{"uid":"cap_JfMNlRf5PLMYrFqYTjhbw","slug":"forgemesh-text-embeddings-api-b51d1da4","name":"ForgeMesh Text Embeddings API","description":"Text embeddings API without an API key: up to 64 texts per call, 768-dimension embedding vectors computed on our own hardware. For semantic search, clustering, dedup, and RAG. Pay per call with USDC over x402; inputs never stored.","url":"https://x402.forgemesh.io/text-embeddings","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"single text alternative to texts[]"},"texts":{"type":"array","items":{"type":"string"},"description":"1-64 texts to embed"}}},"responseSchema":{"type":"json","example":{"model":"nomic-embed-text","dimensions":768,"embeddings":[[0.011,-0.02],[0.03,0.004]]}},"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_BSANkWUnzRT7HsngxvWLm","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 up to 64 texts per call into 768-dimensional embedding vectors for semantic search, clustering, deduplication, and RAG pipelines, billed per call via USDC over x402.","exampleAgentPrompt":"Can you embed these 10 product descriptions into 768-dimensional vectors so I can index them for semantic search: 'wireless noise-cancelling headphones', 'ergonomic office chair', 'standing desk converter', 'USB-C hub', 'mechanical keyboard', 'monitor arm', 'webcam 4K', 'desk lamp LED', 'cable management box', 'laptop stand'?","exampleUseCases":null,"resultDescription":"An array of 768-dimensional float vectors, one per input text, suitable for cosine similarity, nearest-neighbor search, clustering, or as input features to downstream ML models.","failureModes":["More than 64 texts in a single call — API likely rejects or truncates the batch","Empty texts array or missing both 'text' and 'texts' fields — validation error","Payment failure via x402 (insufficient USDC balance or bad payment header) — 402 response","Input text too long for the underlying model — may truncate or error","Network timeout on large batches processed on their own hardware"],"whenToPreferThis":"Choose this endpoint when you need text embeddings without registering for or managing an API key, want to pay per call in USDC rather than a monthly subscription, need a 768-dimension vector space suitable for standard RAG and semantic search pipelines, or want assurance that inputs are not stored. Prefer this over OpenAI or Cohere embeddings when keyless, pay-as-you-go crypto billing is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:53:47.078Z","isFirstParty":false}