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Unterstützt PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch, DuckDB","name","CentralMind/Gateway","readme","![Binärdateien erstellen](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)\n\n🚀 Interaktive Demo hier verfügbar: \n\n## Was ist Centralmind/Gateway\n\nEinfache Möglichkeit, Ihre Datenbank über die Protokolle MCP oder OpenAPI 3.1 dem AI-Agent zugänglich zu machen.\n\n```bash\ndocker run --platform linux/amd64 -p 9090:9090 \\\n ghcr.io/centralmind/gateway:v0.2.14 start \\\n --connection-string \"postgres://db-user:db-password@db-host/db-name?sslmode=require\"\n```\n\nDadurch wird für Sie eine API ausgeführt:\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\nWas Sie in Ihrem KI-Agenten verwenden können:\n\n![mcp-raw-cursor-setup.png](./assets/mcp-raw-cursor-setup.png)\n\nGateway generiert eine KI-optimierte API.\n\n## Warum Centralmind/Gateway\n\nKI-Agenten und LLM-basierte Anwendungen benötigen schnellen und sicheren Datenzugriff. Wir entwickeln eine API-Schicht, die automatisch sichere, LLM-optimierte APIs für Ihre strukturierten Daten generiert.\n\n* Starten Sie schnell mit MCP oder OpenAPI oder verwenden Sie Direct/Raw SQL APIs\n* Filtert PII und sensible Daten heraus, um die Einhaltung von GDPR, CPRA, SOC 2 und anderen Vorschriften sicherzustellen\n* Fügt Rückverfolgbarkeits- und Auditfunktionen hinzu, um sicherzustellen, dass KI-Anwendungen keine Blackboxes sind und Sicherheitsteams die Kontrolle behalten\n* Optimiert für KI-Workloads: unterstützt das Model Context Protocol (MCP) mit erweiterten Metadaten, um KI-Agenten das Verständnis von APIs zu erleichtern, sowie integrierte Caching- und Sicherheitsfunktionen\n\nDies kann während der Entwicklung nützlich sein, wenn ein LLM Daten aus Ihrer Datenbank erstellen, anpassen oder abfragen muss. In analytischen Szenarien ermöglicht es Ihnen, mit Ihrer Datenbank oder Ihrem Data Warehouse zu chatten. Bereichern Sie Ihre KI-Agenten mit Daten aus Ihrer Datenbank durch Remote Function/Tool Calling.\n\n![Demo](/assets/demo.gif)\n\n## Merkmale\n\n* ⚡ **Automatische API-Generierung** – Erstellt APIs automatisch mithilfe von LLM basierend auf Tabellenschema und Stichprobendaten\n* 🗄️ **Strukturierte Datenbankunterstützung** – Unterstützt PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch\n* 🌍 **Unterstützung mehrerer Protokolle** – Bietet APIs als REST- oder MCP-Server einschließlich SSE-Modus\n* 📜 **API-Dokumentation** – Automatisch generierte Swagger-Dokumentation und OpenAPI 3.1.0-Spezifikation\n* 🔒 **PII-Schutz** – Implementiert ein Regex-Plugin oder ein Microsoft Presidio-Plugin für die Schwärzung von PII und sensiblen Daten\n* ⚡ **Flexible Konfiguration** – Einfach erweiterbar über YAML-Konfiguration und Plugin-System\n* 🐳 **Bereitstellungsoptionen** – Ausführung als Binär- oder Docker-Container mit einsatzbereitem Helm-Diagramm\n* 🤖 **Unterstützung mehrerer KI-Anbieter** – Unterstützung für [OpenAI](https://docs.centralmind.ai/providers/openai) , [Anthropic](https://docs.centralmind.ai/providers/anthropic) , [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock) , [Google Gemini](https://docs.centralmind.ai/providers/gemini) und [Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* 📦 **Lokal und vor Ort** – Unterstützung für selbstgehostete LLMs durch konfigurierbare KI-Endpunkte und -Modelle\n* 🔑 **Row-Level Security (RLS)** – Feinkörnige Datenzugriffskontrolle mithilfe von Lua-Skripten\n* 🔐 **Authentifizierungsoptionen** – Integrierte Unterstützung für API-Schlüssel und OAuth\n* 👀 **Umfassende Überwachung** – Integration mit OpenTelemetry (OTel) für Anforderungsverfolgung und Prüfpfade\n* 🏎️ **Leistungsoptimierung** – Implementiert zeitbasierte und LRU-Caching-Strategien\n\n## So funktioniert es\n\n![img.png](assets/diagram.png)\n\n### 1. Verbinden und Entdecken\n\nGateway verbindet sich mit Ihren strukturierten Datenbanken wie PostgreSQL und analysiert automatisch das Schema und die Datenbeispiele, um basierend auf Ihren Vorgaben eine optimierte API-Struktur zu generieren. LLM wird ausschließlich in der Discovery-Phase zur Erstellung der API-Konfiguration verwendet. Das Tool nutzt [KI-Anbieter](https://docs.centralmind.ai/providers) zur Generierung der API-Konfiguration und gewährleistet gleichzeitig die Sicherheit durch PII-Erkennung.\n\n### 2. Bereitstellen\n\nGateway unterstützt verschiedene Bereitstellungsoptionen: Standalone-Binärdateien, Docker oder Kubernetes. Detaillierte Anweisungen finden Sie in unserer Einführungsanleitung. Das System nutzt YAML-Konfiguration und Plugins für eine einfache Anpassung.\n\n### 3. Nutzen & Integrieren\n\nGreifen Sie über REST-APIs oder das Model Context Protocol (MCP) mit integrierten Sicherheitsfunktionen auf Ihre Daten zu. Gateway lässt sich nahtlos in KI-Modelle und Anwendungen wie LangChain, OpenAI und Claude Desktop integrieren, indem es Funktionsaufrufe oder Cursor über MCP nutzt. Sie können auch Telemetriedaten zu lokalen oder entfernten Zielen im Hotelformat einrichten.\n\n## Dokumentation\n\n### Erste Schritte\n\n* Kurzanleitung\n* Installationsanweisungen\n* API-Generierungshandbuch\n* API-Starthandbuch\n\n### Weitere Ressourcen\n\n* ChatGPT-Integrationshandbuch\n* Dokumentation zum Datenbankkonnektor\n* Plugin-Dokumentation\n\n## Wie man baut\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## API-Generierung\n\nGateway verwendet LLM-Modelle, um Ihre API-Konfiguration zu generieren. Führen Sie die folgenden Schritte aus:\n\n1. Wählen Sie einen unserer unterstützten KI-Anbieter:\n\n* [OpenAI](https://docs.centralmind.ai/providers/openai) und alle OpenAI-kompatiblen Anbieter\n* [Anthropisch](https://docs.centralmind.ai/providers/anthropic)\n* [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock)\n* [Google Vertex AI (anthropisch)](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* [Google Gemini](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini](https://docs.centralmind.ai/providers/gemini) bietet eine großzügige **kostenlose Version** . Einen API-Schlüssel erhalten Sie im Google AI Studio:\n\n* [Google AI Studio](https://aistudio.google.com/apikey)\n\nNach der Anmeldung können Sie im API-Bereich von AI Studio einen API-Schlüssel erstellen. Die kostenlose Version beinhaltet eine großzügige monatliche Token-Zuteilung und ermöglicht Ihnen so den Zugriff für Entwicklungs- und Testzwecke.