diff --git a/apps/web/package-lock.json b/apps/web/package-lock.json
index ba825f7..9ac3f20 100644
--- a/apps/web/package-lock.json
+++ b/apps/web/package-lock.json
@@ -1,12 +1,12 @@
{
"name": "ultimate-rag-web",
- "version": "1.0.0",
+ "version": "2.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "ultimate-rag-web",
- "version": "1.0.0",
+ "version": "2.0.0",
"dependencies": {
"@ai-sdk/react": "^4.0.86",
"@radix-ui/react-avatar": "^1.2.6",
diff --git a/apps/web/package.json b/apps/web/package.json
index bc4c24b..20daa38 100644
--- a/apps/web/package.json
+++ b/apps/web/package.json
@@ -1,6 +1,6 @@
{
"name": "ultimate-rag-web",
- "version": "1.0.0",
+ "version": "2.0.0",
"private": true,
"scripts": {
"dev": "next dev",
diff --git a/data/1.png b/data/1.png
new file mode 100644
index 0000000..5897d8b
Binary files /dev/null and b/data/1.png differ
diff --git a/data/2.png b/data/2.png
new file mode 100644
index 0000000..6b3f697
Binary files /dev/null and b/data/2.png differ
diff --git a/data/attention is all you need.pdf b/data/attention is all you need.pdf
new file mode 100644
index 0000000..97d7c51
Binary files /dev/null and b/data/attention is all you need.pdf differ
diff --git a/docker-compose.yml b/docker-compose.yml
index cb964ea..dd2c780 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,3 +1,4 @@
+# Compose 项目名保持稳定，V1 升级 V2 时继续复用已有事实数据卷。
name: ultimate-rag-v1
services:
@@ -82,6 +83,7 @@ services:
restart: unless-stopped
api:
+ image: ultimate-rag-api:local
build:
context: .
dockerfile: Dockerfile.api
@@ -100,6 +102,14 @@ services:
EMBEDDING_MODEL: ${EMBEDDING_MODEL:-text-embedding-v4}
EMBEDDING_DIMENSION: ${EMBEDDING_DIMENSION:-1024}
LLM_MODEL: ${LLM_MODEL:-qwen-plus}
+ OCR_MODEL: ${OCR_MODEL:-qwen3.5-ocr}
+ OCR_MAX_IMAGE_BYTES: ${OCR_MAX_IMAGE_BYTES:-6291456}
+ OCR_MAX_OUTPUT_TOKENS: ${OCR_MAX_OUTPUT_TOKENS:-4096}
+ VISION_MODEL: ${VISION_MODEL:-qwen3-vl-flash}
+ VISION_MAX_IMAGE_BYTES: ${VISION_MAX_IMAGE_BYTES:-6291456}
+ VISION_MAX_OUTPUT_TOKENS: ${VISION_MAX_OUTPUT_TOKENS:-1536}
+ PDF_SCAN_IMAGE_COVERAGE_THRESHOLD: ${PDF_SCAN_IMAGE_COVERAGE_THRESHOLD:-0.65}
+ PDF_SCAN_VISION_TEXT_THRESHOLD: ${PDF_SCAN_VISION_TEXT_THRESHOLD:-300}
CORS_ORIGINS: http://localhost:3000,http://192.168.3.19:3000
ports:
- "8000:8000"
@@ -110,8 +120,47 @@ services:
condition: service_healthy
minio:
condition: service_started
+ healthcheck:
+ test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health', timeout=3)"]
+ interval: 5s
+ timeout: 5s
+ retries: 30
+ start_period: 20s
restart: on-failure
+ # Worker 使用同一镜像和 Parser 配置，但不开放端口，也不处理 HTTP。PostgreSQL 任务让容器
+ # 重启后仍可继续处理；本地模型缓存卷避免每次重建容器重复下载 Docling 模型。
+ worker:
+ image: ultimate-rag-api:local
+ command: ["python", "-m", "ultimate_rag.worker"]
+ env_file:
+ - .env
+ environment:
+ DATABASE_URL: postgresql+asyncpg://ultimate_rag:ultimate_rag@postgres:5432/ultimate_rag
+ MINIO_ENDPOINT: minio:9000
+ MINIO_ACCESS_KEY: ultimate_rag
+ MINIO_SECRET_KEY: ultimate_rag_dev_secret
+ MINIO_SECURE: "false"
+ MINIO_BUCKET: documents
+ MILVUS_URI: http://milvus:19530
+ MILVUS_COLLECTION: knowledge_chunks
+ EMBEDDING_MODEL: ${EMBEDDING_MODEL:-text-embedding-v4}
+ EMBEDDING_DIMENSION: ${EMBEDDING_DIMENSION:-1024}
+ OCR_MODEL: ${OCR_MODEL:-qwen3.5-ocr}
+ OCR_MAX_IMAGE_BYTES: ${OCR_MAX_IMAGE_BYTES:-6291456}
+ OCR_MAX_OUTPUT_TOKENS: ${OCR_MAX_OUTPUT_TOKENS:-4096}
+ VISION_MODEL: ${VISION_MODEL:-qwen3-vl-flash}
+ VISION_MAX_IMAGE_BYTES: ${VISION_MAX_IMAGE_BYTES:-6291456}
+ VISION_MAX_OUTPUT_TOKENS: ${VISION_MAX_OUTPUT_TOKENS:-1536}
+ PDF_SCAN_IMAGE_COVERAGE_THRESHOLD: ${PDF_SCAN_IMAGE_COVERAGE_THRESHOLD:-0.65}
+ PDF_SCAN_VISION_TEXT_THRESHOLD: ${PDF_SCAN_VISION_TEXT_THRESHOLD:-300}
+ volumes:
+ - docling_cache:/root/.cache
+ depends_on:
+ api:
+ condition: service_healthy
+ restart: unless-stopped
+
web:
build:
context: apps/web
@@ -129,3 +178,4 @@ volumes:
minio_data:
etcd_data:
milvus_data:
+ docling_cache:
diff --git a/docs/1.product_description.md b/docs/1.product_description.md
index cdf97a0..d6459ac 100644
--- a/docs/1.product_description.md
+++ b/docs/1.product_description.md
@@ -581,6 +581,9 @@ V2.0 核心目标：
> 新增一种文档类型，不需要修改 RAG 主流程。
+当前实现根据明确产品需求补充了“上传立即返回 + 后台可靠解析 + 前端状态可见”。这是 V2 文档智能
+可用性的必要调整；V4 中的 DLQ 控制台、权限、审计和通用企业任务治理仍保持原规划。
+
---
# 10. 后续版本方向
diff --git a/docs/2.technical_architecture.md b/docs/2.technical_architecture.md
index 3de9073..7ab344c 100644
--- a/docs/2.technical_architecture.md
+++ b/docs/2.technical_architecture.md
@@ -903,7 +903,10 @@ FAILED
即使 V1.0 是同步执行，也提前保留状态模型。
-V4.0 引入异步 Pipeline 时无需重构业务模型。
+当前 V2 实现已按明确产品要求提前引入 PostgreSQL 持久化异步 Pipeline，且复用了这里预留的状态
+模型。V4 仍负责 DLQ、任务运维、审计、权限和大规模调度，而不是重新定义文档处理主链路。
+
+具体决策见 `docs/adr/ADR-001-v2-async-document-intelligence.md`。
---
@@ -1433,6 +1436,9 @@ LangGraph Orchestration Layer
V2.0 核心：
+> 实现修订：上传在 MinIO 与 PostgreSQL 任务可靠落库后返回 `202`，独立 Worker 再进入下图的
+> Parser Registry。该调整来自当前明确需求，不把 V4 的 ACL、审计或通用任务平台提前引入。
+
```text
Parser Registry
diff --git a/docs/4.v2_implementation.md b/docs/4.v2_implementation.md
new file mode 100644
index 0000000..34e3ab3
--- /dev/null
+++ b/docs/4.v2_implementation.md
@@ -0,0 +1,370 @@
+# UltimateRAG V2 实现说明
+
+## 1. 文档目的
+
+本文说明 V2 Document Intelligence 如何在 V1 Naive RAG 闭环上增加多格式解析能力，同时保持
+RAG Core 与原始文件格式解耦。产品范围以 `1.product_description.md` 为准，架构边界以
+`2.technical_architecture.md` 为准。
+
+V2 的核心交付不是“安装更多文件解析库”，而是建立一条稳定边界：
+
+```text
+不可信原始文件
+ ↓
+Parser Registry
+ ↓
+MinIO + PostgreSQL IngestionJob
+ ↓
+Background Worker
+ ↓
+DocumentParser
+ ↓
+ParsedDocument / Block / SourceLocator
+ ↓
+StructureAwareChunker
+ ↓
+V1 RAG Core（Embed → Index → Retrieve → Generate）
+```
+
+新增一种文档类型只需实现 `DocumentParser` 并注册，不需要修改切块、Embedding、Milvus、检索、
+上下文构造或生成流程。
+
+## 2. V2 支持范围
+
+| 类型 | 扩展名 | Parser | 主要定位信息 |
+|---|---|---|---|
+| Markdown | `.md`、`.markdown` | `MarkdownParser` | `heading_path` |
+| PDF | `.pdf` | `PDFParser`（Docling + PDFium + 百炼） | `heading_path`、`page`、`bbox` |
+| Word | `.docx` | `WordParser` | `heading_path` |
+| Excel | `.xlsx` | `ExcelParser` | `sheet`、`cell_range` |
+| PowerPoint | `.pptx` | `PowerPointParser` | `slide`、`heading_path` |
+| HTML | `.html`、`.htm` | `HtmlParser` | `heading_path` |
+| 图片 | PNG/JPEG/WEBP/TIFF/BMP | `ImageOCRParser`（OCR + Vision） | 当前为文档级定位 |
+
+V2 只支持现代 OOXML，不支持 `.doc`、`.xls`、`.ppt`、带宏 Office 文件，也不解析 Office 内嵌
+图片或图表数据模型。根据当前产品要求，V2 已提前实现可靠异步摄取 Worker；它仍不包含网页
+爬取、Reranker、ACL、Agent、DLQ 控制台或通用分布式任务平台。范围调整见
+[`ADR-001`](adr/ADR-001-v2-async-document-intelligence.md)。
+
+## 3. 统一文档模型
+
+每个 Parser 必须输出 UltimateRAG 自有模型，第三方库对象不能越过 Parser 边界。
+
+### BlockType
+
+V2 使用以下语义类型：
+
+```text
+HEADING / TEXT / TABLE / IMAGE / CODE / LIST / QUOTE
+```
+
+类型用于表达原始结构，不决定具体厂商协议。比如 OCR 页面使用 `IMAGE`，其 `content` 仍是可
+Embedding 的识别文本；HTML `` 使用 `CODE`；Office 表格统一转换为 Markdown 表格文本。
+
+### SourceLocator
+
+`SourceLocator` 是跨格式来源位置：
+
+```json
+{
+ "heading_path": ["RAG", "Indexing"],
+ "page": 12,
+ "bbox": null,
+ "sheet": null,
+ "cell_range": null,
+ "slide": null
+}
+```
+
+字段均可选，因为不同格式的定位能力不同。Locator 会沿以下链路传播：
+
+```text
+Block
+ → Chunk
+ → PostgreSQL chunk_metadata.source_locator
+ → Milvus JSON
+ → RetrievalResult
+ → ContextBuilder / Citation
+ → FastAPI / Web
+```
+
+`heading_path` 继续作为 Chunk 的兼容字段保留，避免破坏 V1 API 和数据库结构。
+
+## 4. Parser Registry 与输入边界
+
+`IngestionService` 不再硬编码 Markdown 扩展名。上传流程在访问 MinIO 前完成：
+
+1. 文件名 basename 净化
+2. 空内容与 10 MB 总上传上限检查
+3. MIME 主类型归一化
+4. `ParserRegistry.resolve()` 同时检查扩展名和 MIME
+5. 原文件写入 MinIO
+6. 在一个 PostgreSQL 事务创建 `PENDING` Document 和唯一 IngestionJob
+7. 返回 `202 Accepted`，不等待 Parse、Embedding 或 Milvus
+
+扩展名和 MIME 都来自不可信客户端，因此它们只负责 Parser 路由，不能替代内容校验。例如图片
+由 Pillow 解码并确认真实格式，OOXML 会检查 ZIP Central Directory，HTML/Markdown 必须能按
+UTF-8 解码。
+
+只有找到 Parser 的文件才会进入 MinIO 和 PostgreSQL，未知类型不会留下孤儿对象。Worker 使用
+`FOR UPDATE SKIP LOCKED` 领取任务、心跳续租并回收崩溃任务。临时故障按指数退避有限重试；损坏
+文件与非法输入直接 `FAILED`。前端通过文档列表/详情端点观察状态，无需维持长上传连接。
+
+## 5. 复杂 PDF：版面、分栏、表格、图片与扫描件
+
+`PDFParser` 先使用 PDFium 安全打开并逐页探测原生文字，再选择两条路径：
+
+```text
+Open PDF
+ ↓
+PDFium page text + raster coverage probe
+ ├── 低文字量 + 大图覆盖 → Render JPEG → 百炼 OCR；稀疏文本补 Vision → IMAGE Block
+ └── 文字型页 → 本地 Docling Layout + TableFormer
+ ├── 分栏阅读顺序
+ ├── 标题层级、正文、列表、代码
+ ├── 表格单元格结构 → Markdown TABLE
+ ├── Page + BBox
+ └── Picture crop → 百炼 Qwen-VL → IMAGE Block
+```
+
+扫描判定同时要求“原生文字低于阈值”和“单张栅格图覆盖大部分页面”，避免把只有少量题注的
+架构图页误判为扫描件。这种按页判定支持“前几页原生文本、后几页扫描图”的混合 PDF。Docling 在 Worker 本地运行并关闭
+自身 OCR；它的 Layout 模型负责分栏阅读顺序，TableFormer 使用 Accurate 模式恢复表格结构。
+重复页眉页脚会过滤，元素 BBox 统一转换为左上角原点。
+
+Docling 检出的图表、架构图和示意图会被裁剪、缩放并压缩后调用百炼 `VISION_MODEL`。视觉 Prompt
+要求提取可见文字、图表数值/趋势、流程节点和箭头关系，不允许根据常识补写图中没有的信息。
+装饰图、纯色块可以返回 `NO_RETRIEVABLE_CONTENT` 并跳过。单张附图理解失败时降级为
+OCR，再失败则跳过该附图而保留正文；完整扫描页 OCR 失败属于文档处理失败并进入任务重试。
+
+相关配置：
+
+```dotenv
+PDF_NATIVE_TEXT_THRESHOLD=20
+PDF_SCAN_IMAGE_COVERAGE_THRESHOLD=0.65
+PDF_SCAN_VISION_TEXT_THRESHOLD=300
+PDF_RENDER_SCALE=2.0
+PDF_VISION_CONCURRENCY=2
+PDF_MAX_PICTURES=20
+DOCLING_DEVICE=cpu
+DOCLING_NUM_THREADS=4
+DOCLING_TIMEOUT_SECONDS=600
+DOCLING_IMAGES_SCALE=2.0
+```
+
+V2 限制单份 PDF 最多 500 页。加密、损坏、空页 PDF 会返回明确输入错误。Docling 极少数未产出
+版面元素但 PDFium 能提取文字的页面会显式降级为按页文本，并记录 `fallback_page_count`。
+
+## 6. 百炼 OCR 适配器
+
+`OCRClient` 是领域端口，`BailianOCRClient` 是阿里云百炼 OpenAI-Compatible 适配器。Image 与 PDF
+Parser 只依赖端口，因此单元测试不访问模型服务，未来替换 OCR 厂商也不改变解析流程。
+
+适配器把验证后的图片编码为 Data URL，并要求模型：
+
+- 按自然阅读顺序提取全部可见文字
+- 保留标题、段落和列表
+- 表格输出 Markdown 表格
+- 不解释、不总结、不编造不可见内容
+
+配置：
+
+```dotenv
+OCR_MODEL=qwen3.5-ocr
+OCR_MAX_IMAGE_BYTES=6291456
+OCR_MAX_OUTPUT_TOKENS=4096
+VISION_MODEL=qwen3-vl-flash
+VISION_MAX_IMAGE_BYTES=6291456
+VISION_MAX_OUTPUT_TOKENS=1536
+```
+
+OCR 单图默认限制 6 MB，为百炼 Base64 图片边界留出余量。空图、非图片 MIME、超限输入和空模型
+响应都会显式失败。模型 API Key 只从 `.env` 注入，不写入日志、文档或仓库。
+
+## 7. Office Parser
+
+### DOCX
+
+`WordParser` 使用 python-docx 按正文顺序读取段落和表格：
+
+- Heading Style 更新标题路径
+- List Style 映射为 `LIST`
+- 普通段落映射为 `TEXT`
+- 表格映射为 Markdown `TABLE`
+
+Word 文档没有可靠的静态页码模型，因此 V2 使用标题路径定位。
+
+### XLSX
+
+`ExcelParser` 使用 openpyxl `read_only` 模式读取公式或单元格值，每 100 行形成一个表格 Block，
+并记录工作表与范围，例如：
+
+```text
+工作表 Metrics · 区域 A1:D100
+```
+
+单工作表最大允许 200,000 个声明范围单元格，Workbook 最多 100 个 Sheet。限制用于阻止异常文件
+消耗无界内存与 CPU，不代表生产业务推荐达到上限。
+
+### PPTX
+
+`PowerPointParser` 按幻灯片和 Shape 顺序提取标题、文本框和表格，每个 Block 保存一基 slide
+序号。V2 不对幻灯片内嵌图片再次 OCR。
+
+### OOXML 安全检查
+
+DOCX/XLSX/PPTX 本质都是 ZIP。交给第三方库前统一检查：
+
+- 最多 10,000 个内部条目
+- 解压后总量不超过 100 MB
+- 大条目压缩比不超过 200
+
+该检查降低 ZIP Bomb 风险，同时保留 API 的 10 MB 压缩文件上传上限。
+
+## 8. HTML 与图片
+
+`HtmlParser` 只处理上传的静态 HTML，不发起外部网络请求，不加载图片/iframe，也不执行脚本。
+`script`、`style`、`noscript` 和 `template` 会在提取前删除；标题、段落、列表、引用、代码与表格
+按 DOM 顺序进入统一 Block。V2 因此没有 SSRF 或网页爬取授权范围。
+
+`ImageOCRParser` 支持 PNG、JPEG、WEBP、TIFF 和 BMP。类名/Parser 名称为兼容已有数据保留；
+2.1 起并发融合 OCR 精确文本与 Vision 的嵌套、箭头、流程和图表关系。Pillow 会验证实际编码，
+扩展名伪装会在访问百炼前失败。模型输出经过确定性清理：移除 Markdown 包装、空表格行、无有效
+表头且重复正文的伪表格；两条模型路径都没有有效内容时不创建空 Chunk。
+
+## 9. 结构 + Token + 类型感知混合切块
+
+V1 的 `StructureAwareMarkdownChunker` 已泛化为 `StructureAwareChunker`，旧类名作为兼容别名保留。
+切块不再用字符数估算中英文长度，而是以本地 Tokenizer 执行硬预算。默认配置为：
+
+```dotenv
+CHUNK_MAX_TOKENS=512
+CHUNK_OVERLAP_TOKENS=64
+CHUNK_TOKENIZER=cl100k_base
+```
+
+`cl100k_base` 是可离线复现的预算近似，不是百炼 Embedding 的供应商精确 Tokenizer。512/64 是
+通用基线，最终值必须通过真实查询集的 Recall@k、答案正确率和上下文成本评估。切分顺序如下：
+
+1. 比较完整 Locator，禁止跨页、Sheet/Range、Slide 和标题路径；同页元素 BBox 合并。
+2. `TABLE` 按行装箱，识别 Docling 放在表格前的题注与多级表头；续块在预算允许时完整重复，
+ 极宽单行只有在“表头 + 行”超限时才省略表头，始终保持硬 Token 上限。
+3. `CODE` 按行装箱，每个 Chunk 恢复独立代码围栏。
+4. 正文优先完整段落，超长段落再按中英文句子。
+5. 单句仍超限时才使用 Token 窗口，并保留有限 overlap。
+6. 标题路径写入每个 Chunk，但最多使用四分之一预算，防止异常标题挤掉正文。
+
+来源边界包括：
+
+```text
+heading_path / page / sheet / cell_range / slide
+```
+
+即使两个 PDF 页面标题相同，页码变化也会先提交当前 Section，避免一个 Chunk 混合多个来源位置。
+`token_count` 现在与实际切分使用同一 Tokenizer，不再只是字符启发式观察值。
+
+## 10. 持久化任务与 V1 兼容
+
+Alembic `0002_v2_async_ingestion` 新增 `ingestion_jobs`：
+
+```text
+id / document_id(unique) / status / attempts / max_attempts
+available_at / locked_at / worker_id / error_message / timestamps
+```
+
+迁移会把旧版本异常中断后停在 `PENDING/PARSING/CHUNKING/EMBEDDING/INDEXING` 的文档恢复为
+`PENDING` 并补建任务。`READY/FAILED` 文档不改变。原有 Chunk/Locator 兼容方式保持不变：
+
+- 强类型 Locator 保存在现有 `chunks.chunk_metadata["source_locator"]`
+- Milvus 复用现有 JSON 类型 `heading_path` 字段保存 Locator 字典
+- 读取端兼容 V1 的标题路径数组和 V2 Locator 字典
+
+Schema 只由 Alembic 修改，应用启动不会隐式建表。PostgreSQL + MinIO 仍是事实来源，Milvus 仍是
+可重建派生索引；检索会按 PostgreSQL 的 READY 文档集合过滤 Milvus 候选。
+
+## 11. API 与前端
+
+上传端点不变：
+
+```text
+POST /api/knowledge-bases/{knowledge_base_id}/documents
+```
+
+成功响应改为 `202 Accepted`，Body 是已经可靠持久化的 `PENDING` 文档。前端只在列表中存在非终态
+文档时每两秒轮询，状态进入 `READY` 或 `FAILED` 后自动停止。上传控件在 202 返回后立即恢复，用户
+可以继续提交其他文件。处理中的文档暂不允许删除，避免 Worker 与删除操作并发产生外部孤儿数据。
+
+前端文件选择器展示全部 V2 扩展名。文档列表显示实际 `parser_name@parser_version` 和处理状态。Retrieval 与 Chat
+响应新增可选 `locator`，同时保留 `heading_path`：
+
+```json
+{
+ "filename": "metrics.xlsx",
+ "heading_path": [],
+ "locator": {
+ "heading_path": [],
+ "page": null,
+ "bbox": null,
+ "sheet": "Metrics",
+ "cell_range": "A1:B20",
+ "slide": null
+ }
+}
+```
+
+证据面板会根据格式显示章节、页码、工作表范围或幻灯片。`192.168.3.19:3000` 与
+`localhost:3000` 都在默认 CORS 白名单中；未配置 `NEXT_PUBLIC_API_URL` 时前端继续使用当前浏览器
+主机的 8000 端口。
+
+## 12. 测试与验收
+
+单元测试在内存中动态构造 HTML、DOCX、XLSX、PPTX、PNG 和扫描 PDF，覆盖：
+
+- Parser 类型选择和输入拒绝发生在 MinIO 写入前
+- 每种格式生成统一 Block
+- 标题、页码、Sheet/Range 和 Slide 来源位置
+- HTML 不索引脚本
+- 图片真实格式校验、OCR/Vision 融合与伪表格清理
+- 扫描 PDF 的文字量 + 栅格覆盖判定、OCR 与稀疏页 Vision 路径
+- Docling 版面元素到标题、表格、图片和 BBox 的映射（使用 Stub，不下载模型）
+- 跨页 Block 不被错误合并
+- 同页 BBox 合并、带题注/多级表头表格的重复前缀与严格 Token 上限
+- Worker 成功提交、非法文档不重试与半成品向量清理
+- 百炼 OCR Data URL 与字节上限
+
+本地验证：
+
+```bash
+uv run pytest
+uv run ruff check .
+uv run mypy
+
+cd apps/web
+npm run lint
+npm run build
+```
+
+Docker 全栈与真实百炼验收：
+
+```bash
+docker compose up -d --build
+uv run python scripts/smoke_v2.py --api-url http://localhost:8000
+uv run python scripts/smoke_v2_data.py --api-url http://localhost:8000
+```
+
+Smoke Test 会先验证每个上传都立即返回 `202/PENDING`，再轮询每份文档到 `READY`，验证 Parser
+名称、带 Locator 的检索与流式 Citation，最后清理临时知识库。`smoke_v2_data.py` 额外使用 `data/`
+的真实示意图和 15 页论文，断言图片关系、PDF Table 2 多级表头及第 8 页 BBox 可以实际召回。
+
+## 13. 已知限制与后续版本边界
+
+- V2 已有可靠单类任务 Worker，但没有优先级、任务取消、DLQ UI 和跨服务工作流；这些仍属于 V4
+- OCR 依赖外部百炼服务，网络或额度故障会形成可追踪 `FAILED`
+- 独立图片默认并发调用 OCR 与 Vision，换取精确文字和结构语义，模型用量高于纯 OCR
+- V2 不解析 Office 内嵌图片、图表、批注、修订和宏
+- HTML 不抓取远程资源
+- Docling/Torch 会增加 Worker 镜像体积；首次文字型 PDF 需下载模型，Docker 缓存卷会持久保存
+- PDF 元素保存页码和 BBox，但前端当前只展示页码文字，尚未实现原 PDF 框选预览
+- `cl100k_base` 不是百炼精确 Tokenizer，Chunk 参数仍需用企业真实文档和查询做 Retrieval Evaluation
+- Retrieval 仍是 V1 Dense Search；Hybrid、Reranker、Query Rewrite 属于 V3
+- 认证、ACL、多租户隔离和审计属于 V4，当前不应直接暴露到公网
diff --git a/docs/adr/ADR-001-v2-async-document-intelligence.md b/docs/adr/ADR-001-v2-async-document-intelligence.md
new file mode 100644
index 0000000..2f80e23
--- /dev/null
+++ b/docs/adr/ADR-001-v2-async-document-intelligence.md
@@ -0,0 +1,149 @@
+# ADR-001：V2 提前引入可靠异步摄取与本地 PDF 版面分析
+
+## Status
+
+Accepted — 2026-08-29
+
+## Context / 背景
+
+原路线图把完整异步 Pipeline 放在 V4，V2 初版因此在上传 HTTP 请求内同步执行：
+
+```text
+Upload → Parse → Chunk → Embed → Index → READY → HTTP Response
+```
+
+复杂 PDF 会触发版面推理、扫描页 OCR、图片理解和多批 Embedding。继续同步执行会带来长连接、
+网关超时、浏览器重复提交以及 API 进程 CPU/内存被解析任务占用等问题。用户已明确要求 V2 上传
+成功后立即返回，并能在前端观察后台解析状态，因此当前需求覆盖原路线图的同步限制。
+
+同时，PDFium 的整页文字提取只能提供粗粒度文本，无法可靠处理双栏阅读顺序、标题层级、表格
+结构、图片区域和元素坐标；简单字符窗口也无法同时适配正文、代码和表格。
+
+## Decision / 决策
+
+### 1. 使用 PostgreSQL 持久化摄取任务
+
+新增 `ingestion_jobs` 表，由独立 Worker 使用：
+
+```text
+POST Upload
+ → Validate
+ → MinIO Put
+ → PostgreSQL Transaction(Document + IngestionJob)
+ → 202 Accepted / PENDING
+
+Worker
+ → SELECT ... FOR UPDATE SKIP LOCKED
+ → PARSING → CHUNKING → EMBEDDING → INDEXING → READY
+```
+
+- 文档与任务在同一事务创建，消除“有 PENDING 文档但没有任务”的窗口。
+- `SKIP LOCKED` 允许多个 Worker 互不阻塞地领取不同任务。
+- `locked_at`、`worker_id` 和定时心跳形成任务租约，进程崩溃后可回收。
+- 只对临时故障有限重试；损坏文件、非法格式不重试。
+- 重试仍使用稳定 Block/Chunk ID 和文档级向量删除，避免重复数据。
+- Milvus 命中会由 PostgreSQL `READY` 事实二次过滤，半成品索引不能参与回答。
+
+当前不引入 Redis、Celery、Kafka 或分布式调度器。任务吞吐、优先级或跨服务编排达到明确瓶颈后，
+再通过任务 Repository 边界演进；V4 的 DLQ、运维控制台、审计与大规模调度范围保持不变。
+
+### 2. 文字型 PDF 使用本地 Docling，扫描页使用百炼
+
+PDF 采用双路径：
+
+```text
+PDFium 安全打开与逐页文字/栅格覆盖探测
+ ├─ 低文字量 + 整页大图 → 本地渲染 JPEG → 百炼 Qwen OCR；稀疏结果补 Vision
+ └─ 文字型页 → 本地 Docling Layout + TableFormer
+ ├─ 标题/正文/列表/代码
+ ├─ 分栏阅读顺序
+ ├─ 表格结构 → Markdown
+ ├─ Page + BBox
+ └─ Picture Crop → 百炼 Qwen-VL 视觉理解
+```
+
+- Docling 关闭自身 OCR，避免同时维护两个 OCR 结果来源。
+- 仅靠字符数不足以识别扫描件：低文字页还必须存在覆盖大部分页面的栅格图，避免图片题注页
+ 错误退化；扫描页 OCR 很稀疏时再调用 Vision，兼顾文字精度、图形语义和调用成本。
+- Docling 模型在 Worker 首次文字型 PDF 任务中延迟加载，API 上传进程不加载 Torch。
+- 模型完全在本地运行；只有扫描页和裁剪后的文档图片发送到 `.env` 配置的阿里云百炼。
+- 图片理解失败会降级为 OCR；单张附图失败不使已有正文失效，完整扫描页 OCR 失败则由任务重试。
+- 独立图片并发融合 OCR 与 Vision；模型输出经过确定性 Markdown/伪表格清理，纯装饰图通过
+ `NO_RETRIEVABLE_CONTENT` 协议不进入向量库。
+- 重复页眉页脚不进入索引；页码和左上角原点 BBox 进入 `SourceLocator`。
+- Docker Volume 持久化模型缓存，避免容器重建后重复下载。
+- 默认 uv 锁文件把 `torch/torchvision` 固定到 PyTorch 官方 CPU Index，避免 CPU 容器安装 CUDA 依赖；
+ GPU Worker 应使用独立镜像与锁定策略。
+
+### 3. 使用结构 + Token + 类型感知的混合切块
+
+默认从 `512 tokens / 64 overlap` 起步，但把它视为需要 Retrieval Evaluation 调优的基线，而不是
+脱离文档和查询分布的“数学最优值”。切分顺序为：
+
+1. 严格保留标题、页码、Sheet/Range、Slide 等来源边界；同页元素 BBox 可合并。
+2. 表格按行切分；识别 Docling 的前置题注和多级表头，每个 Chunk 在预算允许时重复完整前缀。
+ 单个超宽行只有在无法同时容纳表头时省略表头，保证硬 Token 上限。
+3. 代码按行切分，每个 Chunk 保持独立围栏。
+4. 正文优先段落，再按句子，最后才用 Token 窗口。
+5. 自然单元间只携带不超过预算的尾部 overlap；新信息优先于强行重叠。
+6. 标题上下文写入每个 Chunk，但限制其预算，防止超长标题挤掉正文。
+
+本地使用 `cl100k_base` 做一致的中英文 Token 预算近似。百炼 Embedding 没有在当前本地 SDK 暴露
+同模型 Tokenizer，因此文档明确记录这不是供应商精确计数；上线前应使用项目评估集比较不同大小。
+
+## Alternatives / 备选方案
+
+### FastAPI `BackgroundTasks`
+
+拒绝。任务只存在于 API 进程内，进程重启会丢失；CPU 密集 PDF 仍与请求服务争用资源，也没有
+跨副本的安全领取与租约恢复。
+
+### Redis/Celery 或 Kafka
+
+暂不采用。它们能提供更高吞吐和成熟调度，但当前只有单一确定性文档任务，引入额外服务、协议、
+监控和故障面不符合“解决当前真实问题的最简单架构”。
+
+### 只使用 PDFium/pdfplumber
+
+拒绝作为主路径。它们适合底层文字、对象和表格规则提取，但复杂分栏、阅读顺序、跨单元格表格与
+图片语义仍需要大量启发式代码。PDFium 继续承担安全打开、扫描判定和页面渲染这一明确职责。
+
+### 每页全部发送在线视觉模型
+
+拒绝。会增加数据外发、模型费用、延迟和限流风险，也浪费本地可确定恢复的原生文字与表格结构。
+
+### Embedding 驱动的语义切分
+
+暂不作为默认。它会在正式 Embedding 前增加额外模型调用和阈值，成本更高且未必改善表格/代码；
+结构 + Token 是可解释、可复现的强基线，后续由评估数据决定是否加入语义断点。
+
+## Consequences / 影响
+
+正向影响：
+
+- 上传延迟与文档复杂度解耦，浏览器能可靠看到处理进度。
+- Worker 可以横向扩展，崩溃任务可自动恢复。
+- 复杂 PDF 的阅读顺序、表格、图片和 BBox 可追踪。
+- 真实 15 页双栏论文样本能恢复 5 个表格区域和 7 个图片区域；所有 Chunk 保留页码/BBox，
+ 带题注表格的续块仍携带列语义。该数字是当前验收样本结果，不是对任意 PDF 的质量承诺。
+- Chunk 预算真实按 Token 执行，表格和代码不再被普通字符窗口破坏。
+
+成本与限制：
+
+- API/Worker 镜像因 Docling/Torch 明显增大，首次文字型 PDF 需要下载本地模型。
+- 当前任务表是轻量数据库队列，不包含优先级、DLQ UI、任务取消和大规模调度能力。
+- 本地版面分析需要 CPU/内存容量；生产环境应为 Worker 单独设置资源限制与副本数。
+- `cl100k_base` 是预算近似，最终参数必须通过真实问答评估而不是只看平均 Chunk 长度。
+- Office 内嵌图表、宏和旧格式仍不在本次范围。
+
+## Primary references / 一手资料
+
+- [PostgreSQL `SELECT` / `SKIP LOCKED`](https://www.postgresql.org/docs/current/sql-select.html)
+- [Docling 官方文档](https://docling-project.github.io/docling/)
+- [Docling PDF pipeline options](https://docling-project.github.io/docling/reference/pipeline_options/)
+- [Docling Hybrid Chunking](https://docling-project.github.io/docling/concepts/chunking/)
+- [pdfplumber 官方 README](https://github.com/jsvine/pdfplumber/blob/stable/README.md)
+- [阿里云百炼视觉模型](https://help.aliyun.com/en/model-studio/vision-model/)
+- [阿里云百炼 Qwen OCR API](https://help.aliyun.com/en/model-studio/qwen-vl-ocr-api-reference)
+- [Microsoft Advanced RAG：chunking 与组织](https://learn.microsoft.com/en-us/azure/developer/ai/advanced-retrieval-augmented-generation)
+- [Milvus RAG chunking 指南](https://milvus.io/ai-quick-reference/how-do-i-implement-efficient-document-chunking-for-rag-applications)
diff --git a/pyproject.toml b/pyproject.toml
index 7e6efb3..1d1a0bb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,12 +1,14 @@
[project]
name = "ultimate-rag"
-version = "1.0.0"
+version = "2.0.0"
description = "An evolvable enterprise-grade RAG platform"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"alembic>=1.16,<2",
"asyncpg>=0.30,<1",
+ "beautifulsoup4>=4.13,<5",
+ "docling>=2.60,<3",
"fastapi>=0.116,<1",
# OpenAI SDK 通过 HTTPX 自动读取 HTTP_PROXY/HTTPS_PROXY/ALL_PROXY。
# 本地开发常使用 SOCKS 代理访问百炼，因此运行时必须包含 socksio 可选传输依赖。
@@ -14,19 +16,39 @@ dependencies = [
"markdown-it-py>=4,<5",
"minio>=7.2,<8",
"openai>=1.99,<3",
+ "openpyxl>=3.1,<4",
+ "pillow>=11,<13",
"pydantic-settings>=2.10,<3",
"pymilvus>=2.5,<3",
+ "pypdfium2>=4.30,<5",
+ "python-docx>=1.2,<2",
"python-multipart>=0.0.20,<1",
+ "python-pptx>=1.0,<2",
"sqlalchemy[asyncio]>=2.0.41,<3",
+ "tiktoken>=0.9,<1",
+ # Docling 默认部署在 CPU Worker；显式依赖配合下方官方 CPU Index，避免 Linux 镜像
+ # 无意拉取数 GB 的 CUDA 运行库。GPU 部署应使用单独锁文件/镜像策略。
+ "torch>=2.12,<2.13",
+ "torchvision>=0.27,<0.28",
"uvicorn[standard]>=0.35,<1",
]
+[tool.uv.sources]
+torch = { index = "pytorch-cpu" }
+torchvision = { index = "pytorch-cpu" }
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
[dependency-groups]
dev = [
"mypy>=1.17,<2",
"pytest>=8.4,<9",
"pytest-asyncio>=1.1,<2",
"ruff>=0.12,<1",
+ "types-openpyxl>=3.1,<4",
]
[build-system]
diff --git a/scripts/smoke_v1.py b/scripts/smoke_v1.py
index 9fffee5..993dc1e 100644
--- a/scripts/smoke_v1.py
+++ b/scripts/smoke_v1.py
@@ -18,6 +18,7 @@
import argparse
import json
+import time
from collections.abc import Iterator
from pathlib import Path
from typing import Any
@@ -61,6 +62,28 @@ def ui_stream_events(response: httpx.Response) -> Iterator[dict[str, Any]]:
yield event
+def wait_until_ready(
+ client: httpx.Client,
+ document_id: str,
+ *,
+ timeout_seconds: float = 180.0,
+) -> dict[str, Any]:
+ """轮询异步文档详情，直到 READY、FAILED 或超时。"""
+
+ deadline = time.monotonic() + timeout_seconds
+ latest: dict[str, Any] = {}
+ while time.monotonic() < deadline:
+ response = client.get(f"/api/documents/{document_id}")
+ require_success(response, "poll document")
+ latest = response.json()
+ if latest.get("status") == "READY":
+ return latest
+ if latest.get("status") == "FAILED":
+ raise RuntimeError(f"background ingestion failed: {latest}")
+ time.sleep(1)
+ raise TimeoutError(f"document did not reach READY: {latest}")
+
+
def run_smoke_test(api_url: str, fixture: Path, question: str) -> None:
"""执行 Create → Upload → Retrieve → Stream Chat → Delete 完整验收。"""
@@ -94,8 +117,9 @@ def run_smoke_test(api_url: str, fixture: Path, question: str) -> None:
)
require_success(upload, "upload document")
document = upload.json()
- if document.get("status") != "READY":
- raise RuntimeError(f"document did not reach READY: {document}")
+ if upload.status_code != 202 or document.get("status") != "PENDING":
+ raise RuntimeError(f"document was not accepted asynchronously: {document}")
+ document = wait_until_ready(client, str(document["id"]))
print(f"[2/5] Document ready: {document['id']}")
retrieval = client.post(
diff --git a/scripts/smoke_v2.py b/scripts/smoke_v2.py
new file mode 100644
index 0000000..c735be3
--- /dev/null
+++ b/scripts/smoke_v2.py
@@ -0,0 +1,315 @@
+"""对已启动的 UltimateRAG 执行一次可回收的 V2 全格式真实闭环验收。
+
+脚本通过公开 API 动态生成并上传 Markdown、HTML、DOCX、XLSX、PPTX、PNG 和扫描 PDF，验证
+Parser、READY、来源定位、Milvus 检索与百炼流式问答，最后清理临时知识库。
+
+执行会真实调用百炼 Embedding、OCR 和 LLM，并产生少量模型用量：
+``uv run python scripts/smoke_v2.py --api-url http://localhost:8000``
+"""
+
+import argparse
+import json
+import time
+from collections.abc import Iterator
+from dataclasses import dataclass
+from io import BytesIO
+from typing import Any
+from uuid import uuid4
+
+import httpx
+from docx import Document as WordDocument
+from openpyxl import Workbook
+from PIL import Image, ImageDraw, ImageFont
+from pptx import Presentation
+from pptx.util import Inches
+
+
+@dataclass(frozen=True, slots=True)
+class SmokeDocument:
+ """一份待上传的内存文档及其期望 Parser。"""
+
+ filename: str
+ mime_type: str
+ content: bytes
+ parser_name: str
+
+
+def parse_arguments() -> argparse.Namespace:
+ """解析 API 地址和验证问题。"""
+
+ parser = argparse.ArgumentParser(description="Run the UltimateRAG V2 smoke test")
+ parser.add_argument("--api-url", default="http://localhost:8000")
+ parser.add_argument("--question", default="What is the UltimateRAG V2 smoke marker?")
