""" 标注编辑器（Label Studio Editor）服务 职责： - 解析 DataMate 标注项目（t_dm_labeling_projects） - 以“文件下载/预览接口”读取文本内容，构造 Label Studio task - 以原始 annotation JSON 形式 upsert 最终标注结果（单人单份） """ from __future__ import annotations import uuid from datetime import datetime from typing import Any, Dict, List, Optional, Tuple from urllib.parse import urlparse import hashlib import json import xml.etree.ElementTree as ET from fastapi import HTTPException from sqlalchemy import case, func, select, or_ from sqlalchemy.ext.asyncio import AsyncSession from app.core.config import settings from app.core.logging import get_logger from app.db.models import AnnotationResult, Dataset, DatasetFiles, LabelingProject, LabelingProjectFile from app.db.models.annotation_management import ( ANNOTATION_STATUS_ANNOTATED, ANNOTATION_STATUS_IN_PROGRESS, ANNOTATION_STATUS_CLIENT_VALUES, ANNOTATION_STATUS_NO_ANNOTATION, ANNOTATION_STATUS_NOT_APPLICABLE, ) from app.module.annotation.config import LabelStudioTagConfig from app.module.annotation.schema.editor import ( EditorProjectInfo, EditorTaskListItem, EditorTaskListResponse, EditorTaskSegmentResponse, EditorTaskResponse, SegmentDetail, SegmentInfo, UpsertAnnotationRequest, UpsertAnnotationResponse, ) from app.module.annotation.service.template import AnnotationTemplateService from app.module.annotation.service.knowledge_sync import KnowledgeSyncService from app.module.annotation.service.annotation_text_splitter import AnnotationTextSplitter from app.module.annotation.service.text_fetcher import fetch_text_content_via_download_api logger = get_logger(__name__) TEXT_DATA_KEY = "text" IMAGE_DATA_KEY = "image" AUDIO_DATA_KEY = "audio" VIDEO_DATA_KEY = "video" DATASET_ID_KEY = "dataset_id" FILE_ID_KEY = "file_id" FILE_NAME_KEY = "file_name" DATASET_ID_CAMEL_KEY = "datasetId" FILE_ID_CAMEL_KEY = "fileId" FILE_NAME_CAMEL_KEY = "fileName" SEGMENT_INDEX_KEY = "segment_index" SEGMENT_INDEX_CAMEL_KEY = "segmentIndex" SEGMENTED_KEY = "segmented" SEGMENTS_KEY = "segments" SEGMENT_TOTAL_KEY = "total_segments" SEGMENT_RESULT_KEY = "result" SEGMENT_CREATED_AT_KEY = "created_at" SEGMENT_UPDATED_AT_KEY = "updated_at" JSONL_EXTENSION = ".jsonl" TEXTUAL_OBJECT_CATEGORIES = {"text", "document"} IMAGE_OBJECT_CATEGORIES = {"image"} MEDIA_OBJECT_CATEGORIES = {"media"} OBJECT_NAME_HEADER_PREFIX = "dm_object_header_" DATASET_TYPE_TEXT = "TEXT" DATASET_TYPE_IMAGE = "IMAGE" DATASET_TYPE_AUDIO = "AUDIO" DATASET_TYPE_VIDEO = "VIDEO" SUPPORTED_EDITOR_DATASET_TYPES = ( DATASET_TYPE_TEXT, DATASET_TYPE_IMAGE, DATASET_TYPE_AUDIO, DATASET_TYPE_VIDEO, ) SEGMENTATION_ENABLED_KEY = "segmentation_enabled" SOURCE_DOCUMENT_EXTENSIONS = (".pdf", ".doc", ".docx") SOURCE_DOCUMENT_TYPES = ("pdf", "doc", "docx") class AnnotationEditorService: """Label Studio Editor 集成服务（TEXT POC 版）""" # 分段阈值：超过此字符数自动分段 SEGMENT_THRESHOLD = 200 def __init__(self, db: AsyncSession): self.db = db self.template_service = AnnotationTemplateService() @staticmethod def _stable_ls_id(seed: str) -> int: """ 生成稳定的 Label Studio 风格整数 ID（JS 安全整数范围内）。 说明： - Label Studio Frontend 的 mobx-state-tree 模型对 task/annotation 的 id 有类型约束（通常为 number）。 - DataMate 使用 UUID 作为 file_id/project_id，因此需映射为整数供编辑器使用。 - 取 sha1 的前 13 个 hex（52bit），落在 JS Number 的安全整数范围。 """ digest = hashlib.sha1(seed.encode("utf-8")).hexdigest() value = int(digest[:13], 16) return value if value > 0 else 1 def _make_ls_task_id(self, project_id: str, file_id: str) -> int: return self._stable_ls_id(f"task:{project_id}:{file_id}") def _make_ls_annotation_id(self, project_id: str, file_id: str) -> int: # 单人单份最终标签：每个 task 只保留一个 annotation，id 直接与 task 绑定即可 return self._stable_ls_id(f"annotation:{project_id}:{file_id}") @staticmethod def _normalize_dataset_type(dataset_type: Optional[str]) -> str: return (dataset_type or "").upper() @staticmethod def _resolve_public_api_prefix() -> str: base = (settings.datamate_backend_base_url or "").strip() if not base: return "/api" parsed = urlparse(base) if parsed.scheme and parsed.netloc: prefix = parsed.path else: prefix = base prefix = prefix.rstrip("/") if not prefix: return "/api" if not prefix.startswith("/"): prefix = "/" + prefix return prefix @classmethod def _build_file_preview_url(cls, dataset_id: str, file_id: str) -> str: prefix = cls._resolve_public_api_prefix() return f"{prefix}/data-management/datasets/{dataset_id}/files/{file_id}/preview" async def _get_project_or_404(self, project_id: str) -> LabelingProject: result = await self.db.execute( select(LabelingProject).where( LabelingProject.id == project_id, LabelingProject.deleted_at.is_(None), ) ) project = result.scalar_one_or_none() if not project: raise HTTPException(status_code=404, detail=f"标注项目不存在: {project_id}") return project async def _get_dataset_type(self, dataset_id: str) -> Optional[str]: result = await self.db.execute( select(Dataset.dataset_type).where(Dataset.id == dataset_id) ) return result.scalar_one_or_none() async def _get_label_config(self, template_id: Optional[str]) -> Optional[str]: if not template_id: return None template = await self.template_service.get_template(self.db, template_id) return getattr(template, "label_config", None) if template else None async def _resolve_project_label_config(self, project: LabelingProject) -> Optional[str]: label_config = None if project.configuration and isinstance(project.configuration, dict): label_config = project.configuration.get("label_config") if not label_config: label_config = await self._get_label_config(project.template_id) return label_config @staticmethod def _resolve_segmentation_enabled(project: LabelingProject) -> bool: config = project.configuration if not isinstance(config, dict): return True value = config.get(SEGMENTATION_ENABLED_KEY) if isinstance(value, bool): return value if value is None: return True return bool(value) @classmethod def _resolve_primary_text_key(cls, label_config: Optional[str]) -> Optional[str]: if not label_config: return None keys = cls._extract_textual_value_keys(label_config) if not keys: return None if TEXT_DATA_KEY in keys: return TEXT_DATA_KEY return keys[0] @classmethod def _resolve_media_value_keys( cls, label_config: Optional[str], default_key: str, categories: Optional[set[str]] = None, ) -> List[str]: if not label_config: return [default_key] target_categories = categories or set() keys = cls._extract_object_value_keys_by_category(label_config, target_categories) if not keys: return [default_key] return keys @staticmethod def _try_parse_json_payload(text_content: str) -> Optional[Dict[str, Any]]: if not text_content: return None stripped = text_content.strip() if not stripped: return None if stripped[0] not in ("{", "["): return None try: parsed = json.loads(stripped) except Exception: return None return parsed if isinstance(parsed, dict) else None @classmethod def _parse_jsonl_records(cls, text_content: str) -> List[Tuple[Optional[Dict[str, Any]], str]]: lines = [line for line in text_content.splitlines() if line.strip()] records: List[Tuple[Optional[Dict[str, Any]], str]] = [] for line in lines: payload = cls._try_parse_json_payload(line) records.append((payload, line)) return records @staticmethod def _is_textual_object_tag(object_tag: str) -> bool: config = LabelStudioTagConfig.get_object_config(object_tag) or {} category = config.get("category") return category in TEXTUAL_OBJECT_CATEGORIES @classmethod def _extract_object_value_keys_by_category( cls, label_config: str, categories: set[str], ) -> List[str]: try: root = ET.fromstring(label_config) except Exception as exc: logger.warning("解析 label_config 失败，已跳过占位填充：%s", exc) return [] object_types = LabelStudioTagConfig.get_object_types() seen: Dict[str, None] = {} for element in root.iter(): if element.tag not in object_types: continue config = LabelStudioTagConfig.get_object_config(element.tag) or {} category = config.get("category") if categories and category not in categories: continue value = element.attrib.get("value", "") if not value.startswith("$"): continue key = value[1:].strip() if not key: continue seen[key] = None return list(seen.keys()) @classmethod def _extract_textual_value_keys(cls, label_config: str) -> List[str]: return cls._extract_object_value_keys_by_category(label_config, TEXTUAL_OBJECT_CATEGORIES) @staticmethod def _needs_placeholder(value: Any) -> bool: if value is None: return True if isinstance(value, str) and not value.strip(): return True return False def _apply_text_placeholders(self, data: Dict[str, Any], label_config: Optional[str]) -> None: if not label_config: return for key in self._extract_textual_value_keys(label_config): if self._needs_placeholder(data.get(key)): data[key] = key @staticmethod def _header_already_present(header: ET.Element, name: str) -> bool: value = header.attrib.get("value", "") if value == name: return True header_text = (header.text or "").strip() return header_text == name def _decorate_label_config_for_editor(self, label_config: str) -> str: try: root = ET.fromstring(label_config) except Exception as exc: logger.warning("解析 label_config 失败，已跳过 name 展示增强：%s", exc) return label_config object_types = LabelStudioTagConfig.get_object_types() used_names = set() for element in root.iter(): name = element.attrib.get("name") if name: used_names.add(name) def allocate_header_name(base: str) -> str: candidate = f"{OBJECT_NAME_HEADER_PREFIX}{base}" if candidate not in used_names: used_names.add(candidate) return candidate idx = 1 while f"{candidate}_{idx}" in used_names: idx += 1 resolved = f"{candidate}_{idx}" used_names.add(resolved) return resolved for parent in root.iter(): children = list(parent) i = 0 while i < len(children): child = children[i] if child.tag not in object_types: i += 1 continue if not self._is_textual_object_tag(child.tag): i += 1 continue obj_name = child.attrib.get("name") if not obj_name: i += 1 continue if i > 0: prev = children[i - 1] if prev.tag == "Header" and self._header_already_present(prev, obj_name): i += 1 continue header = ET.Element("Header") header.set("name", allocate_header_name(obj_name)) header.set("value", obj_name) parent.insert(i, header) children.insert(i, header) i += 2 # continue outer loop return ET.tostring(root, encoding="unicode") @staticmethod def _extract_segment_annotations(payload: Optional[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]: if not payload or not isinstance(payload, dict): return {} segments = payload.get(SEGMENTS_KEY) if isinstance(segments, dict): normalized: Dict[str, Dict[str, Any]] = {} for key, value in segments.items(): if isinstance(value, dict): normalized[str(key)] = value return normalized if isinstance(segments, list): normalized: Dict[str, Dict[str, Any]] = {} for