• MATCH
  • DRIFT
  • MISS
  • HUMAN

How to debug vector similarity with your AI

Connect Couchbase to your AI client to cross-reference search results with your raw NoSQL data.

Ask AI about this page

Short answer

How can I check if my vector search results are accurate?

Your AI performs a similarity search to find the closest matches and then pulls the full document to see if the content actually makes sense. This lets you spot where your embeddings are drifting from your real data. You'll see the specific outcomes below.

Debugging outcomes

Where your findings land.

The AI categorizes what it finds during the verification process.

MATCH

Verified accuracy

The AI confirms the vector match aligns with the text in the document. You get confidence that your embeddings are working.

DRIFT

Embedding mismatch

The AI identifies when a high similarity score returns a document that doesn't actually match the query. This highlights where your model needs tuning.

MISS

False negatives

The AI finds relevant data that the vector search missed. You'll see where your indexing or embedding strategy is failing.

HUMAN

Manual review

The AI flags specific documents that require your eyes to decide if the similarity threshold is too high or too low.

The workflow

What your AI does when the search arrives.

The AI uses specific tools to bridge the gap between math and meaning.

  1. Find candidates

    The AI identifies the most relevant documents based on your query's vector representation.

    vector_search

  2. Fetch content

    The AI pulls the full JSON content for each candidate to inspect the actual text and metadata.

    get_document

  3. Compare logic

    The AI compares the query intent against the retrieved document fields to check for semantic drift.

    get_document

  4. Report findings

    The AI summarizes whether the search results are high quality or if the embeddings are misleading.

    vector_search

Try it

Copy these to start.

Use these prompts to begin your debugging session.

Starting points

Replace the bracketed text with your specific collection names or query terms.

Couchbase Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_wWaJnEVjNwe96vxf5IajtX1h1rZH9VkCF8KWnsvT/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 — Couchbase capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "couchbase-vector-nosql-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_wWaJnEVjNwe96vxf5IajtX1h1rZH9VkCF8KWnsvT/mcp"
    }
  }
}
Copy into chat04

  • Run a vector search for 'high-performance databases' and show me the top 3 documents.

  • Compare the results of my similarity search for 'user authentication' against the actual content in the documents.

  • Find documents related to 'distributed systems' and tell me if the retrieved text actually matches the query.

  • Check why the document with ID 'user_123' is showing up in my recent similarity searches.

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

Start here

Connect Couchbase once, then ask.

Just provide your connection details. Your credentials stay encrypted, and you can immediately start querying your data through your AI client.

Connect Couchbase to your AI

FAQ

How this task behaves

  • 01

    Can the AI change my Couchbase data?

    No. The AI can only read data using the available tools. It cannot update, delete, or insert any records in your database.

  • 02

    Can the AI create new vector indexes?

    No. The AI can only search existing indexes. You must set up your vector indexes within Couchbase before the AI can use them.

  • 03

    Does the AI see my whole database?

    The AI only sees the data it retrieves through the specific tools provided. It doesn't have a blanket view of your entire cluster.

  • 04

    How does it handle large documents?

    The AI uses the document retrieval tool to pull the content. If a document is massive, the AI will process the text it receives to answer your question.

  • 05

    Can I use this with any AI client?

    Yes, as long as your client supports the Model Context Protocol (MCP).

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

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