From 34d72d7ce93729152fd04163ee13679cc5d5e7ce Mon Sep 17 00:00:00 2001 From: ash66 Date: Tue, 26 May 2026 10:08:56 +0800 Subject: [PATCH] feat(document): add database and process documents --- .../document-core-processing-flow.md | 623 ++++++++ .../document-processing-database-design.md | 508 ++++++ pyproject.toml | 1 + requirements.txt | 1 + uv.lock | 1397 +++++++++-------- 5 files changed, 1886 insertions(+), 644 deletions(-) create mode 100644 docs/architecture/document-core-processing-flow.md create mode 100644 docs/architecture/document-processing-database-design.md diff --git a/docs/architecture/document-core-processing-flow.md b/docs/architecture/document-core-processing-flow.md new file mode 100644 index 0000000..edc5b9e --- /dev/null +++ b/docs/architecture/document-core-processing-flow.md @@ -0,0 +1,623 @@ +# 核心文档处理主链路说明 + +本文件说明当前默认生产链路中的核心文档处理流程，也就是： + +- `AliyunDocumentParser` +- `AliyunVectorChunkBuilder` +- `OpenAICompatibleEmbeddingProvider` +- `MilvusVectorIndex` + +目标是回答四个核心问题： + +1. `ParsedDocument` 为什么是多层结构 +2. 这些结构分别保存到哪里 +3. 哪一步才真正做了向量化 +4. Milvus 里最后到底存的是什么 + +数据库表设计、关系模型、DDL 和 PostgreSQL 职责边界已经单独整理到 [document-processing-database-design.md](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/docs/architecture/document-processing-database-design.md:1)。本文件保留流程视角，只在必要处给出与存储分工相关的摘要，不再作为数据库设计 authority。 + +## 1. 主链路总览 + +当前默认实现由 `DocumentCommandService.upload_and_process()` 统一编排。它不是“parse 完直接进向量库”，而是先生成结构化解析产物，再把其中适合检索的一层送去 embedding 和 Milvus。 + +```mermaid +sequenceDiagram + participant API as API / Service + participant MinIO as BinaryStore + participant Parser as AliyunDocumentParser + participant PG as DocumentRepository / ParseArtifactStore + participant Embed as EmbeddingProvider + participant Milvus as VectorIndex + + API->>MinIO: 保存原始文件 + API->>Parser: parse(file_path, doc_id, doc_name) + Parser-->>API: ParsedDocument + API->>MinIO: 保存 layouts/structure_nodes/semantic_blocks/vector_chunks JSON + API->>PG: 更新 documents.status=parsed + API->>PG: 保存 structure_nodes / semantic_blocks + API->>API: chunk_builder.build(parsed_document) + API->>Embed: embed_texts([chunk.embedding_text]) + Embed-->>API: vectors + API->>Milvus: upsert(chunks, vectors) + API->>PG: 更新 documents.status=indexed +``` + +主链路编排代码在 [services.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/application/documents/services.py:83)： + +```python +def upload_and_process( + self, + *, + doc_id: str | None = None, + file_name: str, + content: bytes, + content_type: str, + doc_name: str | None, + regulation_type: str, + version: str, + generate_summary: bool, +) -> DocumentProcessResult: + doc_id = doc_id or str(uuid.uuid4())[:8] + final_doc_name = doc_name or file_name + object_name = f"{doc_id}/{file_name}" + + self.document_repository.create(document) + self.binary_store.save(object_name=object_name, data=content, content_type=content_type, metadata={"doc_id": doc_id}) + self.document_repository.update_status(doc_id, DocumentStatus.STORED) + + parsed_document = self.parser.parse(file_path=temp_path, doc_id=doc_id, doc_name=final_doc_name) + artifact_keys = self._save_parse_artifacts(doc_id=doc_id, parsed_document=parsed_document) + self.document_repository.update_status(doc_id, DocumentStatus.PARSED, parser_name=parsed_document.parser_name, metadata={...}) + + if self.parse_artifact_store: + self.parse_artifact_store.save(doc_id, parsed_document.structure_nodes, parsed_document.semantic_blocks) + + chunks = self.chunk_builder.build(parsed_document=parsed_document, regulation_type=regulation_type, version=version) + vectors = self.embedding_provider.embed_texts([chunk.embedding_text for chunk in chunks]) + inserted = self.vector_index.upsert(chunks, vectors) + + self.document_repository.update_status(doc_id, DocumentStatus.INDEXED, chunk_count=len(chunks), index_name=health.get("collection_name", ""), metadata={...}) +``` + +默认绑定关系在 [bootstrap.