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TurboShop AI Vector Similarity Search
x402
TurboShop AI Vector Similarity Search is a paid API for AI agents from turboshop.ai, paid per call via x402, $0.04/call, status unknown (last checked 2026-10-19).
Computes dot products, cosine similarities, and top-K ranking over 1536-dimensional float32 embeddings against a candidate set
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 the top-K most similar candidate vectors ranked by similarity score, including their IDs, scores, and associated metadata fields from the candidate list.
Example use cases
Semantic product search for e-commerce
“I have a query embedding for 'wireless noise-cancelling headphones' and 200 product embeddings in my catalog — can you find and return the top 10 most semantically similar products?”
Relevant passage retrieval for RAG pipeline
“I've got a 1536-d question embedding and 80 candidate document chunk embeddings — give me the top 5 most relevant passages ranked by similarity score.”
Nearest-neighbor deduplication check
“Compare this embedding to my 30 candidate embeddings and return the top 3 closest matches so I can check for near-duplicate content.”
Technical details
Endpoint
GEThttps://turboshop.ai/api/v1/products/prod-hnsw-vector-indexer/buy?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, single-call vector similarity ranking over a bounded candidate set without maintaining a persistent index — ideal for on-the-fly semantic search, re-ranking, or nearest-neighbor lookup over a list you already have in memory. Prefer a persistent vector database service if you need to store and repeatedly query a large, growing index.
Side effects
- Wrong vector dimensionality (not 1536-d) causes rejection or incorrect results
- Empty candidate_vectors array returns no results
- top_k larger than candidate list returns fewer results than requested
- Malformed float values in query_vector cause computation errors
- Missing required query_vector field returns an error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| top_k | integer | Top K matches to return |
| client_id | string | Optional calling agent ID |
| query_vector | array | 1536-d float embedding vector to search against |
| candidate_vectors | array | Candidate vectors [{ id, vector, metadata }] to rank |
Tags
x402
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