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12 Commits

Author SHA1 Message Date
f381d641ab fix(upload): 修复流式上传中的文件名处理逻辑
- 修正预上传接口调用时传递正确的文件总数而非固定值-1
- 移除导入配置中文件分割时的文件扩展名保留逻辑
- 删除流式上传选项中的fileExtension参数定义
- 移除流式上传实现中的文件扩展名处理相关代码
- 简化新文件名生成逻辑,不再附加扩展名后缀
2026-02-04 07:47:41 +08:00
c8611d29ff feat(upload): 实现流式分割上传,优化大文件上传体验
实现边分割边上传的流式处理,避免大文件一次性加载导致前端卡顿。

修改内容:
1. file.util.ts - 流式分割上传核心功能
   - 新增 streamSplitAndUpload 函数,实现边分割边上传
   - 新增 shouldStreamUpload 函数,判断是否使用流式上传
   - 新增 StreamUploadOptions 和 StreamUploadResult 接口
   - 优化分片大小(默认 5MB)

2. ImportConfiguration.tsx - 智能上传策略
   - 大文件(>5MB)使用流式分割上传
   - 小文件(≤5MB)使用传统分割方式
   - 保持 UI 不变

3. useSliceUpload.tsx - 流式上传处理
   - 新增 handleStreamUpload 处理流式上传事件
   - 支持并发上传和更好的进度管理

4. TaskUpload.tsx - 进度显示优化
   - 注册流式上传事件监听器
   - 显示流式上传信息(已上传行数、当前文件等)

5. dataset.model.ts - 类型定义扩展
   - 新增 StreamUploadInfo 接口
   - TaskItem 接口添加 streamUploadInfo 和 prefix 字段

实现特点:
- 流式读取:使用 Blob.slice 逐块读取,避免一次性加载
- 逐行检测:按换行符分割,形成完整行后立即上传
- 内存优化:buffer 只保留当前块和未完成行,不累积所有分割结果
- 并发控制:支持 3 个并发上传,提升效率
- 进度可见:实时显示已上传行数和总体进度
- 错误处理:单个文件上传失败不影响其他文件
- 向后兼容:小文件仍使用原有分割方式

优势:
- 大文件上传不再卡顿,用户体验大幅提升
- 内存占用显著降低(从加载整个文件到只保留当前块)
- 上传效率提升(边分割边上传,并发上传多个小文件)

相关文件:
- frontend/src/utils/file.util.ts
- frontend/src/pages/DataManagement/Detail/components/ImportConfiguration.tsx
- frontend/src/hooks/useSliceUpload.tsx
- frontend/src/pages/Layout/TaskUpload.tsx
- frontend/src/pages/DataManagement/dataset.model.ts
2026-02-03 13:12:10 +00:00
147beb1ec7 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
2026-02-03 12:59:29 +00:00
699031dae7 fix: 修复编辑数据集时无法清除关联数据集的编译问题
问题分析:
之前尝试使用 @TableField(updateStrategy = FieldStrategy.IGNORED/ALWAYS) 注解
来强制更新 null 值,但 FieldStrategy.ALWAYS 可能不存在于当前
MyBatis-Plus 3.5.14 版本中,导致编译错误。

修复方案:
1. 移除 Dataset.java 中 parentDatasetId 字段的 @TableField(updateStrategy) 注解
2. 移除不需要的 import com.baomidou.mybatisplus.annotation.FieldStrategy
3. 在 DatasetApplicationService.updateDataset 方法中:
   - 添加 import com.baomidou.mybatisplus.core.conditions.update.LambdaUpdateWrapper
   - 保存原始的 parentDatasetId 值用于比较
   - handleParentChange 之后,检查 parentDatasetId 是否发生变化
   - 如果发生变化,使用 LambdaUpdateWrapper 显式地更新 parentDatasetId 字段
   - 这样即使值为 null 也能被正确更新到数据库

原理:
MyBatis-Plus 的 updateById 方法默认只更新非 null 字段。
通过使用 LambdaUpdateWrapper 的 set 方法,可以显式地设置字段值,
包括 null 值,从而确保字段能够被正确更新到数据库。
2026-02-03 11:09:15 +00:00
88b1383653 fix: 恢复前端发送空字符串以支持清除关联数据集
修改说明:
移除了之前将空字符串转换为 undefined 的逻辑,
现在直接发送表单值,包括空字符串。

配合后端修改(commit cc6415c):
1. 当用户选择"无关联数据集"时,发送空字符串 ""
2. 后端 handleParentChange 方法通过 normalizeParentId 将空字符串转为 null
3. Dataset.parentDatasetId 字段添加了 @TableField(updateStrategy = FieldStrategy.IGNORED)
4. 确保即使值为 null 也会被更新到数据库
2026-02-03 10:57:14 +00:00
cc6415c4d9 fix: 修复编辑数据集时无法清除关联数据集的问题
问题描述:
在数据管理的数据集编辑中,如果之前设置了关联数据集,编辑时选择不关联数据集后保存不会生效。

根本原因:
MyBatis-Plus 的 updateById 方法默认使用 FieldStrategy.NOT_NULL 策略,
只有当字段值为非 null 时才会更新到数据库。
当 parentDatasetId 从有值变为 null 时,默认情况下不会更新到数据库。

修复方案:
在 Dataset.java 的 parentDatasetId 字段上添加 @TableField(updateStrategy = FieldStrategy.IGNORED) 注解,
表示即使值为 null 也需要更新到数据库。

配合前端修改(恢复发送空字符串),现在可以正确清除关联数据集:
1. 前端发送空字符串表示"无关联数据集"
2. 后端 handleParentChange 通过 normalizeParentId 将空字符串转为 null
3. dataset.setParentDatasetId(null) 设置为 null
4. 由于添加了 IGNORED 策略,即使为 null 也会更新到数据库
2026-02-03 10:57:08 +00:00
3d036c4cd6 fix: 修复编辑数据集时无法清除关联数据集的问题
问题描述:
在数据管理的数据集编辑中,如果之前设置了关联数据集,编辑时选择不关联数据集后保存不会生效。

问题原因:
后端 updateDataset 方法中的条件判断:
```java
if (updateDatasetRequest.getParentDatasetId() != null) {
    handleParentChange(dataset, updateDatasetRequest.getParentDatasetId());
}
```
当 parentDatasetId 为 null 或空字符串时,条件判断为 false,不会执行 handleParentChange,导致无法清除关联数据集。

修复方案:
去掉条件判断,始终调用 handleParentChange。handleParentChange 内部通过 normalizeParentId 方法将空字符串和 null 都转换为 null,这样既支持设置新的父数据集,也支持清除关联。

配合前端修改(commit 2445235),将空字符串转换为 undefined(被后端反序列化为 null),确保清除关联的操作能够正确执行。
2026-02-03 09:35:09 +00:00
2445235fd2 fix: 修复编辑数据集时清除关联数据集不生效的问题
问题描述:
在数据管理的数据集编辑中,如果之前设置了关联数据集,编辑时选择不关联数据集后保存不会生效。

问题原因:
- BasicInformation.tsx中,"无关联数据集"选项的值是空字符串""
- 当用户选择不关联数据集时,parentDatasetId的值为""
- 后端API将空字符串视为无效值而忽略,而不是识别为"清除关联"的操作

修复方案:
- 在EditDataset.tsx的handleSubmit函数中,将parentDatasetId的空字符串转换为undefined
- 使用 formValues.parentDatasetId || undefined 确保空字符串被转换为 undefined
- 这样后端API能正确识别为要清除关联数据集的操作
2026-02-03 09:23:13 +00:00
893e0a1580 fix: 上传文件时任务中心立即显示
问题描述:
在数据管理的数据集详情页上传文件时,点击确认后,弹窗消失,但是需要等待文件处理(特别是启用按行分割时)后任务中心才弹出来,用户体验不好。

修改内容:
1. useSliceUpload.tsx: 在 createTask 函数中添加立即显示任务中心的逻辑,确保任务创建后立即显示
2. ImportConfiguration.tsx: 在 handleImportData 函数中,在执行耗时的文件处理操作(如文件分割)之前,立即触发 show:task-popover 事件显示任务中心

效果:
- 修改前:点击确认 → 弹窗消失 → (等待文件处理)→ 任务中心弹出
- 修改后:点击确认 → 弹窗消失 + 任务中心立即弹出 → 文件开始处理
2026-02-03 09:14:40 +00:00
05e6842fc8 refactor(DataManagement): 移除不必要的数据集类型过滤逻辑
- 删除了对数据集类型的过滤操作
- 移除了不再使用的 textDatasetTypeOptions 变量
- 简化了 BasicInformation 组件的数据传递逻辑
- 减少了代码冗余,提高了组件性能
2026-02-03 13:33:12 +08:00
da5b18e423 feat(scripts): 添加 APT 缓存预装功能解决离线构建问题
- 新增 APT 缓存目录和相关构建脚本 export-cache.sh
- 添加 build-base-images.sh 脚本用于构建预装 APT 包的基础镜像
- 增加 build-offline-final.sh 最终版离线构建脚本
- 更新 Makefile.offline.mk 添加新的离线构建目标
- 扩展 README.md 文档详细说明 APT 缓存问题解决方案
- 为多个服务添加使用预装基础镜像的离线 Dockerfile
- 修改打包脚本包含 APT 缓存到最终压缩包中
2026-02-03 13:16:17 +08:00
31629ab50b docs(offline): 更新离线构建文档添加传统构建方式和故障排查指南
- 添加传统 docker build 方式作为推荐方案
- 新增离线环境诊断命令 make offline-diagnose
- 扩展故障排查章节,增加多个常见问题解决方案
- 添加文件清单和推荐工作流说明
- 为 BuildKit 构建器无法使用本地镜像问题提供多种解决方法
- 更新构建命令使用说明和重要提示信息
2026-02-03 13:10:28 +08:00
20 changed files with 2418 additions and 568 deletions

View File

@@ -246,28 +246,59 @@ offline-build-classic: offline-setup
offline-diagnose:
@bash scripts/offline/diagnose.sh $(CACHE_DIR)
# 构建 APT 预装基础镜像(有网环境)
.PHONY: offline-build-base-images
offline-build-base-images:
@echo "构建 APT 预装基础镜像..."
@bash scripts/offline/build-base-images.sh $(CACHE_DIR)
# 使用预装基础镜像进行离线构建(推荐)
.PHONY: offline-build-final
offline-build-final: offline-setup
@echo "使用预装 APT 包的基础镜像进行离线构建..."
@bash scripts/offline/build-offline-final.sh $(CACHE_DIR) $(OFFLINE_VERSION)
# 完整离线导出(包含 APT 预装基础镜像)
.PHONY: offline-export-full
offline-export-full:
@echo "======================================"
@echo "完整离线缓存导出(含 APT 预装基础镜像)"
@echo "======================================"
@$(MAKE) offline-build-base-images
@$(MAKE) offline-export
@echo ""
@echo "导出完成!传输时请包含以下文件:"
@echo " - build-cache/images/base-images-with-apt.tar"
@echo " - build-cache-YYYYMMDD.tar.gz"
# ========== 帮助 ==========
.PHONY: help-offline
help-offline:
@echo "离线构建命令:"
@echo " make offline-export [CACHE_DIR=./build-cache] - 在有网环境导出构建缓存"
@echo ""
@echo "【有网环境】"
@echo " make offline-export [CACHE_DIR=./build-cache] - 导出构建缓存"
@echo " make offline-export-full - 导出完整缓存(含 APT 预装基础镜像)"
@echo " make offline-build-base-images - 构建 APT 预装基础镜像"
@echo ""
@echo "【无网环境】"
@echo " make offline-setup [CACHE_DIR=./build-cache] - 解压并准备离线缓存"
@echo " make offline-build [CACHE_DIR=./build-cache] - 在无网环境构建所有服务(BuildKit)"
@echo " make offline-build-classic - 使用传统 docker build(更稳定)"
@echo " make <service>-offline-build - 离线构建单个服务"
@echo " (如: make backend-offline-build)"
@echo " make offline-build-final - 使用预装基础镜像构建(推荐,解决 APT 问题)"
@echo " make offline-build-classic - 使用传统 docker build"
@echo " make offline-build - 使用 BuildKit 构建"
@echo " make offline-diagnose - 诊断离线构建环境"
@echo " make <service>-offline-build - 离线构建单个服务"
@echo ""
@echo "完整工作流程:"
@echo " # 1. 有网环境导出缓存"
@echo " make offline-export"
@echo "完整工作流程(推荐)】"
@echo " # 1. 有网环境导出完整缓存"
@echo " make offline-export-full"
@echo ""
@echo " # 2. 传输缓存到无网环境"
@echo " scp build-cache-*.tar.gz user@offline-server:/path/to/project/"
@echo " # 2. 传输到无网环境(需要传输两个文件)"
@echo " scp build-cache/images/base-images-with-apt.tar user@offline-server:/path/"
@echo " scp build-cache-*.tar.gz user@offline-server:/path/"
@echo ""
@echo " # 3. 无网环境构建(推荐先用传统方式)"
@echo " # 3. 无网环境构建"
@echo " tar -xzf build-cache-*.tar.gz"
@echo " make offline-diagnose # 检查环境"
@echo " make offline-build-classic # 传统构建(推荐)"
@echo " # 或 make offline-build # BuildKit 构建"
@echo " docker load -i build-cache/images/base-images-with-apt.tar"
@echo " make offline-build-final"