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/shared/bootstrap.py:157)： + +```python +def get_parser(): + if settings.parser_backend == "aliyun": + return AliyunDocumentParser() + return LocalDocumentParser() + + +def get_chunk_builder(): + if settings.chunk_backend == "aliyun": + return AliyunVectorChunkBuilder() + return LocalRegulationChunkBuilder(...) + + +def get_embedding_provider() -> OpenAICompatibleEmbeddingProvider: + return OpenAICompatibleEmbeddingProvider() + + +def get_vector_index() -> VectorIndex: + return LazyVectorIndex(_build_vector_index) +``` + +也就是说，当前默认主链路是： + +- parser: `AliyunDocumentParser` +- chunk builder: `AliyunVectorChunkBuilder` +- embedding provider: `OpenAICompatibleEmbeddingProvider` +- vector index: `MilvusVectorIndex` + +## 2. `ParsedDocument` 为什么是三层结构 + +`ParsedDocument` 不是最终入库格式，而是 parser 输出给后续处理步骤的统一中间结构。它把“结构理解”和“向量检索准备”拆成了三层。 + +定义在 [models.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/domain/documents/models.py:49)： + +```python +@dataclass +class ParsedDocument: + doc_id: str + doc_name: str + structure_nodes: list[dict[str, Any]] + semantic_blocks: list[dict[str, Any]] + vector_chunks: list[dict[str, Any]] + parser_name: str + raw_text: str = "" + raw_layouts: list[dict[str, Any]] = field(default_factory=list) + metadata: dict[str, Any] = field(default_factory=dict) +``` + +这三层的职责不同： + +- `structure_nodes` + - 标题层级骨架 + - 描述“文档有哪些章、节、条” + - 用于保留结构，不直接做 embedding + +- `semantic_blocks` + - 语义块层 + - 把正文、表格、图片说明整理成连续的语义单元 + - 是从原始 layout 到检索 chunk 之间的中间层 + +- `vector_chunks` + - 检索和向量化层 + - 已经是适合送给 embedding 模型的 chunk 视图 + - 后续 `ChunkBuilder` 基本就是把这层映射成统一 `Chunk` + +### 2.1 这三层是怎么从 parser 结果生成的 + +`AliyunDocumentParser.parse()` 先通过网关拿到阿里云返回的 `layouts`，再把 `layouts` 转成三层结构。 + +代码在 [aliyun_document_parser.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/parser/aliyun_document_parser.py:28)： + +```python +def parse(self, *, file_path: str, doc_id: str, doc_name: str) -> ParsedDocument: + payload = self.gateway.parse_document(file_path=file_path) + layouts = payload.layouts + structure_nodes = build_structure_nodes(layouts) + semantic_blocks = build_semantic_blocks(layouts) + vector_chunks = build_vector_chunks( + semantic_blocks, + doc_id=doc_id, + doc_title=doc_name, + max_chars=MAX_CHARS, + overlap_chars=OVERLAP_CHARS, + ) + raw_text = "\n\n".join( + block.get("text", "") + for block in semantic_blocks + if block.get("text") + ) + return ParsedDocument( + doc_id=doc_id, + doc_name=doc_name, + structure_nodes=structure_nodes, + semantic_blocks=semantic_blocks, + vector_chunks=vector_chunks, + parser_name=self.parser_name, + raw_text=raw_text, + raw_layouts=layouts, + metadata={...}, + ) +``` + +也就是说： + +- parser 原始输出是 `layouts` +- 当前系统真正消费的是 `ParsedDocument` +- `ParsedDocument` 是由 normalizer 从 `layouts` 规整出来的 + +### 2.2 第一层：`structure_nodes` + +这一层只保留标题和层级。 + +代码在 [aliyun_layout_normalizer.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/parser/aliyun_layout_normalizer.py:85)： + +```python +def build_structure_nodes(layouts: list[dict[str, Any]]) -> list[dict[str, Any]]: + nodes: list[dict[str, Any]] = [] + for layout in layouts: + if not is_title(layout): + continue + text = get_text(layout) + if not text or text in TOC_TITLES: + continue + nodes.append( + { + "unique_id": layout.get("uniqueId"), + "page": get_page(layout), + "index": layout.get("index", 0), + "level": layout.get("level", 0), + "title": text, + "type": layout.get("type"), + "sub_type": layout.get("subType"), + } + ) + return nodes +``` + +示例： + +```json +[ + { + "unique_id": "l-title-001", + "page": 2, + "index": 11, + "level": 1, + "title": "1 范围", + "type": "title", + "sub_type": "para_title" + }, + { + "unique_id": "l-title-002", + "page": 3, + "index": 18, + "level": 2, + "title": "1.1 适用对象", + "type": "title", + "sub_type": "para_title" + } +] +``` + +这层的意义是“保留目录树”，不是直接拿来检索。 + +### 2.3 第二层：`semantic_blocks` + +这一层会把连续正文合并成一个语义块，也会单独处理表格和图片说明。 + +代码在 [aliyun_layout_normalizer.