feat(annotation): 实现文本切片预生成功能

在创建标注任务时自动预生成文本切片结构,避免每次进入标注页面时的实时计算。

修改内容:
1. 在 AnnotationEditorService 中新增 precompute_segmentation_for_project 方法
   - 为项目的所有文本文件预计算切片结构
   - 使用 AnnotationTextSplitter 执行切片
   - 将切片结构持久化到 AnnotationResult 表(状态为 IN_PROGRESS)
   - 支持失败重试机制
   - 返回统计信息

2. 修改 create_mapping 接口
   - 在创建标注任务后,如果启用分段且为文本数据集,自动触发切片预生成
   - 使用 try-except 捕获异常,确保切片失败不影响项目创建

特点:
- 使用现有的 AnnotationTextSplitter 类
- 切片数据结构与现有分段标注格式一致
- 向后兼容(未切片的任务仍然使用实时计算)
- 性能优化:避免进入标注页面时的重复计算

相关文件:
- runtime/datamate-python/app/module/annotation/service/editor.py
- runtime/datamate-python/app/module/annotation/interface/project.py
This commit is contained in:
2026-02-03 12:59:29 +00:00
parent 699031dae7
commit 147beb1ec7
2 changed files with 204 additions and 0 deletions

View File

@@ -1185,3 +1185,195 @@ class AnnotationEditorService:
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,
}