• MATCHING
  • RETRIEVAL
  • FILTERING
  • HUMAN

How to perform semantic vector searches with your AI

Connect SingleStore to your AI client to find data based on meaning. Your agent uses vector similarity to pull relevant context from your database.

Ask AI about this page

Short answer

How can my AI find semantically related data in SingleStore?

Your agent uses the vector_search tool to compare your query's mathematical embeddings against those stored in SingleStore. This finds information that is conceptually similar even if the words don't match. You'll get precise context for your prompts based on semantic meaning.

Search outcomes

What your AI achieves

The search process turns raw embeddings into actionable context.

MATCHING

Semantic relevance

The AI identifies data points that share conceptual meaning. It moves beyond simple keyword matching to find the actual intent behind your questions.

RETRIEVAL

Contextual grounding

The agent pulls specific rows from SingleStore that provide the necessary background. This ensures your AI's answers are rooted in your actual data.

FILTERING

Precise results

The search narrows down massive datasets to the most relevant vectors. You get a tight set of information instead of a wall of irrelevant text.

HUMAN

Review required

The AI presents the most similar data points it found. You decide if that specific context is the right foundation for the final answer.

The workflow

What your AI does when a query arrives.

The agent handles the heavy lifting of mathematical comparison and data retrieval.

  1. Analyze intent

    Your agent looks at your natural language question to understand the underlying concept you are looking for.

    vector_search

  2. Execute search

    The agent triggers a similarity search against your SingleStore embeddings to find the closest mathematical matches.

    vector_search

  3. Extract context

    The tool pulls the raw data associated with those high-scoring vectors so the agent can read the actual content.

    vector_search

  4. Synthesize answer

    The agent uses the retrieved data to construct a response that directly addresses your original question.

    vector_search

Try it

Copy these to start.

Use these prompts to test the connection immediately.

Starting points

Swap the bracketed text for your specific table names or column descriptions.

SingleStore Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_CWK0SovUKcPI3nav2853E8UUtd5snsstATtMZNpM/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 — SingleStore capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "singlestore-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_CWK0SovUKcPI3nav2853E8UUtd5snsstATtMZNpM/mcp"
    }
  }
}
Copy into chat04

  • Find the most semantically similar entries in my [table_name] related to [topic].

  • Search my SingleStore embeddings for any customer feedback that mentions [concept].

  • Using the vector search, find documentation in [table_name] that explains [process].

  • What are the most relevant data points in my database regarding [subject]?

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

Start here

Connect SingleStore once, then ask.

Enter your SingleStore credentials to establish the link. Your login data stays encrypted, and you can immediately start asking questions about your data.

Connect SingleStore to your AI

FAQ

How the search behaves

  • 01

    Does the AI change my data in SingleStore?

    No. The vector_search tool only reads data to find similarities. It cannot modify, delete, or update your records.

  • 02

    Can the AI perform a standard SQL query?

    No. This specific task is limited to performing vector similarity searches via the provided tool.

  • 03

    Do I need to create embeddings myself?

    Yes. The tool searches against existing embeddings stored in your SingleStore instance.

  • 04

    Will the AI always find the exact answer?

    No. The AI finds the most semantically similar data. You must review the results to ensure they meet your specific needs.

  • 05

    Can the AI delete old embeddings?

    No. The tool is strictly for retrieval and does not have permission to manage or delete your data.

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

Connect SingleStore to Claude, Cursor, ChatGPT & more