# RobotShop Vector Similarity Compute

> RobotShop Vector Similarity Compute is a paid API for AI agents from robotshop.up.railway.app, paid per call via x402, $0.04/call, status unknown (last checked 2026-10-10).

Computes dot products, cosine similarities, and ranking scores for 1536-dimensional float32 embeddings via a high-speed vector computation microservice.

## Facts

- Endpoint: GET https://robotshop.up.railway.app/api/v1/products/prod-hnsw-vector-indexer/buy?utm_source=zero.xyz
- Price: $0.04/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-10
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/robotshop-vector-similarity-compute-89a3528f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_UAoIy6OundRqvJ4KVJlWY

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 robotshop-vector-similarity-compute-89a3528f
```

Example prompt: Run a cosine similarity and dot product computation on my 1536-dimensional float32 embeddings and rank the results by similarity score — my client ID is agent-007.

## When to prefer this

Choose this endpoint when you need fast, on-demand vector similarity computation (dot product or cosine) for 1536-dimensional float32 embeddings — the native dimension of OpenAI ada-002 and similar models — without standing up your own vector database or compute infrastructure. Prefer it for lightweight per-call scoring tasks rather than bulk batch indexing.

## Known failure modes

- Malformed or non-1536-dimensional embedding input may return a validation error
- Missing or incorrect float32 format may cause computation failure
- Network timeout under high load conditions
- Invalid client_id may be logged but unlikely to block execution
- Payment failure via x402 protocol will prevent endpoint access

## How this service works

High-speed vector computation microservice to compute dot products, cosine similarities, and ranking for 1536-d float32 embeddings.

## Output

Returns computed dot product values, cosine similarity scores, and ranked ordering for 1536-dimensional float32 embeddings, enabling downstream nearest-neighbor retrieval or semantic ranking decisions.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "client_id": {
   "type": "string",
   "description": "Optional calling agent or client ID"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/robotshop-vector-similarity-compute-89a3528f/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from robotshop.up.railway.app](https://www.zero.xyz/host/robotshop.up.railway.app/llms.txt)