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Feat: TrustGraph i18n & Documentation Translation Updates (#781)
Native CLI i18n: The TrustGraph CLI has built-in translation support that dynamically loads language strings. You can test and use different languages by simply passing the --lang flag (e.g., --lang es for Spanish, --lang ru for Russian) or by configuring your environment's LANG variable. Automated Docs Translations: This PR introduces autonomously translated Markdown documentation into several target languages, including Spanish, Swahili, Portuguese, Turkish, Hindi, Hebrew, Arabic, Simplified Chinese, and Russian.
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docs/tech-specs/structured-data-descriptor.zh-cn.md
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docs/tech-specs/structured-data-descriptor.zh-cn.md
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---
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layout: default
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title: "结构化数据描述符规范"
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parent: "Chinese (Beta)"
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---
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# 结构化数据描述符规范
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> **Beta Translation:** This document was translated via Machine Learning and as such may not be 100% accurate. All non-English languages are currently classified as Beta.
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## 概述
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结构化数据描述符是一种基于 JSON 的配置语言，用于描述如何解析、转换和导入结构化数据到 TrustGraph 中。它提供了一种声明式的数据导入方法，支持多种输入格式和复杂的转换流程，而无需自定义代码。
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## 核心概念
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### 1. 格式定义
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描述输入文件类型和解析选项。确定要使用的解析器以及如何解释源数据。
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### 2. 字段映射
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将源路径映射到目标字段，并进行转换。定义数据如何从输入源流向输出模式字段。
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### 3. 转换流程
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一系列应用于字段值的转换，包括：
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数据清洗（修剪、标准化）
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格式转换（日期解析、类型转换）
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计算（算术运算、字符串操作）
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查找（引用表、替换）
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### 4. 验证规则
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应用于确保数据完整性的数据质量检查：
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类型验证
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范围检查
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模式匹配（正则表达式）
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必填字段验证
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自定义验证逻辑
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### 5. 全局设置
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适用于整个导入过程的配置：
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用于数据增强的查找表
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全局变量和常量
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输出格式规范
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错误处理策略
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## 实施策略
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导入器的实现遵循以下流程：
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1. **解析配置** - 加载和验证 JSON 描述符
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2. **初始化解析器** - 根据 `format.type` 加载适当的解析器（CSV、XML、JSON 等）
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3. **应用预处理** - 执行全局过滤器和转换
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4. **处理记录** - 对于每个输入记录：
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使用源路径（JSONPath、XPath、列名）提取数据
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按照顺序应用字段级别的转换
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根据定义的规则验证结果
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为缺失数据应用默认值
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5. **应用后处理** - 执行去重、聚合等操作
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6. **生成输出** - 以指定的目标格式生成数据
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## 路径表达式支持
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不同的输入格式使用适当的路径表达式语言：
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**CSV**: 列名或索引（`"column_name"` 或 `"[2]"`）
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**JSON**: JSONPath 语法（`"$.user.profile.email"`）
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**XML**: XPath 表达式（`"//product[@id='123']/price"`）
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**定宽**: 来自字段定义的字段名
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## 优点
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**单一代码库** - 一个导入器处理多种输入格式
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**用户友好** - 非技术用户可以创建配置
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**可重用** - 可以共享和版本控制配置
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**灵活** - 无需自定义代码即可进行复杂的转换
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**健壮** - 内置验证和全面的错误处理
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**可维护** - 声明式方法减少了实施复杂性
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## 语言规范
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结构化数据描述符使用基于 JSON 的配置格式，其顶级结构如下：
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```json
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{
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"version": "1.0",
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"metadata": {
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"name": "Configuration Name",
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"description": "Description of what this config does",
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"author": "Author Name",
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"created": "2024-01-01T00:00:00Z"
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},
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"format": { ... },
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"globals": { ... },
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"preprocessing": [ ... ],
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"mappings": [ ... ],
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"postprocessing": [ ... ],
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"output": { ... }
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}
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```
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### 格式定义
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描述输入数据格式和解析选项：
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```json
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{
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"format": {
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"type": "csv|json|xml|fixed-width|excel|parquet",
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"encoding": "utf-8",
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"options": {
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// Format-specific options
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}
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}
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}
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```
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#### CSV 格式选项
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```json
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{
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"format": {
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"type": "csv",
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"options": {
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"delimiter": ",",
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"quote_char": "\"",
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"escape_char": "\\",
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"skip_rows": 1,
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"has_header": true,
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"null_values": ["", "NULL", "null", "N/A"]
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}
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}
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}
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```
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#### JSON 格式选项
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```json
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{
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"format": {
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"type": "json",
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"options": {
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"root_path": "$.data",
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"array_mode": "records|single",
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"flatten": false
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}
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}
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}
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```
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#### XML 格式选项
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```json
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{
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"format": {