View File

@@ -1,5 +1,6 @@
package com.datamate.datamanagement.application;
import com.baomidou.mybatisplus.core.conditions.update.LambdaUpdateWrapper;
import com.baomidou.mybatisplus.core.metadata.IPage;
import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
import com.datamate.common.domain.utils.ChunksSaver;
@@ -101,6 +102,7 @@ public class DatasetApplicationService {
public Dataset updateDataset(String datasetId, UpdateDatasetRequest updateDatasetRequest) {
Dataset dataset = datasetRepository.getById(datasetId);
BusinessAssert.notNull(dataset, DataManagementErrorCode.DATASET_NOT_FOUND);
if (StringUtils.hasText(updateDatasetRequest.getName())) {
dataset.setName(updateDatasetRequest.getName());
}
@@ -113,13 +115,31 @@ public class DatasetApplicationService {
if (Objects.nonNull(updateDatasetRequest.getStatus())) {
dataset.setStatus(updateDatasetRequest.getStatus());
}
if (updateDatasetRequest.getParentDatasetId() != null) {
if (updateDatasetRequest.isParentDatasetIdProvided()) {
// 保存原始的 parentDatasetId 值,用于比较是否发生了变化
String originalParentDatasetId = dataset.getParentDatasetId();
// 处理父数据集变更:仅当请求显式包含 parentDatasetId 时处理
// handleParentChange 内部通过 normalizeParentId 方法将空字符串和 null 都转换为 null
// 这样既支持设置新的父数据集,也支持清除关联
handleParentChange(dataset, updateDatasetRequest.getParentDatasetId());
// 检查 parentDatasetId 是否发生了变化
if (!Objects.equals(originalParentDatasetId, dataset.getParentDatasetId())) {
// 使用 LambdaUpdateWrapper 显式地更新 parentDatasetId 字段
// 这样即使值为 null 也能被正确更新到数据库
datasetRepository.update(null, new LambdaUpdateWrapper<Dataset>()
.eq(Dataset::getId, datasetId)
.set(Dataset::getParentDatasetId, dataset.getParentDatasetId()));
}
}
if (StringUtils.hasText(updateDatasetRequest.getDataSource())) {
// 数据源id不为空,使用异步线程进行文件扫盘落库
processDataSourceAsync(dataset.getId(), updateDatasetRequest.getDataSource());
}
// 更新其他字段(不包括 parentDatasetId,因为它已经在上面的代码中更新了)
datasetRepository.updateById(dataset);
return dataset;
}

View File

@@ -1,8 +1,10 @@
package com.datamate.datamanagement.interfaces.dto;
import com.datamate.datamanagement.common.enums.DatasetStatusType;
import com.fasterxml.jackson.annotation.JsonIgnore;
import jakarta.validation.constraints.NotBlank;
import jakarta.validation.constraints.Size;
import lombok.AccessLevel;
import lombok.Getter;
import lombok.Setter;
@@ -24,9 +26,18 @@ public class UpdateDatasetRequest {
/** 归集任务id */
private String dataSource;
/** 父数据集ID */
@Setter(AccessLevel.NONE)
private String parentDatasetId;
@JsonIgnore
@Setter(AccessLevel.NONE)
private boolean parentDatasetIdProvided;
/** 标签列表 */
private List<String> tags;
/** 数据集状态 */
private DatasetStatusType status;
public void setParentDatasetId(String parentDatasetId) {
this.parentDatasetIdProvided = true;
this.parentDatasetId = parentDatasetId;
}
}

View File

@@ -1,198 +1,348 @@
import { TaskItem } from "@/pages/DataManagement/dataset.model";
import { calculateSHA256, checkIsFilesExist } from "@/utils/file.util";
import { App } from "antd";
import { useRef, useState } from "react";
export function useFileSliceUpload(
{
preUpload,
uploadChunk,
cancelUpload,
}: {
preUpload: (id: string, params: any) => Promise<{ data: number }>;
uploadChunk: (id: string, formData: FormData, config: any) => Promise<any>;
cancelUpload: ((reqId: number) => Promise<any>) | null;
},
showTaskCenter = true // 上传时是否显示任务中心
) {
const { message } = App.useApp();
const [taskList, setTaskList] = useState<TaskItem[]>([]);
const taskListRef = useRef<TaskItem[]>([]); // 用于固定任务顺序
const createTask = (detail: any = {}) => {
const { dataset } = detail;
const title = `上传数据集: ${dataset.name} `;
const controller = new AbortController();
const task: TaskItem = {
key: dataset.id,
title,
percent: 0,
reqId: -1,
controller,
size: 0,
updateEvent: detail.updateEvent,
hasArchive: detail.hasArchive,
prefix: detail.prefix,
};
taskListRef.current = [task, ...taskListRef.current];
setTaskList(taskListRef.current);
return task;
};
const updateTaskList = (task: TaskItem) => {
taskListRef.current = taskListRef.current.map((item) =>
item.key === task.key ? task : item
);
setTaskList(taskListRef.current);
};
const removeTask = (task: TaskItem) => {
const { key } = task;
taskListRef.current = taskListRef.current.filter(
(item) => item.key !== key
);
setTaskList(taskListRef.current);
if (task.isCancel && task.cancelFn) {
task.cancelFn();
}
if (task.updateEvent) {
// 携带前缀信息,便于刷新后仍停留在当前目录
window.dispatchEvent(
new CustomEvent(task.updateEvent, {
detail: { prefix: (task as any).prefix },
})
);
}
if (showTaskCenter) {
window.dispatchEvent(
new CustomEvent("show:task-popover", { detail: { show: false } })
);
}
};
async function buildFormData({ file, reqId, i, j }) {
const formData = new FormData();
const { slices, name, size } = file;
const checkSum = await calculateSHA256(slices[j]);
formData.append("file", slices[j]);
formData.append("reqId", reqId.toString());
formData.append("fileNo", (i + 1).toString());
formData.append("chunkNo", (j + 1).toString());
formData.append("fileName", name);
formData.append("fileSize", size.toString());
formData.append("totalChunkNum", slices.length.toString());
formData.append("checkSumHex", checkSum);
return formData;
}
async function uploadSlice(task: TaskItem, fileInfo) {
if (!task) {
return;
}
const { reqId, key } = task;
const { loaded, i, j, files, totalSize } = fileInfo;
const formData = await buildFormData({
file: files[i],
i,
j,
reqId,
});
let newTask = { ...task };
await uploadChunk(key, formData, {
onUploadProgress: (e) => {
const loadedSize = loaded + e.loaded;
const curPercent = Number((loadedSize / totalSize) * 100).toFixed(2);
newTask = {
...newTask,
...taskListRef.current.find((item) => item.key === key),
size: loadedSize,
percent: curPercent >= 100 ? 99.99 : curPercent,
};
updateTaskList(newTask);
},
});
}
async function uploadFile({ task, files, totalSize }) {
console.log('[useSliceUpload] Calling preUpload with prefix:', task.prefix);
const { data: reqId } = await preUpload(task.key, {
totalFileNum: files.length,
totalSize,
datasetId: task.key,
hasArchive: task.hasArchive,
prefix: task.prefix,
});
console.log('[useSliceUpload] PreUpload response reqId:', reqId);
const newTask: TaskItem = {
...task,
reqId,
isCancel: false,
cancelFn: () => {
task.controller.abort();
cancelUpload?.(reqId);
if (task.updateEvent) window.dispatchEvent(new Event(task.updateEvent));
},
};
updateTaskList(newTask);
if (showTaskCenter) {
window.dispatchEvent(
new CustomEvent("show:task-popover", { detail: { show: true } })
);
}
// // 更新数据状态
if (task.updateEvent) window.dispatchEvent(new Event(task.updateEvent));
let loaded = 0;
for (let i = 0; i < files.length; i++) {
const { slices } = files[i];
for (let j = 0; j < slices.length; j++) {
await uploadSlice(newTask, {
loaded,
i,
j,
files,
totalSize,
});
loaded += slices[j].size;
}
}
removeTask(newTask);
}
const handleUpload = async ({ task, files }) => {
const isErrorFile = await checkIsFilesExist(files);
if (isErrorFile) {
message.error("文件被修改或删除,请重新选择文件上传");
removeTask({
...task,
isCancel: false,
...taskListRef.current.find((item) => item.key === task.key),
});
return;
}
try {
const totalSize = files.reduce((acc, file) => acc + file.size, 0);
await uploadFile({ task, files, totalSize });
} catch (err) {
console.error(err);
message.error("文件上传失败,请稍后重试");
removeTask({
...task,
isCancel: true,
...taskListRef.current.find((item) => item.key === task.key),
});
}
};
return {
taskList,
createTask,
removeTask,
handleUpload,
};
}
import { TaskItem } from "@/pages/DataManagement/dataset.model";
import { calculateSHA256, checkIsFilesExist, streamSplitAndUpload, StreamUploadResult } from "@/utils/file.util";
import { App } from "antd";
import { useRef, useState } from "react";
export function useFileSliceUpload(
{
preUpload,
uploadChunk,
cancelUpload,
}: {
preUpload: (id: string, params: Record<string, unknown>) => Promise<{ data: number }>;
uploadChunk: (id: string, formData: FormData, config: Record<string, unknown>) => Promise<unknown>;
cancelUpload: ((reqId: number) => Promise<unknown>) | null;
},
showTaskCenter = true, // 上传时是否显示任务中心
enableStreamUpload = true // 是否启用流式分割上传
) {
const { message } = App.useApp();
const [taskList, setTaskList] = useState<TaskItem[]>([]);
const taskListRef = useRef<TaskItem[]>([]); // 用于固定任务顺序
const createTask = (detail: Record<string, unknown> = {}) => {
const { dataset } = detail;
const title = `上传数据集: ${dataset.name} `;
const controller = new AbortController();
const task: TaskItem = {
key: dataset.id,
title,
percent: 0,
reqId: -1,
controller,
size: 0,
updateEvent: detail.updateEvent,
hasArchive: detail.hasArchive,
prefix: detail.prefix,
};
taskListRef.current = [task, ...taskListRef.current];
setTaskList(taskListRef.current);
// 立即显示任务中心,让用户感知上传已开始
if (showTaskCenter) {
window.dispatchEvent(
new CustomEvent("show:task-popover", { detail: { show: true } })
);
}
return task;
};
const updateTaskList = (task: TaskItem) => {
taskListRef.current = taskListRef.current.map((item) =>
item.key === task.key ? task : item
);
setTaskList(taskListRef.current);
};
const removeTask = (task: TaskItem) => {
const { key } = task;
taskListRef.current = taskListRef.current.filter(
(item) => item.key !== key
);
setTaskList(taskListRef.current);
if (task.isCancel && task.cancelFn) {
task.cancelFn();
}
if (task.updateEvent) {
// 携带前缀信息,便于刷新后仍停留在当前目录
window.dispatchEvent(
new CustomEvent(task.updateEvent, {
detail: { prefix: task.prefix },
})
);
}
if (showTaskCenter) {
window.dispatchEvent(
new CustomEvent("show:task-popover", { detail: { show: false } })
);
}
};
async function buildFormData({ file, reqId, i, j }: { file: { slices: Blob[]; name: string; size: number }; reqId: number; i: number; j: number }) {
const formData = new FormData();
const { slices, name, size } = file;
const checkSum = await calculateSHA256(slices[j]);
formData.append("file", slices[j]);
formData.append("reqId", reqId.toString());
formData.append("fileNo", (i + 1).toString());
formData.append("chunkNo", (j + 1).toString());
formData.append("fileName", name);
formData.append("fileSize", size.toString());
formData.append("totalChunkNum", slices.length.toString());
formData.append("checkSumHex", checkSum);
return formData;
}
async function uploadSlice(task: TaskItem, fileInfo: { loaded: number; i: number; j: number; files: { slices: Blob[]; name: string; size: number }[]; totalSize: number }) {
if (!task) {
return;
}
const { reqId, key } = task;
const { loaded, i, j, files, totalSize } = fileInfo;
const formData = await buildFormData({
file: files[i],
i,
j,
reqId,
});
let newTask = { ...task };
await uploadChunk(key, formData, {
onUploadProgress: (e) => {
const loadedSize = loaded + e.loaded;
const curPercent = Number((loadedSize / totalSize) * 100).toFixed(2);
newTask = {
...newTask,
...taskListRef.current.find((item) => item.key === key),
size: loadedSize,
percent: curPercent >= 100 ? 99.99 : curPercent,
};
updateTaskList(newTask);
},
});
}
async function uploadFile({ task, files, totalSize }: { task: TaskItem; files: { slices: Blob[]; name: string; size: number; originFile: Blob }[]; totalSize: number }) {
console.log('[useSliceUpload] Calling preUpload with prefix:', task.prefix);
const { data: reqId } = await preUpload(task.key, {
totalFileNum: files.length,
totalSize,
datasetId: task.key,
hasArchive: task.hasArchive,
prefix: task.prefix,
});
console.log('[useSliceUpload] PreUpload response reqId:', reqId);
const newTask: TaskItem = {
...task,
reqId,
isCancel: false,
cancelFn: () => {
task.controller.abort();
cancelUpload?.(reqId);
if (task.updateEvent) window.dispatchEvent(new Event(task.updateEvent));
},
};
updateTaskList(newTask);
// 注意:show:task-popover 事件已在 createTask 中触发,此处不再重复触发
// // 更新数据状态
if (task.updateEvent) window.dispatchEvent(new Event(task.updateEvent));
let loaded = 0;
for (let i = 0; i < files.length; i++) {
const { slices } = files[i];
for (let j = 0; j < slices.length; j++) {
await uploadSlice(newTask, {
loaded,
i,
j,
files,
totalSize,
});
loaded += slices[j].size;
}
}
removeTask(newTask);
}
const handleUpload = async ({ task, files }: { task: TaskItem; files: { slices: Blob[]; name: string; size: number; originFile: Blob }[] }) => {
const isErrorFile = await checkIsFilesExist(files);
if (isErrorFile) {
message.error("文件被修改或删除,请重新选择文件上传");
removeTask({
...task,
isCancel: false,
...taskListRef.current.find((item) => item.key === task.key),
});
return;
}
try {
const totalSize = files.reduce((acc, file) => acc + file.size, 0);
await uploadFile({ task, files, totalSize });
} catch (err) {
console.error(err);
message.error("文件上传失败,请稍后重试");
removeTask({
...task,
isCancel: true,
...taskListRef.current.find((item) => item.key === task.key),
});
}
};
/**
* 流式分割上传处理
* 用于大文件按行分割并立即上传的场景
*/
const handleStreamUpload = async ({ task, files }: { task: TaskItem; files: File[] }) => {
try {
console.log('[useSliceUpload] Starting stream upload for', files.length, 'files');
// 预上传,获取 reqId
const totalSize = files.reduce((acc, file) => acc + file.size, 0);
const { data: reqId } = await preUpload(task.key, {
totalFileNum: files.length,
totalSize,
datasetId: task.key,
hasArchive: task.hasArchive,
prefix: task.prefix,
});
console.log('[useSliceUpload] Stream upload preUpload response reqId:', reqId);
const newTask: TaskItem = {
...task,
reqId,
isCancel: false,
cancelFn: () => {
task.controller.abort();
cancelUpload?.(reqId);
if (task.updateEvent) window.dispatchEvent(new Event(task.updateEvent));
},
};
updateTaskList(newTask);
let totalUploadedLines = 0;
let totalProcessedBytes = 0;
const results: StreamUploadResult[] = [];
// 逐个处理文件
for (let i = 0; i < files.length; i++) {
const file = files[i];
console.log(`[useSliceUpload] Processing file ${i + 1}/${files.length}: ${file.name}`);
const result = await streamSplitAndUpload(
file,
(formData, config) => uploadChunk(task.key, formData, config),
(currentBytes, totalBytes, uploadedLines) => {
// 更新进度
const overallBytes = totalProcessedBytes + currentBytes;
const curPercent = Number((overallBytes / totalSize) * 100).toFixed(2);
const updatedTask: TaskItem = {
...newTask,
...taskListRef.current.find((item) => item.key === task.key),
size: overallBytes,
percent: curPercent >= 100 ? 99.99 : curPercent,
streamUploadInfo: {
currentFile: file.name,
fileIndex: i + 1,
totalFiles: files.length,
uploadedLines: totalUploadedLines + uploadedLines,
},
};
updateTaskList(updatedTask);
},
1024 * 1024, // 1MB chunk size
{
reqId,
hasArchive: task.hasArchive,
prefix: task.prefix,
signal: task.controller.signal,
maxConcurrency: 3,
}
);
results.push(result);
totalUploadedLines += result.uploadedCount;
totalProcessedBytes += file.size;
console.log(`[useSliceUpload] File ${file.name} processed, uploaded ${result.uploadedCount} lines`);
}
console.log('[useSliceUpload] Stream upload completed, total lines:', totalUploadedLines);
removeTask(newTask);
message.success(`成功上传 ${totalUploadedLines} 个文件(按行分割)`);
} catch (err) {
console.error('[useSliceUpload] Stream upload error:', err);
if (err.message === "Upload cancelled") {
message.info("上传已取消");
} else {
message.error("文件上传失败,请稍后重试");
}
removeTask({
...task,
isCancel: true,
...taskListRef.current.find((item) => item.key === task.key),
});
}
};
/**
* 注册流式上传事件监听
* 返回注销函数
*/
const registerStreamUploadListener = () => {
if (!enableStreamUpload) return () => {};
const streamUploadHandler = async (e: Event) => {
const customEvent = e as CustomEvent;
const { dataset, files, updateEvent, hasArchive, prefix } = customEvent.detail;
const controller = new AbortController();
const task: TaskItem = {
key: dataset.id,
title: `上传数据集: ${dataset.name} (按行分割)`,
percent: 0,
reqId: -1,
controller,
size: 0,
updateEvent,
hasArchive,
prefix,
};
taskListRef.current = [task, ...taskListRef.current];
setTaskList(taskListRef.current);
// 显示任务中心
if (showTaskCenter) {
window.dispatchEvent(
new CustomEvent("show:task-popover", { detail: { show: true } })
);
}
await handleStreamUpload({ task, files });
};
window.addEventListener("upload:dataset-stream", streamUploadHandler);
return () => {
window.removeEventListener("upload:dataset-stream", streamUploadHandler);
};
};
return {
taskList,
createTask,
removeTask,
handleUpload,
handleStreamUpload,
registerStreamUploadListener,
};
}

