Cloud Vector Search

Search for vectors using semantic similarity

Prerequisites

• Authenticated with agentuity auth login
• Project context required (run from project directory or use --project-id)

Usage

agentuity cloud vector search <namespace> <query> [options]

Arguments

| Argument | Type | Required | Description | |----------|------|----------|-------------| | <namespace> | string | Yes | - | | <query> | string | Yes | - |

Options

| Option | Type | Required | Default | Description | |--------|------|----------|---------|-------------| | --limit | number | Yes | - | maximum number of results to return (default: 10) | | --similarity | number | Yes | - | minimum similarity threshold (0.0-1.0) | | --metadata | string | Yes | - | filter by metadata (format: key=value or key1=value1,key2=value2) |

Examples

Search for similar products:

bunx @agentuity/cli vector search products "comfortable office chair"

Search knowledge base:

bunx @agentuity/cli vector list knowledge-base "machine learning"

Limit results:

bunx @agentuity/cli vector search docs "API documentation" --limit 5

Set minimum similarity:

bunx @agentuity/cli vector search products "ergonomic" --similarity 0.8

Filter by metadata:

bunx @agentuity/cli vector ls embeddings "neural networks" --metadata category=ai

Output

Returns JSON object:

{
  "namespace": "string",
  "query": "string",
  "results": "array",
  "count": "number"
}

| Field | Type | Description | |-------|------|-------------| | namespace | string | Namespace name | | query | string | Search query used | | results | array | Search results | | count | number | Number of results found |