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.

docs/tech-specs/schema-refactoring-proposal.zh-cn.md

@@ -0,0 +1,99 @@
+---
+layout: default
+title: "方案：Schema 目录重构"
+parent: "Chinese (Beta)"
+---
+
+# 方案：Schema 目录重构
+
+> **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.
+
+## 当前问题
+
+1. **扁平结构** - 所有 Schema 都位于同一目录下，难以理解它们之间的关系
+2. **混杂的关注点** - 核心类型、领域对象和 API 契约都混合在一起
+3. **不明确的命名** - 文件如 "object.py", "types.py", "topic.py" 并不能清晰地表明其用途
+4. **缺乏明确的层级** - 无法轻松地看出哪些依赖于哪些
+
+## 建议的结构
+
+```
+trustgraph-base/trustgraph/schema/
+├── __init__.py
+├── core/              # 核心基本类型，在所有地方使用
+│   ├── __init__.py
+│   ├── primitives.py  # Error, Value, Triple, Field, RowSchema
+│   ├── metadata.py    # 元数据记录
+│   └── topic.py       # Topic 工具
+│
+├── knowledge/         # 知识领域模型和提取
+│   ├── __init__.py
+│   ├── graph.py       # EntityContext, EntityEmbeddings, Triples
+│   ├── document.py    # Document, TextDocument, Chunk
+│   ├── knowledge.py   # 知识提取类型
+│   ├── embeddings.py  # 所有与嵌入相关的类型（从多个文件中移动）
+│   └── nlp.py         # Definition, Topic, Relationship, Fact 类型
+│
+└── services/          # 服务请求/响应契约
+    ├── __init__.py
+    ├── llm.py         # TextCompletion, Embeddings, Tool 请求/响应
+    ├── retrieval.py   # GraphRAG, DocumentRAG 查询/响应
+    ├── query.py       # GraphEmbeddingsRequest/Response, DocumentEmbeddingsRequest/Response
+    ├── agent.py       # Agent 请求/响应
+    ├── flow.py        # Flow 请求/响应
+    ├── prompt.py      # Prompt 服务请求/响应
+    ├── config.py      # 配置服务
+    ├── library.py     # 库服务
+    └── lookup.py      # 查找服务
+```
+
+## 关键变更
+
+1. **分层组织** - 清晰地将核心类型、知识模型和服务契约分开
+2. **更好的命名**：
+   - `types.py` → `core/primitives.py` (更清晰的用途)
+   - `object.py` → 根据实际内容将文件拆分
+   - `documents.py` → `knowledge/document.py` (单数，一致)
+   - `models.py` → `services/llm.py` (更清晰地表明模型类型)
+   - `prompt.py` → 拆分：服务部分到 `services/prompt.py`，数据类型到 `knowledge/nlp.py`
+
+3. **逻辑分组**：
+   - 所有嵌入类型集中在 `knowledge/embeddings.py`
+   - 所有与 LLM 相关的服务契约在 `services/llm.py`
+   - 在 services 目录中明确区分请求/响应对
+   - 知识提取类型与其它知识领域模型分组
+
+4. **依赖关系清晰**：
+   - 核心类型没有依赖
+   - 知识模型仅依赖核心
+   - 服务契约可以依赖核心和知识模型
+
+## 迁移的好处
+
+1. **更轻松的导航** - 开发者可以快速找到所需的内容
+2. **更好的模块化** - 区分不同关注点更清晰
+3. **更简单的导入** - 更有意义的导入路径
+4. **更具未来性** - 轻松添加新的知识类型或服务，而无需增加混乱
+
+## 示例导入变更
+
+```python
+# 之前
+from trustgraph.schema import Error, Triple, GraphEmbeddings, TextCompletionRequest
+
+# 之后
+from trustgraph.schema.core import Error, Triple
+from trustgraph.schema.knowledge import GraphEmbeddings
+from trustgraph.schema.services import TextCompletionRequest
+```
+
+## 实施说明
+
+1. 通过在根的 `__init__.py` 中保持导入，保持与以前的兼容性
+2. 逐步移动文件，并在需要时更新导入
+3. 考虑添加一个 `legacy.py`，用于在过渡期间导入所有内容
+4. 更新文档以反映新的结构
+
+<function_calls>
+<invoke name="TodoWrite">
+<parameter name="todos">[{"id": "1", "content": "检查当前 Schema 目录结构", "status": "completed", "priority": "high"}, {"id": "2", "content": "分析 Schema 文件及其用途", "status": "completed", "priority": "high"}, {"id": "3", "content": "提出改进的命名和结构", "status": "completed", "priority": "high"}]