[{"_1":2,"_344":-5,"_345":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_283":284},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"BOT","id",7616242,"membership","referenceId","019741a0-3a5e-71a9-a406-b44004e747f4","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_81":-5,"_82":40,"_29":83,"_200":201,"_281":282},"availableLocales",[28,40,46,53,60,67,74],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","Ein Model Context Protocol-Server, der es KI-Modellen ermöglicht, über Higress Internet- und Wissenssuchen in Echtzeit durchzuführen und die Modellantworten mit aktuellen Informationen von Google, Bing, Arxiv und internen Wissensdatenbanken zu verbessern.","name","Higress AI-Search MCP Server","readme","[![MseeP.ai-Sicherheitsbewertungsabzeichen](https://mseep.net/pr/cr7258-higress-ai-search-mcp-server-badge.png)](https://mseep.ai/app/cr7258-higress-ai-search-mcp-server)\n\n# Higress AI-Search MCP Server\n\n## Überblick\n\nEin Model Context Protocol (MCP)-Server, der ein KI-Suchtool bereitstellt, um KI-Modellantworten mit Echtzeit-Suchergebnissen aus verschiedenen Suchmaschinen über [die Higress](https://higress.cn/) [-KI-Suchfunktion](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) zu verbessern.\n\n## Demo\n\n### Cline\n\n\n\n### Claude Desktop\n\n\n\n## Merkmale\n\n* **Internetsuche** : Google, Bing, Quark – für allgemeine Webinformationen\n* **Akademische Suche** : Arxiv – für wissenschaftliche Arbeiten und Forschung\n* **Interne Wissenssuche**\n\n## Voraussetzungen\n\n* [UV](https://github.com/astral-sh/uv) für die Paketinstallation.\n* Konfigurieren Sie Higress mit [dem AI-Search-](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) Plugin und [dem AI-Proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md) -Plugin.\n\n## Konfiguration\n\nDer Server kann mithilfe von Umgebungsvariablen konfiguriert werden:\n\n* `HIGRESS_URL` (optional): URL für den Higress-Dienst (Standard: `http://localhost:8080/v1/chat/completions` ).\n* `MODEL` (erforderlich): LLM-Modell, das zum Generieren von Antworten verwendet werden soll.\n* `INTERNAL_KNOWLEDGE_BASES` (optional): Beschreibung der internen Wissensdatenbanken.\n\n### Option 1: Verwendung von uvx\n\nDurch die Verwendung von uvx wird das Paket automatisch von PyPI installiert, es ist nicht erforderlich, das Repository lokal zu klonen.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n### Option 2: Verwenden von UV mit lokaler Entwicklung\n\nDie Verwendung von uv erfordert das lokale Klonen des Repository und die Angabe des Pfads zum Quellcode.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## Lizenz\n\nDieses Projekt ist unter der MIT-Lizenz lizenziert – Einzelheiten finden Sie in der Datei [LICENSE](LICENSE) .","German","tag","de-DE",{"_29":41,"_33":44,"_38":45},{"_31":42,"_33":34,"_35":43},"A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.","[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/cr7258-higress-ai-search-mcp-server-badge.png)](https://mseep.ai/app/cr7258-higress-ai-search-mcp-server)\n\n# Higress AI-Search MCP Server\n\n## Overview\n\nA Model Context Protocol (MCP) server that provides an AI search tool to enhance AI model responses with real-time search results from various search engines through [Higress](https://higress.cn/) [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) feature.\n\n\n $\"Higress$ \n\n\n## Demo\n\n### Cline\n\nhttps://github.com/user-attachments/assets/60a06d99-a46c-40fc-b156-793e395542bb\n\n### Claude Desktop\n\nhttps://github.com/user-attachments/assets/5c9e639f-c21c-4738-ad71-1a88cc0bcb46\n\n## Features\n\n- **Internet Search**: Google, Bing, Quark - for general web information\n- **Academic Search**: Arxiv - for scientific papers and research\n- **Internal Knowledge Search**\n\n## Prerequisites\n\n- [uv](https://github.com/astral-sh/uv) for package installation.