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/parser/aliyun_layout_normalizer.py:163)： + +```python +def build_semantic_blocks(layouts: list[dict[str, Any]]) -> list[dict[str, Any]]: + semantic_blocks: list[dict[str, Any]] = [] + section_stack: list[dict[str, Any]] = [] + pending_text_blocks: list[dict[str, Any]] = [] + block_id = 1 + + for layout in layouts: + text = get_text(layout) + page = get_page(layout) + + if is_title(layout): + block_id = flush_text_block(pending_text_blocks, semantic_blocks, block_id) + pending_text_blocks = [] + section_stack = update_section_path(section_stack, layout) + continue + + section_path = section_path_titles(section_stack) + section_title = section_path[-1] if section_path else "未分类" + section_level = len(section_path) + + if is_table(layout): + ... + semantic_blocks.append( + { + "semantic_id": f"semantic-{block_id}", + "block_type": "table", + "page_start": page, + "page_end": page, + "section_path": section_path, + "section_level": section_level, + "section_title": section_title, + "source_ids": [layout.get("uniqueId")], + "text": table_text, + } + ) + continue + + if is_text(layout) and text: + pending_text_blocks.append( + { + "page": page, + "text": text, + "unique_id": layout.get("uniqueId"), + "section_path": section_path, + "section_level": section_level, + "section_title": section_title, + } + ) +``` + +正文合并后会形成类似这样的语义块： + +```json +[ + { + "semantic_id": "semantic-1", + "block_type": "section_text", + "page_start": 2, + "page_end": 2, + "section_path": ["1 范围", "1.1 适用对象"], + "section_level": 2, + "section_title": "1.1 适用对象", + "source_ids": ["l-text-001", "l-text-002"], + "text": "本标准适用于道路车辆动力电池系统的安全要求。企业应建立一致的测试和验证方法。" + } +] +``` + +## 3. 这些结构分别保存到哪里 + +### 3.1 原始文件和中间 artifacts 先落 MinIO + +当前链路在上传阶段会先把原始文件保存到对象存储；解析完成后，又会把结构化中间产物保存为 JSON。 + +保存 artifacts 的代码在 [services.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/application/documents/services.py:62)： + +```python +def _save_parse_artifacts(self, *, doc_id: str, parsed_document: ParsedDocument) -> dict[str, str]: + prefix = f"{parsed_document.metadata.get('artifact_prefix', 'artifacts').strip('/')}/{doc_id}" + artifact_payloads = { + "layouts": parsed_document.raw_layouts, + "structure_nodes": parsed_document.structure_nodes, + "semantic_blocks": parsed_document.semantic_blocks, + "vector_chunks": parsed_document.vector_chunks, + } + artifact_keys: dict[str, str] = {} + for name, payload in artifact_payloads.items(): + object_name = f"{prefix}/{name}.json" + self.binary_store.save( + object_name=object_name, + data=json.dumps(payload, ensure_ascii=False, indent=2).encode("utf-8"), + content_type="application/json", + metadata={"doc_id": doc_id, "artifact_type": name}, + ) + artifact_keys[name] = object_name + return artifact_keys +``` + +`DocumentBinaryStore` 的当前默认实现是 `MinioDocumentBinaryStore`，也就是： + +- 原始上传文件进 MinIO +- `layouts.json` 进 MinIO +- `structure_nodes.json` 进 MinIO +- `semantic_blocks.json` 进 MinIO +- `vector_chunks.json` 进 MinIO + +### 3.2 PostgreSQL 在流程中的职责摘要 + +当前流程中，PostgreSQL 承担的是“文档元数据 + 结构化快照”的职责，而不是向量或大对象存储： + +- `documents` 保存当前文档主记录、状态、统计和索引信息 +- `structure_nodes` 保存当前最新解析快照的目录结构 +- `semantic_blocks` 保存当前最新解析快照的语义块结构 + +更完整的 PostgreSQL 设计，包括： + +- `documents` +- `document_processing_runs` +- `document_status_history` +- `document_artifacts` +- `structure_nodes` +- `semantic_blocks` + +见 [document-processing-database-design.md](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/docs/architecture/document-processing-database-design.md:1)。 + +### 3.3 存储分工一览 + +| 数据层 | 保存位置 | 是否直接用于 embedding | 是否最终进入 Milvus | 主要用途 | +| --- | --- | --- | --- | --- | +| 原始文件 | MinIO | 否 | 否 | 保留原始上传文档 | +| `raw_layouts` | MinIO `layouts.json` | 否 | 否 | 保留 parser 原始返回 | +| `structure_nodes` | MinIO + PostgreSQL | 否 | 否 | 目录树、层级结构 | +| `semantic_blocks` | MinIO + PostgreSQL | 否 | 间接 | 语义单元、中间层 | +| `vector_chunks` | MinIO | 是 | 间接 | embedding 前的检索块 | +| `Chunk` | 内存态 + Milvus | 是 | 是 | 统一向量入库模型 | +| `documents` 元数据 | PostgreSQL | 否 | 否 | 处理状态、统计、索引信息 | + +## 4. 哪一步才真正“变成向量” + +这是整个流程最关键的点。 + +结论先说清楚： + +- parse 不做向量化 +- 保存 artifacts 不做向量化 +- `ChunkBuilder.build()` 也不做向量化 +- 只有 `EmbeddingProvider.embed_texts()` 才真正调用 embedding 模型 +- 只有 `VectorIndex.upsert()` 才真正把向量写入向量库 + +### 4.1 `vector_chunks` 先被映射成统一 `Chunk` + +`AliyunVectorChunkBuilder` 并不做 embedding，它只负责把 `ParsedDocument.vector_chunks` 转成领域层统一 `Chunk` 模型。 + +代码在 [vector_chunk_builder.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/parser/vector_chunk_builder.py:12)： + +```python +def build( + self, + *, + parsed_document: ParsedDocument, + regulation_type: str, + version: str, +) -> list[Chunk]: + chunks: list[Chunk] = [] + for index, item in enumerate(parsed_document.vector_chunks): + content = item.get("content") or item.get("text") or "" + embedding_text = item.get("embedding_text") or content + if not embedding_text.strip(): + continue + section_path = item.get("section_path") or [] + section_title = item.get("section_title") or (section_path[-1] if section_path else "") + page_number = item.get("page_start") or item.get("page") or 0 + chunk_id = item.get("chunk_id") or f"{parsed_document.doc_id}-chunk-{index}" + metadata = {k: v for k, v in item.items() if k not in {"content", "embedding_text"}} + chunks.append( + Chunk( + chunk_id=str(chunk_id), + doc_id=parsed_document.doc_id, + doc_name=parsed_document.doc_name, + content=content, + embedding_text=embedding_text, + section_title=section_title, + section_path=section_path, + page_number=int(page_number or 0), + regulation_type=regulation_type, + version=version, + semantic_id=item.get("semantic_id", ""), + block_type=item.get("block_type", ""), + metadata=metadata, + ) + ) + return chunks +``` + +`Chunk` 的定义在 [models.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/domain/documents/models.py:63)： + +```python +@dataclass +class Chunk: + chunk_id: str + doc_id: str + doc_name: str + content: str + embedding_text: str + section_title: str = "" + section_path: list[str] = field(default_factory=list) + page_number: int = 0 + regulation_type: str = "" + version: str = "" + semantic_id: str = "" + block_type: str = "" + metadata: dict[str, Any] = field(default_factory=dict) +``` + +一个 `Chunk` 的典型样子如下： + +```json +{ + "chunk_id": "doc-001-chunk-1", + "doc_id": "doc-001", + "doc_name": "动力电池安全规范", + "content": "本标准适用于道路车辆动力电池系统的安全要求。企业应建立一致的测试和验证方法。", + "embedding_text": "标准：动力电池安全规范\n章节：1 范围 > 1.1 适用对象\n\n本标准适用于道路车辆动力电池系统的安全要求。企业应建立一致的测试和验证方法。", + "section_title": "1.1 适用对象", + "section_path": ["1 范围", "1.1 适用对象"], + "page_number": 2, + "regulation_type": "GB", + "version": "2025", + "semantic_id": "semantic-1", + "block_type": "section_text", + "metadata": { + "chunk_index": 1, + "piece_index": 1, + "source_ids": ["l-text-001", "l-text-002"] + } +} +``` + +这里最关键的是要区分两个字段： + +- `content` + - 用于检索命中后的展示内容 + - 更接近用户最终看到的正文片段 + +- `embedding_text` + - 用于送给 embedding 模型 + - 比 `content` 多了“标准名 + 章节路径”的上下文 + +所以“向量化输入”不是纯正文，而是增强后的上下文文本。 + +### 4.2 真正调用 embedding API 的地方 + +真正把文本变成向量的是 `OpenAICompatibleEmbeddingProvider.embed_texts()`。 + +代码在 [openai_compatible_embedding_provider.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/embedding/openai_compatible_embedding_provider.py:64)： + +```python +def embed_texts(self, texts: list[str]) -> list[list[float]]: + if not texts: + return [] +``` + +也就是说，只有在这一步： + +- 输入：`list[str]` 的 `embedding_text` +- 输出：`list[list[float]]` 的 dense vectors + +前面的 parse、normalizer、chunk builder 都只是准备文本，没有任何向量值产生。 + +### 4.3 真正把向量写进 Milvus 的地方 + +向量值生成之后，`MilvusVectorIndex.upsert()` 才会把 `Chunk + vector` 写入向量库。 + +代码在 [milvus_vector_index.