+ return parser.parse_args()
+
+
+def require_success(response: httpx.Response, operation: str) -> None:
+ """把非成功响应转换为带有限正文的验收错误。"""
+
+ if response.is_success:
+ return
+ raise RuntimeError(f"{operation} failed: HTTP {response.status_code}: {response.text[:1000]}")
+
+
+def ui_stream_events(response: httpx.Response) -> Iterator[dict[str, Any]]:
+ """从 AI SDK SSE 中解析 JSON Event。"""
+
+ for line in response.iter_lines():
+ if not line or not line.startswith("data: "):
+ continue
+ payload = line.removeprefix("data: ")
+ if payload == "[DONE]":
+ return
+ event = json.loads(payload)
+ if not isinstance(event, dict):
+ raise RuntimeError("stream event must be a JSON object")
+ yield event
+
+
+def build_documents() -> list[SmokeDocument]:
+ """在内存中生成全部 V2 格式，避免仓库长期保存二进制 Fixture。"""
+
+ marker = "UltimateRAG V2 smoke marker is 8291."
+ documents = [
+ SmokeDocument(
+ "smoke.md",
+ "text/markdown",
+ f"# V2 Smoke\n\n{marker}".encode(),
+ "markdown",
+ ),
+ SmokeDocument(
+ "smoke.html",
+ "text/html",
+ f"V2 Smoke
{marker}
".encode(),
+ "html",
+ ),
+ ]
+
+ word = WordDocument()
+ word.add_heading("V2 Smoke", level=1)
+ word.add_paragraph(marker)
+ word_buffer = BytesIO()
+ word.save(word_buffer)
+ documents.append(
+ SmokeDocument(
+ "smoke.docx",
+ "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+ word_buffer.getvalue(),
+ "docx",
+ )
+ )
+
+ workbook = Workbook()
+ sheet = workbook.active
+ sheet.title = "Smoke"
+ sheet.append(["Name", "Value"])
+ sheet.append(["UltimateRAG V2 smoke marker", 8291])
+ excel_buffer = BytesIO()
+ workbook.save(excel_buffer)
+ documents.append(
+ SmokeDocument(
+ "smoke.xlsx",
+ "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+ excel_buffer.getvalue(),
+ "xlsx",
+ )
+ )
+
+ presentation = Presentation()
+ slide = presentation.slides.add_slide(presentation.slide_layouts[1])
+ slide.shapes.title.text = "V2 Smoke"
+ text_box = slide.shapes.add_textbox(Inches(1), Inches(2), Inches(7), Inches(1))
+ text_box.text = marker
+ ppt_buffer = BytesIO()
+ presentation.save(ppt_buffer)
+ documents.append(
+ SmokeDocument(
+ "smoke.pptx",
+ "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+ ppt_buffer.getvalue(),
+ "pptx",
+ )
+ )
+
+ # 大号高对比英文让 OCR Smoke Test 对本地字体环境不敏感；同一图像同时用于图片和扫描 PDF。
+ image = Image.new("RGB", (1400, 320), "white")
+ font = ImageFont.load_default(size=52)
+ ImageDraw.Draw(image).text((40, 110), marker, fill="black", font=font)
+ png_buffer = BytesIO()
+ image.save(png_buffer, format="PNG")
+ documents.append(SmokeDocument("smoke.png", "image/png", png_buffer.getvalue(), "image-ocr"))
+
+ pdf_buffer = BytesIO()
+ image.save(pdf_buffer, format="PDF", resolution=150)
+ documents.append(
+ SmokeDocument(
+ "smoke.pdf",
+ "application/pdf",
+ pdf_buffer.getvalue(),
+ "pdf-docling",
+ )
+ )
+ return documents
+
+
+def wait_until_ready(
+ client: httpx.Client,
+ document_id: str,
+ filename: str,
+ *,
+ timeout_seconds: float = 600.0,
+) -> dict[str, Any]:
+ """轮询公开详情端点，验证异步任务最终进入 READY 或给出明确失败。"""
+
+ deadline = time.monotonic() + timeout_seconds
+ latest: dict[str, Any] = {}
+ while time.monotonic() < deadline:
+ response = client.get(f"/api/documents/{document_id}")
+ require_success(response, f"poll {filename}")
+ latest = response.json()
+ if latest.get("status") == "READY":
+ return latest
+ if latest.get("status") == "FAILED":
+ raise RuntimeError(f"{filename} background ingestion failed: {latest}")
+ time.sleep(1)
+ raise TimeoutError(f"{filename} did not finish background ingestion: {latest}")
+
+
+def _verify_locator(parser_name: str, payload: dict[str, Any]) -> None:
+ """验证格式特有 Locator 已从向量检索返回，而不只在 Parser 内短暂存在。"""
+
+ locator = payload.get("locator")
+ if not isinstance(locator, dict):
+ raise RuntimeError(f"retrieval result has no locator: {payload}")
+ if parser_name == "pdf-docling" and locator.get("page") is None:
+ raise RuntimeError(f"PDF retrieval result has no page: {payload}")
+ if parser_name == "xlsx" and not locator.get("sheet"):
+ raise RuntimeError(f"XLSX retrieval result has no sheet: {payload}")
+ if parser_name == "pptx" and locator.get("slide") is None:
+ raise RuntimeError(f"PPTX retrieval result has no slide: {payload}")
+
+
+def run_smoke_test(api_url: str, question: str) -> None:
+ """执行 Create → Upload All → Retrieve → Stream Chat → Delete 完整验收。"""
+
+ knowledge_base_id: str | None = None
+ primary_error: Exception | None = None
+ cleanup_error: str | None = None
+ documents = build_documents()
+ with httpx.Client(base_url=api_url.rstrip("/"), timeout=300.0, trust_env=False) as client:
+ try:
+ health = client.get("/api/health")
+ require_success(health, "health check")
+ create = client.post(
+ "/api/knowledge-bases",
+ json={
+ "name": f"V2 Smoke {uuid4().hex[:8]}",
+ "description": "由 scripts/smoke_v2.py 创建，可安全清理。",
+ },
+ )
+ require_success(create, "create knowledge base")
+ knowledge_base_id = str(create.json()["id"])
+ print(f"[1/5] Knowledge base created: {knowledge_base_id}")
+
+ accepted_documents: list[tuple[str, SmokeDocument]] = []
+ for document in documents:
+ response = client.post(
+ f"/api/knowledge-bases/{knowledge_base_id}/documents",
+ files={
+ "file": (document.filename, document.content, document.mime_type),
+ },
+ )
+ require_success(response, f"upload {document.filename}")
+ payload = response.json()
+ if response.status_code != 202 or payload.get("status") != "PENDING":
+ raise RuntimeError(
+ f"{document.filename} was not accepted asynchronously: "
+ f"HTTP {response.status_code} {payload}"
+ )
+ accepted_documents.append((str(payload["id"]), document))
+
+ uploaded: dict[str, str] = {}
+ for document_id, document in accepted_documents:
+ payload = wait_until_ready(client, document_id, document.filename)
+ if payload.get("parser_name") != document.parser_name:
+ raise RuntimeError(
+ f"{document.filename} selected {payload.get('parser_name')}, "
+ f"expected {document.parser_name}"
+ )
+ uploaded[document_id] = document.parser_name
+ print(f"[2/5] All {len(documents)} V2 formats reached READY")
+
+ retrieval = client.post(
+ "/api/retrieval/search",
+ json={"knowledge_base_id": knowledge_base_id, "query": question, "top_k": 20},
+ )
+ require_success(retrieval, "dense retrieval")
+ results = retrieval.json()
+ if not results:
+ raise RuntimeError("retrieval returned no chunks")
+ located_formats: set[str] = set()
+ for result in results:
+ parser_name = uploaded.get(str(result.get("document_id")))
+ if parser_name in {"pdf-docling", "xlsx", "pptx"}:
+ _verify_locator(parser_name, result)
+ located_formats.add(parser_name)
+ if located_formats != {"pdf-docling", "xlsx", "pptx"}:
+ raise RuntimeError(
+ "top-20 retrieval did not return every locator format: "
+ f"found={sorted(located_formats)}"
+ )
+ print(f"[3/5] Retrieval returned {len(results)} chunks with V2 locators")
+
+ with client.stream(
+ "POST",
+ "/api/chat/stream",
+ json={"knowledge_base_id": knowledge_base_id, "question": question, "top_k": 5},
+ ) as stream:
+ require_success(stream, "stream chat")
+ events = list(ui_stream_events(stream))
+ event_types = [str(event.get("type")) for event in events]
+ answer = "".join(
+ str(event.get("delta", "")) for event in events if event.get("type") == "text-delta"
+ )
+ evidence = [event for event in events if event.get("type") == "data-retrieval"]
+ if not answer.strip() or not evidence or "finish" not in event_types:
+ raise RuntimeError(f"invalid chat stream: events={event_types}, answer={answer!r}")
+ evidence_data = evidence[0].get("data")
+ if not isinstance(evidence_data, dict) or not evidence_data.get("citations"):
+ raise RuntimeError("stream retrieval data contains no citations")
+ print(f"[4/5] Stream answer and citations verified ({len(answer)} chars)")
+ except Exception as exc:
+ primary_error = exc
+ finally:
+ if knowledge_base_id is not None:
+ try:
+ cleanup = client.delete(f"/api/knowledge-bases/{knowledge_base_id}")
+ except httpx.HTTPError as exc:
+ cleanup_error = f"knowledge_base_id={knowledge_base_id}, cleanup error: {exc}"
+ else:
+ if cleanup.is_success:
+ print("[5/5] Temporary knowledge base deleted")
+ else:
+ cleanup_error = (
+ f"knowledge_base_id={knowledge_base_id}, cleanup HTTP "
+ f"{cleanup.status_code}: {cleanup.text[:500]}"
+ )
+
+ if primary_error is not None:
+ if cleanup_error:
+ primary_error.add_note(f"Additional cleanup failure: {cleanup_error}")
+ raise primary_error
+ if cleanup_error:
+ raise RuntimeError(cleanup_error)
+
+
+def main() -> None:
+ """运行命令行验收并输出明确发布信号。"""
+
+ arguments = parse_arguments()
+ run_smoke_test(arguments.api_url, arguments.question)
+ print("UltimateRAG V2 smoke test passed.")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/smoke_v2_data.py b/scripts/smoke_v2_data.py
new file mode 100644
index 0000000..63d1f37
--- /dev/null
+++ b/scripts/smoke_v2_data.py
@@ -0,0 +1,191 @@
+"""使用 ``data`` 目录真实图片/PDF 验证 V2 文档智能质量。
+
+与 ``smoke_v2.py`` 的合成格式闭环不同，本脚本专门覆盖真实示意图、15 页双栏论文、PDF 表格和
+内嵌图片。它只通过公开 API 操作，验证上传立即返回、后台终态和 Milvus 实际召回，默认删除自己
+创建的知识库。执行会真实调用 `.env` 中的百炼 OCR、Vision 与 Embedding，并产生模型用量。
+
+运行：``uv run python scripts/smoke_v2_data.py --api-url http://localhost:8000``
+"""
+
+from __future__ import annotations
+
+import argparse
+import mimetypes
+import time
+from pathlib import Path
+from typing import Any
+from uuid import uuid4
+
+import httpx
+from smoke_v2 import require_success, wait_until_ready
+
+
+def parse_arguments() -> argparse.Namespace:
+ """解析 API、样本路径和是否保留验收知识库。"""
+
+ parser = argparse.ArgumentParser(description="Run real V2 data sample validation")
+ parser.add_argument("--api-url", default="http://localhost:8000")
+ parser.add_argument("--data-dir", type=Path, default=Path("data"))
+ parser.add_argument("--keep", action="store_true", help="保留脚本创建的知识库供人工检查")
+ return parser.parse_args()
+
+
+def _sample_paths(data_dir: Path) -> list[Path]:
+ """返回固定验收集并在访问 API 前报告缺失文件。"""
+
+ samples = [data_dir / "1.png", data_dir / "2.png", data_dir / "attention is all you need.pdf"]
+ missing = [str(path) for path in samples if not path.is_file()]
+ if missing:
+ raise FileNotFoundError(f"V2 data samples are missing: {missing}")
+ return samples
+
+
+def _search(
+ client: httpx.Client,
+ knowledge_base_id: str,
+ query: str,
+ *,
+ top_k: int = 10,
+) -> list[dict[str, Any]]:
+ """执行独立 Dense Retrieval 并验证响应是对象数组。"""
+
+ response = client.post(
+ "/api/retrieval/search",
+ json={"knowledge_base_id": knowledge_base_id, "query": query, "top_k": top_k},
+ )
+ require_success(response, f"retrieve {query}")
+ payload = response.json()
+ if not isinstance(payload, list) or not all(isinstance(item, dict) for item in payload):
+ raise RuntimeError(f"invalid retrieval response: {payload!r}")
+ return payload
+
+
+def _require_document_hit(
+ results: list[dict[str, Any]],
+ document_id: str,
+ required_terms: tuple[str, ...],
+) -> dict[str, Any]:
+ """要求同一召回块来自目标文档且包含全部质量标记。"""
+
+ for result in results:
+ content = str(result.get("content", ""))
+ if str(result.get("document_id")) == document_id and all(
+ term in content for term in required_terms
+ ):
+ return result
+ raise RuntimeError(
+ f"document {document_id} has no hit containing {required_terms}: "
+ f"{[str(item.get('content', ''))[:160] for item in results]}"
+ )
+
+
+def run_validation(api_url: str, data_dir: Path, *, keep: bool) -> None:
+ """执行异步上传、终态轮询和三类真实语义召回断言。"""
+
+ samples = _sample_paths(data_dir)
+ knowledge_base_id: str | None = None
+ validation_succeeded = False
+ with httpx.Client(base_url=api_url.rstrip("/"), timeout=300.0, trust_env=False) as client:
+ try:
+ health = client.get("/api/health")
+ require_success(health, "health check")
+ created = client.post(
+ "/api/knowledge-bases",
+ json={
+ "name": f"V2 Real Data {uuid4().hex[:8]}",
+ "description": "scripts/smoke_v2_data.py 可回收真实样本验收。",
+ },
+ )
+ require_success(created, "create knowledge base")
+ knowledge_base_id = str(created.json()["id"])
+ print(f"[1/5] Knowledge base created: {knowledge_base_id}")
+
+ accepted: dict[str, str] = {}
+ for path in samples:
+ mime_type = mimetypes.guess_type(path.name)[0] or "application/octet-stream"
+ started = time.perf_counter()
+ response = client.post(
+ f"/api/knowledge-bases/{knowledge_base_id}/documents",
+ files={"file": (path.name, path.read_bytes(), mime_type)},
+ )
+ elapsed_ms = (time.perf_counter() - started) * 1000
+ require_success(response, f"upload {path.name}")
+ payload = response.json()
+ if response.status_code != 202 or payload.get("status") != "PENDING":
+ raise RuntimeError(f"{path.name} did not return 202/PENDING: {payload}")
+ accepted[path.name] = str(payload["id"])
+ print(f" {path.name}: 202/PENDING in {elapsed_ms:.0f} ms")
+ print("[2/5] Every upload returned before parsing completed")
+
+ for filename, document_id in accepted.items():
+ payload = wait_until_ready(
+ client,
+ document_id,
+ filename,
+ timeout_seconds=1200,
+ )
+ print(
+ f" {filename}: READY via "
+ f"{payload.get('parser_name')}@{payload.get('parser_version')}"
+ )
+ print("[3/5] Background worker completed all real samples")
+
+ containment = _search(
+ client,
+ knowledge_base_id,
+ "图中人工智能、机器学习和深度学习的三层嵌套包含关系是什么？",
+ )
+ containment_hit = _require_document_hit(
+ containment,
+ accepted["1.png"],
+ ("深度学习", "机器学习", "人工智能", "包含关系"),
+ )
+ if str(containment_hit.get("content", "")).count("| |") > 2:
+ raise RuntimeError("1.png retrieval still contains empty OCR pseudo-table noise")
+
+ architecture = _search(
+ client,
+ knowledge_base_id,
+ "编码器怎样通过箭头连接解码器，最后怎样生成 Output Probabilities？",
+ )
+ _require_document_hit(
+ architecture,
+ accepted["2.png"],
+ ("编码器", "解码器", "连接", "Output Probabilities"),
+ )
+
+ table = _search(
+ client,
+ knowledge_base_id,
+ "What BLEU scores and training costs are reported for Transformer big in Table 2?",
+ )
+ table_hit = _require_document_hit(
+ table,
+ accepted["attention is all you need.pdf"],
+ ("Table 2:", "| Model", "EN-DE", "Transformer (big)"),
+ )
+ locator = table_hit.get("locator")
+ if not isinstance(locator, dict) or locator.get("page") != 8 or not locator.get("bbox"):
+ raise RuntimeError(f"PDF table hit has no page-8 BBox: {table_hit}")
+ print("[4/5] Image relations, PDF table headers and page/BBox retrieval verified")
+ validation_succeeded = True
+ finally:
+ if knowledge_base_id is not None and not keep and validation_succeeded:
+ cleanup = client.delete(f"/api/knowledge-bases/{knowledge_base_id}")
+ require_success(cleanup, "cleanup knowledge base")
+ print("[5/5] Temporary knowledge base deleted")
+ elif knowledge_base_id is not None:
+ # 失败时保留现场也避免正在处理文档触发 409 后掩盖真正的验收异常。
+ print(f"[5/5] Knowledge base retained for inspection: {knowledge_base_id}")
+
+
+def main() -> None:
+ """运行真实样本验收并输出明确成功信号。"""
+
+ arguments = parse_arguments()
+ run_validation(arguments.api_url, arguments.data_dir, keep=arguments.keep)
+ print("UltimateRAG V2 real data validation passed.")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/ultimate_rag/__init__.py b/src/ultimate_rag/__init__.py
index 43ddb6b..4a44cd4 100644
--- a/src/ultimate_rag/__init__.py
+++ b/src/ultimate_rag/__init__.py
@@ -1,3 +1,3 @@
-"""UltimateRAG V1 package."""
+"""UltimateRAG V2 Document Intelligence package."""
