624 lines
23 KiB
Markdown
624 lines
23 KiB
Markdown
# 核心文档处理主链路说明
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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：
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- `AliyunDocumentParser`
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- `AliyunVectorChunkBuilder`
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- `OpenAICompatibleEmbeddingProvider`
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- `MilvusVectorIndex`
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目标是回答四个核心问题：
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1. `ParsedDocument` 为什么是多层结构
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2. 这些结构分别保存到哪里
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3. 哪一步才真正做了向量化
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4. Milvus 里最后到底存的是什么
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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。
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## 1. 主链路总览
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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。
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```mermaid
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sequenceDiagram
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participant API as API / Service
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participant MinIO as BinaryStore
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participant Parser as AliyunDocumentParser
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participant PG as DocumentRepository / ParseArtifactStore
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participant Embed as EmbeddingProvider
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participant Milvus as VectorIndex
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API->>MinIO: 保存原始文件
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API->>Parser: parse(file_path, doc_id, doc_name)
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Parser-->>API: ParsedDocument
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API->>MinIO: 保存 layouts/structure_nodes/semantic_blocks/vector_chunks JSON
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API->>PG: 更新 documents.status=parsed
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API->>PG: 保存 structure_nodes / semantic_blocks
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API->>API: chunk_builder.build(parsed_document)
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API->>Embed: embed_texts([chunk.embedding_text])
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Embed-->>API: vectors
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API->>Milvus: upsert(chunks, vectors)
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API->>PG: 更新 documents.status=indexed
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```
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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)：
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```python
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def upload_and_process(
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self,
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*,
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doc_id: str | None = None,
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file_name: str,
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content: bytes,
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content_type: str,
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doc_name: str | None,
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regulation_type: str,
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version: str,
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generate_summary: bool,
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) -> DocumentProcessResult:
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doc_id = doc_id or str(uuid.uuid4())[:8]
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final_doc_name = doc_name or file_name
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object_name = f"{doc_id}/{file_name}"
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self.document_repository.create(document)
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self.binary_store.save(object_name=object_name, data=content, content_type=content_type, metadata={"doc_id": doc_id})
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self.document_repository.update_status(doc_id, DocumentStatus.STORED)
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parsed_document = self.parser.parse(file_path=temp_path, doc_id=doc_id, doc_name=final_doc_name)
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artifact_keys = self._save_parse_artifacts(doc_id=doc_id, parsed_document=parsed_document)
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self.document_repository.update_status(doc_id, DocumentStatus.PARSED, parser_name=parsed_document.parser_name, metadata={...})
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if self.parse_artifact_store:
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self.parse_artifact_store.save(doc_id, parsed_document.structure_nodes, parsed_document.semantic_blocks)
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chunks = self.chunk_builder.build(parsed_document=parsed_document, regulation_type=regulation_type, version=version)
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vectors = self.embedding_provider.embed_texts([chunk.embedding_text for chunk in chunks])
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inserted = self.vector_index.upsert(chunks, vectors)
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self.document_repository.update_status(doc_id, DocumentStatus.INDEXED, chunk_count=len(chunks), index_name=health.get("collection_name", ""), metadata={...})
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```
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默认绑定关系在 [bootstrap.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/shared/bootstrap.py:157)：
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```python
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def get_parser():
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if settings.parser_backend == "aliyun":
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return AliyunDocumentParser()
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return LocalDocumentParser()
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def get_chunk_builder():
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if settings.chunk_backend == "aliyun":
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return AliyunVectorChunkBuilder()
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return LocalRegulationChunkBuilder(...)