\n\nKonfigurieren Sie die Autorisierung des KI-Anbieters. Legen Sie für Google Gemini einen API-Schlüssel fest.\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. Führen Sie den Erkennungsbefehl aus:\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. Viel Spaß beim Generierungsprozess:\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. Überprüfen Sie die generierte Konfiguration in `gateway.yaml` :\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## Ausführen der API\n\n### Lokal ausführen\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### Docker Compose\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### MCP-Protokollintegration\n\nGateway implementiert das MCP-Protokoll für eine nahtlose Integration mit Claude und anderen Tools. Detaillierte Einrichtungsanweisungen finden Sie in unserem Claude-Integrationshandbuch.\n\nUm das MCP-Tool zu Claude Desktop hinzuzufügen, passen Sie einfach Claudes Konfiguration an:\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## Fahrplan\n\nÄnderungen sind jederzeit möglich, und die Roadmap hängt stark vom Nutzerfeedback ab. Aktuell planen wir folgende Features:\n\n#### Datenbank und Konnektivität\n\n* 🗄️ **Erweiterte Datenbankintegrationen** – Redshift, S3 (Iceberg und Parquet), Oracle DB, Microsoft SQL Server, Elasticsearch\n* 🔑 **SSH-Tunneling** – Möglichkeit, Jumphost oder SSH-Bastion zum Tunneln von Verbindungen zu verwenden\n\n#### Erweiterte Funktionalität\n\n* 🔍 **Erweiterte Abfragefunktionen** – Komplexe Filtersyntax und Aggregatfunktionen als Parameter\n* 🔐 **Verbesserte MCP-Sicherheit** – API-Schlüssel und OAuth-Authentifizierung\n\n#### Plattformverbesserungen\n\n* 📦 **Schemaverwaltung** – Automatisierte Schemaentwicklung und API-Versionierung\n* 🚦 **Erweitertes Verkehrsmanagement** – Intelligente Ratenbegrenzung, Anforderungsdrosselung\n* ✍️ **Unterstützung für Schreibvorgänge** – Einfüge- und Aktualisierungsvorgänge","German","tag","de-DE",{"_39":51,"_43":54,"_48":55},{"_41":52,"_43":44,"_45":53},"MCP-Server from your Database optimized for LLMs and AI-Agents. Supports PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch, DuckDB","

\n\n![Build Binaries](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)

\n\n

\n\n\n

CentralMind Gateway: Create API or MCP Server in Minutes

\n\n🚀 Interactive Demo avialable here: https://centralmind.ai\n\n## What is Centralmind/Gateway\n\nSimple way to expose your database to AI-Agent via MCP or OpenAPI 3.1 protocols.\n\n```bash\ndocker run --platform linux/amd64 -p 9090:9090 \\\n ghcr.io/centralmind/gateway:v0.2.14 start \\\n --connection-string \"postgres://db-user:db-password@db-host/db-name?sslmode=require\"\n```\n\nThis will run for you an API:\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\nWhich you can use inside your AI Agent:\n\n![mcp-raw-cursor-setup.png](./assets/mcp-raw-cursor-setup.png)\n\nGateway will generate AI optimized API.\n\n\n## Why Centralmind/Gateway\n\nAI agents and LLM-powered applications need fast, secure access to data. We're building an API layer that automatically generates secure, LLM-optimized APIs for your structured data.\n- Quickly start with MCP or OpenAPI, or use Direct/Raw SQL APIs\n- Filters out PII and sensitive data to ensure compliance with GDPR, CPRA, SOC 2, and other regulations\n- Adds traceability and auditing capabilities, ensuring AI applications aren't black boxes and allowing security teams to maintain control\n- Optimized for AI workloads: supports the Model Context Protocol (MCP) with enhanced metadata to help AI agents understand APIs, along with built-in caching and security features\n\nIt can be useful during development, when an LLM needs to create, adjust, or query data from your database.\nIn analytical scenarios, it enables you to chat with your database or data warehouse.\nEnrich your AI agents with data from your database using remote function/tool calling.\n\n![demo](/assets/demo.gif)\n\n## Features\n\n- ⚡ **Automatic API Generation** – Creates APIs automatically using LLM based on table schema and sampled data\n- 🗄️ **Structured Database Support** – Supports PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch\n- 🌍 **Multiple Protocol Support** – Provides APIs as REST or MCP Server including SSE mode\n- 📜 **API Documentation** – Auto-generated Swagger documentation and OpenAPI 3.1.0 specification\n- 🔒 **PII Protection** – Implements regex plugin or Microsoft Presidio plugin for PII and sensitive data redaction\n- ⚡ **Flexible Configuration** – Easily extensible via YAML configuration and plugin system\n- 🐳 **Deployment Options** – Run as a binary or Docker container with ready-to-use Helm chart\n- 🤖 **Multiple AI Providers Support** - Support for [OpenAI](https://docs.centralmind.ai/providers/openai), [Anthropic](https://docs.centralmind.ai/providers/anthropic), [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock), [Google Gemini](https://docs.centralmind.ai/providers/gemini) & [Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai)\n- 📦 **Local & On-Premises** – Support for self-hosted LLMs through configurable AI endpoints and models\n- 🔑 **Row-Level Security (RLS)** – Fine-grained data access control using Lua scripts\n- 🔐 **Authentication Options** – Built-in support for API keys and OAuth\n- 👀 **Comprehensive Monitoring** – Integration with OpenTelemetry (OTel) for request tracking and audit trails\n- 🏎️ **Performance Optimization** – Implements time-based and LRU caching strategies\n\n## How it Works\n\n

\n\n![img.png](assets/diagram.png)\n\n

