{"uid":"cap_4v-6FoSGZ8_liTG1LfTev","slug":"quartermaster-text-embeddings-bf1eac64","name":"Quartermaster Text Embeddings","description":"Text embeddings for semantic search and RAG: one vector per call, 1024 dimensions.","url":"https://quartermaster.surewhynot.app/v1/embed","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["text"],"properties":{"text":{"type":"string","maxLength":8000,"description":"Text to embed"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_kDv4c2dt-f8mNTCdGagrh","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts a text string (up to 8000 characters) into a 1024-dimensional semantic vector suitable for semantic search and RAG pipelines.","exampleAgentPrompt":"Embed this text for me so I can do a semantic similarity search: 'Retrieval-augmented generation combines a language model with an external knowledge base to produce grounded answers.'","exampleUseCases":null,"resultDescription":"A single 1024-dimensional floating-point vector representing the semantic content of the input text, suitable for cosine similarity comparisons, nearest-neighbor indexing, or feeding into a RAG retrieval pipeline.","failureModes":["Text exceeds 8000-character limit — request rejected","Empty or missing 'text' query parameter — validation error","Payment not included or insufficient — 402 response with x402 challenge","Service unavailable — upstream embedding model timeout or error"],"whenToPreferThis":"Choose this endpoint when you need a single, on-demand 1024-dimensional text embedding billed per call via x402/USDC micropayment, particularly in agentic or autonomous pipelines where pay-per-use is preferable to a subscription API key. Ideal for RAG chunk indexing, semantic search query encoding, or similarity comparisons without maintaining a separate embedding service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T18:35:45.106Z","isFirstParty":false}