docs: make Chinese README the default

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+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/open-metadata/OpenMetadata) · [上游 README](https://github.com/open-metadata/OpenMetadata/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 # OpenMetadata

 ![Commit Activity](https://img.shields.io/github/commit-activity/m/open-metadata/OpenMetadata?style=for-the-badge) [![Release](https://img.shields.io/github/release/open-metadata/OpenMetadata/all.svg?style=for-the-badge)](https://github.com/open-metadata/OpenMetadata/releases)

-## The Open Context Layer for AI
+## AI 的开放上下文层

-**The largest and fastest-growing open-source project for AI context, data cataloging, and metadata management.**
+**面向 AI 上下文、数据目录与元数据管理的最大且增长最快的开源项目。**

-OpenMetadata is the open platform for trusted data context, organizational memory, and business semantics for every data user, AI assistant, and agent.
+OpenMetadata 是一个开放平台，为每位数据用户、AI 助手和智能体提供可信的数据上下文、组织记忆与业务语义。

-OpenMetadata connects technical metadata, data quality signals, lineage, column-level lineage, ownership, usage, policies, conversations, memories, glossaries, classifications, metrics, domains, data contracts, and data products into a unified metadata knowledge graph. With **130+ connectors**, open metadata standards, semantic search, APIs, SDKs, and an MCP server, OpenMetadata gives every user and AI system the governed context it needs to discover, understand, trust, remember, and use data.
+OpenMetadata 将技术元数据、数据质量信号、血缘（lineage）、列级血缘、所有权、使用情况、策略、对话、记忆、术语表、分类、指标、数据域、数据契约和数据产品整合为统一的元数据知识图谱。借助 **130+ 连接器**、开放元数据标准、语义搜索、API、SDK 和 MCP 服务器，OpenMetadata 为每位用户和 AI 系统提供受治理的上下文，以发现、理解、信任、记忆和使用数据。

-**AI does not need another raw database connector. AI needs context + memory.**
+**AI 不需要又一个原始数据库连接器。AI 需要上下文 + 记忆。**

-![OpenMetadata: The Open Context Layer for AI](docs/assets/open-context-layer-hero.png)
+![OpenMetadata：AI 的开放上下文层](docs/assets/open-context-layer-hero.png)

-OpenMetadata provides the context AI needs to know:
+OpenMetadata 提供 AI 需要了解的上下文：

- what data exists
- what it means
- who owns it
- how it is used
- where it came from
- where it flows
- whether it is fresh, tested, certified, and trusted
- which business concepts, classifications, glossary terms, policies, contracts, and data products apply
- what downstream dashboards, pipelines, metrics, ML models, and applications depend on it
- what conversations, decisions, assumptions, and memory nuggets have already been captured about it
+- 存在哪些数据
+- 数据的含义
+- 谁拥有它
+- 如何使用
+- 来源
+- 流向
+- 是否新鲜、经过测试、已认证且可信
+- 适用哪些业务概念、分类、术语表条目、策略、契约和数据产品
+- 哪些下游仪表盘、流水线、指标、ML 模型和应用依赖它
+- 已记录哪些关于它的对话、决策、假设和记忆片段（memory nuggets）

 ---

-## Why OpenMetadata for AI?
+## 为何选择 OpenMetadata 用于 AI？

-AI systems need more than data access. They need governed context, business meaning, trust signals, lineage, usage, ownership, standards, and organizational memory.
+AI 系统需要的不仅是数据访问。它们还需要受治理的上下文、业务含义、信任信号、血缘、使用情况、所有权、标准以及组织记忆。

-A direct connection to a warehouse, lake, dashboard, or pipeline exposes raw structures. It does not tell an AI assistant what the data means, whether it is certified, who owns it, which policies apply, what contract governs it, what breaks if it changes, or what the organization has already learned about it.
+直接连接数据仓库、数据湖、仪表盘或流水线只会暴露原始结构。它无法告诉 AI 助手数据的含义、是否已认证、谁拥有它、适用哪些策略、受哪份契约约束、变更会破坏什么，或组织已从中获得哪些经验。

-OpenMetadata is the open context layer that gives every data user and AI agent the full picture of enterprise data.
+OpenMetadata 是开放的上下文层，为每位数据用户和 AI 智能体提供企业数据的全景视图。

