{"uid":"cap_FOTndLYpIR_5Ep7gjOCrl","slug":"x402-shizu-me-openai-compatible-embeddings-dc42d689","name":"x402.shizu.me OpenAI-Compatible Embeddings","description":"Drop-in OpenAI embeddings endpoint — same base_url as /v1/chat/completions, pay per call in USDC, no account or key. POST {input: string|array (max 16 items, 8KB total), model?, dimensions?}. Returns the standard {data:[{embedding:[...]}], usage} shape. Flat price per call. The bulk-loop primitive for RAG ingestion, semantic search, dedup, clustering — point your OpenAI SDK here and embed.","url":"https://x402.shizu.me/v1/embeddings?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{},"model":{}}},"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_0YYe98dd8colJ1nCqyiKd","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":"Generates text embeddings via a drop-in OpenAI-compatible endpoint, paid per call in USDC with no account or API key required.","exampleAgentPrompt":"Embed these 5 product descriptions into vectors so I can load them into my vector database for semantic search — use the text-embedding-3-small model with 1536 dimensions.","exampleUseCases":[{"title":"RAG document ingestion pipeline","prompt":"I need to embed these 12 text chunks from my knowledge base so I can store them in Pinecone for retrieval-augmented generation — can you vectorize them all in one call?"},{"title":"Semantic deduplication of user content","prompt":"Take these 8 user-submitted product reviews and generate embeddings for each so I can cluster them and remove near-duplicates before storing them."},{"title":"Semantic search index building","prompt":"Convert these 10 FAQ answers into embedding vectors using the text-embedding-3-small model so I can build a semantic search index for my support chatbot."}],"resultDescription":"Returns a standard OpenAI-shaped JSON response with a `data` array of embedding objects (each containing an `embedding` float array and an `index`), plus a `usage` object with token counts. The response is structurally identical to OpenAI's /v1/embeddings output, making it a drop-in replacement.","failureModes":["Input exceeds 16 items or 8KB total — request rejected with an error","Payment fails or USDC balance insufficient — call returns payment error","Unsupported model name passed — returns model-not-found error","Malformed input (non-string, null) — returns validation error","Network timeout if input is near the size limit and processing is slow"],"whenToPreferThis":"Choose this endpoint when you need OpenAI-compatible embeddings without wanting to manage API keys or accounts — ideal for agent pipelines that want pay-per-call USDC billing, or when you want to drop in a compatible embeddings endpoint alongside the same provider's chat completions. Best for batches up to 16 items; for larger bulk jobs, loop calls here rather than using a managed embedding service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T20:39:32.121Z","isFirstParty":false,"canonicalSlug":"x402-shizu-me-openai-compatible-embeddings-dc42d689"}