# Autopilot Studio Text Embedding

> Autopilot Studio Text Embedding is a paid API for AI agents from autopilot-studio.kariguetoazul.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-10).

Converts up to 16 text strings into high-dimensional vector embeddings using the BGE-M3 model (1024 dimensions).

## Facts

- Endpoint: POST https://autopilot-studio.kariguetoazul.workers.dev/v1/embed?utm_source=zero.xyz
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-10
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/autopilot-studio-text-embedding-9707baac
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gbxXgdLF2HE7zpD8DkJnx

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability autopilot-studio-text-embedding-9707baac -d '<json body>'
```

Example prompt: I need to embed these two texts into vectors for semantic search: 'The quick brown fox jumps over the lazy dog' and 'A fast auburn fox leaps over a sleepy hound' — give me their 1024-dimensional BGE-M3 embeddings.

## When to prefer this

Choose this endpoint when you need fast, multilingual, high-quality 1024-dimensional text embeddings from the BGE-M3 model at very low cost ($0.001 per call) via a pay-per-use x402 micropayment model. It supports batches of up to 16 texts per request, making it efficient for small-to-medium embedding jobs. Prefer it over subscription-based embedding APIs when you want no monthly commitment and per-call billing.

## Known failure modes

- More than 16 texts submitted — input array exceeds the supported batch size
- Individual text exceeds 2000 characters — text is too long and may be truncated or rejected
- Empty texts array — no input provided
- Payment failure (x402) — insufficient USDC balance or wallet not configured
- Network timeout on the Cloudflare Workers edge — request may need retry

## How this service works

AI-made art, prompts, stories and guides — fresh every day.

## Output

A JSON object containing the count of embedded texts, the model name ('bge-m3'), the vector dimensions (1024), and an array of float arrays — one embedding vector per input text. Each embedding is a 1024-dimensional dense vector suitable for cosine similarity, dot-product search, or storage in a vector database.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "texts": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "description": "up to 16 texts, 2000 chars each"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "count": 2,
  "model": "bge-m3",
  "dimensions": 1024,
  "embeddings": [
   [
    0.012345,
    -0.045678
   ],
   [
    0.011111,
    -0.022222
   ]
  ]
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/autopilot-studio-text-embedding-9707baac/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from autopilot-studio.kariguetoazul.workers.dev](https://www.zero.xyz/host/autopilot-studio.kariguetoazul.workers.dev/llms.txt)