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"type": "xml",
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"options": {
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"root_element": "//records/record",
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"namespaces": {
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"ns": "http://example.com/namespace"
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}
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}
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}
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}
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```
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### 全局设置
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定义查找表、变量和全局配置：
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```json
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{
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"globals": {
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"variables": {
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"current_date": "2024-01-01",
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"batch_id": "BATCH_001",
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"default_confidence": 0.8
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},
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"lookup_tables": {
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"country_codes": {
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"US": "United States",
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"UK": "United Kingdom",
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"CA": "Canada"
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},
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"status_mapping": {
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"1": "active",
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"0": "inactive"
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}
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},
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"constants": {
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"source_system": "legacy_crm",
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"import_type": "full"
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}
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}
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}
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```
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### 字段映射
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定义如何将源数据映射到目标字段，并进行转换：
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```json
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{
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"mappings": [
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{
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"target_field": "person_name",
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"source": "$.name",
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"transforms": [
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{"type": "trim"},
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{"type": "title_case"},
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{"type": "required"}
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],
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"validation": [
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{"type": "min_length", "value": 2},
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{"type": "max_length", "value": 100},
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{"type": "pattern", "value": "^[A-Za-z\\s]+$"}
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]
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},
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{
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"target_field": "age",
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"source": "$.age",
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"transforms": [
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{"type": "to_int"},
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{"type": "default", "value": 0}
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],
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"validation": [
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{"type": "range", "min": 0, "max": 150}
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]
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},
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{
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"target_field": "country",
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"source": "$.country_code",
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"transforms": [
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{"type": "lookup", "table": "country_codes"},
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{"type": "default", "value": "Unknown"}
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]
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}
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]
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}
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```
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### 转换类型
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可用的转换函数：
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#### 字符串转换
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```json
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{"type": "trim"},
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{"type": "upper"},
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{"type": "lower"},
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{"type": "title_case"},
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{"type": "replace", "pattern": "old", "replacement": "new"},
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{"type": "regex_replace", "pattern": "\\d+", "replacement": "XXX"},
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{"type": "substring", "start": 0, "end": 10},
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{"type": "pad_left", "length": 10, "char": "0"}
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```
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#### 类型转换
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```json
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{"type": "to_string"},
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{"type": "to_int"},
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{"type": "to_float"},
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{"type": "to_bool"},
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{"type": "to_date", "format": "YYYY-MM-DD"},
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{"type": "parse_json"}
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```
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#### 数据操作
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```json
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{"type": "default", "value": "default_value"},
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{"type": "lookup", "table": "table_name"},
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{"type": "concat", "values": ["field1", " - ", "field2"]},
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{"type": "calculate", "expression": "${field1} + ${field2}"},
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{"type": "conditional", "condition": "${age} > 18", "true_value": "adult", "false_value": "minor"}
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```
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### 验证规则
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数据质量检查，具有可配置的错误处理：
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#### 基础验证
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```json
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{"type": "required"},
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{"type": "not_null"},
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{"type": "min_length", "value": 5},
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{"type": "max_length", "value": 100},
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{"type": "range", "min": 0, "max": 1000},
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{"type": "pattern", "value": "^[A-Z]{2,3}$"},
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{"type": "in_list", "values": ["active", "inactive", "pending"]}
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```
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#### 自定义验证
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```json
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{
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"type": "custom",
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"expression": "${age} >= 18 && ${country} == 'US'",
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"message": "Must be 18+ and in US"
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},
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{
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"type": "cross_field",
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"fields": ["start_date", "end_date"],
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"expression": "${start_date} < ${end_date}",