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/vectorstore/milvus_vector_index.py:69)： + +```python +def upsert(self, chunks: list[Chunk], vectors: list[list[float]]) -> int: + if len(chunks) != len(vectors): + raise ValueError("chunks 与 vectors 数量不一致") + data = [] + now = int(time.time()) + for chunk, vector in zip(chunks, vectors): + data.append( + { + "id": chunk.chunk_id, + "doc_id": chunk.doc_id, + "doc_name": chunk.doc_name, + "content": chunk.content[:65535], + "embedding": vector, + "section_title": chunk.section_title[:512], + "section_path": json.dumps(chunk.section_path, ensure_ascii=False)[:4096], + "page_number": chunk.page_number, + "regulation_type": chunk.regulation_type[:128], + "version": chunk.version[:64], + "semantic_id": chunk.semantic_id[:128], + "block_type": chunk.block_type[:64], + "metadata_json": json.dumps(chunk.metadata, ensure_ascii=False)[:65535], + "created_at": now, + } + ) + self.collection.insert(data) + self.collection.flush() + return len(data) +``` + +也就是说，Milvus 最终存进去的是： + +- 主键：`chunk_id` +- 文档维度字段：`doc_id`、`doc_name` +- 检索展示字段：`content` +- 向量字段：`embedding` +- 过滤/回溯字段：`section_title`、`section_path`、`page_number`、`regulation_type`、`version`、`semantic_id`、`block_type` +- 附加元数据：`metadata_json` + +## 5. Milvus 里最后到底存的是什么 + +### 5.1 Collection schema + +当前 `MilvusVectorIndex` 初始化 collection 时定义的 schema 在 [milvus_vector_index.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/infrastructure/vectorstore/milvus_vector_index.py:37)： + +```python +schema = CollectionSchema( + fields=[ + FieldSchema(name="id", dtype=DataType.VARCHAR, max_length=128, is_primary=True, auto_id=False), + FieldSchema(name="doc_id", dtype=DataType.VARCHAR, max_length=64), + FieldSchema(name="doc_name", dtype=DataType.VARCHAR, max_length=256), + FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=65535), + FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=settings.embedding_dim), + FieldSchema(name="section_title", dtype=DataType.VARCHAR, max_length=512), + FieldSchema(name="section_path", dtype=DataType.VARCHAR, max_length=4096), + FieldSchema(name="page_number", dtype=DataType.INT64), + FieldSchema(name="regulation_type", dtype=DataType.VARCHAR, max_length=128), + FieldSchema(name="version", dtype=DataType.VARCHAR, max_length=64), + FieldSchema(name="semantic_id", dtype=DataType.VARCHAR, max_length=128), + FieldSchema(name="block_type", dtype=DataType.VARCHAR, max_length=64), + FieldSchema(name="metadata_json", dtype=DataType.VARCHAR, max_length=65535), + FieldSchema(name="created_at", dtype=DataType.INT64), + ], + description="Dense-only regulations index", + enable_dynamic_field=False, +) +``` + +这说明 Milvus 存的不是“只有 embedding 的极简向量表”，而是： + +- 一个 dense vector +- 一组检索时要返回或过滤的结构化字段 + +但要注意：这并不意味着 Milvus 是业务主记录库。它仍然主要服务于检索，而不是替代 PostgreSQL 的文档管理职责。 + +### 5.2 `list_documents()` 为什么会先看 Milvus + +文档列表查询在 [services.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/application/documents/services.py:271) 中实现，它会： + +1. 从 Milvus 查询当前真的存在向量的文档 +2. 从文档元数据仓储加载文档记录 +3. 以 Milvus 为索引状态真相源进行 merge + +原因不是“Milvus 替代 PostgreSQL”，而是： + +- `indexed` 这个状态最终是否真实成立，要看 Milvus 里有没有对应 chunk +- 但下载、删除、重试、文件定位、错误信息仍然要依赖文档元数据仓储 + +所以： + +- Milvus 是“索引真相源” +- PostgreSQL/JSON 是“文档元数据真相源” + +这两者职责不同，不能互相替代。 diff --git a/docs/architecture/document-processing-database-design.md b/docs/architecture/document-processing-database-design.md new file mode 100644 index 0000000..e998a4e --- /dev/null +++ b/docs/architecture/document-processing-database-design.md @@ -0,0 +1,508 @@ +# 文档处理链路数据库设计 + +## 1. Purpose + +本文档定义当前文档处理主链路的 PostgreSQL 数据库设计，覆盖上传、解析、索引、状态查询、重试、删除这条核心链路，以及围绕该链路的常用运维与审计需求。 + +本文档的目标不是替代 [document-core-processing-flow.md](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/docs/architecture/document-core-processing-flow.md:1) 的流程说明，而是补齐关系型存储的 authority，使后续从 JSON 元数据切换到 PostgreSQL 时有清晰、稳定、可实施的数据库设计基线。 + +## 1.1 Scope And Design Target + +本文档只覆盖以下范围： + +- 文档主记录 +- 文档处理运行记录 +- 文档状态历史 +- 解析产物引用 +- 当前最新结构化解析快照 + +本文档不覆盖以下范围： + +- Agent 会话 +- 反馈和人工审核 +- 合规分析任务 +- Milvus collection schema 的详细实现 + +设计原则采用 `Compat First`： + +- 保持与当前 `DocumentRepository` / `ParseArtifactStore` 主流程兼容 +- 新增关系表以补足运维与审计能力 +- 不为了理想化模型而反推大规模接口重写 + +## 2. Storage Responsibilities + +当前系统采用三类存储，各自职责必须清晰分离： + +| 存储 | 保存内容 | 是否业务主记录 | 说明 | +| --- | --- | --- | --- | +| MinIO | 原始文件、`layouts.json`、`structure_nodes.json`、`semantic_blocks.json`、`vector_chunks.json` | 否 | 负责大对象与产物归档，不承担关系查询 | +| Milvus | chunk 级向量和检索辅助字段 | 否 | 负责向量检索，不承担文档生命周期管理 | +| PostgreSQL | 文档元数据、处理状态、结构化快照、处理历史、artifact 引用 | 是 | 负责文档管理、运维可观测性和关系查询 | + +约束说明： + +- PostgreSQL 不保存 embedding 向量。 +- PostgreSQL 不新增 `vector_chunks` 内容表。 +- Milvus 可以保存 `doc_id`、`doc_name`、`regulation_type`、`version` 等检索辅助字段，但不是业务真相源。 +- 文档下载、删除、重试仍以 PostgreSQL 中的文档主记录为入口。 + +## 3. Design Overview + +### 3.1 Entity Responsibilities + +数据库采用“当前态主记录 + 当前快照 + 历史过程”的分层模型： + +- `documents` + - 当前文档主记录 + - 保存供管理、下载、重试、删除直接使用的元数据和当前状态 +- `document_processing_runs` + - 每次上传或重试对应一次处理运行 + - 保存运行级统计、阶段时间点和失败信息 +- `document_status_history` + - 追加式状态事件流 + - 保存每次状态变更的上下文 +- `document_artifacts` + - 保存 MinIO artifact 的引用信息 + - 不保存 artifact 内容本体 +- `structure_nodes` + - 当前最新解析快照中的目录结构 +- `semantic_blocks` + - 当前最新解析快照中的语义块结构 + +### 3.2 Current Snapshot Vs Historical Records + +本设计显式区分两类数据： + +- 当前快照 + - `documents` + - `structure_nodes` + - `semantic_blocks` +- 历史过程 + - `document_processing_runs` + - `document_status_history` + - `document_artifacts` + +其中： + +- `structure_nodes` 和 `semantic_blocks` 只保存“最新一次成功解析后”的当前快照 +- 历史版本回溯依赖 `document_processing_runs`、`document_artifacts` 和 MinIO 中对应 run 的 artifact 文件 + +## 4. Table Design + +### 4.1 `documents` + +用途： + +- 作为文档生命周期的主记录表 +- 为下载、删除、重试、管理列表、状态查询提供当前态真相 + +字段设计： + +```sql +CREATE TABLE IF NOT EXISTS documents ( + doc_id VARCHAR(128) PRIMARY KEY, + doc_name VARCHAR(512) NOT NULL DEFAULT '', + file_name VARCHAR(512) NOT NULL DEFAULT '', + object_name VARCHAR(1024) NOT NULL DEFAULT '', + content_type VARCHAR(128) NOT NULL DEFAULT '', + size_bytes BIGINT NOT NULL DEFAULT 0, + status VARCHAR(32) NOT NULL DEFAULT 'pending', + regulation_type VARCHAR(128) NOT NULL DEFAULT '', + version VARCHAR(64) NOT NULL DEFAULT '', + summary TEXT NOT NULL DEFAULT '', + summary_latency_ms INTEGER NOT NULL DEFAULT 0, + chunk_count INTEGER NOT NULL DEFAULT 0, + parser_name VARCHAR(128) NOT NULL DEFAULT '', + index_name VARCHAR(128) NOT NULL DEFAULT '', + error_message TEXT NOT NULL DEFAULT '', + metadata JSONB NOT NULL DEFAULT '{}'::jsonb, + created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + CONSTRAINT chk_documents_status + CHECK (status IN ('pending', 'stored', 'parsed', 'indexed', 'failed')) +); + +CREATE INDEX IF NOT EXISTS idx_documents_status_updated_at + ON documents(status, updated_at DESC); + +CREATE INDEX IF NOT EXISTS idx_documents_regulation_version + ON documents(regulation_type, version); + +CREATE INDEX IF NOT EXISTS idx_documents_updated_at + ON documents(updated_at DESC); +``` + +字段说明： + +- `object_name` + - 原始上传文件在 MinIO 中的对象路径 + - 当前实现依赖该字段完成下载、重试和删除，v1 不拆分为独立文件表 +- `status` + - 当前文档处理状态 + - 仅表示当前态，不承担历史审计职责 +- `metadata` + - 保存轻量、变动频率较高、暂不值得列式建模的附加信息 + - 典型内容包括 `parse_task_id`、`processing_stage`、`artifact_keys`、统计计数等 + +### 4.2 `document_processing_runs` + +用途： + +- 记录一次上传或一次重试的完整处理运行 +- 用于解释“这份文档本次处理为什么成功或失败” + +字段设计： + +```sql +CREATE TABLE IF NOT EXISTS document_processing_runs ( + run_id BIGSERIAL PRIMARY KEY, + doc_id VARCHAR(128) NOT NULL, + trigger_type VARCHAR(16) NOT NULL, + run_status VARCHAR(16) NOT NULL, + parser_backend VARCHAR(64) NOT NULL DEFAULT '', + chunk_backend VARCHAR(64) NOT NULL DEFAULT '', + embedding_model VARCHAR(128) NOT NULL DEFAULT '', + index_name VARCHAR(128) NOT NULL DEFAULT '', + started_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + stored_at TIMESTAMPTZ, + parsed_at TIMESTAMPTZ, + indexed_at TIMESTAMPTZ, + finished_at TIMESTAMPTZ, + layout_count INTEGER NOT NULL DEFAULT 0, + structure_node_count INTEGER NOT NULL DEFAULT 0, + semantic_block_count INTEGER NOT NULL DEFAULT 0, + vector_chunk_count INTEGER NOT NULL DEFAULT 0, + chunk_count INTEGER NOT NULL DEFAULT 0, + failure_stage VARCHAR(32) NOT NULL DEFAULT '', + error_message TEXT NOT NULL DEFAULT '', + metadata JSONB NOT NULL DEFAULT '{}'::jsonb, + CONSTRAINT fk_runs_document + FOREIGN KEY (doc_id) REFERENCES documents(doc_id) ON DELETE CASCADE, + CONSTRAINT chk_runs_trigger_type + CHECK (trigger_type IN ('upload', 'retry')), + CONSTRAINT chk_runs_status + CHECK (run_status IN ('running', 'succeeded', 'failed')) +); + +CREATE INDEX IF NOT EXISTS idx_runs_doc_started_at + ON