{"uid":"cap_UAoIy6OundRqvJ4KVJlWY","slug":"robotshop-vector-similarity-compute-89a3528f","name":"RobotShop Vector Similarity Compute","description":"High-speed vector computation microservice to compute dot products, cosine similarities, and ranking for 1536-d float32 embeddings.","url":"https://robotshop.up.railway.app/api/v1/products/prod-hnsw-vector-indexer/buy?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"client_id":{"type":"string","description":"Optional calling agent or client ID"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.04","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.04/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.04","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.04","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_Q00e34dzWMPVUC31iFOl9","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.04","costPer":"request","priority":0,"asset":"0x833589fcd6edb6e08f4c7c32d4f71b54bda02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes dot products, cosine similarities, and ranking scores for 1536-dimensional float32 embeddings via a high-speed vector computation microservice.","exampleAgentPrompt":"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.","exampleUseCases":[{"title":"Semantic search ranking for agent memory","prompt":"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?"},{"title":"Nearest neighbor scoring for RAG pipeline","prompt":"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?"},{"title":"Embedding comparison for deduplication","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T18:30:05.616Z","isFirstParty":false,"canonicalSlug":"robotshop-vector-similarity-compute-89a3528f"}