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def get_embedding_provider() -> OpenAICompatibleEmbeddingProvider:
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return OpenAICompatibleEmbeddingProvider()
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def get_vector_index() -> VectorIndex:
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return LazyVectorIndex(_build_vector_index)
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```
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也就是说，当前默认主链路是：
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- parser: `AliyunDocumentParser`
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- chunk builder: `AliyunVectorChunkBuilder`
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- embedding provider: `OpenAICompatibleEmbeddingProvider`
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- vector index: `MilvusVectorIndex`
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## 2. `ParsedDocument` 为什么是三层结构
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`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。
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定义在 [models.py](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/backend/app/domain/documents/models.py:49)：
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```python
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@dataclass
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class ParsedDocument:
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doc_id: str
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doc_name: str
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structure_nodes: list[dict[str, Any]]
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semantic_blocks: list[dict[str, Any]]
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vector_chunks: list[dict[str, Any]]
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parser_name: str
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raw_text: str = ""
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raw_layouts: list[dict[str, Any]] = field(default_factory=list)
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metadata: dict[str, Any] = field(default_factory=dict)
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```
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这三层的职责不同：
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- `structure_nodes`
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- 标题层级骨架
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- 描述“文档有哪些章、节、条”
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- 用于保留结构，不直接做 embedding
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- `semantic_blocks`
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- 语义块层
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- 把正文、表格、图片说明整理成连续的语义单元
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- 是从原始 layout 到检索 chunk 之间的中间层
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- `vector_chunks`
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- 检索和向量化层
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- 已经是适合送给 embedding 模型的 chunk 视图
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- 后续 `ChunkBuilder` 基本就是把这层映射成统一 `Chunk`
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### 2.1 这三层是怎么从 parser 结果生成的
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`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。
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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)：
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```python
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def parse(self, *, file_path: str, doc_id: str, doc_name: str) -> ParsedDocument:
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payload = self.gateway.parse_document(file_path=file_path)
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layouts = payload.layouts
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structure_nodes = build_structure_nodes(layouts)
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semantic_blocks = build_semantic_blocks(layouts)
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vector_chunks = build_vector_chunks(
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semantic_blocks,
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doc_id=doc_id,
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doc_title=doc_name,
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max_chars=MAX_CHARS,
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overlap_chars=OVERLAP_CHARS,
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)
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raw_text = "\n\n".join(
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block.get("text", "")
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for block in semantic_blocks
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if block.get("text")
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)
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return ParsedDocument(
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doc_id=doc_id,
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doc_name=doc_name,
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structure_nodes=structure_nodes,
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semantic_blocks=semantic_blocks,
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vector_chunks=vector_chunks,
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parser_name=self.parser_name,
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raw_text=raw_text,
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raw_layouts=layouts,
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metadata={...},
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)
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```
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也就是说：
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- parser 原始输出是 `layouts`
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- 当前系统真正消费的是 `ParsedDocument`
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- `ParsedDocument` 是由 normalizer 从 `layouts` 规整出来的
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### 2.2 第一层：`structure_nodes`
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这一层只保留标题和层级。
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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)：
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```python
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def build_structure_nodes(layouts: list[dict[str, Any]]) -> list[dict[str, Any]]:
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nodes: list[dict[str, Any]] = []
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for layout in layouts:
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if not is_title(layout):
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continue
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text = get_text(layout)
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if not text or text in TOC_TITLES:
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continue
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nodes.append(
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{
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"unique_id": layout.get("uniqueId"),
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"page": get_page(layout),
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"index": layout.get("index", 0),
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"level": layout.get("level", 0),
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"title": text,
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"type": layout.get("type"),
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"sub_type": layout.get("subType"),
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}
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)
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return nodes
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```
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示例：
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```json
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[
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{
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"unique_id": "l-title-001",
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"page": 2,
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"index": 11,
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"level": 1,
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"title": "1 范围",
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"type": "title",
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"sub_type": "para_title"
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},
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{
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"unique_id": "l-title-002",
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"page": 3,
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"index": 18,
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"level": 2,
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"title": "1.1 适用对象",
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"type": "title",
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"sub_type": "para_title"
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}
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]
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```
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这层的意义是“保留目录树”，不是直接拿来检索。
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### 2.3 第二层：`semantic_blocks`
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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。
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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)：
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```python
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def build_semantic_blocks(layouts: list[dict[str, Any]]) -> list[dict[str, Any]]:
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semantic_blocks: list[dict[str, Any]] = []
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section_stack: list[dict[str, Any]] = []
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pending_text_blocks: list[dict[str, Any]] = []
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block_id = 1
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for layout in layouts:
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text = get_text(layout)
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page = get_page(layout)
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if is_title(layout):
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block_id = flush_text_block(pending_text_blocks, semantic_blocks, block_id)
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pending_text_blocks = []
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section_stack = update_section_path(section_stack, layout)
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continue
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section_path = section_path_titles(section_stack)
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section_title = section_path[-1] if section_path else "未分类"
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section_level = len(section_path)
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if is_table(layout):
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...
