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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Redis Vector with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 6 capabilities

The complete Redis Vector capability set.

These are the exact actions your AI can choose when you ask it to work with Redis Vector.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Redis Vector.

  1. 01

    Create vector index

    Specify the name and vector dimensions. Creates a new RediSearch vector index

  2. 02

    Search vectors

    Provide the query vector as a JSON array of floats. Performs a KNN similarity search in a vector index

  3. 03

    Upsert vector

    Specify the document key and the vector as a JSON array. Inserts or updates a vector in a Redis hash

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Redis Vector.

  1. 04

    Get index info

    Retrieves details for a specific vector index

  2. 05

    List indexes

    Lists all RediSearch vector indexes

  3. 06

    Delete vector

    Deletes a vector document from Redis

Observed, not estimated

870ms average. Fast in production.

Redis Vector is checked daily against the live service.

Daily averagePeak 1000ms
Aug 20Today
Fastest day
683ms
Slowest day
1000ms
14-day trend
Slowing+18%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Redis Vector, so you can see the experience inside your AI.

It does not authenticate your account with Redis Vector. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Redis Vector Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_zP1SqypHeKOFu0yRPw2yTUOrfnlmdOQBZrAWkuF4/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  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 — Redis Vector capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "redis-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_zP1SqypHeKOFu0yRPw2yTUOrfnlmdOQBZrAWkuF4/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Redis Vector owners ask.

  • 01

    What is the format required for the 'Redis URL' parameter?

    The parameter requires standard Redis URI string formatting. Typically it looks like redis://[username]:[password]@[host]:[port]. For TLS/SSL-enabled endpoints spanning secure setups, use the rediss:// scheme prefix.

  • 02

    Does my Redis instance strictly need the RediSearch module?

    Yes, absolutely. The base Redis product (standard open-source) only manages key-value caching out of the box. You must be running the Redis Stack or a managed tier (like Redis Enterprise or compatible cloud offerings) that explicitly includes RediSearch to generate and query KNN vector indexes.

  • 03

    Can I query using embedding arrays output directly from OpenAI models?

    Yes. Once you receive your numerical float array from an embedding model (like text-embedding-ada-002), you can pipe that exact JSON array into the search_vectors agent capability alongside the relevant index name to perform immediate proximity lookups.

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