View File

@@ -4,14 +4,9 @@ import { ArrowLeft } from "lucide-react";
import { Button, Form, App } from "antd";
import { Link, useLocation, useNavigate } from "react-router";
import { createDatasetUsingPost } from "../dataset.api";
import { datasetTypes } from "../dataset.const";
import { DatasetType } from "../dataset.model";
import BasicInformation from "./components/BasicInformation";
const textDatasetTypeOptions = datasetTypes.filter(
(type) => type.value === DatasetType.TEXT
);
export default function DatasetCreate() {
const navigate = useNavigate();
const location = useLocation();
@@ -87,7 +82,6 @@ export default function DatasetCreate() {
data={newDataset}
setData={setNewDataset}
hidden={["dataSource"]}
datasetTypeOptions={textDatasetTypeOptions}
/>
</Form>
</div>

View File

@@ -1,13 +1,13 @@
import { Select, Input, Form, Radio, Modal, Button, UploadFile, Switch, Tooltip } from "antd";
import { InboxOutlined, QuestionCircleOutlined } from "@ant-design/icons";
import { dataSourceOptions } from "../../dataset.const";
import { Select, Input, Form, Radio, Modal, Button, UploadFile, Switch, Tooltip } from "antd";
import { InboxOutlined, QuestionCircleOutlined } from "@ant-design/icons";
import { dataSourceOptions } from "../../dataset.const";
import { Dataset, DatasetType, DataSource } from "../../dataset.model";
import { useCallback, useEffect, useMemo, useState } from "react";
import { queryTasksUsingGet } from "@/pages/DataCollection/collection.apis";
import { updateDatasetByIdUsingPut } from "../../dataset.api";
import { sliceFile } from "@/utils/file.util";
import Dragger from "antd/es/upload/Dragger";
import { queryTasksUsingGet } from "@/pages/DataCollection/collection.apis";
import { updateDatasetByIdUsingPut } from "../../dataset.api";
import { sliceFile, shouldStreamUpload } from "@/utils/file.util";
import Dragger from "antd/es/upload/Dragger";
const TEXT_FILE_MIME_PREFIX = "text/";
const TEXT_FILE_MIME_TYPES = new Set([
"application/json",
@@ -90,14 +90,16 @@ async function splitFileByLines(file: UploadFile): Promise<UploadFile[]> {
const lines = text.split(/\r?\n/).filter((line: string) => line.trim() !== "");
if (lines.length === 0) return [];
// 生成文件名:原文件名_序号.扩展名
// 生成文件名:原文件名_序号(不保留后缀)
const nameParts = file.name.split(".");
const ext = nameParts.length > 1 ? "." + nameParts.pop() : "";
if (nameParts.length > 1) {
nameParts.pop();
}
const baseName = nameParts.join(".");
const padLength = String(lines.length).length;
return lines.map((line: string, index: number) => {
const newFileName = `${baseName}_${String(index + 1).padStart(padLength, "0")}${ext}`;
const newFileName = `${baseName}_${String(index + 1).padStart(padLength, "0")}`;
const blob = new Blob([line], { type: "text/plain" });
const newFile = new File([blob], newFileName, { type: "text/plain" });
return {
@@ -131,18 +133,18 @@ type ImportConfig = {
};
export default function ImportConfiguration({
data,
open,
onClose,
updateEvent = "update:dataset",
prefix,
}: {
data: Dataset | null;
open: boolean;
onClose: () => void;
updateEvent?: string;
prefix?: string;
}) {
data,
open,
onClose,
updateEvent = "update:dataset",
prefix,
}: {
data: Dataset | null;
open: boolean;
onClose: () => void;
updateEvent?: string;
prefix?: string;
}) {
const [form] = Form.useForm();
const [collectionOptions, setCollectionOptions] = useState<SelectOption[]>([]);
const availableSourceOptions = dataSourceOptions.filter(
@@ -160,23 +162,81 @@ export default function ImportConfiguration({
return files.some((file) => !isTextUploadFile(file));
}, [importConfig.files]);
const isTextDataset = data?.datasetType === DatasetType.TEXT;
// 本地上传文件相关逻辑
const handleUpload = async (dataset: Dataset) => {
let filesToUpload =
// 本地上传文件相关逻辑
const handleUpload = async (dataset: Dataset) => {
const filesToUpload =
(form.getFieldValue("files") as UploadFile[] | undefined) || [];
// 如果启用分行分割,处理文件
// 如果启用分行分割,对大文件使用流式处理
if (importConfig.splitByLine && !hasNonTextFile) {
const splitResults = await Promise.all(
filesToUpload.map((file) => splitFileByLines(file))
);
filesToUpload = splitResults.flat();
// 检查是否有大文件需要流式分割上传
const filesForStreamUpload: File[] = [];
const filesForNormalUpload: UploadFile[] = [];
for (const file of filesToUpload) {
const originFile = file.originFileObj ?? file;
if (originFile instanceof File && shouldStreamUpload(originFile)) {
filesForStreamUpload.push(originFile);
} else {
filesForNormalUpload.push(file);
}
}
// 大文件使用流式分割上传
if (filesForStreamUpload.length > 0) {
window.dispatchEvent(
new CustomEvent("upload:dataset-stream", {
detail: {
dataset,
files: filesForStreamUpload,
updateEvent,
hasArchive: importConfig.hasArchive,
prefix: currentPrefix,
},
})
);
}
// 小文件使用传统分割方式
if (filesForNormalUpload.length > 0) {
const splitResults = await Promise.all(
filesForNormalUpload.map((file) => splitFileByLines(file))
);
const smallFilesToUpload = splitResults.flat();
// 计算分片列表
const sliceList = smallFilesToUpload.map((file) => {
const originFile = (file.originFileObj ?? file) as Blob;
const slices = sliceFile(originFile);
return {
originFile: originFile,
slices,
name: file.name,
size: originFile.size || 0,
};
});
console.log("[ImportConfiguration] Uploading small files with currentPrefix:", currentPrefix);
window.dispatchEvent(
new CustomEvent("upload:dataset", {
detail: {
dataset,
files: sliceList,
updateEvent,
hasArchive: importConfig.hasArchive,
prefix: currentPrefix,
},
})
);
}
return;
}
// 计算分片列表
const sliceList = filesToUpload.map((file) => {
// 未启用分行分割,使用普通上传
// 计算分片列表
const sliceList = filesToUpload.map((file) => {
const originFile = (file.originFileObj ?? file) as Blob;
const slices = sliceFile(originFile);
return {
@@ -185,22 +245,22 @@ export default function ImportConfiguration({
name: file.name,
size: originFile.size || 0,
};
});
console.log("[ImportConfiguration] Uploading with currentPrefix:", currentPrefix);
window.dispatchEvent(
new CustomEvent("upload:dataset", {
detail: {
dataset,
files: sliceList,
updateEvent,
hasArchive: importConfig.hasArchive,
prefix: currentPrefix,
},
})
);
};
});
console.log("[ImportConfiguration] Uploading with currentPrefix:", currentPrefix);
window.dispatchEvent(
new CustomEvent("upload:dataset", {
detail: {
dataset,
files: sliceList,
updateEvent,
hasArchive: importConfig.hasArchive,
prefix: currentPrefix,
},
})
);
};
const fetchCollectionTasks = useCallback(async () => {
if (importConfig.source !== DataSource.COLLECTION) return;
try {
@@ -212,7 +272,7 @@ export default function ImportConfiguration({
label: task.name,
value: task.id,
}));
setCollectionOptions(options);
setCollectionOptions(options);
} catch (error) {
console.error("Error fetching collection tasks:", error);
}
@@ -229,27 +289,31 @@ export default function ImportConfiguration({
});
console.log('[ImportConfiguration] resetState done, currentPrefix still:', currentPrefix);
}, [currentPrefix, form]);
const handleImportData = async () => {
if (!data) return;
console.log('[ImportConfiguration] handleImportData called, currentPrefix:', currentPrefix);
if (importConfig.source === DataSource.UPLOAD) {
await handleUpload(data);
} else if (importConfig.source === DataSource.COLLECTION) {
await updateDatasetByIdUsingPut(data.id, {
...importConfig,
});
}
onClose();
};
const handleImportData = async () => {
if (!data) return;
console.log('[ImportConfiguration] handleImportData called, currentPrefix:', currentPrefix);
if (importConfig.source === DataSource.UPLOAD) {
// 立即显示任务中心,让用户感知上传已开始(在文件分割等耗时操作之前)
window.dispatchEvent(
new CustomEvent("show:task-popover", { detail: { show: true } })
);
await handleUpload(data);
} else if (importConfig.source === DataSource.COLLECTION) {
await updateDatasetByIdUsingPut(data.id, {
...importConfig,
});
}
onClose();
};
useEffect(() => {
if (open) {
setCurrentPrefix(prefix || "");
console.log('[ImportConfiguration] Modal opened with prefix:', prefix);
resetState();
fetchCollectionTasks();
}
console.log('[ImportConfiguration] Modal opened with prefix:', prefix);
resetState();
fetchCollectionTasks();
}
}, [fetchCollectionTasks, open, prefix, resetState]);
useEffect(() => {
@@ -259,111 +323,111 @@ export default function ImportConfiguration({
form.setFieldsValue({ splitByLine: false });
setImportConfig((prev) => ({ ...prev, splitByLine: false }));
}, [form, hasNonTextFile, importConfig.files, importConfig.splitByLine]);
// Separate effect for fetching collection tasks when source changes
useEffect(() => {
if (open && importConfig.source === DataSource.COLLECTION) {
fetchCollectionTasks();
}
// Separate effect for fetching collection tasks when source changes
useEffect(() => {
if (open && importConfig.source === DataSource.COLLECTION) {
fetchCollectionTasks();
}
}, [fetchCollectionTasks, importConfig.source, open]);
return (
<Modal
title="导入数据"
open={open}
width={600}
onCancel={() => {
onClose();
resetState();
}}
maskClosable={false}
footer={
<>
<Button onClick={onClose}></Button>
<Button
type="primary"
disabled={!importConfig?.files?.length && !importConfig.dataSource}
onClick={handleImportData}
>
</Button>
</>
}
>
<Form
form={form}
layout="vertical"
initialValues={importConfig || {}}
onValuesChange={(_, allValues) => setImportConfig(allValues)}
>
<Form.Item
label="数据源"
name="source"
rules={[{ required: true, message: "请选择数据源" }]}
>
return (
<Modal
title="导入数据"
open={open}
width={600}
onCancel={() => {
onClose();
resetState();
}}
maskClosable={false}
footer={
<>
<Button onClick={onClose}></Button>
<Button
type="primary"
disabled={!importConfig?.files?.length && !importConfig.dataSource}
onClick={handleImportData}
>
</Button>
</>
}
>
<Form
form={form}
layout="vertical"
initialValues={importConfig || {}}
onValuesChange={(_, allValues) => setImportConfig(allValues)}
>
<Form.Item
label="数据源"
name="source"
rules={[{ required: true, message: "请选择数据源" }]}
>
<Radio.Group
buttonStyle="solid"
options={availableSourceOptions}
optionType="button"
/>
</Form.Item>
{importConfig?.source === DataSource.COLLECTION && (
<Form.Item name="dataSource" label="归集任务" required>
<Select placeholder="请选择归集任务" options={collectionOptions} />
</Form.Item>
)}
{/* obs import */}
{importConfig?.source === DataSource.OBS && (
<div className="grid grid-cols-2 gap-3 p-4 bg-blue-50 rounded-lg">
<Form.Item
name="endpoint"
rules={[{ required: true }]}
label="Endpoint"
>
<Input
className="h-8 text-xs"
placeholder="obs.cn-north-4.myhuaweicloud.com"
/>
</Form.Item>
<Form.Item
name="bucket"
rules={[{ required: true }]}
label="Bucket"
>
<Input className="h-8 text-xs" placeholder="my-bucket" />
</Form.Item>
<Form.Item
name="accessKey"
rules={[{ required: true }]}
label="Access Key"
>
<Input className="h-8 text-xs" placeholder="Access Key" />
</Form.Item>
<Form.Item
name="secretKey"
rules={[{ required: true }]}
label="Secret Key"
>
<Input
type="password"
className="h-8 text-xs"
placeholder="Secret Key"
/>
</Form.Item>
</div>
)}
{/* Local Upload Component */}
{importConfig?.source === DataSource.UPLOAD && (
<>
<Form.Item
label="自动解压上传的压缩包"
name="hasArchive"
valuePropName="checked"
>
<Switch />
</Form.Item>
{importConfig?.source === DataSource.COLLECTION && (
<Form.Item name="dataSource" label="归集任务" required>
<Select placeholder="请选择归集任务" options={collectionOptions} />
</Form.Item>
)}
{/* obs import */}
{importConfig?.source === DataSource.OBS && (
<div className="grid grid-cols-2 gap-3 p-4 bg-blue-50 rounded-lg">
<Form.Item
name="endpoint"
rules={[{ required: true }]}
label="Endpoint"
>
<Input
className="h-8 text-xs"
placeholder="obs.cn-north-4.myhuaweicloud.com"
/>
</Form.Item>
<Form.Item
name="bucket"
rules={[{ required: true }]}
label="Bucket"
>
<Input className="h-8 text-xs" placeholder="my-bucket" />
</Form.Item>
<Form.Item
name="accessKey"
rules={[{ required: true }]}
label="Access Key"
>
<Input className="h-8 text-xs" placeholder="Access Key" />
</Form.Item>
<Form.Item
name="secretKey"
rules={[{ required: true }]}
label="Secret Key"
>
<Input
type="password"
className="h-8 text-xs"
placeholder="Secret Key"
/>
</Form.Item>
</div>
)}
{/* Local Upload Component */}
{importConfig?.source === DataSource.UPLOAD && (
<>
<Form.Item
label="自动解压上传的压缩包"
name="hasArchive"
valuePropName="checked"
>
<Switch />
</Form.Item>
{isTextDataset && (
<Form.Item
label={
@@ -386,10 +450,10 @@ export default function ImportConfiguration({
<Switch disabled={hasNonTextFile} />
</Form.Item>
)}
<Form.Item
label="上传文件"
name="files"
valuePropName="fileList"
<Form.Item
label="上传文件"
name="files"
valuePropName="fileList"
getValueFromEvent={(
event: { fileList?: UploadFile[] } | UploadFile[]
) => {
@@ -398,69 +462,69 @@ export default function ImportConfiguration({
}
return event?.fileList;
}}
rules={[
{
required: true,
message: "请上传文件",
},
]}
>
<Dragger
className="w-full"
beforeUpload={() => false}
multiple
>
<p className="ant-upload-drag-icon">
<InboxOutlined />
</p>
<p className="ant-upload-text"></p>
<p className="ant-upload-hint"></p>
</Dragger>
</Form.Item>
</>
)}
{/* Target Configuration */}
{importConfig?.target && importConfig?.target !== DataSource.UPLOAD && (
<div className="space-y-3 p-4 bg-blue-50 rounded-lg">
{importConfig?.target === DataSource.DATABASE && (
<div className="grid grid-cols-2 gap-3">
<Form.Item
name="databaseType"
rules={[{ required: true }]}
label="数据库类型"
>
<Select
className="w-full"
options={[
{ label: "MySQL", value: "mysql" },
{ label: "PostgreSQL", value: "postgresql" },
{ label: "MongoDB", value: "mongodb" },
]}
></Select>
</Form.Item>
<Form.Item
name="tableName"
rules={[{ required: true }]}
label="表名"
>
<Input className="h-8 text-xs" placeholder="dataset_table" />
</Form.Item>
<Form.Item
name="connectionString"
rules={[{ required: true }]}
label="连接字符串"
>
<Input
className="h-8 text-xs col-span-2"
placeholder="数据库连接字符串"
/>
</Form.Item>
</div>
)}
</div>
)}
</Form>
</Modal>
);
}
rules={[
{
required: true,
message: "请上传文件",
},
]}
>
<Dragger
className="w-full"
beforeUpload={() => false}
multiple
>
<p className="ant-upload-drag-icon">
<InboxOutlined />
</p>
<p className="ant-upload-text"></p>
<p className="ant-upload-hint"></p>
</Dragger>
</Form.Item>
</>
)}
{/* Target Configuration */}
{importConfig?.target && importConfig?.target !== DataSource.UPLOAD && (
<div className="space-y-3 p-4 bg-blue-50 rounded-lg">
{importConfig?.target === DataSource.DATABASE && (
<div className="grid grid-cols-2 gap-3">
<Form.Item
name="databaseType"
rules={[{ required: true }]}
label="数据库类型"
>
<Select
className="w-full"
options={[
{ label: "MySQL", value: "mysql" },
{ label: "PostgreSQL", value: "postgresql" },
{ label: "MongoDB", value: "mongodb" },
]}
></Select>
</Form.Item>
<Form.Item
name="tableName"
rules={[{ required: true }]}
label="表名"
>
<Input className="h-8 text-xs" placeholder="dataset_table" />
</Form.Item>
<Form.Item
name="connectionString"
rules={[{ required: true }]}
label="连接字符串"
>
<Input
className="h-8 text-xs col-span-2"
placeholder="数据库连接字符串"
/>
</Form.Item>
</div>
)}
</div>
)}
</Form>
</Modal>
);
}

View File

@@ -102,6 +102,13 @@ export interface DatasetTask {
executionHistory?: { time: string; status: string }[];
}
export interface StreamUploadInfo {
currentFile: string;
fileIndex: number;
totalFiles: number;
uploadedLines: number;
}
export interface TaskItem {
key: string;
title: string;
@@ -113,4 +120,6 @@ export interface TaskItem {
updateEvent?: string;
size?: number;
hasArchive?: boolean;
prefix?: string;
streamUploadInfo?: StreamUploadInfo;
}

View File

@@ -1,69 +1,93 @@
import {
cancelUploadUsingPut,
preUploadUsingPost,
uploadFileChunkUsingPost,
} from "@/pages/DataManagement/dataset.api";
import { Button, Empty, Progress } from "antd";
import { DeleteOutlined } from "@ant-design/icons";
import { useEffect } from "react";
import { useFileSliceUpload } from "@/hooks/useSliceUpload";
export default function TaskUpload() {
const { createTask, taskList, removeTask, handleUpload } = useFileSliceUpload(
{
preUpload: preUploadUsingPost,
uploadChunk: uploadFileChunkUsingPost,
cancelUpload: cancelUploadUsingPut,
}
);
useEffect(() => {
const uploadHandler = (e: any) => {
console.log('[TaskUpload] Received upload event detail:', e.detail);
const { files } = e.detail;
const task = createTask(e.detail);
console.log('[TaskUpload] Created task with prefix:', task.prefix);
handleUpload({ task, files });
};
window.addEventListener("upload:dataset", uploadHandler);
return () => {
window.removeEventListener("upload:dataset", uploadHandler);
};
}, []);
return (
<div
className="w-90 max-w-90 max-h-96 overflow-y-auto p-2"
id="header-task-popover"
>
{taskList.length > 0 &&
taskList.map((task) => (
<div key={task.key} className="border-b border-gray-200 pb-2">
<div className="flex items-center justify-between">
<div>{task.title}</div>
<Button
type="text"
danger
disabled={!task?.cancelFn}
onClick={() =>
removeTask({
...task,
isCancel: true,
})
}
icon={<DeleteOutlined />}
></Button>
</div>
<Progress size="small" percent={task.percent} />
</div>
))}
{taskList.length === 0 && (
<Empty
image={Empty.PRESENTED_IMAGE_SIMPLE}
description="暂无上传任务"
/>
)}
</div>
);
}
import {
cancelUploadUsingPut,
preUploadUsingPost,
uploadFileChunkUsingPost,
} from "@/pages/DataManagement/dataset.api";
import { Button, Empty, Progress, Tag } from "antd";
import { DeleteOutlined, FileTextOutlined } from "@ant-design/icons";
import { useEffect } from "react";
import { useFileSliceUpload } from "@/hooks/useSliceUpload";
export default function TaskUpload() {
const { createTask, taskList, removeTask, handleUpload, registerStreamUploadListener } = useFileSliceUpload(
{
preUpload: preUploadUsingPost,
uploadChunk: uploadFileChunkUsingPost,
cancelUpload: cancelUploadUsingPut,
},
true, // showTaskCenter
true // enableStreamUpload
);
useEffect(() => {
const uploadHandler = (e: Event) => {
const customEvent = e as CustomEvent;
console.log('[TaskUpload] Received upload event detail:', customEvent.detail);
const { files } = customEvent.detail;
const task = createTask(customEvent.detail);
console.log('[TaskUpload] Created task with prefix:', task.prefix);
handleUpload({ task, files });
};
window.addEventListener("upload:dataset", uploadHandler);
return () => {
window.removeEventListener("upload:dataset", uploadHandler);
};
}, [createTask, handleUpload]);
// 注册流式上传监听器
useEffect(() => {
const unregister = registerStreamUploadListener();
return unregister;
}, [registerStreamUploadListener]);
return (
<div
className="w-90 max-w-90 max-h-96 overflow-y-auto p-2"
id="header-task-popover"
>
{taskList.length > 0 &&
taskList.map((task) => (
<div key={task.key} className="border-b border-gray-200 pb-2">
<div className="flex items-center justify-between">
<div>{task.title}</div>
<Button
type="text"
danger
disabled={!task?.cancelFn}
onClick={() =>
removeTask({
...task,
isCancel: true,
})
}
icon={<DeleteOutlined />}
></Button>
</div>
<Progress size="small" percent={Number(task.percent)} />
{task.streamUploadInfo && (
<div className="flex items-center gap-2 text-xs text-gray-500 mt-1">
<Tag icon={<FileTextOutlined />} size="small">
</Tag>
<span>
: {task.streamUploadInfo.uploadedLines}
</span>
{task.streamUploadInfo.totalFiles > 1 && (
<span>
({task.streamUploadInfo.fileIndex}/{task.streamUploadInfo.totalFiles} )
</span>
)}
</div>
)}
</div>
))}
{taskList.length === 0 && (
<Empty
image={Empty.PRESENTED_IMAGE_SIMPLE}
description="暂无上传任务"
/>
)}
</div>
);
}

