fix(agent): enforce complete homework answers

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

@@ -122,6 +122,8 @@ class KnowledgeAgentService:
                     limit=cls._selection_limit(retrieval_question),
                     prefer_numbered_practice=practice_overview,
                 )
+                if practice_overview:
+                    selected.sort(key=lambda item: (item.knowledge.name, item.section.sort_order))
                 selected_contents = {
                     item.section.id: cls._protect_content(cls._read_complete_section(db, item.section))
                     for item in selected

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

@@ -116,7 +116,7 @@ class ModelClientService:
         return {
             "ok": True,
             "message": "Agent 调试完成",
-            "answer": answer[:4000],
+            "answer": answer,
             "prompt": rag_result.prompt,
             "modelName": model.model_name,
             "retrieveCount": len(rag_result.chunks),

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

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import re
 from dataclasses import dataclass
 
 from sqlalchemy import select
@@ -127,6 +128,8 @@ class PromptService:
         if not context:
             context = "本轮没有可靠的正式知识章节。对于一般常识可以谨慎回答；涉及课程、老师观点或公司业务时必须说明依据不足，不得编造。"
 
+        completeness_constraint = cls._list_completeness_constraint(question, chunks)
+
         messages: list[dict[str, str]] = [
             {
                 "role": "system",
@@ -163,13 +166,38 @@ class PromptService:
                 "不要求知识库中必须存在一个与‘某课程作业’完全同名的章节。"
                 "回答作业清单类问题时，应直接给出作业名称，并概括每项作业的用途或核心方向；"
                 "正文已经提供基本步骤时可以简要说明，不要把本轮已经取到的内容留到下一轮再问。"
-                "章节标题可以作为作业或练习名称，标题下的子章节是对应的正式说明。\n\n"
+                "章节标题可以作为作业或练习名称，标题下的子章节是对应的正式说明。"
+                f"{completeness_constraint}\n\n"
                 f"{context}"
             ),
         })
         messages.append({"role": "user", "content": question.strip()})
         return messages
 
+    @staticmethod
+    def _list_completeness_constraint(question: str, chunks: list[RetrievedChunk]) -> str:
+        is_practice_list = (
+            any(marker in question for marker in ("作业", "功课", "练习"))
+            and any(marker in question for marker in ("有哪些", "是什么", "包括什么", "都有什么", "列出", "汇总", "总结"))
+        )
+        numbered = [
+            chunk for chunk in chunks
+            if re.search(r"(?:练习|作业|功课)\s*[一二三四五六七八九十百\d]+", chunk.title)
+        ]
+        if not is_practice_list or not numbered:
+            return ""
+        titles = "\n".join(
+            f"{index}. {chunk.knowledge_name} / {chunk.title}"
+            for index, chunk in enumerate(numbered, start=1)
+        )
+        return (
+            "\n\n[作业清单完整性约束]\n"
+            f"本轮共召回 {len(numbered)} 个正式编号练习。回答必须覆盖下面全部 {len(numbered)} 项，"
+            "不得只挑选部分项目，不得合并成少数概念，不得新增清单外的作业。"
+            "应按知识库分组，逐项保留正式练习名称，并根据对应正文简要概括；即使回答较长也必须列完。\n"
+            f"必须覆盖的标题清单：\n{titles}"
+        )
+
     @staticmethod
     def render_messages(messages: list[dict[str, str]]) -> str:
         role_labels = {"system": "系统", "user": "用户", "assistant": "大本营答疑助手"}
@@ -180,8 +208,6 @@ class PromptService:
 
     @staticmethod
     def _clean_history_content(content: str) -> str:
-        import re
-
         return re.sub(r"<think[\s\S]*?</think\s*>", "", content, flags=re.IGNORECASE).strip()
 
     @classmethod