{"uid":"cap_9IAbPWHLodHZKD6OMlxk7","slug":"gambit-embed-text-embeddings-via-x402-usdc-on-base-d60f9d1c","name":"Gambit Embed — Text Embeddings via x402 USDC on Base","description":"Gambit GPU shop. Humans: /signup ($1 credit), X-API-Key on /v1/*, QR Lightning top-up. Bots: L402 Lightning (LIVE) on /l402/*, x402 USDC on Base on /x402/*, or POST /v1/register ($0) then POST /v1/topup/lightning. Handwritten letter POST /x402/letter at $25.00 USDC. Funnel https://gambit.barrioenergy.com. Free probe GET|POST /x402/models.","url":"https://gambit.barrioenergy.com/x402/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{"oneOf":[{"type":"string"},{"type":"array","items":{"type":"string"}}]}}},"responseSchema":{"type":"json","example":{"dim":768,"count":1,"model":"nomic-embed-text:latest","embeddings":[[0.01]]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_e-ZZB5ZHOEssfyF_OHgpn","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","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 strings using nomic-embed-text, paid per-call with USDC on Base via the x402 protocol","exampleAgentPrompt":"Embed this sentence for me using Gambit's nomic-embed-text model so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog.'","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"Take these product descriptions and turn them into embeddings I can store in my vector database for semantic search: ['Wireless noise-cancelling headphones with 30hr battery', 'Portable Bluetooth speaker with waterproof design']."},{"title":"Semantic similarity scoring","prompt":"I need to compare how similar two sentences are — can you embed both 'The restaurant had excellent service' and 'The food and staff were outstanding' so I can calculate their cosine similarity?"},{"title":"Query vectorization for nearest-neighbor search","prompt":"Convert this user search query into an embedding vector so I can run a nearest-neighbor lookup against my indexed documents: 'best hiking boots for winter trails'."}],"resultDescription":"Returns a JSON object containing the embedding dimension (dim: 768), the count of embeddings generated, the model name (nomic-embed-text:latest), and an array of embedding vectors — one float array per input string — suitable for use in vector databases, similarity search, or downstream ML tasks.","failureModes":["Payment not included or insufficient USDC — returns 402 Payment Required with x402 payment details","Input field missing or malformed — returns 400 with validation error","Model unavailable or GPU backend overloaded — returns 503 Service Unavailable","Empty string or empty array input — may return zero-length embeddings or 400 error","Network timeout on large batches — connection may drop before response"],"whenToPreferThis":"Choose this endpoint when you need pay-per-use text embeddings billed in USDC on Base via the x402 protocol, with no subscription or account required for bots. Ideal for AI agents in crypto-native stacks that need to embed text on-demand without pre-purchasing credits. The nomic-embed-text model produces 768-dimensional vectors well-suited for semantic search and RAG pipelines. Prefer this over OpenAI Embeddings or Cohere when you want decentralized, stablecoin-settled compute.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-16T00:38:32.201Z","isFirstParty":false}