idx, value in enumerate(segments): if not isinstance(value, dict): continue key = ( value.get(SEGMENT_INDEX_CAMEL_KEY) or value.get(SEGMENT_INDEX_KEY) or value.get("segment") or value.get("idx") ) if key is None: key = idx normalized[str(key)] = value return normalized return {} @staticmethod def _is_segmented_annotation(payload: Optional[Dict[str, Any]]) -> bool: if not payload or not isinstance(payload, dict): return False if payload.get(SEGMENTED_KEY): return True segments = payload.get(SEGMENTS_KEY) if isinstance(segments, dict): return len(segments) > 0 if isinstance(segments, list): return len(segments) > 0 return False @staticmethod def _has_annotation_result(payload: Optional[Dict[str, Any]]) -> bool: if not payload or not isinstance(payload, dict): return False if AnnotationEditorService._is_segmented_annotation(payload): segments = AnnotationEditorService._extract_segment_annotations(payload) if not segments: return False for segment in segments.values(): if not isinstance(segment, dict): continue result = segment.get(SEGMENT_RESULT_KEY) if isinstance(result, list) and len(result) > 0: return True return False result = payload.get(SEGMENT_RESULT_KEY) return isinstance(result, list) and len(result) > 0 @staticmethod def _resolve_segment_total(payload: Optional[Dict[str, Any]]) -> Optional[int]: if not payload or not isinstance(payload, dict): return None value = payload.get(SEGMENT_TOTAL_KEY) if isinstance(value, int): return value if value > 0 else None if isinstance(value, float) and value.is_integer(): return int(value) if value > 0 else None if isinstance(value, str) and value.isdigit(): parsed = int(value) return parsed if parsed > 0 else None return None async def _compute_segment_total( self, project: LabelingProject, file_record: DatasetFiles, file_id: str, ) -> Optional[int]: dataset_type = self._normalize_dataset_type(await self._get_dataset_type(project.dataset_id)) if dataset_type != DATASET_TYPE_TEXT: return None if not self._resolve_segmentation_enabled(project): return None text_content = await self._fetch_text_content_via_download_api(project.dataset_id, file_id) if not isinstance(text_content, str): return None label_config = await self._resolve_project_label_config(project) primary_text_key = self._resolve_primary_text_key(label_config) file_name = str(getattr(file_record, "file_name", "")).lower() records: List[Tuple[Optional[Dict[str, Any]], str]] = [] if file_name.endswith(JSONL_EXTENSION): records = self._parse_jsonl_records(text_content) else: parsed_payload = self._try_parse_json_payload(text_content) if parsed_payload: records = [(parsed_payload, text_content)] if not records: records = [(None, text_content)] record_texts = [ self._resolve_primary_text_value(payload, raw_text, primary_text_key) for payload, raw_text in records ] if not record_texts: record_texts = [text_content] needs_segmentation = len(records) > 1 or any( len(text or "") > self.SEGMENT_THRESHOLD for text in record_texts ) if not needs_segmentation: return None splitter = AnnotationTextSplitter(max_chars=self.SEGMENT_THRESHOLD) total_segments = 0 for record_text in record_texts: normalized_text = record_text or "" if len(normalized_text) > self.SEGMENT_THRESHOLD: raw_segments = splitter.split(normalized_text) total_segments += len(raw_segments) if raw_segments else 1 else: total_segments += 1 return total_segments if total_segments > 0 else 1 @classmethod def _build_source_document_filter(cls): file_type_lower = func.lower(DatasetFiles.file_type) file_name_lower = func.lower(DatasetFiles.file_name) type_condition = file_type_lower.in_(SOURCE_DOCUMENT_TYPES) name_conditions = [file_name_lower.like(f"%{ext}") for ext in SOURCE_DOCUMENT_EXTENSIONS] return or_(type_condition, *name_conditions) def _build_task_data( self, display_text: str, parsed_payload: Optional[Dict[str, Any]], label_config: Optional[str], file_record: DatasetFiles, dataset_id: str, file_id: str, primary_text_key: Optional[str], ) -> Dict[str, Any]: data: Dict[str, Any] = dict(parsed_payload or {}) text_key = primary_text_key or TEXT_DATA_KEY data[text_key] = display_text file_name = str(getattr(file_record, "file_name", "")) data[FILE_ID_KEY] = file_id data[FILE_ID_CAMEL_KEY] = file_id data[DATASET_ID_KEY] = dataset_id data[DATASET_ID_CAMEL_KEY] = dataset_id data[FILE_NAME_KEY] = file_name data[FILE_NAME_CAMEL_KEY] = file_name self._apply_text_placeholders(data, label_config) return data @classmethod def _resolve_primary_text_value( cls, parsed_payload: Optional[Dict[str, Any]], raw_text: str, primary_text_key: Optional[str], ) -> str: if parsed_payload and primary_text_key: value = parsed_payload.get(primary_text_key) if isinstance(value, str) and value.strip(): return value if parsed_payload and not primary_text_key: value = parsed_payload.get(TEXT_DATA_KEY) if isinstance(value, str) and value.strip(): return value return raw_text def _build_segment_contexts( self, records: List[Tuple[Optional[Dict[str, Any]], str]], record_texts: List[str], segment_annotation_keys: set[str], ) -> Tuple[List[SegmentInfo], List[Tuple[Optional[Dict[str, Any]], str, str, int, int]]]: splitter = AnnotationTextSplitter(max_chars=self.SEGMENT_THRESHOLD) segments: List[SegmentInfo] = [] segment_contexts: List[Tuple[Optional[Dict[str, Any]], str, str, int, int]] = [] segment_cursor = 0 for record_index, ((payload, raw_text), record_text) in enumerate(zip(records, record_texts)): normalized_text = record_text or "" if len(normalized_text) > self.SEGMENT_THRESHOLD: raw_segments = splitter.split(normalized_text) for chunk_index, seg in enumerate(raw_segments): segments.append( SegmentInfo( idx=segment_cursor, hasAnnotation=str(segment_cursor) in segment_annotation_keys, lineIndex=record_index, chunkIndex=chunk_index, ) ) segment_contexts.append((payload, raw_text, seg["text"], record_index, chunk_index)) segment_cursor += 1 else: segments.append( SegmentInfo( idx=segment_cursor, hasAnnotation=str(segment_cursor) in segment_annotation_keys, lineIndex=record_index, chunkIndex=0, ) ) segment_contexts.append((payload, raw_text, normalized_text, record_index, 0)) segment_cursor += 1 if not segments: segments = [SegmentInfo(idx=0, hasAnnotation=False, lineIndex=0, chunkIndex=0)] segment_contexts = [(None, "", "", 0, 0)] return segments, segment_contexts async def get_project_info(self, project_id: str) -> EditorProjectInfo: project = await self._get_project_or_404(project_id) dataset_type = self._normalize_dataset_type(await self._get_dataset_type(project.dataset_id)) supported = dataset_type in SUPPORTED_EDITOR_DATASET_TYPES unsupported_reason = None if not supported: supported_hint = "/".join(SUPPORTED_EDITOR_DATASET_TYPES) unsupported_reason = f"当前仅支持 {supported_hint}，项目数据类型为: {dataset_type or 'UNKNOWN'}" # 优先使用项目配置中的label_config（用户编辑版本），其次使用模板默认配置 label_config = await self._resolve_project_label_config(project) return EditorProjectInfo( projectId=project.id, datasetId=project.dataset_id, datasetType=dataset_type or None, templateId=project.template_id, labelConfig=label_config, supported=supported, unsupportedReason=unsupported_reason, ) async def list_tasks( self, project_id: str, page: int = 0, size: int = 50, exclude_source_documents: Optional[bool] = None, ) -> EditorTaskListResponse: project = await self._get_project_or_404(project_id) base_conditions = [ LabelingProjectFile.project_id == project_id, DatasetFiles.dataset_id == project.dataset_id, ] count_result = await self.db.execute( select(func.count()) .select_from(LabelingProjectFile) .join(DatasetFiles, LabelingProjectFile.file_id == DatasetFiles.id) .where(*base_conditions) ) total = int(count_result.scalar() or 0) annotated_sort_key = case( (AnnotationResult.id.isnot(None), 1), else_=0, ) files_result = await self.db.execute( select( DatasetFiles, AnnotationResult.id, AnnotationResult.updated_at, AnnotationResult.annotation_status, ) .join(LabelingProjectFile, LabelingProjectFile.file_id == DatasetFiles.id) .outerjoin( AnnotationResult, (AnnotationResult.file_id == DatasetFiles.id) & (AnnotationResult.project_id == project_id), ) .where(*base_conditions) .order_by(annotated_sort_key.asc(), DatasetFiles.created_at.desc()) .offset(page * size) .limit(size) ) rows = files_result.all() items: List[EditorTaskListItem] = [] for file_record, annotation_id, annotation_updated_at, annotation_status in rows: fid = str(file_record.id) # type: ignore[arg-type] items.append( EditorTaskListItem( fileId=fid, fileName=str(getattr(file_record, "file_name", "")), fileType=getattr(file_record, "file_type", None), hasAnnotation=annotation_id is not None, annotationUpdatedAt=annotation_updated_at, annotationStatus=annotation_status, ) ) total_pages = (total + size - 1) // size if size > 0 else 0 return EditorTaskListResponse( content=items, totalElements=total, totalPages=total_pages, page=page, size=size, ) async def _fetch_text_content_via_download_api(self, dataset_id: str, file_id: str) -> str: return await fetch_text_content_via_download_api(dataset_id, file_id) async def get_task( self, project_id: str, file_id: str, segment_index: Optional[int] = None, ) -> EditorTaskResponse: project = await self._get_project_or_404(project_id) dataset_type = self._normalize_dataset_type(await self._get_dataset_type(project.dataset_id)) if dataset_type not in SUPPORTED_EDITOR_DATASET_TYPES: raise HTTPException( status_code=400, detail="当前仅支持 TEXT/IMAGE/AUDIO/VIDEO 项目的内嵌编辑器", ) file_result = await self.db.execute( select(DatasetFiles).where( DatasetFiles.id == file_id, DatasetFiles.dataset_id == project.dataset_id, ) ) file_record = file_result.scalar_one_or_none() if not file_record: raise HTTPException(status_code=404, detail=f"文件不存在或不属于该项目: {file_id}") if dataset_type == DATASET_TYPE_IMAGE: return await self._build_image_task(project, file_record, file_id) if dataset_type == DATASET_TYPE_AUDIO: return await self._build_audio_task(project, file_record, file_id) if dataset_type == DATASET_TYPE_VIDEO: return await self._build_video_task(project, file_record, file_id) return await self._build_text_task(project, file_record, file_id, segment_index) async def get_task_segment( self, project_id: str, file_id: str, segment_index: int, ) -> EditorTaskSegmentResponse: project = await self._get_project_or_404(project_id) dataset_type = self._normalize_dataset_type(await self._get_dataset_type(project.dataset_id)) if dataset_type != DATASET_TYPE_TEXT: raise HTTPException( status_code=400, detail="当前仅支持 TEXT 项目的段落内容", ) file_result = await self.db.execute( select(DatasetFiles).where( DatasetFiles.id == file_id, DatasetFiles.dataset_id == project.dataset_id, ) ) file_record = file_result.scalar_one_or_none() if not file_record: raise HTTPException(status_code=404, detail=f"文件不存在或不属于该项目: {file_id}") if not self._resolve_segmentation_enabled(project): return EditorTaskSegmentResponse( segmented=False, segment=None, totalSegments=0, currentSegmentIndex=0, ) text_content = await self._fetch_text_content_via_download_api(project.dataset_id, file_id) assert isinstance(text_content, str) label_config = await self._resolve_project_label_config(project) primary_text_key = self._resolve_primary_text_key(label_config) file_name = str(getattr(file_record, "file_name", "")).lower() records: List[Tuple[Optional[Dict[str, Any]], str]] = [] if file_name.endswith(JSONL_EXTENSION): records = self._parse_jsonl_records(text_content) else: parsed_payload = self._try_parse_json_payload(text_content) if parsed_payload: records = [(parsed_payload, text_content)] if not records: records = [(None, text_content)] record_texts = [ self._resolve_primary_text_value(payload, raw_text, primary_text_key) for payload, raw_text in records ] if not record_texts: record_texts = [text_content] needs_segmentation = len(records) > 1 or any( len(text or "") > self.SEGMENT_THRESHOLD for text in record_texts ) if not needs_segmentation: return EditorTaskSegmentResponse( segmented=False, segment=None, totalSegments=0, currentSegmentIndex=0, ) ann_result = await self.db.execute( select(AnnotationResult).where( AnnotationResult.project_id == project.id, AnnotationResult.file_id == file_id, ) ) ann = ann_result.scalar_one_or_none() segment_annotations: Dict[str, Dict[str, Any]] = {} if ann and isinstance(ann.annotation, dict): segment_annotations = self._extract_segment_annotations(ann.annotation) segment_annotation_keys = set(segment_annotations.keys()) segments, segment_contexts = self._build_segment_contexts( records, record_texts, segment_annotation_keys, ) total_segments = len(segment_contexts) if total_segments == 0: return EditorTaskSegmentResponse( segmented=False, segment=None, totalSegments=0, currentSegmentIndex=0, ) if segment_index < 0 or segment_index >= total_segments: raise HTTPException( status_code=400, detail=f"segmentIndex 超出范围: {segment_index}", ) segment_info = segments[segment_index] _, _, segment_text, line_index, chunk_index = segment_contexts[segment_index] segment_detail = SegmentDetail( idx=segment_info.idx, text=segment_text, hasAnnotation=segment_info.has_annotation, lineIndex=line_index, chunkIndex=chunk_index, ) return EditorTaskSegmentResponse( segmented=True, segment=segment_detail, totalSegments=total_segments, currentSegmentIndex=segment_index, ) async def _build_text_task( self, project: LabelingProject, file_record: DatasetFiles, file_id: str, segment_index: Optional[int], ) -> EditorTaskResponse: text_content = await self._fetch_text_content_via_download_api(project.dataset_id, file_id) assert isinstance(text_content, str) label_config = await self._resolve_project_label_config(project) primary_text_key = self._resolve_primary_text_key(label_config) file_name = str(getattr(file_record, "file_name", "")).lower() records: List[Tuple[Optional[Dict[str, Any]], str]] = [] if file_name.endswith(JSONL_EXTENSION): records = self._parse_jsonl_records(text_content) else: parsed_payload = self._try_parse_json_payload(text_content) if parsed_payload: records = [(parsed_payload, text_content)] if not records: records = [(None, text_content)] record_texts = [ self._resolve_primary_text_value(payload, raw_text, primary_text_key) for payload, raw_text in records ] if not record_texts: record_texts = [text_content] # 获取现有标注 ann_result = await self.db.execute( select(AnnotationResult).where( AnnotationResult.project_id == project.id, AnnotationResult.file_id == file_id, ) ) ann = ann_result.scalar_one_or_none() ls_task_id = self._make_ls_task_id(project.id, file_id) segment_annotations: Dict[str, Dict[str, Any]] = {} has_segmented_annotation = False if ann and isinstance(ann.annotation, dict): segment_annotations = self._extract_segment_annotations(ann.annotation) has_segmented_annotation = self._is_segmented_annotation(ann.annotation) segment_annotation_keys = set(segment_annotations.keys()) # 判断是否需要分段（JSONL 多行或主文本超过阈值） segmentation_enabled = self._resolve_segmentation_enabled(project) if not segmentation_enabled: segment_index = None needs_segmentation = segmentation_enabled and ( len(records) > 1 or any(len(text or "") > self.SEGMENT_THRESHOLD for text in record_texts) ) segments: List[SegmentInfo] = [] segment_contexts: List[Tuple[Optional[Dict[str, Any]], str, str, int, int]] = [] current_segment_index = 0 display_text = record_texts[0] if record_texts else text_content selected_payload = records[0][0] if records else None if not segmentation_enabled and len(records) > 1: selected_payload = None display_text = "\n".join(record_texts) if record_texts else text_content if needs_segmentation: _, segment_contexts = self._build_segment_contexts( records, record_texts, segment_annotation_keys, ) current_segment_index = segment_index if segment_index is not None else 0 if current_segment_index < 0 or current_segment_index >= len(segment_contexts): current_segment_index = 0 selected_payload, _, display_text, _, _ = segment_contexts[current_segment_index] # 构造 task 对象 task_data = self._build_task_data( display_text=display_text, parsed_payload=selected_payload, label_config=label_config, file_record=file_record, dataset_id=project.dataset_id, file_id=file_id, primary_text_key=primary_text_key, ) if needs_segmentation: task_data[SEGMENT_INDEX_KEY] = current_segment_index task_data[SEGMENT_INDEX_CAMEL_KEY] = current_segment_index task: Dict[str, Any] = { "id": ls_task_id, "data": task_data, "annotations": [], } annotation_updated_at = None if ann: annotation_updated_at = ann.updated_at if needs_segmentation and has_segmented_annotation: # 分段模式：获取当前段落的标注 seg_ann = segment_annotations.get(str(current_segment_index), {}) stored = { "id": self._make_ls_annotation_id(project.id, file_id) + current_segment_index, "task": ls_task_id, "result": seg_ann.get(SEGMENT_RESULT_KEY, []), "created_at": seg_ann.get(SEGMENT_CREATED_AT_KEY, datetime.utcnow().isoformat() + "Z"), "updated_at": seg_ann.get(SEGMENT_UPDATED_AT_KEY, datetime.utcnow().isoformat() + "Z"), } task["annotations"] = [stored] elif not needs_segmentation and not has_segmented_annotation: # 非分段模式：直接返回存储的 annotation 原始对象 stored = dict(ann.annotation or {}) stored["task"] = ls_task_id if not isinstance(stored.get("id"), int): stored["id"] = self._make_ls_annotation_id(project.id, file_id) task["annotations"] = [stored] else: # 首次从非分段切换到分段：提供空标注 empty_ann_id = self._make_ls_annotation_id(project.id, file_id) + current_segment_index task["annotations"] = [ { "id": empty_ann_id, "task": ls_task_id, "result": [], "created_at": datetime.utcnow().isoformat() + "Z", "updated_at": datetime.utcnow().isoformat() + "Z", } ] else: # 提供一个空 annotation，避免前端在没有选中 annotation 时无法产生 result empty_ann_id = self._make_ls_annotation_id(project.id, file_id) if needs_segmentation: empty_ann_id += current_segment_index task["annotations"] = [ { "id": empty_ann_id, "task": ls_task_id, "result": [], "created_at": datetime.utcnow().isoformat() + "Z", "updated_at": datetime.utcnow().isoformat() + "Z", } ] return EditorTaskResponse( task=task, annotationUpdatedAt=annotation_updated_at, segmented=needs_segmentation, totalSegments=len(segment_contexts) if needs_segmentation else 1, currentSegmentIndex=current_segment_index, ) async def _build_media_task( self, project: LabelingProject, file_record: DatasetFiles, file_id: str, default_key: str, categories: set[str], ) -> EditorTaskResponse: label_config = await self._resolve_project_label_config(project) media_keys = self._resolve_media_value_keys(label_config, default_key, categories) preview_url = self._build_file_preview_url(project.dataset_id, file_id) file_name = str(getattr(file_record, "file_name", "")) task_data: Dict[str, Any] = { FILE_ID_KEY: file_id, FILE_ID_CAMEL_KEY: file_id, DATASET_ID_KEY: project.dataset_id, DATASET_ID_CAMEL_KEY: project.dataset_id, FILE_NAME_KEY: file_name, FILE_NAME_CAMEL_KEY: file_name, } for key in media_keys: task_data[key] = preview_url self._apply_text_placeholders(task_data, label_config) # 获取现有标注 ann_result = await self.db.execute( select(AnnotationResult).where( AnnotationResult.project_id == project.id, AnnotationResult.file_id == file_id, ) ) ann = ann_result.scalar_one_or_none() ls_task_id = self._make_ls_task_id(project.id, file_id) task: Dict[str, Any] = { "id": ls_task_id, "data": task_data, "annotations": [], } annotation_updated_at = None if ann and not (ann.annotation or {}).get("segmented"): annotation_updated_at = ann.updated_at stored = dict(ann.annotation or {}) stored["task"] = ls_task_id if not isinstance(stored.get("id"), int): stored["id"] = self._make_ls_annotation_id(project.id, file_id) task["annotations"] = [stored] else: empty_ann_id = self._make_ls_annotation_id(project.id, file_id) task["annotations"] = [ { "id": empty_ann_id, "task": ls_task_id, "result": [], "created_at": datetime.utcnow().isoformat() + "Z", "updated_at": datetime.utcnow().isoformat() + "Z", } ] return EditorTaskResponse( task=task, annotationUpdatedAt=annotation_updated_at, segmented=False, segments=None, totalSegments=1, currentSegmentIndex=0, ) async def _build_image_task( self, project: LabelingProject, file_record: DatasetFiles, file_id: str, ) -> EditorTaskResponse: return await self._build_media_task( project=project, file_record=file_record, file_id=file_id, default_key=IMAGE_DATA_KEY, categories=IMAGE_OBJECT_CATEGORIES, ) async def _build_audio_task( self, project: LabelingProject, file_record: DatasetFiles, file_id: str, ) -> EditorTaskResponse: return await self._build_media_task( project=project, file_record=file_record, file_id=file_id, default_key=AUDIO_DATA_KEY, categories=MEDIA_OBJECT_CATEGORIES, ) async def _build_video_task( self, project: LabelingProject, file_record: DatasetFiles, file_id: str, ) -> EditorTaskResponse: return await self._build_media_task( project=project, file_record=file_record, file_id=file_id, default_key=VIDEO_DATA_KEY, categories=MEDIA_OBJECT_CATEGORIES, ) async def upsert_annotation(self, project_id: str, file_id: str, request: UpsertAnnotationRequest) -> UpsertAnnotationResponse: project = await self._get_project_or_404(project_id) # 校验文件归属 file_result = await self.db.execute( select(DatasetFiles) .join(LabelingProjectFile, LabelingProjectFile.file_id == DatasetFiles.id) .where( LabelingProjectFile.project_id == project.id, DatasetFiles.id == file_id, DatasetFiles.dataset_id == project.dataset_id, ) ) file_record = file_result.scalar_one_or_none() if not file_record: raise HTTPException(status_code=404, detail=f"文件不存在或不属于该项目: {file_id}") annotation_payload = dict(request.annotation or {}) result = annotation_payload.get("result") if result is None: annotation_payload["result"] = [] elif not isinstance(result, list): raise HTTPException(status_code=400, detail="annotation.result 必须为数组") ls_task_id = self._make_ls_task_id(project_id, file_id) segment_total_hint = None if request.segment_index is not None: segment_total_hint = self._resolve_segment_total(annotation_payload) if segment_total_hint is None: segment_total_hint = await self._compute_segment_total(project, file_record, file_id) existing_result = await self.db.execute( select(AnnotationResult) .where( AnnotationResult.project_id == project_id, AnnotationResult.file_id == file_id, ) .with_for_update() ) existing = existing_result.scalar_one_or_none() now = datetime.utcnow() # 判断是否为分段保存模式 if request.segment_index is not None: # 分段模式：合并段落标注到整体结构 final_payload = self._merge_segment_annotation( existing.annotation if existing else None, request.segment_index, annotation_payload, ) segment_entries = self._extract_segment_annotations(final_payload) if str(request.segment_index) not in segment_entries: logger.warning( "分段标注合并异常：未找到当前段落 key，project_id=%s file_id=%s segment_index=%s", project_id, file_id, request.segment_index, ) else: # 非分段模式：直接使用传入的 annotation annotation_payload["task"] = ls_task_id if not isinstance(annotation_payload.get("id"), int): annotation_payload["id"] = self._make_ls_annotation_id(project_id, file_id) final_payload = annotation_payload requested_status = request.annotation_status if requested_status is not None and requested_status not in ANNOTATION_STATUS_CLIENT_VALUES: raise HTTPException(status_code=400, detail="annotationStatus 不合法") segment_total = None segment_done = None if request.segment_index is not None: segment_total = self._resolve_segment_total(final_payload) if segment_total is None: segment_total = segment_total_hint if segment_total and segment_total > 0: final_payload[SEGMENT_TOTAL_KEY] = segment_total segment_done = len(self._extract_segment_annotations(final_payload)) if ( segment_total is not None and segment_done is not None and segment_done < segment_total ): final_status = ANNOTATION_STATUS_IN_PROGRESS else: has_result = self._has_annotation_result(final_payload) if has_result: final_status = ANNOTATION_STATUS_ANNOTATED else: if requested_status == ANNOTATION_STATUS_NO_ANNOTATION: final_status = ANNOTATION_STATUS_NO_ANNOTATION elif requested_status == ANNOTATION_STATUS_NOT_APPLICABLE: final_status = ANNOTATION_STATUS_NOT_APPLICABLE else: raise HTTPException(status_code=400, detail="未发现标注内容，请确认无标注/不适用后再保存") if request.segment_index is not None: segment_entries = self._extract_segment_annotations(final_payload) logger.info( "分段标注保存：project_id=%s file_id=%s segment_index=%s segments=%s total=%s status=%s", project_id, file_id, request.segment_index, len(segment_entries), segment_total, final_status, ) if existing: if request.expected_updated_at and existing.updated_at: if existing.updated_at != request.expected_updated_at.replace(tzinfo=None): raise HTTPException(status_code=409, detail="标注已被更新，请刷新后重试") existing.annotation = final_payload # type: ignore[assignment] existing.annotation_status = final_status # type: ignore[assignment] existing.updated_at = now # type: ignore[assignment] await self.db.commit() await self.db.refresh(existing) response = UpsertAnnotationResponse( annotationId=existing.id, updatedAt=existing.updated_at or now, ) await self._sync_annotation_to_knowledge(project, file_record, final_payload, existing.updated_at) return response new_id = str(uuid.uuid4()) record = AnnotationResult( id=new_id, project_id=project_id, file_id=file_id, annotation=final_payload, annotation_status=final_status, created_at=now, updated_at=now, ) self.db.add(record) await self.db.commit() await self.db.refresh(record) response = UpsertAnnotationResponse( annotationId=record.id, updatedAt=record.updated_at or now, ) await self._sync_annotation_to_knowledge(project, file_record, final_payload, record.updated_at) return response def _merge_segment_annotation( self, existing: Optional[Dict[str, Any]], segment_index: int, new_annotation: Dict[str, Any], ) -> Dict[str, Any]: """ 合并段落标注到整体结构 Args: existing: 现有的 annotation 数据 segment_index: 段落索引 new_annotation: 新的段落标注数据 Returns: 合并后的 annotation 结构 """ if not existing or not existing.get(SEGMENTED_KEY): # 初始化分段结构 base: Dict[str, Any] = { SEGMENTED_KEY: True, "version": 1, SEGMENTS_KEY: {}, } else: base = dict(existing) if not base.get(SEGMENTED_KEY): base[SEGMENTED_KEY] = True segments = base.get(SEGMENTS_KEY) if isinstance(segments, dict): # 拷贝一份，避免原地修改导致 SQLAlchemy 变更检测失效 segments = dict(segments) base[SEGMENTS_KEY] = segments elif isinstance(segments, list): # 兼容旧的 list 结构，归一化为 dict 结构 segments = self._extract_segment_annotations(base) base[SEGMENTS_KEY] = segments else: segments = {} base[SEGMENTS_KEY] = segments # 更新指定段落的标注 segments[str(segment_index)] = { SEGMENT_RESULT_KEY: new_annotation.get(SEGMENT_RESULT_KEY, []), SEGMENT_CREATED_AT_KEY: new_annotation.get(SEGMENT_CREATED_AT_KEY, datetime.utcnow().isoformat() + "Z"), SEGMENT_UPDATED_AT_KEY: datetime.utcnow().isoformat() + "Z", } return