{"uid":"cap_HD0bNDWDCnuVTnoOqKlVw","slug":"molcloud-text-embedding-api-e83ceaac","name":"MolCloud Text Embedding API","description":"41 pay-per-request AI, search, headless browser, crypto alpha & memory APIs for autonomous agents settling in USDC on Base via x402 v2.","url":"https://api.molcloud.net/api/ai/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Input text to convert into a vector embedding"}}},"responseSchema":{"type":"json","example":{"data":{"vector":[0.012,-0.034],"dimensions":768},"status":"success","endpoint":"/api/ai/embed"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","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":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_NO-3f-0j6f9TFlLGypLBb","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts input text into a 768-dimensional vector embedding for semantic similarity, search, and retrieval tasks","exampleAgentPrompt":"Turn this sentence into a vector embedding so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog.'","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"Embed this chunk of text so I can store it in my Pinecone index for retrieval-augmented generation: 'Quantum computing leverages superposition to perform calculations exponentially faster than classical computers.'"},{"title":"Semantic similarity search","prompt":"Generate a vector embedding for the query 'best running shoes for flat feet' so I can find the most semantically similar product descriptions in my catalog."},{"title":"Text clustering for topic modeling","prompt":"Convert these customer support messages into vector embeddings so I can cluster them by topic and figure out what issues come up most often."}],"resultDescription":"Returns a JSON object containing a 'data' field with the 768-dimensional float vector array and the dimension count, plus a 'status' field confirming success and the endpoint path. The vector can be directly inserted into any vector store or used for cosine similarity calculations.","failureModes":["Empty or missing 'text' field returns an error response","Extremely long text may exceed token limits and return a truncation or error","Payment failure via x402 (insufficient USDC balance on Base) results in 402 Payment Required before processing","Network timeout on edge gateway returns 503 or similar","Malformed JSON body returns 400 Bad Request"],"whenToPreferThis":"Choose this endpoint when you need pay-per-use text embeddings without a subscription to OpenAI, Cohere, or similar providers, especially in autonomous agent pipelines that settle micropayments in USDC on Base via x402. Ideal for RAG pipelines, semantic search indexing, and similarity scoring where you need 768-dimensional vectors and want crypto-native billing with no API key management.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:40:43.622Z","isFirstParty":false,"canonicalSlug":"molcloud-text-embedding-api-e83ceaac"}