\n- Config Higress with [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) plugin and [ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md) plugin.\n\n## Configuration\n\nThe server can be configured using environment variables:\n\n- `HIGRESS_URL`(optional): URL for the Higress service (default: `http://localhost:8080/v1/chat/completions`).\n- `MODEL`(required): LLM model to use for generating responses.\n- `INTERNAL_KNOWLEDGE_BASES`(optional): Description of internal knowledge bases.\n\n### Option 1: Using uvx\n\nUsing uvx will automatically install the package from PyPI, no need to clone the repository locally.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n### Option 2: Using uv with local development\n\nUsing uv requires cloning the repository locally and specifying the path to the source code.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.","English","en-US",{"_29":47,"_33":51,"_38":52},{"_31":48,"_33":49,"_35":50},"Un servidor de protocolo de contexto de modelo que permite a los modelos de IA realizar búsquedas de Internet y de conocimiento en tiempo real a través de Higress, mejorando las respuestas del modelo con información actualizada de Google, Bing, Arxiv y bases de conocimiento internas.","Servidor MCP de búsqueda de IA de Higress","# Servidor MCP de búsqueda de IA de Higress\n\n## Descripción general\n\nUn servidor de Protocolo de Contexto de Modelo (MCP) que proporciona una herramienta de búsqueda de IA para mejorar las respuestas del modelo de IA con resultados de búsqueda en tiempo real de varios motores de búsqueda a través de la función [de búsqueda de IA](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) [de Higress](https://higress.cn/) .\n\n## Manifestación\n\n### Cline\n\n\n\n### Escritorio de Claude\n\n\n\n## Características\n\n* **Búsqueda en Internet** : Google, Bing, Quark: para información web general\n* **Búsqueda académica** : Arxiv - para artículos científicos e investigaciones\n* **Búsqueda de conocimiento interno**\n\n## Prerrequisitos\n\n* [uv](https://github.com/astral-sh/uv) para instalación de paquetes.\n* Configurar Higress con el complemento [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) y el complemento [ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md) .\n\n## Configuración\n\nEl servidor se puede configurar mediante variables de entorno:\n\n* `HIGRESS_URL` (opcional): URL para el servicio Higress (predeterminado: `http://localhost:8080/v1/chat/completions` ).\n* `MODEL` (requerido): Modelo LLM a utilizar para generar respuestas.\n* `INTERNAL_KNOWLEDGE_BASES` (opcional): Descripción de las bases de conocimiento internas.\n\n### Opción 1: Uso de uvx\n\nEl uso de uvx instalará automáticamente el paquete desde PyPI, sin necesidad de clonar el repositorio localmente.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n### Opción 2: Uso de uv con desarrollo local\n\nEl uso de uv requiere clonar el repositorio localmente y especificar la ruta al código fuente.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## Licencia\n\nEste proyecto está licenciado bajo la licencia MIT: consulte el archivo [de LICENCIA](LICENSE) para obtener más detalles.","Spanish","es-ES",{"_29":54,"_33":58,"_38":59},{"_31":55,"_33":56,"_35":57},"AI モデルが Higress を通じてリアルタイムのインターネット検索や知識検索を実行できるようにし、Google、Bing、Arxiv、内部知識ベースからの最新情報でモデル応答を強化できるようにするモデルコンテキストプロトコルサーバー。","Higress AI-Search MCPサーバー","# Higress AI-Search MCPサーバー\n\n## 概要\n\n[Higress](https://higress.cn/) [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md)機能を通じて、さまざまな検索エンジンからのリアルタイム検索結果を使用して AI モデルの応答を強化する AI 検索ツールを提供するモデルコンテキストプロトコル (MCP) サーバー。