{"slug":"agentsvc-io-api-v1-proxy-text-embed-09601e","title":"Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingu","host":"agentsvc.io","method":"POST","resource":"https://agentsvc.io/api/v1/proxy/text-embed","category":"search","description":"Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingual input works best in English) for semantic search, clustering and deduplication, or rerank mode: pass query + documents to get documents sorted by cosine similarity. Runs locally, texts are not stored. Params: texts (1","price_listed":0.002,"price_asked":0.002,"state":"answering","state_label":"Answering","checks_7d":1,"answered_7d":1,"latency_ms_median":887,"reported_calls_30d":11,"reported_payers_30d":3,"networks":["eip155:137","eip155:42161","eip155:8453","solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp"],"badge":"unverified","paid_checks_7d":0,"paid_ok_7d":0,"example_input":{"body":{"documents":["x402 lets agents pay per call with USDC","The cat sat on the mat","Weather in Berlin is sunny"],"query":"how do AI agents pay for APIs?"},"bodyType":"json","method":"POST","type":"http"},"output_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"input":{"additionalProperties":false,"properties":{"body":{"properties":{"documents":{"items":{"type":"string"},"maxItems":64,"type":"array"},"include_embeddings":{"default":false,"type":"boolean"},"query":{"type":"string"},"texts":{"items":{"type":"string"},"maxItems":32,"type":"array"},"top_k":{"maximum":64,"minimum":1,"type":"integer"}},"required":[]},"bodyType":{"enum":["json","form-data","text"],"type":"string"},"method":{"enum":["POST","PUT","PATCH"],"type":"string"},"type":{"const":"http","type":"string"}},"required":["type","method","bodyType","body"],"type":"object"},"output":{"properties":{"example":{"properties":{"data":{"properties":{"mode":{"type":"string"},"model":{"properties":{"dimensions":{"type":"integer"},"max_tokens":{"type":"integer"},"name":{"type":"string"},"normalized":{"type":"boolean"},"quantization":{"type":"string"},"similarity":{"type":"string"}},"type":"object"},"query":{"type":"string"},"results":{"items":{"properties":{"document":{"type":"string"},"index":{"type":"integer"},"score":{"type":"number"}},"type":"object"},"type":"array"}},"required":["mode","model"],"type":"object"},"success":{"type":"boolean"}},"required":["success","data"],"type":"object"},"type":{"type":"string"}},"required":["type"],"type":"object"}},"required":["input"],"type":"object"},"history":[{"day":"2026-10-10","reachable":true,"status":402,"valid_402":true,"asked_usdc":0.002,"price_match":true,"latency_ms":887,"error":null}],"description_full":"Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingual input works best in English) for semantic search, clustering and deduplication, or rerank mode: pass query + documents to get documents sorted by cosine similarity. Runs locally, texts are not stored. Params: texts (1 to 32 strings, max 2000 chars each) for embed mode, or query + documents (up to 64) and top_k for rerank mode.","last_updated":"2026-10-10T09:09:26.258Z","schemes":["exact"]}