View File

@@ -1,79 +1,568 @@
import { UploadFile } from "antd";
import jsSHA from "jssha";
const CHUNK_SIZE = 1024 * 1024 * 60;
// 默认分片大小:5MB(适合大多数网络环境)
export const DEFAULT_CHUNK_SIZE = 1024 * 1024 * 5;
// 大文件阈值:10MB
export const LARGE_FILE_THRESHOLD = 1024 * 1024 * 10;
// 最大并发上传数
export const MAX_CONCURRENT_UPLOADS = 3;
// 文本文件读取块大小:20MB(用于计算 SHA256)
const BUFFER_CHUNK_SIZE = 1024 * 1024 * 20;
export function sliceFile(file, chunkSize = CHUNK_SIZE): Blob[] {
/**
* 将文件分割为多个分片
* @param file 文件对象
* @param chunkSize 分片大小(字节),默认 5MB
* @returns 分片数组(Blob 列表)
*/
export function sliceFile(file: Blob, chunkSize = DEFAULT_CHUNK_SIZE): Blob[] {
const totalSize = file.size;
const chunks: Blob[] = [];
// 小文件不需要分片
if (totalSize <= chunkSize) {
return [file];
}
let start = 0;
let end = start + chunkSize;
const chunks = [];
while (start < totalSize) {
const end = Math.min(start + chunkSize, totalSize);
const blob = file.slice(start, end);
chunks.push(blob);
start = end;
end = start + chunkSize;
}
return chunks;
}
export function calculateSHA256(file: Blob): Promise<string> {
let count = 0;
const hash = new jsSHA("SHA-256", "ARRAYBUFFER", { encoding: "UTF8" });
/**
* 计算文件的 SHA256 哈希值
* @param file 文件 Blob
* @param onProgress 进度回调(可选)
* @returns SHA256 哈希字符串
*/
export function calculateSHA256(
file: Blob,
onProgress?: (percent: number) => void
): Promise<string> {
return new Promise((resolve, reject) => {
const hash = new jsSHA("SHA-256", "ARRAYBUFFER", { encoding: "UTF8" });
const reader = new FileReader();
let processedSize = 0;
function readChunk(start: number, end: number) {
const slice = file.slice(start, end);
reader.readAsArrayBuffer(slice);
}
const bufferChunkSize = 1024 * 1024 * 20;
function processChunk(offset: number) {
const start = offset;
const end = Math.min(start + bufferChunkSize, file.size);
count = end;
const end = Math.min(start + BUFFER_CHUNK_SIZE, file.size);
readChunk(start, end);
}
reader.onloadend = function () {
const arraybuffer = reader.result;
reader.onloadend = function (e) {
const arraybuffer = reader.result as ArrayBuffer;
if (!arraybuffer) {
reject(new Error("Failed to read file"));
return;
}
hash.update(arraybuffer);
if (count < file.size) {
processChunk(count);
processedSize += (e.target as FileReader).result?.byteLength || 0;
if (onProgress) {
const percent = Math.min(100, Math.round((processedSize / file.size) * 100));
onProgress(percent);
}
if (processedSize < file.size) {
processChunk(processedSize);
} else {
resolve(hash.getHash("HEX", { outputLen: 256 }));
}
};
reader.onerror = () => reject(new Error("File reading failed"));
processChunk(0);
});
}
/**
* 批量计算多个文件的 SHA256
* @param files 文件列表
* @param onFileProgress 单个文件进度回调(可选)
* @returns 哈希值数组
*/
export async function calculateSHA256Batch(
files: Blob[],
onFileProgress?: (index: number, percent: number) => void
): Promise<string[]> {
const results: string[] = [];
for (let i = 0; i < files.length; i++) {
const hash = await calculateSHA256(files[i], (percent) => {
onFileProgress?.(i, percent);
});
results.push(hash);
}
return results;
}
/**
* 检查文件是否存在(未被修改或删除)
* @param fileList 文件列表
* @returns 返回第一个不存在的文件,或 null(如果都存在)
*/
export function checkIsFilesExist(
fileList: UploadFile[]
): Promise<UploadFile | null> {
fileList: Array<{ originFile?: Blob }>
): Promise<{ originFile?: Blob } | null> {
return new Promise((resolve) => {
const loadEndFn = (file: UploadFile, reachEnd: boolean, e) => {
const fileNotExist = !e.target.result;
if (!fileList.length) {
resolve(null);
return;
}
let checkedCount = 0;
const totalCount = fileList.length;
const loadEndFn = (file: { originFile?: Blob }, e: ProgressEvent<FileReader>) => {
checkedCount++;
const fileNotExist = !e.target?.result;
if (fileNotExist) {
resolve(file);
return;
}
if (reachEnd) {
if (checkedCount >= totalCount) {
resolve(null);
}
};
for (let i = 0; i < fileList.length; i++) {
const { originFile: file } = fileList[i];
for (const file of fileList) {
const fileReader = new FileReader();
fileReader.readAsArrayBuffer(file);
fileReader.onloadend = (e) =>
loadEndFn(fileList[i], i === fileList.length - 1, e);
const actualFile = file.originFile;
if (!actualFile) {
checkedCount++;
if (checkedCount >= totalCount) {
resolve(null);
}
continue;
}
fileReader.readAsArrayBuffer(actualFile.slice(0, 1));
fileReader.onloadend = (e) => loadEndFn(file, e);
fileReader.onerror = () => {
checkedCount++;
resolve(file);
};
}
});
}
/**
* 判断文件是否为大文件
* @param size 文件大小(字节)
* @param threshold 阈值(字节),默认 10MB
*/
export function isLargeFile(size: number, threshold = LARGE_FILE_THRESHOLD): boolean {
return size > threshold;
}
/**
* 格式化文件大小为人类可读格式
* @param bytes 字节数
* @param decimals 小数位数
*/
export function formatFileSize(bytes: number, decimals = 2): string {
if (bytes === 0) return "0 B";
const k = 1024;
const sizes = ["B", "KB", "MB", "GB", "TB", "PB"];
const i = Math.floor(Math.log(bytes) / Math.log(k));
return `${parseFloat((bytes / Math.pow(k, i)).toFixed(decimals))} ${sizes[i]}`;
}
/**
* 并发执行异步任务
* @param tasks 任务函数数组
* @param maxConcurrency 最大并发数
* @param onTaskComplete 单个任务完成回调(可选)
*/
export async function runConcurrentTasks<T>(
tasks: (() => Promise<T>)[],
maxConcurrency: number,
onTaskComplete?: (index: number, result: T) => void
): Promise<T[]> {
const results: T[] = new Array(tasks.length);
let index = 0;
async function runNext(): Promise<void> {
const currentIndex = index++;
if (currentIndex >= tasks.length) return;
const result = await tasks[currentIndex]();
results[currentIndex] = result;
onTaskComplete?.(currentIndex, result);
await runNext();
}
const workers = Array(Math.min(maxConcurrency, tasks.length))
.fill(null)
.map(() => runNext());
await Promise.all(workers);
return results;
}
/**
* 按行分割文本文件内容
* @param text 文本内容
* @param skipEmptyLines 是否跳过空行,默认 true
* @returns 行数组
*/
export function splitTextByLines(text: string, skipEmptyLines = true): string[] {
const lines = text.split(/\r?\n/);
if (skipEmptyLines) {
return lines.filter((line) => line.trim() !== "");
}
return lines;
}
/**
* 创建分片信息对象
* @param file 原始文件
* @param chunkSize 分片大小
*/
export function createFileSliceInfo(
file: File | Blob,
chunkSize = DEFAULT_CHUNK_SIZE
): {
originFile: Blob;
slices: Blob[];
name: string;
size: number;
totalChunks: number;
} {
const slices = sliceFile(file, chunkSize);
return {
originFile: file,
slices,
name: (file as File).name || "unnamed",
size: file.size,
totalChunks: slices.length,
};
}
/**
* 支持的文本文件 MIME 类型前缀
*/
export const TEXT_FILE_MIME_PREFIX = "text/";
/**
* 支持的文本文件 MIME 类型集合
*/
export const TEXT_FILE_MIME_TYPES = new Set([
"application/json",
"application/xml",
"application/csv",
"application/ndjson",
"application/x-ndjson",
"application/x-yaml",
"application/yaml",
"application/javascript",
"application/x-javascript",
"application/sql",
"application/rtf",
"application/xhtml+xml",
"application/svg+xml",
]);
/**
* 支持的文本文件扩展名集合
*/
export const TEXT_FILE_EXTENSIONS = new Set([
".txt",
".md",
".markdown",
".csv",
".tsv",
".json",
".jsonl",
".ndjson",
".log",
".xml",
".yaml",
".yml",
".sql",
".js",
".ts",
".jsx",
".tsx",
".html",
".htm",
".css",
".scss",
".less",
".py",
".java",
".c",
".cpp",
".h",
".hpp",
".go",
".rs",
".rb",
".php",
".sh",
".bash",
".zsh",
".ps1",
".bat",
".cmd",
".svg",
".rtf",
]);
/**
* 判断文件是否为文本文件(支持 UploadFile 类型)
* @param file UploadFile 对象
*/
export function isTextUploadFile(file: UploadFile): boolean {
const mimeType = (file.type || "").toLowerCase();
if (mimeType) {
if (mimeType.startsWith(TEXT_FILE_MIME_PREFIX)) return true;
if (TEXT_FILE_MIME_TYPES.has(mimeType)) return true;
}
const fileName = file.name || "";
const dotIndex = fileName.lastIndexOf(".");
if (dotIndex < 0) return false;
const ext = fileName.slice(dotIndex).toLowerCase();
return TEXT_FILE_EXTENSIONS.has(ext);
}
/**
* 判断文件名是否为文本文件
* @param fileName 文件名
*/
export function isTextFileByName(fileName: string): boolean {
const lowerName = fileName.toLowerCase();
// 先检查 MIME 类型(如果有)
// 这里简化处理,主要通过扩展名判断
const dotIndex = lowerName.lastIndexOf(".");
if (dotIndex < 0) return false;
const ext = lowerName.slice(dotIndex);
return TEXT_FILE_EXTENSIONS.has(ext);
}
/**
* 获取文件扩展名
* @param fileName 文件名
*/
export function getFileExtension(fileName: string): string {
const dotIndex = fileName.lastIndexOf(".");
if (dotIndex < 0) return "";
return fileName.slice(dotIndex).toLowerCase();
}
/**
* 安全地读取文件为文本
* @param file 文件对象
* @param encoding 编码,默认 UTF-8
*/
export function readFileAsText(
file: File | Blob,
encoding = "UTF-8"
): Promise<string> {
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.onload = (e) => resolve(e.target?.result as string);
reader.onerror = () => reject(new Error("Failed to read file"));
reader.readAsText(file, encoding);
});
}
/**
* 流式分割文件并逐行上传
* 使用 Blob.slice 逐块读取,避免一次性加载大文件到内存
* @param file 文件对象
* @param datasetId 数据集ID
* @param uploadFn 上传函数,接收 FormData 和配置,返回 Promise
* @param onProgress 进度回调 (currentBytes, totalBytes, uploadedLines)
* @param chunkSize 每次读取的块大小,默认 1MB
* @param options 其他选项
* @returns 上传结果统计
*/
export interface StreamUploadOptions {
reqId: number;
fileNamePrefix?: string;
hasArchive?: boolean;
prefix?: string;
signal?: AbortSignal;
maxConcurrency?: number;
}
export interface StreamUploadResult {
uploadedCount: number;
totalBytes: number;
skippedEmptyCount: number;
}
export async function streamSplitAndUpload(
file: File,
uploadFn: (formData: FormData, config?: { onUploadProgress?: (e: { loaded: number; total: number }) => void }) => Promise<unknown>,
onProgress?: (currentBytes: number, totalBytes: number, uploadedLines: number) => void,
chunkSize: number = 1024 * 1024, // 1MB
options: StreamUploadOptions
): Promise<StreamUploadResult> {
const { reqId, fileNamePrefix, prefix, signal, maxConcurrency = 3 } = options;
const fileSize = file.size;
let offset = 0;
let buffer = "";
let uploadedCount = 0;
let skippedEmptyCount = 0;
let currentBytes = 0;
// 获取文件名基础部分
const baseName = fileNamePrefix || file.name.replace(/\.[^/.]+$/, "");
// 用于并发控制的队列
const uploadQueue: Promise<void>[] = [];
let uploadIndex = 0;
/**
* 上传单行内容
*/
async function uploadLine(line: string, index: number): Promise<void> {
if (!line.trim()) {
skippedEmptyCount++;
return;
}
const newFileName = `${baseName}_${String(index + 1).padStart(6, "0")}`;
const blob = new Blob([line], { type: "text/plain" });
const lineFile = new File([blob], newFileName, { type: "text/plain" });
// 计算分片(小文件通常只需要一个分片)
const slices = sliceFile(lineFile, DEFAULT_CHUNK_SIZE);
const checkSum = await calculateSHA256(slices[0]);
const formData = new FormData();
formData.append("file", slices[0]);
formData.append("reqId", reqId.toString());
formData.append("fileNo", (index + 1).toString());
formData.append("chunkNo", "1");
formData.append("fileName", newFileName);
formData.append("fileSize", lineFile.size.toString());
formData.append("totalChunkNum", "1");
formData.append("checkSumHex", checkSum);
if (prefix !== undefined) {
formData.append("prefix", prefix);
}
await uploadFn(formData, {
onUploadProgress: () => {
// 单行文件很小,进度主要用于追踪上传状态
},
});
}
/**
* 处理并发上传
*/
async function processUploadQueue(): Promise<void> {
let currentIndex = 0;
async function runNext(): Promise<void> {
if (currentIndex >= uploadQueue.length) return;
const task = uploadQueue[currentIndex++];
await task;
await runNext();
}
const workers = Array(Math.min(maxConcurrency, uploadQueue.length))
.fill(null)
.map(() => runNext());
await Promise.all(workers);
}
// 逐块读取文件
while (offset < fileSize) {
// 检查是否已取消
if (signal?.aborted) {
throw new Error("Upload cancelled");
}
const end = Math.min(offset + chunkSize, fileSize);
const chunk = file.slice(offset, end);
const text = await readFileAsText(chunk);
// 将新读取的内容追加到 buffer
const combined = buffer + text;
// 按换行符分割(支持 \n 和 \r\n)
const lines = combined.split(/\r?\n/);
// 保留最后一行(可能不完整)
buffer = lines.pop() || "";
// 将完整行加入上传队列
for (const line of lines) {
if (signal?.aborted) {
throw new Error("Upload cancelled");
}
const currentLineIndex = uploadIndex++;
uploadQueue.push(
uploadLine(line, currentLineIndex).then(() => {
uploadedCount++;
onProgress?.(currentBytes, fileSize, uploadedCount);
})
);
}
currentBytes = end;
offset = end;
// 每处理完一个 chunk,更新进度
onProgress?.(currentBytes, fileSize, uploadedCount);
}
// 处理最后剩余的 buffer(如果文件不以换行符结尾)
if (buffer.trim()) {
const currentLineIndex = uploadIndex++;
uploadQueue.push(
uploadLine(buffer, currentLineIndex).then(() => {
uploadedCount++;
onProgress?.(fileSize, fileSize, uploadedCount);
})
);
}
// 并发执行所有上传任务
await processUploadQueue();
return {
uploadedCount,
totalBytes: fileSize,
skippedEmptyCount,
};
}
/**
* 判断文件是否需要流式分割上传
* @param file 文件对象
* @param threshold 阈值,默认 5MB
*/
export function shouldStreamUpload(file: File, threshold: number = 5 * 1024 * 1024): boolean {
return file.size > threshold;
}