-OpenMetadata brings together five capabilities:
+OpenMetadata 汇聚五大能力：

-1. **Context** — technical, operational, trust, usage, and lineage metadata from across the data ecosystem.
-2. **Semantics** — business meaning through glossaries, metrics, classifications, domains, policies, ontologies, and data products.
-3. **Knowledge Graph** — relationships connecting assets, columns, people, teams, quality, lineage, policies, memories, contracts, and business concepts.
-4. **Memory** — conversations, AI threads, decisions, assumptions, runbooks, remediation notes, and reusable memory nuggets that preserve tribal knowledge.
-5. **Activation** — MCP, Semantic Search, APIs, SDKs, events, and workflows that make context usable by AI assistants, agents, applications, and humans.
+1. **上下文（Context）** — 来自整个数据生态系统的技术、运营、信任、使用与血缘元数据。
+2. **语义（Semantics）** — 通过术语表、指标、分类、数据域、策略、本体（ontologies）和数据产品表达业务含义。
+3. **知识图谱（Knowledge Graph）** — 连接资产、列、人员、团队、质量、血缘、策略、记忆、契约与业务概念的关系。
+4. **记忆（Memory）** — 对话、AI 线程、决策、假设、运行手册、修复说明以及可复用的记忆片段，用于保存隐性知识（tribal knowledge）。
+5. **激活（Activation）** — MCP、语义搜索、API、SDK、事件与工作流，使上下文可被 AI 助手、智能体、应用和人类使用。

-With OpenMetadata, users and AI agents can answer:
+借助 OpenMetadata，用户和 AI 智能体可以回答：

- What does this metric mean and how is it calculated?
- Which datasets power this dashboard?
- Who owns this data product?
- Which data contract applies?
- Is this dataset fresh, tested, certified, and trusted?
- Which downstream dashboards, pipelines, or ML models are affected by this column change?
- Which columns contain sensitive customer information?
- Which glossary terms, policies, standards, and business concepts apply?
- What decisions, assumptions, incidents, or conversations have already been captured about this asset?
+- 该指标的含义是什么，如何计算？
+- 哪些数据集为该仪表盘提供数据？
+- 谁拥有该数据产品？
+- 适用哪份数据契约？
+- 该数据集是否新鲜、经过测试、已认证且可信？
+- 该列变更会影响哪些下游仪表盘、流水线或 ML 模型？
+- 哪些列包含敏感客户信息？
+- 适用哪些术语表条目、策略、标准与业务概念？
+- 关于该资产已记录哪些决策、假设、事件或对话？

 ---

-## The Context OpenMetadata Connects
+## OpenMetadata 连接的上下文

-OpenMetadata collects and connects the context AI needs to reason safely over enterprise data.
+OpenMetadata 收集并连接 AI 安全推理企业数据所需的上下文。