document_processing_runs(doc_id, started_at DESC); + +CREATE INDEX IF NOT EXISTS idx_runs_status_started_at + ON document_processing_runs(run_status, started_at DESC); +``` + +字段说明： + +- `trigger_type` + - 标识该次处理由首次上传还是 retry 触发 +- `run_status` + - 只表示该次运行的最终结果 +- `failure_stage` + - 建议取值与应用层关键阶段一致，例如 `store`、`parse`、`artifact_persist`、`embed`、`index` +- `metadata` + - 保存运行级附加上下文，例如配置快照、后端实现名、provider 返回信息摘要 + +### 4.3 `document_status_history` + +用途： + +- 保存状态变化事件流 +- 用于排障、审计和运行轨迹分析 + +字段设计： + +```sql +CREATE TABLE IF NOT EXISTS document_status_history ( + event_id BIGSERIAL PRIMARY KEY, + doc_id VARCHAR(128) NOT NULL, + run_id BIGINT, + from_status VARCHAR(32) NOT NULL DEFAULT '', + to_status VARCHAR(32) NOT NULL, + stage VARCHAR(32) NOT NULL DEFAULT '', + message TEXT NOT NULL DEFAULT '', + metadata JSONB NOT NULL DEFAULT '{}'::jsonb, + occurred_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + CONSTRAINT fk_status_document + FOREIGN KEY (doc_id) REFERENCES documents(doc_id) ON DELETE CASCADE, + CONSTRAINT fk_status_run + FOREIGN KEY (run_id) REFERENCES document_processing_runs(run_id) ON DELETE CASCADE, + CONSTRAINT chk_status_history_to_status + CHECK (to_status IN ('pending', 'stored', 'parsed', 'indexed', 'failed')) +); + +CREATE INDEX IF NOT EXISTS idx_status_history_doc_occurred_at + ON document_status_history(doc_id, occurred_at DESC); + +CREATE INDEX IF NOT EXISTS idx_status_history_run_occurred_at + ON document_status_history(run_id, occurred_at DESC); +``` + +字段说明： + +- `from_status` 可以为空字符串 + - 用于首个事件，例如文档创建时进入 `pending` +- `stage` + - 用于记录状态推进对应的业务阶段 +- `message` + - 用于记录面向排障的人类可读说明 + +### 4.4 `document_artifacts` + +用途： + +- 保存解析产物在 MinIO 中的位置与基本属性 +- 支持后续定位某次 run 的 artifacts，而不扫描对象存储 + +字段设计： + +```sql +CREATE TABLE IF NOT EXISTS document_artifacts ( + artifact_id BIGSERIAL PRIMARY KEY, + doc_id VARCHAR(128) NOT NULL, + run_id BIGINT, + artifact_type VARCHAR(32) NOT NULL, + object_name VARCHAR(1024) NOT NULL, + content_type VARCHAR(128) NOT NULL DEFAULT 'application/json', + byte_size BIGINT NOT NULL DEFAULT 0, + checksum VARCHAR(128) NOT NULL DEFAULT '', + metadata JSONB NOT NULL DEFAULT '{}'::jsonb, + created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + CONSTRAINT fk_artifacts_document + FOREIGN KEY (doc_id) REFERENCES documents(doc_id) ON DELETE CASCADE, + CONSTRAINT fk_artifacts_run + FOREIGN KEY (run_id) REFERENCES document_processing_runs(run_id) ON DELETE CASCADE, + CONSTRAINT chk_artifact_type + CHECK (artifact_type IN ('layouts', 'structure_nodes', 'semantic_blocks', 'vector_chunks')) +); + +CREATE INDEX IF NOT EXISTS idx_artifacts_doc_created_at + ON document_artifacts(doc_id, created_at DESC); + +CREATE INDEX IF NOT EXISTS idx_artifacts_run_type + ON document_artifacts(run_id, artifact_type); + +CREATE UNIQUE INDEX IF NOT EXISTS uq_artifacts_run_type_object + ON document_artifacts(run_id, artifact_type, object_name); +``` + +字段说明： + +- 该表只记录 artifact 引用，不记录原始文件 +- 原始文件仍由 `documents.object_name` 表达，这是为了保持当前下载和重试逻辑兼容 + +### 4.5 `structure_nodes` + +用途： + +- 保存当前最新解析快照中的标题层级结构 +- 供目录树查询、结构化浏览、调试和审计使用 + +字段设计： + +```sql +CREATE TABLE IF NOT EXISTS structure_nodes ( + id BIGSERIAL PRIMARY KEY, + doc_id VARCHAR(128) NOT NULL, + unique_id VARCHAR(128), + page INTEGER NOT NULL DEFAULT 0, + idx INTEGER NOT NULL DEFAULT 0, + level INTEGER NOT NULL DEFAULT 0, + title TEXT NOT NULL DEFAULT '', + type VARCHAR(64), + sub_type VARCHAR(64), + created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + CONSTRAINT fk_structure_nodes_document + FOREIGN KEY (doc_id) REFERENCES documents(doc_id) ON DELETE CASCADE +); + +CREATE INDEX IF NOT EXISTS idx_structure_nodes_doc_idx + ON structure_nodes(doc_id, idx); + +CREATE INDEX IF NOT EXISTS idx_structure_nodes_doc_level + ON structure_nodes(doc_id, level); +``` + +设计约束： + +- 该表表示当前快照，不做多版本建模 +- 新一轮成功解析会覆盖同一 `doc_id` 的旧快照 + +### 4.6 `semantic_blocks` + +用途： + +- 保存当前最新解析快照中的语义块 +- 供结构回溯、调试和后续关系型查询使用 + +字段设计： + +```sql +CREATE TABLE IF NOT EXISTS semantic_blocks ( + id BIGSERIAL PRIMARY KEY, + doc_id