View File

@@ -150,6 +150,18 @@ async def create_mapping(
labeling_project, snapshot_file_ids
)
# 如果启用了分段且为文本数据集,预生成切片结构
if dataset_type == TEXT_DATASET_TYPE and request.segmentation_enabled:
try:
from ..service.editor import AnnotationEditorService
editor_service = AnnotationEditorService(db)
# 异步预计算切片(不阻塞创建响应)
segmentation_result = await editor_service.precompute_segmentation_for_project(labeling_project.id)
logger.info(f"Precomputed segmentation for project {labeling_project.id}: {segmentation_result}")
except Exception as e:
logger.warning(f"Failed to precompute segmentation for project {labeling_project.id}: {e}")
# 不影响项目创建,只记录警告
response_data = DatasetMappingCreateResponse(
id=mapping.id,
labeling_project_id=str(mapping.labeling_project_id),

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,
}

View File

@@ -0,0 +1,82 @@
# backend-python Dockerfile 离线版本 v2
FROM maven:3-eclipse-temurin-8 AS datax-builder
# 配置 Maven 阿里云镜像
RUN mkdir -p /root/.m2 && \
echo '<?xml version="1.0" encoding="UTF-8"?>\n\
<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"\n\
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"\n\
xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0 http://maven.apache.org/xsd/settings-1.0.0.xsd">\n\
<mirrors>\n\
<mirror>\n\
<id>aliyunmaven</id>\n\
<mirrorOf>*</mirrorOf>\n\
<name>阿里云公共仓库</name>\n\
<url>https://maven.aliyun.com/repository/public</url>\n\
</mirror>\n\
</mirrors>\n\
</settings>' > /root/.m2/settings.xml
# 离线模式: 从构建参数获取本地 DataX 路径
ARG RESOURCES_DIR=./build-cache/resources
ARG DATAX_LOCAL_PATH=${RESOURCES_DIR}/DataX
# 复制本地 DataX 源码
COPY ${DATAX_LOCAL_PATH} /DataX
COPY runtime/datax/ DataX/
RUN cd DataX && \
sed -i "s/com.mysql.jdbc.Driver/com.mysql.cj.jdbc.Driver/g" \
plugin-rdbms-util/src/main/java/com/alibaba/datax/plugin/rdbms/util/DataBaseType.java && \
mvn -U clean package assembly:assembly -Dmaven.test.skip=true
# 使用预装 APT 包的基础镜像
FROM datamate-python-base:latest
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
POETRY_VERSION=2.2.1 \
POETRY_NO_INTERACTION=1 \
POETRY_VIRTUALENVS_CREATE=false \
POETRY_CACHE_DIR=/tmp/poetry_cache
ENV JAVA_HOME=/usr/lib/jvm/java-21-openjdk
ENV PATH="/root/.local/bin:$JAVA_HOME/bin:$PATH"
WORKDIR /app
# 配置 pip 阿里云镜像并安装 Poetry
RUN --mount=type=cache,target=/root/.cache/pip \
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ && \
pip config set global.trusted-host mirrors.aliyun.com && \
pip install --upgrade --root-user-action=ignore pip \
&& pip install --root-user-action=ignore pipx \
&& pipx install "poetry==$POETRY_VERSION"
COPY --from=datax-builder /DataX/target/datax/datax /opt/datax
RUN cp /opt/datax/plugin/reader/mysqlreader/libs/mysql* /opt/datax/plugin/reader/starrocksreader/libs/
# Copy only dependency files first
COPY runtime/datamate-python/pyproject.toml runtime/datamate-python/poetry.lock* /app/
# Install dependencies
RUN --mount=type=cache,target=$POETRY_CACHE_DIR \
poetry install --no-root --only main
# 离线模式: 使用本地 NLTK 数据
ARG RESOURCES_DIR=./build-cache/resources
ARG NLTK_DATA_LOCAL_PATH=${RESOURCES_DIR}/nltk_data
COPY ${NLTK_DATA_LOCAL_PATH} /usr/local/nltk_data
ENV NLTK_DATA=/usr/local/nltk_data
# Copy the rest of the application
COPY runtime/datamate-python /app
COPY runtime/datamate-python/deploy/docker-entrypoint.sh /docker-entrypoint.sh
RUN chmod +x /docker-entrypoint.sh || true
EXPOSE 18000
ENTRYPOINT ["/docker-entrypoint.sh"]

View File

@@ -0,0 +1,71 @@
# backend Dockerfile 离线版本
# 使用预装 APT 包的基础镜像
FROM maven:3-eclipse-temurin-21 AS builder
# 配置 Maven 阿里云镜像
RUN mkdir -p /root/.m2 && \
echo '<?xml version="1.0" encoding="UTF-8"?>\n\
<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"\n\
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"\n\
xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0 http://maven.apache.org/xsd/settings-1.0.0.xsd">\n\
<mirrors>\n\
<mirror>\n\
<id>aliyunmaven</id>\n\
<mirrorOf>*</mirrorOf>\n\
<name>阿里云公共仓库</name>\n\
<url>https://maven.aliyun.com/repository/public</url>\n\
</mirror>\n\
</mirrors>\n\
</settings>' > /root/.m2/settings.xml
WORKDIR /opt/backend
# 先复制所有 pom.xml 文件
COPY backend/pom.xml ./
COPY backend/services/pom.xml ./services/
COPY backend/shared/domain-common/pom.xml ./shared/domain-common/
COPY backend/shared/security-common/pom.xml ./shared/security-common/
COPY backend/services/data-annotation-service/pom.xml ./services/data-annotation-service/
COPY backend/services/data-cleaning-service/pom.xml ./services/data-cleaning-service/
COPY backend/services/data-evaluation-service/pom.xml ./services/data-evaluation-service/
COPY backend/services/data-management-service/pom.xml ./services/data-management-service/
COPY backend/services/data-synthesis-service/pom.xml ./services/data-synthesis-service/
COPY backend/services/execution-engine-service/pom.xml ./services/execution-engine-service/
COPY backend/services/main-application/pom.xml ./services/main-application/
COPY backend/services/operator-market-service/pom.xml ./services/operator-market-service/
COPY backend/services/pipeline-orchestration-service/pom.xml ./services/pipeline-orchestration-service/
COPY backend/services/rag-indexer-service/pom.xml ./services/rag-indexer-service/
COPY backend/services/rag-query-service/pom.xml ./services/rag-query-service/
# 使用缓存卷下载依赖
RUN --mount=type=cache,target=/root/.m2/repository \
cd /opt/backend/services && \
mvn dependency:go-offline -Dmaven.test.skip=true || true
# 复制所有源代码
COPY backend/ /opt/backend
# 编译打包
RUN --mount=type=cache,target=/root/.m2/repository \
cd /opt/backend/services && \
mvn clean package -Dmaven.test.skip=true
# 使用预装 APT 包的基础镜像
FROM datamate-java-base:latest
# 不再执行 apt-get update,因为基础镜像已经预装了所有需要的包
# 如果需要添加额外的包,可以在这里添加,但离线环境下会失败
COPY --from=builder /opt/backend/services/main-application/target/datamate.jar /opt/backend/datamate.jar
COPY scripts/images/backend/start.sh /opt/backend/start.sh
COPY runtime/ops/examples/test_operator/test_operator.tar /opt/backend/test_operator.tar
RUN dos2unix /opt/backend/start.sh \
&& chmod +x /opt/backend/start.sh \
&& ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime
EXPOSE 8080
ENTRYPOINT ["/opt/backend/start.sh"]
CMD ["java", "-Duser.timezone=Asia/Shanghai", "-jar", "/opt/backend/datamate.jar"]

View File

@@ -0,0 +1,62 @@
# 预安装 APT 包的基础镜像
# 在有网环境构建这些镜像,在无网环境作为基础镜像使用
# ==================== backend / gateway 基础镜像 ====================
FROM eclipse-temurin:21-jdk AS datamate-java-base
# 配置 apt 阿里云镜像源
RUN if [ -f /etc/apt/sources.list.d/debian.sources ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources; \
elif [ -f /etc/apt/sources.list.d/ubuntu.sources ]; then \
sed -i 's/archive.ubuntu.com/mirrors.aliyun.com/g; s/security.ubuntu.com/mirrors.aliyun.com/g' /etc/apt/sources.list.d/ubuntu.sources; \
elif [ -f /etc/apt/sources.list ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g; s/archive.ubuntu.com/mirrors.aliyun.com/g; s/security.ubuntu.com/mirrors.aliyun.com/g' /etc/apt/sources.list; \
fi && \
apt-get update && \
apt-get install -y vim wget curl rsync python3 python3-pip python-is-python3 dos2unix libreoffice fonts-noto-cjk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# ==================== backend-python 基础镜像 ====================
FROM python:3.12-slim AS datamate-python-base
RUN if [ -f /etc/apt/sources.list.d/debian.sources ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources; \
elif [ -f /etc/apt/sources.list ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list; \
fi && \
apt-get update && \
apt-get install -y --no-install-recommends vim openjdk-21-jre nfs-common glusterfs-client rsync && \
rm -rf /var/lib/apt/lists/*
# ==================== runtime 基础镜像 ====================
FROM ghcr.nju.edu.cn/astral-sh/uv:python3.11-bookworm AS datamate-runtime-base
RUN if [ -f /etc/apt/sources.list.d/debian.sources ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources; \
elif [ -f /etc/apt/sources.list ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list; \
fi && \
apt update && \
apt install -y libgl1 libglib2.0-0 vim libmagic1 libreoffice dos2unix swig poppler-utils tesseract-ocr && \
rm -rf /var/lib/apt/lists/*
# ==================== deer-flow-backend 基础镜像 ====================
FROM ghcr.nju.edu.cn/astral-sh/uv:python3.12-bookworm AS deer-flow-backend-base
RUN if [ -f /etc/apt/sources.list.d/debian.sources ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources; \
elif [ -f /etc/apt/sources.list ]; then \
sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list; \
fi && \
apt-get update && apt-get install -y libpq-dev git && \
rm -rf /var/lib/apt/lists/*
# ==================== mineru 基础镜像 ====================
FROM python:3.11-slim AS mineru-base
RUN sed -i 's/deb.debian.org/mirrors.huaweicloud.com/g' /etc/apt/sources.list.d/debian.sources && \
apt-get update && \
apt-get install -y curl vim libgl1 libglx0 libopengl0 libglib2.0-0 procps && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*

