from concurrent.futures import ThreadPoolExecutor, as_completed from contextlib import contextmanager import fcntl from sqlalchemy import select, update from .config import get_settings from .db import SessionLocal from .llm import ConsistencyResult, FactResult, ModelError, ScoreResult, validate_score from .models import (CriterionScore, KnowledgeDocument, Project, ScoreEvidence, ScoringBatch, SubmissionFile, now) from .parsing import parse_document from .rag import INDEX_VERSION, ingest, retrieve from .storage import secure_path, sha256 from .settings_store import client_for_batch @contextmanager def file_index_lock(file_id: int): root = get_settings().processed_path root.mkdir(parents=True, exist_ok=True) with (root / f"file-{file_id}.lock").open("a+b") as handle: fcntl.flock(handle, fcntl.LOCK_EX) try: yield finally: fcntl.flock(handle, fcntl.LOCK_UN) def parse_submission(db, file: SubmissionFile, *, embedder=None): with file_index_lock(file.id): db.refresh(file) return _parse_submission_locked(db, file, embedder=embedder) def _parse_submission_locked(db, file: SubmissionFile, *, embedder=None): path = secure_path(file.relative_path) if sha256(path) != file.sha256: file.parse_status, file.parse_error = "changed", "源文件已变化，请重新扫描并确认导入" db.commit() raise ValueError(file.parse_error) if file.parse_status == "ready" and file.parsed: document = db.scalar(select(KnowledgeDocument).where(KnowledgeDocument.submission_file_id == file.id)) if not document or document.source_hash != file.sha256 or document.index_version != INDEX_VERSION or document.status != "ready": project = db.get(Project, file.project_id) kwargs = {"embedder": embedder} if embedder else {} try: ingest(db, course_version_id=project.course_version_id, project_id=project.id, submission_file_id=file.id, title=path.name, category=file.category, source_hash=file.sha256, pages=file.parsed["pages"], **kwargs) except Exception as exc: file.parse_status, file.parse_error = "failed", str(exc)[:1000] db.commit() raise return file.parsed settings = get_settings() try: parsed = parse_document(path, file.category, settings.processed_path / f"file-{file.id}") project = db.get(Project, file.project_id) kwargs = {"embedder": embedder} if embedder else {} ingest(db, course_version_id=project.course_version_id, project_id=project.id, submission_file_id=file.id, title=path.name, category=file.category, source_hash=file.sha256, pages=parsed["pages"], **kwargs) file.parsed, file.parse_status, file.parse_error = parsed, "ready", "" db.commit() return parsed except Exception as exc: file.parse_status, file.parse_error = "failed", str(exc)[:1000] db.commit() raise def _project_facts(db, project, course_snapshot, client, *, retriever): 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)) if not snippets: return {} if client is None: raise ModelError("未配置或无法连接评分模型") facts = client.json_call(prompts["facts"], {"evidence": snippets}, FactResult).model_dump() from .models import Student student = db.get(Student, project.student_id) warnings = [] for key, expected in (("student_no", student.student_no), ("name", student.name), ("topic", project.topic)): if facts.get(key) and facts[key] != expected: warnings.append(f"模型抽取的{key}与教师确认信息不一致") consistency = client.json_call(prompts["consistency"], {"facts": facts, "evidence": snippets}, ConsistencyResult) known = {snippet["chunk_id"] for snippet in snippets} for issue in consistency.issues: if not set(issue.chunk_ids) <= known: raise ModelError("一致性检查引用了未提供的证据") project.facts = {**facts, "identity_warnings": warnings, "consistency_issues": [issue.model_dump() for issue in consistency.issues]} db.commit() return project.facts def _score_criterion(db, batch, project, criterion, client, *, retriever): 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"): return score if not score: score = CriterionScore(batch_id=batch.id, project_id=project.id, criterion_id=criterion["criterion_id"]) db.add(score) db.flush() category = "presentation" if criterion["criterion_id"] == "final.presentation" else ( "final_report" if criterion["category"] == "final" else criterion["category"]) files = db.scalars(select(SubmissionFile).where(SubmissionFile.project_id == project.id, 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" 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) if not chunks: score.status, score.error = "unscorable", "无可检索的材料证据" db.commit() return score if client is None: raise ModelError("未配置或无法连接评分模型") page_paths = {page["page_number"]: page.get("rendered_image_path") for page in ready[0].parsed["pages"]} images = [page_paths[page] for page in dict.fromkeys(chunk["page"] for chunk in chunks[:2]) if page_paths.get(page)] 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"}} 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。") for attempt in range(2): try: result = validate_score(client.json_call(prompt, payload, ScoreResult, images=images), criterion, chunks) break except ModelError: 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", "" 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} for evidence in result.evidence: chunk = by_id[evidence.chunk_id] doc = db.get(KnowledgeDocument, chunk["document_id"]) 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=page_paths.get(evidence.page))) db.commit() return score def _run_project(batch_id, project_id, session_factory, client_factory, embedder, retriever): failures = 0 with session_factory() as db: batch = db.get(ScoringBatch, batch_id) project = db.get(Project, project_id) try: client = client_factory(db, batch) except Exception: client = None files = db.scalars(select(SubmissionFile).where(SubmissionFile.project_id == project.id)).all() for file in files: if file.recognition_status == "recognized": try: parse_submission(db, file, embedder=embedder) except Exception: failures += 1 try: _project_facts(db, project, batch.course_snapshot, client, retriever=retriever) except Exception as exc: project.facts = {"error": str(exc)[:500]} db.commit() 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"): failures += 1 except Exception as exc: score = db.scalar(select(CriterionScore).where(CriterionScore.batch_id == batch.id, CriterionScore.project_id == project.id, CriterionScore.criterion_id == criterion["criterion_id"])) 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] db.commit() failures += 1 db.execute(update(ScoringBatch).where(ScoringBatch.id == batch_id).values(progress_done=ScoringBatch.progress_done + 1)) db.commit() return failures def run_batch(batch_id: int, *, session_factory=SessionLocal, client_factory=client_for_batch, embedder=None, retriever=retrieve): with session_factory() as db: batch = db.get(ScoringBatch, batch_id) if not batch: raise ValueError("评分批次不存在") project_ids = batch.project_ids if batch.project_ids is not None else db.scalars( select(Project.id).where(Project.course_version_id == batch.course_version_id).order_by(Project.id)).all() criteria = batch.rubric_snapshot["criteria"] batch.status, batch.progress_total, batch.progress_done, batch.finished_at = "running", len(project_ids) * len(criteria), 0, None db.commit() failures = 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] for future in as_completed(futures): try: failures += future.result() 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() db.commit()