{"uid":"cap_nI-QlY_3EuWAIiF1NWdxT","slug":"agent-factory-embedding-api-e5d3c301","name":"Agent Factory Embedding API","description":"Pay-per-request datasets and Amazon Bedrock inference for AI agents, settled in USDC over the x402 protocol on Base.","url":"https://d1x8xrm7jrzkbs.cloudfront.net/v1/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"A single text to embed"},"texts":{"type":"array","description":"Multiple texts to embed"}}},"responseSchema":{"type":"object","properties":{"model":{"type":"string"},"dimensions":{"type":"number"},"embeddings":{"type":"array","items":{"type":"array","items":{"type":"number"}}}}},"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_jy4kkgs3jxCcB8vgt9NQN","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 vector embeddings for one or more text inputs using Amazon Bedrock inference, billed per-request in USDC via the x402 protocol on Base.","exampleAgentPrompt":"Embed this text for me using Agent Factory's Bedrock embedding API: 'The quick brown fox jumps over the lazy dog' — I need the vector to index it in my semantic search system.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"I need to embed these 5 product descriptions so I can store them in my vector database for semantic search: 'Wireless noise-cancelling headphones with 30hr battery', 'Portable Bluetooth speaker with waterproof design', 'True wireless earbuds with active noise cancellation', 'Over-ear studio headphones for mixing', 'Sports earbuds with heart rate monitor'. Can you convert all of them to embedding vectors?"},{"title":"Semantic similarity between two texts","prompt":"Can you embed these two sentences so I can measure how similar they are? First: 'I love machine learning and AI research.' Second: 'Artificial intelligence and deep learning are my passion.'"},{"title":"User query vectorization for retrieval","prompt":"My user just typed the search query 'best practices for securing microservices' — please embed it into a vector so I can run a nearest-neighbor search against my knowledge base."}],"resultDescription":"Returns a JSON object containing the model name used for embedding, the vector dimensionality, and an array of embedding vectors (each a float array) corresponding to each input text — one embedding per text provided.","failureModes":["Empty or missing text input returns a validation error","Texts array exceeding model token limits may be truncated or rejected","Payment failure or insufficient USDC balance causes 402 response before processing","CloudFront timeout for very large batches of texts","Model unavailability on Amazon Bedrock returns a 503 or provider error"],"whenToPreferThis":"Choose this endpoint when you need pay-as-you-go text embeddings without managing your own Bedrock account or API keys, particularly in agent workflows where crypto micropayments (USDC on Base via x402) are acceptable or preferred. It is especially suitable for AI agents that need embeddings on-demand without subscription overhead, or for pipelines already integrated with the x402 payment protocol.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T18:39:18.924Z","isFirstParty":false}