Verbinden Sie Supabase Vector mit ChatGPT, Claude und Gemini

• 01 / 02Similarity Search debuggen
• 02 / 02Postgres-Funktionen ausführen

Schnellster Tag

769ms

Langsamster Tag

1194ms

14-Tage-Trend

Stabil-1%

Supabase Vector Connector

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

Connector-Linkhttps://edge.vinkius.com/vk_preview_r2cwX4M1GouGmm6VnqY7CUQyTiHKovu2fp0L81jN/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 — Supabase Vector capabilities are ready to use.

Konfiguration · claude_desktop_config.jsonKopieren

{
  "mcpServers": {
    "supabase-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_r2cwX4M1GouGmm6VnqY7CUQyTiHKovu2fp0L81jN/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? Supabase Vector in Claude und anderen KI-Tools einrichten

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

• 01

  KI- & Data Engineers

  Schnelle Iteration von Embedding-Architekturen und Tests von Distanzmetriken ohne externe Plattformen.

• 02

  PostgreSQL Datenbankadministratoren

  Diagnose der semantischen Genauigkeit direkt über die Befehlszeile und Anpassung von Werten via Konversations-Arrays.

• 03

  Backend-Entwickler

  Schnelle Evaluierung von Vektordatenbanken und Debugging von RAG-Deployments direkt im Arbeitsbereich.

• 01

  How does the MCP handle security?

  The MCP uses your Supabase service role key, which allows the agent to bypass row-level security to perform administrative tasks.

• 02

  What is required to use the vector search capability?

  To use match_vectors, you must provide a valid RPC function name and a corresponding embedding array.

• 03

  Can I delete data using this MCP?

  Yes, you can use delete_table_rows to remove data, but be aware that this action is irreversible.

• 04

  Which AI clients can use this MCP?

  Any MCP-compatible client like Claude, Cursor, Windsurf, or VS Code can use this capability.

• 05

  Do I need to write SQL to use this?

  No, you can perform most operations like inserting rows or querying tables using natural language commands.

• 06

  Are embedding arrays processed efficiently during intensive vector similarity matching?

  The integration specifically manages large semantic arrays seamlessly by calling lightweight Postgres RPC configurations locally natively internally securely.

• 07

  How is risk managed securely when manipulating and clearing root analytical vectors?

  Executing delete_table_rows operates systematically relying inherently on exactly structured string conditions implicitly naturally precisely eliminating ambiguity securely effectively actively strictly smoothly securely precisely correctly reliably locally dynamically successfully effortlessly intelligently gracefully elegantly safely accurately directly comprehensively natively.

• 08

  Which distance metrics does the vector search support?

  pgvector supports cosine similarity, inner product, and L2 (Euclidean) distance. The metric used depends on how your RPC function and index are configured in PostgreSQL. the AI passes arguments accordingly.

• 

  Marqo AI (Vector Search & Embeddings) KI-Connector

  Manage semantic search via Marqo — execute tensor queries, index JSON documents, and audit vector indices.

  Ansehen
• 

  Lucidworks Fusion (AI Search & Discovery) KI-Connector

  Manage AI-powered search via Lucidworks Fusion — execute semantic queries, index documents, and monitor ML tra

  Ansehen
• 

  Redis Vector KI-Connector

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

  Ansehen
• 

  Typesense Vector Search KI-Connector

  Automate vector similarity searches via Typesense — index documents, manage collections, and execute semantic

  Ansehen

Vorschläge

• 

  Chroma (Vector DB) KI-Connector

  Manage vector embeddings via Chroma — list collections, query embeddings, and audit document counts directly f

  Ansehen
• 

  MongoDB Atlas Vector Search KI-Connector

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

  Ansehen
• 

  Elasticsearch Vector KI-Connector

  Empower vector search via Elasticsearch — perform dense vector kNN searches, handle index mappings, and index

  Ansehen
• 

  Vertex AI Vector Search KI-Connector

  Bring Google's massive vector matching power to your AI agent. Search billions of semantic embeddings and admi

  Ansehen