• STORAGE
  • DATA
  • SEARCH
  • HUMAN

How to create and populate vector indices with your AI

Connect your AI to Elasticsearch and manage your vector data through natural language. Once connected, your agent handles the heavy lifting of index management and data ingestion.

Ask AI about this page

Short answer

How do I create and populate vector indices with my AI?

Tell your AI to create a new index with a dense_vector field, then provide the data you want to embed. Your agent uses the connector to build the storage and push the payloads directly into Elasticsearch. You get a searchable vector store without writing any manual API calls.

The results.

Where your data lands.

Your AI handles the technical setup and data movement.

STORAGE

New vector indices

The AI builds the dense_vector storage structures you need. You end up with a ready-to-query index in Elasticsearch.

DATA

Populated embeddings

Your agent pushes embedding payloads into the new index. The data is immediately available for similarity searches.

SEARCH

Ready for retrieval

Once the data is in, your AI can use these vectors to perform semantic searches. This turns raw text into searchable intelligence.

HUMAN

Manual review needed

You still need to verify that the embedding dimensions match your model's output. The AI won't fix a mismatch between your model and the index settings automatically.

The workflow

What your AI does when you request index setup.

The AI translates your intent into specific Elasticsearch operations.

  1. Define the index

    Your agent interprets your requirements for vector dimensions and field types. It then prepares the configuration for the new storage.

    create_index

  2. Provision storage

    The AI executes the command to build the index in your Elasticsearch cluster. This creates the dense_vector field needed for your embeddings.

    create_index

  3. Ingest payloads

    After the index exists, your agent takes your data and pushes it into the vector field. It handles the mapping of the embedding payloads.

    index_document

  4. Confirm ingestion

    The AI verifies that the documents were successfully added to the index. You get a confirmation that the data is live and searchable.

    index_document

Try it

Copy these to start.

Use these prompts to kick off the process in your chat interface.

Starting points

These are starting points. Swap out the index names and dimension counts to match your specific model requirements.

Elasticsearch Vector Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_kUWPWShL0BRuDE0k7qrS1ZYbK8Cj21vwQZnEsEiQ/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 — Elasticsearch Vector capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "elasticsearch-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_kUWPWShL0BRuDE0k7qrS1ZYbK8Cj21vwQZnEsEiQ/mcp"
    }
  }
}
Copy into chat04

  • Create a new Elasticsearch index named 'product_embeddings' with a dense_vector field of 1536 dimensions.

  • Set up a vector index called 'user_profiles' and push these three embedding payloads into it.

  • Build an index for 'document_vectors' and populate it with the following text embeddings.

  • Create a dense_vector index named 'knowledge_base' and add this specific document payload.

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

Start here

Connect Elasticsearch Vector once, then ask.

Just add your credentials once. Your connection stays encrypted, and you can start managing indices immediately through your preferred AI client.

Connect Elasticsearch Vector to your AI

FAQ

How this task behaves.

  • 01

    Can the AI change existing index mappings?

    No. The AI can only create new indices using the create_index tool. It cannot modify the schema of an index that already exists.

  • 02

    Does the AI handle the embedding generation itself?

    No. You must provide the embedding payloads to the AI. The AI uses the index_document tool to push those payloads into Elasticsearch.

  • 03

    Can I delete indices through the chat?

    No. There is no tool provided to delete indices. You must manage deletions through your own Elasticsearch management tools.

  • 04

    Will the AI automatically match my model's dimensions?

    No. You must explicitly tell the AI what the vector dimensions should be when you ask it to create the index.

  • 05

    Can the AI perform similarity searches?

    No. This specific connector is designed for creating and populating indices. It does not include tools for executing search queries.

  • More questions about Elasticsearch Vector? The Connector page answers them. See everything the Elasticsearch Vector Connector can do

Connect Elasticsearch Vector to Claude, Cursor, ChatGPT & more