-__version__ = "1.0.0"
+__version__ = "2.0.0"
diff --git a/src/ultimate_rag/application/__init__.py b/src/ultimate_rag/application/__init__.py
index ee69107..e7abcde 100644
--- a/src/ultimate_rag/application/__init__.py
+++ b/src/ultimate_rag/application/__init__.py
@@ -3,6 +3,7 @@
from ultimate_rag.application.context import ContextBuilder
from ultimate_rag.application.services import (
DocumentLifecycleService,
+ DocumentProcessingService,
IngestionService,
RAGService,
RetrievalService,
@@ -11,6 +12,7 @@
__all__ = [
"ContextBuilder",
"DocumentLifecycleService",
+ "DocumentProcessingService",
"IngestionService",
"RAGService",
"RetrievalService",
diff --git a/src/ultimate_rag/application/context.py b/src/ultimate_rag/application/context.py
index 3c11777..2cdbeaa 100644
--- a/src/ultimate_rag/application/context.py
+++ b/src/ultimate_rag/application/context.py
@@ -48,11 +48,12 @@ def build(self, results: list[RetrievalResult]) -> str:
for index, result in enumerate(results, start=1):
# 每个 Section 都携带用户可读文件名和标题路径。没有标题时使用明确占位文本，
# 避免空标签让模型和用户误以为来源元数据在格式化时丢失。
- heading = " > ".join(result.heading_path) or "未命名章节"
+ locator = result.locator.display() if result.locator else ""
+ heading = locator or " > ".join(result.heading_path) or "未提供原文定位"
section = (
f"[来源 {index}]\n"
f"文档：{result.filename}\n"
- f"章节：{heading}\n"
+ f"位置：{heading}\n"
f"内容：\n{result.content}"
)
diff --git a/src/ultimate_rag/application/services.py b/src/ultimate_rag/application/services.py
index 6f5913b..3e20d16 100644
--- a/src/ultimate_rag/application/services.py
+++ b/src/ultimate_rag/application/services.py
@@ -1,31 +1,29 @@
-"""V1 文档生命周期、检索与生成应用服务。
+"""V2 文档生命周期、后台处理、检索与生成应用服务。
模块职责：
- 以普通 Python Service 显式编排两条 RAG 主链路：
- ``Upload → Parse → Chunk → Embed → Index`` 与 ``Query → Retrieve → Generate``。
+ 以普通 Python Service 显式编排上传入队、后台文档处理与 RAG 查询链路。
架构边界：
- 本模块只依赖领域模型、领域端口和 Repository，不实现 Markdown 语法、Embedding 协议、
+ 本模块只依赖领域模型、领域端口和 Repository，不实现具体文件语法、Embedding 协议、
Milvus SDK 或 HTTP 路由。基础设施异常在这里转换为用户可理解的业务失败状态。
设计背景：
- V1 的处理流程是确定性的顺序工作流，不需要 LangGraph、事件总线或任务编排框架。
- 显式方法调用让学习者能够从上到下看到状态如何变化，也让每个阶段可以单独测试和替换。
+ 处理流程本身仍是确定性的顺序工作流，不需要 LangGraph。耗时工作由持久化 Worker 执行，
+ 使 HTTP 上传在原文件和任务可靠落库后立即返回。
数据一致性：
- PostgreSQL 与 MinIO 保存事实数据，Milvus 保存可重建的派生索引。V1 不实现跨存储事务，
+ PostgreSQL 与 MinIO 保存事实数据，Milvus 保存可重建的派生索引。V2 不实现跨存储事务，
而是使用稳定 ID、明确的 FAILED 状态和有限补偿降低部分失败造成的不一致。
"""
import hashlib
-import logging
from collections.abc import AsyncIterator
from dataclasses import replace
from pathlib import PurePath
from uuid import uuid4
from ultimate_rag.application.context import ContextBuilder
-from ultimate_rag.domain.exceptions import DocumentProcessingError, InvalidDocumentError
+from ultimate_rag.domain.exceptions import DocumentBusyError, InvalidDocumentError
from ultimate_rag.domain.models import (
Citation,
Document,
@@ -38,75 +36,54 @@
from ultimate_rag.infrastructure.database.repository import Repository
from ultimate_rag.parsers.registry import ParserRegistry
-logger = logging.getLogger(__name__)
-
class IngestionService:
- """编排 Markdown 从上传字节到可检索向量的同步摄取流程。
-
- 本类位于 Application 层，负责输入边界、阶段顺序、文档状态和失败语义；Parser、Chunker、
- Embedder 与 VectorStore 的具体算法或外部协议由注入的端口实现，不在本类中处理。
+ """校验上传、保存原文件并原子创建文档与后台任务。
- V1 不模拟分布式事务。原文件先落 MinIO，后续失败保留原文件与文档事实并标记 ``FAILED``；
- 只有 PostgreSQL Chunk 与 Milvus 向量均成功写入后，文档才进入 ``READY``。
+ 这里刻意不解析、切块或调用模型。HTTP 请求只等待输入校验、MinIO 写入和 PostgreSQL
+ 事务，因此复杂 PDF 不会长期占用上传连接；后续阶段由 ``DocumentProcessingService`` 执行。
"""
- # 浏览器和操作系统对 Markdown 的声明不统一，允许常见文本类型与通用二进制回退值。
- MARKDOWN_MIME_TYPES = frozenset(
- {
- "text/markdown",
- "text/plain",
- "application/x-markdown",
- "application/octet-stream",
- }
- )
-
def __init__(
self,
*,
repository: Repository,
storage: ObjectStorage,
parser_registry: ParserRegistry,
- chunker: Chunker,
- embedder: Embedder,
- vector_store: VectorStore,
max_upload_bytes: int,
+ job_max_attempts: int,
) -> None:
- """注入事实存储、处理策略和最大上传限制，不在构造时访问外部服务。"""
+ """注入事实存储、格式注册表和上传/重试边界。"""
self._repository = repository
self._storage = storage
self._parser_registry = parser_registry
- self._chunker = chunker
- self._embedder = embedder
- self._vector_store = vector_store
self._max_upload_bytes = max_upload_bytes
+ self._job_max_attempts = job_max_attempts
- async def ingest(
+ async def submit(
self,
knowledge_base_id: str,
filename: str,
mime_type: str,
content: bytes,
) -> Document:
- """校验、保存并同步处理一份 Markdown 文档。
+ """校验并可靠提交一份后台处理文档。
Args:
knowledge_base_id: 文档所属知识库 ID。
filename: 浏览器上传的展示文件名；只取 basename，不用于构造本地路径。
- mime_type: 客户端声明的 MIME 类型，缺失时回退为 ``text/markdown``。
- content: 原始文件字节，大小和 UTF-8 编码会在管线中验证。
+ mime_type: 客户端声明的 MIME 类型，缺失时回退为通用二进制类型。
+ content: 原始文件字节；Parser 会继续验证实际格式和内容。
Returns:
- 已完成处理且状态为 ``READY`` 的文档。
+ 已持久化且状态为 ``PENDING`` 的文档；返回不代表解析已经完成。
Raises:
ResourceNotFoundError: 所属知识库不存在。
InvalidDocumentError: 文件类型、大小、编码或内容不合法。
- DocumentProcessingError: 解析、向量化或索引失败；文档会保留为 ``FAILED``。
-
Side Effects:
- 写入 MinIO、PostgreSQL 和 Milvus；元数据创建失败时补偿删除刚上传的 MinIO 对象，
- 处理阶段失败时保留原文件并把文档状态更新为 ``FAILED``。
+ 写入 MinIO，并在一个 PostgreSQL 事务中创建 Document 与 IngestionJob。事务失败时
+ 补偿删除刚上传的对象，避免形成没有业务事实可追踪的 MinIO 孤儿。
"""
# 阶段 1：在触碰外部存储前验证所属知识库和上传边界。
@@ -117,8 +94,8 @@ async def ingest(
# 真正的 Object Key 会使用系统 UUID 构造，因此同名上传不会覆盖，也不能路径穿越。
safe_filename = PurePath(filename).name
extension = PurePath(safe_filename).suffix.lower()
- if extension not in {".md", ".markdown"}:
- raise InvalidDocumentError("V1 仅支持 .md 或 .markdown 文件")
+ if not safe_filename or not extension:
+ raise InvalidDocumentError("上传文件必须包含安全的文件名和扩展名")
if not content:
raise InvalidDocumentError("上传文件不能为空")
if len(content) > self._max_upload_bytes:
@@ -126,11 +103,8 @@ async def ingest(
# MIME 可能包含 ``charset`` 参数，比较前先归一化主类型。MIME 仍只是客户端声明，
# 所以后续 Parser 必须继续验证 UTF-8 解码和实际文本内容，不能把它当作可信证据。
- normalized_mime_type = (
- mime_type.split(";", maxsplit=1)[0].strip().lower() or "text/markdown"
- )
- if normalized_mime_type not in self.MARKDOWN_MIME_TYPES:
- raise InvalidDocumentError(f"不支持的 Markdown MIME 类型：{normalized_mime_type}")
+ normalized_mime_type = mime_type.split(";", maxsplit=1)[0].strip().lower()
+ normalized_mime_type = normalized_mime_type or "application/octet-stream"
# 阶段 2：为原始文件生成稳定的系统定位信息，然后先保存文件、再创建文档事实。
# Object Key 使用知识库 ID 与文档 UUID 隔离对象；SHA-256 记录上传内容指纹，
@@ -139,11 +113,17 @@ async def ingest(
object_key = f"{knowledge_base_id}/{document_id}/source{extension}"
sha256 = hashlib.sha256(content).hexdigest()
- # 原文件先于解析和索引持久化。进入处理阶段后即使失败，也不要求用户重新上传，
+ # 在保存原文件前让 Registry 同时检查扩展名和 MIME，未知格式不会产生 MinIO 孤儿对象。
+ # Parser 后续仍需检查真实文件签名/结构，因为 MIME 与扩展名都来自不可信客户端。
+ self._parser_registry.resolve(
+ DocumentSource(document_id, safe_filename, normalized_mime_type, content)
+ )
+
+ # 原文件先于后台任务持久化。进入处理阶段后即使失败，也不要求用户重新上传，
# 并且可以使用 MinIO 中的事实数据重新构建 PostgreSQL Chunk 与 Milvus 派生索引。
await self._storage.put(object_key, content, normalized_mime_type)
try:
- document = await self._repository.create_document(
+ document = await self._repository.create_document_with_job(
document_id=document_id,
knowledge_base_id=knowledge_base_id,
filename=safe_filename,
@@ -151,6 +131,7 @@ async def ingest(
extension=extension,
object_key=object_key,
sha256=sha256,
+ max_attempts=self._job_max_attempts,
)
except Exception:
# 此时 PENDING 文档事实尚未创建，刚上传的对象没有任何业务记录可以追踪，
@@ -158,40 +139,65 @@ async def ingest(
await self._storage.delete(object_key)
raise
- # 阶段 3：执行确定性的处理 Pipeline，并把任意阶段异常投影为 FAILED 业务状态。
- # 已创建的文档和原文件在失败后继续保留，用户可以看到错误，后续任务也能重试。
- try:
- await self._process(document, content)
- except Exception as exc:
- logger.exception("Document ingestion failed", extra={"document_id": document_id})
- await self._repository.update_document_status(
- document_id,
- DocumentStatus.FAILED,
- error_message=self._safe_error(exc),
- )
- raise DocumentProcessingError(
- f"文档处理失败，document_id={document_id}：{self._safe_error(exc)}"
- ) from exc
+ # Document 与 Job 已原子提交。此处立即返回 PENDING 快照，Worker 即使尚未启动也不会
+ # 丢任务；前端通过列表或详情端点轮询后续阶段，而不是保持上传请求等待。
+ return document
+
+ async def ingest(
+ self,
+ knowledge_base_id: str,
+ filename: str,
+ mime_type: str,
+ content: bytes,
+ ) -> Document:
+ """保留旧应用调用名；语义已变为提交后台任务并立即返回。"""
+
+ return await self.submit(knowledge_base_id, filename, mime_type, content)
+
+
+class DocumentProcessingService:
+ """由 Worker 调用的确定性 ``Parse → Chunk → Embed → Index`` 管线。"""
+
+ def __init__(
+ self,
+ *,
+ repository: Repository,
+ storage: ObjectStorage,
+ parser_registry: ParserRegistry,
+ chunker: Chunker,
+ embedder: Embedder,
+ vector_store: VectorStore,
+ ) -> None:
+ """注入处理阶段需要的事实存储、策略与外部端口。"""
- # 状态更新使用独立短事务，重新读取可以返回数据库最终保存的 READY、解析器信息和时间戳，
- # 而不是继续返回创建时仍处于 PENDING 的旧领域对象快照。
- return await self._repository.get_document(document_id)
+ self._repository = repository
+ self._storage = storage
+ self._parser_registry = parser_registry
+ self._chunker = chunker
+ self._embedder = embedder
+ self._vector_store = vector_store
- async def _process(self, document: Document, content: bytes) -> None:
- """依次执行 Parse、Chunk、Embed 和 Index，并显式记录每个处理状态。
+ async def process(self, document_id: str) -> Document:
+ """幂等处理一份已入队文档，并显式记录每个阶段。
Args:
- document: 已持久化且处于 ``PENDING`` 的文档事实快照。
- content: 与文档 Object Key 对应的原始上传字节。
+ document_id: 已持久化文档 ID；原文件由 Object Key 从 MinIO 重新读取。
Raises:
InvalidDocumentError: Parser 或 Chunker 无法生成可索引内容。
- Exception: 外部处理阶段失败时保留原始异常，由 ``ingest()`` 统一记录 ``FAILED``。
+ Exception: 外部处理失败时保留原始异常，由 Worker 决定是否有限重试。
Side Effects:
更新 PostgreSQL 文档状态和 Chunk，删除并重建该文档的 Milvus 向量。
"""
+ document = await self._repository.get_document(document_id)
+ if document.status == DocumentStatus.READY:
+ # Worker 可能在 READY 写入后、任务完成提交前退出。重领时直接返回可以关闭这一
+ # 很小的提交窗口，避免再次支付解析和向量化成本。
+ return document
+ content = await self._storage.get(document.object_key)
+
# 阶段 1 — Parse：先建立不依赖 FastAPI、MinIO SDK 或 ORM 的 DocumentSource。
# Parser 只接收这个领域输入，并把不同原始格式统一映射为 ParsedDocument/Block。
source = DocumentSource(
@@ -221,7 +227,17 @@ async def _process(self, document: Document, content: bytes) -> None:
# Chunk 保持不可变；使用 dataclasses.replace 只添加展示用文件名。Milvus 检索命中后
# 可以直接构造 Citation，避免为了每个 Hit 再查询一次 PostgreSQL 形成 N+1。
- chunks = [replace(chunk, metadata={"filename": document.filename}) for chunk in chunks]
+ chunks = [
+ replace(
+ chunk,
+ metadata={
+ **chunk.metadata,
+ "filename": document.filename,
+ "source_locator": chunk.locator.to_metadata() if chunk.locator else {},
+ },
+ )
+ for chunk in chunks
+ ]
# 阶段 3 — Embed：应用层一次提交全部文本，Adapter 再按供应商 Batch 上限有界分批。
# 这样业务流程不依赖百炼限制，也避免每个 Chunk 单独发一次网络请求。
@@ -242,15 +258,19 @@ async def _process(self, document: Document, content: bytes) -> None:
await self._vector_store.delete_by_document(document.id)
await self._vector_store.upsert(embedded_chunks)
- # READY 是 Pipeline 的提交标志，只能放在最后。此前任一步失败都会回到 ingest()，
- # 由它记录 FAILED，确保用户永远不会检索到只完成部分索引的文档。
+ # READY 是 Pipeline 的提交标志，只能放在最后。此前异常向 Worker 冒泡，由 Worker
+ # 在同一数据库事务内更新任务和文档失败/重试状态。
await self._repository.update_document_status(document.id, DocumentStatus.READY)
+ return await self._repository.get_document(document.id)
- @staticmethod
- def _safe_error(exc: Exception) -> str:
- """截断外部异常文本，避免无界错误内容写入数据库或返回客户端。"""
- message = str(exc).strip() or exc.__class__.__name__
- return message[:1000]
+ async def cleanup_partial_index(self, document_id: str) -> None:
+ """失败后尽力清理可能只写入一部分的 Milvus 派生向量。
+
+ PostgreSQL Chunk 与 MinIO 原文件继续保留用于诊断和重试；只有派生索引需要清理，
+ 防止尚未 READY 的文档在异常窗口内进入检索结果。
+ """
+
+ await self._vector_store.delete_by_document(document_id)
class RetrievalService:
@@ -260,10 +280,16 @@ class RetrievalService:
它不负责构造 Prompt 或调用 LLM，因此 Retrieval 可以独立测试和调试。
"""
- def __init__(self, embedder: Embedder, vector_store: VectorStore) -> None:
- """注入共享向量编码器和向量索引。"""
+ def __init__(
+ self,
+ embedder: Embedder,
+ vector_store: VectorStore,
+ repository: Repository,
+ ) -> None:
+ """注入共享向量编码器、向量索引与文档事实读取端。"""
self._embedder = embedder
self._vector_store = vector_store
+ self._repository = repository
async def search(
self,
@@ -285,7 +311,16 @@ async def search(
# 查询必须沿用文档入库时的 Embedder；更换模型会改变向量空间，
# 即使维度相同，使用旧 Collection 检索也不会得到有意义的相似度。
query_vector = await self._embedder.embed_query(query)
- return await self._vector_store.search(query_vector, knowledge_base_id, top_k)
+
+ # Milvus 是派生索引，索引写入与 PostgreSQL READY 状态无法形成跨系统原子事务。
+ # 多取少量候选后按事实状态过滤，可避免 Worker 崩溃时的半成品向量参与回答。
+ candidates = await self._vector_store.search(
+ query_vector,
+ knowledge_base_id,
+ min(top_k * 3, 60),
+ )
+ ready_document_ids = await self._repository.list_ready_document_ids(knowledge_base_id)
+ return [result for result in candidates if result.document_id in ready_document_ids][:top_k]
class RAGService:
@@ -390,6 +425,7 @@ async def _prepare_generation(
filename=result.filename,
chunk_id=result.chunk_id,
heading_path=result.heading_path,
+ locator=result.locator,
)
for result in results
]
@@ -428,6 +464,10 @@ async def delete_document(self, document_id: str) -> None:
# 删除开始前读取文档事实，既验证资源存在，也取得系统生成的 MinIO Object Key。
document = await self._repository.get_document(document_id)
+ if document.status not in {DocumentStatus.READY, DocumentStatus.FAILED}:
+ # 没有取消协议时直接删除事实记录，会让已领取任务继续向 MinIO/Milvus 写入并形成孤儿。
+ # 当前版本明确拒绝该竞态，用户可在任务进入终态后重试删除。
+ raise DocumentBusyError("文档正在后台处理，完成或失败后才能删除")
# PostgreSQL 放在最后删除：前两步中断时，事实记录仍能告诉补偿操作应该清理什么。
# 任一步异常都继续上抛，API 不能在外部资源仍残留时返回虚假的 204 成功。
@@ -441,6 +481,11 @@ async def delete_knowledge_base(self, knowledge_base_id: str) -> None:
# 删除数据库前先获取文档快照；若先触发级联删除，之后将失去所有 MinIO Object Key，
# 无法知道知识库曾经包含哪些需要清理的原始文件。
documents = await self._repository.list_documents(knowledge_base_id)
+ if any(
+ document.status not in {DocumentStatus.READY, DocumentStatus.FAILED}
+ for document in documents
+ ):
+ raise DocumentBusyError("知识库仍有文档正在后台处理，完成或失败后才能删除")
# Milvus 支持按知识库过滤条件批量删除，MinIO V1 则按系统 Object Key 逐个删除。
# PostgreSQL 事实仍然最后提交删除，使中途失败后可以使用同一调用重新清理。
diff --git a/src/ultimate_rag/chunkers/__init__.py b/src/ultimate_rag/chunkers/__init__.py
index cc330dd..f988c0f 100644
--- a/src/ultimate_rag/chunkers/__init__.py
+++ b/src/ultimate_rag/chunkers/__init__.py
@@ -1,5 +1,5 @@
"""文档切块策略公开入口。"""
-from ultimate_rag.chunkers.markdown import StructureAwareMarkdownChunker
+from ultimate_rag.chunkers.markdown import StructureAwareChunker, StructureAwareMarkdownChunker
-__all__ = ["StructureAwareMarkdownChunker"]
+__all__ = ["StructureAwareChunker", "StructureAwareMarkdownChunker"]
diff --git a/src/ultimate_rag/chunkers/markdown.py b/src/ultimate_rag/chunkers/markdown.py
index 47395d9..89bfe2b 100644
--- a/src/ultimate_rag/chunkers/markdown.py
+++ b/src/ultimate_rag/chunkers/markdown.py
@@ -1,109 +1,98 @@
-"""结构感知 Markdown 切块模块。
+"""格式无关的结构、Token 与内容类型混合切块器。
-模块职责：
- 把 Parser 生成的 ``ParsedDocument`` 切分为适合 Embedding 和 Retrieval 的 ``Chunk``，
- 同时保留标题路径、代码块边界、原始顺序和可重复计算的稳定 ID。
+最佳实践不是一个对所有文档都固定的字符窗口。本实现先尊重 Parser 已恢复的标题、页码、表格、
+代码和图片边界，再使用 Token 预算切分超长内容：正文优先段落/句子，代码优先行，表格优先行并
+重复表头，只有单个自然单元仍超限时才退化为带重叠的 Token 窗口。
-架构边界：
- 本模块只依赖 UltimateRAG 领域模型，不解析原始 Markdown 字节、不调用 Embedding API，
- 也不写入 PostgreSQL 或 Milvus。
-
-设计背景：
- V1 先按标题路径聚合章节，只有章节超过字符预算时才继续切分。相比对整篇文档直接使用
- 固定窗口，这种方式优先保留章节语义；相邻硬切窗口使用 Overlap，降低边界信息丢失。
-
-典型调用位置：
- ``IngestionService`` 在 Markdown Parser 之后、Embedder 之前调用本模块。
-
-注意事项 / 已知限制：
- ``max_chars`` 和 ``overlap_chars`` 是尚待 Retrieval Evaluation 校准的 V1 字符级默认值，
- 不是模型 Token 上限。``token_count`` 只是观察指标，不可用于强制模型输入限制。
+``cl100k_base`` 不是百炼 Embedding 的官方精确 Tokenizer，而是稳定、可离线运行的预算近似器。
+它用于避免中英文字符比例造成的巨大偏差；真实最佳大小仍需通过项目评估集的 Recall@k 调优。
"""
import re
+from dataclasses import dataclass, field
from uuid import NAMESPACE_URL, uuid5
-from ultimate_rag.domain.models import BlockType, Chunk, ParsedDocument
+import tiktoken
+from tiktoken import Encoding
+from ultimate_rag.domain.models import (
+ Block,
+ BlockType,
+ Chunk,
+ JsonValue,
+ ParsedDocument,
+ SourceLocator,
+)
-class StructureAwareMarkdownChunker:
- """按章节结构和字符预算生成可追溯、可重复计算的 Chunk。
- 本类是无状态切块策略；构造后只保存字符预算。相同 ``document_id``、Block 顺序和内容
- 会生成相同 Chunk ID，以支持摄取重试时的数据库替换与向量 Upsert。
- """
+@dataclass(slots=True)
+class _Section:
+ """连续、同来源且切分策略兼容的 Block 聚合。"""
- def __init__(self, max_chars: int = 1600, overlap_chars: int = 160) -> None:
- """验证字符预算与重叠范围，防止零步长或无界切块。"""
- if max_chars < 100:
- raise ValueError("max_chars must be at least 100")
- if overlap_chars < 0 or overlap_chars >= max_chars:
- raise ValueError("overlap_chars must be between 0 and max_chars")
- self._max_chars = max_chars
- self._overlap_chars = overlap_chars
+ locator: SourceLocator
+ kind: str
+ parts: list[str] = field(default_factory=list)
+ block_types: set[BlockType] = field(default_factory=set)
+ source_labels: set[str] = field(default_factory=set)
+ extraction_methods: set[str] = field(default_factory=set)
+ layout_engines: set[str] = field(default_factory=set)
- async def split(self, document: ParsedDocument, knowledge_base_id: str) -> list[Chunk]:
- """先按标题聚合章节，再按字符预算切分并生成稳定 Chunk ID。
- Args:
- document: Parser 输出的统一文档，其中 Block 按原文顺序排列。
- knowledge_base_id: Chunk 所属检索隔离范围，会写入每个领域对象。
+class StructureAwareChunker:
+ """按结构边界与 Token 预算生成稳定、可追溯 Chunk。"""
- Returns:
- 按原文顺序排列的 Chunk；空文档返回空列表，由 Application Service 决定失败语义。
+ _ATOMIC_KINDS = frozenset({"table", "code", "image"})
- 复杂度:
- 主流程相对 Block 数和文本字符数为 O(n)；每个内容片段只进行有限次拼接与扫描。
- """
+ def __init__(
+ self,
+ max_tokens: int = 512,
+ overlap_tokens: int = 64,
+ tokenizer_name: str = "cl100k_base",
+ ) -> None:
+ """验证预算并加载本地 Tokenizer；构造过程不访问网络。"""
- # 阶段 1 — Build Sections：先把连续正文 Block 聚合到各自的标题路径下。
- # 标题路径与正文一起保存，章节稍后即使拆成多个 Chunk，Citation 仍能定位原始章节。
- sections: list[tuple[tuple[str, ...], str]] = []
- current_path: tuple[str, ...] = ()
- current_parts: list[str] = []
+ if max_tokens < 64:
+ raise ValueError("max_tokens must be at least 64")
+ if overlap_tokens < 0 or overlap_tokens >= max_tokens:
+ raise ValueError("overlap_tokens must be between 0 and max_tokens")
+ self._max_tokens = max_tokens
+ self._overlap_tokens = overlap_tokens
+ self._encoding: Encoding = tiktoken.get_encoding(tokenizer_name)
+
+ async def split(self, document: ParsedDocument, knowledge_base_id: str) -> list[Chunk]:
+ """先建立语义 Section，再按内容类型切分并生成稳定 ID。"""
- for block in document.blocks:
- block_path = block.locator.heading_path if block.locator else ()
- if block.type == BlockType.HEADING:
- # Heading Block 标记上一章节结束。标题文字已经存在于 heading_path，
- # 因此不把它重复追加到正文；最终由 _with_heading() 统一添加一次完整路径。
- self._flush_section(sections, current_path, current_parts)
- current_path = block_path
- current_parts = []
- continue
- if block_path != current_path and current_parts:
- # Markdown Parser 通常会先产生 Heading Block；这个保护分支也支持未来 Parser
- # 直接改变 SourceLocator，避免不同来源路径的正文被错误合并为同一章节。
- self._flush_section(sections, current_path, current_parts)
- current_parts = []
- current_path = block_path
-
- # CODE Block 只保存代码正文，这里恢复通用围栏，让 Embedding 输入仍能区分代码
- # 与普通段落。V1 没有在领域模型中保存原始语言标记，因此不会尝试伪造语言名称。
- prefix = "```\n" if block.type == BlockType.CODE else ""
- suffix = "\n```" if block.type == BlockType.CODE else ""
- current_parts.append(f"{prefix}{block.content}{suffix}")
-
- # 最后一个章节后面没有新的 Heading 触发 Flush，循环结束时必须显式提交。
- self._flush_section(sections, current_path, current_parts)
-
- # 阶段 2 — Split Sections：短章节保持完整，只有超预算章节才按段落和窗口继续切分。
- # 标题路径会写入每一段最终文本，使向量自身也携带局部结构语义。
chunks: list[Chunk] = []
- for heading_path, section in sections:
- for piece in self._split_text(section):
- content = self._with_heading(heading_path, piece)
+ for section in self._build_sections(document.blocks):
+ prefix = self._heading_prefix(section.locator.heading_path)
+ body_budget = self._body_budget(prefix)
+ for piece, strategy in self._split_section(section, body_budget):
+ content = f"{prefix}\n\n{piece}" if prefix else piece
+ token_count = self._count_tokens(content)
+ if not content.strip():
+ continue
+ if token_count > self._max_tokens:
+ # 所有内部路径都应遵守预算；显式断言比把超限文本交给模型后隐蔽失败更安全。
+ raise RuntimeError("Chunker produced content above configured token budget")
index = len(chunks)
-
- # 阶段 3 — Build Domain Chunk：ID 包含文档、最终顺序和实际索引文本。
- # 对同一 document_id 重试相同内容会得到相同 UUID，使 Milvus Upsert 保持幂等；
- # 内容或顺序改变则生成新 ID，旧向量由 Application Service 在 Upsert 前删除。
chunk_id = str(
uuid5(
NAMESPACE_URL,
f"{document.document_id}:chunk:{index}:{content}",
)
)
+ block_types = list[JsonValue](sorted(value.value for value in section.block_types))
+ source_labels = list[JsonValue](sorted(section.source_labels))
+ extraction_methods = list[JsonValue](sorted(section.extraction_methods))
+ layout_engines = list[JsonValue](sorted(section.layout_engines))
+ metadata: dict[str, JsonValue] = {
+ "block_types": block_types,
+ "source_labels": source_labels,
+ "extraction_methods": extraction_methods,
+ "layout_engines": layout_engines,
+ "split_strategy": strategy,
+ "tokenizer": self._encoding.name,
+ }
chunks.append(
Chunk(
id=chunk_id,
@@ -111,84 +100,346 @@ async def split(self, document: ParsedDocument, knowledge_base_id: str) -> list[
document_id=document.document_id,
index=index,
content=content,
- heading_path=heading_path,
- token_count=self._estimate_tokens(content),
+ heading_path=section.locator.heading_path,
+ token_count=token_count,
+ locator=section.locator,
+ metadata=metadata,
)
)
return chunks
- @staticmethod
- def _flush_section(
- sections: list[tuple[tuple[str, ...], str]],
- heading_path: tuple[str, ...],
- parts: list[str],
- ) -> None:
- """把当前非空章节规范化后追加到待切分列表。"""
- content = "\n\n".join(part.strip() for part in parts if part.strip()).strip()
- if content:
- sections.append((heading_path, content))
+ def _build_sections(self, blocks: tuple[Block, ...]) -> list[_Section]:
+ """合并兼容正文，同时禁止跨页、跨 Sheet、跨 Slide 或跨语义类型。"""
- def _split_text(self, text: str) -> list[str]:
- """优先把完整段落装入字符窗口，只对超长段落使用带 Overlap 的硬切分。
+ sections: list[_Section] = []
+ current: _Section | None = None
+ bounding_boxes: list[tuple[float, float, float, float]] = []
- 普通段落之间不人为创建 Overlap，因为它们已经具有自然语义边界；只有单个段落超过
- ``max_chars`` 时，才需要重叠窗口保护落在人工切分边界附近的信息。
+ def flush() -> None:
+ nonlocal current, bounding_boxes
+ if current is not None and any(part.strip() for part in current.parts):
+ current.locator = self._merge_bbox(current.locator, bounding_boxes)
+ sections.append(current)
+ current = None
+ bounding_boxes = []
+
+ for block in blocks:
+ if block.type == BlockType.HEADING:
+ # 标题已由 Parser 写入后续 Block 的 heading_path，不重复进入正文向量。
+ flush()
+ continue
+ content = block.content.strip()
+ if not content:
+ continue
+
+ locator = block.locator or SourceLocator()
+ kind = self._section_kind(block.type)
+ should_start = (
+ current is None
+ or current.kind != kind
+ or self._location_key(current.locator) != self._location_key(locator)
+ or current.kind in self._ATOMIC_KINDS
+ )
+ if should_start:
+ flush()
+ current = _Section(locator=locator, kind=kind)
+
+ assert current is not None
+ current.parts.append(self._format_block(block))
+ current.block_types.add(block.type)
+ label = block.metadata.get("layout_label")
+ if isinstance(label, str) and label:
+ current.source_labels.add(label)
+ extraction = block.metadata.get("extraction")
+ if isinstance(extraction, str) and extraction:
+ current.extraction_methods.update(extraction.split("+"))
+ layout_engine = block.metadata.get("layout_engine")
+ if isinstance(layout_engine, str) and layout_engine:
+ current.layout_engines.add(layout_engine)
+ if locator.bbox is not None:
+ bounding_boxes.append(locator.bbox)
+
+ # 表格、代码和图片各自形成独立 Section，避免与后续正文混合后破坏专用切分策略。
+ if kind in self._ATOMIC_KINDS:
+ flush()
+
+ flush()
+ return sections
+
+ def _split_section(self, section: _Section, budget: int) -> list[tuple[str, str]]:
+ """按 Section 内容类型选择确定性的切分策略。"""
+
+ text = "\n\n".join(part for part in section.parts if part).strip()
+ if not text:
+ return []
+ if self._count_tokens(text) <= budget:
+ return [(text, "structure")]
+ if section.kind == "table":
+ return [(piece, "table_rows") for piece in self._split_table(text, budget)]
+ if section.kind == "code":
+ code_budget = max(8, budget - self._count_tokens("```\n\n```"))
+ return [
+ (f"```\n{piece}\n```", "code_lines")
+ for piece in self._split_natural(
+ text,
+ code_budget,
+ separator="\n",
+ units=text.splitlines(),
+ )
+ ]
+
+ paragraphs = [value.strip() for value in re.split(r"\n\s*\n", text) if value.strip()]
+ units: list[str] = []
+ for paragraph in paragraphs:
+ if self._count_tokens(paragraph) <= budget:
+ units.append(paragraph)
+ continue
+ for sentence in self._sentences(paragraph):
+ if self._count_tokens(sentence) <= budget:
+ units.append(sentence)
+ else:
+ units.extend(self._hard_token_split(sentence, budget))
+ return [
+ (piece, "paragraph_sentence_tokens")
+ for piece in self._split_natural(text, budget, separator="\n\n", units=units)
+ ]
+
+ def _split_table(self, table: str, budget: int) -> list[str]:
+ """按 Markdown 表格行切分，并在预算允许时重复题注、表头和二级表头。
+
+ Docling 会把题注放在 Markdown 表格之前，旧实现因此没有识别到第二行之后的真正表头，
+ 最终退化为 Token 窗口并让续块丢失列语义。这里显式定位分隔行；超宽单行无法同时容纳
+ 表头时，采用 Docling 官方同类策略：优先保持该行完整并只对该块省略表头。
"""
- if len(text) <= self._max_chars:
- return [text]
+ lines = [line.strip() for line in table.splitlines() if line.strip()]
+ separator_index = next(
+ (
+ index
+ for index in range(1, len(lines))
+ if lines[index - 1].startswith("|")
+ and self._is_table_separator(lines[index])
+ ),
+ None,
+ )
+ if separator_index is None:
+ return self._hard_token_split(table, budget)
+
+ header_start = separator_index - 1
+ prefix_lines = [*lines[:header_start], lines[header_start], lines[separator_index]]
+ data_start = separator_index + 1
+ if data_start < len(lines) and self._looks_like_secondary_header(lines[data_start]):
+ # Docling 用合并单元格表达多级表头时，第二级常作为首个“数据行”导出且首列为空。
+ prefix_lines.append(lines[data_start])
+ data_start += 1
+ prefix = "\n".join(prefix_lines)
+ rows = lines[data_start:]
+ if not rows:
+ return self._hard_token_split(prefix, budget, overlap_tokens=0)