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semantic_blocks.append(
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{
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"semantic_id": f"semantic-{block_id}",
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"block_type": "table",
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"page_start": page,
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"page_end": page,
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"section_path": section_path,
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"section_level": section_level,
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"section_title": section_title,
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"source_ids": [layout.get("uniqueId")],
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"text": table_text,
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}
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)
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continue
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if is_text(layout) and text:
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pending_text_blocks.append(
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{
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"page": page,
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"text": text,
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"unique_id": layout.get("uniqueId"),
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"section_path": section_path,
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"section_level": section_level,
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"section_title": section_title,
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}
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)
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```
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正文合并后会形成类似这样的语义块：
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```json
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[
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{
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"semantic_id": "semantic-1",
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"block_type": "section_text",
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"page_start": 2,
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"page_end": 2,
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"section_path": ["1 范围", "1.1 适用对象"],
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"section_level": 2,
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"section_title": "1.1 适用对象",
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"source_ids": ["l-text-001", "l-text-002"],
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"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。"
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}
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]
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```
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## 3. 这些结构分别保存到哪里
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### 3.1 原始文件和中间 artifacts 先落 MinIO
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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。
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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)：
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```python
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def _save_parse_artifacts(self, *, doc_id: str, parsed_document: ParsedDocument) -> dict[str, str]:
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prefix = f"{parsed_document.metadata.get('artifact_prefix', 'artifacts').strip('/')}/{doc_id}"
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artifact_payloads = {
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"layouts": parsed_document.raw_layouts,
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"structure_nodes": parsed_document.structure_nodes,
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"semantic_blocks": parsed_document.semantic_blocks,
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"vector_chunks": parsed_document.vector_chunks,
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}
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artifact_keys: dict[str, str] = {}
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for name, payload in artifact_payloads.items():
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object_name = f"{prefix}/{name}.json"
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self.binary_store.save(
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object_name=object_name,
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data=json.dumps(payload, ensure_ascii=False, indent=2).encode("utf-8"),
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content_type="application/json",
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metadata={"doc_id": doc_id, "artifact_type": name},
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)
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artifact_keys[name] = object_name
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return artifact_keys
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```
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`DocumentBinaryStore` 的当前默认实现是 `MinioDocumentBinaryStore`，也就是：
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- 原始上传文件进 MinIO
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- `layouts.json` 进 MinIO
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- `structure_nodes.json` 进 MinIO
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- `semantic_blocks.json` 进 MinIO
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- `vector_chunks.json` 进 MinIO
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### 3.2 PostgreSQL 在流程中的职责摘要
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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：
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- `documents` 保存当前文档主记录、状态、统计和索引信息
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- `structure_nodes` 保存当前最新解析快照的目录结构
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- `semantic_blocks` 保存当前最新解析快照的语义块结构
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更完整的 PostgreSQL 设计，包括：
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- `documents`
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- `document_processing_runs`
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- `document_status_history`
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- `document_artifacts`
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- `structure_nodes`
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- `semantic_blocks`
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见 [document-processing-database-design.md](/abs/path/C:/Users/A200477427/Developers/AIRegulation/AIRegulation-DocAnalysis/docs/architecture/document-processing-database-design.md:1)。
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### 3.3 存储分工一览
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| 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 |
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| --- | --- | --- | --- | --- |
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| 原始文件 | MinIO | 否 | 否 | 保留原始上传文档 |
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| `raw_layouts` | MinIO `layouts.json` | 否 | 否 | 保留 parser 原始返回 |
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| `structure_nodes` | MinIO + PostgreSQL | 否 | 否 | 目录树、层级结构 |
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| `semantic_blocks` | MinIO + PostgreSQL | 否 | 间接 | 语义单元、中间层 |
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| `vector_chunks` | MinIO | 是 | 间接 | embedding 前的检索块 |
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| `Chunk` | 内存态 + Milvus | 是 | 是 | 统一向量入库模型 |
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| `documents` 元数据 | PostgreSQL | 否 | 否 | 处理状态、统计、索引信息 |
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## 4. 哪一步才真正“变成向量”
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这是整个流程最关键的点。
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结论先说清楚：
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- parse 不做向量化
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- 保存 artifacts 不做向量化
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- `ChunkBuilder.build()` 也不做向量化
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- 只有 `EmbeddingProvider.embed_texts()` 才真正调用 embedding 模型
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- 只有 `VectorIndex.upsert()` 才真正把向量写入向量库
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### 4.1 `vector_chunks` 先被映射成统一 `Chunk`
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`AliyunVectorChunkBuilder` 并不做 embedding，它只负责把 `ParsedDocument.vector_chunks` 转成领域层统一 `Chunk` 模型。
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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)：
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```python
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def build(
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self,
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*,
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parsed_document: ParsedDocument,
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regulation_type: str,
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version: str,
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) -> list[Chunk]:
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chunks: list[Chunk] = []
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for index, item in enumerate(parsed_document.vector_chunks):
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content = item.get("content") or item.get("text") or ""
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embedding_text = item.get("embedding_text") or content
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if not embedding_text.strip():
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continue
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section_path = item.get("section_path") or []
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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。
|