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RobotShop Vector Similarity Compute
x402
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.
Price / call$0.04
Success—
StatusUnknown
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.
Example use cases
Semantic search ranking for agent memory
“I have a set of 1536-d float32 embeddings representing stored memories and a query embedding — can you rank all the memories by cosine similarity to the query so I know which ones are most relevant?”
Nearest neighbor scoring for RAG pipeline
“I'm building a retrieval-augmented generation pipeline and need to score a candidate embedding against my indexed 1536-dimensional vectors using dot product — can you run that computation and return ranked results?”
Embedding comparison for deduplication
“I have two 1536-dimensional float32 embeddings from different documents and I want to know how similar they are — can you compute the cosine similarity score between them?”
Technical details
Endpoint
GEThttps://robotshop.up.railway.app/api/v1/products/prod-hnsw-vector-indexer/buy?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| client_id | string | Optional calling agent or client ID |
Tags
x402
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