- # 阶段 1：Markdown 空行是自然段落边界，先按它拆分可以优先保留完整逻辑段。
- # 单段仍然超预算时，_hard_split() 才退化为固定字符窗口。
- paragraphs = [part.strip() for part in re.split(r"\n\s*\n", text) if part.strip()]
pieces: list[str] = []
- current = ""
- for paragraph in paragraphs:
- for segment in self._hard_split(paragraph):
- # 阶段 2：尝试把下一段装入当前窗口。候选文本恢复段落空行，因此长度判断
- # 针对最终送入 Embedding 的实际字符串，而不是忽略分隔符的近似长度。
- candidate = f"{current}\n\n{segment}".strip() if current else segment
- if len(candidate) <= self._max_chars:
- current = candidate
+ current_rows: list[str] = []
+ for row in rows:
+ row_units = (
+ [row]
+ if self._count_tokens(row) <= budget
+ else self._hard_token_split(row, budget, overlap_tokens=0)
+ )
+ for unit in row_units:
+ candidate = "\n".join([prefix, *current_rows, unit])
+ if self._count_tokens(candidate) <= budget:
+ current_rows.append(unit)
continue
- if current:
- # 候选超过预算时提交旧窗口，并从当前 Segment 开始下一窗口。
- # 超长段落已经由 _hard_split() 添加 Overlap，这里不能再次重叠，
- # 否则同一边界会被重复计算，并可能让最终 Piece 超过 max_chars。
- pieces.append(current)
- current = segment
+ if current_rows:
+ pieces.append("\n".join([prefix, *current_rows]))
+ current_rows = []
+
+ with_header = f"{prefix}\n{unit}"
+ if self._count_tokens(with_header) <= budget:
+ current_rows = [unit]
else:
- current = segment
- if current:
- pieces.append(current)
+ # 对极宽表格行，行本身比重复表头更重要；该行为与官方
+ # omit_header_on_overflow 选项一致，并且仍严格遵守 Token 上限。
+ pieces.append(unit)
+ if current_rows:
+ pieces.append("\n".join([prefix, *current_rows]))
return pieces
- def _hard_split(self, text: str) -> list[str]:
- """使用固定字符窗口切分无法按自然段落拆开的超长文本。
+ @staticmethod
+ def _is_table_separator(line: str) -> bool:
+ """严格识别 ``| --- | :---: |`` 形式的 Markdown 分隔行。"""
- 相邻窗口共享 ``overlap_chars`` 个字符，降低实体、定义或代码语句恰好跨越边界时
- 两个 Chunk 都缺少完整上下文的风险。
- """
+ cells = [cell.strip() for cell in line.strip().strip("|").split("|") if cell.strip()]
+ return bool(cells) and all(re.fullmatch(r":?-{3,}:?", cell) for cell in cells)
+
+ @staticmethod
+ def _looks_like_secondary_header(line: str) -> bool:
+ """识别 Docling 从跨列表头导出的首列为空二级表头。"""
+
+ cells = [cell.strip() for cell in line.strip().strip("|").split("|")]
+ return bool(cells) and not cells[0] and any(cells[1:])
+
+ def _split_natural(
+ self,
+ _text: str,
+ budget: int,
+ *,
+ separator: str,
+ units: list[str],
+ ) -> list[str]:
+ """贪心装入自然单元，并把上一块末尾少量单元带入下一块。"""
+
+ normalized = [unit.strip() for unit in units if unit.strip()]
+ if not normalized:
+ return []
+ pieces: list[str] = []
+ current: list[str] = []
+ for unit in normalized:
+ candidate = separator.join([*current, unit])
+ if not current or self._count_tokens(candidate) <= budget:
+ current.append(unit)
+ continue
+
+ pieces.append(separator.join(current))
+ carry = self._overlap_tail(current, separator)
+ candidate_with_overlap = separator.join([*carry, unit])
+ # 若重叠会挤不下完整新单元，优先保留新信息，不能为了 overlap 超出预算。
+ current = (
+ [*carry, unit] if self._count_tokens(candidate_with_overlap) <= budget else [unit]
+ )
+ if current:
+ pieces.append(separator.join(current))
+ return pieces
- if len(text) <= self._max_chars:
+ def _overlap_tail(self, units: list[str], separator: str) -> list[str]:
+ """从完整自然单元中选择不超过 overlap 预算的后缀。"""
+
+ if self._overlap_tokens == 0:
+ return []
+ tail: list[str] = []
+ for unit in reversed(units):
+ candidate = separator.join([unit, *tail])
+ if self._count_tokens(candidate) > self._overlap_tokens:
+ break
+ tail.insert(0, unit)
+ # 不复制完整上一 Chunk，否则只有一个短单元时可能生成重复内容。
+ return [] if len(tail) == len(units) else tail
+
+ def _hard_token_split(
+ self,
+ text: str,
+ budget: int,
+ *,
+ overlap_tokens: int | None = None,
+ ) -> list[str]:
+ """最后退路：按 Token ID 使用有限重叠窗口切分单个超长自然单元。"""
+
+ tokens = self._encode(text)
+ if len(tokens) <= budget:
return [text]
+ overlap = self._overlap_tokens if overlap_tokens is None else overlap_tokens
+ overlap = min(overlap, budget - 1)
+ step = budget - overlap
+ return [
+ self._encoding.decode(tokens[start : start + budget]).strip()
+ for start in range(0, len(tokens), step)
+ if tokens[start : start + budget]
+ ]
+
+ def _body_budget(self, prefix: str) -> int:
+ """为重复标题预留 Token；异常长标题会先截断到总预算四分之一。"""
+
+ if not prefix:
+ return self._max_tokens
+ prefix_tokens = self._count_tokens(prefix) + 1
+ if prefix_tokens <= self._max_tokens // 4:
+ return self._max_tokens - prefix_tokens
+ # _heading_prefix 已限制标题；此分支只防御 Tokenizer 特殊编码导致的预算偏差。
+ return max(32, self._max_tokens - prefix_tokens)
+
+ def _heading_prefix(self, heading_path: tuple[str, ...]) -> str:
+ """生成受限标题上下文，防止恶意超长 Heading 吞掉正文预算。"""
- # 构造函数已经保证 ``0 <= overlap_chars < max_chars``，因此步长一定为正，
- # range() 不会进入零步长错误或无限切分。
- step = self._max_chars - self._overlap_chars
- return [text[start : start + self._max_chars] for start in range(0, len(text), step)]
+ if not heading_path:
+ return ""
+ prefix = f"章节：{' > '.join(heading_path)}"
+ limit = max(16, self._max_tokens // 4)
+ tokens = self._encode(prefix)
+ return prefix if len(tokens) <= limit else self._encoding.decode(tokens[:limit]).strip()
@staticmethod
- def _with_heading(heading_path: tuple[str, ...], content: str) -> str:
- """把章节路径写入 Chunk 文本，使向量本身保留结构语义。"""
- if not heading_path:
- return content
- return f"章节：{' > '.join(heading_path)}\n\n{content}"
+ def _section_kind(block_type: BlockType) -> str:
+ """把领域类型映射到少量真实有差异的切分策略。"""
+
+ if block_type == BlockType.TABLE:
+ return "table"
+ if block_type == BlockType.CODE:
+ return "code"
+ if block_type == BlockType.IMAGE:
+ return "image"
+ return "prose"
@staticmethod
- def _estimate_tokens(text: str) -> int:
- """估算中英文 Token 数，仅用于元数据观察而非模型限额判断。"""
- chinese_chars = len(re.findall(r"[\u4e00-\u9fff]", text))
- other_words = len(re.findall(r"[A-Za-z0-9_]+", text))
- punctuation = len(re.findall(r"[^\w\s\u4e00-\u9fff]", text))
- return chinese_chars + other_words + punctuation // 2
+ def _format_block(block: Block) -> str:
+ """保留 Parser 生成的结构文本；代码围栏会在每个最终 Chunk 单独恢复。"""
+
+ return block.content.strip()
+
+ @staticmethod
+ def _location_key(locator: SourceLocator) -> tuple[object, ...]:
+ """BBox 可合并，其他来源字段变化必须形成新的 Citation 边界。"""
+
+ return (
+ locator.heading_path,
+ locator.page,
+ locator.sheet,
+ locator.cell_range,
+ locator.slide,
+ )
+
+ @staticmethod
+ def _merge_bbox(
+ locator: SourceLocator,
+ boxes: list[tuple[float, float, float, float]],
+ ) -> SourceLocator:
+ """把同一页同一 Chunk 的元素 BBox 合并为可点击的最小包围框。"""
+
+ if not boxes:
+ return locator
+ return SourceLocator(
+ heading_path=locator.heading_path,
+ page=locator.page,
+ bbox=(
+ min(box[0] for box in boxes),
+ min(box[1] for box in boxes),
+ max(box[2] for box in boxes),
+ max(box[3] for box in boxes),
+ ),
+ sheet=locator.sheet,
+ cell_range=locator.cell_range,
+ slide=locator.slide,
+ )
+
+ @staticmethod
+ def _sentences(paragraph: str) -> list[str]:
+ """对超长段落按中英文句末和换行拆分，短段落不会进入该路径。"""
+
+ return [
+ value.strip()
+ for value in re.split(r"(?<=[。！？!?；;])|(?<=[.!?])\s+|\n+", paragraph)
+ if value.strip()
+ ]
+
+ def _encode(self, text: str) -> list[int]:
+ """把任意不可信正文编码为 Token，特殊标记按普通文本处理。"""
+
+ return self._encoding.encode(text, disallowed_special=())
+
+ def _count_tokens(self, text: str) -> int:
+ """返回与实际切分使用同一 Tokenizer 的精确本地计数。"""
+
+ return len(self._encode(text))
+
+
+# 兼容 V1 的公开类名，已有调用方无需迁移；新代码使用格式无关名称表达真实职责。
+StructureAwareMarkdownChunker = StructureAwareChunker
diff --git a/src/ultimate_rag/config.py b/src/ultimate_rag/config.py
index 3cae493..b3fef71 100644
--- a/src/ultimate_rag/config.py
+++ b/src/ultimate_rag/config.py
@@ -6,12 +6,12 @@
from functools import lru_cache
from typing import Annotated
-from pydantic import Field, field_validator
+from pydantic import Field, field_validator, model_validator
from pydantic_settings import BaseSettings, NoDecode, SettingsConfigDict
class Settings(BaseSettings):
- """UltimateRAG V1 的集中配置模型。
+ """UltimateRAG V2 的集中配置模型。
默认值面向本地 Docker Compose 开发环境；密钥必须由环境变量或未提交的 ``.env`` 提供。
"""
@@ -28,7 +28,7 @@ class Settings(BaseSettings):
log_level: str = "INFO"
# NoDecode 把原始字符串交给下方校验器，避免 Settings 在校验前强制按 JSON 解码。
cors_origins: Annotated[list[str], NoDecode] = Field(
- default_factory=lambda: ["http://localhost:3000"]
+ default_factory=lambda: ["http://localhost:3000", "http://192.168.3.19:3000"]
)
database_url: str = "postgresql+asyncpg://ultimate_rag:ultimate_rag@localhost:5432/ultimate_rag"
@@ -49,14 +49,44 @@ class Settings(BaseSettings):
embedding_dimension: int = 1024
embedding_batch_size: int = 10
llm_model: str = "qwen-plus"
+ ocr_model: str = "qwen3.5-ocr"
+ vision_model: str = "qwen3-vl-flash"
+ # 百炼 Base64 OCR 接口要求原图小于 7 MB；默认留出编码和服务端校验余量。
+ ocr_max_image_bytes: int = 6 * 1024 * 1024
+ ocr_max_output_tokens: int = Field(default=4096, ge=256, le=32_768)
+ vision_max_image_bytes: int = 6 * 1024 * 1024
+ vision_max_output_tokens: int = Field(default=1536, ge=256, le=8192)
model_timeout_seconds: float = 60.0
max_upload_bytes: int = 10 * 1024 * 1024
- chunk_max_chars: int = 1600
- chunk_overlap_chars: int = 160
+ # 512 Token + 12.5% overlap 是通用起点，不是脱离评估集即可宣称的全局最优值。
+ chunk_max_tokens: int = Field(default=512, ge=64, le=8192)
+ chunk_overlap_tokens: int = Field(default=64, ge=0, le=2048)
+ chunk_tokenizer: str = "cl100k_base"
retrieval_top_k: int = 5
context_max_chars: int = 12000
+ # PostgreSQL 持久化队列采用有限重试与租约回收，不允许无限重试或永久 RUNNING。
+ ingestion_job_max_attempts: int = Field(default=3, ge=1, le=10)
+ worker_poll_interval_seconds: float = Field(default=1.0, ge=0.1, le=60.0)
+ worker_lease_seconds: int = Field(default=900, ge=30, le=7200)
+ worker_heartbeat_seconds: int = Field(default=30, ge=5, le=600)
+ worker_retry_delay_seconds: int = Field(default=10, ge=0, le=3600)
+
+ pdf_native_text_threshold: int = 20
+ # 低文字页只有被大幅栅格图覆盖时才判为扫描页，避免把“少量题注 + 大图”误当纯 OCR 页。
+ pdf_scan_image_coverage_threshold: float = Field(default=0.65, ge=0.1, le=1.0)
+ pdf_scan_vision_text_threshold: int = Field(default=300, ge=0, le=10_000)
+ pdf_render_scale: float = 2.0
+ pdf_vision_concurrency: int = Field(default=2, ge=1, le=8)
+ pdf_max_pictures: int = Field(default=20, ge=0, le=200)
+ pdf_min_picture_pixels: int = Field(default=10_000, ge=1)
+ docling_device: str = "cpu"
+ docling_num_threads: int = Field(default=4, ge=1, le=64)
+ docling_timeout_seconds: float = Field(default=600.0, ge=30.0, le=3600.0)
+ docling_images_scale: float = Field(default=2.0, ge=1.0, le=3.0)
+ docling_artifacts_path: str | None = None
+
@field_validator("cors_origins", mode="before")
@classmethod
def parse_cors_origins(cls, value: object) -> object:
@@ -65,6 +95,16 @@ def parse_cors_origins(cls, value: object) -> object:
return [origin.strip() for origin in value.split(",") if origin.strip()]
return value
+ @model_validator(mode="after")
+ def validate_cross_field_limits(self) -> "Settings":
+ """验证需要同时比较两个字段的 Token 与 Worker 租约约束。"""
+
+ if self.chunk_overlap_tokens >= self.chunk_max_tokens:
+ raise ValueError("chunk_overlap_tokens must be smaller than chunk_max_tokens")
+ if self.worker_heartbeat_seconds >= self.worker_lease_seconds:
+ raise ValueError("worker_heartbeat_seconds must be smaller than worker_lease_seconds")
+ return self
+
@lru_cache
def get_settings() -> Settings:
diff --git a/src/ultimate_rag/domain/exceptions.py b/src/ultimate_rag/domain/exceptions.py
index ed66008..0b8ded5 100644
--- a/src/ultimate_rag/domain/exceptions.py
+++ b/src/ultimate_rag/domain/exceptions.py
@@ -32,6 +32,12 @@ class DocumentProcessingError(UltimateRAGError):
pass
+class DocumentBusyError(UltimateRAGError):
+ """文档仍由后台 Worker 处理，当前操作会破坏状态一致性。"""
+
+ pass
+
+
class ExternalServiceError(UltimateRAGError):
"""外部基础设施或模型服务发生可识别故障。"""
diff --git a/src/ultimate_rag/domain/models.py b/src/ultimate_rag/domain/models.py
index ba1833a..7eaa710 100644
--- a/src/ultimate_rag/domain/models.py
+++ b/src/ultimate_rag/domain/models.py
@@ -4,6 +4,7 @@
或模型厂商 SDK。
"""
+from collections.abc import Mapping
from dataclasses import dataclass, field
from datetime import datetime
from enum import StrEnum
@@ -13,7 +14,7 @@
class DocumentStatus(StrEnum):
- """同步摄取管线的可观察状态；只有完整索引成功后才能进入 ``READY``。"""
+ """后台摄取管线的用户可观察状态；完整索引成功前绝不能进入 ``READY``。"""
PENDING = "PENDING"
PARSING = "PARSING"
@@ -24,21 +25,109 @@ class DocumentStatus(StrEnum):
FAILED = "FAILED"
+class IngestionJobStatus(StrEnum):
+ """持久化摄取任务的内部调度状态。
+
+ 文档状态面向用户表达当前处理阶段；任务状态只负责 Worker 领取、重试与完成语义，
+ 二者刻意分开，避免把队列实现细节暴露到公开 API。
+ """
+
+ PENDING = "PENDING"
+ RUNNING = "RUNNING"
+ SUCCEEDED = "SUCCEEDED"
+ FAILED = "FAILED"
+
+
class BlockType(StrEnum):
- """统一文档模型中 V1 已识别的语义块类型。"""
+ """统一文档模型中可由不同 Parser 产生的语义块类型。"""
HEADING = "HEADING"
TEXT = "TEXT"
CODE = "CODE"
LIST = "LIST"
QUOTE = "QUOTE"
+ TABLE = "TABLE"
+ IMAGE = "IMAGE"
@dataclass(frozen=True, slots=True)
class SourceLocator:
- """内容在原文中的可追溯位置；V1 使用 Markdown 标题路径定位。"""
+ """跨文档格式的来源位置，供 Chunk、检索结果和 Citation 统一复用。
+
+ 字段均为可选，因为不同格式能提供的定位精度不同：Markdown/HTML 使用标题路径，PDF
+ 使用页码与可选边界框，Excel 使用 Sheet 与单元格范围，PowerPoint 使用幻灯片序号。
+ """
heading_path: tuple[str, ...] = ()
+ page: int | None = None
+ bbox: tuple[float, float, float, float] | None = None
+ sheet: str | None = None
+ cell_range: str | None = None
+ slide: int | None = None
+
+ def to_metadata(self) -> dict[str, JsonValue]:
+ """转换为 PostgreSQL JSONB、Milvus JSON 和 API 都可接受的稳定字典。"""
+
+ value: dict[str, JsonValue] = {"heading_path": list(self.heading_path)}
+ if self.page is not None:
+ value["page"] = self.page
+ if self.bbox is not None:
+ value["bbox"] = list(self.bbox)
+ if self.sheet is not None:
+ value["sheet"] = self.sheet
+ if self.cell_range is not None:
+ value["cell_range"] = self.cell_range
+ if self.slide is not None:
+ value["slide"] = self.slide
+ return value
+
+ @classmethod
+ def from_metadata(cls, value: Mapping[str, object] | None) -> "SourceLocator":
+ """兼容缺失字段并从持久化 JSON 恢复强类型定位信息。"""
+
+ if not value:
+ return cls()
+ raw_bbox = value.get("bbox")
+ bbox = None
+ if isinstance(raw_bbox, list) and len(raw_bbox) == 4:
+ bbox = (
+ float(raw_bbox[0]),
+ float(raw_bbox[1]),
+ float(raw_bbox[2]),
+ float(raw_bbox[3]),
+ )
+ raw_heading = value.get("heading_path")
+ heading_path = (
+ tuple(str(item) for item in raw_heading) if isinstance(raw_heading, list) else ()
+ )
+ raw_page = value.get("page")
+ page = int(raw_page) if isinstance(raw_page, (str, int, float)) else None
+ raw_slide = value.get("slide")
+ slide = int(raw_slide) if isinstance(raw_slide, (str, int, float)) else None
+ return cls(
+ heading_path=heading_path,
+ page=page,
+ bbox=bbox,
+ sheet=str(value["sheet"]) if value.get("sheet") is not None else None,
+ cell_range=(str(value["cell_range"]) if value.get("cell_range") is not None else None),
+ slide=slide,
+ )
+
+ def display(self) -> str:
+ """生成面向 Prompt 和前端的紧凑定位文本。"""
+
+ parts: list[str] = []
+ if self.heading_path:
+ parts.append(" / ".join(self.heading_path))
+ if self.page is not None:
+ parts.append(f"第 {self.page} 页")
+ if self.sheet:
+ parts.append(f"工作表 {self.sheet}")
+ if self.cell_range:
+ parts.append(f"区域 {self.cell_range}")
+ if self.slide is not None:
+ parts.append(f"第 {self.slide} 张幻灯片")
+ return " · ".join(parts) or "未提供原文定位"
@dataclass(frozen=True, slots=True)
@@ -82,6 +171,7 @@ class Chunk:
content: str
heading_path: tuple[str, ...]
token_count: int
+ locator: SourceLocator | None = None
metadata: dict[str, JsonValue] = field(default_factory=dict)
@@ -104,6 +194,7 @@ class RetrievalResult:
content: str
heading_path: tuple[str, ...]
score: float
+ locator: SourceLocator | None = None
@dataclass(frozen=True, slots=True)
@@ -114,6 +205,7 @@ class Citation:
filename: str
chunk_id: str
heading_path: tuple[str, ...]
+ locator: SourceLocator | None = None
@dataclass(frozen=True, slots=True)
@@ -144,3 +236,20 @@ class Document:
error_message: str | None
created_at: datetime
updated_at: datetime
+
+
+@dataclass(frozen=True, slots=True)
+class IngestionJob:
+ """Worker 已领取或可领取的持久化文档摄取任务快照。"""
+
+ id: str
+ document_id: str
+ status: IngestionJobStatus
+ attempts: int
+ max_attempts: int
+ available_at: datetime
+ locked_at: datetime | None
+ worker_id: str | None
+ error_message: str | None
+ created_at: datetime
+ updated_at: datetime
diff --git a/src/ultimate_rag/domain/ports.py b/src/ultimate_rag/domain/ports.py
index 22c75d9..6451414 100644
--- a/src/ultimate_rag/domain/ports.py
+++ b/src/ultimate_rag/domain/ports.py
@@ -1,6 +1,6 @@
"""核心可替换能力的最小端口协议。
-协议由应用层依赖、外围适配器实现；这里只描述当前 V1 真实需要的行为，不预设未来插件运行时。
+协议由应用层依赖、外围适配器实现；这里只描述当前 V2 真实需要的行为，不预设未来插件运行时。
"""
from collections.abc import AsyncIterator, Sequence
@@ -113,3 +113,19 @@ def stream(self, system_prompt: str, user_prompt: str) -> AsyncIterator[str]:
不需要知道 OpenAI-Compatible SDK 的 Chunk 结构，也不会用假逐字动画掩盖模型延迟。
"""
...
+
+
+class OCRClient(Protocol):
+ """把图片字节转换为纯文本的视觉 OCR 边界。"""
+
+ async def extract_text(self, image: bytes, mime_type: str) -> str:
+ """识别单张可信格式图片，返回保持阅读顺序的非空文本。"""
+ ...
+
+
+class VisionClient(Protocol):
+ """把图表、示意图等视觉内容转换为可检索文字的模型边界。"""
+
+ async def describe(self, image: bytes, mime_type: str, caption: str = "") -> str:
+ """提取可见文字、结构和图形语义，不推断图片中不存在的事实。"""
+ ...
diff --git a/src/ultimate_rag/infrastructure/database/models.py b/src/ultimate_rag/infrastructure/database/models.py
index abb9525..fa631a6 100644
--- a/src/ultimate_rag/infrastructure/database/models.py
+++ b/src/ultimate_rag/infrastructure/database/models.py
@@ -5,11 +5,11 @@
from datetime import UTC, datetime
-from sqlalchemy import DateTime, ForeignKey, Integer, String, Text
+from sqlalchemy import DateTime, ForeignKey, Index, Integer, String, Text
from sqlalchemy.dialects.postgresql import JSONB
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
-from ultimate_rag.domain.models import DocumentStatus
+from ultimate_rag.domain.models import DocumentStatus, IngestionJobStatus
def utc_now() -> datetime:
@@ -41,7 +41,7 @@ class KnowledgeBaseModel(Base):
class DocumentModel(Base):
- """原始文档元数据和同步摄取状态记录。"""
+ """原始文档元数据和后台摄取状态记录。"""
__tablename__ = "documents"
@@ -66,6 +66,33 @@ class DocumentModel(Base):
chunks: Mapped[list["ChunkModel"]] = relationship(
back_populates="document", cascade="all, delete-orphan"
)
+ ingestion_job: Mapped["IngestionJobModel | None"] = relationship(
+ back_populates="document", cascade="all, delete-orphan", uselist=False
+ )
+
+
+class IngestionJobModel(Base):
+ """PostgreSQL 持久化任务；进程重启后仍可继续领取和有限重试。"""
+
+ __tablename__ = "ingestion_jobs"
+ __table_args__ = (Index("ix_ingestion_jobs_claim", "status", "available_at"),)
+
+ id: Mapped[str] = mapped_column(String(36), primary_key=True)
+ document_id: Mapped[str] = mapped_column(
+ ForeignKey("documents.id", ondelete="CASCADE"), unique=True
+ )
+ status: Mapped[str] = mapped_column(String(20), default=IngestionJobStatus.PENDING.value)
+ attempts: Mapped[int] = mapped_column(Integer, default=0)
+ max_attempts: Mapped[int] = mapped_column(Integer, default=3)
+ available_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utc_now)
+ locked_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
+ worker_id: Mapped[str | None] = mapped_column(String(200), nullable=True)
+ error_message: Mapped[str | None] = mapped_column(Text, nullable=True)
+ created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utc_now)
+ updated_at: Mapped[datetime] = mapped_column(
+ DateTime(timezone=True), default=utc_now, onupdate=utc_now
+ )
+ document: Mapped[DocumentModel] = relationship(back_populates="ingestion_job")
class ChunkModel(Base):
diff --git a/src/ultimate_rag/infrastructure/database/repository.py b/src/ultimate_rag/infrastructure/database/repository.py
index 002fd64..ed0671f 100644
--- a/src/ultimate_rag/infrastructure/database/repository.py
+++ b/src/ultimate_rag/infrastructure/database/repository.py
@@ -4,23 +4,31 @@
"""
from collections.abc import Sequence
-from datetime import datetime
+from datetime import UTC, datetime, timedelta
from uuid import uuid4
-from sqlalchemy import delete, select
+from sqlalchemy import and_, delete, or_, select
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
from ultimate_rag.domain.exceptions import ResourceNotFoundError
-from ultimate_rag.domain.models import Chunk, Document, DocumentStatus, KnowledgeBase
+from ultimate_rag.domain.models import (
+ Chunk,
+ Document,
+ DocumentStatus,
+ IngestionJob,
+ IngestionJobStatus,
+ KnowledgeBase,
+)
from ultimate_rag.infrastructure.database.models import (
ChunkModel,
DocumentModel,
+ IngestionJobModel,
KnowledgeBaseModel,
)
class Repository:
- """封装 V1 知识库、文档和 Chunk 的数据库读写。"""
+ """封装知识库、文档和 Chunk 的数据库事实读写。"""
def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
"""接收可复用 Session 工厂，每个公共操作自行定义短事务。"""
@@ -103,6 +111,218 @@ async def create_document(
session.add(model)
return self._document(model)
+ async def create_document_with_job(
+ self,
+ *,
+ document_id: str,
+ knowledge_base_id: str,
+ filename: str,
+ mime_type: str,
+ extension: str,
+ object_key: str,
+ sha256: str,
+ max_attempts: int,
+ ) -> Document:
+ """在一个事务内创建 ``PENDING`` 文档和唯一摄取任务。
+
+ 文档事实与任务必须原子提交。若先创建文档、后创建任务时进程退出，用户会看到一个
+ 永远停留在 ``PENDING`` 的文档；同一事务可以从根源上消除这个丢任务窗口。
+ """
+
+ if max_attempts < 1:
+ raise ValueError("max_attempts must be at least 1")
+ now = datetime.now(UTC)
+ document = DocumentModel(
+ id=document_id,
+ knowledge_base_id=knowledge_base_id,
+ filename=filename,
+ mime_type=mime_type,
+ extension=extension,
+ object_key=object_key,
+ sha256=sha256,
+ status=DocumentStatus.PENDING.value,
+ )
+ job = IngestionJobModel(
+ id=str(uuid4()),
+ document_id=document_id,
+ status=IngestionJobStatus.PENDING.value,
+ attempts=0,
+ max_attempts=max_attempts,
+ available_at=now,
+ )
+ async with self._session_factory() as session, session.begin():
+ if await session.get(KnowledgeBaseModel, knowledge_base_id) is None:
+ raise ResourceNotFoundError("知识库不存在")
+ session.add_all([document, job])
+ return self._document(document)
+
+ async def claim_ingestion_job(
+ self,
+ worker_id: str,
+ *,
+ lease_seconds: int,
+ ) -> IngestionJob | None:
+ """以行锁跳过其他 Worker 正在领取的任务，并回收租约过期任务。
+
+ ``FOR UPDATE SKIP LOCKED`` 只用于队列表这一明确场景：多个 Worker 可以并行领取，
+ 但同一事务瞬间只有一个 Worker 能修改某行。``locked_at`` 租约让进程崩溃后的
+ ``RUNNING`` 任务重新可见，不需要人工修改数据库。
+ """
+
+ if not worker_id.strip():
+ raise ValueError("worker_id cannot be empty")
+ if lease_seconds < 30:
+ raise ValueError("lease_seconds must be at least 30")
+
+ now = datetime.now(UTC)
+ stale_before = now - timedelta(seconds=lease_seconds)
+ async with self._session_factory() as session, session.begin():
+ # 最后一次尝试若随进程崩溃而租约过期，普通领取条件不会再选中。先在短事务内
+ # 对这种行做终态收敛；若文档已 READY，说明只是“任务完成提交”窗口崩溃，任务可
+ # 直接成功，否则明确 FAILED，不能永久留在 RUNNING。
+ exhausted_statement = (
+ select(IngestionJobModel)
+ .where(
+ IngestionJobModel.status == IngestionJobStatus.RUNNING.value,
+ IngestionJobModel.locked_at.is_not(None),
+ IngestionJobModel.locked_at <= stale_before,
+ IngestionJobModel.attempts >= IngestionJobModel.max_attempts,
+ )
+ .order_by(IngestionJobModel.locked_at)
+ .with_for_update(skip_locked=True)
+ .limit(1)
+ )
+ exhausted = await session.scalar(exhausted_statement)
+ if exhausted is not None:
+ document = await session.get(DocumentModel, exhausted.document_id)
+ is_ready = document is not None and document.status == DocumentStatus.READY.value
+ exhausted.status = (
+ IngestionJobStatus.SUCCEEDED.value
+ if is_ready
+ else IngestionJobStatus.FAILED.value
+ )
+ exhausted.locked_at = None
+ exhausted.worker_id = None
+ if not is_ready:
+ message = "Worker 租约过期且已达到最大尝试次数"
+ exhausted.error_message = message
+ if document is not None:
+ document.status = DocumentStatus.FAILED.value
+ document.error_message = message
+ return None
+
+ statement = (
+ select(IngestionJobModel)
+ .where(
+ IngestionJobModel.attempts < IngestionJobModel.max_attempts,
+ or_(
+ and_(
+ IngestionJobModel.status == IngestionJobStatus.PENDING.value,
+ IngestionJobModel.available_at <= now,
+ ),
+ and_(
+ IngestionJobModel.status == IngestionJobStatus.RUNNING.value,
+ IngestionJobModel.locked_at.is_not(None),
+ IngestionJobModel.locked_at <= stale_before,
+ ),
+ ),
+ )
+ .order_by(IngestionJobModel.available_at, IngestionJobModel.created_at)
+ .with_for_update(skip_locked=True)
+ .limit(1)
+ )
+ model = await session.scalar(statement)
+ if model is None:
+ return None
+
+ model.status = IngestionJobStatus.RUNNING.value
+ model.attempts += 1
+ model.locked_at = now
+ model.worker_id = worker_id
+ model.error_message = None
+ return self._ingestion_job(model)
+
+ async def heartbeat_ingestion_job(self, job_id: str, worker_id: str) -> bool:
+ """续租当前 Worker 拥有的运行中任务；所有权变化时返回 ``False``。"""
+
+ async with self._session_factory() as session, session.begin():
+ model = await session.get(IngestionJobModel, job_id)
+ if (
+ model is None
+ or model.status != IngestionJobStatus.RUNNING.value
+ or model.worker_id != worker_id
+ ):
+ return False
+ model.locked_at = datetime.now(UTC)
+ return True
+
+ async def complete_ingestion_job(self, job_id: str, worker_id: str) -> None:
+ """在文档已经 ``READY`` 后，把当前租约拥有者的任务标记为完成。"""
+
+ async with self._session_factory() as session, session.begin():
+ model = await session.get(IngestionJobModel, job_id)
+ if (
+ model is None
+ or model.status != IngestionJobStatus.RUNNING.value
+ or model.worker_id != worker_id
+ ):
+ raise RuntimeError("摄取任务租约已丢失，不能提交完成状态")
+ document = await session.get(DocumentModel, model.document_id)
+ if document is None or document.status != DocumentStatus.READY.value:
+ raise RuntimeError("文档尚未 READY，不能提交摄取任务")
+ model.status = IngestionJobStatus.SUCCEEDED.value
+ model.locked_at = None
+ model.worker_id = None
+ model.error_message = None
+
+ async def fail_ingestion_job(
+ self,
+ job_id: str,
+ worker_id: str,
+ *,
+ error_message: str,
+ retryable: bool,
+ retry_delay_seconds: int,
+ ) -> bool:
+ """记录一次失败，并返回任务是否已安排有限重试。
+
+ 参数错误和损坏文件直接终止；临时网络/服务故障仅在剩余尝试次数内重试。