\n\n## デモ\n\n### クライン\n\n\n\n### クロードデスクトップ\n\n\n\n## 特徴\n\n* **インターネット検索**: Google、Bing、Quark - 一般的なウェブ情報\n* **学術検索**：Arxiv - 科学論文と研究用\n* **社内知識検索**\n\n## 前提条件\n\n* パッケージのインストール用の[uv](https://github.com/astral-sh/uv) 。\n* [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md)プラグインと[ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md)プラグインを使用して Higress を設定します。\n\n## 構成\n\nサーバーは環境変数を使用して設定できます。\n\n* `HIGRESS_URL` (オプション): Higress サービスの URL (デフォルト: `http://localhost:8080/v1/chat/completions` )。\n* `MODEL` (必須): 応答の生成に使用する LLM モデル。\n* `INTERNAL_KNOWLEDGE_BASES` (オプション): 内部ナレッジベースの説明。\n\n### オプション1: uvxを使用する\n\nuvx を使用すると、PyPI からパッケージが自動的にインストールされるため、リポジトリをローカルにクローンする必要はありません。\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n### オプション2: ローカル開発でUVを使用する\n\nuv を使用するには、リポジトリをローカルに複製し、ソースコードへのパスを指定する必要があります。\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## ライセンス\n\nこのプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細については[LICENSE](LICENSE)ファイルを参照してください。","Japanese","ja-JP",{"_29":61,"_33":65,"_38":66},{"_31":62,"_33":63,"_35":64},"Higress를 통해 AI 모델이 실시간 인터넷 및 지식 검색을 수행하고 Google, Bing, Arxiv 및 내부 지식 기반의 최신 정보로 모델 응답을 향상시킬 수 있도록 하는 모델 컨텍스트 프로토콜 서버입니다.","Higress AI-Search MCP 서버","# Higress AI-Search MCP 서버\n\n## 개요\n\n[Higress](https://higress.cn/) [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) 기능을 통해 다양한 검색 엔진의 실시간 검색 결과로 AI 모델의 응답을 향상시키는 AI 검색 도구를 제공하는 MCP(Model Context Protocol) 서버입니다.\n\n## 데모\n\n### 클라인\n\n\n\n### 클로드 데스크탑\n\n\n\n## 특징\n\n* **인터넷 검색** : Google, Bing, Quark - 일반 웹 정보 검색\n* **학술 검색** : Arxiv - 과학 논문 및 연구용\n* **내부 지식 검색**\n\n## 필수 조건\n\n* 패키지 설치를 위한 [uv](https://github.com/astral-sh/uv) .\n* [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) 플러그인과 [ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md) 플러그인을 사용하여 Higress를 구성합니다.\n\n## 구성\n\n서버는 환경 변수를 사용하여 구성할 수 있습니다.\n\n* `HIGRESS_URL` (선택 사항): Higress 서비스의 URL(기본값: `http://localhost:8080/v1/chat/completions` ).\n* `MODEL` (필수): 응답을 생성하는 데 사용할 LLM 모델입니다.\n* `INTERNAL_KNOWLEDGE_BASES` (선택 사항): 내부 지식 기반에 대한 설명입니다.\n\n### 옵션 1: uvx 사용\n\nuvx를 사용하면 PyPI에서 패키지가 자동으로 설치되므로 로컬로 저장소를 복제할 필요가 없습니다.\n\n지엑스피1\n\n### 옵션 2: 로컬 개발에 uv 사용\n\nuv를 사용하려면 로컬로 저장소를 복제하고 소스 코드 경로를 지정해야 합니다.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## 특허\n\n이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 [라이선스](LICENSE) 파일을 참조하세요.","Korean","ko-KR",{"_29":68,"_33":72,"_38":73},{"_31":69,"_33":70,"_35":71},"Сервер протокола контекста модели, который позволяет моделям ИИ выполнять поиск в Интернете и знаниях в режиме реального времени с помощью Higress, дополняя ответы моделей актуальной информацией из Google, Bing, Arxiv и внутренних баз знаний.","Higress AI-Search MCP-сервер","[![Значок оценки безопасности MseeP.ai](https://mseep.net/pr/cr7258-higress-ai-search-mcp-server-badge.png)](https://mseep.ai/app/cr7258-higress-ai-search-mcp-server)\n\n# Higress AI-Search MCP-сервер\n\n## Обзор\n\nСервер протокола контекста модели (MCP), предоставляющий инструмент поиска на основе ИИ для улучшения ответов модели ИИ с помощью результатов поиска в реальном времени из различных поисковых систем с помощью функции поиска [Higress](https://higress.cn/) [AI-Search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) .\n\n## Демо\n\n### Клайн\n\n\n\n### Клод Десктоп\n\n\n\n## Функции\n\n* **Поиск в Интернете** : Google, Bing, Quark — для общей веб-информации.