\n\n### 1. Connect & Discover\n\nGateway connects to your structured databases like PostgreSQL and automatically analyzes the schema and data samples\nto generate an optimized API structure based on your prompt. LLM is used only on discovery stage to produce API configuration.\nThe tool uses [AI Providers](https://docs.centralmind.ai/providers) to generate the API configuration while ensuring security\nthrough PII detection.\n\n### 2. Deploy\n\nGateway supports multiple deployment options from standalone binary, docker or Kubernetes.\nCheck our launching guide for detailed\ninstructions. The system uses YAML configuration and plugins for easy customization.\n\n### 3. Use & Integrate\n\nAccess your data through REST APIs or Model Context Protocol (MCP) with built-in security features.\nGateway seamlessly integrates with AI models and applications like LangChain,\nOpenAI and\nClaude Desktop using function calling\nor Cursor through MCP. You can also setup telemetry to local or remote destination in otel format.\n\n## Documentation\n\n### Getting Started\n\n- Quickstart Guide\n- Installation Instructions\n- API Generation Guide\n- API Launch Guide\n\n### Additional Resources\n\n- ChatGPT Integration Guide\n- Database Connector Documentation\n- Plugin Documentation\n\n## How to Build\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## API Generation\n\nGateway uses LLM models to generate your API configuration. Follow these steps:\n\n\n1. Choose one of our supported AI providers:\n\n- [OpenAI](https://docs.centralmind.ai/providers/openai) and all OpenAI-compatible providers\n- [Anthropic](https://docs.centralmind.ai/providers/anthropic)\n- [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock)\n- [Google Vertex AI (Anthropic)](https://docs.centralmind.ai/providers/anthropic-vertexai)\n- [Google Gemini](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini](https://docs.centralmind.ai/providers/gemini) provides a generous **free tier**. You can obtain an API key by visiting Google AI Studio:\n\n- [Google AI Studio](https://aistudio.google.com/apikey)\n\nOnce logged in, you can create an API key in the API section of AI Studio. The free tier includes a generous monthly token allocation, making it accessible for development and testing purposes.\n\nConfigure AI provider authorization. For Google Gemini, set an API key.\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. Run the discovery command:\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. Enjoy the generation process:\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. Review the generated configuration in `gateway.yaml`:\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## Running the API\n\n### Run locally\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### Docker Compose\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### MCP Protocol Integration\n\nGateway implements the MCP protocol for seamless integration with Claude and other tools. For detailed setup instructions, see our Claude integration guide.\n\nTo add MCP Tool to Claude Desktop just adjust Claude's config :\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## Roadmap\n\nIt is always subject to change, and the roadmap will highly depend on user feedback. At this moment,\nwe are planning the following features:\n\n#### Database and Connectivity\n\n- 🗄️ **Extended Database Integrations** - Redshift, S3 (Iceberg and Parquet), Oracle DB, Microsoft SQL Server, Elasticsearch\n- 🔑 **SSH tunneling** - ability to use jumphost or ssh bastion to tunnel connections\n\n#### Enhanced Functionality\n\n- 🔍 **Advanced Query Capabilities** - Complex filtering syntax and Aggregation functions as parameters\n- 🔐 **Enhanced MCP Security** - API key and OAuth authentication\n\n#### Platform Improvements\n\n- 📦 **Schema Management** - Automated schema evolution and API versioning\n- 🚦 **Advanced Traffic Management** - Intelligent rate limiting, Request throttling\n- ✍️ **Write Operations Support** - Insert, Update operations\n","English","en-US",{"_39":57,"_43":60,"_48":61},{"_41":58,"_43":44,"_45":59},"Servidor MCP de su base de datos, optimizado para LLM y agentes de IA. Compatible con PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch y DuckDB.","![Construir binarios](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)\n\n🚀 Demostración interactiva disponible aquí: \n\n## ¿Qué es Centralmind/Gateway?\n\nUna forma sencilla de exponer su base de datos a AI-Agent a través de los protocolos MCP o OpenAPI 3.1.\n\n```bash\ndocker run --platform linux/amd64 -p 9090:9090 \\\n ghcr.io/centralmind/gateway:v0.2.14 start \\\n --connection-string \"postgres://db-user:db-password@db-host/db-name?sslmode=require\"\n```\n\nEsto ejecutará una API para usted:\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\nQue puedes utilizar dentro de tu agente de IA:\n\n![configuración del cursor mcp-raw.png](./assets/mcp-raw-cursor-setup.png)\n\nGateway generará una API optimizada para IA.\n\n## ¿Por qué Centralmind/Gateway?\n\nLos agentes de IA y las aplicaciones basadas en LLM necesitan un acceso rápido y seguro a los datos. Estamos desarrollando una capa de API que genera automáticamente API seguras y optimizadas para LLM para sus datos estructurados.\n\n* Comience rápidamente con MCP o OpenAPI, o utilice las API de SQL directo/sin procesar\n* Filtra información personal identificable (PII) y datos confidenciales para garantizar el cumplimiento de GDPR, CPRA, SOC 2 y otras regulaciones.\n* Agrega capacidades de trazabilidad y auditoría, lo que garantiza que las aplicaciones de IA no sean cajas negras y permite que los equipos de seguridad mantengan el control.\n* Optimizado para cargas de trabajo de IA: admite el Protocolo de contexto de modelo (MCP) con metadatos mejorados para ayudar a los agentes de IA a comprender las API, junto con funciones de seguridad y almacenamiento en caché integradas.\n\nPuede ser útil durante el desarrollo, cuando un LLM necesita crear, ajustar o consultar datos de su base de datos. En escenarios analíticos, le permite interactuar con su base de datos o almacén de datos. Enriquezca a sus agentes de IA con datos de su base de datos mediante llamadas remotas a funciones/herramientas.