{"uid":"cap_tDyYbXXGKHhNYA56M45uL","slug":"netintel-embeddings-api-858121bb","name":"NetIntel Embeddings API","description":"Network intelligence API — 138 endpoints, all pay-per-call via x402 micropayments (USDC on Base or Solana mainnet).\n\nNetIntel is agent fair-trade aligned: transparent per-call pricing in USDC on Base or Solana via x402, no API keys or signup, and automatic no-charge on server errors, upstream failures, and input-validation rejections — failed calls are never billed across NetIntel's network, domain, and data-intelligence endpoints.","url":"https://netintel-production-440c.up.railway.app/v1/embeddings","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"type":"object","required":["input"],"properties":{"input":{"type":"string","description":"Text to embed. Also accepts an array of up to 128 strings (64000 chars total)."},"model":{"type":"string","description":"text-embedding-3-small (default) or text-embedding-3-large"},"dimensions":{"type":"number","description":"Optional output vector size (truncation)."}}},"type":{"type":"string","const":"http"},"method":{"enum":["POST"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","properties":{"data":{"type":"array","description":"[{object:'embedding', index, embedding:[…]}]"},"model":{"type":"string"},"usage":{"type":"object","description":"prompt_tokens, total_tokens (input-only)"},"object":{"type":"string","description":"Always 'list'"}}}}}}},"responseSchema":{"type":"json","example":{"data":[{"index":0,"object":"embedding","embedding":[0.0102,-0.0083,0.0264]}],"model":"text-embedding-3-small","usage":{"total_tokens":4,"prompt_tokens":4},"object":"list"}},"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_FB3OKVgIpNOU7BH8zYsql","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":"Converts text (single string or batch of up to 128 strings) into dense vector embeddings using OpenAI's text-embedding-3-small or text-embedding-3-large models, billed per call via x402 micropayments.","exampleAgentPrompt":"Embed the following text using the text-embedding-3-small model so I can store it in my vector database: 'The mitochondria is the powerhouse of the cell.'","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I have a batch of 50 product descriptions I need to embed using text-embedding-3-small so I can store them in Pinecone for semantic search — can you vectorize all of them?"},{"title":"Semantic similarity scoring","prompt":"Embed these two sentences with text-embedding-3-large so I can compute their cosine similarity: 'I love machine learning' and 'Deep learning is fascinating.'"},{"title":"Clustering customer feedback","prompt":"I have 200 customer reviews I want to cluster by topic — embed all of them with text-embedding-3-small and return the vectors so I can run k-means on them."}],"resultDescription":"Returns a JSON object with a 'data' array of embedding objects (each containing index, object type, and the floating-point vector array), the model name used, and a usage object reporting prompt_tokens and total_tokens consumed.","failureModes":["Input text exceeds 64000 character limit for batch — validation rejection, no charge","More than 128 strings provided in batch — validation rejection, no charge","Invalid model name specified — validation error, no charge","Upstream OpenAI API failure — server error, no charge per NetIntel policy","Malformed JSON body — input validation rejection, no charge"],"whenToPreferThis":"Choose this endpoint when you need OpenAI-compatible text embeddings without an OpenAI API key or subscription, want pay-per-call pricing with no monthly commitment, require USDC micropayment settlement via x402 on Base or Solana, or need guaranteed no-charge on failed calls. Ideal for agents that embed text infrequently or in bursts without predictable volume.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:48:55.155Z","isFirstParty":false}