VARCHAR(128) NOT NULL, + semantic_id VARCHAR(128) NOT NULL, + block_type VARCHAR(64) NOT NULL DEFAULT '', + page_start INTEGER NOT NULL DEFAULT 0, + page_end INTEGER NOT NULL DEFAULT 0, + section_path JSONB NOT NULL DEFAULT '[]'::jsonb, + section_level INTEGER NOT NULL DEFAULT 0, + section_title VARCHAR(512) NOT NULL DEFAULT '', + source_ids JSONB NOT NULL DEFAULT '[]'::jsonb, + text TEXT NOT NULL DEFAULT '', + created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), + CONSTRAINT fk_semantic_blocks_document + FOREIGN KEY (doc_id) REFERENCES documents(doc_id) ON DELETE CASCADE, + CONSTRAINT uq_semantic_blocks_doc_semantic + UNIQUE (doc_id, semantic_id) +); + +CREATE INDEX IF NOT EXISTS idx_semantic_blocks_doc_id + ON semantic_blocks(doc_id); + +CREATE INDEX IF NOT EXISTS idx_semantic_blocks_doc_section_title + ON semantic_blocks(doc_id, section_title); + +CREATE INDEX IF NOT EXISTS idx_semantic_blocks_doc_block_type + ON semantic_blocks(doc_id, block_type); +``` + +设计约束： + +- 该表表示当前快照，不保存历史版本 +- 历史回溯应通过 run 对应的 artifact 文件完成 + +## 5. Relationship Model + +实体关系如下： + +```mermaid +erDiagram + documents ||--o{ document_processing_runs : has + documents ||--o{ document_status_history : has + documents ||--o{ document_artifacts : has + documents ||--o{ structure_nodes : has + documents ||--o{ semantic_blocks : has + document_processing_runs ||--o{ document_status_history : emits + document_processing_runs ||--o{ document_artifacts : produces +``` + +关系语义： + +- `documents` 是聚合根 +- `document_processing_runs` 记录一次完整处理尝试 +- `document_status_history` 记录状态推进轨迹 +- `document_artifacts` 记录 MinIO 中可回放的结构化产物 +- `structure_nodes` / `semantic_blocks` 代表“当前版本”的关系型快照 + +## 6. Flow-To-Table Mapping + +### 6.1 Upload + +上传开始时： + +1. 创建 `documents` +2. 创建一条 `document_processing_runs` +3. 写入一条 `document_status_history`，`to_status='pending'` + +### 6.2 Store Original File + +原始文件写入 MinIO 成功后： + +1. 更新 `documents.status='stored'` +2. 更新当前 run 的 `stored_at` +3. 追加 `document_status_history` + +### 6.3 Parse And Persist Artifacts + +解析成功后： + +1. 更新当前 run 的 `parsed_at` +2. 更新 run 的 `layout_count`、`structure_node_count`、`semantic_block_count`、`vector_chunk_count` +3. 更新 `documents.status='parsed'` +4. 刷新 `structure_nodes` +5. 刷新 `semantic_blocks` +6. 为 `layouts`、`structure_nodes`、`semantic_blocks`、`vector_chunks` 写入 `document_artifacts` +7. 追加 `document_status_history` + +### 6.4 Embed And Index + +向量化和入库成功后： + +1. 更新当前 run 的 `indexed_at`、`finished_at` +2. 更新当前 run 的 `run_status='succeeded'` +3. 更新 `documents.status='indexed'` +4. 更新 `documents.chunk_count`、`index_name` +5. 追加 `document_status_history` + +### 6.5 Failure + +任一阶段失败时： + +1. 更新当前 run 的 `run_status='failed'` +2. 记录 `failure_stage` 和 `error_message` +3. 更新 `finished_at` +4. 更新 `documents.status='failed'` +5. 更新 `documents.error_message` +6. 追加 `document_status_history` + +### 6.6 Retry + +重试时： + +1. 保留现有 `documents.doc_id` +2. 新建一条 `document_processing_runs` +3. 为本次重试重新写入状态历史 +4. 本次重试成功后覆盖 `structure_nodes` / `semantic_blocks` 当前快照 +5. 历史 run 和 artifact 记录继续保留 + +### 6.7 Delete + +删除文档时： + +1. 应用层先删除 MinIO 原始文件和 artifacts +2. 应用层删除 Milvus 中按 `doc_id` 关联的向量 +3. 最后删除 `documents` +4. 依赖外键 `ON DELETE CASCADE` 清理 run、status history、artifacts、structure nodes、semantic blocks + +## 7. Alignment With Current Backend + +### 7.1 Compatible Parts + +当前代码已天然兼容以下设计： + +- `documents` +- `structure_nodes` +- `semantic_blocks` +- 当前快照覆盖式更新 +- `doc_id` 作为跨 MinIO / Milvus / PostgreSQL 的统一关联键 + +### 7.2 Required Future Additions + +若后续正式切到 PostgreSQL 默认元数据后端，应新增以下内部 store 或 repository： + +- `DocumentProcessingRunStore` +- `DocumentStatusEventStore` +- `DocumentArtifactStore` + +这些新增能力属于内部增强，不要求修改现有 HTTP API。 + +### 7.3 Migration Guidance + +从当前 JSON 元数据切换到 PostgreSQL 时，建议按以下顺序进行： + +1. 迁移 `documents.json` 中已有文档主记录到 `documents` +2. 将 `DOCUMENT_REPOSITORY_BACKEND` 切换为 `postgres` +3. 为新上传或重试的文档开始写入 run / status history / artifact records +4. 历史文档若缺少 run 级数据，可允许为空，不阻塞切换 + +## 8. 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