-| Context type | What OpenMetadata captures | Why it matters for AI |
+| 上下文类型 | OpenMetadata 捕获的内容 | 对 AI 的意义 |
 | --- | --- | --- |
-| **Technical metadata** | Databases, schemas, tables, columns, topics, dashboards, charts, pipelines, APIs, search indexes, ML models, storage assets, data types, constraints, descriptions, joins, sample queries, service metadata, owners, teams, usage, domains, and data products | Helps AI discover what exists and understand how assets are structured |
-| **Quality and trust** | Test cases, test suites, freshness checks, volume checks, null, uniqueness, distribution, custom tests, profiling results, observability signals, incidents, alerts, and quality history | Helps AI avoid treating every dataset as equally trustworthy |
-| **Lineage and impact** | Upstream and downstream lineage, table lineage, column-level lineage, dashboard lineage, pipeline lineage, metric lineage, ML model lineage, API and topic dependencies, and OpenLineage events | Helps AI explain where data came from, where it flows, and what changes may break |
-| **Semantics** | Glossaries, business terms, synonyms, related terms, metrics, KPIs, classifications, tags, domains, data products, policies, personas, lifecycle states, and ontologies | Helps AI map technical names to business meaning |
-| **Governance** | Owners, stewards, teams, policies, roles, classifications, access context, certification, review workflows, lifecycle states, and data contracts | Helps AI act with policy-aware context |
-| **Memory and tribal knowledge** | Conversations, AI threads, decisions, assumptions, runbooks, remediation notes, incident learnings, and reusable memory nuggets attached to assets, users, teams, data products, and agent workflows | Helps humans and agents inherit what the organization already learned instead of rediscovering it in every conversation |
-| **Standards and interoperability** | DCAT, DPROD, PROV-O, OpenLineage, ODCS, RDF/OWL, JSON-LD, SHACL, JSON Schema, APIs, events, and metadata schemas | Helps context move across tools, agents, catalogs, contracts, and knowledge graphs |
+| **技术元数据** | 数据库、模式、表、列、主题、仪表盘、图表、流水线、API、搜索索引、ML 模型、存储资产、数据类型、约束、描述、连接、示例查询、服务元数据、所有者、团队、使用情况、数据域和数据产品 | 帮助 AI 发现存在哪些资产并理解其结构 |
+| **质量与信任** | 测试用例、测试套件、新鲜度检查、数据量检查、空值、唯一性、分布、自定义测试、剖析结果、可观测性信号、事件、告警与质量历史 | 帮助 AI 避免将每个数据集都视为同等可信 |
+| **血缘与影响** | 上游与下游血缘、表级血缘、列级血缘、仪表盘血缘、流水线血缘、指标血缘、ML 模型血缘、API 与主题依赖，以及 OpenLineage 事件 | 帮助 AI 说明数据来源、流向以及哪些变更可能引发破坏 |
+| **语义** | 术语表、业务术语、同义词、相关术语、指标、KPI、分类、标签、数据域、数据产品、策略、角色画像（personas）、生命周期状态与本体 | 帮助 AI 将技术名称映射到业务含义 |
+| **治理** | 所有者、数据管家（stewards）、团队、策略、角色、分类、访问上下文、认证、评审工作流、生命周期状态与数据契约 | 帮助 AI 在具备策略感知的上下文中行动 |
+| **记忆与隐性知识** | 对话、AI 线程、决策、假设、运行手册、修复说明、事件经验以及附加于资产、用户、团队、数据产品和智能体工作流的可复用记忆片段 | 帮助人类与智能体继承组织已有经验，而非在每次对话中重新发现 |
+| **标准与互操作性** | DCAT、DPROD、PROV-O、OpenLineage、ODCS、RDF/OWL、JSON-LD、SHACL、JSON Schema、API、事件与元数据模式 | 帮助上下文在工具、智能体、目录、契约与知识图谱之间流转 |

 ---

-## Architecture: Context + Memory Graph
+## 架构：上下文 + 记忆图谱

-![How OpenMetadata Works](docs/assets/open-context-layer-architecture.png)
+![OpenMetadata 如何工作](docs/assets/open-context-layer-architecture.png)

-OpenMetadata is built around an open, schema-first metadata graph.
+OpenMetadata 围绕开放的、模式优先（schema-first）的元数据图谱构建。

-1. **Collect** metadata from warehouses, lakes, BI tools, pipelines, ML platforms, messaging systems, storage systems, APIs, search systems, SaaS applications, metadata systems, documents, conversations, and agent workflows through **130+ connectors**, ingestion APIs, events, and SDKs.
-2. **Normalize** metadata with open schemas and standards so every asset, relationship, policy, contract, lineage event, and memory can be represented consistently.
-3. **Connect** technical metadata, quality signals, lineage, ownership, usage, policies, conversations, memories, semantics, domains, contracts, and data products into one graph.
-4. **Preserve Memory** by turning conversations, AI threads, decisions, assumptions, runbooks, and remediation notes into reusable governed memory nuggets tied to data assets and business context.
-5. **Govern** context with open standards, classifications, policies, roles, data quality, review workflows, data contracts, and stewardship.
-6. **Activate** that context through Semantic Search, MCP, APIs, SDKs, events, webhooks, metadata applications, and AI workflows.
+1. **收集（Collect）** 通过 **130+ 连接器**、摄取 API、事件与 SDK，从数据仓库、数据湖、BI 工具、流水线、ML 平台、消息系统、存储系统、API、搜索系统、SaaS 应用、元数据系统、文档、对话与智能体工作流收集元数据。
+2. **规范化（Normalize）** 使用开放模式与标准，使每项资产、关系、策略、契约、血缘事件与记忆都能一致表示。
+3. **连接（Connect）** 将技术元数据、质量信号、血缘、所有权、使用情况、策略、对话、记忆、语义、数据域、契约与数据产品整合为一张图谱。
+4. **保存记忆（Preserve Memory）** 将对话、AI 线程、决策、假设、运行手册与修复说明转化为可复用、受治理的记忆片段，并与数据资产及业务上下文关联。
+5. **治理（Govern）** 通过开放标准、分类、策略、角色、数据质量、评审工作流、数据契约与数据管理（stewardship）对上下文进行治理。
+6. **激活（Activate）** 通过语义搜索、MCP、API、SDK、事件、Webhook、元数据应用与 AI 工作流使上下文可用。