+ 文档与任务状态在同一 PostgreSQL 事务更新，前端不会观察到相互矛盾的状态。
+ """
+
+ if retry_delay_seconds < 0:
+ raise ValueError("retry_delay_seconds cannot be negative")
+ safe_error = (error_message.strip() or "Unknown ingestion error")[:1000]
+ async with self._session_factory() as session, session.begin():
+ model = await session.get(IngestionJobModel, job_id)
+ if (
+ model is None
+ or model.status != IngestionJobStatus.RUNNING.value
+ or model.worker_id != worker_id
+ ):
+ raise RuntimeError("摄取任务租约已丢失，不能提交失败状态")
+ document = await session.get(DocumentModel, model.document_id)
+ if document is None:
+ raise ResourceNotFoundError("文档不存在")
+
+ should_retry = retryable and model.attempts < model.max_attempts
+ model.error_message = safe_error
+ model.locked_at = None
+ model.worker_id = None
+ if should_retry:
+ model.status = IngestionJobStatus.PENDING.value
+ model.available_at = datetime.now(UTC) + timedelta(seconds=retry_delay_seconds)
+ document.status = DocumentStatus.PENDING.value
+ document.error_message = (
+ f"第 {model.attempts}/{model.max_attempts} 次处理暂时失败，"
+ f"系统将自动重试：{safe_error}"
+ )[:1000]
+ else:
+ model.status = IngestionJobStatus.FAILED.value
+ document.status = DocumentStatus.FAILED.value
+ document.error_message = safe_error
+ return should_retry
+
async def list_documents(self, knowledge_base_id: str) -> list[Document]:
"""返回知识库文档；知识库不存在与空知识库使用不同语义。"""
# 先显式读取知识库，确保“不存在”抛出 404，而真实存在但没有文档时返回空列表。
@@ -124,6 +344,18 @@ async def get_document(self, document_id: str) -> Document:
raise ResourceNotFoundError("文档不存在")
return self._document(model)
+ async def list_ready_document_ids(self, knowledge_base_id: str) -> set[str]:
+ """返回知识库内允许参与检索的 ``READY`` 文档 ID 集合。"""
+
+ async with self._session_factory() as session:
+ result = await session.scalars(
+ select(DocumentModel.id).where(
+ DocumentModel.knowledge_base_id == knowledge_base_id,
+ DocumentModel.status == DocumentStatus.READY.value,
+ )
+ )
+ return set(result)
+
async def update_document_status(
self,
document_id: str,
@@ -212,6 +444,24 @@ def _document(model: DocumentModel) -> Document:
updated_at=Repository._datetime(model.updated_at),
)
+ @staticmethod
+ def _ingestion_job(model: IngestionJobModel) -> IngestionJob:
+ """把 ORM 任务映射为 Worker 可安全持有的不可变快照。"""
+
+ return IngestionJob(
+ id=model.id,
+ document_id=model.document_id,
+ status=IngestionJobStatus(model.status),
+ attempts=model.attempts,
+ max_attempts=model.max_attempts,
+ available_at=Repository._datetime(model.available_at),
+ locked_at=model.locked_at,
+ worker_id=model.worker_id,
+ error_message=model.error_message,
+ created_at=Repository._datetime(model.created_at),
+ updated_at=Repository._datetime(model.updated_at),
+ )
+
@staticmethod
def _datetime(value: datetime | None) -> datetime:
"""断言数据库默认时间已回填，避免构造不完整领域对象。"""
diff --git a/src/ultimate_rag/infrastructure/storage/minio.py b/src/ultimate_rag/infrastructure/storage/minio.py
index 9f5af14..ff1f22e 100644
--- a/src/ultimate_rag/infrastructure/storage/minio.py
+++ b/src/ultimate_rag/infrastructure/storage/minio.py
@@ -40,7 +40,7 @@ def ensure() -> None:
await asyncio.to_thread(ensure)
async def put(self, object_key: str, content: bytes, content_type: str) -> None:
- """上传内存中的 V1 文档内容，并保存标准 MIME 类型。"""
+ """上传内存中的原始文档内容，并保存标准 MIME 类型。"""
await asyncio.to_thread(
self._client.put_object,
self._bucket,
diff --git a/src/ultimate_rag/ocr/__init__.py b/src/ultimate_rag/ocr/__init__.py
new file mode 100644
index 0000000..e3bd132
--- /dev/null
+++ b/src/ultimate_rag/ocr/__init__.py
@@ -0,0 +1,5 @@
+"""图片文字识别适配器公开入口。"""
+
+from ultimate_rag.ocr.bailian import BailianOCRClient
+
+__all__ = ["BailianOCRClient"]
diff --git a/src/ultimate_rag/ocr/bailian.py b/src/ultimate_rag/ocr/bailian.py
new file mode 100644
index 0000000..ac5dcce
--- /dev/null
+++ b/src/ultimate_rag/ocr/bailian.py
@@ -0,0 +1,156 @@
+"""阿里云百炼 Qwen-OCR 适配器。
+
+模块职责：
+ 把图片字节编码为 Data URL，通过百炼 OpenAI-Compatible Chat Completions API 提取文本。
+
+架构边界：
+ 本模块只实现 ``OCRClient`` 外部协议，不判断文件扩展名、不渲染 PDF，也不创建领域 Block。
+ 图片验证、页码定位和 Parser 选择分别由 Image/PDF Parser 与 Parser Registry 负责。
+
+外部约束：
+ 百炼 OpenAI 兼容接口的 Base64 图片原文件必须小于 7 MB。调用使用配置提供的有界超时，
+ 不做无限重试；空响应和超限输入都显式失败，避免把空 OCR 文本继续送入 Embedding。
+
+手动验证：
+ 本模块附带命令行入口，可直接对单张图片调用百炼 OCR 验证连通性：
+ ``uv run python src/ultimate_rag/ocr/bailian.py <图片路径>``
+ 密钥、模型与超时读取自集中配置（环境变量或 ``.env``），会真实产生少量模型用量。
+"""
+
+import argparse
+import asyncio
+import base64
+import mimetypes
+from pathlib import Path
+from typing import cast
+
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam
+
+from ultimate_rag.config import get_settings
+
+
+class BailianOCRClient:
+ """使用 Qwen-OCR 把单张图片识别为保持阅读顺序的纯文本。"""
+
+ _PROMPT = (
+ "请按自然阅读顺序提取图片中的全部可见文字。保留标题、段落、列表和表格的行列关系，"
+ "只有原图明确存在行列结构时才使用 Markdown 表格，禁止输出空行、空单元格组成的伪表格。"
+ "示意图只提取文字，不要猜测图形关系。只输出识别结果，不要解释、总结或编造不可见内容。"
+ )
+
+ def __init__(
+ self,
+ *,
+ api_key: str,
+ base_url: str,
+ model: str,
+ max_image_bytes: int,
+ max_output_tokens: int,
+ timeout: float,
+ ) -> None:
+ """创建可复用客户端并固定 OCR 模型与单图字节上限。"""
+
+ self._client = AsyncOpenAI(api_key=api_key, base_url=base_url, timeout=timeout)
+ self._model = model
+ self._max_image_bytes = max_image_bytes
+ self._max_output_tokens = max_output_tokens
+
+ async def extract_text(self, image: bytes, mime_type: str) -> str:
+ """识别图片并拒绝空内容或超过百炼 Base64 接口限制的输入。
+
+ Args:
+ image: 已经由 Parser 验证过格式的图片原始字节。
+ mime_type: Data URL 使用的标准图片 MIME 类型。
+
+ Returns:
+ 去除首尾空白但保留内部换行的 OCR 文本。
+
+ Raises:
+ ValueError: 图片为空、MIME 非图片或超过配置上限。
+ RuntimeError: 模型没有返回可索引文字。
+ """
+
+ if not image:
+ raise ValueError("OCR 图片不能为空")
+ if not mime_type.startswith("image/"):
+ raise ValueError(f"OCR 只接受图片 MIME：{mime_type}")
+ if len(image) > self._max_image_bytes:
+ limit_mb = self._max_image_bytes // (1024 * 1024)
+ raise ValueError(f"OCR 图片不能超过 {limit_mb} MB")
+
+ data_url = f"data:{mime_type};base64,{base64.b64encode(image).decode('ascii')}"
+ # OpenAI SDK 的 TypedDict 联合类型无法从嵌套字面量稳定推导；cast 只隔离 SDK 类型，
+ # 请求结构仍严格遵循百炼 image_url + text 的 OpenAI-Compatible 协议。
+ messages = cast(
+ list[ChatCompletionMessageParam],
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "image_url", "image_url": {"url": data_url}},
+ {"type": "text", "text": self._PROMPT},
+ ],
+ }
+ ],
+ )
+ response = await self._client.chat.completions.create(
+ model=self._model,
+ messages=messages,
+ temperature=0,
+ # 有界输出可防止模型把图形边框误识别为数千行空表格并污染后续 Chunk。
+ max_tokens=self._max_output_tokens,
+ )
+ content = response.choices[0].message.content
+ if not content or not content.strip():
+ raise RuntimeError("OCR model returned empty text")
+ return content.strip()
+
+
+
+
+# uv run python src/ultimate_rag/ocr/bailian.py ./data/1.png
+def main() -> None:
+ """命令行验证入口：对单张本地图片调用百炼 OCR 并打印识别文本。
+
+ 该入口只用于开发期人工验证模型连通性与识别效果，不属于生产调用链；
+ 生产路径统一经过 runtime.py 装配的 Parser。
+
+ Raises:
+ SystemExit: 扩展名无法映射为图片 MIME 类型时终止。
+ ValueError: 图片为空或超过配置的字节上限。
+ RuntimeError: 百炼返回空文本，或网络 / 鉴权失败（由 OpenAI SDK 异常上抛）。
+
+ Side Effects:
+ 读取本地图片文件，真实调用百炼 Chat Completions 接口并产生模型用量。
+ """
+
+ parser = argparse.ArgumentParser(description="用百炼 Qwen-OCR 识别一张本地图片")
+ parser.add_argument("image", help="待识别的图片文件路径，例如 ./sample.png")
+ arguments = parser.parse_args()
+
+ # 扩展名到 MIME 的映射交给标准库；识别失败直接终止，避免把错误 MIME 送进 Data URL。
+ # 这里的扩展名判断仅服务于命令行入口，生产上传链路仍由 IngestionService 统一校验。
+ mime_type, _ = mimetypes.guess_type(arguments.image)
+ if not mime_type or not mime_type.startswith("image/"):
+ raise SystemExit(f"无法从扩展名识别图片 MIME 类型：{arguments.image}")
+
+ # 客户端参数与 runtime.py 的生产装配保持同源，验证结论才能代表 Worker 的真实行为。
+ settings = get_settings()
+ client = BailianOCRClient(
+ api_key=settings.dashscope_api_key,
+ base_url=settings.dashscope_base_url,
+ model=settings.ocr_model,
+ max_image_bytes=settings.ocr_max_image_bytes,
+ max_output_tokens=settings.ocr_max_output_tokens,
+ timeout=settings.model_timeout_seconds,
+ )
+
+ # extract_text 是异步方法；脚本只发起一次调用，用 asyncio.run 驱动即可，无需常驻事件循环。
+ image = Path(arguments.image).read_bytes()
+ text = asyncio.run(client.extract_text(image, mime_type))
+ print(text)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/ultimate_rag/parsers/__init__.py b/src/ultimate_rag/parsers/__init__.py
index 56e06f9..e150dc5 100644
--- a/src/ultimate_rag/parsers/__init__.py
+++ b/src/ultimate_rag/parsers/__init__.py
@@ -1,6 +1,19 @@
"""文档解析器与解析器注册表公开入口。"""
+from ultimate_rag.parsers.html import HtmlParser
+from ultimate_rag.parsers.image import ImageOCRParser
from ultimate_rag.parsers.markdown import MarkdownParser
+from ultimate_rag.parsers.office import ExcelParser, PowerPointParser, WordParser
+from ultimate_rag.parsers.pdf import PDFParser
from ultimate_rag.parsers.registry import ParserRegistry
-__all__ = ["MarkdownParser", "ParserRegistry"]
+__all__ = [
+ "ExcelParser",
+ "HtmlParser",
+ "ImageOCRParser",
+ "MarkdownParser",
+ "ParserRegistry",
+ "PDFParser",
+ "PowerPointParser",
+ "WordParser",
+]
diff --git a/src/ultimate_rag/parsers/_model_output.py b/src/ultimate_rag/parsers/_model_output.py
new file mode 100644
index 0000000..0fc5826
--- /dev/null
+++ b/src/ultimate_rag/parsers/_model_output.py
@@ -0,0 +1,162 @@
+"""清理 OCR / Vision 模型返回的可检索 Markdown。
+
+模型输出属于不可信外部数据。即使 Prompt 要求只返回正文，OCR 仍可能用 Markdown 围栏包裹
+结果，或把示意图背景误判成数百行空表格。本模块只做确定性、保守的格式清理：不改写正文，
+只移除 Markdown 包装、空表格行和明显重复于正文的单单元格伪表格。
+"""
+
+from __future__ import annotations
+
+import re
+
+_MARKDOWN_FENCE = re.compile(r"^```(?:markdown|md)\s*$", re.IGNORECASE)
+_TABLE_SEPARATOR_CELL = re.compile(r"^:?-{3,}:?$")
+NO_RETRIEVABLE_CONTENT = "NO_RETRIEVABLE_CONTENT"
+
+
+def normalize_model_markdown(value: str) -> str:
+ """返回适合进入领域 Block 的模型文本，并过滤无信息的 Markdown 表格噪声。
+
+ 真实表格至少需要一个有效表头或两个有内容的数据行。只有一个非空单元格、且该文字已在
+ 普通正文出现的“表格”通常是 OCR 对图形边框的误判，删除它可以避免无意义 Chunk 污染召回。
+ """
+
+ canonical = value.replace("\r\n", "\n").replace("\r", "\n").strip()
+ if canonical.upper().strip("` .\n") == NO_RETRIEVABLE_CONTENT:
+ return ""
+ lines = _remove_markdown_fences(canonical.split("\n"))
+ plain_text = "\n".join(line for line in lines if not _is_table_line(line))
+ normalized_plain = _search_key(plain_text)
+
+ cleaned: list[str] = []
+ index = 0
+ while index < len(lines):
+ if not _is_table_line(lines[index]):
+ cleaned.append(lines[index].rstrip())
+ index += 1
+ continue
+
+ # OCR 偶尔在每个表格行之间插入空行；这些空行仍属于当前表格区域，不能把一个伪表格
+ # 拆成数百个独立片段后绕过清理。
+ table_lines: list[str] = []
+ cursor = index
+ while cursor < len(lines):
+ line = lines[cursor]
+ if _is_table_line(line):
+ table_lines.append(line.strip())
+ cursor += 1
+ continue
+ if not line.strip() and _next_nonblank_is_table(lines, cursor + 1):
+ cursor += 1
+ continue
+ break
+ cleaned.extend(_clean_table(table_lines, normalized_plain))
+ index = cursor
+
+ result = "\n".join(cleaned).strip()
+ return re.sub(r"\n{3,}", "\n\n", result)
+
+
+def combine_ocr_and_vision(ocr_text: str, vision_text: str) -> str:
+ """以显式标签融合精确 OCR 与视觉关系描述，避免下游误解两种证据来源。"""
+
+ ocr = normalize_model_markdown(ocr_text)
+ vision = normalize_model_markdown(vision_text)
+ if ocr and vision:
+ return f"## OCR 文本\n\n{ocr}\n\n## 视觉结构\n\n{vision}"
+ return ocr or vision
+
+
+def _remove_markdown_fences(lines: list[str]) -> list[str]:
+ """仅删除模型用于包装 Markdown 的围栏，不触碰正文中的其他语言代码块。"""
+
+ cleaned: list[str] = []
+ inside_markdown_fence = False
+ for line in lines:
+ stripped = line.strip()
+ if _MARKDOWN_FENCE.fullmatch(stripped):
+ inside_markdown_fence = True
+ continue
+ if inside_markdown_fence and stripped == "```":
+ inside_markdown_fence = False
+ continue
+ cleaned.append(line)
+ return cleaned
+
+
+def _clean_table(lines: list[str], normalized_plain: str) -> list[str]:
+ """删除空行并判断表格是否携带足够独立信息。"""
+
+ rows = [line for line in lines if _is_separator_row(line) or any(_table_cells(line))]
+ if not rows:
+ return []
+
+ separator_indexes = [index for index, line in enumerate(rows) if _is_separator_row(line)]
+ has_valid_header = any(
+ index > 0 and any(_table_cells(rows[index - 1])) for index in separator_indexes
+ )
+ semantic_rows = [line for line in rows if not _is_separator_row(line)]
+ unique_cells = {
+ _search_key(cell)
+ for row in semantic_rows
+ for cell in _table_cells(row)
+ if _search_key(cell)
+ }
+
+ if not has_valid_header:
+ # 没有有效表头时不能声称保留了行列结构。把仍有价值的单元格降为普通文本，并删除已经
+ # 出现在正文中的碎片；这既保住无表头矩阵中的独立值，也能清掉示意图边框产生的伪表格。
+ flattened_rows: list[str] = []
+ seen: set[str] = set()
+ for row in semantic_rows:
+ flattened = " ".join(cell for cell in _table_cells(row) if cell).strip()
+ key = _search_key(flattened)
+ if not key or key in normalized_plain or key in seen:
+ continue
+ seen.add(key)
+ flattened_rows.append(flattened)
+ return flattened_rows
+ if len(unique_cells) == 1 and next(iter(unique_cells)) in normalized_plain:
+ return []
+
+ # 分隔行没有前置表头时会生成非法 Markdown；保留语义行比保留损坏格式更有价值。
+ return [
+ line
+ for index, line in enumerate(rows)
+ if not _is_separator_row(line) or (index > 0 and any(_table_cells(rows[index - 1])))
+ ]
+
+
+def _table_cells(line: str) -> list[str]:
+ """返回去除边界竖线和空白后的 Markdown 单元格。"""
+
+ return [cell.strip() for cell in line.strip().strip("|").split("|")]
+
+
+def _is_separator_row(line: str) -> bool:
+ """判断一行是否为 Markdown 表头分隔行。"""
+
+ cells = [cell for cell in _table_cells(line) if cell]
+ return bool(cells) and all(_TABLE_SEPARATOR_CELL.fullmatch(cell) for cell in cells)
+
+
+def _is_table_line(line: str) -> bool:
+ """使用严格边界识别模型生成的 Markdown 表格行。"""
+
+ stripped = line.strip()
+ return stripped.startswith("|") and stripped.endswith("|") and stripped.count("|") >= 2
+
+
+def _next_nonblank_is_table(lines: list[str], start: int) -> bool:
+ """判断后续第一个非空行是否仍属于表格。"""
+
+ for line in lines[start:]:
+ if line.strip():
+ return _is_table_line(line)
+ return False
+
+
+def _search_key(value: str) -> str:
+ """生成只用于重复判断的大小写与空白无关键。"""
+
+ return "".join(value.casefold().split())
diff --git a/src/ultimate_rag/parsers/_shared.py b/src/ultimate_rag/parsers/_shared.py
new file mode 100644
index 0000000..928326a
--- /dev/null
+++ b/src/ultimate_rag/parsers/_shared.py
@@ -0,0 +1,107 @@
+"""多格式 Parser 共用的安全校验与领域映射小工具。"""
+
+from collections.abc import Sequence
+from io import BytesIO
+from pathlib import PurePosixPath
+from uuid import NAMESPACE_URL, uuid5
+from zipfile import BadZipFile, ZipFile
+
+from ultimate_rag.domain.exceptions import InvalidDocumentError
+from ultimate_rag.domain.models import Block, BlockType, DocumentSource, JsonValue, SourceLocator
+
+
+def source_extension(source: DocumentSource) -> str:
+ """统一按 POSIX 分隔符读取已净化文件名扩展名。"""
+
+ return PurePosixPath(source.filename.replace("\\", "/")).suffix.lower()
+
+
+def source_mime(source: DocumentSource) -> str:
+ """移除 MIME 参数并转换为小写，兼容浏览器附带 charset。"""
+
+ return source.mime_type.split(";", maxsplit=1)[0].strip().lower()
+
+
+def supports_source(
+ source: DocumentSource,
+ extensions: frozenset[str],
+ mime_types: frozenset[str],
+) -> bool:
+ """同时校验扩展名与 MIME，通用二进制 MIME 交给 Parser 检查实际结构。"""
+
+ return source_extension(source) in extensions and source_mime(source) in (
+ mime_types | {"application/octet-stream"}
+ )
+
+
+def stable_block(
+ document_id: str,
+ index: int,
+ block_type: BlockType,
+ content: str,
+ locator: SourceLocator,
+ metadata: dict[str, JsonValue] | None = None,
+) -> Block:
+ """使用文档、顺序、类型和内容生成可重复 Block，并复制可选解析元数据。"""
+
+ block_id = str(
+ uuid5(NAMESPACE_URL, f"{document_id}:block:{index}:{block_type.value}:{content}")
+ )
+ return Block(
+ id=block_id,
+ type=block_type,
+ content=content,
+ locator=locator,
+ metadata=dict(metadata or {}),
+ )
+
+
+def table_to_markdown(rows: Sequence[Sequence[object]]) -> str:
+ """把二维单元格值转换为结构稳定、适合 Embedding 的 Markdown 表格。"""
+
+ normalized = [
+ [
+ str(value).strip().replace("|", "\\|").replace("\n", " ") if value is not None else ""
+ for value in row
+ ]
+ for row in rows
+ ]
+ normalized = [row for row in normalized if any(cell for cell in row)]
+ if not normalized:
+ return ""
+ width = max(len(row) for row in normalized)
+ padded = [row + [""] * (width - len(row)) for row in normalized]
+ header = padded[0]
+ separator = ["---"] * width
+ body = padded[1:]
+ lines = [f"| {' | '.join(header)} |", f"| {' | '.join(separator)} |"]
+ lines.extend(f"| {' | '.join(row)} |" for row in body)
+ return "\n".join(lines)
+
+
+def validate_ooxml_archive(
+ content: bytes,
+ *,
+ max_entries: int = 10_000,
+ max_uncompressed_bytes: int = 100 * 1024 * 1024,
+ max_compression_ratio: int = 200,
+) -> None:
+ """在 Office 库解压前拒绝损坏文件和明显 ZIP Bomb。
+
+ OOXML 本质是 ZIP。仅限制上传压缩包大小不足以限制解压后的内存与 CPU，因此同时约束
+ 条目数量、总解压体积和单条目压缩比。校验只读取 Central Directory，不提取文件。
+ """
+
+ try:
+ with ZipFile(BytesIO(content)) as archive:
+ entries = archive.infolist()
+ except BadZipFile as exc:
+ raise InvalidDocumentError("Office 文件不是有效的 OOXML 压缩包") from exc
+ if len(entries) > max_entries:
+ raise InvalidDocumentError("Office 文件包含过多内部条目")
+ if sum(entry.file_size for entry in entries) > max_uncompressed_bytes:
+ raise InvalidDocumentError("Office 文件解压后内容过大")
+ for entry in entries:
+ compressed = max(entry.compress_size, 1)
+ if entry.file_size > 1024 * 1024 and entry.file_size / compressed > max_compression_ratio:
+ raise InvalidDocumentError("Office 文件包含异常高压缩比条目")
diff --git a/src/ultimate_rag/parsers/html.py b/src/ultimate_rag/parsers/html.py
new file mode 100644
index 0000000..ba06954
--- /dev/null
+++ b/src/ultimate_rag/parsers/html.py
@@ -0,0 +1,139 @@
+"""HTML 到统一文档模型的 Parser。
+
+模块职责：
+ 清理脚本与样式内容，按照 DOM 文档顺序提取标题、正文、列表、引用、代码和表格，并把
+ HTML 结构立即转换为 UltimateRAG 自有的 Block 与 SourceLocator。
+
+安全边界：
+ 本 Parser 只解析上传的静态字节，不访问外部 URL、不执行 JavaScript，也不加载 CSS、图片
+ 或 iframe。V2 不实现网页爬取，避免 SSRF 与不受控网络访问进入文档摄取链路。
+"""
+
+import asyncio
+
+from bs4 import BeautifulSoup, Tag
+
+from ultimate_rag.domain.exceptions import InvalidDocumentError
+from ultimate_rag.domain.models import (
+ Block,
+ BlockType,
+ DocumentSource,
+ ParsedDocument,
+ SourceLocator,
+)
+from ultimate_rag.parsers._shared import stable_block, supports_source, table_to_markdown
+
+
+class HtmlParser:
+ """把静态 HTML 的可见主要内容转换为有序语义 Block。"""
+
+ name = "html"
+ version = "2.0"
+ _EXTENSIONS = frozenset({".html", ".htm"})
+ _MIME_TYPES = frozenset({"text/html", "application/xhtml+xml"})
+ _CONTENT_TAGS = frozenset(
+ {"h1", "h2", "h3", "h4", "h5", "h6", "p", "li", "blockquote", "pre", "table"}
+ )
+
+ def supports(self, source: DocumentSource) -> bool:
+ """同时要求 HTML 扩展名以及 HTML 或通用二进制 MIME。"""
+
+ return supports_source(source, self._EXTENSIONS, self._MIME_TYPES)
+
+ async def parse(self, source: DocumentSource) -> ParsedDocument:
+ """在线程中解析 HTML，避免较大 DOM 构建阻塞事件循环。"""
+
+ return await asyncio.to_thread(self._parse_sync, source)
+
+ def _parse_sync(self, source: DocumentSource) -> ParsedDocument:
+ """清理不可见节点，并保持主要内容在 DOM 中的原始顺序。"""
+
+ try:
+ text = source.content.decode("utf-8-sig")
+ except UnicodeDecodeError as exc:
+ raise InvalidDocumentError("HTML 文件必须使用 UTF-8 编码") from exc
+ if not text.strip():
+ raise InvalidDocumentError("HTML 文件不能为空")
+
+ try:
+ soup = BeautifulSoup(text, "html.parser")
+ for element in soup.find_all(["script", "style", "noscript", "template"]):
+ element.decompose()
+ blocks = self._extract_blocks(source.document_id, soup)
+ except InvalidDocumentError:
+ raise
+ except Exception as exc:
+ raise InvalidDocumentError("HTML 文件损坏或无法解析") from exc
+ if not blocks:
+ raise InvalidDocumentError("HTML 文件没有可索引的可见文本")
+ return ParsedDocument(
+ document_id=source.document_id,
+ blocks=tuple(blocks),
+ metadata={"parser": self.name, "parser_version": self.version},
+ )
+
+ def _extract_blocks(self, document_id: str, soup: BeautifulSoup) -> list[Block]:
+ """提取顶层内容标签，防止同一段嵌套文本被父子标签重复索引。"""
+
+ blocks: list[Block] = []
+ heading_path: list[str] = []
+ for element in soup.find_all(self._CONTENT_TAGS):
+ if not isinstance(element, Tag) or self._has_content_ancestor(element):
+ continue
+ tag_name = element.name.lower()
+ if tag_name == "table":
+ content = self._table_content(element)
+ block_type = BlockType.TABLE
+ else:
+ separator = "\n" if tag_name == "pre" else " "
+ content = element.get_text(separator=separator, strip=True)
+ block_type = self._block_type(tag_name)
+ if not content:
+ continue
+ if tag_name.startswith("h") and len(tag_name) == 2 and tag_name[1].isdigit():
+ level = int(tag_name[1])
+ heading_path = heading_path[: level - 1] + [content]
+ blocks.append(
+ stable_block(
+ document_id,
+ len(blocks),
+ block_type,
+ content,
+ SourceLocator(heading_path=tuple(heading_path)),
+ )
+ )
+ return blocks
+
+ def _has_content_ancestor(self, element: Tag) -> bool:
+ """判断元素是否已包含在另一个将被整体提取的内容节点中。"""
+
+ parent = element.parent
+ while isinstance(parent, Tag):
+ if parent.name.lower() in self._CONTENT_TAGS:
+ return True
+ parent = parent.parent
+ return False
+
+ @staticmethod
+ def _table_content(table: Tag) -> str:
+ """把 HTML 表格按行列转换为稳定 Markdown 表格。"""
+
+ rows = [
+ [cell.get_text(" ", strip=True) for cell in row.find_all(["th", "td"])]
+ for row in table.find_all("tr")
+ ]
+ return table_to_markdown(rows)
+
+ @staticmethod
+ def _block_type(tag_name: str) -> BlockType:
+ """把少量明确 HTML 标签映射为统一 BlockType。"""
+
+ if tag_name.startswith("h"):
+ return BlockType.HEADING
+ if tag_name == "li":
+ return BlockType.LIST
+ if tag_name == "blockquote":
+ return BlockType.QUOTE
+ if tag_name == "pre":
+ return BlockType.CODE
+ return BlockType.TEXT
diff --git a/src/ultimate_rag/parsers/image.py b/src/ultimate_rag/parsers/image.py
new file mode 100644
index 0000000..3153f9a
--- /dev/null
+++ b/src/ultimate_rag/parsers/image.py
@@ -0,0 +1,153 @@
+"""常见图片到统一文档模型的多模态 Parser。
+
+模块职责：
+ 使用 Pillow 校验真实图片格式，并融合 OCR 精确文字与 Vision 图形关系生成 Image Block。
+
+架构边界：
+ Parser 不依赖具体模型厂商。百炼调用封装在基础设施适配器中，测试可注入内存 Stub；
+ Vision 是向后兼容的可选能力，只有 OCR 的既有部署仍可工作。
+"""
+
+import asyncio
+import logging
+from collections.abc import Awaitable
+from io import BytesIO
+
+from PIL import Image, UnidentifiedImageError
+
+from ultimate_rag.domain.exceptions import InvalidDocumentError
+from ultimate_rag.domain.models import (
+ BlockType,
+ DocumentSource,
+ JsonValue,
+ ParsedDocument,
+ SourceLocator,
+)
+from ultimate_rag.domain.ports import OCRClient, VisionClient
+from ultimate_rag.parsers._model_output import combine_ocr_and_vision, normalize_model_markdown
+from ultimate_rag.parsers._shared import source_extension, source_mime, stable_block
+
+logger = logging.getLogger(__name__)
+
+
+class ImageOCRParser:
+ """验证图片后融合可见文字与流程、层级、箭头等视觉关系。
+
+ 类名和 Parser 名称为兼容 V2 已有数据继续保留 ``image-ocr``；2.1 起实际能力是 OCR +
+ Vision。调用方可通过 Block 的 ``extraction`` 元数据判断本次是否发生了降级。
+ """
+
+ name = "image-ocr"
+ version = "2.1"
+ _EXTENSION_FORMATS = {
+ ".png": frozenset({"PNG"}),
+ ".jpg": frozenset({"JPEG"}),
+ ".jpeg": frozenset({"JPEG"}),
+ ".webp": frozenset({"WEBP"}),
+ ".tif": frozenset({"TIFF"}),
+ ".tiff": frozenset({"TIFF"}),
+ ".bmp": frozenset({"BMP"}),
+ }
+ _FORMAT_MIME = {
+ "PNG": "image/png",
+ "JPEG": "image/jpeg",
+ "WEBP": "image/webp",
+ "TIFF": "image/tiff",
+ "BMP": "image/bmp",
+ }
+
+ def __init__(self, ocr_client: OCRClient, vision_client: VisionClient | None = None) -> None:
+ """注入厂商无关 OCR 与可选 Vision 协议，使解析逻辑可独立测试。"""
+
+ self._ocr_client = ocr_client
+ self._vision_client = vision_client
+
+ def supports(self, source: DocumentSource) -> bool:
+ """按受支持扩展名与 image MIME 选择图片 Parser。"""
+
+ extension = source_extension(source)
+ mime_type = source_mime(source)
+ return extension in self._EXTENSION_FORMATS and (
+ mime_type.startswith("image/") or mime_type == "application/octet-stream"
+ )
+
+ async def parse(self, source: DocumentSource) -> ParsedDocument:
+ """并发提取精确文字和视觉关系，单路失败时保留另一条可用结果。"""
+
+ image_format = await asyncio.to_thread(self._validated_format, source)
+ mime_type = self._FORMAT_MIME[image_format]
+ ocr_task = self._attempt(self._ocr_client.extract_text(source.content, mime_type))
+ if self._vision_client is None:
+ ocr_text, ocr_error = await ocr_task
+ vision_text, vision_error = "", None
+ else:
+ ocr_result, vision_result = await asyncio.gather(
+ ocr_task,
+ self._attempt(self._vision_client.describe(source.content, mime_type)),
+ )
+ ocr_text, ocr_error = ocr_result
+ vision_text, vision_error = vision_result
+
+ if ocr_error is not None:
+ logger.warning("Image OCR extraction failed; using vision result", exc_info=ocr_error)
+ if vision_error is not None:
+ logger.warning(
+ "Image vision extraction failed; using OCR result",
+ exc_info=vision_error,
+ )
+
+ content = combine_ocr_and_vision(ocr_text, vision_text)
+ if not content:
+ error = ocr_error or vision_error
+ if isinstance(error, ValueError):
+ raise InvalidDocumentError(f"图片模型输入无效：{error}") from error
+ if error is not None:
+ # 网络或模型故障属于可重试外部失败，不能误标成永久损坏文件。
+ raise RuntimeError("图片 OCR 与视觉理解均失败") from error
+ raise InvalidDocumentError("图片没有可索引的文字或视觉语义")
+
+ extraction_methods = []
+ if normalize_model_markdown(ocr_text):
+ extraction_methods.append("bailian_ocr")
+ if normalize_model_markdown(vision_text):
+ extraction_methods.append("bailian_vision")
+ block = stable_block(
+ source.document_id,
+ 0,
+ BlockType.IMAGE,
+ content,
+ SourceLocator(),
+ {"extraction": "+".join(extraction_methods)},
+ )
+ return ParsedDocument(
+ document_id=source.document_id,
+ blocks=(block,),
+ metadata={
+ "parser": self.name,
+ "parser_version": self.version,
+ "extraction_methods": list[JsonValue](extraction_methods),
+ },
+ )
+
+ @staticmethod
+ async def _attempt(operation: Awaitable[str]) -> tuple[str, Exception | None]:
+ """捕获单条外部模型路径，使另一条路径可以独立降级成功。"""
+
+ try:
+ return normalize_model_markdown(await operation), None
+ except Exception as exc:
+ return "", exc
+
+ def _validated_format(self, source: DocumentSource) -> str:
+ """使用图片解码器校验内容，并拒绝扩展名与真实编码不一致。"""
+
+ extension = source_extension(source)
+ try:
+ with Image.open(BytesIO(source.content)) as image:
+ image_format = (image.format or "").upper()
+ image.verify()
+ except (UnidentifiedImageError, OSError, ValueError) as exc:
+ raise InvalidDocumentError("图片文件损坏或格式不受支持") from exc
+ if image_format not in self._EXTENSION_FORMATS[extension]:
+ raise InvalidDocumentError("图片扩展名与实际编码格式不一致")
+ return image_format
diff --git a/src/ultimate_rag/parsers/markdown.py b/src/ultimate_rag/parsers/markdown.py
index 3d950a1..f5cd68d 100644
--- a/src/ultimate_rag/parsers/markdown.py
+++ b/src/ultimate_rag/parsers/markdown.py
@@ -19,7 +19,6 @@
V1 只保留标题、普通文本和代码块，不保留链接目标、图片、表格结构或代码语言标记。
"""
-from pathlib import PurePath
from uuid import NAMESPACE_URL, uuid5
from markdown_it import MarkdownIt
@@ -33,6 +32,7 @@
ParsedDocument,
SourceLocator,
)
+from ultimate_rag.parsers._shared import supports_source
class MarkdownParser:
@@ -43,15 +43,17 @@ class MarkdownParser:
"""
name = "markdown"
- version = "1.0"
+ version = "2.0"
+ _EXTENSIONS = frozenset({".md", ".markdown"})
+ _MIME_TYPES = frozenset({"text/markdown", "text/plain", "application/x-markdown"})
def __init__(self) -> None:
"""创建启用 CommonMark 规则的无状态 Token 解析器。"""
self._markdown = MarkdownIt("commonmark")
def supports(self, source: DocumentSource) -> bool:
- """V1 按安全归一化后的文件扩展名识别 Markdown。"""
- return PurePath(source.filename).suffix.lower() in {".md", ".markdown"}
+ """同时校验 Markdown 扩展名与常见 MIME，通用二进制由内容校验兜底。"""
+ return supports_source(source, self._EXTENSIONS, self._MIME_TYPES)
async def parse(self, source: DocumentSource) -> ParsedDocument:
"""校验并解析 Markdown 字节，返回带章节定位的统一文档。
diff --git a/src/ultimate_rag/parsers/office.py b/src/ultimate_rag/parsers/office.py
new file mode 100644
index 0000000..561a182
--- /dev/null
+++ b/src/ultimate_rag/parsers/office.py
@@ -0,0 +1,253 @@
+"""DOCX、XLSX 与 PPTX 到统一文档模型的 Office Parser。
+
+模块职责：
+ 使用各格式成熟库提取标题、正文、列表与表格，立即映射为 UltimateRAG 的 Block 和
+ SourceLocator。所有同步 ZIP/XML 解析在线程中执行，避免阻塞 FastAPI Event Loop。
+
+安全边界：
+ 三种 OOXML 格式在交给第三方库前统一检查 ZIP 条目数、解压总量和异常压缩比，降低压缩炸弹
+ 风险。V2 只支持现代 OOXML，不支持带宏格式或旧版 `.doc`、`.xls`、`.ppt`。
+
+已知限制：
+ V2 提取 Office 原生文字与表格，不对文档内嵌图片再次 OCR，不计算 Word 真实页码，也不解析
+ 图表背后的数据模型；这些内容不会阻止同一文档中的可见文字进入 RAG。
+"""
+
+import asyncio
+from io import BytesIO
+
+from docx import Document as OpenDocument
+from docx.table import Table as WordTable
+from docx.text.paragraph import Paragraph
+from openpyxl import load_workbook
+from pptx import Presentation
+
+from ultimate_rag.domain.exceptions import InvalidDocumentError
+from ultimate_rag.domain.models import (
+ Block,
+ BlockType,
+ DocumentSource,
+ ParsedDocument,
+ SourceLocator,
+)
+from ultimate_rag.parsers._shared import (
+ stable_block,
+ supports_source,
+ table_to_markdown,
+ validate_ooxml_archive,
+)
+
+
+class WordParser:
+ """把 DOCX 段落、标题、列表和表格转换为有序 Block。"""
+
+ name = "docx"
+ version = "2.0"
+ _EXTENSIONS = frozenset({".docx"})
+ _MIME_TYPES = frozenset(
+ {"application/vnd.openxmlformats-officedocument.wordprocessingml.document"}
+ )
+
+ def supports(self, source: DocumentSource) -> bool:
+ """按 DOCX 扩展名和标准 MIME 识别来源。"""
+
+ return supports_source(source, self._EXTENSIONS, self._MIME_TYPES)
+
+ async def parse(self, source: DocumentSource) -> ParsedDocument:
+ """在线程中解析 DOCX，保留标题路径和表格结构。"""
+
+ validate_ooxml_archive(source.content)
+ return await asyncio.to_thread(self._parse_sync, source)
+
+ def _parse_sync(self, source: DocumentSource) -> ParsedDocument:
+ """执行 python-docx 同步解析并映射领域模型。"""
+
+ try:
+ document = OpenDocument(BytesIO(source.content))
+ blocks: list[Block] = []
+ heading_path: list[str] = []
+ for element in document.iter_inner_content():
+ if isinstance(element, Paragraph):
+ content = element.text.strip()
+ if not content:
+ continue
+ style_name = element.style.name if element.style is not None else ""
+ block_type = BlockType.TEXT
+ if style_name.startswith("Heading"):
+ level_text = style_name.removeprefix("Heading").strip()
+ level = int(level_text) if level_text.isdigit() else 1
+ heading_path = heading_path[: level - 1] + [content]
+ block_type = BlockType.HEADING
+ elif style_name.startswith("List"):
+ block_type = BlockType.LIST
+ locator = SourceLocator(heading_path=tuple(heading_path))
+ blocks.append(
+ stable_block(source.document_id, len(blocks), block_type, content, locator)
+ )
+ elif isinstance(element, WordTable):
+ content = table_to_markdown(
+ [[cell.text for cell in row.cells] for row in element.rows]
+ )
+ if content:
+ locator = SourceLocator(heading_path=tuple(heading_path))
+ blocks.append(
+ stable_block(
+ source.document_id,
+ len(blocks),
+ BlockType.TABLE,
+ content,
+ locator,
+ )
+ )
+ except InvalidDocumentError:
+ raise
+ except Exception as exc:
+ raise InvalidDocumentError("DOCX 文件损坏或无法解析") from exc
+ return _parsed(source, self.name, self.version, blocks)
+
+
+class ExcelParser:
+ """把 XLSX 工作表按有限行窗口转换为 Markdown Table Block。"""
+
+ name = "xlsx"
+ version = "2.0"
+ _EXTENSIONS = frozenset({".xlsx"})
+ _MIME_TYPES = frozenset({"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"})
+ _MAX_SHEETS = 100
+ _MAX_CELLS_PER_SHEET = 200_000
+ _ROWS_PER_BLOCK = 100
+
+ def supports(self, source: DocumentSource) -> bool:
+ """按 XLSX 扩展名和标准 MIME 识别来源。"""
+
+ return supports_source(source, self._EXTENSIONS, self._MIME_TYPES)
+
+ async def parse(self, source: DocumentSource) -> ParsedDocument:
+ """在线程中以 read-only 模式读取工作表，避免复制完整 Workbook。"""
+
+ validate_ooxml_archive(source.content)
+ return await asyncio.to_thread(self._parse_sync, source)
+
+ def _parse_sync(self, source: DocumentSource) -> ParsedDocument:
+ """执行 openpyxl 同步读取，并给每个表格窗口记录 Sheet 与 Range。"""
+
+ try:
+ workbook = load_workbook(BytesIO(source.content), read_only=True, data_only=False)
+ if len(workbook.worksheets) > self._MAX_SHEETS:
+ raise InvalidDocumentError("XLSX 工作表数量超过限制")
+ blocks: list[Block] = []
+ for worksheet in workbook.worksheets:
+ if worksheet.max_row * worksheet.max_column > self._MAX_CELLS_PER_SHEET:
+ raise InvalidDocumentError(f"XLSX 工作表 {worksheet.title} 单元格范围过大")
+ rows = list(worksheet.iter_rows(values_only=True))
+ for start in range(0, len(rows), self._ROWS_PER_BLOCK):
+ window = rows[start : start + self._ROWS_PER_BLOCK]
+ content = table_to_markdown(window)
+ if not content:
+ continue
+ end_row = min(start + len(window), worksheet.max_row)
+ end_cell = worksheet.cell(end_row, worksheet.max_column).coordinate
+ cell_range = f"A{start + 1}:{end_cell}"
+ locator = SourceLocator(sheet=worksheet.title, cell_range=cell_range)
+ blocks.append(
+ stable_block(
+ source.document_id,
+ len(blocks),
+ BlockType.TABLE,
+ content,
+ locator,
+ )
+ )
+ workbook.close()
+ except InvalidDocumentError:
+ raise
+ except Exception as exc:
+ raise InvalidDocumentError("XLSX 文件损坏或无法解析") from exc
+ return _parsed(source, self.name, self.version, blocks)
+
+
+class PowerPointParser:
+ """把 PPTX 每张幻灯片中的标题、文本框和表格转换为 Block。"""
+
+ name = "pptx"
+ version = "2.0"
+ _EXTENSIONS = frozenset({".pptx"})
+ _MIME_TYPES = frozenset(
+ {"application/vnd.openxmlformats-officedocument.presentationml.presentation"}
+ )
+
+ def supports(self, source: DocumentSource) -> bool:
+ """按 PPTX 扩展名和标准 MIME 识别来源。"""
+
+ return supports_source(source, self._EXTENSIONS, self._MIME_TYPES)
+
+ async def parse(self, source: DocumentSource) -> ParsedDocument:
+ """在线程中解析 PPTX，并记录一基幻灯片序号。"""
+
+ validate_ooxml_archive(source.content)
+ return await asyncio.to_thread(self._parse_sync, source)
+
+ def _parse_sync(self, source: DocumentSource) -> ParsedDocument:
+ """执行 python-pptx 同步解析并保持 Shape 原始顺序。"""
+
+ try:
+ presentation = Presentation(BytesIO(source.content))
+ blocks: list[Block] = []
+ for slide_number, slide in enumerate(presentation.slides, start=1):
+ title_shape = slide.shapes.title
+ title = title_shape.text.strip() if title_shape is not None else ""
+ heading_path = (title,) if title else ()
+ if title:
+ blocks.append(
+ stable_block(
+ source.document_id,
+ len(blocks),
+ BlockType.HEADING,
+ title,
+ SourceLocator(heading_path=heading_path, slide=slide_number),
+ )
+ )
+ for shape in slide.shapes:
+ if shape is title_shape:
+ continue
+ locator = SourceLocator(heading_path=heading_path, slide=slide_number)
+ if shape.has_table:
+ content = table_to_markdown(
+ [[cell.text for cell in row.cells] for row in shape.table.rows]
+ )
+ block_type = BlockType.TABLE
+ elif shape.has_text_frame:
+ content = shape.text.strip()
+ block_type = BlockType.TEXT
+ else:
+ continue
+ if content:
+ blocks.append(
+ stable_block(
+ source.document_id,
+ len(blocks),
+ block_type,
+ content,
+ locator,
+ )
+ )
+ except Exception as exc:
+ raise InvalidDocumentError("PPTX 文件损坏或无法解析") from exc
+ return _parsed(source, self.name, self.version, blocks)
+
+
+def _parsed(
+ source: DocumentSource,
+ parser_name: str,
+ parser_version: str,
+ blocks: list[Block],
+) -> ParsedDocument:
+ """统一拒绝无文本 Office 文件并构造 Parser 元数据。"""
+
+ if not blocks:
+ raise InvalidDocumentError(f"{source.filename} 没有可索引的文本或表格")
+ return ParsedDocument(
+ document_id=source.document_id,
+ blocks=tuple(blocks),
+ metadata={"parser": parser_name, "parser_version": parser_version},
+ )
diff --git a/src/ultimate_rag/parsers/pdf.py b/src/ultimate_rag/parsers/pdf.py
new file mode 100644
index 0000000..f709bac
--- /dev/null
+++ b/src/ultimate_rag/parsers/pdf.py
@@ -0,0 +1,647 @@
+"""复杂 PDF 到统一文档模型的版面感知 Parser。
+
+处理策略分为两条互补路径：
+
+1. 文字型页面由本地 Docling 还原阅读顺序、标题层级、表格结构、图片区域与 BBox；
+2. 低文字量扫描页由 PDFium 渲染后交给阿里云百炼 OCR。
+
+Docling 的模型只在后台 Worker 首次真正处理文字型 PDF 时延迟加载，FastAPI 上传进程不会下载
+或初始化版面模型。第三方文档对象全部收敛为本文件的不可变中间对象，不会越过 Parser 边界。
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from dataclasses import dataclass, field
+from io import BytesIO
+from typing import TYPE_CHECKING, Protocol
+
+import pypdfium2 as pdfium # type: ignore[import-untyped]
+from PIL import Image
+
+from ultimate_rag.domain.exceptions import InvalidDocumentError
+from ultimate_rag.domain.models import (
+ Block,
+ BlockType,
+ DocumentSource,
+ JsonValue,
+ ParsedDocument,
+ SourceLocator,
+)
+from ultimate_rag.domain.ports import OCRClient, VisionClient
+from ultimate_rag.parsers._model_output import combine_ocr_and_vision, normalize_model_markdown
+from ultimate_rag.parsers._shared import stable_block, supports_source
+
+if TYPE_CHECKING:
+ from docling_core.types.doc.base import BoundingBox
+ from docling_core.types.doc.document import DoclingDocument
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True, slots=True)
+class _PDFPage:
+ """PDFium 页面检查结果；只有扫描候选页携带渲染图片。"""
+
+ number: int
+ native_text: str
+ rendered_image: bytes | None
+
+
+@dataclass(frozen=True, slots=True)
+class _LayoutElement:
+ """Docling 边界内归一化后的版面元素。"""
+
+ order: int
+ page: int
+ block_type: BlockType
+ content: str
+ locator: SourceLocator
+ metadata: dict[str, JsonValue] = field(default_factory=dict)
+ image: bytes | None = None
+ caption: str = ""
+
+
+class _PDFLayoutAnalyzer(Protocol):
+ """同步本地版面分析边界，便于 Parser 单元测试替换重量模型。"""
+
+ def analyze(
+ self,
+ content: bytes,
+ filename: str,
+ skipped_pages: frozenset[int],
+ ) -> list[_LayoutElement]:
+ """按自然阅读顺序返回非扫描页面的结构元素。"""
+ ...
+
+
+class DoclingPDFLayoutAnalyzer:
+ """使用 Docling Layout + TableFormer 的本地 PDF 版面分析器。"""
+
+ _SKIPPED_LABELS = frozenset({"page_header", "page_footer"})
+
+ def __init__(
+ self,
+ *,
+ device: str,
+ num_threads: int,
+ document_timeout: float,
+ images_scale: float,
+ max_picture_bytes: int,
+ max_pictures: int,
+ min_picture_pixels: int,
+ artifacts_path: str | None = None,
+ ) -> None:
+ """保存模型、图片和资源边界；构造时不导入模型运行时。"""
+
+ self._device = device
+ self._num_threads = num_threads
+ self._document_timeout = document_timeout
+ self._images_scale = images_scale
+ self._max_picture_bytes = max_picture_bytes
+ self._max_pictures = max_pictures
+ self._min_picture_pixels = min_picture_pixels
+ self._artifacts_path = artifacts_path
+
+ def analyze(
+ self,
+ content: bytes,
+ filename: str,
+ skipped_pages: frozenset[int],
+ ) -> list[_LayoutElement]:
+ """执行本地版面推理，并在适配器边界内导出文本、表格和图片。"""
+
+ # 重量依赖在 Worker 真正收到文字型 PDF 后才导入。这样 API 进程能够快速启动，
+ # 普通 Markdown/Office 任务也不会承担 Torch 与模型初始化成本。
+ from docling.datamodel.accelerator_options import AcceleratorOptions
+ from docling.datamodel.base_models import InputFormat
+ from docling.datamodel.pipeline_options import (
+ HeadingHierarchyOptions,
+ PdfPipelineOptions,
+ TableFormerMode,
+ TableStructureOptions,
+ )
+ from docling.document_converter import DocumentConverter, PdfFormatOption
+ from docling_core.types.doc.items.picture.picture import PictureItem
+ from docling_core.types.doc.items.table.table import TableItem
+ from docling_core.types.doc.items.text import SectionHeaderItem, TextItem
+ from docling_core.types.io import DocumentStream
+
+ try:
+ pipeline_options = PdfPipelineOptions(
+ # 扫描 OCR 明确交给百炼；本地模型只负责确定性的 Layout 与表格结构。
+ do_ocr=False,
+ do_table_structure=True,
+ table_structure_options=TableStructureOptions(
+ do_cell_matching=True,
+ mode=TableFormerMode.ACCURATE,
+ ),
+ generate_picture_images=True,
+ images_scale=self._images_scale,
+ generate_parsed_pages=True,
+ heading_hierarchy_options=HeadingHierarchyOptions(enabled=True),
+ document_timeout=self._document_timeout,
+ accelerator_options=AcceleratorOptions(
+ device=self._device,
+ num_threads=self._num_threads,
+ ),
+ artifacts_path=self._artifacts_path,
+ )
+ converter = DocumentConverter(
+ allowed_formats=[InputFormat.PDF],
+ format_options={
+ InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
+ },
+ )
+ result = converter.convert(
+ DocumentStream(name=filename, stream=BytesIO(content)),
+ raises_on_error=True,
+ max_num_pages=PDFParser._MAX_PAGES,
+ max_file_size=len(content),
+ )
+ document = result.document
+ except Exception as exc:
+ # PDFium 已在调用本方法前验证文件可打开。此处失败更可能是模型下载、资源或推理
+ # 故障，因此保留为可重试 RuntimeError，而不是错误归类为不可重试的损坏文件。
+ raise RuntimeError("PDF 本地版面分析失败") from exc
+
+ elements: list[_LayoutElement] = []
+ heading_path: list[str] = []
+ picture_count = 0
+ for order, (item, _level) in enumerate(document.iterate_items()):
+ if not isinstance(item, (PictureItem, TableItem, TextItem)):
+ continue
+ if not getattr(item, "prov", None):
+ continue
+ provenance = item.prov[0]
+ page = int(provenance.page_no)
+ if page in skipped_pages:
+ continue
+
+ label = str(item.label.value)
+ if label in self._SKIPPED_LABELS:
+ # 重复页眉页脚通常会污染召回；页码仍由 SourceLocator 保留，不依赖正文中的页脚。
+ continue
+ locator = SourceLocator(
+ heading_path=tuple(heading_path),
+ page=page,
+ bbox=self._top_left_bbox(document, page, provenance.bbox),
+ )
+ metadata: dict[str, JsonValue] = {
+ "layout_label": label,
+ "reading_order": order,
+ "layout_engine": "docling",
+ }
+
+ if isinstance(item, SectionHeaderItem) or label in {"title", "section_header"}:
+ text = str(getattr(item, "text", "")).strip()
+ if not text:
+ continue
+ level = max(1, min(int(getattr(item, "level", 1)), 6))
+ heading_path = heading_path[: level - 1]
+ heading_path.append(text)
+ locator = SourceLocator(
+ heading_path=tuple(heading_path),
+ page=page,
+ bbox=locator.bbox,
+ )
+ elements.append(
+ _LayoutElement(
+ order,
+ page,
+ BlockType.HEADING,
+ text,
+ locator,
+ {**metadata, "heading_level": level},
+ )
+ )
+ continue
+
+ if isinstance(item, TableItem):
+ table = item.export_to_markdown(document).strip()
+ if table:
+ elements.append(
+ _LayoutElement(
+ order,
+ page,
+ BlockType.TABLE,
+ table,
+ locator,
+ metadata,
+ caption=item.caption_text(document).strip(),
+ )
+ )
+ continue
+
+ if isinstance(item, PictureItem):
+ if picture_count >= self._max_pictures:
+ continue
+ image = item.get_image(document)
+ if image is None or image.width * image.height < self._min_picture_pixels:
+ continue
+ encoded = self._encode_picture(image)
+ if not encoded:
+ continue
+ picture_count += 1
+ elements.append(
+ _LayoutElement(
+ order,
+ page,
+ BlockType.IMAGE,
+ "",
+ locator,
+ metadata,
+ image=encoded,
+ caption=item.caption_text(document).strip(),
+ )
+ )
+ continue
+
+ if isinstance(item, TextItem):
+ text = item.text.strip()
+ if not text:
+ continue
+ block_type = {
+ "code": BlockType.CODE,
+ "list_item": BlockType.LIST,
+ "footnote": BlockType.QUOTE,
+ }.get(label, BlockType.TEXT)
+ elements.append(_LayoutElement(order, page, block_type, text, locator, metadata))
+
+ return elements
+
+ @staticmethod
+ def _top_left_bbox(
+ document: DoclingDocument,
+ page: int,
+ bbox: BoundingBox,
+ ) -> tuple[float, float, float, float]:
+ """把 Docling BBox 统一为左上角原点，并返回可 JSON 化坐标。"""
+
+ # Docling 页对象与 BoundingBox 都在重量依赖内部，使用局部动态属性可以避免模块导入时
+ # 加载整个运行时；这段代码只接受 Docling 自己刚产生的受控对象。
+ page_height = float(document.pages[page].size.height)
+ normalized = bbox.to_top_left_origin(page_height)
+ left, top, right, bottom = normalized.as_tuple()
+ return (float(left), float(top), float(right), float(bottom))
+
+ def _encode_picture(self, image: Image.Image) -> bytes:
+ """把版面裁图压缩到百炼 Base64 上限以内，避免上传整页无界位图。"""
+
+ picture = image.convert("RGB")
+ picture.thumbnail((2048, 2048))
+ for quality in (88, 76, 64, 52):
+ buffer = BytesIO()
+ picture.save(buffer, format="JPEG", quality=quality, optimize=True)
+ value = buffer.getvalue()
+ if len(value) <= self._max_picture_bytes:
+ return value
+ # 单纯降低质量仍超限时同步缩小像素；循环有固定次数，不会无限处理恶意大图。
+ picture.thumbnail((max(1, picture.width * 3 // 4), max(1, picture.height * 3 // 4)))
+ return b""
+
+
+class PDFParser:
+ """组合本地版面分析、百炼扫描 OCR 与嵌入图片理解。"""
+
+ name = "pdf-docling"
+ version = "2.2"
+ _EXTENSIONS = frozenset({".pdf"})
+ _MIME_TYPES = frozenset({"application/pdf"})
+ _MAX_PAGES = 500
+
+ def __init__(
+ self,
+ ocr_client: OCRClient,
+ vision_client: VisionClient | None = None,
+ *,
+ native_text_threshold: int = 20,
+ scan_image_coverage_threshold: float = 0.65,
+ scan_vision_text_threshold: int = 300,
+ render_scale: float = 2.0,
+ vision_concurrency: int = 2,
+ layout_analyzer: _PDFLayoutAnalyzer | None = None,
+ docling_device: str = "auto",
+ docling_num_threads: int = 4,
+ docling_timeout: float = 600.0,
+ docling_images_scale: float = 2.0,
+ docling_artifacts_path: str | None = None,
+ max_picture_bytes: int = 6 * 1024 * 1024,
+ max_pictures: int = 20,
+ min_picture_pixels: int = 10_000,
+ ) -> None:
+ """配置扫描判定、并发和本地 Docling 资源上限。"""
+
+ if native_text_threshold < 0:
+ raise ValueError("PDF native text threshold cannot be negative")
+ if not 0.1 <= scan_image_coverage_threshold <= 1.0:
+ raise ValueError("PDF scan image coverage threshold must be between 0.1 and 1.0")
+ if scan_vision_text_threshold < 0:
+ raise ValueError("PDF scan vision text threshold cannot be negative")
+ if not 0.5 <= render_scale <= 3.0:
+ raise ValueError("PDF render scale must be between 0.5 and 3.0")
+ if vision_concurrency < 1:
+ raise ValueError("PDF vision concurrency must be at least 1")
+ self._ocr_client = ocr_client
+ self._vision_client = vision_client
+ self._native_text_threshold = native_text_threshold
+ self._scan_image_coverage_threshold = scan_image_coverage_threshold
+ self._scan_vision_text_threshold = scan_vision_text_threshold
+ self._render_scale = render_scale
+ self._vision_concurrency = vision_concurrency
+ self._layout_analyzer = layout_analyzer or DoclingPDFLayoutAnalyzer(
+ device=docling_device,
+ num_threads=docling_num_threads,
+ document_timeout=docling_timeout,
+ images_scale=docling_images_scale,
+ max_picture_bytes=max_picture_bytes,
+ max_pictures=max_pictures,
+ min_picture_pixels=min_picture_pixels,
+ artifacts_path=docling_artifacts_path,
+ )
+
+ def supports(self, source: DocumentSource) -> bool:
+ """同时要求 PDF 扩展名以及 PDF 或通用二进制 MIME。"""
+
+ return supports_source(source, self._EXTENSIONS, self._MIME_TYPES)
+
+ async def parse(self, source: DocumentSource) -> ParsedDocument:
+ """按页选择布局或 OCR 路径，并保留阅读顺序、页码与 BBox。"""
+
+ pages = await asyncio.to_thread(self._inspect_pages, source.content)
+ scanned_pages = frozenset(page.number for page in pages if page.rendered_image is not None)
+ layout_elements: list[_LayoutElement] = []
+ if len(scanned_pages) < len(pages):
+ layout_elements = await asyncio.to_thread(
+ self._layout_analyzer.analyze,
+ source.content,
+ source.filename,
+ scanned_pages,
+ )
+
+ resolved_layout = await self._resolve_layout_images(layout_elements)
+ elements_by_page: dict[int, list[_LayoutElement]] = {}
+ for element in resolved_layout:
+ elements_by_page.setdefault(element.page, []).append(element)
+
+ scan_results = await self._ocr_scanned_pages(pages)
+ blocks: list[Block] = []
+ fallback_page_count = 0
+ for page in pages:
+ page_elements = sorted(
+ elements_by_page.get(page.number, []), key=lambda item: item.order
+ )
+ if page.number in scan_results:
+ text, extraction = scan_results[page.number]
+ if text:
+ page_elements = [
+ _LayoutElement(
+ 0,
+ page.number,
+ BlockType.IMAGE,
+ text,
+ SourceLocator(page=page.number),
+ {"extraction": extraction},
+ )
+ ]
+ elif not page_elements and page.native_text.strip():
+ # 极少数 PDF 能被 PDFium 提取文字但版面模型没有产出元素。保留按页文本比静默
+ # 丢页更安全，同时通过 metadata 让评估可以识别这一降级路径。
+ fallback_page_count += 1
+ page_elements = [
+ _LayoutElement(
+ 0,
+ page.number,
+ BlockType.TEXT,
+ page.native_text.strip(),
+ SourceLocator(page=page.number),
+ {"extraction": "pdfium_text_fallback"},
+ )
+ ]
+
+ for element in page_elements:
+ content = element.content.strip()
+ if not content:
+ continue
+ blocks.append(
+ stable_block(
+ source.document_id,
+ len(blocks),
+ element.block_type,
+ content,
+ element.locator,
+ element.metadata,
+ )
+ )
+
+ if not blocks:
+ raise InvalidDocumentError("PDF 没有可索引的文本、表格、图片语义或 OCR 结果")
+ return ParsedDocument(
+ document_id=source.document_id,
+ blocks=tuple(blocks),
+ metadata={
+ "parser": self.name,
+ "parser_version": self.version,
+ "page_count": len(pages),
+ "scan_page_count": len(scanned_pages),
+ "ocr_page_count": sum(
+ "ocr" in extraction for _text, extraction in scan_results.values()
+ ),
+ "scan_vision_page_count": sum(
+ "vision" in extraction for _text, extraction in scan_results.values()
+ ),
+ "table_count": sum(block.type == BlockType.TABLE for block in blocks),
+ "image_count": sum(block.type == BlockType.IMAGE for block in blocks),
+ "fallback_page_count": fallback_page_count,
+ "layout_engine": "docling",
+ },
+ )
+
+ async def _resolve_layout_images(
+ self,
+ elements: list[_LayoutElement],
+ ) -> list[_LayoutElement]:
+ """有界并发理解 Docling 裁出的图片，并保持原始元素顺序。"""
+
+ semaphore = asyncio.Semaphore(self._vision_concurrency)
+
+ async def resolve(element: _LayoutElement) -> _LayoutElement | None:
+ if element.image is None:
+ return element
+ async with semaphore:
+ content = ""
+ extraction = ""
+ if self._vision_client is not None:
+ try:
+ content = normalize_model_markdown(
+ await self._vision_client.describe(
+ element.image,
+ "image/jpeg",
+ element.caption,
+ )
+ )
+ extraction = "bailian_vision"
+ except Exception:
+ # 单个附图理解失败不应使正文完全不可用；继续尝试 OCR，完整异常只写日志。
+ logger.warning(
+ "PDF embedded-image vision analysis failed",
+ extra={"page": element.page},
+ exc_info=True,
+ )
+ if not content:
+ try:
+ content = normalize_model_markdown(
+ await self._ocr_client.extract_text(
+ element.image,
+ "image/jpeg",
+ )
+ )
+ extraction = "bailian_ocr_fallback"
+ except Exception:
+ logger.warning(
+ "PDF embedded-image OCR fallback failed",
+ extra={"page": element.page},
+ exc_info=True,
+ )
+ content = ""
+ if element.caption:
+ # 题注来自 Docling 的相邻结构，是图片最稳定的检索锚点；即使模型降级也应保留。
+ content = f"图片题注：{element.caption}\n\n{content}".strip()
+ if not content:
+ return None
+ return _LayoutElement(
+ element.order,
+ element.page,
+ element.block_type,
+ content,
+ element.locator,
+ {
+ **element.metadata,
+ "extraction": extraction or "docling_caption",
+ "caption": element.caption,
+ },
+ caption=element.caption,
+ )
+
+ values = await asyncio.gather(*(resolve(element) for element in elements))
+ return [value for value in values if value is not None]
+
+ async def _ocr_scanned_pages(self, pages: list[_PDFPage]) -> dict[int, tuple[str, str]]:
+ """有界并发解析扫描页；稀疏 OCR 页面追加 Vision 以保留图形关系。"""
+
+ semaphore = asyncio.Semaphore(self._vision_concurrency)
+
+ async def recognize(page: _PDFPage) -> tuple[int, str, str] | None:
+ if page.rendered_image is None:
+ return None
+ async with semaphore:
+ ocr_text = ""
+ ocr_error: Exception | None = None
+ try:
+ ocr_text = normalize_model_markdown(
+ await self._ocr_client.extract_text(page.rendered_image, "image/jpeg")
+ )
+ except ValueError as exc:
+ raise InvalidDocumentError(
+ f"PDF 第 {page.number} 页 OCR 输入无效：{exc}"
+ ) from exc
+ except Exception as exc:
+ ocr_error = exc
+ logger.warning(
+ "PDF scanned-page OCR failed; trying vision",
+ extra={"page": page.number},
+ exc_info=True,
+ )
+
+ vision_text = ""
+ compact_ocr_length = len("".join(ocr_text.split()))
+ should_use_vision = (
+ self._vision_client is not None
+ and compact_ocr_length < self._scan_vision_text_threshold
+ )
+ if should_use_vision and self._vision_client is not None:
+ try:
+ vision_text = normalize_model_markdown(
+ await self._vision_client.describe(page.rendered_image, "image/jpeg")
+ )
+ except Exception:
+ logger.warning(
+ "PDF scanned-page vision analysis failed",
+ extra={"page": page.number},
+ exc_info=True,
+ )
+
+ content = combine_ocr_and_vision(ocr_text, vision_text)
+ if not content and ocr_error is not None:
+ raise RuntimeError(f"PDF 第 {page.number} 页扫描解析失败") from ocr_error
+ extraction = "+".join(
+ method
+ for method, value in (
+ ("bailian_page_ocr", ocr_text),
+ ("bailian_page_vision", vision_text),
+ )
+ if value
+ )
+ return page.number, content, extraction
+
+ values = await asyncio.gather(*(recognize(page) for page in pages))
+ return {value[0]: (value[1], value[2]) for value in values if value is not None}
