[{"_1":2,"_301":-5,"_302":-5},"loaderData",{"_3":4,"_29":30,"_31":32,"_248":249},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","019726ab-5cb2-721b-855b-6627db6b8285","title","You need to sign in to access this page","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"BOT","id",7058884,"membership","referenceId","01971406-43e3-74e8-b880-2f000258007a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_33":34,"_75":-5,"_76":35,"_36":77,"_167":168,"_247":136},"availableLocales",[35,47,54,61,68],{"_36":37,"_40":44,"_45":46},"mcpServer",{"_38":39,"_40":41,"_42":43},"description","A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.","name","MCP Server for Vertex AI Search","readme","# MCP Server for Vertex AI Search\n\nThis is a MCP server to search documents using Vertex AI.\n\n## Architecture\n\nThis solution uses Gemini with Vertex AI grounding to search documents using your private data.\nGrounding improves the quality of search results by grounding Gemini's responses in your data stored in Vertex AI Datastore.\nWe can integrate one or multiple Vertex AI data stores to the MCP server.\nFor more details on grounding, refer to [Vertex AI Grounding Documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/ground-with-your-data).\n\n![Architecture](./docs/img//archirecture.png)\n\n## How to use\n\nThere are two ways to use this MCP server.\nIf you want to run this on Docker, the first approach would be good as Dockerfile is provided in the project.\n\n### 1. Clone the repository\n\n```shell\n# Clone the repository\ngit clone git@github.com:ubie-oss/mcp-vertexai-search.git\n\n# Create a virtual environment\nuv venv\n# Install the dependencies\nuv sync --all-extras\n\n# Check the command\nuv run mcp-vertexai-search\n```\n\n### Install the python package\n\nThe package isn't published to PyPI yet, but we can install it from the repository.\nWe need a config file derives from [config.yml.template](./config.yml.template) to run the MCP server, because the python package doesn't include the config template.\nPlease refer to [Appendix A: Config file](#appendix-a-config-file) for the details of the config file.\n\n```shell\n# Install the package\npip install git+https://github.com/ubie-oss/mcp-vertexai-search.git\n\n# Check the command\nmcp-vertexai-search --help\n```\n\n## Development\n\n### Prerequisites\n\n- [uv](https://docs.astral.sh/uv/getting-started/installation/)\n- Vertex AI data store\n - Please look into [the official documentation about data stores](https://cloud.google.com/generative-ai-app-builder/docs/create-datastore-ingest) for more information\n\n### Set up Local Environment\n\n```shell\n# Optional: Install uv\npython -m pip install -r requirements.setup.txt\n\n# Create a virtual environment\nuv venv\nuv sync --all-extras\n```\n\n### Run the MCP server\n\nThis supports two transports for SSE (Server-Sent Events) and stdio (Standard Input Output).\nWe can control the transport by setting the `--transport` flag.\n\nWe can configure the MCP server with a YAML file.\n[config.yml.template](./config.yml.template) is a template for the config file.\nPlease modify the config file to fit your needs.\n\n```bash\nuv run mcp-vertexai-search serve \\\n --config config.yml \\\n --transport \n```\n\n### Test the Vertex AI Search\n\nWe can test the Vertex AI Search by using the `mcp-vertexai-search search` command without the MCP server.