feat(memoir): 回忆录分段两阶段管线（Phase1 分类 / Phase2 叙事）与配置、测试

api/app/agents/memoir/classification_agent.py

@@ -14,8 +14,11 @@ from dataclasses import dataclass
 from typing import Any
 
 from app.agents.memoir.prompts import get_chapter_classification_json_prompt
-from app.agents.stage_constants import CHAPTER_CATEGORIES
-from app.agents.stage_constants import STAGE_TO_DEFAULT_CATEGORY
+from app.agents.stage_constants import (
+    CHAPTER_CATEGORIES,
+    STAGE_KEYWORD_WEIGHTS,
+    STAGE_TO_DEFAULT_CATEGORY,
+)
 from app.core.json_utils import extract_json_payload
 from app.core.langchain_llm import invoke_json_object
 from app.core.logging import get_logger
@@ -40,21 +43,12 @@ _SHORT_HUKOU_STYLE = re.compile(
     re.UNICODE,
 )
 
-# 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
-STAGE_KEYWORDS = {
-    "childhood": ["童年", "小时候", "家乡", "小镇"],
-    "education": ["上学", "学校", "老师", "同学", "教育", "大学"],
-    "career": ["工作", "职业", "事业", "公司", "同事", "创业"],
-    "family": ["伴侣", "孩子", "家庭", "家人", "结婚", "父母"],
-    "belief": ["信念", "价值观", "座右铭", "坚持", "原则"],
-}
-
 
 def _detect_stage(text: str, fallback_stage: str) -> str:
-    """根据关键词检测消息所属的 5-stage 阶段"""
+    """根据关键词检测消息所属的 5-stage 阶段（与 stage_constants.STAGE_KEYWORD_WEIGHTS 同源；匹配方式为子串，非加权）。"""
     message = (text or "").lower()
-    for stage, keywords in STAGE_KEYWORDS.items():
-        if any(word in message for word in keywords):
+    for stage, pairs in STAGE_KEYWORD_WEIGHTS.items():
+        if any(word in message for word, _w in pairs):
             return stage
     return fallback_stage
 

api/app/agents/memoir/extraction_agent.py

@@ -10,6 +10,7 @@ from dataclasses import dataclass
 from typing import Any, Dict
 
 from app.agents.memoir.prompts import get_state_extraction_prompt
+from app.agents.stage_constants import normalize_chat_stage
 from app.core.langchain_llm import invoke_json_object
 from app.core.logging import get_logger
 from app.core.json_utils import extract_json_payload
@@ -63,7 +64,11 @@ class ExtractionAgent:
                 agent="ExtractionAgent.extract",
             )
             parsed = json.loads(extract_json_payload(raw))
-            detected_stage = parsed.get("detected_stage", detected_stage)
+            raw_detected = parsed.get("detected_stage", detected_stage)
+            detected_stage = normalize_chat_stage(
+                str(raw_detected) if raw_detected is not None else None,
+                fallback=current_stage,
+            )
             raw_slots = parsed.get("slots", {}) or {}
             extracted_slots = {
                 k: v if isinstance(v, str) else str(v) for k, v in raw_slots.items()

api/app/agents/memoir/narrative_agent.py

@@ -8,6 +8,7 @@ from __future__ import annotations
 import json
 from typing import Any, Dict, Optional
 
+from app.agents.stage_constants import CHAPTER_CATEGORIES
 from app.agents.memoir.prompts import (
     get_creative_title_json_prompt,
     get_narrative_json_prompt,
@@ -34,7 +35,7 @@ class NarrativeAgent:
     ) -> str:
         """生成创意标题。若无 LLM 则返回默认标题"""
         if not llm:
-            return f"{stage} 回忆"
+            return f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
         try:
             prompt = get_creative_title_json_prompt(
                 stage=stage,
@@ -53,10 +54,10 @@ class NarrativeAgent:
             title = (data.get("title") or "").strip() if isinstance(data, dict) else ""
             if title:
                 return title.strip('"')
-            return f"{stage} 回忆"
+            return f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
         except Exception as e:
             logger.warning("NarrativeAgent 生成标题失败: {}", e)
-            return f"{stage} 回忆"
+            return f"{CHAPTER_CATEGORIES.get(stage, stage)} 回忆"
 
     def generate_narrative(
         self,

api/app/agents/memoir/orchestrator.py

@@ -33,6 +33,8 @@ class PreparedMemoirBatches:
     category_to_segments: Dict[str, List[Segment]]
     #: segment id 在「LLM 判 none 且 extraction slots 为空」时加入；batch 级短路见 memoir_tasks
     segment_skip_story_ids: Set[str]
+    #: 每个 segment → Phase 1 分类 chapter_category（持久化到 Segment.topic_category）
+    segment_chapter_category: Dict[str, str]
 
 
 class MemoirOrchestrator:
@@ -64,6 +66,7 @@ class MemoirOrchestrator:
         state = get_or_create_state()
         category_to_segments: Dict[str, List[Segment]] = {}
         segment_skip_story_ids: Set[str] = set()
+        segment_chapter_category: Dict[str, str] = {}
         classify_extract_llm = llm_fast if llm_fast is not None else llm
 
         for segment in segments:
@@ -103,6 +106,7 @@ class MemoirOrchestrator:
             chapter_category = classify_result.category
             if (not result.slots) and classify_result.llm_said_none:
                 segment_skip_story_ids.add(str(segment.id))
+            segment_chapter_category[str(segment.id)] = chapter_category
 
             if agent_summary_enabled():
                 logger.info(
@@ -126,6 +130,7 @@ class MemoirOrchestrator:
             state=state,
             category_to_segments=category_to_segments,
             segment_skip_story_ids=segment_skip_story_ids,
+            segment_chapter_category=segment_chapter_category,
         )
 
     def run(