-Memory is part of the architecture, not a side channel. It lets engineers use APIs, SDKs, MCP, or AI workflows to preserve conversational context and convert tribal knowledge into reusable organizational knowledge.
+记忆是架构的一部分，而非旁路通道。它使工程师可通过 API、SDK、MCP 或 AI 工作流保存对话上下文，并将隐性知识转化为可复用的组织知识。

 ---

-## Context Graph, Semantics, and Memory
+## 上下文图谱、语义与记忆

-![OpenMetadata Context Graph](docs/assets/open-context-layer-graph.png)
+![OpenMetadata 上下文图谱](docs/assets/open-context-layer-graph.png)

-The OpenMetadata graph does not only store data assets. It stores the relationships between assets, columns, owners, teams, policies, quality tests, lineage, classifications, glossary terms, metrics, domains, data contracts, data products, conversations, and memory nuggets.
+OpenMetadata 图谱不仅存储数据资产，还存储资产、列、所有者、团队、策略、质量测试、血缘、分类、术语表条目、指标、数据域、数据契约、数据产品、对话与记忆片段之间的关系。

-Example relationships:
+示例关系：

 ```text
 Table               ──hasColumn────────────> Column
@@ -120,191 +126,198 @@ Memory              ──documentsDecisionFor> Metric
 Memory              ──attachedTo──────────> Table / Column / Topic / Dashboard / Pipeline / API
 ```

-This graph gives AI systems the relationships, meaning, memory, and governance they need to reason across the data estate.
+该图谱为 AI 系统提供跨数据资产域进行推理所需的关系、语义、记忆与治理能力。

 ---

-## Memories: Organizational Context for Humans and Agents
+## 记忆（Memories）：面向人类与智能体的组织上下文

 ![Memory Primitives](docs/assets/memory-primitives.png)

-Memories preserve the important context that usually disappears inside chats, tickets, meetings, notebooks, and AI agent threads.
+记忆会保留那些通常会在聊天、工单、会议、笔记本和 AI 智能体线程中消失的重要上下文。

-A memory is an open, governed OpenMetadata entity that can be tied to data assets, users, teams, threads, domains, data products, metrics, policies, incidents, and workflows. Engineers can capture and retrieve memories through APIs, SDKs, MCP, chat, or AI applications.
+记忆（memory）是一种开放、受治理的 OpenMetadata 实体，可与数据资产、用户、团队、线程、域、数据产品、指标、策略、事件和工作流关联。工程师可通过 API、SDK、MCP、聊天或 AI 应用来捕获和检索记忆。

-Use memories to preserve:
+使用记忆来保留：

- why a metric changed
- why a column was renamed
- what assumption was used in an analysis
- which remediation fixed a data quality issue
- which dashboard or data product a decision applies to
- what an AI agent learned while investigating an incident
- what a domain expert explained in a conversation
+- 某个指标为何发生变化
+- 某列为何被重命名
+- 某次分析使用了哪些假设
+- 哪项修复措施解决了数据质量问题
+- 某项决策适用于哪个仪表板或数据产品
+- AI 智能体在调查事件过程中学到了什么
+- 领域专家在对话中解释了什么

-Memories unlock tribal knowledge by making it reusable, governed, searchable, and available to every human, assistant, and agent that touches your data.
+记忆通过让隐性知识可复用、受治理、可搜索，并可供每一位接触你数据的人类、助手和智能体使用，从而释放组织内的隐性知识（tribal knowledge）。

 ---

-## MCP, Semantic Search, APIs, AI SDK, and Memory
+## MCP、语义搜索、API、AI SDK 与记忆

-OpenMetadata makes context actionable through AI- and developer-friendly interfaces.
+OpenMetadata 通过面向 AI 和开发者的友好接口，让上下文可付诸行动。

 ### MCP Server

-OpenMetadata includes an MCP server that lets MCP-compatible assistants and agents interact with the metadata graph through natural language.
+OpenMetadata 内置 MCP 服务器，让兼容 MCP 的助手和智能体能够通过自然语言与元数据图谱交互。

