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Connect Redis Vector to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Manage embeddings and run similarity searches directly from your chat.

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

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 6 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Redis Vector.

  1. 01

    Create vector index

    This capability builds a new RediSearch vector index. You specify the name and the required dimensions for the vectors.

  2. 02

    Search vectors

    This capability executes a KNN similarity search. It uses a JSON array of floats to find the nearest neighbors in your index.

  3. 03

    Upsert vector

    This capability handles vector insertion and updates. It maps a JSON array of vector data to a specific document key 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

    This capability pulls metadata for a specific index. It returns the configuration and schema details for your vector indexes.

  2. 05

    List indexes

    This capability retrieves a list of all active RediSearch vector indexes currently loaded in your Redis instance.

  3. 06

    Delete vector

    This capability removes a vector document from your Redis database to keep your semantic storage clean.

Real work

What teams actually run

01

RAG Architecture Validation

Check if your vector dimensions and metrics match your embedding model's output.

02

Rapid Prototyping

Create and test new vector indexes as you experiment with different similarity algorithms.

03

Semantic Data Cleanup

Remove obsolete or outdated embeddings to maintain index performance.

04

Interactive Debugging

Query specific vectors to verify that your similarity search is returning the expected neighbors.

Automate a task

Put Redis Vector to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Redis Vector works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Redis Vector to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

942ms average. Fast in production.

Redis Vector is checked daily against the live service.

Daily averagePeak 1228ms
Oct 3Today
Fastest day
781ms
Slowest day
1228ms
14-day trend
Slowing+24%

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

Guided setup for Claude? Set up Redis Vector in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Redis Vector owners hand off.

This MCP is built for technical users who need to bridge the gap between machine learning models and Redis storage.

  • 01

    AI & ML Engineers

    They use the agent to iterate on similarity tuning and store embeddings without writing Python scripts.

  • 02

    Backend Developers

    They manage semantic storage logic and audit index properties directly from their AI client.

  • 03

    Data Architects

    They validate Redis vector environments and confirm architecture readiness for RAG applications.

FAQ

Questions Redis Vector owners ask.

  • 01

    Does this MCP work with any Redis instance?

    No, your Redis instance must have the RediSearch module installed to support vector operations.

  • 02

    What AI clients can I use with this MCP?

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

  • 03

    Can I create new indexes using my AI agent?

    Yes, you can use the create_vector_index capability to specify names and dimensions for new indexes.

  • 04

    How do I perform a similarity search?

    You can use the search_vectors capability by providing a JSON array of floats representing your query vector.

  • 05

    Is my Redis URL stored by Vinkius?

    Vinkius manages the connection and credentials so you can connect your client once and start working.

  • 06

    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.

  • 07

    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.

  • 08

    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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