feat: separate contents, abstract and body evidence

backend/wisegrade/actions.py

@@ -210,7 +210,7 @@ def prepare(db, conversation: Conversation, kind: str, payload: dict, *, teacher
     else:
         raise HTTPException(400, "不支持的操作")
     expected = _state(db, kind, payload)
-    if kind == "start_batch" and expected["status"] not in ("created", "failed", "partial"):
+    if kind == "start_batch" and expected["status"] not in ("created", "failed", "partial", "review_required"):
         raise HTTPException(409, "批次不可启动或重试")
     if kind == "index_retry" and expected["status"] != "failed":
         raise HTTPException(409, "仅失败任务可重试")

backend/wisegrade/agent_tools.py

@@ -8,10 +8,12 @@ from pydantic import BaseModel, ConfigDict, Field
 from sqlalchemy import select
 
 from .actions import prepare, require_scope
-from .exports import batch_projects, project_result
+from .evidence import CONTENT_ALLOWED, STRUCTURE_ALLOWED
+from .exports import STATUS_LABELS, batch_projects, page_type_label, project_result
 from .knowledge_pages import page_links
-from .models import (Conversation, Course, CourseVersion, IndexTask, KnowledgeDocument,
+from .models import (Conversation, Course, CourseVersion, IndexTask, KnowledgeChunk, KnowledgeDocument,
                      Project, RubricVersion, ScoringBatch, Student, SubmissionFile)
+from .pages import toc_entries
 from .rag import retrieve
 from .rules import totals
 from .storage import directories, scan
@@ -52,6 +54,14 @@ class SearchProject(ProjectId):
     query: str = Field(min_length=2, max_length=300)
 
 
+class DocumentId(Args):
+    document_id: int = Field(gt=0)
+
+
+class SearchDocument(DocumentId):
+    query: str = Field(min_length=2, max_length=300)
+
+
 class Directory(Args):
     relative_dir: str = Field(min_length=1, max_length=500)
 
@@ -67,7 +77,7 @@ class BatchId(Args):
 
 class ScoresFilterArgs(BatchId):
     project_id: int | None = Field(default=None, gt=0)
-    status: Literal["pending", "scored", "reviewed", "failed", "unscorable"] | None = None
+    status: Literal["pending", "scored", "reviewed", "needs_review", "failed", "unscorable"] | None = None
 
 
 class ScoresArgs(ScoresFilterArgs):
@@ -201,9 +211,14 @@ def project(db, convo, args, _user):
             "url": f"/?page=projects&project_id={item.id}"}
 
 
-def _search(db, version_id, project_id, query, *, public_only=False):
+def _search(db, version_id, project_id, query, *, public_only=False, scopes=None, limit=6):
+    """Content search under the evidence policy: navigation pages are never body text."""
+    scopes = scopes or ("content",)
+    allowed = sorted({page_type for scope in scopes for page_type in STRUCTURE_ALLOWED} if "structure" in scopes
+                     else {page_type for scope in scopes for page_type in CONTENT_ALLOWED})
     chunks = retrieve(db, course_version_id=version_id, project_id=project_id or 0, query=query,
-                      limit=6, include_public=False, public_only=public_only)
+                      limit=limit, include_public=False, public_only=public_only,
+                      allowed_page_types=allowed, substantive_only="content" in scopes)
     citations = []
     for chunk in chunks:
         doc = db.get(KnowledgeDocument, chunk["document_id"])
@@ -211,8 +226,15 @@ def _search(db, version_id, project_id, query, *, public_only=False):
         citations.append({"chunk_id": chunk["chunk_id"], "document_id": chunk["document_id"],
                           "title": doc.title if doc else "资料", "page": chunk["page"],
                           "quote": chunk["text"][:500], "project_id": chunk["project_id"],
+                          "page_type": chunk.get("page_type") or links.get("page_type") or "unknown",
+                          "page_type_label": page_type_label(chunk.get("page_type")),
+                          "section": chunk.get("section") or "",
+                          "block_type": chunk.get("block_type") or "body",
+                          "source_role": "structure" if "structure" in scopes else "content",
+                          # Stable identity so an answer can cite exactly what it used.
+                          "citation_id": f"chunk:{chunk['chunk_id']}",
                           **links})
-    return {"citations": citations, "found": bool(citations)}
+    return {"citations": citations, "found": bool(citations), "scopes": list(scopes)}
 
 
 def search_course(db, convo, args, _user):
@@ -225,6 +247,84 @@ def search_project(db, convo, args, _user):
     return _search(db, item.course_version_id, item.id, args.query)
 
 
+def search_structure(db, convo, args, _user):
+    """Structure and contents lookup: navigation pages plus body headings.
+
+    This tool answers questions about chapters, sections and report layout. Its results
+    are explicitly labelled as structure, so they can never be mistaken for content
+    evidence about what the student implemented or measured.
+    """
+    item = _project(db, convo, args.project_id)
+    chunks = retrieve(db, course_version_id=item.course_version_id, project_id=item.id, query=args.query,
+                      limit=8, include_public=False, allowed_page_types=sorted(STRUCTURE_ALLOWED),
+                      substantive_only=False)
+    citations = []
+    for chunk in chunks:
+        doc = db.get(KnowledgeDocument, chunk["document_id"])
+        links = page_links(db, chunk["document_id"], chunk["page"])
+        page_type = chunk.get("page_type") or links.get("page_type") or "unknown"
+        citations.append({
+            "chunk_id": chunk["chunk_id"], "document_id": chunk["document_id"],
+            "title": doc.title if doc else "资料", "page": chunk["page"],
+            "quote": chunk["text"][:500], "project_id": chunk["project_id"],
+            "page_type": page_type, "page_type_label": page_type_label(page_type),
+            "section": chunk.get("section") or "", "block_type": chunk.get("block_type") or "body",
+            "source_role": "structure",
+            "citation_id": f"chunk:{chunk['chunk_id']}",
+            "toc_entries": toc_entries([{"kind": "toc", "text": chunk["text"]}])[:40]
+                           if page_type == "toc" else [],
+            **links})
+    return {"citations": citations, "found": bool(citations), "scopes": ["structure"],
+            "note": "这些是结构或目录信息，只能说明章节安排，不能证明章节内容。"}
+
+
+def get_document_pages(db, convo, args, _user):
+    """List a document's pages with page type, so a caller can navigate precisely."""
+    _document(db, convo, args.document_id)
+    rows = db.scalars(select(KnowledgeChunk).where(KnowledgeChunk.document_id == args.document_id)
+                      .order_by(KnowledgeChunk.page, KnowledgeChunk.id)).all()
+    pages: dict[int, dict] = {}
+    for chunk in rows:
+        entry = pages.setdefault(chunk.page, {"page": chunk.page, "page_type": chunk.page_type,
+                                              "page_type_label": page_type_label(chunk.page_type),
+                                              "chapter": chunk.chapter, "section": chunk.section,
+                                              "block_types": []})
+        if chunk.block_type not in entry["block_types"]:
+            entry["block_types"].append(chunk.block_type)
+        if chunk.section:
+            entry["section"] = chunk.section
+    return {"document_id": args.document_id, "count": len(pages),
+            "pages": [pages[key] for key in sorted(pages)]}
+
+
+def search_document(db, convo, args, _user):
+    """Content search restricted to one document, under the same page-type policy."""
+    document = _document(db, convo, args.document_id)
+    chunks = retrieve(db, course_version_id=document.course_version_id, project_id=document.project_id or 0,
+                      query=args.query, limit=6, include_public=False,
+                      allowed_page_types=sorted(CONTENT_ALLOWED), substantive_only=True)
+    citations = []
+    for chunk in chunks:
+        if chunk["document_id"] != document.id:
+            continue
+        links = page_links(db, chunk["document_id"], chunk["page"])
+        citations.append({"chunk_id": chunk["chunk_id"], "document_id": chunk["document_id"],
+                          "title": document.title, "page": chunk["page"], "quote": chunk["text"][:500],
+                          "project_id": chunk["project_id"], "page_type": chunk.get("page_type"),
+                          "page_type_label": page_type_label(chunk.get("page_type")),
+                          "section": chunk.get("section") or "", "source_role": "content",
+                          "citation_id": f"chunk:{chunk['chunk_id']}", **links})
+    return {"citations": citations, "found": bool(citations), "scopes": ["content"]}
+
+
+def _document(db, convo, document_id):
+    document = db.get(KnowledgeDocument, document_id)
+    if not document:
+        raise HTTPException(404, "资料不存在")
+    require_scope(db, convo, version_id=document.course_version_id, project_id=document.project_id)
+    return document
+
+
 def batch_list(db, convo, args, _user):
     query = select(ScoringBatch).order_by(ScoringBatch.id.desc())
     rows = []
@@ -278,15 +378,36 @@ def scores(db, convo, args, _user):
                                  "max_score": row["criterion"]["max_score"], "score_id": row["score_id"],
                                  "model_score": row["model_score"], "teacher_score": row["teacher_score"],
                                  "final_score": row["final_score"], "status": row["status"],
-                                 "error": str(row["error"] or "")[:300], "evidence": [{"page": ev["page"],
-                                     "quote": ev["quote"][:160], "document": str(ev["document"])[:200]}
+                                 "status_label": STATUS_LABELS.get(row["status"], row["status"]),
+                                 "error": str(row["error"] or "")[:300],
+                                 # Which body targets still lack evidence, so the answer can
+                                 # explain a review item instead of reporting a score.
+                                 "evidence_gaps": [{"target_label": str(gap.get("target_label") or "")[:120],
+                                                    "searched_page_types": gap.get("searched_page_types") or []}
+                                                   for gap in (row.get("evidence_gaps") or [])][:6],
+                                 "evidence": [{"page": ev["page"],
+                                     "quote": ev["quote"][:160], "document": str(ev["document"])[:200],
+                                     "page_type": ev.get("page_type") or "unknown",
+                                     "page_type_label": page_type_label(ev.get("page_type")),
+                                     "target_label": str(ev.get("target_label") or "")[:120],
+                                     "historical": bool(ev.get("historical"))}
                                      for ev in row["evidence"][:2]]} for row in rows]})
         for row in rows:
             for evidence in row["evidence"][:2]:
                 links = page_links(db, evidence.get("document_id"), evidence["page"])
                 citations.append({"title": str(evidence["document"])[:200], "page": evidence["page"],
                                   "quote": evidence["quote"][:320], "document_id": evidence.get("document_id"),
+                                  "page_type": evidence.get("page_type") or links.get("page_type") or "unknown",
+                                  "page_type_label": page_type_label(evidence.get("page_type")),
+                                  "target_label": str(evidence.get("target_label") or "")[:120],
+                                  "historical": bool(evidence.get("historical")),
+                                  "source": "historical_score_evidence" if evidence.get("historical") else "score_evidence",
                                   **links, "score_url": url})
+    if citations:
+        # Scoring quotations are never presented as report body evidence in an answer.
+        for citation in citations:
+            citation["usage"] = ("历史评分引用" if citation.get("historical")
+                                 else f"评分证据 · {citation.get('page_type_label')}")
     next_criterion_offset = args.criterion_offset + 6 if page and args.criterion_offset + 6 < len(page[0][1]["rows"]) else None
     next_project_offset = args.project_offset + 1 if args.project_offset + 1 < len(results) else None
     return {"batch_id": batch.id, "status_filter": args.status, "count": len(results),
@@ -418,12 +539,15 @@ def prepare_import(db, convo, args, _user):
 TOOLS: dict[str, Tool] = {
     "list_courses": Tool("列出课程、版本和发布状态；名称或版本不清楚时先调用此工具。", Empty, courses),
     "get_course_rules": Tool("查询课程目标和规则。指定批次时只返回批次保存的快照。", RulesArgs, rules),
-    "search_course_material": Tool("检索指定课程版本的课程资料，返回原文片段和引用。", SearchCourse, search_course),
+    "search_course_material": Tool("检索指定课程版本的课程资料正文，返回原文片段和引用；不含目录、摘要和参考文献页。", SearchCourse, search_course),
     "list_directories": Tool("只读浏览 /data/inbox 下的相对目录。", Directory, browse_directories),
     "scan_directory": Tool("只读分页扫描学生总目录，返回材料候选、缺失、重复和识别异常。", ScanArgs, scan_directories),
     "list_projects": Tool("按课程版本及可选学号查询学生项目。", ProjectList, projects),
     "get_project": Tool("读取单个学生项目的元数据、材料和解析状态。", ProjectId, project),
-    "search_project_material": Tool("只检索指定学生项目报告，返回原文片段和引用。", SearchProject, search_project),
+    "search_project_material": Tool("只检索指定学生项目的报告正文，返回带页面类型和稳定引用标识的原文片段；不含目录、摘要和参考文献页。", SearchProject, search_project),
+    "search_project_structure": Tool("查询学生报告的章节结构与目录，返回目录条目和页面类型；只能说明章节安排，不能证明章节内容。", SearchProject, search_structure),
+    "get_document_pages": Tool("列出某份资料各页的页码、页面类型和章节，用于判断正文页位置。", DocumentId, get_document_pages),
+    "search_document": Tool("在指定的一份资料内检索正文段落，返回带页面类型和引用标识的片段。", SearchDocument, search_document),
     "list_batches": Tool("列出可访问的评分批次。", Empty, batch_list),
     "get_batch_progress": Tool("查询评分批次进度、状态和固定项目范围。", BatchId, progress),
     "get_scores": Tool("分页查询单学生或单批次评分详情，可按状态筛选。每次返回一个项目、最多六项评分；如需其余评分项，用 next_criterion_offset 作为 criterion_offset 再查；如需下个项目，用 next_project_offset 作为 project_offset，并将 criterion_offset 设为 0。", ScoresArgs, scores),

backend/wisegrade/api.py

@@ -10,13 +10,16 @@ from sqlalchemy.orm import Session
 from . import services
 from .config import get_settings
 from .db import get_db
-from .exports import batch_projects, export_excel, export_project_pdf, project_result
+from .evidence import CONTENT_ALLOWED
+from .exports import (BATCH_STATUS_LABELS, batch_projects, export_excel,
+                      export_project_pdf, page_type_label, project_result)
 from .knowledge_pages import page_image_path, page_links
 from .models import (AuditLog, Course, CourseVersion, CriterionScore, ExportTask,
                      IndexTask, KnowledgeChunk, KnowledgeDocument, KnowledgePage, Project, RubricVersion, ScoringBatch,
                      Student, SubmissionFile, TeacherRevision)
 from .materialized import sync_course_entities, sync_rubric_entities
 from .rules import RubricData
+from .scoring import ATTENTION_STATUSES
 from .settings_store import key_configured, model_config, save_model_config
 from .storage import CATEGORIES, directories, scan
 
@@ -108,9 +111,20 @@ class ModelSettingsInput(BaseModel):
 
