{"uid":"cap_Hdj_Oe7dHdhtOYwxX8iVp","slug":"zeroreader-bge-small-en-v1-5-embedding-716f1fc0","name":"ZeroReader BGE Small EN v1.5 Embedding","description":"BGE Small EN v1.5 — Fast, lightweight English embeddings.","url":"https://api.zeroreader.com/v1/ai/embed-bge-small?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"oneOf":[{"type":"string"},{"type":"array","items":{"type":"string"}}]}}},"responseSchema":{"data":[{"index":0,"object":"embedding","embedding":[0.1,0.2,0.3]}],"object":"list"},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_n5UROqV7FyuHHT5BQwWve","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates fast, lightweight English text embeddings using the BGE Small EN v1.5 model, returning dense float vectors for semantic similarity and retrieval tasks.","exampleAgentPrompt":"Can you embed this text using a fast, lightweight English model — 'The latest AI breakthroughs are reshaping the tech industry' — and give me back the vector?","exampleUseCases":null,"resultDescription":"Returns a JSON object with an 'object' field set to 'list' and a 'data' array containing embedding objects, each with an index, object type 'embedding', and a dense float array representing the semantic vector of the input text.","failureModes":["Empty or missing 'text' field returns a 400 validation error","Input text exceeding model token limit may be truncated or return an error","Network timeout for very large batches of strings","Payment failure via x402 protocol results in 402 response blocking the call","Non-English text may produce lower-quality embeddings due to model training scope"],"whenToPreferThis":"Choose this endpoint when you need fast, cost-efficient English text embeddings and latency or throughput matters more than maximum quality. Prefer the BGE Large EN v1.5 sibling when embedding quality is paramount. Use this model for high-volume RAG chunking, real-time semantic search, or resource-constrained pipelines where the small model's speed advantage is valuable.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-03T00:33:59.922Z","isFirstParty":false,"canonicalSlug":"zeroreader-bge-small-en-v1-5-embedding-716f1fc0"}