\n\n![manifestación](/assets/demo.gif)\n\n## Características\n\n* ⚡ **Generación automática de API** : crea API automáticamente utilizando LLM según el esquema de tabla y los datos muestreados\n* 🗄️ **Compatibilidad con bases de datos estructuradas** : compatible con PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch\n* 🌍 **Compatibilidad con múltiples protocolos** : proporciona API como servidor REST o MCP, incluido el modo SSE\n* 📜 **Documentación API** : documentación Swagger generada automáticamente y especificación OpenAPI 3.1.0\n* **Protección de PII** : implementa el complemento de expresiones regulares o el complemento de Microsoft Presidio para la redacción de PII y datos confidenciales\n* ⚡ **Configuración flexible** : fácilmente extensible a través de la configuración YAML y el sistema de complementos\n* 🐳 **Opciones de implementación** : Ejecútelo como un contenedor binario o Docker con un gráfico Helm listo para usar\n* 🤖 **Compatibilidad con varios proveedores de IA** : compatibilidad con [OpenAI](https://docs.centralmind.ai/providers/openai) , [Anthropic](https://docs.centralmind.ai/providers/anthropic) , [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock) , [Google Gemini](https://docs.centralmind.ai/providers/gemini) y [Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* 📦 **Local y en las instalaciones** : compatibilidad con LLM autoalojados mediante puntos finales y modelos de IA configurables\n* **Seguridad a nivel de fila (RLS):** control de acceso a datos de grano fino mediante scripts Lua\n* 🔐 **Opciones de autenticación** : soporte integrado para claves API y OAuth\n* 👀 **Monitoreo integral** : integración con OpenTelemetry (OTel) para seguimiento de solicitudes y registros de auditoría\n* 🏎️ **Optimización del rendimiento** : implementa estrategias de almacenamiento en caché LRU y basadas en tiempo\n\n## Cómo funciona\n\n![img.png](assets/diagram.png)\n\n### 1. Conéctate y descubre\n\nGateway se conecta a sus bases de datos estructuradas, como PostgreSQL, y analiza automáticamente el esquema y las muestras de datos para generar una estructura de API optimizada según sus indicaciones. LLM se utiliza únicamente en la etapa de descubrimiento para generar la configuración de la API. La herramienta utiliza [proveedores de IA](https://docs.centralmind.ai/providers) para generar la configuración de la API, garantizando al mismo tiempo la seguridad mediante la detección de información de identificación personal (PII).\n\n### 2. Implementar\n\nGateway admite múltiples opciones de implementación: desde binarios independientes, Docker o Kubernetes. Consulta nuestra guía de lanzamiento para obtener instrucciones detalladas. El sistema utiliza configuración YAML y complementos para una fácil personalización.\n\n### 3. Usar e integrar\n\nAcceda a sus datos mediante API REST o el Protocolo de Contexto de Modelo (MCP) con funciones de seguridad integradas. Gateway se integra a la perfección con modelos de IA y aplicaciones como LangChain, OpenAI y Claude Desktop mediante llamadas a funciones o Cursor a través de MCP. También puede configurar la telemetría a destinos locales o remotos en formato hotel.\n\n## Documentación\n\n### Empezando\n\n* Guía de inicio rápido\n* Instrucciones de instalación\n* Guía de generación de API\n* Guía de lanzamiento de API\n\n### Recursos adicionales\n\n* Guía de integración de ChatGPT\n* Documentación del conector de base de datos\n* Documentación del complemento\n\n## Cómo construir\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## Generación de API\n\nGateway utiliza modelos LLM para generar la configuración de su API. Siga estos pasos:\n\n1. Elija uno de nuestros proveedores de inteligencia artificial compatibles:\n\n* [OpenAI](https://docs.centralmind.ai/providers/openai) y todos los proveedores compatibles con OpenAI\n* [Antrópico](https://docs.centralmind.ai/providers/anthropic)\n* [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock)\n* [Google Vertex AI (Antrópico)](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* [Google Géminis](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini](https://docs.centralmind.ai/providers/gemini) ofrece un generoso **nivel gratuito** . Puedes obtener una clave API visitando Google AI Studio:\n\n* [Estudio de inteligencia artificial de Google](https://aistudio.google.com/apikey)\n\nUna vez iniciada la sesión, puede crear una clave API en la sección API de AI Studio. El plan gratuito incluye una generosa asignación mensual de tokens, lo que lo hace accesible para fines de desarrollo y pruebas.\n\nConfigurar la autorización del proveedor de IA. Para Google Gemini, configure una clave API.\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. Ejecute el comando de descubrimiento:\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. Disfruta del proceso de generación:\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. Revise la configuración generada en `gateway.yaml` :\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## Ejecución de la API\n\n### Ejecutar localmente\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### Docker Compose\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### Integración del protocolo MCP\n\nGateway implementa el protocolo MCP para una integración fluida con Claude y otras herramientas. Para obtener instrucciones detalladas de configuración, consulte nuestra guía de integración con Claude.\n\nPara agregar la herramienta MCP a Claude Desktop simplemente ajuste la configuración de Claude:\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## Hoja de ruta\n\nEstá sujeto a cambios constantes y la hoja de ruta dependerá en gran medida de los comentarios de los usuarios. Actualmente, estamos planificando las siguientes funciones:\n\n#### Base de datos y conectividad\n\n* 🗄️ **Integraciones de bases de datos extendidas** : Redshift, S3 (Iceberg y Parquet), Oracle DB, Microsoft SQL Server, Elasticsearch\n* 🔑 **Túnel SSH** : capacidad de usar jumphost o ssh bastion para tunelizar conexiones\n\n#### Funcionalidad mejorada\n\n* 🔍 **Capacidades de consulta avanzadas** : sintaxis de filtrado compleja y funciones de agregación como parámetros\n* 🔐 **Seguridad MCP mejorada** : clave API y autenticación OAuth\n\n#### Mejoras de la plataforma\n\n* 📦 **Gestión de esquemas** : evolución automatizada de esquemas y control de versiones de API\n* 🚦 **Gestión avanzada del tráfico** : limitación inteligente de velocidad, limitación de solicitudes\n* ✍️ **Soporte de operaciones de escritura** : operaciones de inserción y actualización","Spanish","es-ES",{"_39":63,"_43":67,"_48":68},{"_41":64,"_43":65,"_45":66},"LLMおよびAIエージェント向けに最適化されたデータベースからのMCPサーバー。