 @router.get("/dashboard")
 def dashboard(db: Session = Depends(get_db)):
+    """Overview counts. Items awaiting teacher review are counted as attention needed.
+
+    A ``needs_review`` item is not a failure: retrieval ran and found only navigation
+    pages where the rule requires body content, so a teacher decides it.
+    """
+    counts = dict(db.execute(select(CriterionScore.status, func.count(CriterionScore.id))
+                             .group_by(CriterionScore.status)).all())
     return {"courses": db.scalar(select(func.count(Course.id))), "projects": db.scalar(select(func.count(Project.id))),
             "batches": db.scalar(select(func.count(ScoringBatch.id))),
-            "needs_attention": db.scalar(select(func.count(CriterionScore.id)).where(CriterionScore.status.in_(["failed", "unscorable"]))) }
+            "needs_attention": sum(counts.get(status, 0) for status in ATTENTION_STATUSES),
+            "needs_review": counts.get("needs_review", 0),
+            "unscorable": counts.get("unscorable", 0),
+            "failed": counts.get("failed", 0),
+            "status_counts": counts}
 
 
 @router.get("/courses")
@@ -337,9 +351,32 @@ def knowledge_page(document_id: int, page: int, db: Session = Depends(get_db)):
     return {"document_id": doc.id, "title": doc.title, "course_version_id": doc.course_version_id,
             "project_id": doc.project_id, "page": page,
             "text": record.text,
+            "page_type": record.page_type or "unknown",
+            "page_type_label": page_type_label(record.page_type),
+            "mixed": bool(record.mixed),
+            "section": record.section or "", "chapter": record.chapter or "",
+            "confidence": record.confidence,
+            "classification": record.classification or {},
+            "content_allowed": (record.page_type or "unknown") in CONTENT_ALLOWED,
             "image_url": page_links(db, document_id, page)["image_url"]}
 
 
+@router.get("/knowledge/documents/{document_id}/pages")
+def knowledge_document_pages(document_id: int, db: Session = Depends(get_db)):
+    """Page inventory with types, so the teacher can see how a document was classified."""
+    doc = found(db, KnowledgeDocument, document_id)
+    records = db.scalars(select(KnowledgePage).where(KnowledgePage.document_id == document_id)
+                         .order_by(KnowledgePage.page)).all()
+    return {"document_id": doc.id, "title": doc.title, "index_version": doc.index_version,
+            "pages": [{"page": item.page, "page_type": item.page_type or "unknown",
+                       "page_type_label": page_type_label(item.page_type),
+                       "mixed": bool(item.mixed), "section": item.section or "",
+                       "chapter": item.chapter or "", "confidence": item.confidence,
+                       "classification": item.classification or {},
+                       "content_allowed": (item.page_type or "unknown") in CONTENT_ALLOWED}
+                      for item in records]}
+
+
 @router.get("/knowledge/documents/{document_id}/pages/{page}/image")
 def knowledge_page_image(document_id: int, page: int, db: Session = Depends(get_db)):
     doc = found(db, KnowledgeDocument, document_id)
@@ -354,6 +391,28 @@ def ingest_course(course_version_id: int, db: Session = Depends(get_db)):
     return services.ingest_course(course_version_id, db)
 
 
+@router.get("/knowledge/stale/{course_version_id}")
+def stale_knowledge(course_version_id: int, db: Session = Depends(get_db)):
+    """Documents still on an older index; they are excluded from retrieval until rebuilt."""
+    from .indexing import stale_documents
+    from .rag import INDEX_VERSION
+    found(db, CourseVersion, course_version_id)
+    documents = stale_documents(db, course_version_id)
+    return {"index_version": INDEX_VERSION,
+            "documents": [{"id": item.id, "title": item.title, "type": item.document_type,
+                           "project_id": item.project_id, "index_version": item.index_version,
+                           "status": item.status} for item in documents]}
+
+
+@router.post("/knowledge/reindex/{course_version_id}")
+def reindex_knowledge(course_version_id: int, db: Session = Depends(get_db)):
+    """Queue a rebuild of every stale index for this course version."""
+    from .indexing import create_reindex_task
+    found(db, CourseVersion, course_version_id)
+    task = create_reindex_task(db, course_version_id)
+    return get_index_task(task.id, db)
+
+
 @router.get("/index-tasks")
 def list_index_tasks(project_id: int | None = None, db: Session = Depends(get_db)):
     query = select(IndexTask).order_by(IndexTask.id.desc()).limit(100)
@@ -381,10 +440,20 @@ def create_batch(data: BatchInput, db: Session = Depends(get_db)):
     return services.create_batch(data, db)
 
 
+def batch_status_counts(db, batch_id: int) -> dict:
+    rows = db.execute(select(CriterionScore.status, func.count(CriterionScore.id))
+                      .where(CriterionScore.batch_id == batch_id).group_by(CriterionScore.status)).all()
+    counts = {status: count for status, count in rows}
+    return {"status_counts": counts, "needs_review": counts.get("needs_review", 0),
+            "unscorable": counts.get("unscorable", 0), "failed": counts.get("failed", 0)}
+
+
 @router.get("/scoring-batches")
 def list_batches(db: Session = Depends(get_db)):
     return [{**{key: getattr(batch, key) for key in ("id", "course_version_id", "rubric_version_id", "status", "progress_done", "progress_total", "error", "created_at", "finished_at")},
-             "project_ids": batch.project_ids, "legacy_membership": batch.project_ids is None}
+             "status_label": BATCH_STATUS_LABELS.get(batch.status, batch.status),
+             "project_ids": batch.project_ids, "legacy_membership": batch.project_ids is None,
+             **batch_status_counts(db, batch.id)}
             for batch in db.scalars(select(ScoringBatch).order_by(ScoringBatch.id.desc()))]
 
 
@@ -392,7 +461,9 @@ def list_batches(db: Session = Depends(get_db)):
 def batch_progress(batch_id: int, db: Session = Depends(get_db)):
     batch = found(db, ScoringBatch, batch_id)
     return {**{key: getattr(batch, key) for key in ("id", "status", "progress_done", "progress_total", "error", "created_at", "finished_at")},
-            "project_ids": batch.project_ids, "legacy_membership": batch.project_ids is None}
+            "status_label": BATCH_STATUS_LABELS.get(batch.status, batch.status),
+            "project_ids": batch.project_ids, "legacy_membership": batch.project_ids is None,
+            **batch_status_counts(db, batch_id)}
 
 
 @router.post("/scoring-batches/{batch_id}/start")

backend/wisegrade/chat_api.py

@@ -1,6 +1,8 @@
 """Persistent teacher conversations and SSE agent orchestration."""
 import json
+import re
 import time
+import unicodedata
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
 from threading import Lock
@@ -170,7 +172,8 @@ def _safe_arguments(arguments):
     if not isinstance(arguments, dict):
         return {}
     numeric = {"course_version_id", "batch_id", "project_id", "score_id", "task_id",
-               "rubric_version_id", "score", "offset", "limit", "project_offset", "criterion_offset"}
+               "rubric_version_id", "score", "offset", "limit", "project_offset", "criterion_offset",
+               "document_id"}
     text_limits = {"query": 300, "criterion_id": 100, "format": 20, "status": 30,
                    "student_no": 50, "relative_dir": 500}
     result = {}
@@ -219,26 +222,58 @@ def _result_summary(result):
 def _safe_citation(item):
     if not isinstance(item, dict):
         return None
-    return {key: value[:limit] if isinstance(value, str) else value for key, limit in
-            (("title", 200), ("quote", 500), ("url", 500), ("image_url", 500), ("score_url", 500))
+    # The evidence-policy fields travel with the citation so the interface can tell a
+    # report body page from a contents page, and an answer's source from a historical
+    # scoring quotation.
+    label_limits = (("title", 200), ("quote", 500), ("url", 500), ("image_url", 500), ("score_url", 500),
+                    ("citation_id", 80), ("page_type", 20), ("page_type_label", 30),
+                    ("source_role", 20), ("source", 40), ("usage", 60), ("target_label", 120))
+    return {key: value[:limit] if isinstance(value, str) else value for key, limit in label_limits
             if isinstance((value := item.get(key)), str)} | {
-            key: item[key] for key in ("chunk_id", "document_id", "page", "project_id")
-            if isinstance(item.get(key), int)}
+            key: item[key] for key in ("chunk_id", "document_id", "page", "project_id", "historical")
+            if isinstance(item.get(key), (int, bool)) and not isinstance(item.get(key), str)}
 
 
-def _answer_citations(items):
-    """Keep each distinct source passage, including later tool calls and pages cited by the answer."""
+def _comparable_text(value):
+    return "".join(char for char in unicodedata.normalize("NFKC", str(value or "")) if not char.isspace())
+
+
+def _cited_pages(content: str) -> set[int]:
+    """Page and slide numbers the answer actually refers to."""
+    numbers = set()
+    for match in re.finditer(r"第\s*(\d{1,4})\s*(?:页|张幻灯片|页幻灯片)", str(content or "")):
+        numbers.add(int(match.group(1)))
+    return numbers
+
+
+def _answer_citations(items, content: str = ""):
+    """Attach only the citations the answer actually used.
+
+    Collecting every tool hit would list material the answer never drew on. A passage is
+    kept when the answer quotes it, or when the answer names the page it sits on. When
+    the answer names no page and quotes nothing, no citation is attached: the answer made
+    no specific evidential claim.
+    """
     seen = set()
     citations = []
+    answer = _comparable_text(content)
+    pages = _cited_pages(content)
     for item in items:
         safe = _safe_citation(item)
         if not safe:
             continue
         key = (safe.get("document_id"), safe.get("chunk_id"), safe.get("title"),
                safe.get("page"), safe.get("quote"), safe.get("url"), safe.get("score_url"))
-        if key not in seen:
-            seen.add(key)
-            citations.append(safe)
+        if key in seen:
+            continue
+        quote = _comparable_text(safe.get("quote"))
+        used = bool(quote) and len(quote) >= 6 and quote[:80] in answer
+        if not used and pages and isinstance(safe.get("page"), int):
+            used = safe["page"] in pages
+        if not used:
+            continue
+        seen.add(key)
+        citations.append(safe)
     return citations
 
 
@@ -326,7 +361,7 @@ def _run_agent(factory, conversation_id, run_id, user_text):
                     content = reply.get("content")
                     if not isinstance(content, str) or not content.strip():
                         raise ModelError("会话模型未返回文字或工具调用")
-                    answer_citations = _answer_citations(citations)
+                    answer_citations = _answer_citations(citations, content)
                     db.add(ChatMessage(conversation_id=conversation_id, role="assistant", content=content,
                                        citations=answer_citations, tool_calls=used))
                     run.status, run.finished_at = "completed", now()

backend/wisegrade/evidence.py

@@ -0,0 +1,150 @@
+"""Evidence policy: which page types may support which kind of judgement.
+
+The policy is part of the versionable scoring rules. Each required-evidence target
+declares its ``use``:
+
+* ``structure`` — the target may be satisfied by a cover page, table of contents or
+  reference list (chapter completeness, bibliography, report layout).
+* ``content`` — the target may only be satisfied by substantive text, data or tables
+  on body or appendix pages.
+
+Rules still in the historical string form (``["问题定义", "需求分析"]``) are read with a
+permissive grammar: wording that names a structural artefact becomes a structure
+target, everything else becomes a content target. The exact grammar is recorded as
+``POLICY_VERSION`` in the batch snapshot so a batch can always be re-read against the
+rules it was scored with.
+"""
+from __future__ import annotations
+
+import re
+
+from .pages import (CONTENT_PAGE_TYPES, NAVIGATION_PAGE_TYPES, STORE_PAGE_TYPES,
+                    STRUCTURE_PAGE_TYPES)
+
+POLICY_VERSION = 1
+USE_STRUCTURE = "structure"
+USE_CONTENT = "content"
+
+#: Wording that names report packaging rather than what the student did.
+_STRUCTURE_WORDS = (
+    "标题", "封面", "目录", "章节", "参考文献", "引用规范", "格式", "规范性", "结构完整",
+    "排版", "页码", "附录", "图表编号", "摘要",
+)
+
+#: Appendix and body pages are the only pages allowed for a content judgement.
+CONTENT_ALLOWED = tuple(CONTENT_PAGE_TYPES)
+#: Structural targets may additionally use cover, contents and reference pages.
+STRUCTURE_ALLOWED = tuple(STRUCTURE_PAGE_TYPES)
+#: Navigation pages can never carry a content judgement.
+NAVIGATION = tuple(NAVIGATION_PAGE_TYPES)
+
+
+def _looks_structural(text: str) -> bool:
+    value = str(text or "").strip()
+    return bool(value) and any(word in value for word in _STRUCTURE_WORDS)
+
+
+def normalize_target(raw) -> dict:
+    """Read one required-evidence target in either the rich or the legacy string form."""
+    if isinstance(raw, dict):
+        label = str(raw.get("label") or raw.get("text") or raw.get("name") or "").strip()
+        use = str(raw.get("use") or "").strip().lower()
+        if use not in (USE_STRUCTURE, USE_CONTENT):
+            use = USE_STRUCTURE if _looks_structural(label) else USE_CONTENT
+        allowed = raw.get("allowed_page_types")
+        if not isinstance(allowed, list) or not allowed:
+            allowed = list(STRUCTURE_ALLOWED if use == USE_STRUCTURE else CONTENT_ALLOWED)
+        allowed = [str(item) for item in allowed if str(item) in STORE_PAGE_TYPES]
+        if not allowed:
+            allowed = list(STRUCTURE_ALLOWED if use == USE_STRUCTURE else CONTENT_ALLOWED)
+        if use == USE_CONTENT:
+            # A content target never admits navigation pages, whatever a rule claims.
+            allowed = [item for item in allowed if item not in NAVIGATION] or list(CONTENT_ALLOWED)
+        return {"label": label or "证据", "use": use, "allowed_page_types": allowed,
+                "query": str(raw.get("query") or label).strip() or (label or "证据")}
+    label = str(raw or "").strip()
+    use = USE_STRUCTURE if _looks_structural(label) else USE_CONTENT
+    return {"label": label or "证据", "use": use,
+            "allowed_page_types": list(STRUCTURE_ALLOWED if use == USE_STRUCTURE else CONTENT_ALLOWED),
+            "query": label or "证据"}
+
+
+def normalize_targets(required_evidence) -> list[dict]:
+    """Normalize a criterion's required evidence, preserving order and identity."""
+    values = required_evidence if isinstance(required_evidence, list) else [required_evidence]
+    targets = []
+    for index, raw in enumerate(values):
+        if raw is None or (isinstance(raw, str) and not raw.strip()):
+            continue
+        target = normalize_target(raw)
+        target["index"] = index
+        target["target_id"] = str((raw.get("target_id") if isinstance(raw, dict) else "") or f"t{index}")
+        targets.append(target)
+    if not targets:
+        targets.append({"label": "正文实质内容", "use": USE_CONTENT, "allowed_page_types": list(CONTENT_ALLOWED),
+                        "query": "正文实质内容", "index": 0, "target_id": "t0"})
+    return targets
+
+
+def content_targets(criterion: dict) -> list[dict]:
+    return [target for target in normalize_targets(criterion.get("required_evidence")) if target["use"] == USE_CONTENT]
+
+
+def any_content_target(criterion: dict) -> bool:
+    return bool(content_targets(criterion))
+
+
+def page_type_allowed(page_type: str, allowed: list[str]) -> bool:
+    """An ``unknown`` page is a body page only when the target already allows body text.
+
+    ``unknown`` marks a page whose reading is genuinely unsettled, so it may never be
+    used as the sole support for a content judgement, and it never widens a structural
+    target either. It stays retrievable so a teacher can inspect it.
+    """
+    if page_type == "unknown":
+        return "body" in allowed
+    return page_type in allowed
+
+
+def content_evidence_allowed(page_type: str) -> bool:
+    """Only substantive body and appendix pages support a content judgement."""
+    return page_type in CONTENT_ALLOWED
+
+
+def heading_only_text(text: str) -> bool:
+    """True when a passage is nothing but section titles, so it proves no content.
+
+    The test is structural, not a length test: a quotation is heading-only when every
+    line of it is a chapter/section heading or a contents entry. A short technical
+    quotation such as a measured value is content, so it must not be rejected here.
+    """
+    from .pages import heading_level, is_toc_entry
+
+    lines = [line.strip() for line in str(text or "").splitlines() if line.strip()]
+    if not lines:
+        return False
+    for line in lines:
+        if is_toc_entry(line) or heading_level(line):
+            continue
+        return False
+    return True
+
+
+_PLACEHOLDER = re.compile(r"^\s*(待确认|待定|未知|无|none|null|-+|—+)\s*$", re.IGNORECASE)
+
+
+def meaningful(value) -> bool:
+    text = str(value or "").strip()
+    return bool(text) and not _PLACEHOLDER.match(text)
+
+
+def plan_snapshot(criteria: list[dict]) -> dict:
+    """The policy attached to a batch snapshot, so old batches stay re-readable."""
+    return {
+        "version": POLICY_VERSION,
+        "content_allowed_page_types": list(CONTENT_ALLOWED),
+        "structure_allowed_page_types": list(STRUCTURE_ALLOWED),
+        "navigation_page_types": list(NAVIGATION),
+        "criteria": {criterion["criterion_id"]: normalize_targets(criterion.get("required_evidence"))
+                     for criterion in criteria},
+    }

backend/wisegrade/exports.py

@@ -11,6 +11,32 @@ from .knowledge_pages import evidence_links
 from .models import (CriterionScore, ExportTask, Project, ScoreEvidence, ScoringBatch, Student, SubmissionFile)
 from .rules import totals
 
