Connectez Supabase Vector à ChatGPT, Claude et Gemini

• 01 / 02Déboguer la similarité
• 02 / 02Exécuter des fonctions Postgres

Jour le plus rapide

769ms

Jour le plus lent

1194ms

Tendance sur 14 jours

Stable-1%

Supabase Vector Connector

Tout est prêt. Choisissez votre client MCP et suivez les instructions de configuration.

Lien du connecteurhttps://edge.vinkius.com/vk_preview_r2cwX4M1GouGmm6VnqY7CUQyTiHKovu2fp0L81jN/mcpCopier

Claude Desktop

Suivez les étapes ci-dessous pour vous connecter en quelques secondes.

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.

Configuration · claude_desktop_config.jsonCopier

{
  "mcpServers": {
    "supabase-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_r2cwX4M1GouGmm6VnqY7CUQyTiHKovu2fp0L81jN/mcp"
    }
  }
}

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

Les instructions pas à pas pour chaque client sont dans le guide. Comment connecter

Configuration guidée pour Claude ? Configurer Supabase Vector avec Claude et les autres IA

Voyez tous les clients IA avec lesquels ce connector fonctionne ↑

• 01

  Ingénieurs IA et Data

  Pour itérer rapidement sur les architectures d'embeddings et tester les métriques de distance sans interface externe.

• 02

  Administrateurs de bases de données PostgreSQL

  Pour diagnostiquer la précision sémantique directement depuis la ligne de commande.

• 03

  Développeurs Backend

  Pour déboguer les infrastructures sémantiques et les déploiements RAG directement dans l'espace de travail.

• 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.

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