-AI assistants can use OpenMetadata MCP to:
+AI 助手可使用 OpenMetadata MCP 来：

- search metadata
- run semantic search
- retrieve entity details
- inspect lineage
- understand data contracts and policy context
- retrieve or preserve memory nuggets
- update descriptions, tags, owners, and other metadata
- create glossary terms and lineage
- list and create data quality tests
- analyze root causes of data quality failures
+- 搜索元数据
+- 运行语义搜索
+- 检索实体详情
+- 检查血缘（lineage）
+- 理解数据契约与策略上下文
+- 检索或保存记忆片段（memory nuggets）
+- 更新描述、标签、所有者及其他元数据
+- 创建术语表条目与血缘
+- 列出并创建数据质量测试
+- 分析数据质量失败的根本原因

-Get started: [OpenMetadata MCP Server Documentation](https://docs.open-metadata.org/latest/how-to-guides/mcp)
+入门指南：[OpenMetadata MCP Server Documentation](https://docs.open-metadata.org/latest/how-to-guides/mcp)

 ### Semantic Search

-Semantic Search lets users and AI assistants find data assets by meaning, not only exact keywords.
+语义搜索（Semantic Search）让用户和 AI 助手不仅能按精确关键词，还能按语义含义查找数据资产。

 ```text
 Find trusted customer purchase datasets with known data quality issues and recent remediation notes.
 ```

-OpenMetadata can surface conceptually related assets, metrics, glossary terms, data products, memory nuggets, and governance context even when names differ across domains, tools, and teams.
+即使名称在不同域、工具和团队之间不一致，OpenMetadata 也能呈现概念上相关的资产、指标、术语表条目、数据产品、记忆片段和治理上下文。

 ### APIs, SDKs, Events, and Webhooks

-OpenMetadata exposes APIs, SDKs, events, and webhooks so teams can ingest, update, search, subscribe to, and automate metadata across their ecosystem.
+OpenMetadata 提供 API、SDK、事件和 Webhook，供团队在生态系统中摄取、更新、搜索、订阅和自动化元数据。

-Developers can use the AI SDK to build custom AI applications that use OpenMetadata context and memory programmatically.
+开发者可使用 AI SDK 构建自定义 AI 应用，以编程方式利用 OpenMetadata 的上下文与记忆。

 ---

-## What You Can Build
+## 你可以构建什么

 ### AI Data Discovery
-Ask natural-language questions over the metadata graph and find relevant assets even when names and keywords do not match exactly.
+
+对元数据图谱提出自然语言问题，即使名称和关键词不完全匹配，也能找到相关资产。

 ### Trusted AI Assistants
-Ground AI responses in governed metadata: owners, descriptions, glossary terms, classifications, quality, freshness, usage, lineage, policies, contracts, and memory.
+
+将 AI 回答建立在受治理的元数据之上：所有者、描述、术语表条目、分类、质量、新鲜度、使用情况、血缘、策略、契约和记忆。

 ### Agent Memory and Tribal Knowledge
-Capture conversations, decisions, assumptions, runbooks, and agent learnings as governed memory nuggets that can be reused by every data user and AI agent.
+
+将对话、决策、假设、运行手册（runbook）和智能体所学内容捕获为受治理的记忆片段，供每位数据用户和 AI 智能体复用。

 ### Impact Analysis Agents
-Ask what will break if a table, column, pipeline, dashboard, metric, ML feature, contract, or data product changes.
+
+询问如果表、列、流水线、仪表板、指标、ML 特征、契约或数据产品发生变化，将会破坏什么。

 ### Governance Automation
-Use agents to suggest descriptions, assign glossary terms, identify sensitive data, propose ownership, enforce contract context, and manage stewardship workflows.
+
+使用智能体建议描述、分配术语表条目、识别敏感数据、提议所有权、强制执行契约上下文，并管理数据管理（stewardship）工作流。

 ### Data Quality Automation
-Use AI workflows to create tests, summarize failures, identify root causes, preserve remediation memory, and recommend next actions.
+
+使用 AI 工作流创建测试、汇总失败、识别根本原因、保留修复记忆，并推荐后续行动。

 ### Developer and Coding Agent Workflows
-Connect coding agents to OpenMetadata so they understand schemas, owners, lineage, business definitions, quality expectations, contracts, and memory before generating SQL, dbt models, documentation, tests, migration plans, or impact analysis.
+
+将编码智能体连接到 OpenMetadata，使其在生成 SQL、dbt 模型、文档、测试、迁移计划或影响分析之前，先理解模式、所有者、血缘、业务定义、质量预期、契约和记忆。