View File

@@ -0,0 +1,47 @@
# gateway Dockerfile 离线版本
FROM maven:3-eclipse-temurin-21 AS builder
# 配置 Maven 阿里云镜像
RUN mkdir -p /root/.m2 && \
echo '<?xml version="1.0" encoding="UTF-8"?>\n\
<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"\n\
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"\n\
xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0 http://maven.apache.org/xsd/settings-1.0.0.xsd">\n\
<mirrors>\n\
<mirror>\n\
<id>aliyunmaven</id>\n\
<mirrorOf>*</mirrorOf>\n\
<name>阿里云公共仓库</name>\n\
<url>https://maven.aliyun.com/repository/public</url>\n\
</mirror>\n\
</mirrors>\n\
</settings>' > /root/.m2/settings.xml
WORKDIR /opt/gateway
COPY backend/pom.xml ./
COPY backend/api-gateway/pom.xml ./api-gateway/
RUN --mount=type=cache,target=/root/.m2/repository \
cd /opt/gateway/api-gateway && \
mvn dependency:go-offline -Dmaven.test.skip=true || true
COPY backend/api-gateway /opt/gateway/api-gateway
RUN --mount=type=cache,target=/root/.m2/repository \
cd /opt/gateway/api-gateway && \
mvn clean package -Dmaven.test.skip=true
FROM datamate-java-base:latest
COPY --from=builder /opt/gateway/api-gateway/target/gateway.jar /opt/gateway/gateway.jar
COPY scripts/images/gateway/start.sh /opt/gateway/start.sh
RUN dos2unix /opt/gateway/start.sh \
&& chmod +x /opt/gateway/start.sh \
&& ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime
EXPOSE 8080
ENTRYPOINT ["/opt/gateway/start.sh"]
CMD ["java", "-Duser.timezone=Asia/Shanghai", "-jar", "/opt/gateway/gateway.jar"]

View File

@@ -0,0 +1,42 @@
# runtime Dockerfile 离线版本 v2
# 使用预装 APT 包的基础镜像
FROM datamate-runtime-base:latest
# 离线模式: 本地模型文件路径
ARG RESOURCES_DIR=./build-cache/resources
ARG MODELS_DIR=${RESOURCES_DIR}/models
# 复制本地 PaddleOCR 模型
RUN mkdir -p /home/models
COPY ${MODELS_DIR}/ch_ppocr_mobile_v2.0_cls_infer.tar /home/models/
RUN tar -xf /home/models/ch_ppocr_mobile_v2.0_cls_infer.tar -C /home/models
COPY runtime/python-executor /opt/runtime
COPY runtime/ops /opt/runtime/datamate/ops
COPY runtime/ops/user /opt/runtime/user
COPY scripts/images/runtime/start.sh /opt/runtime/start.sh
ENV PYTHONPATH=/opt/runtime/datamate/
ENV UV_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
ENV UV_INDEX_STRATEGY=unsafe-best-match
ENV UV_INDEX_URL="https://mirrors.aliyun.com/pypi/simple/"
WORKDIR /opt/runtime
# 复制本地 spaCy 模型
COPY ${MODELS_DIR}/zh_core_web_sm-3.8.0-py3-none-any.whl /tmp/
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e .[all] --system \
&& uv pip install -r /opt/runtime/datamate/ops/pyproject.toml --system \
&& uv pip install /tmp/zh_core_web_sm-3.8.0-py3-none-any.whl --system \
&& echo "/usr/local/lib/ops/site-packages" > /usr/local/lib/python3.11/site-packages/ops.pth
RUN ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime \
&& chmod +x /opt/runtime/start.sh \
&& dos2unix /opt/runtime/start.sh
EXPOSE 8081
ENTRYPOINT ["/opt/runtime/start.sh"]