+
+ def _inspect_pages(self, content: bytes) -> list[_PDFPage]:
+ """用 PDFium 验证 PDF、识别低文本页，并只渲染需要百炼 OCR 的页面。"""
+
+ try:
+ document = pdfium.PdfDocument(content)
+ page_count = len(document)
+ if page_count == 0:
+ raise InvalidDocumentError("PDF 文件没有页面")
+ if page_count > self._MAX_PAGES:
+ raise InvalidDocumentError(f"PDF 页数不能超过 {self._MAX_PAGES} 页")
+ pages: list[_PDFPage] = []
+ for page_index in range(page_count):
+ page = document[page_index]
+ text_page = page.get_textpage()
+ native_text = text_page.get_text_bounded().strip()
+ text_page.close()
+ rendered = None
+ visible_character_count = len("".join(native_text.split()))
+ raster_coverage = self._largest_raster_coverage(page)
+ is_scanned_page = (
+ visible_character_count < self._native_text_threshold
+ and raster_coverage >= self._scan_image_coverage_threshold
+ )
+ if is_scanned_page:
+ bitmap = page.render(scale=self._render_scale)
+ image = bitmap.to_pil().convert("RGB")
+ buffer = BytesIO()
+ image.save(buffer, format="JPEG", quality=88, optimize=True)
+ rendered = buffer.getvalue()
+ bitmap.close()
+ page.close()
+ pages.append(_PDFPage(page_index + 1, native_text, rendered))
+ document.close()
+ return pages
+ except InvalidDocumentError:
+ raise
+ except Exception as exc:
+ raise InvalidDocumentError("PDF 文件损坏、加密或无法解析") from exc
+
+ @staticmethod
+ def _largest_raster_coverage(page: object) -> float:
+ """估算最大栅格图占页比例，用于区分扫描页与含少量文字的图文页。
+
+ 仅用“原生文字少于 N 个字符”会把整页架构图或少量题注页面错误路由到纯 OCR。扫描件通常
+ 包含一张覆盖大部分页面的栅格图，因此同时要求最大图片覆盖率达到阈值更可靠。
+ """
+
+ width, height = page.get_size() # type: ignore[attr-defined]
+ page_area = max(float(width) * float(height), 1.0)
+ largest = 0.0
+ for image in page.get_objects( # type: ignore[attr-defined]
+ filter=[pdfium.raw.FPDF_PAGEOBJ_IMAGE]
+ ):
+ left, bottom, right, top = image.get_pos()
+ area = abs(float(right) - float(left)) * abs(float(top) - float(bottom))
+ largest = max(largest, min(area / page_area, 1.0))
+ return largest
diff --git a/src/ultimate_rag/parsers/registry.py b/src/ultimate_rag/parsers/registry.py
index 77da415..706564b 100644
--- a/src/ultimate_rag/parsers/registry.py
+++ b/src/ultimate_rag/parsers/registry.py
@@ -1,6 +1,6 @@
"""解析器注册与解析器选择。
-V1 使用显式内存注册即可满足单进程需求；本模块不实现包发现或远程插件加载。
+V2 使用显式内存注册即可满足单进程需求；本模块不实现包发现或远程插件加载。
"""
from ultimate_rag.domain.exceptions import UnsupportedDocumentTypeError
diff --git a/src/ultimate_rag/runtime.py b/src/ultimate_rag/runtime.py
new file mode 100644
index 0000000..aab5a4b
--- /dev/null
+++ b/src/ultimate_rag/runtime.py
@@ -0,0 +1,157 @@
+"""API 与 Worker 共用的文档处理依赖装配入口。
+
+该模块只是 Composition Root：它知道具体适配器并把它们注入应用服务，但不包含业务流程。
+集中装配可保证 API 上传校验与 Worker 真正解析使用同一 Parser Registry 和配置，避免两套进程
+支持格式不一致。当前依赖数量有限，因此无需引入依赖注入框架。
+"""
+
+from dataclasses import dataclass
+
+from sqlalchemy.ext.asyncio import AsyncEngine
+
+from ultimate_rag.application import DocumentProcessingService, IngestionService
+from ultimate_rag.chunkers import StructureAwareChunker
+from ultimate_rag.config import Settings
+from ultimate_rag.domain.ports import Embedder, ObjectStorage, VectorStore
+from ultimate_rag.embeddings import BailianEmbedder
+from ultimate_rag.infrastructure.database import create_database
+from ultimate_rag.infrastructure.database.repository import Repository
+from ultimate_rag.infrastructure.storage import MinioObjectStorage
+from ultimate_rag.ocr import BailianOCRClient
+from ultimate_rag.parsers import (
+ ExcelParser,
+ HtmlParser,
+ ImageOCRParser,
+ MarkdownParser,
+ ParserRegistry,
+ PDFParser,
+ PowerPointParser,
+ WordParser,
+)
+from ultimate_rag.vectorstores import MilvusVectorStore
+from ultimate_rag.vision import BailianVisionClient
+
+
+@dataclass(slots=True)
+class ProcessingRuntime:
+ """持有一个进程内可复用的文档处理依赖与应用服务。"""
+
+ engine: AsyncEngine
+ repository: Repository
+ storage: ObjectStorage
+ embedder: Embedder
+ vector_store: VectorStore
+ ingestion: IngestionService
+ processor: DocumentProcessingService
+
+ async def initialize(self) -> None:
+ """在进程开放服务或领取任务前幂等准备外部存储。"""
+
+ await self.storage.ensure_bucket()
+ await self.vector_store.ensure_collection()
+
+ async def close(self) -> None:
+ """释放本进程持有的 SQLAlchemy 连接池。"""
+
+ await self.engine.dispose()
+
+
+def create_processing_runtime(settings: Settings) -> ProcessingRuntime:
+ """根据集中配置装配 API 与 Worker 必须保持一致的处理组件。"""
+
+ engine, repository = create_database(settings.database_url)
+ storage = MinioObjectStorage(
+ settings.minio_endpoint,
+ settings.minio_access_key,
+ settings.minio_secret_key,
+ settings.minio_bucket,
+ settings.minio_secure,
+ )
+ embedder = BailianEmbedder(
+ api_key=settings.dashscope_api_key,
+ base_url=settings.dashscope_base_url,
+ model=settings.embedding_model,
+ dimension=settings.embedding_dimension,
+ batch_size=settings.embedding_batch_size,
+ timeout=settings.model_timeout_seconds,
+ )
+ vector_store = MilvusVectorStore(
+ uri=settings.milvus_uri,
+ token=settings.milvus_token,
+ collection=settings.milvus_collection,
+ dimension=settings.embedding_dimension,
+ )
+ ocr = BailianOCRClient(
+ api_key=settings.dashscope_api_key,
+ base_url=settings.dashscope_base_url,
+ model=settings.ocr_model,
+ max_image_bytes=settings.ocr_max_image_bytes,
+ max_output_tokens=settings.ocr_max_output_tokens,
+ timeout=settings.model_timeout_seconds,
+ )
+ vision = BailianVisionClient(
+ api_key=settings.dashscope_api_key,
+ base_url=settings.dashscope_base_url,
+ model=settings.vision_model,
+ max_image_bytes=settings.vision_max_image_bytes,
+ max_output_tokens=settings.vision_max_output_tokens,
+ timeout=settings.model_timeout_seconds,
+ )
+ registry = ParserRegistry(
+ [
+ MarkdownParser(),
+ WordParser(),
+ ExcelParser(),
+ PowerPointParser(),
+ HtmlParser(),
+ PDFParser(
+ ocr,
+ vision,
+ native_text_threshold=settings.pdf_native_text_threshold,
+ scan_image_coverage_threshold=settings.pdf_scan_image_coverage_threshold,
+ scan_vision_text_threshold=settings.pdf_scan_vision_text_threshold,
+ render_scale=settings.pdf_render_scale,
+ vision_concurrency=settings.pdf_vision_concurrency,
+ docling_device=settings.docling_device,
+ docling_num_threads=settings.docling_num_threads,
+ docling_timeout=settings.docling_timeout_seconds,
+ docling_images_scale=settings.docling_images_scale,
+ docling_artifacts_path=settings.docling_artifacts_path,
+ max_picture_bytes=settings.vision_max_image_bytes,
+ max_pictures=settings.pdf_max_pictures,
+ min_picture_pixels=settings.pdf_min_picture_pixels,
+ ),
+ # 独立图片与 PDF 内嵌图片使用同一组可替换模型边界：OCR 保留精确文字，
+ # Vision 补充箭头、包含关系和图表趋势，避免示意图只剩零散标签。
+ ImageOCRParser(ocr, vision),
+ ]
+ )
+ chunker = StructureAwareChunker(
+ max_tokens=settings.chunk_max_tokens,
+ overlap_tokens=settings.chunk_overlap_tokens,
+ tokenizer_name=settings.chunk_tokenizer,
+ )
+ ingestion = IngestionService(
+ repository=repository,
+ storage=storage,
+ parser_registry=registry,
+ max_upload_bytes=settings.max_upload_bytes,
+ job_max_attempts=settings.ingestion_job_max_attempts,
+ )
+ processor = DocumentProcessingService(
+ repository=repository,
+ storage=storage,
+ parser_registry=registry,
+ chunker=chunker,
+ embedder=embedder,
+ vector_store=vector_store,
+ )
+ return ProcessingRuntime(
+ engine=engine,
+ repository=repository,
+ storage=storage,
+ embedder=embedder,
+ vector_store=vector_store,
+ ingestion=ingestion,
+ processor=processor,
+ )
diff --git a/src/ultimate_rag/vectorstores/milvus.py b/src/ultimate_rag/vectorstores/milvus.py
index d2fd6c4..aa5e509 100644
--- a/src/ultimate_rag/vectorstores/milvus.py
+++ b/src/ultimate_rag/vectorstores/milvus.py
@@ -27,7 +27,7 @@
from pymilvus import DataType, MilvusClient # type: ignore[import-untyped]
-from ultimate_rag.domain.models import EmbeddedChunk, RetrievalResult
+from ultimate_rag.domain.models import EmbeddedChunk, RetrievalResult, SourceLocator
class MilvusVectorStore:
@@ -117,7 +117,13 @@ async def upsert(self, chunks: Sequence[EmbeddedChunk]) -> None:
"chunk_id": item.chunk.id,
"filename": str(item.chunk.metadata.get("filename", "")),
"content": item.chunk.content,
- "heading_path": list(item.chunk.heading_path),
+ # 复用 V1 已有 JSON 字段保存 V2 SourceLocator，避免在应用启动时隐式迁移
+ # Milvus Schema。读取端兼容旧版 list，现有 V1 索引可以原地继续检索。
+ "heading_path": (
+ item.chunk.locator.to_metadata()
+ if item.chunk.locator
+ else {"heading_path": list(item.chunk.heading_path)}
+ ),
"embedding": list(item.embedding),
}
for item in chunks
@@ -196,12 +202,19 @@ def _retrieval_result(hit: dict[str, Any]) -> RetrievalResult:
# ``output_fields`` 位于 entity，COSINE 分数位于 Hit 顶层 distance。
# heading_path 兼容缺失字段或 JSON null，并在领域边界冻结为 Tuple。
entity = hit["entity"]
+ raw_locator = entity.get("heading_path")
+ if isinstance(raw_locator, dict):
+ locator = SourceLocator.from_metadata(raw_locator)
+ else:
+ # V1 行只保存标题数组；转换为统一 Locator 后无需重建已有 Collection。
+ locator = SourceLocator(heading_path=tuple(str(item) for item in (raw_locator or [])))
return RetrievalResult(
chunk_id=str(entity["chunk_id"]),
knowledge_base_id=str(entity["knowledge_base_id"]),
document_id=str(entity["document_id"]),
filename=str(entity["filename"]),
content=str(entity["content"]),
- heading_path=tuple(entity.get("heading_path") or []),
+ heading_path=locator.heading_path,
score=float(hit["distance"]),
+ locator=locator,
)
diff --git a/src/ultimate_rag/vision/__init__.py b/src/ultimate_rag/vision/__init__.py
new file mode 100644
index 0000000..126f839
--- /dev/null
+++ b/src/ultimate_rag/vision/__init__.py
@@ -0,0 +1,5 @@
+"""视觉内容理解适配器公开入口。"""
+
+from ultimate_rag.vision.bailian import BailianVisionClient
+
+__all__ = ["BailianVisionClient"]
diff --git a/src/ultimate_rag/vision/bailian.py b/src/ultimate_rag/vision/bailian.py
new file mode 100644
index 0000000..48d8f01
--- /dev/null
+++ b/src/ultimate_rag/vision/bailian.py
@@ -0,0 +1,83 @@
+"""阿里云百炼视觉理解适配器。
+
+PDF 中的图表、架构图和示意图往往没有足够 OCR 文字，却包含检索所需的关系、趋势或流程。
+本适配器把经过 Parser 裁剪和压缩的图片交给通用视觉模型，输出可向量化的忠实 Markdown；
+它与 OCR 端口分离，避免让“逐字识别”和“视觉语义描述”共用一个含糊 Prompt。
+"""
+
+import base64
+from typing import cast
+
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam
+
+
+class BailianVisionClient:
+ """使用百炼 Qwen-VL 提取图片中的文字、结构和非文本语义。"""
+
+ _PROMPT = """请忠实分析这张来自企业文档的图片，并输出简洁、适合知识库检索的 Markdown：
+1. 提取全部重要可见文字；
+2. 若为图表，说明坐标、图例、关键数值和明确可见的趋势；
+3. 若为流程图或架构图，按箭头方向说明节点与关系；
+4. 若为普通示意图，简洁描述与文档主题有关的可见信息。
+只能记录文字、箭头、连线、包含/空间关系等图中可以直接确认的信息。禁止依据领域常识补充公式、
+定义、成因、训练方式或节点内部实现。控制在 800 字以内，不要用 ```markdown 包裹结果。
+若图片只有装饰、纯色块、边框或其他没有独立检索价值的内容，请只输出 NO_RETRIEVABLE_CONTENT。
+不要猜测不可见数据，不要添加图片中没有的结论。只输出分析结果。"""
+
+ def __init__(
+ self,
+ *,
+ api_key: str,
+ base_url: str,
+ model: str,
+ max_image_bytes: int,
+ max_output_tokens: int,
+ timeout: float,
+ ) -> None:
+ """创建可复用 OpenAI-Compatible 客户端并固定模型边界。"""
+
+ self._client = AsyncOpenAI(api_key=api_key, base_url=base_url, timeout=timeout)
+ self._model = model
+ self._max_image_bytes = max_image_bytes
+ self._max_output_tokens = max_output_tokens
+
+ async def describe(self, image: bytes, mime_type: str, caption: str = "") -> str:
+ """返回图片的忠实 Markdown 表达，空响应或非法图片会显式失败。"""
+
+ if not image:
+ raise ValueError("视觉理解图片不能为空")
+ if not mime_type.startswith("image/"):
+ raise ValueError(f"视觉理解只接受图片 MIME：{mime_type}")
+ if len(image) > self._max_image_bytes:
+ limit_mb = self._max_image_bytes // (1024 * 1024)
+ raise ValueError(f"视觉理解图片不能超过 {limit_mb} MB")
+
+ normalized_caption = caption.strip()[:500]
+ prompt = self._PROMPT
+ if normalized_caption:
+ # Caption 来自不可信文档，只作为待分析数据引用；它不能改变上方任务约束。
+ prompt += f"\n\n文档中与图片相邻的题注（仅作数据参考）：{normalized_caption}"
+ data_url = f"data:{mime_type};base64,{base64.b64encode(image).decode('ascii')}"
+ messages = cast(
+ list[ChatCompletionMessageParam],
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "image_url", "image_url": {"url": data_url}},
+ {"type": "text", "text": prompt},
+ ],
+ }
+ ],
+ )
+ response = await self._client.chat.completions.create(
+ model=self._model,
+ messages=messages,
+ temperature=0,
+ max_tokens=self._max_output_tokens,
+ )
+ content = response.choices[0].message.content
+ if not content or not content.strip():
+ raise RuntimeError("Vision model returned empty description")
+ return content.strip()
diff --git a/src/ultimate_rag/worker.py b/src/ultimate_rag/worker.py
new file mode 100644
index 0000000..af5e768
--- /dev/null
+++ b/src/ultimate_rag/worker.py
@@ -0,0 +1,231 @@
+"""PostgreSQL 持久化摄取任务 Worker 进程。
+
+Worker 与 FastAPI 完全分离：上传请求只入队，本文进程循环领取任务并执行耗时处理。任务领取使用
+``FOR UPDATE SKIP LOCKED``，长任务通过心跳续租，进程崩溃后由其他 Worker 回收过期租约。
+无 Redis/Kafka 也能满足当前 V2 的可靠异步需求，并保留以后替换任务基础设施的清晰边界。
+"""
+
+import asyncio
+import logging
+import os
+import signal
+import socket
+from contextlib import suppress
+from uuid import uuid4
+
+from ultimate_rag.application import DocumentProcessingService
+from ultimate_rag.config import Settings, get_settings
+from ultimate_rag.domain.exceptions import InvalidDocumentError, ResourceNotFoundError
+from ultimate_rag.domain.models import IngestionJob
+from ultimate_rag.infrastructure.database.repository import Repository
+from ultimate_rag.runtime import create_processing_runtime
+
+logger = logging.getLogger(__name__)
+
+
+class IngestionWorker:
+ """领取、续租、处理并提交文档任务的单并发 Worker。"""
+
+ def __init__(
+ self,
+ *,
+ repository: Repository,
+ processor: DocumentProcessingService,
+ worker_id: str,
+ poll_interval: float,
+ lease_seconds: int,
+ heartbeat_seconds: int,
+ retry_delay_seconds: int,
+ ) -> None:
+ """注入任务事实端、处理服务与全部有界时间参数。"""
+
+ self._repository = repository
+ self._processor = processor
+ self._worker_id = worker_id
+ self._poll_interval = poll_interval
+ self._lease_seconds = lease_seconds
+ self._heartbeat_seconds = heartbeat_seconds
+ self._retry_delay_seconds = retry_delay_seconds
+
+ async def run(self, stop_event: asyncio.Event) -> None:
+ """持续处理任务直到收到进程停止信号。"""
+
+ logger.info("Ingestion worker started", extra={"worker_id": self._worker_id})
+ while not stop_event.is_set():
+ processed = await self.run_once()
+ if processed:
+ continue
+ # wait_for 同时提供低延迟停止和空队列退避，避免无任务时频繁查询 PostgreSQL。
+ with suppress(TimeoutError):
+ await asyncio.wait_for(stop_event.wait(), timeout=self._poll_interval)
+ logger.info("Ingestion worker stopped", extra={"worker_id": self._worker_id})
+
+ async def run_once(self) -> bool:
+ """领取并处理至多一个任务；空队列返回 ``False``。"""
+
+ job = await self._repository.claim_ingestion_job(
+ self._worker_id,
+ lease_seconds=self._lease_seconds,
+ )
+ if job is None:
+ return False
+
+ logger.info(
+ "Ingestion job claimed",
+ extra={
+ "worker_id": self._worker_id,
+ "document_id": job.document_id,
+ "job_id": job.id,
+ "attempt": job.attempts,
+ },
+ )
+ lease_lost = asyncio.Event()
+ heartbeat = asyncio.create_task(self._heartbeat(job, lease_lost))
+ try:
+ await self._processor.process(job.document_id)
+ except asyncio.CancelledError:
+ # 进程退出时不篡改任务状态；租约到期后其他 Worker 会安全回收并重跑幂等管线。
+ raise
+ except Exception as exc:
+ await self._handle_processing_failure(job, exc, lease_lost.is_set())
+ else:
+ if lease_lost.is_set():
+ logger.error(
+ "Ingestion completed after lease was lost; another worker will reconcile it",
+ extra={"document_id": job.document_id, "job_id": job.id},
+ )
+ else:
+ await self._repository.complete_ingestion_job(job.id, self._worker_id)
+ logger.info(
+ "Ingestion job completed",
+ extra={"document_id": job.document_id, "job_id": job.id},
+ )
+ finally:
+ heartbeat.cancel()
+ with suppress(asyncio.CancelledError):
+ await heartbeat
+ return True
+
+ async def _heartbeat(self, job: IngestionJob, lease_lost: asyncio.Event) -> None:
+ """在处理期间定期续租，所有权丢失后通知主任务停止提交状态。"""
+
+ while True:
+ await asyncio.sleep(self._heartbeat_seconds)
+ try:
+ renewed = await self._repository.heartbeat_ingestion_job(job.id, self._worker_id)
+ except Exception:
+ # 数据库不可达时无法证明仍拥有租约，按租约丢失处理，禁止随后提交状态。
+ lease_lost.set()
+ logger.exception(
+ "Ingestion job heartbeat failed",
+ extra={"document_id": job.document_id, "job_id": job.id},
+ )
+ return
+ if renewed:
+ continue
+ lease_lost.set()
+ logger.error(
+ "Ingestion job lease lost",
+ extra={"document_id": job.document_id, "job_id": job.id},
+ )
+ return
+
+ async def _handle_processing_failure(
+ self,
+ job: IngestionJob,
+ exc: Exception,
+ lease_lost: bool,
+ ) -> None:
+ """清理半成品向量，并按错误类型提交有限重试或终态失败。"""
+
+ logger.exception(
+ "Ingestion job failed",
+ extra={
+ "document_id": job.document_id,
+ "job_id": job.id,
+ "attempt": job.attempts,
+ },
+ )
+ if lease_lost:
+ # 失去所有权后不能覆盖新 Worker 的状态，也不能删除它可能已经写入的向量。
+ return
+
+ try:
+ await self._processor.cleanup_partial_index(job.document_id)
+ except Exception:
+ # 清理失败会完整记录，但原处理异常仍用于任务重试决策；下一次索引阶段会再次按
+ # document_id 删除旧向量，READY 过滤也会阻止半成品被检索。
+ logger.exception(
+ "Failed to clean partial vector index",
+ extra={"document_id": job.document_id, "job_id": job.id},
+ )
+
+ retryable = not isinstance(exc, (InvalidDocumentError, ResourceNotFoundError))
+ # 指数退避只作用于剩余的有限尝试，且封顶五分钟，避免外部服务故障时形成请求风暴。
+ retry_delay = min(self._retry_delay_seconds * (2 ** max(job.attempts - 1, 0)), 300)
+ message = (str(exc).strip() or exc.__class__.__name__)[:1000]
+ will_retry = await self._repository.fail_ingestion_job(
+ job.id,
+ self._worker_id,
+ error_message=message,
+ retryable=retryable,
+ retry_delay_seconds=retry_delay,
+ )
+ logger.info(
+ "Ingestion failure persisted",
+ extra={
+ "document_id": job.document_id,
+ "job_id": job.id,
+ "will_retry": will_retry,
+ },
+ )
+
+
+def _worker_id() -> str:
+ """生成便于日志定位且跨进程唯一的短 Worker 标识。"""
+
+ return f"{socket.gethostname()}:{os.getpid()}:{str(uuid4())[:8]}"
+
+
+async def run_worker(settings: Settings) -> None:
+ """装配运行时、注册优雅停止信号并启动 Worker 循环。"""
+
+ runtime = create_processing_runtime(settings)
+ stop_event = asyncio.Event()
+ loop = asyncio.get_running_loop()
+ for signal_name in (signal.SIGINT, signal.SIGTERM):
+ try:
+ loop.add_signal_handler(signal_name, stop_event.set)
+ except NotImplementedError:
+ # Windows Proactor Loop 不支持 add_signal_handler，本地 Ctrl+C 仍由 asyncio.run 处理。
+ pass
+
+ try:
+ await runtime.initialize()
+ worker = IngestionWorker(
+ repository=runtime.repository,
+ processor=runtime.processor,
+ worker_id=_worker_id(),
+ poll_interval=settings.worker_poll_interval_seconds,
+ lease_seconds=settings.worker_lease_seconds,
+ heartbeat_seconds=settings.worker_heartbeat_seconds,
+ retry_delay_seconds=settings.worker_retry_delay_seconds,
+ )
+ await worker.run(stop_event)
+ finally:
+ await runtime.close()
+
+
+def main() -> None:
+ """配置结构化基础日志并运行独立 Worker 进程。"""
+
+ settings = get_settings()
+ logging.basicConfig(
+ level=settings.log_level,
+ format="%(asctime)s %(levelname)s %(name)s %(message)s",
+ )
+ asyncio.run(run_worker(settings))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tests/unit/test_bailian_ocr.py b/tests/unit/test_bailian_ocr.py
new file mode 100644
index 0000000..05e1f64
--- /dev/null
+++ b/tests/unit/test_bailian_ocr.py
@@ -0,0 +1,69 @@
+"""验证百炼 OCR 适配器的 Data URL 请求与输入边界。"""
+
+from types import SimpleNamespace
+from typing import cast
+
+import pytest
+
+from ultimate_rag.ocr import BailianOCRClient
+
+
+class FakeCompletionsAPI:
+ """记录 Chat Completions 请求并返回固定 OCR 文本。"""
+
+ def __init__(self) -> None:
+ """初始化空请求记录。"""
+
+ self.requests: list[dict[str, object]] = []
+
+ async def create(self, **kwargs: object) -> SimpleNamespace:
+ """保存请求并模拟 OpenAI-Compatible 响应结构。"""
+
+ self.requests.append(kwargs)
+ return SimpleNamespace(
+ choices=[SimpleNamespace(message=SimpleNamespace(content=" OCR text "))]
+ )
+
+
+@pytest.mark.asyncio
+async def test_ocr_uses_base64_data_url_and_strips_response() -> None:
+ """图片字节应编码为正确 MIME 的 Data URL，响应首尾空白应被移除。"""
+
+ client = BailianOCRClient(
+ api_key="test",
+ base_url="https://example.test/v1",
+ model="qwen-vl-ocr-latest",
+ max_image_bytes=100,
+ max_output_tokens=512,
+ timeout=1,
+ )
+ api = FakeCompletionsAPI()
+ client._client = SimpleNamespace( # type: ignore[assignment]
+ chat=SimpleNamespace(completions=api)
+ )
+
+ result = await client.extract_text(b"png", "image/png")
+
+ assert result == "OCR text"
+ messages = cast(list[dict[str, object]], api.requests[0]["messages"])
+ content = cast(list[dict[str, object]], messages[0]["content"])
+ image_url = cast(dict[str, str], content[0]["image_url"])
+ assert image_url["url"].startswith("data:image/png;base64,")
+ assert api.requests[0]["max_tokens"] == 512
+
+
+@pytest.mark.asyncio
+async def test_ocr_rejects_image_over_configured_limit() -> None:
+ """超限图片必须在访问模型服务前失败。"""
+
+ client = BailianOCRClient(
+ api_key="test",
+ base_url="https://example.test/v1",
+ model="qwen-vl-ocr-latest",
+ max_image_bytes=2,
+ max_output_tokens=512,
+ timeout=1,
+ )
+
+ with pytest.raises(ValueError, match="不能超过"):
+ await client.extract_text(b"123", "image/png")
diff --git a/tests/unit/test_chunker.py b/tests/unit/test_chunker.py
index d0c39c9..f12ba0b 100644
--- a/tests/unit/test_chunker.py
+++ b/tests/unit/test_chunker.py
@@ -5,7 +5,13 @@
import pytest
from ultimate_rag.chunkers import StructureAwareMarkdownChunker
-from ultimate_rag.domain.models import DocumentSource
+from ultimate_rag.domain.models import (
+ Block,
+ BlockType,
+ DocumentSource,
+ ParsedDocument,
+ SourceLocator,
+)
from ultimate_rag.parsers import MarkdownParser
FIXTURE_CONTENT = Path("tests/fixtures/rag.md").read_bytes()
@@ -17,7 +23,7 @@ async def test_chunker_is_structure_aware_and_stable() -> None:
source = DocumentSource("doc-1", "rag.md", "text/markdown", FIXTURE_CONTENT)
parsed = await MarkdownParser().parse(source)
- chunker = StructureAwareMarkdownChunker(max_chars=300, overlap_chars=30)
+ chunker = StructureAwareMarkdownChunker(max_tokens=160, overlap_tokens=20)
first = await chunker.split(parsed, "kb-1")
second = await chunker.split(parsed, "kb-1")
@@ -38,7 +44,94 @@ async def test_chunker_splits_oversized_paragraph() -> None:
)
parsed = await MarkdownParser().parse(source)
- chunks = await StructureAwareMarkdownChunker(200, 20).split(parsed, "kb-1")
+ chunks = await StructureAwareMarkdownChunker(100, 12).split(parsed, "kb-1")
assert len(chunks) > 1
- assert all(len(chunk.content) <= 220 for chunk in chunks)
+ assert all(chunk.token_count <= 100 for chunk in chunks)
+
+
+@pytest.mark.asyncio
+async def test_chunker_keeps_pdf_pages_as_separate_source_locations() -> None:
+ """标题相同但页码不同的 Block 不能合并，否则 Citation 会丢失精确页码。"""
+
+ parsed = ParsedDocument(
+ document_id="pdf-1",
+ blocks=(
+ Block("b1", BlockType.TEXT, "第一页内容", SourceLocator(page=1)),
+ Block("b2", BlockType.TEXT, "第二页内容", SourceLocator(page=2)),
+ ),
+ )
+
+ chunks = await StructureAwareMarkdownChunker(100, 12).split(parsed, "kb-1")
+
+ assert [chunk.locator.page if chunk.locator else None for chunk in chunks] == [1, 2]
+
+
+@pytest.mark.asyncio
+async def test_chunker_repeats_table_header_when_splitting_rows() -> None:
+ """长表格按行切分后每块都应带表头，避免召回中间行时失去列语义。"""
+
+ rows = "\n".join(f"| 指标 {index} | {index * 10} |" for index in range(30))
+ table = f"| 指标 | 数值 |\n| --- | --- |\n{rows}"
+ parsed = ParsedDocument(
+ document_id="table-1",
+ blocks=(Block("table", BlockType.TABLE, table, SourceLocator(page=3)),),
+ )
+
+ chunks = await StructureAwareMarkdownChunker(64, 8).split(parsed, "kb-1")
+
+ assert len(chunks) > 1
+ assert all(chunk.content.startswith("| 指标 | 数值 |\n| --- | --- |") for chunk in chunks)
+ assert all(chunk.token_count <= 64 for chunk in chunks)
+ assert all(chunk.metadata["split_strategy"] == "table_rows" for chunk in chunks)
+
+
+@pytest.mark.asyncio
+async def test_chunker_repeats_docling_caption_and_multilevel_table_header() -> None:
+ """Docling 题注和跨列表头都应出现在续块中，不能退化为无列语义 Token 窗口。"""
+
+ rows = "\n".join(f"| Model {index} | {20 + index}.0 | {30 + index}.0 |" for index in range(20))
+ table = (
+ "Table 2: Translation quality.\n"
+ "| Model | BLEU | BLEU |\n"
+ "| --- | --- | --- |\n"
+ "| | EN-DE | EN-FR |\n"
+ f"{rows}"
+ )
+ parsed = ParsedDocument(
+ document_id="table-caption",
+ blocks=(Block("table", BlockType.TABLE, table, SourceLocator(page=8)),),
+ )
+
+ chunks = await StructureAwareMarkdownChunker(96, 8).split(parsed, "kb-1")
+
+ expected_prefix = (
+ "Table 2: Translation quality.\n"
+ "| Model | BLEU | BLEU |\n"
+ "| --- | --- | --- |\n"
+ "| | EN-DE | EN-FR |"
+ )
+ assert len(chunks) > 1
+ assert all(chunk.content.startswith(expected_prefix) for chunk in chunks)
+ assert all(chunk.token_count <= 96 for chunk in chunks)
+
+
+@pytest.mark.asyncio
+async def test_chunker_merges_bboxes_without_crossing_page_boundary() -> None:
+ """同页相邻正文可合并并扩大 BBox，不同页仍必须生成不同 Citation。"""
+
+ parsed = ParsedDocument(
+ document_id="layout-1",
+ blocks=(
+ Block("b1", BlockType.TEXT, "第一段", SourceLocator(page=1, bbox=(10, 20, 100, 40))),
+ Block("b2", BlockType.TEXT, "第二段", SourceLocator(page=1, bbox=(12, 50, 120, 80))),
+ Block("b3", BlockType.TEXT, "下一页", SourceLocator(page=2, bbox=(5, 10, 90, 30))),
+ ),
+ )
+
+ chunks = await StructureAwareMarkdownChunker(100, 12).split(parsed, "kb-1")
+
+ assert len(chunks) == 2
+ assert chunks[0].locator is not None
+ assert chunks[0].locator.bbox == (10, 20, 120, 80)
+ assert chunks[1].locator is not None and chunks[1].locator.page == 2
diff --git a/tests/unit/test_document_lifecycle_service.py b/tests/unit/test_document_lifecycle_service.py
index 5873f55..3e8b024 100644
--- a/tests/unit/test_document_lifecycle_service.py
+++ b/tests/unit/test_document_lifecycle_service.py
@@ -7,6 +7,7 @@
import pytest
from ultimate_rag.application import DocumentLifecycleService
+from ultimate_rag.domain.models import DocumentStatus
from ultimate_rag.domain.ports import ObjectStorage, VectorStore
from ultimate_rag.infrastructure.database.repository import Repository