 ---

-## Open Standards and Interoperability
+## 开放标准与互操作性

-OpenMetadata is built on open metadata standards.
+OpenMetadata 构建于开放的元数据标准之上。

-[OpenMetadata Standards](https://openmetadatastandards.org/) is the open-source home for schemas, APIs, ontologies, event models, and semantic specifications behind OpenMetadata.
+[OpenMetadata Standards](https://openmetadatastandards.org/) 是 OpenMetadata 背后的模式、API、本体、事件模型和语义规范的开放源码之家。

-It provides:
+它提供：

- 700+ JSON Schemas for metadata entities, APIs, configurations, events, and relationships
- RDF/OWL ontologies for semantic web, linked data, and knowledge graph use cases
- SHACL shapes for validation
- JSON-LD contexts for semantic interoperability
- standards for governance, lineage, quality, observability, teams, users, roles, policies, events, contracts, and data products
+- 700+ 个用于元数据实体、API、配置、事件和关系的 JSON Schema
+- 面向语义网、关联数据和知识图谱用例的 RDF/OWL 本体
+- 用于验证的 SHACL 形状（shapes）
+- 用于语义互操作的 JSON-LD 上下文
+- 涵盖治理、血缘、质量、可观测性、团队、用户、角色、策略、事件、契约和数据产品的标准

-OpenMetadata supports and aligns with the standards that matter for AI context and data ecosystems:
+OpenMetadata 支持并与对 AI 上下文和数据生态系统至关重要的标准保持一致：

 | Standard | How OpenMetadata uses it |
 | --- | --- |
-| **DCAT / DPROD** | Represents catalog and data-product context in interoperable semantic models, including datasets, data services, distributions, domains, owners, input and output datasets, lifecycle state, purpose, and policies. |
-| **PROV-O** | Uses W3C provenance semantics for lineage, generated/derived data, agents, activities, ownership, and explainable context. |
-| **OpenLineage Support** | Accepts and connects OpenLineage-compatible lineage events so pipeline execution metadata can enrich the broader OpenMetadata graph. |
-| **ODCS Support** | Supports Open Data Contract Standard 3.1 for interoperable data contracts, contract import/export, schema expectations, quality rules, SLAs, support channels, roles, and producer-consumer agreements. |
-| **RDF/OWL, JSON-LD, SHACL** | Makes metadata graph-friendly, semantically interoperable, and validatable for linked data, knowledge graph, and AI use cases. |
-| **JSON Schema, APIs, Events** | Keeps metadata portable, automation-friendly, and extensible across tools, agents, and custom applications. |
+| **DCAT / DPROD** | 在可互操作的语义模型中表示目录和数据产品上下文，包括数据集、数据服务、分发、域、所有者、输入与输出数据集、生命周期状态、用途和策略。 |
+| **PROV-O** | 使用 W3C 溯源语义表示血缘、生成/派生数据、智能体、活动、所有权和可解释上下文。 |
+| **OpenLineage Support** | 接收并连接兼容 OpenLineage 的血缘事件，使流水线执行元数据能够丰富更广泛的 OpenMetadata 图谱。 |
+| **ODCS Support** | 支持 Open Data Contract Standard 3.1，实现可互操作的数据契约、契约导入/导出、模式预期、质量规则、SLA、支持渠道、角色以及生产者-消费者协议。 |
+| **RDF/OWL, JSON-LD, SHACL** | 使元数据对图谱友好、语义可互操作，并可在关联数据、知识图谱和 AI 用例中进行验证。 |
+| **JSON Schema, APIs, Events** | 使元数据可移植、便于自动化，并可在工具、智能体和自定义应用之间扩展。 |

-These standards make OpenMetadata a foundation for interoperable semantic metadata, linked data, data products, data contracts, lineage, provenance, and enterprise knowledge graphs.
+这些标准使 OpenMetadata 成为可互操作语义元数据、关联数据、数据产品、数据契约、血缘、溯源和企业知识图谱的基础。