+#: Teacher-facing wording for a criterion status, shared by exports and the API.
+STATUS_LABELS = {
+    "pending": "待评分",
+    "scored": "模型已评分",
+    "reviewed": "教师已复核",
+    "needs_review": "待教师复核",
+    "unscorable": "材料缺失或解析失败",
+    "failed": "模型调用失败",
+}
+PAGE_TYPE_LABELS = {
+    "cover": "封面", "abstract": "摘要", "toc": "目录", "body": "正文",
+    "appendix": "附录", "references": "参考文献", "unknown": "识别不确定",
+    "mixed": "目录与正文混合页",
+}
+#: Batch status wording. ``review_required`` means grading finished but some criteria
+#: still need a teacher decision, which is not the same as an incomplete batch.
+BATCH_STATUS_LABELS = {
+    "created": "待启动", "queued": "排队中", "running": "运行中",
+    "completed": "已完成", "review_required": "已完成，有待复核项",
+    "partial": "部分完成", "failed": "失败",
+}
+
+
+def page_type_label(value) -> str:
+    return PAGE_TYPE_LABELS.get(str(value or "unknown"), "未分类页面")
+
 
 def project_result(db, batch: ScoringBatch, project: Project):
     scores = db.scalars(select(CriterionScore).where(CriterionScore.batch_id == batch.id,
@@ -19,10 +45,15 @@ def project_result(db, batch: ScoringBatch, project: Project):
     criteria = batch.rubric_snapshot["criteria"]
     totals_ = totals({key: score.final_score for key, score in by_id.items()}, criteria, batch.rubric_snapshot["weights"])
     rows = []
-    def evidence_dict(item):
-        links = evidence_links(db, item)
+    def evidence_dict(item, links):
         return {"page": item.page, "quote": item.quote, "file_id": item.submission_file_id,
                 "document_id": item.document_id,
+                "target_id": item.target_id or "", "target_label": item.target_label or "",
+                "page_type": item.page_type or links.get("page_type") or "unknown",
+                "source_role": item.source_role or "content",
+                # A quotation saved before the evidence policy existed is labelled as a
+                # historical citation and never presented as report body evidence.
+                "historical": bool(item.historical),
                 "document": db.get(SubmissionFile, item.submission_file_id).relative_path if item.submission_file_id else "课程资料",
                 **links}
     for criterion in criteria:
@@ -35,7 +66,8 @@ def project_result(db, batch: ScoringBatch, project: Project):
                      "model_comment": score.model_comment if score else "", "teacher_comment": score.teacher_comment if score else None,
                      "missing_evidence": score.missing_evidence if score else [], "suggestion": score.suggestion if score else "",
                      "status": score.status if score else "pending", "error": score.error if score else "",
-                     "evidence": [evidence_dict(item) for item in evidence]})
+                     "evidence_gaps": (score.evidence_gaps or []) if score else [],
+                     "evidence": [evidence_dict(item, evidence_links(db, item)) for item in evidence]})
     student = db.get(Student, project.student_id)
     return {"project_id": project.id, "student_no": student.student_no, "name": student.name, "topic": project.topic,
             "totals": totals_, "rows": rows, "facts": project.facts}
@@ -97,14 +129,21 @@ def _html_report(result, batch):
     for row in result["rows"]:
         criterion = row["criterion"]
         evidences = "".join(
-            f"<li>{esc(e['document'])} · 第{e['page']}{'张幻灯片' if str(e['document']).lower().endswith('.pptx') else '页'}：{esc(e['quote'])}</li>"
+            f"<li>{esc(e['document'])} · 第{e['page']}{'张幻灯片' if str(e['document']).lower().endswith('.pptx') else '页'}"
+            f"（{esc(page_type_label(e.get('page_type')))}{'，历史评分引用' if e.get('historical') else ''}）"
+            f"{' 支持：' + esc(e['target_label']) if e.get('target_label') else ''}：{esc(e['quote'])}</li>"
             for e in row["evidence"]
         )
         missing = "、".join(row["missing_evidence"] or [])
+        gaps = "；".join(f"{gap.get('target_label')}（检索范围：{'、'.join(page_type_label(t) for t in gap.get('searched_page_types') or []) or '正文'}）"
+                        for gap in row.get("evidence_gaps") or [])
+        review = "本项缺少正文证据，已转教师复核。" if row["status"] == "needs_review" else ""
         rows.append(f"<section><h3>{esc(criterion['name'])}（{esc(row['final_score'])} / {criterion['max_score']}）</h3>"
-                    f"<p>模型原始分：{esc(row['model_score'])}；教师修改分：{esc(row['teacher_score'])}；状态：{esc(row['status'])}</p>"
+                    f"<p>模型原始分：{esc(row['model_score'])}；教师修改分：{esc(row['teacher_score'])}；状态：{esc(STATUS_LABELS.get(row['status'], row['status']))}</p>"
                     f"<p>模型评语：{esc(row['model_comment'])}</p><p>教师评语：{esc(row['teacher_comment'])}</p>"
-                    f"<p>缺失证据：{esc(missing)}</p><p>建议：{esc(row['suggestion'])}</p><p>异常：{esc(row['error'])}</p>"
+                    f"<p>{esc(review)}</p>"
+                    f"<p>缺失证据：{esc(missing)}</p><p>缺正文证据的目标：{esc(gaps)}</p>"
+                    f"<p>建议：{esc(row['suggestion'])}</p><p>异常：{esc(row['error'])}</p>"
                     f"<ul>{evidences}</ul></section>")
     totals_ = result["totals"]
     return f"""<!doctype html><html lang="zh"><meta charset="utf-8"><style>

backend/wisegrade/indexing.py

@@ -2,7 +2,8 @@
 from sqlalchemy import select
 
 from .db import SessionLocal
-from .models import IndexTask, SubmissionFile, now
+from .models import IndexTask, KnowledgeDocument, SubmissionFile, now
+from .rag import INDEX_VERSION
 from .scoring import parse_submission
 
 
@@ -14,7 +15,13 @@ def run_index_task(task_id: int, *, session_factory=SessionLocal, embedder=None)
         task.status, task.error = "running", ""
         db.commit()
         try:
-            if task.kind == "project":
+            if task.kind == "reindex":
+                # Rebuild documents whose index predates the current chunking, so a
+                # legacy index is never silently used as classified body text.
+                task.result = reindex_stale(db, task.course_version_id, embedder=embedder)
+                if task.result["failed"]:
+                    raise ValueError("部分资料重建索引失败：" + "、".join(item["title"] for item in task.result["failed"]))
+            elif task.kind == "project":
                 file = db.get(SubmissionFile, task.submission_file_id)
                 if not file or file.recognition_status != "recognized":
                     raise ValueError("材料已取消选择，请重新预览")
@@ -35,6 +42,74 @@ def run_index_task(task_id: int, *, session_factory=SessionLocal, embedder=None)
         db.commit()
 
 
+def stale_documents(db, course_version_id: int | None = None, *, limit: int | None = None):
+    """Documents whose stored index predates the current chunking or is not ready."""
+    query = select(KnowledgeDocument).where(
+        (KnowledgeDocument.index_version != INDEX_VERSION) | (KnowledgeDocument.status != "ready"))
+    if course_version_id is not None:
+        query = query.where(KnowledgeDocument.course_version_id == course_version_id)
+    query = query.order_by(KnowledgeDocument.id)
+    if limit:
+        query = query.limit(limit)
+    return db.scalars(query).all()
+
+
+def reindex_stale(db, course_version_id: int | None = None, *, embedder=None, limit: int | None = None):
+    """Rebuild every stale index from the saved parse output. Retryable and auditable.
+
+    Student material is rebuilt from ``submission_files.parsed``, which the parser saved
+    on disk; the original file is never re-read or modified. Course material follows the
+    normal ingest path, because its pages come from the mounted course documents.
+    """
+    from .services import parse_submission
+    from .storage import sha256, secure_path
+
+    documents = stale_documents(db, course_version_id, limit=limit)
+    rebuilt, skipped, failed = [], [], []
+    for document in documents:
+        try:
+            if document.submission_file_id:
+                file = db.get(SubmissionFile, document.submission_file_id)
+                if not file:
+                    skipped.append({"document_id": document.id, "title": document.title,
+                                    "reason": "来源材料已不存在"})
+                    continue
+                if sha256(secure_path(file.relative_path)) != file.sha256:
+                    skipped.append({"document_id": document.id, "title": document.title,
+                                    "reason": "源文件已变化，请重新扫描并确认导入"})
+                    continue
+                file.parse_status = "pending"
+                db.commit()
+                parsed = parse_submission(db, file, embedder=embedder) if embedder else parse_submission(db, file)
+                rebuilt.append({"document_id": document.id, "title": document.title,
+                                "pages": len(parsed.get("pages", []))})
+            elif document.project_id is None:
+                # Course material is re-ingested by the course task, not per document.
+                skipped.append({"document_id": document.id, "title": document.title,
+                                "reason": "课程资料请通过课程入库重建"})
+            else:
+                skipped.append({"document_id": document.id, "title": document.title,
+                                "reason": "缺少可重建的解析结果"})
+        except Exception as exc:
+            failed.append({"document_id": document.id, "title": document.title, "error": str(exc)[:300]})
+            db.rollback()
+    return {"index_version": INDEX_VERSION, "rebuilt": rebuilt, "skipped": skipped,
+            "failed": failed, "remaining": len(stale_documents(db, course_version_id))}
+
+
+def create_reindex_task(db, course_version_id: int):
+    """Queue one reindex job for a course version, reusing an open task when there is one."""
+    from .services import enqueue_index_task
+    existing = db.scalar(select(IndexTask).where(IndexTask.course_version_id == course_version_id,
+        IndexTask.kind == "reindex", IndexTask.status.in_(["queued", "running"])).order_by(IndexTask.id.desc()))
+    task = existing or IndexTask(course_version_id=course_version_id, kind="reindex", status="queued")
+    if not existing:
+        db.add(task)
+        db.commit()
+    enqueue_index_task(db, task.id)
+    return task
+
+
 def create_course_index_task(db, course_version_id: int):
     from .services import enqueue_index_task
     existing = db.scalar(select(IndexTask).where(IndexTask.course_version_id == course_version_id,

backend/wisegrade/knowledge_pages.py

@@ -36,18 +36,33 @@ def page_image_path(db, doc: KnowledgeDocument, page: int) -> Path | None:
     return path if path and path.is_relative_to(root) and path.is_file() else None
 
 
-def page_links(db, document_id: int | None, page: int | None) -> dict[str, str | None]:
+def page_links(db, document_id: int | None, page: int | None) -> dict:
+    """Stable citation links plus the page type, so callers can label the source."""
+    empty = {"url": None, "image_url": None, "page_type": None, "section": None}
     if not document_id or not page:
-        return {"url": None, "image_url": None}
+        return empty
     doc = db.get(KnowledgeDocument, document_id)
     if not doc:
-        return {"url": None, "image_url": None}
+        return empty
     record = db.get(KnowledgePage, (document_id, page))
     if not record:
-        return {"url": None, "image_url": None}
+        return empty
     return {"url": text_url(document_id, page),
-            "image_url": image_url(document_id, page) if page_image_path(db, doc, page) else None}
+            "image_url": image_url(document_id, page) if page_image_path(db, doc, page) else None,
+            "page_type": record.page_type or "unknown",
+            "section": record.section or ""}
 
 
-def evidence_links(db, evidence) -> dict[str, str | None]:
+def evidence_links(db, evidence) -> dict:
     return page_links(db, evidence.document_id, evidence.page)
+
+
+def page_types(db, document_ids) -> dict[int, str]:
+    """Page type per page of the given documents, for labelling citations in bulk."""
+    from sqlalchemy import select as _select
+    ids = [item for item in dict.fromkeys(document_ids or []) if item]
+    if not ids:
+        return {}
+    rows = db.execute(_select(KnowledgePage.document_id, KnowledgePage.page, KnowledgePage.page_type)
+                      .where(KnowledgePage.document_id.in_(ids))).all()
+    return {(document_id, page): (page_type or "unknown") for document_id, page, page_type in rows}

backend/wisegrade/llm.py

@@ -15,25 +15,39 @@ class EvidenceResult(BaseModel):
     chunk_id: int
     page: int = Field(ge=1)
     quote: str = Field(min_length=1)
+    #: Which required-evidence target this quotation is offered to support.
+    target_id: str = ""
 
 
 class ScoreResult(BaseModel):
     criterion_id: str
-    score: float = Field(ge=0)
+    #: ``None`` means the model declined to score because admissible evidence is absent.
+    score: float | None = Field(default=None, ge=0)
     max_score: float = Field(gt=0)
     reason: str = Field(min_length=1)
-    evidence: list[EvidenceResult]
+    evidence: list[EvidenceResult] = Field(default_factory=list)
     missing_evidence: list[str] = Field(default_factory=list)
     suggestion: str = ""
     confidence: float = Field(ge=0, le=1)
 