\n\n```bash\nuv run mcp-vertexai-search search \\\n --config config.yml \\\n --query \n```\n\n## Appendix A: Config file\n\n[config.yml.template](./config.yml.template) is a template for the config file.\n\n- `server`\n - `server.name`: The name of the MCP server\n- `model`\n - `model.model_name`: The name of the Vertex AI model\n - `model.project_id`: The project ID of the Vertex AI model\n - `model.location`: The location of the model (e.g. us-central1)\n - `model.impersonate_service_account`: The service account to impersonate\n - `model.generate_content_config`: The configuration for the generate content API\n- `data_stores`: The list of Vertex AI data stores\n - `data_stores.project_id`: The project ID of the Vertex AI data store\n - `data_stores.location`: The location of the Vertex AI data store (e.g. us)\n - `data_stores.datastore_id`: The ID of the Vertex AI data store\n - `data_stores.tool_name`: The name of the tool\n - `data_stores.description`: The description of the Vertex AI data store\n","English","tag","en-US",{"_36":48,"_40":52,"_45":53},{"_38":49,"_40":50,"_42":51},"Un servidor que permite la búsqueda de documentos utilizando Vertex AI con conexión a tierra Gemini, mejorando los resultados de búsqueda al conectar las respuestas a datos privados almacenados en Vertex AI Datastore.","Servidor MCP para Vertex AI Search","# Servidor MCP para Vertex AI Search\n\nEste es un servidor MCP para buscar documentos utilizando Vertex AI.\n\n## Arquitectura\n\nEsta solución utiliza Gemini con Vertex AI Grounding para buscar documentos utilizando sus datos privados. Grounding mejora la calidad de los resultados de búsqueda al integrar las respuestas de Gemini con sus datos almacenados en el almacén de datos de Vertex AI. Podemos integrar uno o varios almacenes de datos de Vertex AI en el servidor MCP. Para más información sobre grounding, consulte [la documentación de Vertex AI Grounding](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/ground-with-your-data) .\n\n![Arquitectura](./docs/img//archirecture.png)\n\n## Cómo utilizar\n\nHay dos maneras de usar este servidor MCP. Si desea ejecutarlo en Docker, la primera opción es recomendable, ya que el proyecto incluye Dockerfile.\n\n### 1. Clonar el repositorio\n\n```shell\n# Clone the repository\ngit clone git@github.com:ubie-oss/mcp-vertexai-search.git\n\n# Create a virtual environment\nuv venv\n# Install the dependencies\nuv sync --all-extras\n\n# Check the command\nuv run mcp-vertexai-search\n```\n\n### Instalar el paquete de Python\n\nEl paquete aún no está publicado en PyPI, pero podemos instalarlo desde el repositorio. Necesitamos un archivo de configuración derivado de [config.yml.template](./config.yml.template) para ejecutar el servidor MCP, ya que el paquete de Python no incluye la plantilla de configuración. Consulte el [Apéndice A: Archivo de configuración](#appendix-a-config-file) para obtener más información sobre el archivo de configuración.\n\n```shell\n# Install the package\npip install git+https://github.com/ubie-oss/mcp-vertexai-search.git\n\n# Check the command\nmcp-vertexai-search --help\n```\n\n## Desarrollo\n\n### Prerrequisitos\n\n* [ultravioleta](https://docs.astral.sh/uv/getting-started/installation/)\n* Almacén de datos de Vertex AI\n * Consulte [la documentación oficial sobre almacenes de datos](https://cloud.google.com/generative-ai-app-builder/docs/create-datastore-ingest) para obtener más información.