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+# MCP + openGauss
+随着AI从静态推理向动态交互演进,智能体(Agent)逐渐成为焦点。Agent不仅能够调用LLM进行推理,还能访问数据库、调用API、执行任务。然而,当前LLM和Agent之间缺乏标准化交互协议, 每个新数据源都需要自定义实现,使得真正互联的系统难以扩展。MCP(Model Context Protocol, 模型上下文协议)解决了这一挑战,MCP是为LLM和Agent系统设计的标准化交互框架,使LLM可以与外部数据库、API和工具进行高效交互。
+
+## openGauss + MCP + LLM 架构
+
+**图 1** openGauss + MCP + LLM 架构
+
+

+
+
+## 快速搭建openGauss + MCP + LLM的AI Agent应用
+### 环境准备
+- 安装python3环境,安装uv。
+- 通过[容器部署](../InstallationGuide/容器镜像安装.md)并启动openGauss数据库。
+- 下载Claude Desktop配合MCP协议进行问答操作。
+
+### 获取openGauss_mcp_server源码
+访问链接 https://github.com/vincentsunx/mcp-openGauss.git 获取openGauss_mcp_server源码,当前版本为(0.1.0)。
+
+### 配置参数
+- 打开Claude Desktop设置,编辑配置文件。
+
+**图 2** Claude Desktop配置页面
+
+

+
+
+- 通过Edit Config增加配置
+
+```
+{
+ "mcpServers": {
+ "openGauss": {
+ "command": "uv",
+ "args": [
+ "--directory",
+ "path/to/openGauss_mcp_server",
+ "run",
+ "server.py"
+ ],
+ "env": {
+ "OPENGAUSS_HOST": "localhost",
+ "OPENGAUSS_PORT": "8888",
+ "OPENGAUSS_USER": "your_username",
+ "OPENGAUSS_PASSWORD": "your_password",
+ "OPENGAUSS_DBNAME": "your_database"
+ }
+ }
+ }
+}
+```
+## AI服务集成
+### 重新启动Claude Desktop
+可以看到可用MCP Tool, 执行sql通过openGauss server
+
+**图 3** Claude Desktop可用MCP Tool
+
+

+
+
+### 使用Cluade Desktop通过openGauss进行问答
+**图 4** Claude Desktop问答演示
+
+

+
+
+
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diff --git a/content/docs-lite/zh/menu/index.md b/content/docs-lite/zh/menu/index.md
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--- a/content/docs-lite/zh/menu/index.md
+++ b/content/docs-lite/zh/menu/index.md
@@ -634,6 +634,8 @@ headless: true
- 嵌入模型
- [BGE-M3]({{< relref "./docs/DataVec/embedding-bgem3.md" >}})
- [Nomic]({{< relref "./docs/DataVec/embedding-nomic.md" >}})
+ - Agents
+ - [MCP]({{< relref "./docs/DataVec/mcp.md" >}})
- 数据迁移
- [从Milvus迁移至openGauss DataVec]({{< relref "./docs/DataVec/milvus2datavec.md" >}})
- API Reference
diff --git a/content/zh/docs/DataVec/MCP.md b/content/zh/docs/DataVec/MCP.md
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index 0000000000000000000000000000000000000000..b5c256009c8ab5e34ca18645ad3163283cf34893
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@@ -0,0 +1,67 @@
+# MCP + openGauss
+随着AI从静态推理向动态交互演进,智能体(Agent)逐渐成为焦点。Agent不仅能够调用LLM进行推理,还能访问数据库、调用API、执行任务。然而,当前LLM和Agent之间缺乏标准化交互协议, 每个新数据源都需要自定义实现,使得真正互联的系统难以扩展。MCP(Model Context Protocol, 模型上下文协议)解决了这一挑战,MCP是为LLM和Agent系统设计的标准化交互框架,使LLM可以与外部数据库、API和工具进行高效交互。
+
+## openGauss + MCP + LLM 架构
+
+**图 1** openGauss + MCP + LLM 架构
+
+

+
+
+## 快速搭建openGauss + MCP + LLM的AI Agent应用
+### 环境准备
+- 安装python3环境,安装uv。
+- 通过[容器部署](../InstallationGuide/容器镜像安装.md)并启动openGauss数据库。
+- 下载Claude Desktop配合MCP协议进行问答操作。
+
+### 获取openGauss_mcp_server源码
+访问链接 https://github.com/vincentsunx/mcp-openGauss.git 获取openGauss_mcp_server源码,当前版本为(0.1.0)。
+
+### 配置参数
+- 打开Claude Desktop设置,编辑配置文件。
+
+**图 2** Claude Desktop配置页面
+
+

+
+
+- 通过Edit Config增加配置
+
+```
+{
+ "mcpServers": {
+ "openGauss": {
+ "command": "uv",
+ "args": [
+ "--directory",
+ "path/to/openGauss_mcp_server",s
+ "run",
+ "server.py"
+ ],
+ "env": {
+ "OPENGAUSS_HOST": "localhost",
+ "OPENGAUSS_PORT": "8888",
+ "OPENGAUSS_USER": "your_username",
+ "OPENGAUSS_PASSWORD": "your_password",
+ "OPENGAUSS_DBNAME": "your_database"
+ }
+ }
+ }
+}
+```
+## AI服务集成
+### 重新启动Claude Desktop
+可以看到可用MCP Tool, 执行sql通过openGauss server
+
+**图 3** Claude Desktop可用MCP Tool
+
+

+
+
+### 使用Cluade Desktop通过openGauss进行问答
+**图 4** Claude Desktop问答演示
+
+

+
+
+
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--- a/content/zh/menu/index.md
+++ b/content/zh/menu/index.md
@@ -919,6 +919,8 @@ headless: true
- 嵌入模型
- [BGE-M3]({{< relref "./docs/DataVec/embedding-bgem3.md" >}})
- [Nomic]({{< relref "./docs/DataVec/embedding-nomic.md" >}})
+ - Agents
+ - [MCP]({{< relref "./docs/DataVec/mcp.md" >}})
- 数据迁移
- [从Milvus迁移至openGauss DataVec]({{< relref "./docs/DataVec/milvus2datavec.md" >}})
- API Reference