View File

@@ -74,20 +74,29 @@ scp build-cache-20250202.tar.gz user@offline-server:/opt/datamate/
# 或者使用 U 盘等物理介质
```
#### 4. 无网环境构建
#### 4. 无网环境构建(推荐使用传统方式)
```bash
# 解压缓存
tar -xzf build-cache-20250202.tar.gz
# 设置环境并构建
# 诊断环境(检查基础镜像等)
make offline-diagnose
# 方法 A:传统 docker build(推荐,更稳定)
make offline-setup
make offline-build-classic
# 方法 B:BuildKit 构建(如果方法 A 失败)
make offline-setup
make offline-build
# 或者指定版本号
make offline-build OFFLINE_VERSION=v1.0.0
make offline-build-classic OFFLINE_VERSION=v1.0.0
```
**⚠️ 重要提示**:如果遇到镜像拉取问题,请使用 `make offline-build-classic` 而不是 `make offline-build`
### 方法二:使用独立脚本
#### 导出缓存
@@ -162,9 +171,133 @@ tar -czf build-cache-partial.tar.gz build-cache/buildkit/backend-cache
make backend-offline-build
```
## APT 缓存问题详解
### 问题描述
即使使用了 `--mount=type=cache,target=/var/cache/apt`,Dockerfile 中的 `apt-get update` 仍会尝试从网络获取包列表(list 数据),导致无网环境下构建失败:
```
Err:1 http://mirrors.aliyun.com/debian bookworm InRelease
Could not resolve 'mirrors.aliyun.com'
Reading package lists...
E: Failed to fetch http://mirrors.aliyun.com/debian/dists/bookworm/InRelease
```
### 根本原因
- `--mount=type=cache,target=/var/cache/apt` 只缓存下载的 `.deb`
- `apt-get update` 会尝试从配置的源获取最新的包索引(InRelease/Packages 文件)
- `/var/lib/apt/lists/` 目录存储包索引,但通常不在缓存范围内
### 解决方案
#### 方案 1: 使用预装 APT 包的基础镜像(推荐)
这是最有效的方法:
**步骤 1**: 在有网环境构建预装所有依赖的基础镜像
```bash
# 构建并保存带 APT 预装包的基础镜像
./scripts/offline/build-base-images.sh
```
这会创建以下预装基础镜像:
- `datamate-java-base` - 用于 backend、gateway(预装 vim、python3、libreoffice 等)
- `datamate-python-base` - 用于 backend-python(预装 openjdk、nfs-common 等)
- `datamate-runtime-base` - 用于 runtime(预装 libgl1、tesseract-ocr 等)
- `deer-flow-backend-base` - 用于 deer-flow-backend
- `mineru-base` - 用于 mineru
**步骤 2**: 在无网环境使用这些基础镜像构建
```bash
# 加载包含预装基础镜像的 tar 包
docker load -i build-cache/images/base-images-with-apt.tar
# 使用最终版构建脚本
./scripts/offline/build-offline-final.sh
```
#### 方案 2: 修改 Dockerfile 跳过 apt update
如果确定不需要安装新包,可以修改 Dockerfile:
```dockerfile
# 原代码
RUN apt-get update && apt-get install -y xxx
# 修改为(离线环境)
# RUN apt-get update && \
RUN apt-get install -y xxx || true
```
#### 方案 3: 挂载 apt lists 缓存
在有网环境预先下载并保存 apt lists:
```bash
# 有网环境:保存 apt lists
docker run --rm \
-v "$(pwd)/apt-lists:/var/lib/apt/lists" \
eclipse-temurin:21-jdk \
apt-get update
# 无网环境:挂载保存的 lists
docker build \
--mount=type=bind,source=$(pwd)/apt-lists,target=/var/lib/apt/lists,ro \
-f Dockerfile .
```
**注意**: BuildKit 的 `--mount=type=bind``docker build` 中不直接支持,需要在 Dockerfile 中使用。
---
## 故障排查
### 问题 1: 缓存导入失败
### 问题 1: 构建时仍然尝试拉取镜像(最常见)
**现象**:
```
ERROR: failed to solve: pulling from host ...
ERROR: pull access denied, repository does not exist or may require authorization
```
**原因**:
- 基础镜像未正确加载
- BuildKit 尝试验证远程镜像
**解决方案**:
1. **使用传统构建方式(推荐)**:
```bash
make offline-build-classic
```
2. **手动加载基础镜像**:
```bash
# 加载基础镜像
docker load -i build-cache/images/base-images.tar
# 验证镜像存在
docker images | grep -E "(maven|eclipse-temurin|mysql|node|nginx)"
```
3. **使用 Docker 守护进程的离线模式**:
```bash
# 编辑 /etc/docker/daemon.json
{
"registry-mirrors": [],
"insecure-registries": []
}
# 重启 Docker
sudo systemctl restart docker
```
### 问题 2: 缓存导入失败
```
ERROR: failed to solve: failed to read cache metadata
@@ -172,23 +305,26 @@ ERROR: failed to solve: failed to read cache metadata
**解决**: 缓存目录可能损坏,重新在有网环境导出。
### 问题 2: 基础镜像不存在
### 问题 3: 基础镜像不存在
```
ERROR: pull access denied
```
**解决**: 先执行 `make offline-setup` 加载基础镜像。
**解决**:
1. 先执行 `make offline-setup` 加载基础镜像
2. 运行 `make offline-diagnose` 检查缺失的镜像
3. 重新导出缓存时确保包含所有基础镜像
### 问题 3: 网络连接错误(无网环境)
### 问题 4: 网络连接错误(无网环境)
```
ERROR: failed to do request: dial tcp: lookup ...
```
**解决**: 检查 Dockerfile 中是否还有网络依赖,可能需要修改 Dockerfile 使用本地资源。
**解决**: 检查 Dockerfile 中是否还有网络依赖(如 `git clone``wget``pip install` 等),可能需要修改 Dockerfile 使用本地资源。
### 问题 4: 内存不足
### 问题 5: 内存不足
BuildKit 缓存可能占用大量内存,可以设置资源限制:
@@ -200,6 +336,39 @@ docker buildx create --name offline-builder \
--use
```
### 问题 6: BuildKit 构建器无法使用本地镜像
**现象**: 即使镜像已加载,BuildKit 仍提示找不到镜像
**解决**: BuildKit 的 `docker-container` 驱动无法直接访问本地镜像。使用以下方法之一:
**方法 A**: 使用传统 Docker 构建(推荐)
```bash
make offline-build-classic
```
**方法 B**: 将镜像推送到本地 registry
```bash
# 启动本地 registry
docker run -d -p 5000:5000 --name registry registry:2
# 标记并推送镜像到本地 registry
docker tag maven:3-eclipse-temurin-21 localhost:5000/maven:3-eclipse-temurin-21
docker push localhost:5000/maven:3-eclipse-temurin-21
# 修改 Dockerfile 使用本地 registry
# FROM localhost:5000/maven:3-eclipse-temurin-21
```
**方法 C**: 使用 `docker` 驱动的 buildx 构建器(不需要推送镜像,但有其他限制)
```bash
# 创建使用 docker 驱动的构建器
docker buildx create --name offline-builder --driver docker --use
# 但这种方式无法使用 --cache-from type=local
# 仅适用于简单的离线构建场景
```
## 限制说明
1. **镜像版本**: 基础镜像版本必须与缓存导出时一致
@@ -239,6 +408,81 @@ docker buildx build \
-f scripts/images/backend/Dockerfile .
```
## 文件清单
```
scripts/offline/
├── export-cache.sh # 有网环境导出缓存脚本
├── build-base-images.sh # 构建 APT 预装基础镜像
├── build-offline.sh # 基础离线构建脚本(BuildKit)
├── build-offline-v2.sh # 增强版离线构建脚本
├── build-offline-classic.sh # 传统 docker build 脚本
├── build-offline-final.sh # 最终版(使用预装基础镜像,推荐)
├── diagnose.sh # 环境诊断脚本
├── Dockerfile.base-images # 预装 APT 包的基础镜像定义
├── Dockerfile.backend.offline # backend 离线 Dockerfile(使用预装基础镜像)
├── Dockerfile.gateway.offline # gateway 离线 Dockerfile(使用预装基础镜像)
├── Dockerfile.backend-python.offline # backend-python 离线 Dockerfile
├── Dockerfile.backend-python.offline-v2 # backend-python 离线 Dockerfile v2(使用预装基础镜像)
├── Dockerfile.runtime.offline # runtime 离线 Dockerfile
├── Dockerfile.runtime.offline-v2 # runtime 离线 Dockerfile v2(使用预装基础镜像)
├── Dockerfile.deer-flow-backend.offline # deer-flow-backend 离线 Dockerfile
├── Dockerfile.deer-flow-frontend.offline # deer-flow-frontend 离线 Dockerfile
├── Makefile.offline # 独立离线构建 Makefile
└── README.md # 本文档
Makefile.offline.mk # Makefile 扩展(追加到主 Makefile)
```
## 推荐工作流(解决 APT 问题版)
### 工作流 A: 使用预装 APT 包的基础镜像(彻底解决 APT 问题)
```bash
# ========== 有网环境 ==========
# 1. 构建并保存带 APT 预装包的基础镜像
./scripts/offline/build-base-images.sh
# 输出: build-cache/images/base-images-with-apt.tar
# 2. 导出其他缓存(BuildKit 缓存、外部资源)
./scripts/offline/export-cache.sh
# 3. 打包传输
scp build-cache/images/base-images-with-apt.tar user@offline-server:/opt/datamate/build-cache/images/
scp build-cache-*.tar.gz user@offline-server:/opt/datamate/
# ========== 无网环境 ==========
cd /opt/datamate
# 4. 解压
tar -xzf build-cache-*.tar.gz
# 5. 加载预装基础镜像(关键!)
docker load -i build-cache/images/base-images-with-apt.tar
# 6. 使用最终版脚本构建
./scripts/offline/build-offline-final.sh
```
### 工作流 B: 简单场景(使用传统构建)
如果 APT 包需求简单,可以直接使用传统构建:
```bash
# 有网环境
make offline-export
# 传输到无网环境
scp build-cache-*.tar.gz offline-server:/path/
# 无网环境
tar -xzf build-cache-*.tar.gz
make offline-diagnose # 检查环境
make offline-build-classic # 使用传统构建
```
## 参考
- [Docker BuildKit Documentation](https://docs.docker.com/build/buildkit/)

View File

@@ -0,0 +1,87 @@
#!/bin/bash
# 构建带有预装 APT 包的基础镜像
# Usage: ./build-base-images.sh [output-dir]
set -e
OUTPUT_DIR="${1:-./build-cache}"
IMAGES_DIR="$OUTPUT_DIR/images"
mkdir -p "$IMAGES_DIR"
echo "======================================"
echo "构建预装 APT 包的基础镜像"
echo "======================================"
# 构建各个基础镜像
echo ""
echo "1. 构建 datamate-java-base (用于 backend, gateway)..."
docker build \
-t datamate-java-base:latest \
--target datamate-java-base \
-f scripts/offline/Dockerfile.base-images \
. || echo "Warning: datamate-java-base 构建失败"
echo ""
echo "2. 构建 datamate-python-base (用于 backend-python)..."
docker build \
-t datamate-python-base:latest \
--target datamate-python-base \
-f scripts/offline/Dockerfile.base-images \
. || echo "Warning: datamate-python-base 构建失败"
echo ""
echo "3. 构建 datamate-runtime-base (用于 runtime)..."
docker build \
-t datamate-runtime-base:latest \
--target datamate-runtime-base \
-f scripts/offline/Dockerfile.base-images \
. || echo "Warning: datamate-runtime-base 构建失败"
echo ""
echo "4. 构建 deer-flow-backend-base (用于 deer-flow-backend)..."
docker build \
-t deer-flow-backend-base:latest \
