fix(rag): route exercise titles to knowledge base

ai_knowledge_base_v2/apps/backend/app/services/knowledge_agent_service.py

@@ -8,7 +8,7 @@ import re
 from dataclasses import dataclass
 from time import perf_counter
 
-from sqlalchemy import select
+from sqlalchemy import or_, select
 from sqlalchemy.orm import Session
 
 from app.core.config import get_settings
@@ -40,7 +40,13 @@ MAX_LEXICAL_CANDIDATES = 12
 MAX_SELECTED_SECTIONS = 4
 MAX_OVERVIEW_LEXICAL_CANDIDATES = 48
 MAX_OVERVIEW_SELECTED_SECTIONS = 24
-BUSINESS_MARKERS = {"课程", "大本营", "训练营", "老师", "卢慧", "功课", "学员", "课堂", "练习", "觉察", "内在"}
+BUSINESS_MARKERS = {
+    "课程", "大本营", "训练营", "老师", "卢慧", "功课", "作业", "学员",
+    "课堂", "练习", "静心", "觉察", "内在",
+}
+_ROUTING_STOP_TERMS = {
+    "课程", "练习", "功课", "作业", "静心", "具体", "详细", "内容", "怎么", "操作", "什么", "方法",
+}
 
 
 @dataclass
@@ -101,6 +107,14 @@ class KnowledgeAgentService:
         )
         trace.append(rewrite_trace)
         need_knowledge, selected_ids, reason = cls._decide(retrieval_question, catalog)
+        terms = cls._query_terms(retrieval_question)
+        title_routes = cls._route_knowledge_by_titles(db, terms, catalog)
+        if title_routes:
+            need_knowledge = True
+            selected_ids = list(dict.fromkeys(
+                [item["knowledgeId"] for item in title_routes] + selected_ids
+            ))[:4]
+            reason = "章节标题直接命中知识库"
         trace.append(cls._trace(
             "agent_decision",
             len(trace) + 1,
@@ -108,10 +122,16 @@ class KnowledgeAgentService:
             {"needKnowledge": need_knowledge, "selectedKnowledgeIds": selected_ids, "reason": reason},
             started,
         ))
+        trace.append(cls._trace(
+            "route_knowledge_by_titles",
+            len(trace) + 1,
+            {"queryTerms": terms},
+            {"count": len(title_routes), "matches": title_routes},
+            started,
+        ))
         chunks: list[RetrievedChunk] = []
         try:
             if need_knowledge and selected_ids:
-                terms = cls._query_terms(retrieval_question)
                 practice_overview = cls._is_practice_overview(retrieval_question)
                 candidate_limit = cls._candidate_limit(retrieval_question)
                 candidates = cls.search_knowledge(
@@ -557,6 +577,41 @@ class KnowledgeAgentService:
             return True, selected, "问题与知识目录主题相关"
         return False, [], "问题与当前知识目录无关，无需调用知识库"
 
+    @staticmethod
+    def _route_knowledge_by_titles(db: Session, terms: list[str], catalog: list[dict]) -> list[dict]:
+        """Map a named exercise/chapter back to its knowledge base before retrieval."""
+        version_ids = [int(item["versionId"]) for item in catalog if item.get("versionId")]
+        if not version_ids:
+            return []
+        distinctive_terms = list(dict.fromkeys(
+            term.lower() for term in terms
+            if len(term) >= 2 and term not in _ROUTING_STOP_TERMS
+        ))
+        if not distinctive_terms:
+            return []
+        distinctive_terms = sorted(distinctive_terms, key=len, reverse=True)[:12]
+
+        rows = db.execute(
+            select(KnowledgeChunk.knowledge_id, KnowledgeChunk.title)
+            .where(
+                KnowledgeChunk.version_id.in_(version_ids),
+                or_(*(KnowledgeChunk.title.contains(term) for term in distinctive_terms)),
+            )
+        ).all()
+        best_by_knowledge: dict[int, tuple[float, str]] = {}
+        for knowledge_id, title in rows:
+            normalized_title = title.lower()
+            score = sum(min(len(term), 4) ** 2 for term in distinctive_terms if term in normalized_title)
+            current = best_by_knowledge.get(knowledge_id)
+            if score > 0 and (current is None or score > current[0]):
+                best_by_knowledge[knowledge_id] = (float(score), title)
+
+        ranked = sorted(best_by_knowledge.items(), key=lambda item: (item[1][0], item[0]), reverse=True)
+        return [
+            {"knowledgeId": knowledge_id, "matchedTitle": title, "score": score}
+            for knowledge_id, (score, title) in ranked[:4]
+        ]
+
     @staticmethod
     def _query_terms(question: str) -> list[str]:
         base = extract_terms(question)