@@ -18,8 +19,8 @@ async def test_delete_knowledge_base_keeps_fact_delete_last() -> None:
events: list[str] = []
repository = AsyncMock()
repository.list_documents.return_value = [
- SimpleNamespace(object_key="kb-1/doc-1/source.md"),
- SimpleNamespace(object_key="kb-1/doc-2/source.md"),
+ SimpleNamespace(object_key="kb-1/doc-1/source.md", status=DocumentStatus.READY),
+ SimpleNamespace(object_key="kb-1/doc-2/source.md", status=DocumentStatus.READY),
]
repository.delete_knowledge_base.side_effect = lambda _knowledge_base_id: events.append(
"delete-facts"
@@ -51,7 +52,9 @@ async def test_delete_knowledge_base_preserves_facts_when_object_cleanup_fails()
"""MinIO 清理中断时必须保留数据库事实，供后续定位和补偿。"""
repository = AsyncMock()
- repository.list_documents.return_value = [SimpleNamespace(object_key="kb-1/doc-1/source.md")]
+ repository.list_documents.return_value = [
+ SimpleNamespace(object_key="kb-1/doc-1/source.md", status=DocumentStatus.READY)
+ ]
storage = AsyncMock()
storage.delete.side_effect = RuntimeError("MinIO unavailable")
vector_store = AsyncMock()
diff --git a/tests/unit/test_ingestion_service.py b/tests/unit/test_ingestion_service.py
index 3270644..1e4dcf0 100644
--- a/tests/unit/test_ingestion_service.py
+++ b/tests/unit/test_ingestion_service.py
@@ -7,14 +7,14 @@
from ultimate_rag.application import IngestionService
from ultimate_rag.domain.exceptions import InvalidDocumentError
-from ultimate_rag.domain.ports import Chunker, Embedder, ObjectStorage, VectorStore
+from ultimate_rag.domain.ports import ObjectStorage
from ultimate_rag.infrastructure.database.repository import Repository
from ultimate_rag.parsers import MarkdownParser, ParserRegistry
@pytest.mark.asyncio
-async def test_ingestion_rejects_non_markdown_mime_before_storage() -> None:
- """明显非文本 MIME 即使使用 .md 后缀，也不应写入 MinIO。"""
+async def test_ingestion_rejects_extension_mime_mismatch_before_storage() -> None:
+ """扩展名与 MIME 没有任何 Parser 同时支持时，不应写入 MinIO。"""
repository = AsyncMock()
repository.get_knowledge_base.return_value = object()
@@ -23,13 +23,11 @@ async def test_ingestion_rejects_non_markdown_mime_before_storage() -> None:
repository=cast(Repository, repository),
storage=cast(ObjectStorage, storage),
parser_registry=ParserRegistry([MarkdownParser()]),
- chunker=cast(Chunker, AsyncMock()),
- embedder=cast(Embedder, AsyncMock()),
- vector_store=cast(VectorStore, AsyncMock()),
max_upload_bytes=1024,
+ job_max_attempts=3,
)
- with pytest.raises(InvalidDocumentError, match="MIME"):
+ with pytest.raises(InvalidDocumentError, match="不支持的文档类型"):
await service.ingest(
knowledge_base_id="kb-1",
filename="disguised.md",
diff --git a/tests/unit/test_milvus_vector_store.py b/tests/unit/test_milvus_vector_store.py
index 3b3ea28..1bfc19e 100644
--- a/tests/unit/test_milvus_vector_store.py
+++ b/tests/unit/test_milvus_vector_store.py
@@ -64,3 +64,25 @@ def test_delete_flushes_after_data_change() -> None:
("delete", 'knowledge_chunks:document_id == "doc-1"'),
("flush", "knowledge_chunks"),
]
+
+
+def test_retrieval_result_reads_v2_locator_and_v1_heading_path() -> None:
+ """同一 JSON 字段必须兼容 V2 Locator 字典与升级前的 V1 标题数组。"""
+
+ common = {
+ "chunk_id": "chunk-1",
+ "knowledge_base_id": "kb-1",
+ "document_id": "doc-1",
+ "filename": "source.pdf",
+ "content": "content",
+ }
+ v2 = MilvusVectorStore._retrieval_result(
+ {"distance": 0.9, "entity": {**common, "heading_path": {"page": 3}}}
+ )
+ v1 = MilvusVectorStore._retrieval_result(
+ {"distance": 0.8, "entity": {**common, "heading_path": ["RAG", "Index"]}}
+ )
+
+ assert v2.locator is not None and v2.locator.page == 3
+ assert v1.heading_path == ("RAG", "Index")
+ assert v1.locator is not None and v1.locator.heading_path == ("RAG", "Index")
diff --git a/tests/unit/test_model_output.py b/tests/unit/test_model_output.py
new file mode 100644
index 0000000..155893b
--- /dev/null
+++ b/tests/unit/test_model_output.py
@@ -0,0 +1,72 @@
+"""验证模型 Markdown 清理不会让 OCR 伪表格污染检索。"""
+
+from ultimate_rag.parsers._model_output import combine_ocr_and_vision, normalize_model_markdown
+
+
+def test_normalizer_removes_empty_ocr_table_repeated_from_plain_text() -> None:
+ """背景边框误识别出的空表格应删除，已正确提取的正文必须保留。"""
+
+ value = """深度学习
+Deep Learning
+
+```markdown
+| | |
+
+| --- | --- |
+
+| | Deep Learning |
+
+| | |
+
+| | |
+```
+"""
+
+ assert normalize_model_markdown(value) == "深度学习\nDeep Learning"
+
+
+def test_normalizer_preserves_real_table_and_removes_empty_rows() -> None:
+ """具有表头和数据的真实表格只删除全空数据行。"""
+
+ value = """| Metric | Value |
+| --- | --- |
+| Recall | 0.92 |
+| | |
+"""
+
+ assert normalize_model_markdown(value) == (
+ "| Metric | Value |\n| --- | --- |\n| Recall | 0.92 |"
+ )
+
+
+def test_normalizer_drops_headerless_table_fragments_already_in_prose() -> None:
+ """多行单单元格伪表格即使非空，也不应重复污染已正确提取的正文。"""
+
+ value = """机器学习是人工智能的一个子领域，主要关注多层神经网络。
+
+| | |
+| --- | --- |
+| | 机器学习是人工智能的一个子领域 |
+| 主要关注多层神经网络 | |
+| | |
+"""
+
+ assert normalize_model_markdown(value) == (
+ "机器学习是人工智能的一个子领域，主要关注多层神经网络。"
+ )
+
+
+def test_combiner_labels_ocr_and_visual_evidence() -> None:
+ """融合内容应明确区分原文字与图形关系，便于检索和调试。"""
+
+ result = combine_ocr_and_vision("Encoder", "Encoder 通过箭头连接 Decoder。")
+
+ assert result == (
+ "## OCR 文本\n\nEncoder\n\n## 视觉结构\n\nEncoder 通过箭头连接 Decoder。"
+ )
+
+
+def test_normalizer_drops_explicit_no_retrievable_content_sentinel() -> None:
+ """视觉模型确认无检索价值的装饰图不得生成空洞向量。"""
+
+ assert normalize_model_markdown("`NO_RETRIEVABLE_CONTENT`") == ""
diff --git a/tests/unit/test_v2_parsers.py b/tests/unit/test_v2_parsers.py
new file mode 100644
index 0000000..f5c5b95
--- /dev/null
+++ b/tests/unit/test_v2_parsers.py
@@ -0,0 +1,402 @@
+"""验证 V2 多格式 Parser 都映射到同一领域模型并保留格式特有位置。"""
+
+from io import BytesIO
+
+import pytest
+from docx import Document as WordDocument
+from openpyxl import Workbook
+from PIL import Image
+from pptx import Presentation
+from pptx.util import Inches
+
+from ultimate_rag.domain.models import BlockType, DocumentSource, SourceLocator
+from ultimate_rag.parsers import (
+ ExcelParser,
+ HtmlParser,
+ ImageOCRParser,
+ PDFParser,
+ PowerPointParser,
+ WordParser,
+)
+from ultimate_rag.parsers.pdf import _LayoutElement
+
+
+class StubOCRClient:
+ """返回固定识别文本并记录图片 MIME，避免单元测试访问百炼。"""
+
+ def __init__(self, text: str = "识别出的文字") -> None:
+ """保存固定结果和空调用记录。"""
+
+ self.text = text
+ self.mime_types: list[str] = []
+
+ async def extract_text(self, image: bytes, mime_type: str) -> str:
+ """验证 Parser 确实提交了非空图片，再返回确定性结果。"""
+
+ assert image
+ self.mime_types.append(mime_type)
+ return self.text
+
+
+class StubVisionClient:
+ """返回固定图像语义，证明 PDF 内嵌图片走视觉理解而非丢弃。"""
+
+ async def describe(self, image: bytes, mime_type: str, caption: str = "") -> str:
+ """验证图片、MIME 与题注均已越过版面适配边界。"""
+
+ assert image
+ assert mime_type == "image/jpeg"
+ assert caption == "系统架构"
+ return "架构图显示 API 指向 Worker。"
+
+
+class StubStandaloneVisionClient:
+ """返回独立图片中的箭头关系，覆盖 OCR 无法表达的视觉语义。"""
+
+ async def describe(self, image: bytes, mime_type: str, caption: str = "") -> str:
+ """验证独立 PNG 使用真实 MIME 且没有伪造题注。"""
+
+ assert image
+ assert mime_type == "image/png"
+ assert caption == ""
+ return "Encoder 通过箭头连接 Decoder。"
+
+
+class StubLayoutAnalyzer:
+ """以确定性元素替代重量 Docling 模型，单测只验证 Parser 编排和映射。"""
+
+ def __init__(self, elements: list[_LayoutElement]) -> None:
+ """保存应按阅读顺序返回的版面元素。"""
+
+ self.elements = elements
+ self.calls: list[frozenset[int]] = []
+
+ def analyze(
+ self,
+ content: bytes,
+ filename: str,
+ skipped_pages: frozenset[int],
+ ) -> list[_LayoutElement]:
+ """记录扫描页集合并返回固定结果，不访问本地模型或网络。"""
+
+ assert content
+ assert filename.endswith(".pdf")
+ self.calls.append(skipped_pages)
+ return self.elements
+
+
+def _source(filename: str, mime_type: str, content: bytes) -> DocumentSource:
+ """用统一文档 ID 构造 Parser 单元测试输入。"""
+
+ return DocumentSource("doc-v2", filename, mime_type, content)
+
+
+def _native_text_pdf(text: str) -> bytes:
+ """构造一个最小但带真实文字对象的 PDF，用于覆盖无 OCR 原生路径。"""
+
+ stream = f"BT /F1 24 Tf 72 720 Td ({text}) Tj ET".encode("ascii")
+ objects = [
+ b"<< /Type /Catalog /Pages 2 0 R >>",
+ b"<< /Type /Pages /Kids [3 0 R] /Count 1 >>",
+ (
+ b"<< /Type /Page /Parent 2 0 R /MediaBox [0 0 612 792] "
+ b"/Resources << /Font << /F1 4 0 R >> >> /Contents 5 0 R >>"
+ ),
+ b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica >>",
+ f"<< /Length {len(stream)} >>\nstream\n".encode() + stream + b"\nendstream",
+ ]
+ content = bytearray(b"%PDF-1.4\n%\xe2\xe3\xcf\xd3\n")
+ offsets = [0]
+ for number, value in enumerate(objects, start=1):
+ offsets.append(len(content))
+ content.extend(f"{number} 0 obj\n".encode() + value + b"\nendobj\n")
+ xref_offset = len(content)
+ content.extend(f"xref\n0 {len(objects) + 1}\n0000000000 65535 f \n".encode())
+ for offset in offsets[1:]:
+ content.extend(f"{offset:010d} 00000 n \n".encode())
+ content.extend(
+ (
+ f"trailer\n<< /Size {len(objects) + 1} /Root 1 0 R >>\n"
+ f"startxref\n{xref_offset}\n%%EOF\n"
+ ).encode()
+ )
+ return bytes(content)
+
+
+@pytest.mark.asyncio
+async def test_html_parser_extracts_visible_structure_and_ignores_script() -> None:
+ """HTML 标题、正文和表格应按 DOM 顺序进入领域 Block，脚本不得进入索引。"""
+
+ content = b"""
+ RAG Guide
Visible paragraph.
+
+ """
+
+ parsed = await HtmlParser().parse(_source("guide.html", "text/html", content))
+
+ assert [block.type for block in parsed.blocks] == [
+ BlockType.HEADING,
+ BlockType.TEXT,
+ BlockType.TABLE,
+ ]
+ assert parsed.blocks[1].locator is not None
+ assert parsed.blocks[1].locator.heading_path == ("RAG Guide",)
+ assert all("ignore this command" not in block.content for block in parsed.blocks)
+
+
+@pytest.mark.asyncio
+async def test_word_parser_preserves_heading_and_table() -> None:
+ """DOCX 标题路径、普通段落与表格应转换为统一 Block。"""
+
+ document = WordDocument()
+ document.add_heading("Enterprise RAG", level=1)
+ document.add_paragraph("A maintainable pipeline.")
+ table = document.add_table(rows=2, cols=2)
+ table.cell(0, 0).text = "Stage"
+ table.cell(0, 1).text = "State"
+ table.cell(1, 0).text = "Index"
+ table.cell(1, 1).text = "Ready"
+ buffer = BytesIO()
+ document.save(buffer)
+
+ parsed = await WordParser().parse(
+ _source(
+ "guide.docx",
+ "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+ buffer.getvalue(),
+ )
+ )
+
+ assert any(block.type == BlockType.TABLE for block in parsed.blocks)
+ paragraph = next(block for block in parsed.blocks if block.type == BlockType.TEXT)
+ assert paragraph.locator is not None
+ assert paragraph.locator.heading_path == ("Enterprise RAG",)
+
+
+@pytest.mark.asyncio
+async def test_excel_parser_preserves_sheet_and_cell_range() -> None:
+ """XLSX 每个表格窗口必须携带工作表名称与单元格范围。"""
+
+ workbook = Workbook()
+ sheet = workbook.active
+ sheet.title = "Metrics"
+ sheet.append(["Metric", "Value"])
+ sheet.append(["Recall", 0.92])
+ buffer = BytesIO()
+ workbook.save(buffer)
+
+ parsed = await ExcelParser().parse(
+ _source(
+ "metrics.xlsx",
+ "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+ buffer.getvalue(),
+ )
+ )
+
+ assert len(parsed.blocks) == 1
+ locator = parsed.blocks[0].locator
+ assert locator is not None
+ assert locator.sheet == "Metrics"
+ assert locator.cell_range == "A1:B2"
+ assert "Recall" in parsed.blocks[0].content
+
+
+@pytest.mark.asyncio
+async def test_powerpoint_parser_preserves_slide_number() -> None:
+ """PPTX 标题和正文应保留一基幻灯片序号。"""
+
+ presentation = Presentation()
+ slide = presentation.slides.add_slide(presentation.slide_layouts[1])
+ slide.shapes.title.text = "Architecture"
+ text_box = slide.shapes.add_textbox(Inches(1), Inches(2), Inches(5), Inches(1))
+ text_box.text = "Parse then chunk."
+ buffer = BytesIO()
+ presentation.save(buffer)
+
+ parsed = await PowerPointParser().parse(
+ _source(
+ "architecture.pptx",
+ "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+ buffer.getvalue(),
+ )
+ )
+
+ assert parsed.blocks
+ assert all(block.locator and block.locator.slide == 1 for block in parsed.blocks)
+
+
+@pytest.mark.asyncio
+async def test_image_parser_validates_format_before_ocr() -> None:
+ """图片 Parser 应向 OCR 提交由真实编码推导的 MIME，而不是盲信客户端。"""
+
+ buffer = BytesIO()
+ Image.new("RGB", (80, 40), "white").save(buffer, format="PNG")
+ ocr = StubOCRClient()
+
+ parsed = await ImageOCRParser(ocr).parse(
+ _source("scan.png", "application/octet-stream", buffer.getvalue())
+ )
+
+ assert parsed.blocks[0].type == BlockType.IMAGE
+ assert parsed.blocks[0].content == "识别出的文字"
+ assert ocr.mime_types == ["image/png"]
+
+
+@pytest.mark.asyncio
+async def test_image_parser_fuses_ocr_text_and_visual_relationships() -> None:
+ """示意图应同时保留精确标签与箭头关系，不能只索引零散 OCR 文字。"""
+
+ buffer = BytesIO()
+ Image.new("RGB", (80, 40), "white").save(buffer, format="PNG")
+
+ parsed = await ImageOCRParser(
+ StubOCRClient("Encoder\nDecoder"),
+ StubStandaloneVisionClient(),
+ ).parse(_source("diagram.png", "image/png", buffer.getvalue()))
+
+ assert "## OCR 文本" in parsed.blocks[0].content
+ assert "## 视觉结构" in parsed.blocks[0].content
+ assert "Encoder 通过箭头连接 Decoder" in parsed.blocks[0].content
+ assert parsed.blocks[0].metadata["extraction"] == "bailian_ocr+bailian_vision"
+
+
+@pytest.mark.asyncio
+async def test_pdf_parser_ocr_scan_page_and_keeps_page_number() -> None:
+ """无文本扫描 PDF 应渲染页面执行 OCR，并把一基页码带到 Block。"""
+
+ buffer = BytesIO()
+ Image.new("RGB", (240, 120), "white").save(buffer, format="PDF")
+ ocr = StubOCRClient("扫描页内容")
+
+ parsed = await PDFParser(ocr).parse(_source("scan.pdf", "application/pdf", buffer.getvalue()))
+
+ assert parsed.blocks[0].type == BlockType.IMAGE
+ assert parsed.blocks[0].locator is not None
+ assert parsed.blocks[0].locator.page == 1
+ assert parsed.metadata["ocr_page_count"] == 1
+ assert ocr.mime_types == ["image/jpeg"]
+
+
+@pytest.mark.asyncio
+async def test_pdf_parser_uses_native_text_without_ocr() -> None:
+ """文字型 PDF 达到阈值时必须直接提取，避免不必要的付费 OCR。"""
+
+ ocr = StubOCRClient()
+ layout = StubLayoutAnalyzer(
+ [
+ _LayoutElement(
+ 0,
+ 1,
+ BlockType.TEXT,
+ "UltimateRAG native PDF text",
+ SourceLocator(page=1, bbox=(10, 20, 300, 60)),
+ )
+ ]
+ )
+
+ parsed = await PDFParser(
+ ocr,
+ native_text_threshold=10,
+ layout_analyzer=layout,
+ ).parse(
+ _source(
+ "native.pdf",
+ "application/pdf",
+ _native_text_pdf("UltimateRAG native PDF text"),
+ )
+ )
+
+ assert parsed.blocks[0].type == BlockType.TEXT
+ assert "UltimateRAG native PDF text" in parsed.blocks[0].content
+ assert parsed.blocks[0].locator is not None and parsed.blocks[0].locator.page == 1
+ assert parsed.metadata["ocr_page_count"] == 0
+ assert ocr.mime_types == []
+ assert layout.calls == [frozenset()]
+
+
+@pytest.mark.asyncio
+async def test_pdf_parser_does_not_treat_short_vector_page_as_scan() -> None:
+ """原生文字少但没有整页栅格图时仍应走 Layout，避免图文页退化为纯 OCR。"""
+
+ ocr = StubOCRClient()
+ layout = StubLayoutAnalyzer(
+ [
+ _LayoutElement(
+ 0,
+ 1,
+ BlockType.TEXT,
+ "Short vector page",
+ SourceLocator(page=1, bbox=(10, 20, 180, 50)),
+ )
+ ]
+ )
+
+ parsed = await PDFParser(ocr, native_text_threshold=20, layout_analyzer=layout).parse(
+ _source("short.pdf", "application/pdf", _native_text_pdf("Short"))
+ )
+
+ assert parsed.blocks[0].content == "Short vector page"
+ assert parsed.metadata["scan_page_count"] == 0
+ assert ocr.mime_types == []
+ assert layout.calls == [frozenset()]
+
+
+@pytest.mark.asyncio
+async def test_pdf_parser_preserves_layout_table_picture_and_bbox() -> None:
+ """复杂文字 PDF 的阅读顺序、表格、图片语义与 BBox 应全部进入统一 Block。"""
+
+ image = BytesIO()
+ Image.new("RGB", (120, 80), "white").save(image, format="JPEG")
+ layout = StubLayoutAnalyzer(
+ [
+ _LayoutElement(
+ 0,
+ 1,
+ BlockType.HEADING,
+ "架构",
+ SourceLocator(heading_path=("架构",), page=1, bbox=(10, 10, 200, 40)),
+ ),
+ _LayoutElement(
+ 1,
+ 1,
+ BlockType.TABLE,
+ "| 组件 | 职责 |\n| --- | --- |\n| Worker | 解析 |",
+ SourceLocator(heading_path=("架构",), page=1, bbox=(10, 50, 300, 150)),
+ ),
+ _LayoutElement(
+ 2,
+ 1,
+ BlockType.IMAGE,
+ "",
+ SourceLocator(heading_path=("架构",), page=1, bbox=(10, 160, 300, 300)),
+ image=image.getvalue(),
+ caption="系统架构",
+ ),
+ ]
+ )
+
+ parsed = await PDFParser(
+ StubOCRClient(),
+ StubVisionClient(),
+ native_text_threshold=10,
+ layout_analyzer=layout,
+ ).parse(
+ _source(
+ "complex.pdf",
+ "application/pdf",
+ _native_text_pdf("Complex native PDF with enough text"),
+ )
+ )
+
+ assert [block.type for block in parsed.blocks] == [
+ BlockType.HEADING,
+ BlockType.TABLE,
+ BlockType.IMAGE,
+ ]
+ assert parsed.blocks[1].locator is not None
+ assert parsed.blocks[1].locator.bbox == (10, 50, 300, 150)
+ assert parsed.blocks[2].content == (
+ "图片题注：系统架构\n\n架构图显示 API 指向 Worker。"
+ )
+ assert parsed.blocks[2].metadata["extraction"] == "bailian_vision"
+ assert parsed.metadata["table_count"] == 1
diff --git a/tests/unit/test_worker.py b/tests/unit/test_worker.py
new file mode 100644
index 0000000..e87f871
--- /dev/null
+++ b/tests/unit/test_worker.py
@@ -0,0 +1,83 @@
+"""验证后台 Worker 的任务提交、不可重试错误和清理语义。"""
+
+from datetime import UTC, datetime
+from typing import cast
+from unittest.mock import AsyncMock
+
+import pytest
+
+from ultimate_rag.application import DocumentProcessingService
+from ultimate_rag.domain.exceptions import InvalidDocumentError
+from ultimate_rag.domain.models import IngestionJob, IngestionJobStatus
+from ultimate_rag.infrastructure.database.repository import Repository
+from ultimate_rag.worker import IngestionWorker
+
+
+def _job() -> IngestionJob:
+ """构造一次已经由测试 Worker 领取的任务快照。"""
+
+ now = datetime.now(UTC)
+ return IngestionJob(
+ id="job-1",
+ document_id="doc-1",
+ status=IngestionJobStatus.RUNNING,
+ attempts=1,
+ max_attempts=3,
+ available_at=now,
+ locked_at=now,
+ worker_id="worker-1",
+ error_message=None,
+ created_at=now,
+ updated_at=now,
+ )
+
+
+def _worker(repository: AsyncMock, processor: AsyncMock) -> IngestionWorker:
+ """以长心跳间隔构造单次测试 Worker，避免测试产生真实等待。"""
+
+ return IngestionWorker(
+ repository=cast(Repository, repository),
+ processor=cast(DocumentProcessingService, processor),
+ worker_id="worker-1",
+ poll_interval=0.1,
+ lease_seconds=900,
+ heartbeat_seconds=300,
+ retry_delay_seconds=10,
+ )
+
+
+@pytest.mark.asyncio
+async def test_worker_completes_job_after_document_is_ready() -> None:
+ """处理服务成功后才可提交任务完成，且不应执行失败清理。"""
+
+ repository = AsyncMock()
+ repository.claim_ingestion_job.return_value = _job()
+ processor = AsyncMock()
+
+ assert await _worker(repository, processor).run_once() is True
+
+ processor.process.assert_awaited_once_with("doc-1")
+ repository.complete_ingestion_job.assert_awaited_once_with("job-1", "worker-1")
+ processor.cleanup_partial_index.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_worker_does_not_retry_invalid_document() -> None:
+ """损坏或非法文件不会因重试而变好，应清理半成品并直接进入终态失败。"""
+
+ repository = AsyncMock()
+ repository.claim_ingestion_job.return_value = _job()
+ repository.fail_ingestion_job.return_value = False
+ processor = AsyncMock()
+ processor.process.side_effect = InvalidDocumentError("PDF 文件损坏")
+
+ assert await _worker(repository, processor).run_once() is True
+
+ processor.cleanup_partial_index.assert_awaited_once_with("doc-1")
+ repository.fail_ingestion_job.assert_awaited_once_with(
+ "job-1",
+ "worker-1",
+ error_message="PDF 文件损坏",
+ retryable=False,
+ retry_delay_seconds=10,
+ )
diff --git a/uv.lock b/uv.lock
index 8646387..5868c26 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4,13 +4,35 @@ requires-python = ">=3.12"
resolution-markers = [
"python_full_version >= '3.15' and sys_platform == 'win32'",
"python_full_version >= '3.15' and sys_platform == 'emscripten'",
- "python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version >= '3.15' and sys_platform == 'darwin'",
+ "python_full_version >= '3.15' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
"python_full_version == '3.14.*' and sys_platform == 'win32'",
"python_full_version == '3.14.*' and sys_platform == 'emscripten'",
- "python_full_version == '3.14.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version == '3.14.*' and sys_platform == 'darwin'",
+ "python_full_version == '3.14.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
"python_full_version < '3.14' and sys_platform == 'win32'",
"python_full_version < '3.14' and sys_platform == 'emscripten'",
- "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version < '3.14' and sys_platform == 'darwin'",
+ "python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "accelerate"
+version = "1.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "huggingface-hub" },
+ { name = "numpy" },
+ { name = "packaging" },
+ { name = "psutil" },
+ { name = "pyyaml" },
+ { name = "safetensors" },
+ { name = "torch", version = "2.12.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "python_full_version < '3.15' and sys_platform == 'darwin'" },
+ { name = "torch", version = "2.12.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "python_full_version >= '3.15' or sys_platform != 'darwin'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8d/75/94cd5d389649578aca399e5aa822637eec18319a1dadc400ffe2f9a7493f/accelerate-1.14.0.tar.gz", hash = "sha256:41b9c4377a54e0b460a959b0defa1b736e4ca0a2373252d9a539964c2afe3c8d", size = 412167, upload-time = "2026-06-11T13:45:52.326Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/a8/db/253133d7e7cb40d3af384bb2f5c0b4a2b7fdcffbc95c688cc67a20a3c103/accelerate-1.14.0-py3-none-any.whl", hash = "sha256:e94390c2863b873be18f623f9df48a0d8fe5eff13ea7f1a00092b0a7904888c6", size = 389246, upload-time = "2026-06-11T13:45:50.477Z" },
]
[[package]]
@@ -45,6 +67,12 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
]
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" }
+
[[package]]
name = "anyio"
version = "4.14.2"
@@ -153,6 +181,28 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/3c/d7/8fb3044eaef08a310acfe23dae9a8e2e07d305edc29a53497e52bc76eca7/asyncpg-0.31.0-cp314-cp314t-win_amd64.whl", hash = "sha256:bd4107bb7cdd0e9e65fae66a62afd3a249663b844fa34d479f6d5b3bef9c04c3", size = 706062, upload-time = "2025-11-24T23:26:44.086Z" },
]
+[[package]]
+name = "attrs"
+version = "26.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055, upload-time = "2026-03-19T14:22:25.026Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" },
+]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.15.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "soupsieve" },
+ { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/43/65/318323f98dbee45d42dff61d8f047181bc6f2268a9068cfad035a46be5af/beautifulsoup4-4.15.0.tar.gz", hash = "sha256:288e3ca7d54b06f2ac191970bc275c1939cb46d450b255bf6718b04aa37ab4f7", size = 632571, upload-time = "2026-06-07T16:44:20.453Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/88/c6/92fcd42f1ba33e1184263f25bfabf3d27c383410470f169e4b8163bf9c17/beautifulsoup4-4.15.0-py3-none-any.whl", hash = "sha256:d6f88de62e1d4e38ecb1077eb9724cd0eff29d2a08ca16a401e9b9e93f117cf9", size = 109924, upload-time = "2026-06-07T16:44:21.566Z" },
+]
+
[[package]]
name = "cachetools"
version = "7.1.7"
@@ -405,6 +455,36 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
]
+[[package]]
+name = "colorlog"
+version = "6.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8c/55/ba79756cb90c8d69d599d57785398ac87bba7b19c80e87f4e8a562197c93/colorlog-6.12.0.tar.gz", hash = "sha256:2a7924c1dadf18b22a0eb8b06d1c7b01d5341707ec1641eb6fcc4fde0c3e8e5f", size = 18151, upload-time = "2026-07-23T13:40:40.71Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/d4/19/0b6647bf5e331521e55d2b63bfbdc210bd9cd605189273f03614a05f702d/colorlog-6.12.0-py3-none-any.whl", hash = "sha256:30d392604e9110045a2c2aeefc27d7a017abbab63f3a8aee594eac0801df784e", size = 12239, upload-time = "2026-07-23T13:40:39.562Z" },
+]
+
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
+]
+
+[[package]]
+name = "dill"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315, upload-time = "2026-01-19T02:36:56.85Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019, upload-time = "2026-01-19T02:36:55.663Z" },
+]
+
[[package]]
name = "distro"
version = "1.9.0"
@@ -414,6 +494,191 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
]
+[[package]]
+name = "doclang"
+version = "0.7.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "lxml" },
+ { name = "typer" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f5/3a/005e4856ad8e9b9879414a4df4dbc56dc3663b96f9d8c920ef210e8931cf/doclang-0.7.3.tar.gz", hash = "sha256:ca50615357e46ebf9597bb9065b9112367103ec24bd539f8ae12649224cf50b0", size = 31569, upload-time = "2026-07-15T08:11:02.917Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/a5/81/334ccc0f0cd7c3d75996b6b596e7f4c62c4c46a0ca042003315c28170159/doclang-0.7.3-py3-none-any.whl", hash = "sha256:9440c4ca9f7e061a7b8d33bdf15b1029be69a4c13cd8952dd6ce541884e4c685", size = 32267, upload-time = "2026-07-15T08:11:01.977Z" },
+]
+
+[[package]]
+name = "docling"
+version = "2.123.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "docling-slim", extra = ["standard"] },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dc/44/0b1c143a5156c170ee1247b60e7272d4b6e1b1ef07e6c7e14c6129338888/docling-2.123.1.tar.gz", hash = "sha256:6ded4775206b40a8959956d0e17c5e6f6fbe9425a6e4eb12e6032a40e5796853", size = 9068, upload-time = "2026-08-28T15:15:45.899Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/d2/e2/21defca766bffa7c93c7dfee2a32a098121dbd734307b170a6592676f48c/docling-2.123.1-py3-none-any.whl", hash = "sha256:ec012ac0cd6bfc97b357f6aedd33aadbbc9a23ba08e5cee598081baff756030e", size = 5226, upload-time = "2026-08-28T15:15:44.521Z" },
+]
+
+[[package]]
+name = "docling-core"
+version = "2.92.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "defusedxml" },
+ { name = "doclang" },
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