--target deer-flow-backend-base \
-f scripts/offline/Dockerfile.base-images \
. || echo "Warning: deer-flow-backend-base 构建失败"
echo ""
echo "5. 构建 mineru-base (用于 mineru)..."
docker build \
-t mineru-base:latest \
--target mineru-base \
-f scripts/offline/Dockerfile.base-images \
. || echo "Warning: mineru-base 构建失败"
echo ""
echo "======================================"
echo "保存基础镜像集合"
echo "======================================"
docker save -o "$IMAGES_DIR/base-images-with-apt.tar" \
maven:3-eclipse-temurin-21 \
maven:3-eclipse-temurin-8 \
eclipse-temurin:21-jdk \
mysql:8 \
node:20-alpine \
nginx:1.29 \
ghcr.nju.edu.cn/astral-sh/uv:python3.11-bookworm \
ghcr.nju.edu.cn/astral-sh/uv:python3.12-bookworm \
ghcr.nju.edu.cn/astral-sh/uv:latest \
python:3.12-slim \
python:3.11-slim \
gcr.io/distroless/nodejs20-debian12 \
datamate-java-base:latest \
datamate-python-base:latest \
datamate-runtime-base:latest \
deer-flow-backend-base:latest \
mineru-base:latest \
2>/dev/null || echo "Warning: 部分镜像保存失败"
echo ""
echo "======================================"
echo "✓ 基础镜像构建完成"
echo "======================================"
echo "镜像列表:"
docker images | grep -E "(datamate-|deer-flow-|mineru-)base" || true

View File

@@ -0,0 +1,181 @@
#!/bin/bash
# 最终版离线构建脚本 - 使用预装 APT 包的基础镜像
# Usage: ./build-offline-final.sh [cache-dir] [version]
set -e
CACHE_DIR="${1:-./build-cache}"
VERSION="${2:-latest}"
IMAGES_DIR="$CACHE_DIR/images"
RESOURCES_DIR="$CACHE_DIR/resources"
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m'
log_info() { echo -e "${GREEN}[INFO]${NC} $1"; }
log_warn() { echo -e "${YELLOW}[WARN]${NC} $1"; }
log_error() { echo -e "${RED}[ERROR]${NC} $1"; }
# 检查缓存目录
if [ ! -d "$CACHE_DIR" ]; then
log_error "缓存目录 $CACHE_DIR 不存在"
exit 1
fi
# 加载基础镜像
load_images() {
log_info "加载基础镜像..."
# 优先加载带 APT 预装包的镜像集合
if [ -f "$IMAGES_DIR/base-images-with-apt.tar" ]; then
log_info "加载带 APT 预装包的基础镜像..."
docker load -i "$IMAGES_DIR/base-images-with-apt.tar"
elif [ -f "$IMAGES_DIR/base-images.tar" ]; then
log_warn "加载普通基础镜像(不含 APT 预装包)..."
docker load -i "$IMAGES_DIR/base-images.tar"
else
log_warn "基础镜像 tar 包不存在,检查本地镜像..."
fi
log_info "✓ 镜像加载完成"
}
# 验证镜像是否存在
verify_image() {
docker inspect "$1" > /dev/null 2>&1
}
# 构建函数
build_service() {
local service_name=$1
local image_name=$2
local dockerfile=$3
local base_image=$4 # 必需的基础镜像
log_info "----------------------------------------"
log_info "构建 $service_name"
log_info "----------------------------------------"
if [ ! -f "$dockerfile" ]; then
log_error "Dockerfile 不存在: $dockerfile"
return 1
fi
# 检查必需的基础镜像
if [ -n "$base_image" ]; then
if verify_image "$base_image"; then
log_info "✓ 基础镜像存在: $base_image"
else
log_error "✗ 缺少基础镜像: $base_image"
log_info "请确保已加载正确的 base-images-with-apt.tar"
return 1
fi
fi
# 准备构建参数
local build_args=()
# 添加资源目录参数
if [ -d "$RESOURCES_DIR" ]; then
build_args+=("--build-arg" "RESOURCES_DIR=$RESOURCES_DIR")
fi
# 执行构建
log_info "开始构建..."
if docker build \
--pull=false \
"${build_args[@]}" \
-f "$dockerfile" \
-t "$image_name:$VERSION" \
. 2>&1; then
log_info "$service_name 构建成功"
return 0
else
log_error "$service_name 构建失败"
return 1
fi
}
# 主流程
main() {
log_info "======================================"
log_info "最终版离线构建 (使用 APT 预装基础镜像)"
log_info "======================================"
# 加载基础镜像
load_images
# 验证关键基础镜像
log_info ""
log_info "验证预装基础镜像..."
REQUIRED_BASE_IMAGES=(
"datamate-java-base:latest"
"datamate-python-base:latest"
"datamate-runtime-base:latest"
)
for img in "${REQUIRED_BASE_IMAGES[@]}"; do
if verify_image "$img"; then
log_info "$img"
else
log_warn "$img (缺失)"
fi
done
# 定义服务配置
declare -A SERVICES=(
["database"]="datamate-database:scripts/images/database/Dockerfile:"
["gateway"]="datamate-gateway:scripts/offline/Dockerfile.gateway.offline:datamate-java-base:latest"
["backend"]="datamate-backend:scripts/offline/Dockerfile.backend.offline:datamate-java-base:latest"
["frontend"]="datamate-frontend:scripts/images/frontend/Dockerfile:"
["runtime"]="datamate-runtime:scripts/offline/Dockerfile.runtime.offline-v2:datamate-runtime-base:latest"
["backend-python"]="datamate-backend-python:scripts/offline/Dockerfile.backend-python.offline-v2:datamate-python-base:latest"
)
log_info ""
log_info "======================================"
log_info "开始构建服务"
log_info "======================================"
local failed=()
local succeeded=()
for service_name in "${!SERVICES[@]}"; do
IFS=':' read -r image_name dockerfile base_image <<< "${SERVICES[$service_name]}"
if build_service "$service_name" "$image_name" "$dockerfile" "$base_image"; then
succeeded+=("$service_name")
else
failed+=("$service_name")
fi
echo ""
done
# 汇总
log_info "======================================"
log_info "构建结果"
log_info "======================================"
if [ ${#succeeded[@]} -gt 0 ]; then
log_info "成功 (${#succeeded[@]}): ${succeeded[*]}"
fi
if [ ${#failed[@]} -gt 0 ]; then
log_error "失败 (${#failed[@]}): ${failed[*]}"
log_info ""
log_info "提示: 如果失败是因为缺少预装基础镜像,请确保:"
log_info " 1. 在有网环境执行: ./scripts/offline/build-base-images.sh"
log_info " 2. 将生成的 base-images-with-apt.tar 传输到无网环境"
log_info " 3. 在无网环境加载: docker load -i base-images-with-apt.tar"
exit 1
else
log_info "✓ 所有服务构建成功!"
echo ""
docker images --format "table {{.Repository}}:{{.Tag}}\t{{.Size}}" | grep -E "(datamate-|deer-flow-)" || true
fi
}
main "$@"

View File

@@ -8,6 +8,7 @@ OUTPUT_DIR="${1:-./build-cache}"
BUILDKIT_CACHE_DIR="$OUTPUT_DIR/buildkit"
IMAGES_DIR="$OUTPUT_DIR/images"
RESOURCES_DIR="$OUTPUT_DIR/resources"
APT_CACHE_DIR="$OUTPUT_DIR/apt-cache"
# 确保 buildx 构建器存在
if ! docker buildx inspect offline-builder > /dev/null 2>&1; then
@@ -17,7 +18,7 @@ else
docker buildx use offline-builder
fi
mkdir -p "$BUILDKIT_CACHE_DIR" "$IMAGES_DIR" "$RESOURCES_DIR"
mkdir -p "$BUILDKIT_CACHE_DIR" "$IMAGES_DIR" "$RESOURCES_DIR" "$APT_CACHE_DIR"
echo "======================================"
echo "1. 导出基础镜像"
@@ -117,11 +118,42 @@ fi
echo ""
echo "======================================"
echo "4. 打包缓存"
echo "4. 导出 APT 缓存"
echo "======================================"
# 为需要 apt 的镜像预生成 apt 缓存
echo "生成 APT list 缓存..."
# eclipse-temurin:21-jdk 的 apt 缓存
docker run --rm \
-v "$APT_CACHE_DIR/eclipse-temurin:/var/cache/apt/archives" \
-v "$APT_CACHE_DIR/eclipse-temurin-lists:/var/lib/apt/lists" \
eclipse-temurin:21-jdk \
bash -c "apt-get update && apt-get install -y --download-only vim wget curl rsync python3 python3-pip python-is-python3 dos2unix libreoffice fonts-noto-cjk 2>/dev/null || true" 2>/dev/null || echo " Warning: eclipse-temurin apt 缓存导出失败"
# python:3.12-slim 的 apt 缓存
docker run --rm \
-v "$APT_CACHE_DIR/python312:/var/cache/apt/archives" \
-v "$APT_CACHE_DIR/python312-lists:/var/lib/apt/lists" \
python:3.12-slim \
bash -c "apt-get update && apt-get install -y --download-only vim openjdk-21-jre nfs-common glusterfs-client rsync 2>/dev/null || true" 2>/dev/null || echo " Warning: python3.12 apt 缓存导出失败"
# python:3.11-slim 的 apt 缓存
docker run --rm \
-v "$APT_CACHE_DIR/python311:/var/cache/apt/archives" \
-v "$APT_CACHE_DIR/python311-lists:/var/lib/apt/lists" \
python:3.11-slim \
bash -c "apt-get update && apt-get install -y --download-only curl vim libgl1 libglx0 libopengl0 libglib2.0-0 procps 2>/dev/null || true" 2>/dev/null || echo " Warning: python3.11 apt 缓存导出失败"
echo "✓ APT 缓存导出完成"
echo ""
echo "======================================"
echo "5. 打包缓存"
echo "======================================"
cd "$OUTPUT_DIR"
tar -czf "build-cache-$(date +%Y%m%d).tar.gz" buildkit images resources
tar -czf "build-cache-$(date +%Y%m%d).tar.gz" buildkit images resources apt-cache
cd - > /dev/null
echo ""
@@ -131,4 +163,10 @@ echo "======================================"
echo "缓存位置: $OUTPUT_DIR"
echo "传输文件: $OUTPUT_DIR/build-cache-$(date +%Y%m%d).tar.gz"
echo ""
echo "包含内容:"
echo " - 基础镜像 (images/)"
echo " - BuildKit 缓存 (buildkit/)"
echo " - 外部资源 (resources/)"
echo " - APT 缓存 (apt-cache/)"
echo ""
echo "请将此压缩包传输到无网环境后解压使用"