\n\n### Configurar el entorno local\n\n```shell\n# Optional: Install uv\npython -m pip install -r requirements.setup.txt\n\n# Create a virtual environment\nuv venv\nuv sync --all-extras\n```\n\n### Ejecutar el servidor MCP\n\nEsto admite dos transportes para SSE (Eventos enviados por el servidor) y stdio (Entrada/Salida estándar). Podemos controlar el transporte configurando el indicador `--transport` .\n\nPodemos configurar el servidor MCP con un archivo YAML. [config.yml.template](./config.yml.template) es una plantilla para el archivo de configuración. Por favor, modifique el archivo de configuración según sus necesidades.\n\n```bash\nuv run mcp-vertexai-search serve \\\n --config config.yml \\\n --transport \n```\n\n### Prueba la búsqueda de Vertex AI\n\nPodemos probar la búsqueda Vertex AI usando el comando `mcp-vertexai-search search` sin el servidor MCP.\n\n```bash\nuv run mcp-vertexai-search search \\\n --config config.yml \\\n --query \n```\n\n## Apéndice A: Archivo de configuración\n\n[config.yml.template](./config.yml.template) es una plantilla para el archivo de configuración.\n\n* `server`\n\n * `server.name` : El nombre del servidor MCP\n* `model`\n\n * `model.model_name` : El nombre del modelo Vertex AI\n * `model.project_id` : El ID del proyecto del modelo Vertex AI\n * `model.location` : La ubicación del modelo (por ejemplo, us-central1)\n * `model.impersonate_service_account` : La cuenta de servicio que se suplantará\n * `model.generate_content_config` : La configuración para la API de generación de contenido\n* `data_stores` : La lista de almacenes de datos de Vertex AI\n * `data_stores.project_id` : El ID del proyecto del almacén de datos de Vertex AI\n * `data_stores.location` : La ubicación del almacén de datos de Vertex AI (por ejemplo, nosotros)\n * `data_stores.datastore_id` : El ID del almacén de datos de Vertex AI\n * `data_stores.tool_name` : El nombre de la herramienta\n * `data_stores.description` : La descripción del almacén de datos de Vertex AI","Spanish","es-ES",{"_36":55,"_40":59,"_45":60},{"_38":56,"_40":57,"_42":58},"Vertex AI と Gemini グラウンディングを使用したドキュメント検索を可能にし、Vertex AI データストアに保存されているプライベートデータに応答をグラウンディングすることで検索結果を向上させるサーバーです。","Vertex AI Search 用 MCP サーバー","# Vertex AI Search 用 MCP サーバー\n\nこれは、Vertex AI を使用してドキュメントを検索するための MCP サーバーです。\n\n## 建築\n\nこのソリューションは、GeminiとVertex AI Groundingを活用し、お客様のプライベートデータを使用してドキュメントを検索します。Groundingは、GeminiのレスポンスをVertex AI Datastoreに保存されているデータにグラウンディングすることで、検索結果の品質を向上させます。MCPサーバーには、1つまたは複数のVertex AIデータストアを統合できます。Groundingの詳細については、 [Vertex AI Groundingのドキュメント](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/ground-with-your-data)をご覧ください。\n\n![建築](./docs/img//archirecture.png)\n\n## 使い方\n\nこのMCPサーバーを使用するには2つの方法があります。Docker上で実行したい場合は、プロジェクト内にDockerfileが提供されているため、最初の方法をお勧めします。\n\n### 1. リポジトリをクローンする\n\n```shell\n# Clone the repository\ngit clone git@github.com:ubie-oss/mcp-vertexai-search.git\n\n# Create a virtual environment\nuv venv\n# Install the dependencies\nuv sync --all-extras\n\n# Check the command\nuv run mcp-vertexai-search\n```\n\n### Pythonパッケージをインストールする\n\nこのパッケージはまだPyPIに公開されていませんが、リポジトリからインストールできます。MCPサーバーを実行するには、 [config.yml.template](./config.yml.template)から派生した設定ファイルが必要です。Pythonパッケージには設定テンプレートが含まれていないためです。設定ファイルの詳細については[、付録A「設定ファイル」](#appendix-a-config-file)を参照してください。\n\n```shell\n# Install the package\npip install git+https://github.com/ubie-oss/mcp-vertexai-search.git\n\n# Check the command\nmcp-vertexai-search --help\n```\n\n## 発達\n\n### 前提条件\n\n* [紫外線](https://docs.astral.sh/uv/getting-started/installation/)\n* Vertex AI データストア\n * 詳細については、[データストアに関する公式ドキュメント](https://cloud.google.com/generative-ai-app-builder/docs/create-datastore-ingest)をご覧ください。