 ---

-## Core Platform Capabilities
+## 核心平台能力

 | Capability | Includes |
 | --- | --- |
-| **AI Context and Memory** | memory nuggets, conversations, agent threads, decisions, assumptions, remediation notes, runbooks, context retrieval, and governed agent memory |
-| **Discovery and Understanding** | asset search, semantic search, descriptions, sample data, usage, ownership, conversations, tasks, announcements |
-| **Governance and Semantics** | glossaries, classifications, tags, metrics, KPIs, domains, data products, policies, roles, certification, lifecycle states |
-| **Data Contracts and Standards** | ODCS 3.1 support, contract import/export, schema expectations, SLAs, terms of service, semantic relationships, data product context, DCAT/DPROD, PROV-O, RDF/OWL, JSON-LD, SHACL |
-| **Data Quality and Observability** | tests, profiling, freshness, volume, null, uniqueness, distribution checks, alerts, incidents, root-cause workflows |
-| **Lineage and Impact Analysis** | table lineage, column-level lineage, dashboard lineage, pipeline lineage, metric lineage, ML model lineage, OpenLineage support, impact analysis |
-| **Security and Access Control** | authentication, authorization, roles, policies, SSO, bot tokens, user tokens, MCP authentication, governed metadata actions |
-| **Extensibility and Automation** | 130+ connectors, APIs, SDKs, webhooks, events, applications, ingestion framework, custom connectors, custom properties, MCP tools, AI SDK workflows |
+| **AI Context and Memory** | 记忆片段、对话、智能体线程、决策、假设、修复备注、运行手册、上下文检索，以及受治理的智能体记忆 |
+| **Discovery and Understanding** | 资产搜索、语义搜索、描述、样本数据、使用情况、所有权、对话、任务、公告 |
+| **Governance and Semantics** | 术语表、分类、标签、指标、KPI、域、数据产品、策略、角色、认证、生命周期状态 |
+| **Data Contracts and Standards** | ODCS 3.1 支持、契约导入/导出、模式预期、SLA、服务条款、语义关系、数据产品上下文、DCAT/DPROD、PROV-O、RDF/OWL、JSON-LD、SHACL |
+| **Data Quality and Observability** | 测试、剖析、新鲜度、数据量、空值、唯一性、分布检查、告警、事件、根因工作流 |
+| **Lineage and Impact Analysis** | 表级血缘、列级血缘、仪表板血缘、流水线血缘、指标血缘、ML 模型血缘、OpenLineage 支持、影响分析 |
+| **Security and Access Control** | 身份验证、授权、角色、策略、SSO、机器人令牌、用户令牌、MCP 身份验证、受治理的元数据操作 |
+| **Extensibility and Automation** | 130+ 连接器、API、SDK、Webhook、事件、应用、摄取框架、自定义连接器、自定义属性、MCP 工具、AI SDK 工作流 |

 ---

-## Quickstart
+## 快速入门

-1. **Try OpenMetadata**: [OpenMetadata Sandbox](https://sandbox.open-metadata.org)
-2. **Install OpenMetadata**: [Quickstart Guide](https://docs.open-metadata.org/latest/quick-start)
-3. **Ingest Metadata** from a warehouse, BI tool, pipeline system, data quality tool, lineage source, contract source, or memory-producing workflow.
-4. **Build Context** with descriptions, owners, teams, domains, data products, quality tests, freshness, usage, lineage, and data contracts.
-5. **Add Semantics** with glossaries, classifications, tags, metrics, KPIs, policies, domains, DCAT/DPROD-aligned data products, and PROV-O lineage context.
-6. **Capture Memory** from conversations, AI threads, incidents, remediation notes, assumptions, and decisions.
-7. **Enable Semantic Search** so users and AI assistants can search by meaning.
-8. **Connect an MCP Client** to give AI assistants and agents governed access to OpenMetadata context and memory.
-9. **Build AI Applications** using OpenMetadata APIs, SDKs, MCP tools, events, and AI SDK workflows.
+1. **Try OpenMetadata**：[OpenMetadata Sandbox](https://sandbox.open-metadata.org)
+2. **Install OpenMetadata**：[Quickstart Guide](https://docs.open-metadata.org/latest/quick-start)
+3. **Ingest Metadata**：从数据仓库、BI 工具、流水线系统、数据质量工具、血缘来源、契约来源或产生记忆的工作流中摄取元数据。
+4. **Build Context**：通过描述、所有者、团队、域、数据产品、质量测试、新鲜度、使用情况、血缘和数据契约来构建上下文。
+5. **Add Semantics**：通过术语表、分类、标签、指标、KPI、策略、域、与 DCAT/DPROD 对齐的数据产品，以及 PROV-O 血缘上下文来添加语义。
+6. **Capture Memory**：从对话、AI 线程、事件、修复备注、假设和决策中捕获记忆。
+7. **Enable Semantic Search**：让用户和 AI 助手能够按语义含义进行搜索。
+8. **Connect an MCP Client**：为 AI 助手和智能体提供对 OpenMetadata 上下文与记忆的受治理访问。
+9. **Build AI Applications**：使用 OpenMetadata API、SDK、MCP 工具、事件和 AI SDK 工作流构建 AI 应用。