base async def _sync_annotation_to_knowledge( self, project: LabelingProject, file_record: DatasetFiles, annotation: Dict[str, Any], annotation_updated_at: Optional[datetime], ) -> None: """同步标注结果到知识管理（失败不影响标注保存）""" try: await KnowledgeSyncService(self.db).sync_annotation_to_knowledge( project=project, file_record=file_record, annotation=annotation, annotation_updated_at=annotation_updated_at, ) except Exception as exc: logger.warning("标注同步知识管理失败：%s", exc) async def precompute_segmentation_for_project( self, project_id: str, max_retries: int = 3 ) -> Dict[str, Any]: """ 为指定项目的所有文本文件预计算切片结构并持久化到数据库 Args: project_id: 标注项目ID max_retries: 失败重试次数 Returns: 统计信息：{total_files, succeeded, failed} """ project = await self._get_project_or_404(project_id) dataset_type = self._normalize_dataset_type(await self._get_dataset_type(project.dataset_id)) # 只处理文本数据集 if dataset_type != DATASET_TYPE_TEXT: logger.info(f"项目 {project_id} 不是文本数据集，跳过切片预生成") return {"total_files": 0, "succeeded": 0, "failed": 0} # 检查是否启用分段 if not self._resolve_segmentation_enabled(project): logger.info(f"项目 {project_id} 未启用分段，跳过切片预生成") return {"total_files": 0, "succeeded": 0, "failed": 0} # 获取项目的所有文本文件（排除源文档） files_result = await self.db.execute( select(DatasetFiles) .join(LabelingProjectFile, LabelingProjectFile.file_id == DatasetFiles.id) .where( LabelingProjectFile.project_id == project_id, DatasetFiles.dataset_id == project.dataset_id, ) ) file_records = files_result.scalars().all() if not file_records: logger.info(f"项目 {project_id} 没有文件，跳过切片预生成") return {"total_files": 0, "succeeded": 0, "failed": 0} # 过滤源文档文件 valid_files = [] for file_record in file_records: file_type = str(getattr(file_record, "file_type", "") or "").lower() file_name = str(getattr(file_record, "file_name", "")).lower() is_source_document = ( file_type in SOURCE_DOCUMENT_TYPES or any(file_name.endswith(ext) for ext in SOURCE_DOCUMENT_EXTENSIONS) ) if not is_source_document: valid_files.append(file_record) total_files = len(valid_files) succeeded = 0 failed = 0 label_config = await self._resolve_project_label_config(project) primary_text_key = self._resolve_primary_text_key(label_config) for file_record in valid_files: file_id = str(file_record.id) # type: ignore file_name = str(getattr(file_record, "file_name", "")) for retry in range(max_retries): try: # 读取文本内容 text_content = await self._fetch_text_content_via_download_api(project.dataset_id, file_id) if not isinstance(text_content, str): logger.warning(f"文件 {file_id} 内容不是字符串，跳过切片") failed += 1 break # 解析文本记录 records: List[Tuple[Optional[Dict[str, Any]], str]] = [] if file_name.lower().endswith(JSONL_EXTENSION): records = self._parse_jsonl_records(text_content) else: parsed_payload = self._try_parse_json_payload(text_content) if parsed_payload: records = [(parsed_payload, text_content)] if not records: records = [(None, text_content)] record_texts = [ self._resolve_primary_text_value(payload, raw_text, primary_text_key) for payload, raw_text in records ] if not record_texts: record_texts = [text_content] # 判断是否需要分段 needs_segmentation = len(records) > 1 or any( len(text or "") > self.SEGMENT_THRESHOLD for text in record_texts ) if not needs_segmentation: # 不需要分段的文件，跳过 succeeded += 1 break # 执行切片 splitter = AnnotationTextSplitter(max_chars=self.SEGMENT_THRESHOLD) segment_cursor = 0 segments = {} for record_index, ((payload, raw_text), record_text) in enumerate(zip(records, record_texts)): normalized_text = record_text or "" if len(normalized_text) > self.SEGMENT_THRESHOLD: raw_segments = splitter.split(normalized_text) for chunk_index, seg in enumerate(raw_segments): segments[str(segment_cursor)] = { SEGMENT_RESULT_KEY: [], SEGMENT_CREATED_AT_KEY: datetime.utcnow().isoformat() + "Z", SEGMENT_UPDATED_AT_KEY: datetime.utcnow().isoformat() + "Z", } segment_cursor += 1 else: segments[str(segment_cursor)] = { SEGMENT_RESULT_KEY: [], SEGMENT_CREATED_AT_KEY: datetime.utcnow().isoformat() + "Z", SEGMENT_UPDATED_AT_KEY: datetime.utcnow().isoformat() + "Z", } segment_cursor += 1 if not segments: succeeded += 1 break # 构造分段标注结构 final_payload = { SEGMENTED_KEY: True, "version": 1, SEGMENTS_KEY: segments, SEGMENT_TOTAL_KEY: segment_cursor, } # 检查是否已存在标注 existing_result = await self.db.execute( select(AnnotationResult).where( AnnotationResult.project_id == project_id, AnnotationResult.file_id == file_id, ) ) existing = existing_result.scalar_one_or_none() now = datetime.utcnow() if existing: # 更新现有标注 existing.annotation = final_payload # type: ignore[assignment] existing.annotation_status = ANNOTATION_STATUS_IN_PROGRESS # type: ignore[assignment] existing.updated_at = now # type: ignore[assignment] else: # 创建新标注记录 record = AnnotationResult( id=str(uuid.uuid4()), project_id=project_id, file_id=file_id, annotation=final_payload, annotation_status=ANNOTATION_STATUS_IN_PROGRESS, created_at=now, updated_at=now, ) self.db.add(record) await self.db.commit() succeeded += 1 logger.info(f"成功为文件 {file_id} 预生成 {segment_cursor} 个切片") break except Exception as e: logger.warning( f"为文件 {file_id} 预生成切片失败 (重试 {retry + 1}/{max_retries}): {e}" ) if retry == max_retries - 1: failed += 1 await self.db.rollback() logger.info( f"项目 {project_id} 切片预生成完成: 总计 {total_files}, 成功 {succeeded}, 失败 {failed}" ) return { "total_files": total_files, "succeeded": succeeded, "failed": failed, }