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Vinkius

OpenSearch Vector Connector for AI agents.

6 live capabilities

Manage your vector database and run similarity searches on your OpenSearch cluster.

Live agent request OpenSearch Vector / Connector

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AI Agent

Why people use OpenSearch Vector

OpenSearch Vector for Faster RAG Development

With this Connector, you just ask your agent to find the most similar documents. It handles the query, pulls the results, and shows them to you immediately. You stay in your editor and get your work done.

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

What Vinkius changes

You get a conversational interface for your entire OpenSearch vector store.

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

One account · 5,900+ Connectors

  1. Real-world use case 01

    Testing RAG context

    A developer asks the agent to find the 5 most similar documents to a new query to see if the retrieval is accurate.

  2. Real-world use case 02

    Quick index provisioning

    An ML engineer needs a new 1536-dimension index for a new project and has the agent create it in one go.

  3. Real-world use case 03

    Health checks

    A data team member asks the agent to list all indexes to check for yellow health statuses during a migration.

Complete set · 6capabilities

The complete OpenSearch Vector capability set.

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

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through OpenSearch Vector.

  1. 01 Capability

    Search

    Run a k-NN vector search against a specific index using a dense float vector array.

  2. 02 Capability

    List indexes

    Get a full list of all explicit indexes currently residing on your OpenSearch cluster.

  3. 03 Capability

    Get index

    Retrieve the exact mapping and settings for a specific OpenSearch index.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through OpenSearch Vector.

  1. 04 Capability

    Index document

    Perform a fast, atomic insertion of a single vector document into your embedding space.

  2. 05 Capability

    Delete document

    Remove a specific vector document from your OpenSearch cluster by its ID.

  3. 06 Capability

    Create index

    Provision a new native OpenSearch KNN index optimized for cosine similarity.

Set up in minutes

One URL. Then ask OpenSearch Vector to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use OpenSearch 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_6R4dQ885yexHhsyH4M5e8fLiT1BjuMlzaUlJMumQ/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 OpenSearch Vector, and paste the URL above.

  3. Step 03

    Turn it on in chat

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

Where the request belongs

Work OpenSearch can move forward.

Built around the request

This is for the engineer who is tired of copy-pasting JSON blocks into a browser just to see why a vector search is failing. It is for anyone building RAG systems who needs to move fast.

01

ML Engineer

Testing similarity queries against production embeddings without writing curl commands.

02

RAG Developer

Indexing and retrieving context documents for retrieval-augmented generation pipelines.

03

Data Engineer

Inspecting index health and document counts through conversation instead of Kibana dashboards.

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.

View Connector
Chroma (Vector DB) logo
03 7 capabilities

Chroma (Vector DB)

Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly 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
Redis Vector logo
05 6 capabilities

Redis Vector

Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

View Connector
Vertex AI Vector Search logo
06 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

Bring your own AI

Change the model, client or framework. Keep OpenSearch connected.

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Before you connect

Questions about OpenSearch.

The practical details behind the request, access and result.

Can the OpenSearch Vector MCP help me build a RAG system?

Yes, it is designed specifically for RAG. It allows your agent to query your vector store and retrieve relevant context documents for your generation pipeline.

How do I use OpenSearch Vector MCP to manage my embeddings?

You can manage them through natural conversation. Ask your agent to add new documents with metadata or delete specific entries from your vector space.

Does the OpenSearch Vector MCP support cosine similarity?

Yes, the Connector includes capabilities to create and manage indexes optimized specifically for cosine similarity and other k-NN metrics.

Can I use OpenSearch Vector MCP to check my cluster health?

You can. It provides a way to list all indexes and see their health status and document counts without opening a dashboard.

How does OpenSearch Vector MCP handle document deletion?

It allows you to remove specific vector documents from your OpenSearch cluster by providing the unique document ID to your agent.

Is OpenSearch Vector MCP good for ML engineers?

It is a great capability for ML engineers because it removes the need to write manual curl commands for testing similarity queries against production embeddings.

What vector dimensions does it support?

Any dimension supported by OpenSearch k-NN. Common values: 384 (MiniLM), 768 (BERT/all-mpnet), 1536 (OpenAI text-embedding-ada-002), 3072 (text-embedding-3-large). When creating an index, specify the exact dimension and the agent provisions the mapping automatically.

Can I delete an entire index or just individual documents?

Currently, the agent supports deleting individual documents by ID from an index. Full index deletion is not exposed through this integration to prevent accidental data loss. If you need to drop an index, use the OpenSearch Dashboards or direct API calls.

Does this work with Amazon OpenSearch Service (managed)?

Yes. Provide the Amazon OpenSearch Service endpoint as the host (e.g., https://search-xxx.us-east-1.es.amazonaws.com) along with the master username and password. The integration uses standard REST APIs that work identically on managed and self-hosted clusters.

One connection away

Give your agent a direct line to OpenSearch.

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

Explore every Connector No credit card required · Free tier available