{"slug":"api-x402ai-dev-api-embed-c6e21a","title":"Convert text into a 768-dimension embedding vector (nomic-embed-text) for semant","host":"api.x402ai.dev","method":"POST","resource":"https://api.x402ai.dev/api/embed","category":"search","description":"Convert text into a 768-dimension embedding vector (nomic-embed-text) for semantic search, RAG retrieval, clustering, deduplication or similarity scoring. POST JSON {\"input\": string}, up to 3000 characters; returns {embedding: number[768], model, dimensions, elapsed_ms}. One text per call; English w","price_listed":0.01,"price_asked":0.01,"state":"answering","state_label":"Answering","checks_7d":1,"answered_7d":1,"latency_ms_median":536,"reported_calls_30d":1,"reported_payers_30d":1,"networks":["eip155:8453"],"badge":"unverified","paid_checks_7d":0,"paid_ok_7d":0,"example_input":{"body":{"input":"The quick brown fox jumps over the lazy dog."},"bodyType":"json","method":"POST","type":"http"},"output_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"input":{"additionalProperties":false,"properties":{"body":{"properties":{"input":{"description":"Text to embed. 1-3000 characters; longer input is rejected with HTTP 400.","maxLength":3000,"minLength":1,"type":"string"}},"required":["input"]},"bodyType":{"enum":["json","form-data","text"],"type":"string"},"method":{"enum":["POST"],"type":"string"},"type":{"const":"http","type":"string"}},"required":["type","method","bodyType","body"],"type":"object"},"output":{"properties":{"example":{"properties":{"dimensions":{"description":"Always 768","type":"integer"},"elapsed_ms":{"type":"integer"},"embedding":{"description":"768-dimension embedding vector","items":{"type":"number"},"type":"array"},"model":{"type":"string"}},"required":["embedding","dimensions"],"type":"object"},"type":{"type":"string"}},"required":["type"],"type":"object"}},"required":["input"],"type":"object"},"history":[{"day":"2026-09-30","reachable":true,"status":402,"valid_402":true,"asked_usdc":0.01,"price_match":true,"latency_ms":536,"error":null}],"description_full":"Convert text into a 768-dimension embedding vector (nomic-embed-text) for semantic search, RAG retrieval, clustering, deduplication or similarity scoring. POST JSON {\"input\": string}, up to 3000 characters; returns {embedding: number[768], model, dimensions, elapsed_ms}. One text per call; English works best.","last_updated":"2026-09-30T16:09:37.301Z","schemes":["exact"]}