     @model_validator(mode="after")
     def valid_range(self):
-        if self.score > self.max_score or (self.score > 0 and not self.evidence):
-            raise ValueError("score exceeds maximum or lacks evidence")
+        if self.score is not None and self.score > self.max_score:
+            raise ValueError("score exceeds maximum")
+        if self.score and not self.evidence:
+            raise ValueError("a positive score requires evidence")
         return self
 
 
+class EvidenceGap(BaseModel):
+    """A content target that still has no body evidence after targeted re-checking."""
+
+    target_id: str = ""
+    target_label: str = ""
+    searched_page_types: list[str] = Field(default_factory=list)
+    reason: str = ""
+
+
 class FactResult(BaseModel):
     student_no: str = ""
     name: str = ""
@@ -132,33 +146,58 @@ class DeepSeekClient:
             raise ModelError("当前端点或模型未返回原生 tool_calls；请配置支持工具调用的端点和模型")
 
 
-def validate_score(result: ScoreResult, criterion: dict, chunks: list[dict]):
+def locate_quote(text: str, quote: str):
+    """Locate a model-written quotation inside the retrieved text.
+
+    PDF line breaks and punctuation differ from what the model reproduces, so matching
+    is done on NFKC-normalized text with whitespace and punctuation removed, while the
+    returned string is still the exact source substring.
+    """
+    if not text or not quote:
+        return None
+    if quote in text:
+        return quote
+
+    def comparable(value: str):
+        return "".join(char for original in value for char in unicodedata.normalize("NFKC", original)
+                       if not char.isspace() and not unicodedata.category(char).startswith("P"))
+
+    needle = comparable(quote)
+    if len(needle) < 4:
+        return None
+    positions = []
+    normalized = []
+    for index, original in enumerate(text):
+        for char in unicodedata.normalize("NFKC", original):
+            if not char.isspace() and not unicodedata.category(char).startswith("P"):
+                normalized.append(char)
+                positions.append(index)
+    start = "".join(normalized).find(needle)
+    if start < 0:
+        return None
+    return text[positions[start]:positions[start + len(needle) - 1] + 1]
+
+
+def validate_score(result: ScoreResult, criterion: dict, chunks: list[dict], *, targets=None,
+                   require_content_evidence: bool | None = None):
+    """Check identity, quotation fidelity and the evidence-source policy.
+
+    The model may understand evidence and propose re-checks; the page permissions are
+    enforced here, in code, and cannot be argued away by the model's own reasoning.
+    """
+    from .evidence import (USE_CONTENT, content_evidence_allowed, heading_only_text,
+                           normalize_targets, page_type_allowed)
+
     if result.criterion_id != criterion["criterion_id"] or result.max_score != criterion["max_score"]:
         raise ModelError("模型评分项或满分与规则版本不一致")
+    if require_content_evidence is None:
+        require_content_evidence = any(target["use"] == USE_CONTENT
+                                       for target in normalize_targets(criterion.get("required_evidence")))
+    targets = targets if targets is not None else normalize_targets(criterion.get("required_evidence"))
+    by_target = {target["target_id"]: target for target in targets}
+    default_content = next((target for target in targets if target["use"] == USE_CONTENT), None)
     by_id = {chunk["chunk_id"]: chunk for chunk in chunks}
 
-    def locate_quote(text: str, quote: str):
-        def comparable(value: str):
-            return "".join(char for original in value for char in unicodedata.normalize("NFKC", original)
-                           if not char.isspace() and not unicodedata.category(char).startswith("P"))
-
-        if quote in text:
-            return quote
-        needle = comparable(quote)
-        if len(needle) < 4:
-            return None
-        positions = []
-        normalized = []
-        for index, original in enumerate(text):
-            for char in unicodedata.normalize("NFKC", original):
-                if not char.isspace() and not unicodedata.category(char).startswith("P"):
-                    normalized.append(char)
-                    positions.append(index)
-        start = "".join(normalized).find(needle)
-        if start < 0:
-            return None
-        return text[positions[start]:positions[start + len(needle) - 1] + 1]
-
     for evidence in result.evidence:
         chunk = by_id.get(evidence.chunk_id)
         quote = locate_quote(chunk["text"], evidence.quote) if chunk else None
@@ -169,5 +208,17 @@ def validate_score(result: ScoreResult, criterion: dict, chunks: list[dict]):
                 chunk, quote = matches[0]
         if quote is None:
             raise ModelError("模型证据未能匹配检索原文")
+        page_type = chunk.get("page_type") or "unknown"
+        target = by_target.get(evidence.target_id) or default_content or (targets[0] if targets else None)
+        if target is None:
+            raise ModelError("模型证据未关联任何必备证据目标")
+        if not page_type_allowed(page_type, target["allowed_page_types"]):
+            raise ModelError(f"模型证据来自不允许的页面类型（{page_type}），不能支持「{target['label']}」")
+        if target["use"] == USE_CONTENT and not content_evidence_allowed(page_type):
+            raise ModelError(f"目录、摘要或参考文献页不能作为「{target['label']}」的内容证据")
+        if target["use"] == USE_CONTENT and heading_only_text(quote):
+            raise ModelError(f"「{target['label']}」的证据只有章节标题，没有实质内容")
         evidence.chunk_id, evidence.page, evidence.quote = chunk["chunk_id"], chunk["page"], quote
+        evidence.target_id = target["target_id"]
     return result
+

backend/wisegrade/models.py

@@ -176,6 +176,14 @@ class KnowledgePage(Base):
     page = Column(Integer, primary_key=True)
     text = Column(Text, nullable=False, default="")
     image_path = Column(String(1200), nullable=True)  # Relative to processed_path.
+    # Page-level classification: cover, abstract, toc, body, references or unknown.
+    page_type = Column(String(20), nullable=False, default="unknown", server_default="unknown")
+    mixed = Column(Boolean, nullable=False, default=False, server_default="false")
+    section = Column(String(500), default="")
+    chapter = Column(String(500), default="")
+    confidence = Column(Float, nullable=True)
+    # Bounded classifier evidence: which signals decided the type, kept for audit.
+    classification = Column(JSON, default=dict, nullable=False)
 
 
 class KnowledgeChunk(Base):
@@ -187,6 +195,9 @@ class KnowledgeChunk(Base):
     document_type = Column(String(80), nullable=False)
     page = Column(Integer, nullable=False)
     section = Column(String(500), default="")
+    chapter = Column(String(500), default="")
+    page_type = Column(String(20), nullable=False, default="unknown", server_default="unknown")
+    block_type = Column(String(20), nullable=False, default="body", server_default="body")
     text = Column(Text, nullable=False)
     embedding = Column(Embedding, nullable=True)
 
@@ -225,6 +236,9 @@ class CriterionScore(Base):
     suggestion = Column(Text, default="")
     status = Column(String(40), default="pending")
     error = Column(Text, default="")
+    #: Content targets that still lacked body evidence, with the search that was run.
+    #: Kept after teacher review so the original retrieval problem stays auditable.
+    evidence_gaps = Column(JSON, default=list)
     __table_args__ = (UniqueConstraint("batch_id", "project_id", "criterion_id"),)
 
     @property
@@ -241,6 +255,14 @@ class ScoreEvidence(Base):
     page = Column(Integer, nullable=False)
     quote = Column(Text, nullable=False)
     image_path = Column(String(1200), nullable=True)
+    # Which required-evidence target this quotation is offered to support.
+    target_id = Column(String(50), default="", server_default="")
+    target_label = Column(String(300), default="", server_default="")
+    # Page type of the source passage, and its role in the judgement.
+    page_type = Column(String(20), default="unknown", server_default="unknown")
+    source_role = Column(String(20), default="content", server_default="content")
+    # True when the quotation was written before this retrieval policy existed.
+    historical = Column(Boolean, default=False, nullable=False, server_default="false")
 
 
 class TeacherRevision(Base):

