Verbinden Sie Opensearch Vector mit ChatGPT und Claude

• 01 / 03Vektorsuche starten
• 02 / 03k-NN Indizes verwalten
• 03 / 03Update und Löschung

Schnellster Tag

751ms

Langsamster Tag

1525ms

14-Tage-Trend

Verlangsamend+80%

OpenSearch Connector

Alles bereit. Wählen Sie Ihren MCP-Client und folgen Sie den Einrichtungsschritten.

Connector-Linkhttps://edge.vinkius.com/vk_preview_6R4dQ885yexHhsyH4M5e8fLiT1BjuMlzaUlJMumQ/mcpKopieren

Claude Desktop

Folgen Sie den Schritten unten, um sich in Sekunden zu verbinden.

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — OpenSearch capabilities are ready to use.

Konfiguration · claude_desktop_config.jsonKopieren

{
  "mcpServers": {
    "opensearch-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_6R4dQ885yexHhsyH4M5e8fLiT1BjuMlzaUlJMumQ/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

Schritt-für-Schritt-Anleitungen für jeden Client finden Sie im Guide. Verbinden

Geführte Einrichtung für Claude? OpenSearch in Claude und anderen KI-Tools einrichten

Alle KI-Clients ansehen, mit denen dieser Connector funktioniert ↑

• 01

  ML Engineers

  Testen Ähnlichkeitsabfragen gegen Produktions-Embeddings, ohne manuelle Curl-Befehle schreiben zu müssen.

• 02

  RAG Entwickler

  Indizieren und rufen Kontextdokumente für Retrieval-Augmented Generation Pipelines ab.

• 03

  Data Teams

  Überwachen die Index-Gesundheit und Vektor-Konfigurationen per Chat statt über komplexe Kibana-Dashboards.

• 01

  What is this MCP used for?

  This MCP allows your AI client to perform k-NN vector searches, manage k-NN indexes, and handle document upserts within an OpenSearch cluster.

• 02

  Which AI clients can I use with this?

  You can use this MCP with any compatible client, including Claude, Cursor, Windsurf, and VS Code.

• 03

  Can I create indexes with specific dimensions?

  Yes, you can instruct your agent to create new k-NN indexes with configurable dimensions such as 384, 768, or 1536.

• 04

  Does this work with existing OpenSearch clusters?

  Yes, you simply provide your existing host credentials to connect the MCP to your cluster.

• 05

  How do I perform similarity searches?

  You can ask your agent to execute a k-NN search by providing the index name and a dense float vector.

• 06

  What vector dimensions does it support?

  Any dimension supported by OpenSearch k-NN. Common values: 384 (MiniLM), 768 (BERT/all-mpnet), 1536 (OpenAI text-embedding-ada-002), 3072 (text-embedding-3-large). When creating an index, specify the exact dimension and the agent provisions the mapping automatically.

• 07

  Can I delete an entire index or just individual documents?

  Currently, the agent supports deleting individual documents by ID from an index. Full index deletion is not exposed through this integration to prevent accidental data loss. If you need to drop an index, use the OpenSearch Dashboards or direct API calls.

• 08

  Does this work with Amazon OpenSearch Service (managed)?

  Yes. Provide the Amazon OpenSearch Service endpoint as the host (e.g., https://search-xxx.us-east-1.es.amazonaws.com) along with the master username and password. The integration uses standard REST APIs that work identically on managed and self-hosted clusters.

• 

  Marqo AI (Vector Search & Embeddings) KI-Connector

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  Ansehen
• 

  Typesense Vector Search KI-Connector

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• 

  Elasticsearch Vector KI-Connector

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• 

  pgvector (Vector Database) KI-Connector

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Vorschläge

• 

  MyScale (SQL Vector Database API) KI-Connector

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• 

  Redis Vector KI-Connector

  Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes

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• 

  Oracle Vector DB KI-Connector

  Run vector similarity searches on Oracle 23ai — execute VECTOR_DISTANCE queries, inspect schemas, list vector

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• 

  MongoDB Atlas Vector Search KI-Connector

  Manage vector storage via MongoDB Atlas — perform similarity searches, query MQL documents, and audit collecti

  Ansehen