\n\n### ローカル環境の設定\n\n```shell\n# Optional: Install uv\npython -m pip install -r requirements.setup.txt\n\n# Create a virtual environment\nuv venv\nuv sync --all-extras\n```\n\n### MCPサーバーを実行する\n\nこれはSSE（Server-Sent Events）とstdio（Standard Input Output）の2つのトランスポートをサポートしています。-- `--transport`フラグを設定することでトランスポートを制御できます。\n\nMCPサーバーはYAMLファイルで設定できます。config.yml.template[は](./config.yml.template)設定ファイルのテンプレートです。必要に応じて設定ファイルを修正してください。\n\n```bash\nuv run mcp-vertexai-search serve \\\n --config config.yml \\\n --transport \n```\n\n### Vertex AI Searchをテストする\n\nMCP サーバーなしで`mcp-vertexai-search search`コマンドを使用して Vertex AI Search をテストできます。\n\n```bash\nuv run mcp-vertexai-search search \\\n --config config.yml \\\n --query \n```\n\n## 付録A: 設定ファイル\n\n[config.yml.template](./config.yml.template)は設定ファイルのテンプレートです。\n\n* `server`\n\n * `server.name` : MCPサーバーの名前\n* `model`\n\n * `model.model_name` : Vertex AI モデルの名前\n * `model.project_id` : Vertex AI モデルのプロジェクト ID\n * `model.location` : モデルの場所（例：us-central1）\n * `model.impersonate_service_account` : 偽装するサービスアカウント\n * `model.generate_content_config` : コンテンツ生成APIの設定\n* `data_stores` : Vertex AI データストアのリスト\n * `data_stores.project_id` : Vertex AI データストアのプロジェクト ID\n * `data_stores.location` : Vertex AI データストアの場所 (例: us)\n * `data_stores.datastore_id` : Vertex AI データストアのID\n * `data_stores.tool_name` : ツールの名前\n * `data_stores.description` : Vertex AI データストアの説明","Japanese","ja-JP",{"_36":62,"_40":66,"_45":67},{"_38":63,"_40":64,"_42":65},"Vertex AI와 Gemini 기반 ...","Vertex AI Search용 MCP 서버","# Vertex AI Search용 MCP 서버\n\n이는 Vertex AI를 사용하여 문서를 검색하는 MCP 서버입니다.\n\n## 건축학\n\n이 솔루션은 Vertex AI 기반 기능을 갖춘 Gemini를 사용하여 사용자의 개인 데이터를 기반으로 문서를 검색합니다. 기반 기능은 Vertex AI 데이터스토어에 저장된 사용자 데이터를 기반으로 Gemini의 응답을 분석하여 검색 결과의 품질을 향상시킵니다. 하나 이상의 Vertex AI 데이터스토어를 MCP 서버에 통합할 수 있습니다. 기반 기능에 대한 자세한 내용은 [Vertex AI 기반 기능 설명서를](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/ground-with-your-data) 참조하세요.\n\n![건축학](./docs/img//archirecture.png)\n\n## 사용 방법\n\n이 MCP 서버를 사용하는 방법은 두 가지가 있습니다. Docker에서 실행하려면 첫 번째 방법이 좋습니다. Dockerfile이 프로젝트에 포함되어 있기 때문입니다.\n\n### 1. 저장소를 복제합니다.\n\n지엑스피1\n\n### 파이썬 패키지를 설치하세요\n\n이 패키지는 아직 PyPI에 공개되지 않았지만, 저장소에서 설치할 수 있습니다. Python 패키지에는 구성 템플릿이 포함되어 있지 않으므로 MCP 서버를 실행하려면 [config.yml.template](./config.yml.template) 에서 파생된 구성 파일이 필요합니다. 구성 파일에 대한 자세한 내용은 [부록 A: 구성 파일을](#appendix-a-config-file) 참조하세요.\n\n```shell\n# Install the package\npip install git+https://github.com/ubie-oss/mcp-vertexai-search.git\n\n# Check the command\nmcp-vertexai-search --help\n```\n\n## 개발\n\n### 필수 조건\n\n* [자외선](https://docs.astral.sh/uv/getting-started/installation/)\n* Vertex AI 데이터 저장소\n * 자세한 내용은 [데이터 저장소에 대한 공식 문서를](https://cloud.google.com/generative-ai-app-builder/docs/create-datastore-ingest) 참조하세요.\n\n### 로컬 환경 설정\n\n```shell\n# Optional: Install uv\npython -m pip install -r requirements.setup.txt\n\n# Create a virtual environment\nuv venv\nuv sync --all-extras\n```\n\n### MCP 서버 실행\n\n이 기능은 SSE(Server-Sent Events)와 stdio(Standard Input Output) 두 가지 전송 방식을 지원합니다. `--transport` 플래그를 설정하여 전송 방식을 제어할 수 있습니다.\n\nYAML 파일을 사용하여 MCP 서버를 구성할 수 있습니다. [config.yml.template은](./config.yml.template) 구성 파일 템플릿입니다. 필요에 맞게 구성 파일을 수정하세요.\n\n```bash\nuv run mcp-vertexai-search serve \\\n --config config.yml \\\n --transport \n```\n\n### Vertex AI 검색 테스트\n\nMCP 서버 없이 `mcp-vertexai-search search` 명령을 사용하여 Vertex AI 검색을 테스트할 수 있습니다.