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"message": "Start date must be before end date"
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}
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```
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### 预处理和后处理
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在字段映射之前/之后应用的全局操作：
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|
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```json
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{
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"preprocessing": [
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{
|
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"type": "filter",
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"condition": "${status} != 'deleted'"
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},
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{
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"type": "sort",
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"field": "created_date",
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"order": "asc"
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}
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],
|
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"postprocessing": [
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{
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"type": "deduplicate",
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"key_fields": ["email", "phone"]
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},
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{
|
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"type": "aggregate",
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"group_by": ["country"],
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"functions": {
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"total_count": {"type": "count"},
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"avg_age": {"type": "avg", "field": "age"}
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}
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}
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]
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}
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```
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### 输出配置
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定义如何输出处理后的数据：
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```json
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{
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"output": {
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"format": "trustgraph-objects",
|
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"schema_name": "person",
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"options": {
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"batch_size": 1000,
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"confidence": 0.9,
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"source_span_field": "raw_text",
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"metadata": {
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"source": "crm_import",
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"version": "1.0"
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}
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},
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"error_handling": {
|
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"on_validation_error": "skip|fail|log",
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"on_transform_error": "skip|fail|default",
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"max_errors": 100,
|
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"error_output": "errors.json"
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}
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}
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}
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```
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## 完整示例
|
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```json
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{
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"version": "1.0",
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"metadata": {
|
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"name": "Customer Import from CRM CSV",
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"description": "Imports customer data from legacy CRM system",
|
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"author": "Data Team",
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"created": "2024-01-01T00:00:00Z"
|
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},
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"format": {
|
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"type": "csv",
|
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"encoding": "utf-8",
|
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"options": {
|
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"delimiter": ",",
|
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"has_header": true,
|
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"skip_rows": 1
|
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}
|
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},
|
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"globals": {
|
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"variables": {
|
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"import_date": "2024-01-01",
|
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"default_confidence": 0.85
|
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},
|
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"lookup_tables": {
|
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"country_codes": {
|
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"US": "United States",
|
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"CA": "Canada",
|
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"UK": "United Kingdom"
|
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}
|
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}
|
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},
|
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"preprocessing": [
|
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{
|
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"type": "filter",
|
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"condition": "${status} == 'active'"
|
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}
|
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],
|
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"mappings": [
|
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{
|
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"target_field": "full_name",
|
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"source": "customer_name",
|
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"transforms": [
|
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{"type": "trim"},
|
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{"type": "title_case"}
|
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],
|
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"validation": [
|
||||
{"type": "required"},
|
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{"type": "min_length", "value": 2}
|
||||
]
|
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},
|
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{
|
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"target_field": "email",
|
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"source": "email_address",
|
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"transforms": [
|
||||
{"type": "trim"},
|
||||
{"type": "lower"}
|
||||
],
|
||||
"validation": [
|
||||
{"type": "pattern", "value": "^[\\w.-]+@[\\w.-]+\\.[a-zA-Z]{2,}$"}
|
||||
]
|
||||
},
|
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{
|
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"target_field": "age",
|
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"source": "age",
|
||||
"transforms": [
|
||||
{"type": "to_int"},
|
||||
{"type": "default", "value": 0}
|
||||
],
|
||||
"validation": [
|
||||
{"type": "range", "min": 0, "max": 120}
|
||||
]
|
||||
},
|
||||
{
|
||||
"target_field": "country",
|
||||
"source": "country_code",
|
||||
"transforms": [
|
||||
{"type": "lookup", "table": "country_codes"},
|
||||
{"type": "default", "value": "Unknown"}
|
||||
]
|
||||
}
|
||||
],
|
||||
"output": {
|
||||
"format": "trustgraph-objects",
|
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"schema_name": "customer",
|
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"options": {
|
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"confidence": "${default_confidence}",
|
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"batch_size": 500
|
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},
|
||||
"error_handling": {
|
||||
"on_validation_error": "log",
|
||||
"max_errors": 50
|
||||
}
|
||||
}
|
||||
}
|
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```
|
||||
|
||||
## 用于描述符生成的 LLM 提示
|
||||
|
||||
以下提示可用于让 LLM 分析样本数据并生成描述符配置：
|
||||
|
||||
```
|
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I need you to analyze the provided data sample and create a Structured Data Descriptor configuration in JSON format.
