agentuity-cli-cloud-vector-upsert
Add or update vectors in the vector storage. Requires authentication. Use for Agentuity cloud platform operations
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentuity-cli-cloud-vector-upsert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentuity-cli-cloud-vector-upsert Compares
| Feature / Agent | agentuity-cli-cloud-vector-upsert | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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 |Related Skills
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