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Vinkius

Redis Vector Connector for AI agents.

6 live capabilities

Manage your Redis vector database and KNN similarity searches through your AI client.

Live agent request Redis Vector / Connector

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AI Agent

Why people use Redis Vector

Redis Vector for Managing Semantic Search at Scale

With this Connector, you can just describe the goal in plain English. Your agent handles the interaction with the RediSearch module, letting you find similar records or audit index status without ever leaving your chat window. You get a direct line to your data.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

This Connector turns your AI agent into a native administrator for your Redis vector database.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Finding similar products

    A customer support agent finds the 3 most relevant FAQ entries based on a user's specific problem description using search_vectors.

  2. Real-world use case 02

    Dynamic index creation

    An ML engineer tells the agent to create a new 1536-dimensional index for a new batch of product images using create_vector_index.

  3. Real-world use case 03

    Data cleanup

    A backend dev asks the agent to purge all embeddings associated with deleted user accounts to maintain privacy using delete_vector.

Complete set · 6capabilities

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 Capability

    Search vectors

    Perform a KNN similarity search using a JSON array of floats. This is the primary way to find related content.

  2. 02 Capability

    Get index info

    Pull the metadata and configuration details for a specific index. It helps you verify dimensions and search metrics.

  3. 03 Capability

    List indexes

    Get a full list of all vector indexes in your Redis instance. This is perfect for auditing your current setup.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through Redis Vector.

  1. 04 Capability

    Upsert vector

    Insert or update a vector in a Redis hash using a document key. Use this to keep your embeddings synced with your data.

  2. 05 Capability

    Create vector index

    Create a new RediSearch vector index by specifying the name and dimensions. This is how you set up new search spaces for your data.

  3. 06 Capability

    Delete vector

    Remove a specific vector document from your Redis database. Use this to keep your storage clean and up to date.

Set up in minutes

One URL. Then ask Redis Vector to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Redis Vector from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_zP1SqypHeKOFu0yRPw2yTUOrfnlmdOQBZrAWkuF4/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Redis Vector, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Redis Vector for the conversation.

Where the request belongs

Work Redis Vector can move forward.

Built around the request

This is for the AI/ML engineer who's tired of writing repetitive Python scripts to query vector spaces during RAG development, or the backend developer who needs to manage semantic search schemas without building a custom UI.

01

AI/ML Engineer

You'll use this to quickly iterate on similarity tuning and manage chunk embeddings on the fly without leaving your chat interface.

02

Backend Developer

You'll use this to audit schemas, map out active index properties, and delete obsolete hashes holding raw vector models.

03

Data Architect

You'll use this to validate your Redis vector environments and confirm architecture viability for RAG applications.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Elasticsearch Vector logo
01 6 capabilities

Elasticsearch Vector

Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

View Connector
Milvus (Open-Source Vector Database) logo
02 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

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OpenSearch Vector logo
03 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector
Qdrant logo
04 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector
Vertex AI Vector Search logo
05 6 capabilities

Vertex AI Vector Search

Bring Google's massive vector matching power to your AI agent. Search billions of semantic embeddings and administer Vertex Index endpoints directly in chat.

View Connector
Typesense Vector Search logo
06 6 capabilities

Typesense Vector Search

Automate vector similarity searches via Typesense. index documents, manage collections, and execute semantic queries directly from your AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep Redis Vector connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Redis Vector.

The practical details behind the request, access and result.

Can the Redis Vector MCP help with my RAG pipeline?

Yes, it connects your AI agent to your Redis vector store. This allows your agent to perform similarity searches and manage embeddings directly, which is a core part of building a Retrieval-Augmented Generation system.

Do I need to write code to use the Redis Vector MCP?

No, you don't need to write any custom drivers. Once the Connector is connected to your AI client, you can just use plain English to ask your agent to perform searches, update records, or manage your indexes.

How does the Redis Vector MCP handle similarity searches?

It allows your agent to execute native KNN similarity searches. You just provide the embedding array, and the agent retrieves the nearest neighbors from your Redis instance for you.

Can I use the Redis Vector MCP to manage my embeddings?

Absolutely. You can use it to inject new embeddings, update existing ones, or delete old records to keep your semantic data clean and accurate.

Does the Redis Vector MCP work with any Redis instance?

It works with Redis instances that have the RediSearch module installed and enabled for vector search. This is the standard way to handle vector data in the Redis ecosystem.

How do I check my index dimensions with the Redis Vector MCP?

You can simply ask your agent to check the info for a specific index. It will pull the metadata for you, showing dimensions, algorithms, and other configuration details.

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.

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.

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.

One connection away

Give your agent a direct line to Redis Vector.

Connect Redis Vector once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available