|
||||
|
||||
The descriptor should follow this specification:
|
||||
- version: "1.0"
|
||||
- metadata: Configuration name, description, author, and creation date
|
||||
- format: Input format type and parsing options
|
||||
- globals: Variables, lookup tables, and constants
|
||||
- preprocessing: Filters and transformations applied before mapping
|
||||
- mappings: Field-by-field mapping from source to target with transformations and validations
|
||||
- postprocessing: Operations like deduplication or aggregation
|
||||
- output: Target format and error handling configuration
|
||||
|
||||
ANALYZE THE DATA:
|
||||
1. Identify the format (CSV, JSON, XML, etc.)
|
||||
2. Detect delimiters, encodings, and structure
|
||||
3. Find data types for each field
|
||||
4. Identify patterns and constraints
|
||||
5. Look for fields that need cleaning or transformation
|
||||
6. Find relationships between fields
|
||||
7. Identify lookup opportunities (codes that map to values)
|
||||
8. Detect required vs optional fields
|
||||
|
||||
CREATE THE DESCRIPTOR:
|
||||
For each field in the sample data:
|
||||
- Map it to an appropriate target field name
|
||||
- Add necessary transformations (trim, case conversion, type casting)
|
||||
- Include appropriate validations (required, patterns, ranges)
|
||||
- Set defaults for missing values
|
||||
|
||||
Include preprocessing if needed:
|
||||
- Filters to exclude invalid records
|
||||
- Sorting requirements
|
||||
|
||||
Include postprocessing if beneficial:
|
||||
- Deduplication on key fields
|
||||
- Aggregation for summary data
|
||||
|
||||
Configure output for TrustGraph:
|
||||
- format: "trustgraph-objects"
|
||||
- schema_name: Based on the data entity type
|
||||
- Appropriate error handling
|
||||
|
||||
DATA SAMPLE:
|
||||
[Insert data sample here]
|
||||
|
||||
ADDITIONAL CONTEXT (optional):
|
||||
- Target schema name: [if known]
|
||||
- Business rules: [any specific requirements]
|
||||
- Data quality issues to address: [known problems]
|
||||
|
||||
Generate a complete, valid Structured Data Descriptor configuration that will properly import this data into TrustGraph. Include comments explaining key decisions.
|
||||
```
|
||||
|
||||
### 示例用法提示
|
||||
|
||||
```
|
||||
I need you to analyze the provided data sample and create a Structured Data Descriptor configuration in JSON format.
|
||||
|
||||
[Standard instructions from above...]
|
||||
|
||||
DATA SAMPLE:
|
||||
```csv
|
||||
客户ID,姓名,电子邮件,年龄,国家,状态,加入日期,总购买额
|
||||
1001,"Smith, John",john.smith@email.com,35,美国,1,2023-01-15,5420.50
|
||||
1002,"doe, jane",JANE.DOE@GMAIL.COM,28,加拿大,1,2023-03-22,3200.00
|
||||
1003,"Bob Johnson",bob@,62,英国,0,2022-11-01,0
|
||||
1004,"Alice Chen","alice.chen@company.org",41,美国,1,2023-06-10,8900.25
|
||||
1005,,invalid-email,25,XX,1,2024-01-01,100
|
||||
```
|
||||
|
||||
ADDITIONAL CONTEXT:
|
||||
- Target schema name: customer
|
||||
- Business rules: Email should be valid and lowercase, names should be title case
|
||||
- Data quality issues: Some emails are invalid, some names are missing, country codes need mapping
|
||||
```
|
||||
|
||||
### 用于分析现有数据而无需样本的提示
|
||||
|
||||
```
|
||||
I need you to help me create a Structured Data Descriptor configuration for importing [data type] data.
|
||||
|
||||
The source data has these characteristics:
|
||||
- Format: [CSV/JSON/XML/etc]
|
||||
- Fields: [list the fields]
|
||||
- Data quality issues: [describe any known issues]
|
||||
- Volume: [approximate number of records]
|
||||
|
||||
Requirements:
|
||||
- [List any specific transformation needs]
|
||||
- [List any validation requirements]
|
||||
- [List any business rules]
|
||||
|
||||
Please generate a Structured Data Descriptor configuration that will:
|
||||
1. Parse the input format correctly
|
||||
2. Clean and standardize the data
|
||||
3. Validate according to the requirements
|
||||
4. Handle errors gracefully
|
||||
5. Output in TrustGraph ExtractedObject format
|
||||
|
||||
Focus on making the configuration robust and reusable.
|
||||
```
|
||||
Loading…
Add table
Add a link
Reference in a new issue