{"uid":"cap__k5CkK7GZxOdjw-gMlaZ6","slug":"netintel-text-embeddings-284ecc15","name":"NetIntel Text Embeddings","description":"OpenAI-compatible text embeddings API — standard /v1/embeddings request shape: input as a string or a batch of up to 128 strings (64000 chars total on text-embedding-3-small, the default; 24000 on text-embedding-3-large). Flat $0.005 per call in USDC via x402, no OpenAI account or API key. Returns float vectors for RAG, semantic search, clustering, and dedup.","url":"https://netintel.dev/v1/embeddings","method":"POST","headers":{},"bodySchema":{"type":"object","$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_PMEtc3hOJlTzS-A8_PP8Y","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 input text into a vector embedding using text-embedding-3-small, returning a float array suitable for semantic search, similarity, or downstream ML tasks.","exampleAgentPrompt":"Can you generate a text embedding vector for the sentence 'The quick brown fox jumps over the lazy dog' so I can store it in my vector database for semantic search?","exampleUseCases":null,"resultDescription":"Returns a JSON object containing a list of embedding objects, each with an index, object type, and a float array (the embedding vector); also includes the model name ('text-embedding-3-small') and token usage counts (prompt_tokens, total_tokens).","failureModes":["Empty or missing input text — input validation rejection, no charge","Text exceeds token limit — validation error returned, no charge","Upstream model provider failure — server error returned, no charge","Malformed JSON request body — 400-level error, no charge","Network timeout or infrastructure error — no charge per NetIntel policy"],"whenToPreferThis":"Choose this endpoint when you need pay-per-call text embeddings with no signup, no API key, and automatic no-charge on failures — ideal for agents that need on-demand embeddings without committing to a subscription, or when building RAG pipelines, semantic search, or clustering workflows that fire infrequently or unpredictably.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:47:52.542Z","isFirstParty":false}