 ---

-## Documentation and Community
+## 文档与社区

- Documentation: [docs.open-metadata.org](https://docs.open-metadata.org/)
- MCP Server: [OpenMetadata MCP Documentation](https://docs.open-metadata.org/latest/how-to-guides/mcp)
- OpenLineage Connector: [OpenMetadata OpenLineage Documentation](https://docs.open-metadata.org/latest/connectors/pipeline/openlineage)
- OpenMetadata Standards: [openmetadatastandards.org](https://openmetadatastandards.org/)
- Website: [open-metadata.org](https://open-metadata.org/)
- Slack Community: [slack.open-metadata.org](https://slack.open-metadata.org/)
- Blog: [blog.open-metadata.org](https://blog.open-metadata.org/)
+- 文档：[docs.open-metadata.org](https://docs.open-metadata.org/)
+- MCP 服务器：[OpenMetadata MCP Documentation](https://docs.open-metadata.org/latest/how-to-guides/mcp)
+- OpenLineage 连接器：[OpenMetadata OpenLineage Documentation](https://docs.open-metadata.org/latest/connectors/pipeline/openlineage)
+- OpenMetadata 标准：[openmetadatastandards.org](https://openmetadatastandards.org/)
+- 网站：[open-metadata.org](https://open-metadata.org/)
+- Slack 社区：[slack.open-metadata.org](https://slack.open-metadata.org/)
+- 博客：[blog.open-metadata.org](https://blog.open-metadata.org/)

 ---

-## Open Source and Enterprise AI
+## 开源与企业 AI

-OpenMetadata is the open-source foundation for AI context, metadata, organizational memory, semantics, governance, quality, lineage, data contracts, open standards, APIs, MCP, and AI SDK workflows.
+OpenMetadata 是面向 AI 上下文（AI context）、元数据（metadata）、组织记忆（organizational memory）、语义（semantics）、治理（governance）、质量（quality）、血缘（lineage）、数据契约（data contracts）、开放标准（open standards）、API、MCP 与 AI SDK 工作流的开源基础。

-For managed enterprise capabilities, AI agents, automation, AI Studio, enterprise MCP workflows, commercial support, and managed operations, see Collate:
+如需托管式企业能力、AI 智能体（AI agents）、自动化、AI Studio、企业级 MCP 工作流、商业支持与托管运维，请参阅 Collate：

 - [Collate](https://www.getcollate.io/)
 - [Collate AI](https://www.getcollate.io/collate-ai)

 ---

-## Contributing
+## 贡献

-We welcome contributions from the community. You can help improve metadata schemas and standards, add connectors, improve ingestion workflows, enhance MCP tools, improve semantic search, add memory workflows, add documentation, fix bugs, and improve the user experience.
+我们欢迎来自社区的贡献。你可以帮助改进元数据模式与标准、添加连接器、改进摄取（ingestion）工作流、增强 MCP 工具、改进语义搜索、添加记忆（memory）工作流、补充文档、修复缺陷并提升用户体验。

-See the contribution guide in this repository to get started.
+请参阅本仓库中的贡献指南以开始参与。

- [How To Contribute](https://docs.open-metadata.org/v1.12.x/developers/contribute)
- [Development Environment Setup](https://docs.open-metadata.org/v1.12.x/developers/contribute/development-environment-setup)
- [Build Code & Run Tests](https://docs.open-metadata.org/v1.12.x/developers/contribute/build-code-and-run-tests)
+- [如何贡献](https://docs.open-metadata.org/v1.12.x/developers/contribute)
+- [开发环境搭建](https://docs.open-metadata.org/v1.12.x/developers/contribute/development-environment-setup)
+- [构建代码与运行测试](https://docs.open-metadata.org/v1.12.x/developers/contribute/build-code-and-run-tests)

 ---

-## License
+## 许可证

-OpenMetadata is released under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
+OpenMetadata 根据 [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). 发布。