agentuity-cli-cloud-vector-upsert

Add or update vectors in the vector storage. Requires authentication. Use for Agentuity cloud platform operations

16 stars

Best use case

agentuity-cli-cloud-vector-upsert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Add or update vectors in the vector storage. Requires authentication. Use for Agentuity cloud platform operations

Teams using agentuity-cli-cloud-vector-upsert should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/agentuity-cli-cloud-vector-upsert/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/ai-agents/agentuity-cli-cloud-vector-upsert/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/agentuity-cli-cloud-vector-upsert/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How agentuity-cli-cloud-vector-upsert Compares

Feature / Agentagentuity-cli-cloud-vector-upsertStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Add or update vectors in the vector storage. Requires authentication. Use for Agentuity cloud platform operations

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Cloud Vector Upsert

Add or update vectors in the vector storage

## Prerequisites

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

## Usage

```bash
agentuity cloud vector upsert <namespace> [key] [options]
```

## Arguments

| Argument | Type | Required | Description |
|----------|------|----------|-------------|
| `<namespace>` | string | Yes | - |
| `<key>` | string | No | - |

## Options

| Option | Type | Required | Default | Description |
|--------|------|----------|---------|-------------|
| `--document` | string | Yes | - | document text to embed |
| `--embeddings` | string | Yes | - | pre-computed embeddings as JSON array |
| `--metadata` | string | Yes | - | metadata as JSON object |
| `--file` | string | Yes | - | path to JSON file containing vectors, or "-" for stdin |

## Examples

Upsert a single vector with document text:

```bash
bunx @agentuity/cli vector upsert products doc1 --document "Comfortable office chair"
```

Upsert with metadata:

```bash
bunx @agentuity/cli vector upsert products doc1 --document "Chair" --metadata '{"category":"furniture"}'
```

Upsert with pre-computed embeddings:

```bash
bunx @agentuity/cli vector upsert embeddings vec1 --embeddings "[0.1, 0.2, 0.3]"
```

Bulk upsert from JSON file:

```bash
bunx @agentuity/cli vector upsert products --file vectors.json
```

Bulk upsert from stdin:

```bash
cat vectors.json | bunx @agentuity/cli vector upsert products -
```

## Output

Returns JSON object:

```json
{
  "success": "boolean",
  "namespace": "string",
  "count": "number",
  "results": "array",
  "durationMs": "number"
}
```

| Field | Type | Description |
|-------|------|-------------|
| `success` | boolean | Whether the operation succeeded |
| `namespace` | string | Namespace name |
| `count` | number | Number of vectors upserted |
| `results` | array | Upsert results with key-to-id mappings |
| `durationMs` | number | Operation duration in milliseconds |

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