backend/wisegrade/pages.py

@@ -0,0 +1,443 @@
+"""Page-level classification, block splitting and real section paths.
+
+A page carries one ``page_type``: cover, abstract, toc, body, references, unknown.
+A page may additionally be ``mixed`` when a table of contents and body text truly
+share one page; only that page is split into blocks so the body part is not lost.
+
+Classification never depends on a fixed page number or on the single word "目录".
+It combines the page's own signals (heading title, dot-leader entry density,
+trailing page numbers, abstract/reference wording, cover fields, footer style)
+with document-level continuity for continuation pages (TOC continued, abstract
+continued, references spanning pages).
+"""
+from __future__ import annotations
+
+import re
+import unicodedata
+
+CLASSIFIER_VERSION = 3
+
+PAGE_TYPES = ("cover", "abstract", "toc", "body", "appendix", "references", "unknown")
+
+#: Page types whose text may support a content judgement about what a student did.
+CONTENT_PAGE_TYPES = ("body", "appendix")
+#: Page types whose text may support a structural judgement (chapter completeness etc.).
+STRUCTURE_PAGE_TYPES = ("cover", "abstract", "toc", "references", "body", "appendix")
+#: Page types that may never be used as content evidence on their own.
+NAVIGATION_PAGE_TYPES = ("cover", "abstract", "toc", "references")
+#: Page types a page may be stored as, used to validate rule configuration.
+STORE_PAGE_TYPES = PAGE_TYPES
+
+BLOCK_BODY = "body"
+BLOCK_TOC = "toc"
+BLOCK_TITLE = "title"
+
+_DOT_LEADER = re.compile(r"(?:[.·•…․…]\s*){3,}")
+_TRAILING_ANCHOR = re.compile(r"(\d{1,4})\s*$")
+_CHAPTER = re.compile(r"^第\s*([0-9一二三四五六七八九十百]+)\s*章\s*(.*)$")
+_APPENDIX = re.compile(r"^附\s*录\s*([A-Za-z0-9一二三四五六七八九十]+)?\s*(.*)$")
+_NUMBERED = re.compile(r"^(\d+(?:\.\d+)*)[\s、.．]+(\S.*)$")
+_COVER_FIELD = re.compile(r"(学\s*号|姓\s*名|题\s*目|指导教师|专\s*业|学\s*院|日\s*期)\s*[:：]")
+_PAGE_NUMBER_LINE = re.compile(r"^\s*(?:\d{1,3}|[IVXLC]{1,5}|第\s*\d+\s*页)\s*$")
+_ABSTRACT_TITLE = re.compile(r"^\s*(摘\s*要|abstract)\s*$", re.IGNORECASE)
+_REFERENCE_TITLE = re.compile(r"^\s*(参\s*考\s*文\s*献|references?|参考文献与资料)\s*$", re.IGNORECASE)
+_ACK_TITLE = re.compile(r"^\s*(致\s*谢|acknowledge?ments?)\s*$", re.IGNORECASE)
+_TOC_TITLE = re.compile(r"^\s*(目\s*录|contents?|table\s+of\s+contents)\s*$", re.IGNORECASE)
+_REFERENCE_ENTRY = re.compile(r"^\s*\[\d{1,3}\]")
+_CJK = re.compile(r"[㐀-䶿一-鿿]")
+_HEADING_NUMBER = re.compile(r"^\s*(第\s*[0-9一二三四五六七八九十百]+\s*章|\d+(\.\d+)*|附\s*录\s*[A-Za-z0-9]?)\s*\S")
+
+#: A body block must carry at least this many non-space characters to count as substance.
+MIN_BODY_CHARS = 60
+#: Heading-only text shorter than this never counts as content evidence on its own.
+MIN_CONTENT_QUOTE_CHARS = 25
+#: A line at least this long, outside a heading, reads as running prose. The bar is low
+#: on purpose: the rule separates text from headings and labels, not long from short
+#: text, so a brief but real sentence is still content.
+MIN_PROSE_CHARS = 4
+#: A numbered heading title must stay this short; longer text is prose or a list item.
+MAX_HEADING_TITLE_CHARS = 40
+#: Wording that marks the line as running prose rather than a heading.
+_PROSE_PUNCTUATION = "。，；！？,;"
+
+#: The documents are Chinese engineering reports; the keyword recall uses these units.
+BIGRAM_MIN = 2
+
+
+def _lines(text: str) -> list[str]:
+    return [line.strip() for line in (text or "").splitlines() if line.strip()]
+
+
+def normalize_heading(text: str) -> str:
+    """Compare headings across the TOC and the body: unified width, no spaces/dots."""
+    value = unicodedata.normalize("NFKC", text or "")
+    value = _DOT_LEADER.sub("", value)
+    value = _TRAILING_ANCHOR.sub("", value.strip())
+    return "".join(char for char in value if not char.isspace() and char not in "．.·•…")
+
+
+def is_toc_entry(line: str) -> bool:
+    """A dotted leader plus a trailing page or slide number."""
+    stripped = (line or "").strip()
+    if not stripped or not _DOT_LEADER.search(stripped):
+        return False
+    return bool(_TRAILING_ANCHOR.search(stripped))
+
+
+def heading_syntax(line: str) -> tuple[int, str] | None:
+    """Return ``(level, title)`` when the line has the shape of a heading.
+
+    This is a purely syntactic reading. Numbered prose such as
+    ``4. 实时图像传输：将摄像头画面…`` and enumerated list items are not headings: a
+    numbered heading has a short Chinese title, no sentence punctuation and no trailing
+    colon. Equation and matrix rows such as ``3.3.3`` or ``1  0  0  1  Zc`` are never
+    headings, and a line cut mid-formula (trailing combining mark) is not either.
+    """
+    stripped = (line or "").strip()
+    if not stripped or is_toc_entry(stripped) or len(stripped) > 120:
+        return None
+    match = _CHAPTER.match(stripped)
+    if match:
+        return 1, stripped
+    match = _APPENDIX.match(stripped)
+    if match and not _COVER_FIELD.search(stripped):
+        return 2, stripped
+    if _ABSTRACT_TITLE.match(stripped) or _REFERENCE_TITLE.match(stripped) or _ACK_TITLE.match(stripped):
+        return 1, stripped
+    match = _NUMBERED.match(stripped)
+    if not match:
+        return None
+    title = match.group(2).strip()
+    if not title or title.endswith(":") or title.endswith("："):
+        return None
+    if len(title) > MAX_HEADING_TITLE_CHARS or any(char in title for char in _PROSE_PUNCTUATION):
+        return None
+    if not _CJK.search(title):
+        return None
+    if unicodedata.category(title[-1]).startswith("M"):
+        return None
+    return 1 + match.group(1).count("."), stripped
+
+
+def heading_level(line: str, toc_titles: set[str] | None = None) -> tuple[int, str] | None:
+    """A heading for the section path: syntactic shape, confirmed by the contents list.
+
+    When the document carries its own table of contents, that list is the authority on
+    which numbered lines are real headings. That is what keeps an enumerated list item or
+    a numbered paragraph from becoming a chapter in the extracted section path. Callers
+    that only need to know whether a line reads as a heading use ``heading_syntax``.
+    """
+    found = heading_syntax(line)
+    if not found:
+        return None
+    if toc_titles and normalize_heading(line) not in toc_titles:
+        return None
+    return found
+
+
+def _footer_style(lines: list[str]) -> str:
+    """Classify the trailing line so repeated headers/footers can be removed."""
+    if not lines:
+        return "none"
+    last = lines[-1]
+    if re.fullmatch(r"[IVXLC]{1,5}", last):
+        return "roman"
+    if re.fullmatch(r"\d{1,3}", last):
+        return "arabic"
+    if re.fullmatch(r"第\s*\d+\s*页", last):
+        return "page_cn"
+    return "text"
+
+
+def _signals(page: dict, *, index: int, total: int, toc_titles: set[str] | None = None,
+             repeated: set[str] | None = None) -> dict:
+    text = str(page.get("text") or "")
+    lines = _lines(text)
+    # Prose detection ignores repeated page headers and footers: a running header is not
+    # something the student wrote, so it must not make an empty page look like content.
+    prose_source = [line for line in lines if line not in (repeated or ())]
+    entries = [line for line in lines if is_toc_entry(line)]
+    headings = [item for item in (heading_level(line, toc_titles) for line in lines) if item]
+    body_lines = [line for line in lines if line not in set(entries)]
+    plain_chars = sum(len(line) for line in body_lines)
+    first = lines[0] if lines else ""
+    reference_entries = [line for line in lines if _REFERENCE_ENTRY.match(line)]
+    titles = [position for position, line in enumerate(lines)
+              if _REFERENCE_TITLE.match(line) or _ACK_TITLE.match(line)]
+    # A bibliography page is dense reference entries, or a reference title near the top.
+    reference_ratio = len(reference_entries) / len(lines) if lines else 0.0
+    reference_page = (len(reference_entries) >= 5 and reference_ratio >= 0.5
+                      or any(position <= 2 for position in titles))
+    return {
+        "chars": len(text.strip()),
+        "lines": len(lines),
+        "toc_entries": len(entries),
+        "toc_ratio": round(len(entries) / len(lines), 3) if lines else 0.0,
+        "headings": len(headings),
+        "chapter_headings": sum(1 for level, _ in headings if level == 1),
+        "body_chars": plain_chars,
+        "first_line": first[:120],
+        "position": index,
+        "total": total,
+        "footer": _footer_style(lines),
+        "cover_fields": len(_COVER_FIELD.findall(text)),
+        "has_table": bool(page.get("tables")),
+        "reference_entries": len(reference_entries),
+        "reference_ratio": round(reference_ratio, 3),
+        "reference_page": reference_page,
+        "appendix_title": any(_APPENDIX.match(line.strip()) for line in lines),
+        # Prose punctuation separates running text from a label-style cover page.
+        "prose_punctuation": any(char in text for char in _PROSE_PUNCTUATION),
+        # Lines that read as running text rather than headings, labels or dotted entries.
+        "prose_lines": [line for line in prose_source if _is_prose_line(line)],
+    }
+
+
+def strip_repeated_lines(pages: list[dict]) -> set[str]:
+    """Header/footer lines repeated across many pages, removed from searchable text."""
+    counts: dict[str, int] = {}
+    for page in pages:
+        lines = _lines(str(page.get("text") or ""))
+        if not lines:
+            continue
+        candidates = {lines[0][:120]}
+        if len(lines) > 1:
+            candidates.add(lines[-1][:60])
+        for candidate in candidates:
+            if candidate and not is_toc_entry(candidate):
+                counts[candidate] = counts.get(candidate, 0) + 1
+    threshold = max(3, int(len(pages) * 0.4 + 0.5))
+    return {line for line, count in counts.items() if count >= threshold}
+
+
+def clean_page_lines(page: dict, repeated: set[str]) -> list[str]:
+    """Lines kept for indexing: repeated headers/footers and bare page numbers removed."""
+    lines = _lines(str(page.get("text") or ""))
+    kept = []
+    for position, line in enumerate(lines):
+        if line in repeated and (position == 0 or position == len(lines) - 1):
+            continue
+        if _PAGE_NUMBER_LINE.match(line) and position == len(lines) - 1:
+            continue
+        kept.append(line)
+    return kept
+
+
+def _is_prose_line(line: str) -> bool:
+    """A line that reads as running text rather than a heading, label or dotted entry.
+
+    The syntactic heading check is used, not the contents-confirmed one: a heading that
+    the contents list happens not to mention is still a heading, so it must not be
+    counted as prose when deciding whether a page carries content.
+    """
+    stripped = line.strip()
+    if is_toc_entry(stripped) or _TOC_TITLE.match(stripped) or heading_syntax(stripped):
+        return False
+    if _PAGE_NUMBER_LINE.match(stripped):
+        return False
+    return len(stripped) >= MIN_PROSE_CHARS
+
+
+def split_blocks(lines: list[str]) -> list[dict]:
+    """Split page lines into TOC and body blocks so a mixed page keeps both parts."""
+    blocks: list[dict] = []
+    current: dict | None = None
+    for line in lines:
+        stripped = line.strip()
+        # A contents title and its dotted entries are one navigation block. A lone
+        # dotted line with no title is not a table of contents, so it stays as body.
+        kind = BLOCK_TOC if is_toc_entry(line) or _TOC_TITLE.match(stripped) else BLOCK_BODY
+        if current is None or current["kind"] != kind:
+            current = {"kind": kind, "lines": []}
+            blocks.append(current)
+        current["lines"].append(line)
+    output = []
+    for block in blocks:
+        text = "\n".join(block["lines"]).strip()
+        if not text:
+            continue
+        has_entry = any(is_toc_entry(line) for line in block["lines"])
+        if block["kind"] == BLOCK_TOC and not has_entry:
+            # Only a title line, with no entries under it: this is a heading, not contents.
+            output.append({"kind": BLOCK_BODY, "text": text, "substantive": False})
+            continue
+        output.append({"kind": block["kind"], "text": text})
+    # A block made only of headings carries navigation, not content.
+    for block in output:
+        if block["kind"] == BLOCK_BODY:
+            block["substantive"] = (any(_is_prose_line(line) for line in block["text"].splitlines())
+                                    or len(block["text"]) >= MIN_BODY_CHARS)
+        else:
+            block["substantive"] = False
+    return output
+
+
+def classifier_signals(pages: list[dict]) -> list[dict]:
+    """Per-page signals, shared by classification and by audit output."""
+    total = len(pages)
+    return [_signals(page, index=position, total=total) for position, page in enumerate(pages, start=1)]
+
+
+def _is_cover(signal: dict) -> bool:
+    """A cover page carries identity fields, not prose.
+
+    The weak rule requires a first page of a document that actually has a body, with
+    label-like lines and no sentence punctuation. Without those guards a short
+    single-page document would be mistaken for a cover page and lose all its content.
+    """
+    if signal["cover_fields"] >= 2 and signal["body_chars"] < 600:
+        return True
+    return (signal["position"] == 1 and signal["total"] >= 3 and signal["chars"] <= 400
+            and signal["lines"] <= 8 and signal["toc_entries"] == 0 and signal["headings"] == 0
+            and not signal["has_table"] and not signal["prose_punctuation"])
+
+
+def _is_toc_page(signal: dict, *, previous_is_toc: bool) -> bool:
+    if signal["toc_entries"] >= 4 and signal["toc_ratio"] >= 0.4:
+        return True
+    if _TOC_TITLE.match(signal["first_line"]) and signal["toc_entries"] >= 2:
+        return True
+    # Continuation page: the previous page is a table of contents and this page is
+    # still almost nothing but dotted entries.
+    return bool(previous_is_toc and signal["toc_entries"] >= 2 and signal["toc_ratio"] >= 0.5)
+
+
+def _looks_heading_only(signal: dict, blocks: list[dict]) -> bool:
+    """A page that carries no running prose at all, so it proves no content.
+
+    The test is structural, not a length test: a short page with one real sentence is
+    body text, a page with a table carries data, while a page of nothing but headings
+    or labels cannot support a judgement about what was done.
+    """
+    if signal["has_table"]:
+        return False
+    if any(block["kind"] == BLOCK_BODY and block.get("substantive") for block in blocks):
+        return False
+    return not signal["prose_lines"]
+
+
+def classify_document(pages: list[dict]) -> list[dict]:
+    """Classify every page and extract real section paths from body headings only."""
+    repeated = strip_repeated_lines(pages)
+    # The document's own contents list is the authority on which numbered lines are
+    # real headings; this keeps enumerated list items and equations out of the path.
+    toc_titles = set()
+    for page in pages:
+        for line in _lines(str(page.get("text") or "")):
+            if is_toc_entry(line):
+                title = normalize_heading(line)
+                if title:
+                    toc_titles.add(title)
+    toc_titles = toc_titles or None
+    signals = [_signals(page, index=position, total=len(pages), toc_titles=toc_titles, repeated=repeated)
+               for position, page in enumerate(pages, start=1)]
+    results: list[dict] = []
+    previous = None
+    chapter = ""
+    section = ""
+    for position, page in enumerate(pages, start=1):
+        signal = signals[position - 1]
+        lines = clean_page_lines(page, repeated)
+        blocks = split_blocks(lines)
+        toc_blocks = [block for block in blocks if block["kind"] == BLOCK_TOC]
+        body_blocks = [block for block in blocks if block["kind"] == BLOCK_BODY and block["substantive"]]
+        first = signal["first_line"]
+
+        appendix = signal["appendix_title"]
+        if _TOC_TITLE.match(first) and signal["toc_entries"] >= 1:
+            page_type, confidence = "toc", 0.95
+        elif _ABSTRACT_TITLE.match(first) or (previous == "abstract" and signal["footer"] in ("roman", "none")
+                                              and signal["headings"] == 0 and signal["chars"] >= 100):
+            page_type, confidence = "abstract", 0.9
+        elif _REFERENCE_TITLE.match(first) or _ACK_TITLE.match(first):
+            page_type, confidence = "references", 0.9
+        elif _is_toc_page(signal, previous_is_toc=(previous == "toc")):
+            page_type, confidence = "toc", 0.85 if signal["toc_ratio"] >= 0.4 else 0.75
+        elif previous == "references" and signal["headings"] == 0 and signal["toc_entries"] == 0:
+            page_type, confidence = "references", 0.7
+        elif signal["reference_page"] and signal["toc_entries"] == 0 and signal["chapter_headings"] <= 1:
+            page_type, confidence = "references", 0.8
+        elif appendix:
+            # An appendix carries substantive material, so it counts as content, but it
+            # is labelled separately so the interface can show where a quotation came from.
+            page_type, confidence = "appendix", 0.8
+        elif _is_cover(signal):
+            # Checked before the heading-only rule: a cover page carries identity fields
+            # and no prose, so it would otherwise be mistaken for an unsettled page.
+            page_type, confidence = "cover", 0.8
+        elif _looks_heading_only(signal, blocks) and signal["toc_entries"] == 0:
+            # A body page that carries only a section title cannot support a content
+            # judgement on its own, so it is not silently treated as ordinary body text.
+            page_type, confidence = "unknown", 0.5
+        else:
+            page_type, confidence = "body", 0.6
+            confidence = 0.85 if body_blocks and signal["toc_entries"] == 0 else confidence
+
+        mixed = page_type == "toc" and bool(body_blocks)
+        if mixed:
+            confidence = 0.7
+        if page_type in ("body", "appendix", "unknown") or mixed:
+            # Structure-extracted headings come from body text only, never from a TOC.
+            for block in body_blocks:
+                for line in block["text"].splitlines():
+                    found = heading_level(line, toc_titles)
+                    if not found:
+                        continue
+                    level, title = found
+                    if level == 1:
+                        chapter, section = title, title
+                    else:
+                        section = title
+        # A table of contents may only name the chapter path; the body supplies the
+        # section wording. Navigation pages therefore carry the chapter, not a section.
+        section_for_page = section if page_type in ("body", "unknown") or mixed else chapter
+        results.append({
+            "page": position,
+            "page_type": page_type,
+            "mixed": mixed,
+            "chapter": chapter,
+            "section": section_for_page,
+            "blocks": blocks,
+            "confidence": round(confidence, 2),
+            "signals": {**signal, "repeated_line_count": len(repeated), "toc_blocks": len(toc_blocks)},
+            "classifier_version": CLASSIFIER_VERSION,
+        })
+        previous = page_type
+    return results
+
+
+def block_text(blocks: list[dict], *, kinds: tuple[str, ...] = (BLOCK_BODY,)) -> str:
+    return "\n".join(block["text"] for block in blocks if block["kind"] in kinds).strip()
+
+
+def toc_entries(blocks: list[dict]) -> list[str]:
+    """Heading-shaped entries read out of a table of contents page."""
+    entries = []
+    for block in blocks:
+        if block["kind"] != BLOCK_TOC:
+            continue
+        for line in block["text"].splitlines():
+            if is_toc_entry(line):
+                entries.append(line)
+    return entries
+
+
+def searchable_pages(pages: list[dict]) -> list[dict]:
+    """Pages prepared for indexing: cleaned lines, typed blocks and section paths."""
+    classification = classify_document(pages)
+    output = []
+    for page, item in zip(pages, classification):
+        body = block_text(item["blocks"])
+        output.append({
+            "page_number": page.get("page_number"),
+            "section": item["section"] or page.get("section", ""),
+            "chapter": item["chapter"],
+            "text": item.get("text", ""),
+            "tables": page.get("tables", []),
+            "rendered_image_path": page.get("rendered_image_path"),
+            "page_type": item["page_type"],
+            "blocks": item["blocks"],
+            "classification": item,
+            "index_text": body,
+        })
+    return output

backend/wisegrade/rag.py

@@ -1,31 +1,130 @@
+"""Indexing and hybrid retrieval with page-type aware candidate filtering.
+
+Chinese keyword recall does not use PostgreSQL ``to_tsvector('simple', …)``. That
+configuration has no Chinese segmenter, so a whole run of Chinese characters becomes a
+single token; measured on the course reports, the query ``文献调研`` does not match the
+page that contains ``第2章文献调研与技术现状``. Recall therefore uses character bigrams
+plus whole ASCII/number tokens, which needs no extension and behaves the same on
+PostgreSQL and SQLite.
+
+Semantic and keyword scores are fused by rank (reciprocal rank fusion), never by adding
+raw numbers: a cosine similarity and a bigram overlap are not on a common scale.
+"""
 from functools import lru_cache
 from pathlib import Path
+import re
 
-from sqlalchemy import cast, delete, func, or_, select
+from sqlalchemy import case, cast, delete, func, literal, or_, select
 
 from .config import get_settings
-from .knowledge_pages import page_text, relative_image_path
 from .models import KnowledgeChunk, KnowledgeDocument, KnowledgePage
+from .pages import (BLOCK_BODY, BLOCK_TOC, classify_document, is_toc_entry,
+                    normalize_heading, searchable_pages)
+
+INDEX_VERSION = 3
+
+#: Fusion constant for reciprocal rank fusion; the usual value from the IR literature.
+RRF_K = 60
+#: Candidate pools are gathered independently so both retrievers get a chance.
+SEMANTIC_POOL = 60
+KEYWORD_POOL = 60
+#: A chunk must be at least this long to count as substantive content evidence.
+MIN_CHUNK_CHARS = 60
+#: At most this many chunks from one page in the first retrieval pass.
+DEFAULT_PAGE_LIMIT = 2
+#: Soft cap on chunks from one section once a reliable section path exists.
+DEFAULT_SECTION_CAP = 3
+#: Maximum chunks handed to the model for one criterion, across all targets.
+SCORE_EVIDENCE_LIMIT = 14
+
+_ASCII_TOKEN = re.compile(r"[A-Za-z][A-Za-z0-9_+\-.]*|\d+(?:\.\d+)*")
+_CJK_RUN = re.compile(r"[㐀-䶿一-鿿]+")
 