\n\n```bash\nuv run mcp-vertexai-search search \\\n --config config.yml \\\n --query \n```\n\n## 부록 A: 구성 파일\n\n[config.yml.template은](./config.yml.template) 구성 파일의 템플릿입니다.\n\n* `server`\n\n * `server.name` : MCP 서버의 이름\n* `model`\n\n * `model.model_name` : Vertex AI 모델의 이름\n * `model.project_id` : Vertex AI 모델의 프로젝트 ID\n * `model.location` : 모델의 위치(예: us-central1)\n * `model.impersonate_service_account` : 가장할 서비스 계정\n * `model.generate_content_config` : 콘텐츠 생성 API에 대한 구성\n* `data_stores` : Vertex AI 데이터 저장소 목록\n * `data_stores.project_id` : Vertex AI 데이터 저장소의 프로젝트 ID\n * `data_stores.location` : Vertex AI 데이터 저장소의 위치(예: 미국)\n * `data_stores.datastore_id` : Vertex AI 데이터 저장소의 ID\n * `data_stores.tool_name` : 도구의 이름\n * `data_stores.description` : Vertex AI 데이터 저장소에 대한 설명","Korean","ko-KR",{"_36":69,"_40":73,"_45":74},{"_38":70,"_40":71,"_42":72},"该服务器使用基于 Gemini 的 Vertex AI 进行文档搜索，通过将响应基于 Vertex AI 数据存储区中存储的私人数据来改善搜索结果。","Vertex AI Search 的 MCP 服务器","# Vertex AI Search 的 MCP 服务器\n\n这是一个使用 Vertex AI 搜索文档的 MCP 服务器。\n\n## 建筑学\n\n此解决方案结合 Gemini 与 Vertex AI 基础功能，使用您的私人数据搜索文档。基础功能通过将 Gemini 的响应与存储在 Vertex AI 数据存储区中的数据相结合，从而提高搜索结果的质量。我们可以将一个或多个 Vertex AI 数据存储区集成到 MCP 服务器。有关基础功能的更多详细信息，请参阅[Vertex AI 基础文档](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/ground-with-your-data)。\n\n![建筑学](./docs/img//archirecture.png)\n\n## 如何使用\n\n有两种方法可以使用此 MCP 服务器。如果您想在 Docker 上运行它，则第一种方法比较好，因为项目中提供了 Dockerfile。\n\n### 1. 克隆存储库\n\n```shell\n# Clone the repository\ngit clone git@github.com:ubie-oss/mcp-vertexai-search.git\n\n# Create a virtual environment\nuv venv\n# Install the dependencies\nuv sync --all-extras\n\n# Check the command\nuv run mcp-vertexai-search\n```\n\n### 安装python包\n\n该软件包尚未发布到 PyPI，但我们可以从仓库中安装它。我们需要一个源自[config.yml.template](./config.yml.template)的配置文件来运行 MCP 服务器，因为 Python 软件包中不包含配置模板。有关配置文件的详细信息，请参阅[附录 A：配置文件](#appendix-a-config-file)。\n\n```shell\n# Install the package\npip install git+https://github.com/ubie-oss/mcp-vertexai-search.git\n\n# Check the command\nmcp-vertexai-search --help\n```\n\n## 发展\n\n### 先决条件\n\n* [紫外线](https://docs.astral.sh/uv/getting-started/installation/)\n* Vertex AI 数据存储\n * 请参阅[有关数据存储的官方文档](https://cloud.google.com/generative-ai-app-builder/docs/create-datastore-ingest)以获取更多信息\n\n### 设置本地环境\n\n```shell\n# Optional: Install uv\npython -m pip install -r requirements.setup.txt\n\n# Create a virtual environment\nuv venv\nuv sync --all-extras\n```\n\n### 运行 MCP 服务器\n\n它支持两种传输方式：SSE（服务器发送事件）和 stdio（标准输入输出）。我们可以通过设置`--transport`标志来控制传输。\n\n我们可以使用 YAML 文件配置 MCP 服务器[。config.yml.template](./config.yml.template)是配置文件的模板。请根据自己的需求修改配置文件。\n\n```bash\nuv run mcp-vertexai-search serve \\\n --config config.yml \\\n --transport \n```\n\n### 测试 Vertex AI 搜索\n\n我们可以在不使用 MCP 服务器的情况下使用`mcp-vertexai-search search`命令来测试 Vertex AI Search。\n\n```bash\nuv run mcp-vertexai-search search \\\n --config config.yml \\\n --query \n```\n\n## 附录 A：配置文件\n\n[config.yml.template](./config.yml.template)是配置文件的模板。\n\n* `server`\n\n * `server.name` ：MCP 服务器的名称\n* `model`\n\n * `model.model_name` ：Vertex AI 模型的名称\n * `model.project_id` ：Vertex AI 模型的项目 ID\n * `model.location` ：模型的位置（例如 us-central1）\n * `model.impersonate_service_account` ：要模拟的服务帐户\n * `model.generate_content_config` ：生成内容 API 的配置\n* `data_stores` ：Vertex AI 数据存储列表\n * `data_stores.project_id` ：Vertex AI 数据存储的项目 ID\n * `data_stores.location` ：Vertex AI 数据存储的位置（例如我们）\n * `data_stores.datastore_id` ：Vertex AI 数据存储的 ID\n * `data_stores.tool_name` ：工具的名称\n * `data_stores.description` ：Vertex AI 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