PostgreSQL、MySQL、ClickHouse、Snowflake、MSSQL、BigQuery、Oracle Database、SQLite、ElasticSearch、DuckDBをサポート","セントラルマインド/ゲートウェイ","![バイナリをビルドする](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)\n\n🚀 インタラクティブデモはこちらからご覧いただけます: \n\n## Centralmind/Gatewayとは\n\nMCP または OpenAPI 3.1 プロトコルを介してデータベースを AI-Agent に公開する簡単な方法。\n\n```bash\ndocker run --platform linux/amd64 -p 9090:9090 \\\n ghcr.io/centralmind/gateway:v0.2.14 start \\\n --connection-string \"postgres://db-user:db-password@db-host/db-name?sslmode=require\"\n```\n\nこれにより、API が実行されます。\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\nAI エージェント内で使用できるもの:\n\n![mcp-raw-cursor-setup.png](./assets/mcp-raw-cursor-setup.png)\n\nゲートウェイは AI に最適化された API を生成します。\n\n## Centralmind/Gatewayを選ぶ理由\n\nAIエージェントとLLMを活用したアプリケーションには、高速かつ安全なデータアクセスが必要です。私たちは、構造化データ向けにLLMに最適化された安全なAPIを自動生成するAPIレイヤーを構築しています。\n\n* MCP または OpenAPI をすぐに開始するか、Direct/Raw SQL API を使用します\n* GDPR、CPRA、SOC 2、その他の規制への準拠を確実にするために、PII と機密データをフィルタリングします。\n* トレーサビリティと監査機能を追加し、AIアプリケーションがブラックボックスにならないようにし、セキュリティチームが制御を維持できるようにします。\n* AIワークロード向けに最適化：AIエージェントがAPIを理解するのに役立つ拡張メタデータを備えたモデルコンテキストプロトコル（MCP）をサポートし、キャッシュとセキュリティ機能も組み込まれています。\n\n開発段階において、LLMがデータベースからデータを作成、調整、またはクエリする必要がある場合に役立ちます。分析シナリオでは、データベースまたはデータウェアハウスとチャットできるようになります。リモート関数/ツール呼び出しを使用して、データベースのデータでAIエージェントを強化できます。\n\n![デモ](/assets/demo.gif)\n\n## 特徴\n\n* ⚡**自動 API 生成**– テーブルスキーマとサンプルデータに基づいて LLM を使用して API を自動的に作成します。\n* 🗄️**構造化データベースのサポート**– PostgreSQL、MySQL、ClickHouse、Snowflake、MSSQL、BigQuery、Oracle Database、SQLite、ElasticSearch をサポート\n* 🌍**複数プロトコルのサポート**– SSEモードを含むRESTまたはMCPサーバーとしてAPIを提供します\n* 📜 **APIドキュメント**– 自動生成されたSwaggerドキュメントとOpenAPI 3.1.0仕様\n* 🔒 **PII 保護**– PII および機密データの編集用に正規表現プラグインまたは Microsoft Presidio プラグインを実装します\n* ⚡**柔軟な構成**– YAML構成とプラグインシステムで簡単に拡張可能\n* 🐳**デプロイメントオプション**– すぐに使えるHelmチャートを使用してバイナリまたはDockerコンテナとして実行\n* 🤖**複数の AI プロバイダーのサポート**- [OpenAI](https://docs.centralmind.ai/providers/openai) 、 [Anthropic](https://docs.centralmind.ai/providers/anthropic) 、 [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock) 、 [Google Gemini](https://docs.centralmind.ai/providers/gemini) 、 [Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai)のサポート\n* 📦**ローカルおよびオンプレミス**– 構成可能な AI エンドポイントとモデルを通じてセルフホスト型 LLM をサポート\n* 🔑**行レベルセキュリティ（RLS）** – Luaスクリプトを使用したきめ細かなデータアクセス制御\n* 🔐**認証オプション**– APIキーとOAuthの組み込みサポート\n* 👀**包括的な監視**– リクエストの追跡と監査証跡のための OpenTelemetry (OTel) との統合\n* 🏎️**パフォーマンス最適化**– 時間ベースおよびLRUキャッシュ戦略を実装\n\n## 仕組み\n\n![画像.png](assets/diagram.png)\n\n### 1. 接続と発見\n\nGatewayはPostgreSQLなどの構造化データベースに接続し、スキーマとデータサンプルを自動的に分析して、ユーザーの指示に基づいて最適化されたAPI構造を生成します。LLMは、API構成を生成するためのディスカバリー段階でのみ使用されます。このツールは[AIプロバイダー](https://docs.centralmind.ai/providers)を活用し、PII検出によってセキュリティを確保しながらAPI構成を生成します。\n\n### 2. デプロイ\n\nゲートウェイは、スタンドアロンバイナリ、Docker、Kubernetesなど、複数のデプロイメントオプションをサポートしています。詳細な手順については、起動ガイドをご覧ください。システムはYAML構成とプラグインを使用して簡単にカスタマイズできます。\n\n### 3. 使用と統合\n\nREST APIまたはモデルコンテキストプロトコル（MCP）を介してデータにアクセスし、セキュリティ機能を内蔵しています。Gatewayは、MCPを介した関数呼び出しやカーソル機能を使用して、LangChain、OpenAI、Claude DesktopなどのAIモデルやアプリケーションとシームレスに統合できます。また、ローカルまたはリモートの宛先にOtel形式でテレメトリを設定することもできます。\n\n## ドキュメント\n\n### はじめる\n\n* クイックスタートガイド\n* インストール手順\n* API生成ガイド\n* API 起動ガイド\n\n### 追加リソース\n\n* ChatGPT統合ガイド\n* データベースコネクタのドキュメント\n* プラグインのドキュメント\n\n## 構築方法\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## API生成\n\nゲートウェイはLLMモデルを使用してAPI構成を生成します。以下の手順に従ってください。\n\n1. サポートされている AI プロバイダーのいずれかを選択します。\n\n* [OpenAI](https://docs.centralmind.ai/providers/openai)およびすべてのOpenAI互換プロバイダー\n* [人類学的](https://docs.centralmind.ai/providers/anthropic)\n* [アマゾンの岩盤](https://docs.centralmind.ai/providers/bedrock)\n* [Google Vertex AI（人間中心）](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* [Google ジェミニ](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini は、](https://docs.centralmind.ai/providers/gemini)**無料枠**を豊富に提供しています。API キーは Google AI Studio から取得できます。\n\n* [Google AIスタジオ](https://aistudio.google.com/apikey)\n\nログイン後、AI StudioのAPIセクションでAPIキーを作成できます。無料プランには毎月のトークン割り当てが含まれており、開発やテストにご利用いただけます。\n\nAIプロバイダーの認証を設定します。Google Geminiの場合は、APIキーを設定します。\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. 検出コマンドを実行します。\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. 生成プロセスをお楽しみください:\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. `gateway.yaml`で生成された構成を確認します。\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## APIの実行\n\n### ローカルで実行\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### Dockerコンポーズ\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### MCPプロトコル統合\n\nGatewayはMCPプロトコルを実装しており、Claudeやその他のツールとのシームレスな統合を実現します。詳細な設定手順については、Claude統合ガイドをご覧ください。\n\nMCP ツールを Claude Desktop に追加するには、Claude の設定を調整するだけです。\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## ロードマップ\n\nロードマップは常に変更される可能性があり、ユーザーからのフィードバックに大きく左右されます。現時点では、以下の機能を予定しています。\n\n#### データベースと接続性\n\n* 🗄️**拡張データベース統合**- Redshift、S3 (Iceberg および Parquet)、Oracle DB、Microsoft SQL Server、Elasticsearch\n* 🔑 **SSH トンネリング**- ジャンプホストまたは SSH 要塞を使用して接続をトンネリングする機能\n\n#### 強化された機能\n\n* 🔍**高度なクエリ機能**- 複雑なフィルタリング構文と集計関数をパラメータとして使用可能\n* 🔐**強化された MCP セキュリティ**- API キーと OAuth 認証\n\n#### プラットフォームの改善\n\n* 📦**スキーマ管理**- 自動化されたスキーマ進化と API バージョン管理\n* 🚦**高度なトラフィック管理**- インテリジェントなレート制限、リクエストスロットリング\n* ✍️**書き込み操作のサポート**- 挿入、更新操作","Japanese","ja-JP",{"_39":70,"_43":74,"_48":75},{"_41":71,"_43":72,"_45":73},"LLM 및 AI 에이전트에 최적화된 데이터베이스의 MCP 서버입니다. PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch, DuckDB를 지원합니다.","센트럴마인드/게이트웨이","![바이너리 빌드](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)\n\n🚀 대화형 데모는 여기에서 이용 가능합니다: \n\n## 센트럴마인드/게이트웨이란 무엇인가요?\n\nMCP 또는 OpenAPI 3.1 프로토콜을 통해 AI-Agent에 데이터베이스를 노출하는 간단한 방법입니다.\n\n지엑스피1\n\n이렇게 하면 API가 실행됩니다.\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\nAI 에이전트 내부에서 사용할 수 있는 항목:\n\n![mcp-raw-cursor-setup.png](./assets/mcp-raw-cursor-setup.png)\n\n게이트웨이는 AI에 최적화된 API를 생성합니다.\n\n## 센트럴마인드/게이트웨이를 선택해야 하는 이유\n\nAI 에이전트와 LLM 기반 애플리케이션은 데이터에 빠르고 안전하게 액세스할 수 있어야 합니다. 구조화된 데이터에 대해 안전하고 LLM에 최적화된 API를 자동으로 생성하는 API 계층을 구축하고 있습니다.\n\n* MCP 또는 OpenAPI로 빠르게 시작하거나 직접/원시 SQL API를 사용하세요.\n* GDPR, CPRA, SOC 2 및 기타 규정을 준수하기 위해 PII 및 민감한 데이터를 필터링합니다.\n* 추적 가능성과 감사 기능을 추가하여 AI 애플리케이션이 블랙박스가 아닌지 확인하고 보안 팀이 제어를 유지할 수 있도록 합니다.\n* AI 워크로드에 최적화: AI 에이전트가 API를 이해하는 데 도움이 되는 향상된 메타데이터와 내장 캐싱 및 보안 기능을 갖춘 MCP(모델 컨텍스트 프로토콜)를 지원합니다.