\n* **Академический поиск** : Arxiv — для научных работ и исследований\n* **Внутренний поиск знаний**\n\n## Предпосылки\n\n* [uv](https://github.com/astral-sh/uv) для установки пакета.\n* Настройте Higress с плагином [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) и плагином [ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md) .\n\n## Конфигурация\n\nСервер можно настроить с помощью переменных среды:\n\n* `HIGRESS_URL` (необязательно): URL-адрес для службы Higress (по умолчанию: `http://localhost:8080/v1/chat/completions` ).\n* `MODEL` (обязательно): модель LLM, используемая для генерации ответов.\n* `INTERNAL_KNOWLEDGE_BASES` (необязательно): Описание внутренних баз знаний.\n\n### Вариант 1: Использование uvx\n\nИспользование uvx автоматически установит пакет из PyPI, нет необходимости клонировать репозиторий локально.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n### Вариант 2: Использование УФ-излучения с локальной проявкой\n\nИспользование uv требует локального клонирования репозитория и указания пути к исходному коду.\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## Лицензия\n\nДанный проект лицензирован по лицензии MIT — подробности см. в файле [LICENSE](LICENSE) .","Russian","ru-RU",{"_29":75,"_33":79,"_38":80},{"_31":76,"_33":77,"_35":78},"模型上下文协议服务器，使 AI 模型能够通过 Higress 执行实时互联网和知识搜索，并利用来自 Google、Bing、Arxiv 和内部知识库的最新信息增强模型响应。","Higress AI-Search MCP 服务器","# Higress AI-Search MCP 服务器\n\n## 概述\n\n模型上下文协议 (MCP) 服务器提供 AI 搜索工具，通过[Higress](https://higress.cn/) [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md)功能利用来自各种搜索引擎的实时搜索结果增强 AI 模型响应。\n\n## 演示\n\n### 克莱恩\n\n\n\n### 克劳德桌面\n\n\n\n## 特征\n\n* **互联网搜索**：Google、Bing、Quark - 用于一般网络信息\n* **学术搜索**：Arxiv - 用于科学论文和研究\n* **内部知识搜索**\n\n## 先决条件\n\n* [uv](https://github.com/astral-sh/uv)用于包安装。\n* 使用[ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md)插件和[ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md)插件配置 Higress。\n\n## 配置\n\n可以使用环境变量来配置服务器：\n\n* `HIGRESS_URL` (可选)：Higress 服务的 URL（默认值： `http://localhost:8080/v1/chat/completions` ）。\n* `MODEL` （必需）：用于生成响应的 LLM 模型。\n* `INTERNAL_KNOWLEDGE_BASES` （可选）：内部知识库的描述。\n\n### 选项 1：使用 uvx\n\n使用 uvx 将自动从 PyPI 安装包，无需在本地克隆存储库。\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n### 选项 2：使用 uv 进行本地开发\n\n使用 uv 需要在本地克隆存储库并指定源代码的路径。\n\n```bash\n{\n \"mcpServers\": {\n \"higress-ai-search-mcp-server\": {\n \"command\": \"uv\",\n \"args\": [\n \"--directory\",\n \"path/to/src/higress-ai-search-mcp-server\",\n \"run\",\n \"higress-ai-search-mcp-server\"\n ],\n \"env\": {\n \"HIGRESS_URL\": \"http://localhost:8080/v1/chat/completions\",\n \"MODEL\": \"qwen-turbo\",\n \"INTERNAL_KNOWLEDGE_BASES\": \"Employee handbook, company policies, internal process documents\"\n }\n }\n }\n}\n```\n\n## 执照\n\n该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅[LICENSE](LICENSE)文件。","Chinese","zh-CN","githubUser","locale",{"_84":85,"_86":87,"_13":106,"_116":42,"_117":42,"_118":119,"_122":123,"_146":-5,"_16":147,"_148":149,"_150":151,"_33":34,"_169":136,"_170":-5,"_171":-5,"_172":173,"_174":-5,"_175":176,"_181":-5,"_158":132,"_182":-5,"_183":184,"_137":197,"_198":199},"addedAt","2025-03-09T01:06:58.642642984Z","argumentsJsonSchema",{"_88":89,"_103":104,"_93":105},"properties",{"_90":91,"_95":96,"_100":101},"MODEL",{"_31":92,"_93":94},"LLM model to use for generating responses","type","string","HIGRESS_URL",{"_97":98,"_31":99,"_93":94},"default","http://localhost:8080/v1/chat/completions","URL for the Higress service","INTERNAL_KNOWLEDGE_BASES",{"_31":102,"_93":94},"Description of internal knowledge 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