{"uid":"cap_895zZGx19J_wFaJjzplka","slug":"netintel-embeddings-api-df4788e2","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/api/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"},"object":{"type":"string"}}}}}}},"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_Q-gfn1kIKzlr0WJCo47UC","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 (or batches 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 this text using text-embedding-3-small and give me the vector: 'The quick brown fox jumps over the lazy dog'","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I need to vectorize these 50 product descriptions so I can store them in my Pinecone index for semantic search — embed them all using text-embedding-3-small."},{"title":"Semantic similarity scoring","prompt":"Generate embeddings for both of these two customer support messages using text-embedding-3-large so I can compute their cosine similarity and detect if they're duplicates."},{"title":"Keyword-free content clustering","prompt":"Embed all 30 of these news headlines with text-embedding-3-small at 256 dimensions so I can cluster them by topic without any keyword matching."}],"resultDescription":"Returns a JSON object with a 'data' array where each element contains the embedding vector (floating-point array), its index, and object type. Also includes the model name used and token usage stats (prompt_tokens, total_tokens).","failureModes":["Input text exceeds 64,000 character limit for batch requests — returns validation error, no charge","Batch size exceeds 128 strings — rejected at input validation, no charge","Invalid model name provided — returns validation error","Upstream OpenAI API failure — server error returned, no charge applied","Malformed JSON body — HTTP 400, no charge"],"whenToPreferThis":"Choose this endpoint when you need OpenAI-compatible text embeddings without API keys or monthly subscriptions, pay only per call, and want guaranteed no-charge on failures. Ideal for agents that embed text intermittently or at variable volume, or when operating in a crypto-native / x402 payment environment. Supports batching up to 128 strings per call for efficiency.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:49:43.495Z","isFirstParty":false}