\n\n개발 과정에서 LLM이 데이터베이스에서 데이터를 생성, 조정 또는 쿼리해야 할 때 유용할 수 있습니다. 분석 시나리오에서는 데이터베이스 또는 데이터웨어하우스와 통신할 수 있습니다. 원격 함수/도구 호출을 사용하여 데이터베이스의 데이터로 AI 에이전트를 강화할 수 있습니다.\n\n![데모](/assets/demo.gif)\n\n## 특징\n\n* ⚡ **자동 API 생성** – 테이블 스키마와 샘플링된 데이터를 기반으로 LLM을 사용하여 자동으로 API를 생성합니다.\n* 🗄️ **구조화된 데이터베이스 지원** – PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch 지원\n* 🌍 **다중 프로토콜 지원** – SSE 모드를 포함한 REST 또는 MCP 서버로 API 제공\n* 📜 **API 문서** – 자동 생성된 Swagger 문서 및 OpenAPI 3.1.0 사양\n* 🔒 **PII 보호** – PII 및 민감한 데이터 삭제를 위한 정규식 플러그인 또는 Microsoft Presidio 플러그인 구현\n* ⚡ **유연한 구성** – YAML 구성 및 플러그인 시스템을 통해 쉽게 확장 가능\n* 🐳 **배포 옵션** – 사용 가능한 Helm 차트를 사용하여 바이너리 또는 Docker 컨테이너로 실행\n* 🤖 **다양한 AI 공급자 지원** - [OpenAI](https://docs.centralmind.ai/providers/openai) , [Anthropic](https://docs.centralmind.ai/providers/anthropic) , [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock) , [Google Gemini](https://docs.centralmind.ai/providers/gemini) 및 [Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai) 지원\n* 📦 **로컬 및 온프레미스** – 구성 가능한 AI 엔드포인트 및 모델을 통해 자체 호스팅 LLM 지원\n* 🔑 **행 수준 보안(RLS)** – Lua 스크립트를 사용한 세분화된 데이터 액세스 제어\n* 🔐 **인증 옵션** – API 키 및 OAuth에 대한 기본 지원\n* 👀 **종합 모니터링** – 요청 추적 및 감사 추적을 위한 OpenTelemetry(OTel)와의 통합\n* 🏎️ **성능 최적화** – 시간 기반 및 LRU 캐싱 전략 구현\n\n## 작동 원리\n\n![이미지.png](assets/diagram.png)\n\n### 1. 연결하고 발견하세요\n\nGateway는 PostgreSQL과 같은 구조화된 데이터베이스에 연결하여 스키마와 데이터 샘플을 자동으로 분석하여 사용자의 요청에 따라 최적화된 API 구조를 생성합니다. LLM은 API 구성을 생성하는 검색 단계에서만 사용됩니다. 이 도구는 [AI 제공자를](https://docs.centralmind.ai/providers) 사용하여 PII 탐지를 통해 보안을 강화하는 동시에 API 구성을 생성합니다.\n\n### 2. 배포\n\nGateway는 독립 실행형 바이너리, Docker 또는 Kubernetes 등 다양한 배포 옵션을 지원합니다. 자세한 내용은 시작 가이드를 참조하세요. 시스템은 YAML 구성 및 플러그인을 사용하여 손쉽게 사용자 지정할 수 있습니다.\n\n### 3. 사용 및 통합\n\n내장된 보안 기능이 포함된 REST API 또는 MCP(Model Context Protocol)를 통해 데이터에 액세스하세요. Gateway는 MCP를 통해 함수 호출 또는 Cursor를 사용하여 LangChain, OpenAI, Claude Desktop과 같은 AI 모델 및 애플리케이션과 완벽하게 통합됩니다. 또한 호텔 형식으로 로컬 또는 원격 대상에 대한 원격 측정을 설정할 수 있습니다.\n\n## 선적 서류 비치\n\n### 시작하기\n\n* 빠른 시작 가이드\n* 설치 지침\n* API 생성 가이드\n* API 출시 가이드\n\n### 추가 자료\n\n* ChatGPT 통합 가이드\n* 데이터베이스 커넥터 문서\n* 플러그인 문서\n\n## 빌드하는 방법\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## API 생성\n\nGateway는 LLM 모델을 사용하여 API 구성을 생성합니다. 다음 단계를 따르세요.\n\n1. 지원되는 AI 공급업체 중 하나를 선택하세요.\n\n* [OpenAI](https://docs.centralmind.ai/providers/openai) 및 모든 OpenAI 호환 공급자\n* [인류학적](https://docs.centralmind.ai/providers/anthropic)\n* [아마존 베드락](https://docs.centralmind.ai/providers/bedrock)\n* [Google Vertex AI(인류학)](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* [구글 제미니](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini는](https://docs.centralmind.ai/providers/gemini) 넉넉한 **무료 등급을** 제공합니다. Google AI Studio를 방문하여 API 키를 받으실 수 있습니다.\n\n* [구글 AI 스튜디오](https://aistudio.google.com/apikey)\n\n로그인 후 AI Studio의 API 섹션에서 API 키를 생성할 수 있습니다. 무료 티어에는 넉넉한 월별 토큰 할당이 포함되어 있어 개발 및 테스트 목적으로 사용할 수 있습니다.\n\nAI 공급자 권한을 구성하세요. Google Gemini의 경우 API 키를 설정하세요.\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. 검색 명령을 실행합니다.\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. 생성 과정을 즐겨보세요:\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. `gateway.yaml` 에서 생성된 구성을 검토하세요.\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## API 실행\n\n### 지역적으로 실행\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### 도커 컴포즈\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### MCP 프로토콜 통합\n\nGateway는 Claude 및 기타 도구와의 원활한 통합을 위해 MCP 프로토콜을 구현합니다. 자세한 설정 지침은 Claude 통합 가이드를 참조하세요.\n\nClaude Desktop에 MCP 도구를 추가하려면 Claude의 구성을 조정하기만 하면 됩니다.\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## 로드맵\n\n로드맵은 항상 변경될 수 있으며, 사용자 피드백을 바탕으로 작성될 예정입니다. 현재 다음과 같은 기능을 계획하고 있습니다.\n\n#### 데이터베이스 및 연결성\n\n* 🗄️ **확장 데이터베이스 통합** - Redshift, S3(Iceberg 및 Parquet), Oracle DB, Microsoft SQL Server, Elasticsearch\n* 🔑 **SSH 터널링** - jumphost 또는 ssh bastion을 사용하여 연결을 터널링하는 기능\n\n#### 향상된 기능\n\n* 🔍 **고급 쿼리 기능** - 복잡한 필터링 구문 및 매개변수로서의 집계 함수\n* 🔐 **향상된 MCP 보안** - API 키 및 OAuth 인증\n\n#### 플랫폼 개선\n\n* 📦 **스키마 관리** - 자동화된 스키마 진화 및 API 버전 관리\n* 🚦 **고급 트래픽 관리** - 지능형 속도 제한, 요청 조절\n* ✍️ **쓰기 작업 지원** - 삽입, 업데이트 작업","Korean","ko-KR",{"_39":77,"_43":81,"_48":82},{"_41":78,"_43":79,"_45":80},"MCP-сервер из вашей базы данных, оптимизированный для LLM и AI-агентов. Поддерживает PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch, DuckDB","CentralMind/Шлюз","![Сборка двоичных файлов](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)\n\n🚀 Интерактивная демоверсия доступна здесь: \n\n## Что такое Centralmind/Gateway\n\nПростой способ предоставить доступ к вашей базе данных AI-Agent через протоколы MCP или OpenAPI 3.1.\n\n```bash\ndocker run --platform linux/amd64 -p 9090:9090 \\\n ghcr.io/centralmind/gateway:v0.2.14 start \\\n --connection-string \"postgres://db-user:db-password@db-host/db-name?sslmode=require\"\n```\n\nЭто запустит для вас API:\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\nЧто вы можете использовать внутри своего ИИ-агента:\n\n![mcp-raw-cursor-setup.png](./assets/mcp-raw-cursor-setup.png)\n\nGateway сгенерирует оптимизированный для ИИ API.\n\n## Почему Centralmind/Gateway\n\nАгентам ИИ и приложениям на базе LLM нужен быстрый и безопасный доступ к данным. Мы создаем уровень API, который автоматически генерирует безопасные API, оптимизированные для LLM, для ваших структурированных данных.\n\n* Быстро начните с MCP или OpenAPI или используйте API Direct/Raw SQL\n* Фильтрует персональные данные и конфиденциальные данные для обеспечения соответствия GDPR, CPRA, SOC 2 и другим нормативным актам.\n* Добавляет возможности отслеживания и аудита, гарантируя, что приложения ИИ не являются черными ящиками, и позволяя службам безопасности сохранять контроль\n* Оптимизирован для рабочих нагрузок ИИ: поддерживает протокол контекста модели (MCP) с расширенными метаданными, помогающими агентам ИИ понимать API, а также встроенные функции кэширования и безопасности\n\nЭто может быть полезно во время разработки, когда LLM необходимо создавать, корректировать или запрашивать данные из вашей базы данных. В аналитических сценариях это позволяет вам общаться с вашей базой данных или хранилищем данных. Обогатите ваших агентов ИИ данными из вашей базы данных с помощью удаленного вызова функций/инструментов.