失败时的兜底）
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_STAGE_TO_DEFAULT_CATEGORY = {
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"childhood": "childhood",
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"education": "education",
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"career": "career_early",
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"family": "family",
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"belief": "beliefs",
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}
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def _detect_stage(text: str, fallback_stage: str) -> str:
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"""根据关键词检测消息所属的 5-stage 阶段"""
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message = (text or "").lower()
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for stage, keywords in STAGE_KEYWORDS.items():
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if any(word in message for word in keywords):
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return stage
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return fallback_stage
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def _looks_like_fragment_only(text: str) -> bool:
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"""
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保守启发式：明显为档案点/标签句，不足以作为 Story 叙事单元。
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与 get_chapter_classification_prompt 中「应返回 none」的情形一致；误判风险通过窄正则控制。
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"""
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s = (text or "").strip()
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if not s:
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return True
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if _BIRTH_YEAR_LINE.match(s):
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return True
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if len(s) <= _FRAGMENT_SHORT_MAX_LEN and _SHORT_HUKOU_STYLE.match(s):
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return True
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return False
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def _normalize_llm_category(raw: str) -> str:
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"""去掉模型偶发的引号、反引号包裹。"""
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s = (raw or "").strip().lower()
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if s.startswith("`"):
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s = s.strip("`").strip()
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if (s.startswith('"') and s.endswith('"')) or (
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s.startswith("'") and s.endswith("'")
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):
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s = s[1:-1].strip()
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return s
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def _parse_category_from_llm_response(raw: str) -> str:
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"""优先解析 JSON ``{"category": "..."}``，失败则按纯文本 key 处理。"""
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s = (raw or "").strip()
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if not s:
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return ""
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try:
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data = json.loads(extract_json_payload(s))
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if isinstance(data, dict) and "category" in data:
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return _normalize_llm_category(str(data["category"]))
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except (json.JSONDecodeError, TypeError, ValueError):
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pass
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return _normalize_llm_category(s)
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class ClassificationAgent:
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"""将内容分类到 8 个章节类别之一，或判定无价值返回 None"""
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def classify(
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self,
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text: str,
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fallback_stage: str,
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llm: Any,
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) -> Optional[str]:
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"""
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分类到 8 个章节类别之一。
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若 LLM 判定内容不足以独立成篇（none）或启发式判定为零散档案点，返回 None。
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llm 需支持 .invoke(prompt) 同步调用。
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"""
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if _looks_like_fragment_only(text):
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logger.debug(
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"零散档案/极短标签句，跳过回忆录 Story: text_len={} text={}",
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len(text or ""),
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text or "",
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)
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return None
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if llm:
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try:
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prompt = get_chapter_classification_json_prompt(text)
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raw = invoke_json_object(
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llm,
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prompt,
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max_tokens=256,
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agent="ClassificationAgent.classify",
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)
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category = _parse_category_from_llm_response(raw)
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if category == "none":
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logger.debug(
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"LLM 判定内容不足以成篇，跳过: text_len={} text={}",
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len(text or ""),
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text or "",
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)
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return None
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if category in CHAPTER_CATEGORIES:
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return category
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except Exception as e:
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logger.warning("ClassificationAgent LLM 章节分类失败: {}", e)
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stage = _detect_stage(text, fallback_stage)
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return _STAGE_TO_DEFAULT_CATEGORY.get(
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stage,
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_STAGE_TO_DEFAULT_CATEGORY.get(fallback_stage, "childhood"),
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)
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