agentuity 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 |