\n\n![демо](/assets/demo.gif)\n\n## Функции\n\n* ⚡ **Автоматическая генерация API** — автоматически создает API с использованием LLM на основе схемы таблицы и выборочных данных.\n* 🗄️ **Поддержка структурированных баз данных** – поддерживает PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch\n* 🌍 **Поддержка нескольких протоколов** — предоставляет API как сервер REST или MCP, включая режим SSE\n* 📜 **Документация API** – автоматически сгенерированная документация Swagger и спецификация OpenAPI 3.1.0\n* 🔒 **Защита персональных данных** — реализует плагин регулярных выражений или плагин Microsoft Presidio для редактирования персональных данных и конфиденциальных данных.\n* ⚡ **Гибкая конфигурация** — легко расширяется с помощью конфигурации YAML и системы плагинов\n* 🐳 **Варианты развертывания** — запуск в виде двоичного файла или контейнера Docker с готовым к использованию чартом Helm\n* 🤖 **Поддержка нескольких поставщиков ИИ** - поддержка [OpenAI](https://docs.centralmind.ai/providers/openai) , [Anthropic](https://docs.centralmind.ai/providers/anthropic) , [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock) , [Google Gemini](https://docs.centralmind.ai/providers/gemini) и [Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* 📦 **Локально и локально** — поддержка размещенных самостоятельно LLM с помощью настраиваемых конечных точек и моделей ИИ\n* 🔑 **Безопасность на уровне строк (RLS)** — детальный контроль доступа к данным с использованием скриптов Lua\n* 🔐 **Варианты аутентификации** – Встроенная поддержка ключей API и OAuth\n* 👀 **Комплексный мониторинг** — интеграция с OpenTelemetry (OTel) для отслеживания запросов и аудита\n* 🏎️ **Оптимизация производительности** — реализует стратегии кэширования на основе времени и LRU\n\n## Как это работает\n\n![img.png](assets/diagram.png)\n\n### 1. Подключайтесь и открывайте новое\n\nGateway подключается к вашим структурированным базам данных, таким как PostgreSQL, и автоматически анализирует схему и образцы данных для создания оптимизированной структуры API на основе вашего запроса. LLM используется только на этапе обнаружения для создания конфигурации API. Инструмент использует [поставщиков AI](https://docs.centralmind.ai/providers) для создания конфигурации API, обеспечивая безопасность посредством обнаружения PII.\n\n### 2. Развертывание\n\nGateway поддерживает несколько вариантов развертывания из автономного двоичного файла, docker или Kubernetes. Ознакомьтесь с нашим руководством по запуску для получения подробных инструкций. Система использует конфигурацию YAML и плагины для легкой настройки.\n\n### 3. Использование и интеграция\n\nДоступ к данным через REST API или Model Context Protocol (MCP) со встроенными функциями безопасности. Gateway легко интегрируется с моделями и приложениями ИИ, такими как LangChain, OpenAI и Claude Desktop, с помощью вызова функций или Cursor через MCP. Вы также можете настроить телеметрию для локального или удаленного назначения в формате otel.\n\n## Документация\n\n### Начиная\n\n* Краткое руководство\n* Инструкция по установке\n* Руководство по созданию API\n* Руководство по запуску API\n\n### Дополнительные ресурсы\n\n* Руководство по интеграции ChatGPT\n* Документация по коннектору базы данных\n* Документация по плагину\n\n## Как построить\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## Генерация API\n\nGateway использует модели LLM для генерации конфигурации API. Выполните следующие шаги:\n\n1. Выберите одного из поддерживаемых нами поставщиков ИИ:\n\n* [OpenAI](https://docs.centralmind.ai/providers/openai) и все OpenAI-совместимые поставщики\n* [Антропный](https://docs.centralmind.ai/providers/anthropic)\n* [Амазонка Бедрок](https://docs.centralmind.ai/providers/bedrock)\n* [Google Vertex AI (антропный)](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* [Google Близнецы](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini](https://docs.centralmind.ai/providers/gemini) предоставляет щедрый **бесплатный уровень** . Вы можете получить ключ API, посетив Google AI Studio:\n\n* [Студия искусственного интеллекта Google](https://aistudio.google.com/apikey)\n\nПосле входа в систему вы можете создать ключ API в разделе API студии AI. Бесплатный уровень включает щедрое ежемесячное распределение токенов, что делает его доступным для целей разработки и тестирования.\n\nНастройте авторизацию поставщика AI. Для Google Gemini установите ключ API.\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. Запустите команду обнаружения:\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. Наслаждайтесь процессом генерации:\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. Просмотрите сгенерированную конфигурацию в `gateway.yaml` :\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## Запуск API\n\n### Запустить локально\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### Docker-композитор\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### Интеграция протокола MCP\n\nGateway реализует протокол MCP для бесшовной интеграции с Claude и другими инструментами. Подробные инструкции по настройке см. в нашем руководстве по интеграции Claude.\n\nЧтобы добавить MCP Tool в Claude Desktop, просто измените конфигурацию Claude:\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## Дорожная карта\n\nОн всегда может измениться, и дорожная карта будет сильно зависеть от отзывов пользователей. На данный момент мы планируем следующие функции:\n\n#### База данных и связь\n\n* 🗄️ **Расширенная интеграция с базами данных** — Redshift, S3 (Iceberg и Parquet), Oracle DB, Microsoft SQL Server, Elasticsearch\n* 🔑 **SSH-туннелирование** — возможность использовать jumphost или ssh bastion для туннелирования соединений\n\n#### Расширенная функциональность\n\n* 🔍 **Расширенные возможности запросов** — сложный синтаксис фильтрации и функции агрегации в качестве параметров\n* 🔐 **Улучшенная безопасность MCP** — API-ключ и аутентификация OAuth\n\n#### Улучшения платформы\n\n* 📦 **Управление схемами** — автоматическая эволюция схем и управление версиями API\n* 🚦 **Расширенное управление трафиком** — интеллектуальное ограничение скорости, регулирование запросов\n* ✍️ **Поддержка операций записи** — операции вставки, обновления","Russian","ru-RU",{"_39":84,"_43":88,"_48":89},{"_41":85,"_43":86,"_45":87},"数据库的 MCP 服务器已针对 LLM 和 AI 代理进行了优化。