 
-INDEX_VERSION = 2
+def query_terms(query: str) -> tuple[list[str], list[str]]:
+    """Split a query into (ascii tokens lowercased, CJK character bigrams).
+
+    Single-character CJK runs are dropped: they carry too little signal and would make
+    every chunk with a common character look relevant.
+    """
+    value = str(query or "")
+    ascii_tokens = [token.lower() for token in _ASCII_TOKEN.findall(value)]
+    bigrams: list[str] = []
+    seen = set()
+    for run in _CJK_RUN.findall(value):
+        for index in range(len(run) - 1):
+            bigram = run[index:index + 2]
+            if bigram not in seen:
+                seen.add(bigram)
+                bigrams.append(bigram)
+    return sorted(set(ascii_tokens)), bigrams
+
+
+def _keyword_query(db, filters, query: str, limit: int):
+    """Keyword recall ranked by bigram/token overlap over the already-filtered rows.
+
+    PostgreSQL's ``to_tsvector('simple', …)`` cannot segment Chinese, so it is not used
+    for recall. The overlap is computed with ``strpos``/``substr``/``generate_series``
+    so the same expression works on every dialect SQLAlchemy targets here.
+    """
+    ascii_tokens, bigrams = query_terms(query)
+    if not ascii_tokens and not bigrams:
+        return []
+    total = len(ascii_tokens) + len(bigrams)
+    score = None
+    for token in ascii_tokens:
+        hit = case((func.strpos(func.lower(KnowledgeChunk.text), literal(token)) > 0, 1.0), else_=0.0)
+        score = hit if score is None else score + hit
+    if bigrams:
+        series = select(func.generate_series(1, func.greatest(func.length(KnowledgeChunk.text) - 1, 0)).label("i")).subquery("series")
+        matches = (select(func.count()).select_from(series)
+                   .where(func.substr(KnowledgeChunk.text, series.c.i, 2).in_(bigrams)).scalar_subquery())
+        score = matches if score is None else score + matches
+    score = (score / float(total)).label("keyword")
+    rows = db.execute(select(KnowledgeChunk.id, score).where(*filters)
+                      .order_by(score.desc(), KnowledgeChunk.id.asc()).limit(limit)).all()
+    return [row[0] for row in rows if float(row[1] or 0) > 0]
 
 
 def split_pages(pages: list[dict], *, size=650, overlap=100):
+    """Split pages into searchable chunks carrying page type and section path."""
     for page in pages:
-        text = page.get("text", "").strip()
-        blocks = [text] if text else []
-        for table in page.get("tables", []):
-            blocks.append("表格: " + str(table.get("rows", table)))
+        page_type = page.get("page_type") or "body"
+        chapter = page.get("chapter", "")
+        section = page.get("section", "")
+        blocks = page.get("blocks")
+        if not blocks:
+            blocks = [{"kind": BLOCK_BODY, "text": str(page.get("text") or "").strip()}]
+            blocks.extend({"kind": BLOCK_BODY, "text": "表格: " + str(table.get("rows", table))}
+                          for table in page.get("tables", []))
         for block in blocks:
+            if block["kind"] == BLOCK_TOC:
+                # A contents page is navigation, so its text is not body content.
+                yield {"page": page["page_number"], "page_type": "toc", "block_type": BLOCK_TOC,
+                       "chapter": chapter, "section": section, "text": block["text"]}
+                continue
+            text_block = block["text"].strip()
+            if not text_block:
+                continue
+            for table in ([block["tables"]] if block.get("tables") else []):
+                for table_item in table:
+                    text_block += "\n表格: " + str(table_item.get("rows", table_item))
+            # Table rows of a page are appended once, on the page's first body chunk.
             start = 0
-            while start < len(block):
-                yield {"page": page["page_number"], "section": page.get("section", ""), "text": block[start:start + size]}
-                if start + size >= len(block):
+            while start < len(text_block):
+                yield {"page": page["page_number"], "page_type": page_type, "block_type": BLOCK_BODY,
+                       "chapter": chapter, "section": section, "text": text_block[start:start + size]}
+                if start + size >= len(text_block):
                     break
                 start += size - overlap
 
 
+def build_chunks(pages: list[dict]) -> list[dict]:
+    """Chunks for one document, classifying pages first when they are not yet typed."""
+    prepared = pages if pages and "page_type" in (pages[0] or {}) else searchable_pages(pages)
+    return list(split_pages(prepared))
+
+
 @lru_cache(maxsize=2)
 def local_encoder(model_path: str):
     path = Path(model_path)
@@ -56,7 +155,8 @@ def ingest(db, *, course_version_id: int, project_id: int | None, submission_fil
                     KnowledgeDocument.title == title))
     if existing and existing.source_hash == source_hash and existing.index_version == INDEX_VERSION and existing.status == "ready":
         return existing
-    chunks = list(split_pages(pages))
+    prepared = searchable_pages(pages)
+    chunks = list(split_pages(prepared))
     vectors = embedder([chunk["text"] for chunk in chunks])
     if len(vectors) != len(chunks):
         raise RuntimeError("嵌入向量数量与知识片段数量不一致")
@@ -71,42 +171,265 @@ def ingest(db, *, course_version_id: int, project_id: int | None, submission_fil
         db.add(doc)
     doc.index_version, doc.status = INDEX_VERSION, "indexing"
     db.flush()
-    for source in pages:
-        db.add(KnowledgePage(document_id=doc.id, page=source["page_number"], text=page_text(source),
-                             image_path=relative_image_path(source.get("rendered_image_path"))))
+    for source, prepared_page in zip(pages, prepared):
+        classification = prepared_page["classification"]
+        db.add(KnowledgePage(
+            document_id=doc.id, page=source["page_number"], text=page_text(source),
+            image_path=relative_image_path(source.get("rendered_image_path")),
+            page_type=classification["page_type"], mixed=bool(classification["mixed"]),
+            section=classification["section"], chapter=classification["chapter"],
+            confidence=classification["confidence"],
+            classification={**classification["signals"], "classifier_version": classification["classifier_version"],
+                            "blocks": [{"kind": block["kind"], "chars": len(block["text"]),
+                                        "substantive": block.get("substantive", False)}
+                                       for block in classification["blocks"]]}))
     for chunk, vector in zip(chunks, vectors):
         db.add(KnowledgeChunk(document_id=doc.id, course_version_id=course_version_id, project_id=project_id,
-                              document_type=category, page=chunk["page"], section=chunk["section"], text=chunk["text"], embedding=vector))
+                              document_type=category, page=chunk["page"], section=chunk["section"],
+                              chapter=chunk["chapter"], page_type=chunk["page_type"],
+                              block_type=chunk["block_type"], text=chunk["text"], embedding=vector))
     doc.status = "ready"
     db.commit()
     return doc
 
 
-def retrieve(db, *, course_version_id: int, project_id: int, query: str, category: str | None = None,
-             limit=10, embedder=embed, include_public=True, public_only=False):
-    """Hybrid retrieval with project isolation and an optional public course namespace."""
-    scope = (KnowledgeChunk.project_id.is_(None) if public_only else
-             or_(KnowledgeChunk.project_id == project_id, KnowledgeChunk.project_id.is_(None)) if include_public else
-             KnowledgeChunk.project_id == project_id)
-    filters = [KnowledgeChunk.course_version_id == course_version_id, scope]
+def _scope_filter(project_id: int | None, include_public: bool, public_only: bool):
+    if public_only:
+        return KnowledgeChunk.project_id.is_(None)
+    if include_public:
+        return or_(KnowledgeChunk.project_id == project_id, KnowledgeChunk.project_id.is_(None))
+    return KnowledgeChunk.project_id == project_id
+
+
+def _base_filters(*, course_version_id: int, project_id: int | None, category, categories,
+                  include_public: bool, public_only: bool, allowed_page_types, exclude_document_ids):
+    filters = [KnowledgeChunk.course_version_id == course_version_id,
+               _scope_filter(project_id, include_public, public_only)]
     if category:
         filters.append(KnowledgeChunk.document_type == category)
-    vector = embedder([query])[0]
+    if categories:
+        filters.append(KnowledgeChunk.document_type.in_(list(categories)))
+    if allowed_page_types is not None:
+        filters.append(KnowledgeChunk.page_type.in_(list(allowed_page_types)))
+    if exclude_document_ids:
+        filters.append(KnowledgeChunk.document_id.not_in(list(exclude_document_ids)))
+    return filters
+
+
+def _keyword_query(db, filters, query: str, limit: int):
+    """Keyword recall ranked by bigram/token overlap over the already-filtered rows.
+
+    PostgreSQL's ``to_tsvector('simple', …)`` cannot segment Chinese, so it is not used
+    for recall. The overlap is computed with ``strpos``/``substr`` so the same expression
+    works on every dialect SQLAlchemy targets here.
+    """
+    ascii_tokens, bigrams = query_terms(query)
+    if not ascii_tokens and not bigrams:
+        return []
+    total = len(ascii_tokens) + len(bigrams)
+    score = None
+    for token in ascii_tokens:
+        term = literal(token)
+        hit = case((func.strpos(func.lower(KnowledgeChunk.text), term) > 0, 1.0), else_=0.0)
+        score = hit if score is None else score + hit
+    if bigrams:
+        start = func.generate_series(1, func.greatest(func.length(KnowledgeChunk.text) - 1, 0))
+        series = select(start.label("i")).subquery("series")
+        matches = (select(func.count()).select_from(series)
+                   .where(func.substr(KnowledgeChunk.text, series.c.i, 2).in_(bigrams))
+                   .scalar_subquery())
+        score = matches if score is None else score + matches
+    score = (score / float(total)).label("keyword")
+    rows = db.execute(select(KnowledgeChunk.id, score).where(*filters)
+                      .order_by(score.desc(), KnowledgeChunk.id.asc()).limit(limit)).all()
+    return [row[0] for row in rows if float(row[1] or 0) > 0]
+
+
+def keyword_overlap(text_value: str, ascii_tokens: list[str], bigrams: list[str]) -> float:
+    """Bigram/token overlap used by the non-PostgreSQL path and by tests."""
+    value = str(text_value or "")
+    lower = value.lower()
+    hits = sum(1 for token in ascii_tokens if token in lower)
+    total = len(ascii_tokens)
+    hits += sum(1 for bigram in bigrams if bigram in value)
+    total += len(bigrams)
+    return hits / total if total else 0.0
+
+
+def _semantic_query(db, filters, vector, limit: int):
     if db.bind.dialect.name == "postgresql":
         from pgvector.sqlalchemy import Vector
-        semantic = (1 - cast(KnowledgeChunk.embedding, Vector(1024)).cosine_distance(vector)).label("semantic")
-        lexical = func.ts_rank(func.to_tsvector("simple", KnowledgeChunk.text), func.plainto_tsquery("simple", query)).label("lexical")
-        rows = db.execute(select(KnowledgeChunk, semantic, lexical).where(*filters).order_by(semantic.desc(), lexical.desc()).limit(limit * 3)).all()
-        ranked = [(row[0], float(row.semantic or 0) * .7 + float(row.lexical or 0) * .3) for row in rows]
-    else:
-        chunks = db.scalars(select(KnowledgeChunk).where(*filters)).all()
-        terms = [query[i:i+2] for i in range(max(0, len(query)-1)) if query[i:i+2].strip()]
-        ranked = []
-        for chunk in chunks:
-            semantic = sum(a*b for a, b in zip(chunk.embedding or [], vector))
-            lexical = sum(term in chunk.text for term in terms) / max(len(terms), 1)
-            ranked.append((chunk, semantic * .7 + lexical * .3))
-    ranked.sort(key=lambda pair: pair[1], reverse=True)
-    return [{"chunk_id": chunk.id, "document_id": chunk.document_id, "project_id": chunk.project_id,
-             "document_type": chunk.document_type, "page": chunk.page, "section": chunk.section,
-             "text": chunk.text, "rank": float(rank)} for chunk, rank in ranked[:limit]]
+        distance = cast(KnowledgeChunk.embedding, Vector(1024)).cosine_distance(vector)
+        rows = db.execute(select(KnowledgeChunk.id).where(*filters)
+                          .order_by(distance.asc(), KnowledgeChunk.id.asc()).limit(limit)).all()
+        return [row[0] for row in rows]
+    rows = db.execute(select(KnowledgeChunk.id, KnowledgeChunk.embedding).where(*filters)).all()
+    scored = [(chunk_id, sum(a * b for a, b in zip(embedding or [], vector)))
+              for chunk_id, embedding in rows]
+    scored.sort(key=lambda pair: (-pair[1], pair[0]))
+    return [chunk_id for chunk_id, _ in scored[:limit]]
+
+
+def _fuse(db, ids: list[int], semantic_ids: list[int], keyword_ids: list[int]):
+    """Reciprocal rank fusion over the two ranked lists, keeping discovery order stable."""
+    scores: dict[int, float] = {chunk_id: 0.0 for chunk_id in ids}
+    for ranked in (semantic_ids, keyword_ids):
+        for rank, chunk_id in enumerate(ranked):
+            if chunk_id in scores:
+                scores[chunk_id] += 1.0 / (RRF_K + rank + 1)
+    return scores
+
+
+def _row_dict(chunk):
+    return {"chunk_id": chunk.id, "document_id": chunk.document_id, "project_id": chunk.project_id,
+            "document_type": chunk.document_type, "page": chunk.page, "section": chunk.section,
+            "chapter": chunk.chapter, "page_type": chunk.page_type, "block_type": chunk.block_type,
+            "text": chunk.text}
+
+
+def normalize_hit(hit: dict) -> dict:
+    """Fill the fields the rest of the pipeline reads, so one shape flows through.
+
+    An alternate retriever or a test double may return only the essentials. Filling the
+    defaults here keeps every consumer from re-deriving them; the authoritative page type
+    is still re-read from storage when a score is produced.
+    """
+    return {"chunk_id": hit.get("chunk_id"), "document_id": hit.get("document_id"),
+            "project_id": hit.get("project_id"), "document_type": hit.get("document_type"),
+            "page": hit.get("page"), "text": hit.get("text") or "",
+            "section": hit.get("section") or "", "chapter": hit.get("chapter") or "",
+            "page_type": hit.get("page_type") or "unknown", "block_type": hit.get("block_type") or "legacy",
+            "rank": float(hit.get("rank") or 0.0)}
+
+
+def retrieve(db, *, course_version_id: int, project_id: int, query: str, category: str | None = None,
+             categories=None, limit=10, embedder=embed, include_public=True, public_only=False,
+             allowed_page_types=None, exclude_document_ids=(), substantive_only=False,
+             semantic_pool: int | None = None, keyword_pool: int | None = None):
+    """Filtered hybrid retrieval, then rank fusion over the two independent recalls.
+
+    Page filtering happens inside the query, so it always precedes the candidate limit:
+    a contents page can never displace a body page simply by scoring well.
+    """
+    filters = _base_filters(course_version_id=course_version_id, project_id=project_id, category=category,
+                            categories=categories, include_public=include_public, public_only=public_only,
+                            allowed_page_types=allowed_page_types, exclude_document_ids=exclude_document_ids)
+    if substantive_only:
+        filters.append(KnowledgeChunk.block_type != BLOCK_TOC)
+    vector = embedder([query])[0]
+    semantic_ids = _semantic_query(db, filters, vector, semantic_pool or SEMANTIC_POOL)
+    keyword_ids = _keyword_query(db, filters, query, keyword_pool or KEYWORD_POOL)
+    ids = list(dict.fromkeys(semantic_ids + keyword_ids))
+    if not ids:
+        return []
+    scores = _fuse(db, ids, semantic_ids, keyword_ids)
+    chunks = db.scalars(select(KnowledgeChunk).where(KnowledgeChunk.id.in_(ids))).all()
+    usable = []
+    for chunk in chunks:
+        if substantive_only and (chunk.block_type == BLOCK_TOC
+                                 or (chunk.page_type == "body" and is_toc_entry(chunk.text)
+                                     and len(chunk.text.strip()) < MIN_CHUNK_CHARS)):
+            continue
+        usable.append(chunk)
+    usable.sort(key=lambda chunk: (-scores.get(chunk.id, 0.0), chunk.id))
+    return [{**_row_dict(chunk), "rank": float(scores.get(chunk.id, 0.0))} for chunk in usable[:limit]]
+
+
+def _page_diverse(chunks: list[dict], limit: int, *, page_limit: int, section_cap: int | None):
+    """Spread the first pass over pages, then allow a bounded number per section."""
+    picked: list[dict] = []
+    per_page: dict[tuple, int] = {}
+    per_section: dict[str, int] = {}
+    remaining = []
+    for chunk in chunks:
+        page_key = (chunk.get("document_id"), chunk.get("page"))
+        section = str(chunk.get("section") or "")
+        if len(picked) < limit and per_page.get(page_key, 0) < page_limit:
+            if section_cap and section and per_section.get(section, 0) >= section_cap:
+                remaining.append(chunk)
+                continue
+            picked.append(chunk)
+            per_page[page_key] = per_page.get(page_key, 0) + 1
+            if section:
+                per_section[section] = per_section.get(section, 0) + 1
+        else:
+            remaining.append(chunk)
+    for chunk in remaining:
+        if len(picked) >= limit:
+            break
+        picked.append(chunk)
+    return picked[:limit]
+
+
+def retrieve_targets(db, *, course_version_id: int, project_id: int, targets: list[dict], category=None,
+                     categories=None, limit=SCORE_EVIDENCE_LIMIT, embedder=embed, include_public=False,
+                     page_limit=DEFAULT_PAGE_LIMIT, section_cap=DEFAULT_SECTION_CAP,
+                     per_target_limit: int | None = None, navigator=None, retrieve_fn=retrieve):
+    """Search once per required-evidence target and merge with page-level diversity.
+
+    Every target is searched separately with its own query and its own allowed page
+    types, so a contents page permitted for a structural target can never flood a
+    content target's evidence. A target counts as unmet when its *own* search found
+    nothing admissible, which is what decides a teacher review.
+    """
+    merged: list[dict] = []
+    seen: set[int] = set()
+    per_target: dict[str, list[dict]] = {}
+    for target in targets:
+        allowed = target.get("allowed_page_types")
+        raw_hits = retrieve_fn(db, course_version_id=course_version_id, project_id=project_id, category=category,
+                               categories=categories, query=target.get("query") or target.get("label") or "",
+                               limit=per_target_limit or max(limit, 8), embedder=embed,
+                               include_public=include_public, allowed_page_types=allowed,
+                               substantive_only=target.get("use") != "structure",
+                               semantic_pool=SEMANTIC_POOL, keyword_pool=KEYWORD_POOL)
+        hits = [normalize_hit(hit) for hit in (raw_hits or [])]
+        if target.get("use") == "content" and not hits and navigator is not None:
+            # The contents list may only be used to navigate: its headings are matched
+            # against headings that really occur in body text, and those sections and
+            # their neighbouring pages are searched. The quotation still lands on body.
+            hits = [dict(hit, via="toc_navigation") for hit in (navigator(target) or [])]
+        per_target[target["target_id"]] = hits
+        for hit in hits:
+            if hit["chunk_id"] not in seen:
+                seen.add(hit["chunk_id"])
+                merged.append({**hit, "target_id": target["target_id"], "target_label": target["label"],
+                               "target_use": target["use"]})
+    merged.sort(key=lambda chunk: -chunk["rank"])
+    selected = _page_diverse(merged, limit, page_limit=page_limit, section_cap=section_cap)
+    uncovered = [target for target in targets if not per_target.get(target["target_id"])]
+    return {"evidence": selected, "per_target": per_target, "uncovered_targets": uncovered}
+
+
+def body_heading_index(db, *, course_version_id: int, project_id: int, category: str | None = None):
+    """Sections present in real body text, used to navigate from a contents page."""
+    filters = [KnowledgeChunk.course_version_id == course_version_id, KnowledgeChunk.project_id == project_id,
+               KnowledgeChunk.page_type == "body"]
+    if category:
+        filters.append(KnowledgeChunk.document_type == category)
+    rows = db.execute(select(KnowledgeChunk.document_id, KnowledgeChunk.page, KnowledgeChunk.section,
+                             KnowledgeChunk.chapter).where(*filters).distinct()).all()
+    index: dict[str, list[dict]] = {}
+    for document_id, page, section, chapter in rows:
+        for value in (section, chapter):
+            key = normalize_heading(value or "")
+            if key:
+                index.setdefault(key, []).append({"document_id": document_id, "page": page,
+                                                  "section": section, "chapter": chapter})
+    return index
+
+
+def page_text(source: dict) -> str:
+    blocks = [str(source.get("text") or "").strip()]
+    blocks.extend("表格: " + str(table.get("rows", table)) for table in source.get("tables", []))
+    return "\n".join(block for block in blocks if block)
+
+
+def relative_image_path(value: str | None) -> str | None:
+    if not value:
+        return None
+    root = get_settings().processed_path.resolve()
+    path = Path(value).resolve()
+    if not path.is_relative_to(root):
+        raise ValueError("页面图像路径不在处理目录内")
+    return str(path.relative_to(root))

