{"uid":"cap_82mKdJWZ7a5Q0c65jYrE5","slug":"forgemesh-text-vectorizer-c38d46c0","name":"ForgeMesh Text Vectorizer","description":"Vector embeddings API: converts up to 64 pieces of text per call into 768-dimensional numeric vectors suitable for similarity comparison, no separate provider account or key needed. Pay per call in USDC, and nothing submitted is retained afterward. Use it to build semantic search indexes, cluster related documents, or de-duplicate near-identical text at scale.","url":"https://x402.forgemesh.io/text-vectorizer","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_jcY5b6PnUctoE6u1RiU8L","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 text strings per call into 768-dimensional numeric embedding vectors for semantic similarity, clustering, and deduplication tasks.","exampleAgentPrompt":"Turn these three sentences into 768-dimensional embedding vectors so I can compare them for semantic similarity: 'The cat sat on the mat.', 'A feline rested on the rug.', 'The stock market closed higher today.'","exampleUseCases":null,"resultDescription":"Returns an array of 768-dimensional floating-point vectors, one per input text, suitable for cosine similarity comparisons, nearest-neighbor lookups, clustering algorithms, or insertion into a vector database. No input data is retained after the call.","failureModes":["More than 64 texts submitted in a single call — request rejected","Empty texts array or missing both text and texts fields — validation error","Individual text string exceeds model token limits — truncation or error","Payment in USDC not provided or insufficient — 402 Payment Required response","Network timeout for very large batches near the 64-item limit"],"whenToPreferThis":"Choose this endpoint when you need text embeddings without setting up a separate provider account (OpenAI, Cohere, etc.) or managing API keys, and when a pay-per-call USDC model is acceptable. Ideal for one-off embedding jobs, privacy-sensitive text (no retention), or agent workflows that need embeddings as a commodity service. Best when batch sizes fit within 64 texts per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:53:30.156Z","isFirstParty":false}