vector_similarity

Active

Tool of IA-QA — 130+ QA & Dev Tools for AI Agents

declared in 1.0.0

Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scoring, embedding evaluation, and nearest-neighbor testing.

Parameters schema

{
  "type": "object",
  "required": [
    "vector_a",
    "vector_b"
  ],
  "properties": {
    "metric": {
      "enum": [
        "cosine",
        "dot_product",
        "euclidean",
        "manhattan",
        "all"
      ],
      "type": "string",
      "description": "Distance metric (default: all)"
    },
    "vector_a": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "description": "First vector as array of floats"
    },
    "vector_b": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "description": "Second vector as array of floats"
    }
  }
}

What this tool wraps· 0 endpoints

min confidence0.70 0.50

No endpoints wrapped at confidence ≥ 0.70.

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IA-QA — 130+ QA & Dev Tools for AI Agents

https://github.com/jcjamet/ia-qa

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