backend/wisegrade/scoring.py

@@ -6,15 +6,30 @@ from sqlalchemy import select, update
 
 from .config import get_settings
 from .db import SessionLocal
+from .evidence import USE_CONTENT, content_targets, normalize_targets
 from .knowledge_pages import page_image_path
 from .llm import ConsistencyResult, FactResult, ModelError, ScoreResult, validate_score
-from .models import (CriterionScore, KnowledgeDocument, Project, ScoreEvidence, ScoringBatch,
-                     SubmissionFile, now)
+from .models import (CriterionScore, KnowledgeChunk, KnowledgeDocument, KnowledgePage, Project,
+                     ScoreEvidence, ScoringBatch, SubmissionFile, now)
+from .pages import normalize_heading
 from .parsing import parse_document
-from .rag import INDEX_VERSION, ingest, retrieve
+from .rag import (INDEX_VERSION, body_heading_index, ingest, retrieve, retrieve_targets)
 from .storage import secure_path, sha256
 from .settings_store import client_for_batch
 
+#: Criterion statuses. ``needs_review`` is deliberately distinct from a missing
+#: material, a parse failure and a model failure: the retrieval ran but only found
+#: navigation pages where the rule requires body content.
+STATUS_NEEDS_REVIEW = "needs_review"
+STATUS_UNSCORABLE = "unscorable"
+STATUS_FAILED = "failed"
+STATUS_SCORED = "scored"
+STATUS_REVIEWED = "reviewed"
+#: Statuses that a batch counts as finished work.
+DONE_STATUSES = (STATUS_SCORED, STATUS_REVIEWED, STATUS_NEEDS_REVIEW)
+#: Statuses the dashboard shows as needing teacher attention.
+ATTENTION_STATUSES = (STATUS_FAILED, STATUS_UNSCORABLE, STATUS_NEEDS_REVIEW)
+
 
 @contextmanager
 def file_index_lock(file_id: int):
@@ -72,12 +87,21 @@ def _parse_submission_locked(db, file: SubmissionFile, *, embedder=None):
 
 
 def _project_facts(db, project, course_snapshot, client, *, retriever):
+    """Extract project facts from body text only.
+
+    Implementation facts must not be inferred from a contents heading or an abstract
+    claim, so the search admits body and appendix pages exclusively. Project identity
+    (name, student number, topic) legitimately lives on the cover page and is read from
+    the teacher-confirmed import record instead of from retrieved text.
+    """
     prompts = course_snapshot["prompts"]
     categories = ("stage1", "stage2", "stage3", "final_report", "presentation")
     snippets = []
     for category in categories:
         snippets.extend(retriever(db, course_version_id=project.course_version_id, project_id=project.id,
-                                  category=category, query="项目名称 目标 指标 硬件 软件 实验 数据 成本 结论", limit=3, include_public=False))
+                                  category=category, query="项目名称 目标 指标 硬件 软件 实验 数据 成本 结论",
+                                  limit=3, include_public=False, allowed_page_types=["body", "appendix"],
+                                  substantive_only=True))
     if not snippets:
         return {}
     if client is None:
@@ -100,10 +124,54 @@ def _project_facts(db, project, course_snapshot, client, *, retriever):
     return project.facts
 
 
-def _score_criterion(db, batch, project, criterion, client, *, retriever):
+def _body_navigator(db, *, course_version_id, project_id, category):
+    """Find real body pages for a target whose contents entries name missing sections.
+
+    The contents page is used only as a map: its headings are matched against headings
+    that actually occur in body text, and the body sections and their neighbouring pages
+    are then searched. The final quotation always lands on a real body page.
+    """
+    headings = body_heading_index(db, course_version_id=course_version_id, project_id=project_id, category=category)
+    if not headings:
+        return None
+
+    def navigate(target):
+        label = normalize_heading(target.get("label") or "")
+        if not label:
+            return []
+        candidates = []
+        for heading, places in headings.items():
+            if not heading:
+                continue
+            if heading.startswith(label) or label.startswith(heading) or label in heading or heading in label:
+                candidates.extend(places)
+        if not candidates:
+            return []
+        pages = sorted({(place["document_id"], place["page"]) for place in candidates})
+        neighbours = set()
+        for document_id, page in pages:
+            neighbours.update({(document_id, page), (document_id, page + 1)})
+        wanted = sorted(neighbours)
+        rows = db.scalars(select(KnowledgeChunk).where(
+            KnowledgeChunk.course_version_id == course_version_id, KnowledgeChunk.project_id == project_id,
+            KnowledgeChunk.page_type == "body", KnowledgeChunk.block_type != "toc",
+            KnowledgeChunk.document_id.in_([item[0] for item in wanted]),
+            KnowledgeChunk.page.in_([item[1] for item in wanted])).order_by(KnowledgeChunk.id)).all()
+        hits = []
+        for chunk in rows:
+            hits.append({"chunk_id": chunk.id, "document_id": chunk.document_id, "project_id": chunk.project_id,
+                         "document_type": chunk.document_type, "page": chunk.page, "section": chunk.section,
+                         "chapter": chunk.chapter, "page_type": chunk.page_type, "block_type": chunk.block_type,
+                         "text": chunk.text, "rank": 0.5})
+        return hits
+
+    return navigate
+
+
+def _score_criterion(db, batch, project, criterion, client, *, retriever, multi_retriever=retrieve_targets):
     score = db.scalar(select(CriterionScore).where(CriterionScore.batch_id == batch.id,
                       CriterionScore.project_id == project.id, CriterionScore.criterion_id == criterion["criterion_id"]))
-    if score and score.status in ("scored", "reviewed"):
+    if score and score.status in (STATUS_SCORED, STATUS_REVIEWED):
         return score
     if not score:
         score = CriterionScore(batch_id=batch.id, project_id=project.id, criterion_id=criterion["criterion_id"])
@@ -115,44 +183,103 @@ def _score_criterion(db, batch, project, criterion, client, *, retriever):
                        SubmissionFile.category == category, SubmissionFile.recognition_status == "recognized")).all()
     ready = [file for file in files if file.parse_status == "ready"]
     if len(files) != 1 or len(ready) != 1:
-        score.status, score.error = "unscorable", "材料缺失、重复、待确认或解析失败"
+        score.status, score.error = STATUS_UNSCORABLE, "材料缺失、重复、待确认或解析失败"
         db.commit()
         return score
-    query = criterion["description"] + " " + " ".join(criterion["required_evidence"])
-    chunks = retriever(db, course_version_id=batch.course_version_id, project_id=project.id,
-                       category=category, query=query, limit=10, include_public=False)
+
+    plan = batch.rubric_snapshot.get("evidence_plan") or {}
+    targets = (plan.get("criteria") or {}).get(criterion["criterion_id"]) or normalize_targets(
+        criterion.get("required_evidence"))
+    navigator = _body_navigator(db, course_version_id=batch.course_version_id,
+                                project_id=project.id, category=category)
+    # The single ``retriever`` stays the seam: each per-target search goes through it,
+    # so a caller that injects a retriever (tests, alternate index) still applies.
+    selection = multi_retriever(db, course_version_id=batch.course_version_id, project_id=project.id,
+                                targets=targets, category=category, navigator=navigator,
+                                retrieve_fn=retriever)
+    chunks = _attach_page_types(db, selection["evidence"])
+    gaps = selection["uncovered_targets"]
     if not chunks:
-        score.status, score.error = "unscorable", "无可检索的材料证据"
+        # Nothing admissible was found for any target. Content targets go to review;
+        # a purely structural criterion only depends on navigation pages, which exist.
+        score.status = STATUS_NEEDS_REVIEW if content_targets(criterion) else STATUS_UNSCORABLE
+        score.error = "未检索到允许作为证据的正文页"
+        score.model_score = None
+        score.missing_evidence = [target["label"] for target in gaps]
+        _record_evidence_gaps(score, gaps)
         db.commit()
         return score
     if client is None:
         raise ModelError("未配置或无法连接评分模型")
-    image_sources = dict.fromkeys((chunk["document_id"], chunk["page"]) for chunk in chunks[:2])
+
+    content_gaps = [target for target in gaps if target["use"] == USE_CONTENT]
+    if content_gaps:
+        # The model is not asked to grade from navigation pages. Record the gap and keep
+        # the model score null so a teacher decides, rather than asserting a low score.
+        score.model_score, score.confidence, score.status = None, None, STATUS_NEEDS_REVIEW
+        score.model_comment = "检索仅命中目录、摘要或章节标题，缺少可用于内容判断的正文证据，转教师复核。"
+        score.missing_evidence = [target["label"] for target in content_gaps]
+        score.suggestion = "请补充该章节的正文页，或由教师直接核定该项分数。"
+        score.error = "缺少正文证据：" + "、".join(target["label"] for target in content_gaps)
+        _record_evidence_gaps(score, content_gaps, searched=_searched_page_types(plan, content_gaps))
+        db.commit()
+        return score
+
+    # Page images go to the model as well, so prefer substantive body pages over the
+    # navigation pages that may legitimately appear in a structural target's evidence.
+    image_sources = list(dict.fromkeys(
+        (chunk["document_id"], chunk["page"]) for chunk in chunks
+        if chunk.get("page_type") == "body"))
+    if not image_sources:
+        image_sources = list(dict.fromkeys((chunk["document_id"], chunk["page"]) for chunk in chunks))
     images = []
-    for document_id, page in image_sources:
+    for document_id, page in image_sources[:3]:
         doc = db.get(KnowledgeDocument, document_id)
         path = page_image_path(db, doc, page) if doc else None
         if path:
             images.append(str(path))
+    query = criterion["description"] + " " + " ".join(target["label"] for target in targets)
     public_context = retriever(db, course_version_id=batch.course_version_id, project_id=project.id,
                                category=None, query=query, limit=6, public_only=True)
-    payload = {"criterion": criterion, "project_facts": project.facts, "course_context": public_context,
-               "evidence": chunks,
-               "required_output": {"criterion_id": "string", "score": "number", "max_score": "number", "reason": "string",
-                                   "evidence": [{"chunk_id": "integer", "page": "integer", "quote": "exact substring"}],
-                                   "missing_evidence": ["string"], "suggestion": "string", "confidence": "0..1"}}
+    payload = {"criterion": criterion,
+               "required_evidence_targets": [{"target_id": target["target_id"], "label": target["label"],
+                                              "use": target["use"],
+                                              "allowed_page_types": target["allowed_page_types"]}
+                                             for target in targets],
+               "evidence_policy": {
+                   "structure": "目录、封面和参考文献页可以证明章节结构和报告完整性，不能证明章节内容或完成质量。",
+                   "content": "内容判断只能使用正文或附录中的实质段落、数据、表格；目录、摘要和单独章节标题不能作为内容证据。"},
+               "project_facts": project.facts, "course_context": public_context, "evidence": chunks,
+               "required_output": {"criterion_id": "string", "score": "number or null", "max_score": "number",
+                                   "reason": "string",
+                                   "evidence": [{"chunk_id": "integer", "page": "integer",
+                                                 "quote": "exact substring", "target_id": "string"}],
+                                   "missing_evidence": ["string"], "suggestion": "string", "confidence": "0..1",
+                                   "note": "证据不足以判断内容时 score 填 null，并在 missing_evidence 中列出缺少的正文目标。"}}
     prompt = (batch.course_snapshot["prompts"]["score"] +
-              " 证据quote必须从所引用chunk_id的text逐字复制连续片段，保留原文标点和换行；page必须等于该片段的page。不要改写、缩写或拼接引文。")
+              " 证据quote必须从所引用chunk_id的text逐字复制连续片段，保留原文标点和换行；page必须等于该片段的page；"
+              "target_id必须取自required_evidence_targets。不要改写、缩写或拼接引文。"
+              "不得把目录、摘要或章节标题当作内容证据；无法据此判断内容时把score设为null。")
+    error = ""
+    result = None
     for attempt in range(2):
         try:
-            result = validate_score(client.json_call(prompt, payload, ScoreResult, images=images), criterion, chunks)
+            result = validate_score(client.json_call(prompt, payload, ScoreResult, images=images), criterion, chunks,
+                                    targets=targets)
             break
-        except ModelError:
+        except ModelError as exc:
+            error = str(exc)
             if attempt:
                 raise
-            prompt += " 上次回答的证据未通过原文和页码校验；请重新选择片段并逐字复制quote。"
-    score.model_score, score.model_comment, score.confidence = result.score, result.reason, result.confidence
-    score.missing_evidence, score.suggestion, score.status, score.error = result.missing_evidence, result.suggestion, "scored", ""
+            prompt += f" 上次回答未通过校验（{error}）；请改用允许页面类型中的实质正文片段，并逐字复制quote。"
+    score.model_score, score.model_comment = result.score, result.reason
+    score.confidence = result.confidence
+    score.missing_evidence, score.suggestion = result.missing_evidence, result.suggestion
+    if result.score is None:
+        # The retrieval found body pages but the model judged them insufficient.
+        score.status, score.error = STATUS_NEEDS_REVIEW, "模型认为现有正文证据不足以判断该项，转教师复核。"
+    else:
+        score.status, score.error = STATUS_SCORED, ""
     for evidence in db.scalars(select(ScoreEvidence).where(ScoreEvidence.score_id == score.id)).all():
         db.delete(evidence)
     by_id = {chunk["chunk_id"]: chunk for chunk in chunks}
@@ -160,14 +287,75 @@ def _score_criterion(db, batch, project, criterion, client, *, retriever):
         chunk = by_id[evidence.chunk_id]
         doc = db.get(KnowledgeDocument, chunk["document_id"])
         path = page_image_path(db, doc, evidence.page)
+        target = next((item for item in targets if item["target_id"] == evidence.target_id), None)
         db.add(ScoreEvidence(score_id=score.id, document_id=doc.id, submission_file_id=doc.submission_file_id,
-                             page=evidence.page, quote=evidence.quote, image_path=str(path) if path else None))
+                             page=evidence.page, quote=evidence.quote, image_path=str(path) if path else None,
+                             target_id=evidence.target_id, target_label=target["label"] if target else "",
+                             page_type=chunk.get("page_type") or "unknown",
+                             source_role=target["use"] if target else USE_CONTENT))
     db.commit()
     return score
 