支持 PostgreSQL、MySQL、ClickHouse、Snowflake、MSSQL、BigQuery、Oracle Database、SQLite、ElasticSearch、DuckDB","CentralMind/网关","![构建二进制文件](https://github.com/centralmind/gateway/actions/workflows/build-binaries.yml/badge.svg)\n\n🚀 交互式演示可在此处获取： \n\n## 什么是 Centralmind/Gateway\n\n通过 MCP 或 OpenAPI 3.1 协议向 AI-Agent 公开数据库的简单方法。\n\n```bash\ndocker run --platform linux/amd64 -p 9090:9090 \\\n ghcr.io/centralmind/gateway:v0.2.14 start \\\n --connection-string \"postgres://db-user:db-password@db-host/db-name?sslmode=require\"\n```\n\n这将为您运行一个 API：\n\n```shell\nINFO Gateway server started successfully! \nINFO MCP SSE server for AI agents is running at: http://localhost:9090/sse \nINFO REST API with Swagger UI is available at: http://localhost:9090/ \n```\n\n您可以在 AI 代理中使用它：\n\n![mcp-原始光标设置.png](./assets/mcp-raw-cursor-setup.png)\n\n网关将生成 AI 优化的 API。\n\n## 为什么选择 Centralmind/Gateway\n\nAI 代理和 LLM 驱动的应用程序需要快速、安全地访问数据。我们正在构建一个 API 层，可自动为您的结构化数据生成安全且经过 LLM 优化的 API。\n\n* 快速启动 MCP 或 OpenAPI，或使用 Direct/Raw SQL API\n* 过滤掉 PII 和敏感数据，以确保符合 GDPR、CPRA、SOC 2 和其他法规\n* 增加可追溯性和审计功能，确保人工智能应用程序不是黑匣子，并允许安全团队保持控制\n* 针对 AI 工作负载进行了优化：支持模型上下文协议 (MCP)，并增强元数据以帮助 AI 代理理解 API，同时还具有内置缓存和安全功能\n\n在开发过程中，当 LLM 需要创建、调整或查询数据库中的数据时，它会非常有用。在分析场景中，它使您能够与数据库或数据仓库进行交互。使用远程函数/工具调用，利用数据库中的数据丰富您的 AI 代理。\n\n![演示](/assets/demo.gif)\n\n## 特征\n\n* ⚡**自动 API 生成**– 使用基于表架构和采样数据的 LLM 自动创建 API\n* 🗄️**结构化数据库支持**- 支持 PostgreSQL、MySQL、ClickHouse、Snowflake、MSSQL、BigQuery、Oracle Database、SQLite、ElasticSearch\n* 🌍**多协议支持**– 提供 REST 或 MCP 服务器 API，包括 SSE 模式\n* 📜 **API 文档**– 自动生成的 Swagger 文档和 OpenAPI 3.1.0 规范\n* 🔒 **PII 保护**– 实现正则表达式插件或 Microsoft Presidio 插件，用于 PII 和敏感数据编辑\n* ⚡**灵活的配置**– 通过 YAML 配置和插件系统轻松扩展\n* 🐳**部署选项**– 作为二进制文件或 Docker 容器运行，并附带可立即使用的 Helm 图表\n* 🤖**支持多家 AI 提供商**- 支持[OpenAI](https://docs.centralmind.ai/providers/openai) 、 [Anthropic](https://docs.centralmind.ai/providers/anthropic) 、 [Amazon Bedrock](https://docs.centralmind.ai/providers/bedrock) 、 [Google Gemini](https://docs.centralmind.ai/providers/gemini)和[Google VertexAI](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* 📦**本地和内部部署**– 通过可配置的 AI 端点和模型支持自托管 LLM\n* 🔑**行级安全性（RLS）** ——使用 Lua 脚本进行细粒度的数据访问控制\n* 🔐**身份验证选项**– 内置对 API 密钥和 OAuth 的支持\n* 👀**全面监控**——与 OpenTelemetry（OTel）集成，用于请求跟踪和审计跟踪\n* 🏎️**性能优化**——实现基于时间和 LRU 缓存策略\n\n## 工作原理\n\n![图片.png](assets/diagram.png)\n\n### 1. 连接与发现\n\nGateway 连接到您的结构化数据库（例如 PostgreSQL），并自动分析架构和数据样本，根据您的提示生成优化的 API 结构。LLM 仅在发现阶段用于生成 API 配置。该工具使用[AI 提供程序](https://docs.centralmind.ai/providers)生成 API 配置，同时通过 PII 检测确保安全性。\n\n### 2.部署\n\nGateway 支持多种部署选项，包括独立二进制文件、Docker 或 Kubernetes。查看我们的启动指南，了解详细说明。该系统使用 YAML 配置和插件，方便用户轻松定制。\n\n### 3. 使用与整合\n\n通过内置安全功能的 REST API 或模型上下文协议 (MCP) 访问您的数据。网关通过 MCP 使用函数调用或 Cursor 与 LangChain、OpenAI 和 Claude Desktop 等 AI 模型和应用程序无缝集成。您还可以以 Otel 格式设置遥测到本地或远程目标。\n\n## 文档\n\n### 入门\n\n* 快速入门指南\n* 安装说明\n* API 生成指南\n* API 启动指南\n\n### 其他资源\n\n* ChatGPT 集成指南\n* 数据库连接器文档\n* 插件文档\n\n## 如何构建\n\n```shell\n# Clone the repository\ngit clone https://github.com/centralmind/gateway.git\n\n# Navigate to project directory\ncd gateway\n\n# Install dependencies\ngo mod download\n\n# Build the project\ngo build .\n```\n\n## API 生成\n\nGateway 使用 LLM 模型生成您的 API 配置。请按以下步骤操作：\n\n1. 选择我们支持的 AI 提供商之一：\n\n* [OpenAI](https://docs.centralmind.ai/providers/openai)和所有与 OpenAI 兼容的提供商\n* [人择](https://docs.centralmind.ai/providers/anthropic)\n* [亚马逊基岩](https://docs.centralmind.ai/providers/bedrock)\n* [Google Vertex AI（人类学）](https://docs.centralmind.ai/providers/anthropic-vertexai)\n* [谷歌双子座](https://docs.centralmind.ai/providers/gemini)\n\n[Google Gemini](https://docs.centralmind.ai/providers/gemini)提供了慷慨的**免费套餐**。您可以通过访问 Google AI Studio 获取 API 密钥：\n\n* [谷歌人工智能工作室](https://aistudio.google.com/apikey)\n\n登录后，您可以在 AI Studio 的 API 部分创建 API 密钥。免费套餐包含丰厚的每月令牌分配，可用于开发和测试目的。\n\n配置 AI 提供商授权。对于 Google Gemini，请设置 API 密钥。\n\n```bash\nexport GEMINI_API_KEY='yourkey'\n```\n\n2. 运行发现命令：\n\n```shell\n./gateway discover \\\n --ai-provider gemini \\\n --connection-string \"postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require\" \\\n --prompt \"Generate for me awesome readonly API\"\n```\n\n3. 享受生成过程：\n\n```shell\nINFO 🚀 API Discovery Process\nINFO Step 1: Read configs\nINFO ✅ Step 1 completed. Done.\n\nINFO Step 2: Discover data\nINFO Discovered Tables:\nINFO - payment_dim: 3 columns, 39 rows\nINFO - fact_table: 9 columns, 1000000 rows\nINFO ✅ Step 2 completed. Done.\n\n# Additional steps and output...\n\nINFO ✅ All steps completed. Done.\n\nINFO --- Execution Statistics ---\nINFO Total time taken: 1m10s\nINFO Tokens used: 16543 (Estimated cost: $0.0616)\nINFO Tables processed: 6\nINFO API methods created: 18\nINFO Total number of columns with PII data: 2\n```\n\n4. 查看`gateway.yaml`中生成的配置：\n\n```yaml\napi:\n name: Awesome Readonly API\n description: ''\n version: '1.0'\ndatabase:\n type: postgres\n connection: YOUR_CONNECTION_INFO\n tables:\n - name: payment_dim\n columns: # Table columns\n endpoints:\n - http_method: GET\n http_path: /some_path\n mcp_method: some_method\n summary: Some readable summary\n description: 'Some description'\n query: SQL Query with params\n params: # Query parameters\n```\n\n## 运行 API\n\n### 本地运行\n\n```shell\n./gateway start --config gateway.yaml\n```\n\n### Docker Compose\n\n```shell\ndocker compose -f ./example/simple/docker-compose.yml up\n```\n\n### MCP 协议集成\n\nGateway 实现了 MCP 协议，可与 Claude 及其他工具无缝集成。有关详细的设置说明，请参阅我们的 Claude 集成指南。\n\n要将 MCP 工具添加到 Claude 桌面，只需调整 Claude 的配置：\n\n```json\n{\n \"mcpServers\": {\n \"gateway\": {\n \"command\": \"PATH_TO_GATEWAY_BINARY\",\n \"args\": [\"start\", \"--config\", \"PATH_TO_GATEWAY_YAML_CONFIG\", \"mcp-stdio\"]\n }\n }\n}\n```\n\n## 路线图\n\n路线图随时可能更改，并且高度依赖于用户反馈。目前，我们正在规划以下功能：\n\n#### 数据库和连接\n\n* 🗄️**扩展数据库集成**- Redshift、S3（Iceberg 和 Parquet）、Oracle DB、Microsoft SQL Server、Elasticsearch\n* 🔑 **SSH 隧道**- 能够使用 jumphost 或 ssh 堡垒来建立隧道连接\n\n#### 增强功能\n\n* 🔍**高级查询功能**- 复杂的过滤语法和聚合函数作为参数\n* 🔐**增强的 MCP 安全性**- API 密钥和 OAuth 身份验证\n\n#### 平台改进\n\n* 📦 **Schema 管理**- 自动进行 Schema 演变和 API 版本控制\n* 🚦**高级流量管理**- 智能速率限制、请求节流\n* ✍️**写操作支持**- 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Rate & Review

MCP directory API