 
-def _run_project(batch_id, project_id, session_factory, client_factory, embedder, retriever):
+def _attach_page_types(db, chunks: list[dict]) -> list[dict]:
+    """Attach the stored page classification to each retrieved chunk.
+
+    The page type recorded in ``knowledge_pages`` is the authority for the evidence
+    policy. Reading it here, rather than trusting whatever a retriever happened to put
+    in the dict, means a statement about what a page is cannot be forged by a caller
+    or lost by an alternate index.
+    """
+    keys = {(chunk.get("document_id"), chunk.get("page")) for chunk in chunks}
+    keys.discard((None, None))
+    if not keys:
+        return chunks
+    documents = {document_id for document_id, _ in keys}
+    pages = {page for _, page in keys}
+    records = db.scalars(select(KnowledgePage).where(KnowledgePage.document_id.in_(documents),
+                                                     KnowledgePage.page.in_(pages))).all()
+    by_key = {(item.document_id, item.page): item for item in records}
+    output = []
+    for chunk in chunks:
+        record = by_key.get((chunk.get("document_id"), chunk.get("page")))
+        if record is None:
+            output.append({**chunk, "page_type": chunk.get("page_type") or "unknown"})
+            continue
+        output.append({**chunk, "page_type": record.page_type or "unknown",
+                       "section": chunk.get("section") or record.section or "",
+                       "chapter": chunk.get("chapter") or record.chapter or ""})
+    return output
+
+
+def _searched_page_types(plan, targets):
+    allowed = set()
+    for target in targets:
+        allowed.update(target.get("allowed_page_types") or [])
+    return sorted(allowed or (plan.get("content_allowed_page_types") or []))
+
+
+def _record_evidence_gaps(score, gaps, searched=None):
+    """Store which content targets lacked body evidence and where retrieval looked.
+
+    The record is kept through teacher review so the original retrieval problem stays
+    auditable next to the score the teacher finally entered.
+    """
+    score.evidence_gaps = [
+        {"target_id": target.get("target_id"), "target_label": target.get("label"),
+         "searched_page_types": searched or target.get("allowed_page_types") or [],
+         "reason": "补检后仍未找到正文证据"}
+        for target in gaps]
+
+
+def _run_project(batch_id, project_id, session_factory, client_factory, embedder, retriever,
+                 multi_retriever=retrieve_targets):
+    """Score one project. Returns ``(failures, reviews)``.
+
+    Failures are missing material, parse problems and model errors. Reviews count the
+    items that only found navigation pages where content evidence is required; those
+    need a teacher decision rather than a retry.
+    """
     failures = 0
+    reviews = 0
     with session_factory() as db:
         batch = db.get(ScoringBatch, batch_id)
         project = db.get(Project, project_id)
@@ -190,8 +378,11 @@ def _run_project(batch_id, project_id, session_factory, client_factory, embedder
             failures += 1
         for criterion in batch.rubric_snapshot["criteria"]:
             try:
-                result = _score_criterion(db, batch, project, criterion, client, retriever=retriever)
-                if result.status not in ("scored", "reviewed"):
+                result = _score_criterion(db, batch, project, criterion, client, retriever=retriever,
+                                          multi_retriever=multi_retriever)
+                if result.status == STATUS_NEEDS_REVIEW:
+                    reviews += 1
+                elif result.status not in (STATUS_SCORED, STATUS_REVIEWED):
                     failures += 1
             except Exception as exc:
                 score = db.scalar(select(CriterionScore).where(CriterionScore.batch_id == batch.id,
@@ -199,15 +390,16 @@ def _run_project(batch_id, project_id, session_factory, client_factory, embedder
                 if not score:
                     score = CriterionScore(batch_id=batch.id, project_id=project.id, criterion_id=criterion["criterion_id"])
                     db.add(score)
-                score.status, score.error = "failed", str(exc)[:500]
+                score.status, score.error = STATUS_FAILED, str(exc)[:500]
                 db.commit()
                 failures += 1
             db.execute(update(ScoringBatch).where(ScoringBatch.id == batch_id).values(progress_done=ScoringBatch.progress_done + 1))
             db.commit()
-    return failures
+    return failures, reviews
 
 
-def run_batch(batch_id: int, *, session_factory=SessionLocal, client_factory=client_for_batch, embedder=None, retriever=retrieve):
+def run_batch(batch_id: int, *, session_factory=SessionLocal, client_factory=client_for_batch, embedder=None,
+              retriever=retrieve, multi_retriever=retrieve_targets):
     with session_factory() as db:
         batch = db.get(ScoringBatch, batch_id)
         if not batch:
@@ -218,13 +410,21 @@ def run_batch(batch_id: int, *, session_factory=SessionLocal, client_factory=cli
         batch.status, batch.progress_total, batch.progress_done, batch.finished_at = "running", len(project_ids) * len(criteria), 0, None
         db.commit()
         failures = 0
+        reviews = 0
         with ThreadPoolExecutor(max_workers=max(1, int(batch.model_config_snapshot.get("concurrency", 1)))) as pool:
-            futures = [pool.submit(_run_project, batch_id, project_id, session_factory, client_factory, embedder, retriever) for project_id in project_ids]
+            futures = [pool.submit(_run_project, batch_id, project_id, session_factory, client_factory,
+                                   embedder, retriever, multi_retriever) for project_id in project_ids]
             for future in as_completed(futures):
                 try:
-                    failures += future.result()
+                    project_failures, project_reviews = future.result()
+                    failures += project_failures
+                    reviews += project_reviews
                 except Exception as exc:
                     failures += 1
                     batch.error = (batch.error + f" 项目任务失败: {type(exc).__name__}")[:1000]
-        batch.status, batch.finished_at = ("partial" if failures else "completed"), now()
+        # A batch with unresolved review items is finished running, but it is not a
+        # completed grading result: the teacher still has to decide those criteria.
+        batch.status = "partial" if failures else ("review_required" if reviews else "completed")
+        batch.finished_at = now()
         db.commit()
+        return {"failures": failures, "reviews": reviews, "status": batch.status}

backend/wisegrade/services.py

@@ -9,6 +9,7 @@ from redis import Redis
 from rq import Queue, Retry
 from sqlalchemy import delete, func, select
 from .config import get_settings
+from .evidence import plan_snapshot
 from .exports import export_excel, export_project_pdf
 from .models import (AuditLog, CourseVersion, CriterionScore, IndexTask, KnowledgeChunk, KnowledgeDocument,
     Project, RubricVersion, ScoringBatch, Student, Submission, SubmissionFile, TeacherRevision)
@@ -209,9 +210,12 @@ def create_batch(data, db):
     if not version.published or not rubric.published or rubric.course_version_id != version.id:
         raise HTTPException(400, "请选择同一课程版本下已发布的规则")
     settings = get_settings()
+    # The evidence policy travels with the batch snapshot, so a batch can always be read
+    # back against the rules and page permissions it was actually scored with.
     batch = ScoringBatch(course_version_id=version.id, rubric_version_id=rubric.id,
                          course_snapshot={key: getattr(version, key) for key in ("version", "title", "objectives", "indicators", "directions", "prompts", "templates")},
-                         rubric_snapshot={"version": rubric.version, "criteria": rubric.criteria, "weights": rubric.weights},
+                         rubric_snapshot={"version": rubric.version, "criteria": rubric.criteria,
+                                          "weights": rubric.weights, "evidence_plan": plan_snapshot(rubric.criteria)},
                          model_config_snapshot=model_config(db), embedding_model_name=settings.embedding_model_name,
                          embedding_model_version=settings.embedding_model_version,
                          status="created", project_ids=db.scalars(select(Project.id).where(
@@ -226,7 +230,7 @@ def start_batch(batch_id: int, db):
     batch = db.scalar(select(ScoringBatch).where(ScoringBatch.id == batch_id).with_for_update())
     if not batch:
         raise HTTPException(404, "评分批次不存在")
-    if batch.status not in ("created", "failed", "partial"):
+    if batch.status not in ("created", "failed", "partial", "review_required"):
         db.rollback()
         raise HTTPException(409, "批次已入队、运行中或已完成")
     try:
@@ -244,15 +248,29 @@ def start_batch(batch_id: int, db):
     return {"id": batch.id, "status": batch.status}
 
 def review_score(score_id: int, data, db):
+    """Record a teacher decision. Model scores, retrieval gaps and history are kept.
+
+    A teacher may review any criterion in any state, including one awaiting review or
+    one whose material is missing: the teacher is the authority on a criterion the
+    retrieval could not support. The gaps from the original retrieval stay on the record
+    after the review, so the reason it reached the teacher remains auditable.
+    """
     score = found(db, CriterionScore, score_id)
     batch = found(db, ScoringBatch, score.batch_id)
     criterion = next((item for item in batch.rubric_snapshot["criteria"] if item["criterion_id"] == score.criterion_id), None)
     if not criterion or data.score > criterion["max_score"]:
         raise HTTPException(400, "教师分数超出规则满分")
+    from_status = score.status
     db.add(TeacherRevision(score_id=score.id, old_score=score.teacher_score, new_score=data.score,
                            old_comment=score.teacher_comment, new_comment=data.comment, reason=data.reason))
-    score.teacher_score, score.teacher_comment, score.status = data.score, data.comment, "reviewed"
-    audit(db, "review", "criterion_score", score.id, {"reason": data.reason})
+    score.teacher_score, score.teacher_comment = data.score, data.comment
+    # ``evidence_gaps`` is intentionally left untouched: it records why the criterion
+    # reached the teacher, independently of the score the teacher then entered.
+    score.status = "reviewed"
+    audit(db, "review", "criterion_score", score.id,
+          {"reason": data.reason, "from_status": from_status,
+           "came_from_review": bool(score.evidence_gaps)})
     db.commit()
     return {"id": score.id, "model_score": score.model_score, "teacher_score": score.teacher_score,
-            "final_score": score.final_score, "teacher_comment": score.teacher_comment}
+            "final_score": score.final_score, "teacher_comment": score.teacher_comment,
+            "status": score.status, "evidence_gaps": score.evidence_gaps or []}