{"uid":"cap_DuiW85DZOMzVDWN13Km5o","slug":"x402-deployer-x402-deployer-workers-dev-e8a56194","name":"x402 Text Embedding via Venice (Gemini / BGE-M3)","description":"Text embedding / vector embedding / semantic vector / Venice embeddings / Gemini embeddings / BGE-M3. Embeds 1 to 100 strings via Venice. Tier shorthand: 'default' → gemini-embedding-2-preview (newest, recommended), 'fast' → text-embedding-bge-m3, 'openai-compat' → text-embedding-3-small. You can also pass a full Venice embedding model name. Returns a list of vectors aligned with input order.","url":"https://x402-deployer.x402-deployer.workers.dev/text-embedding","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":{"required":["texts"],"properties":{"model":{"type":"string","description":"Tier shorthand ('default'|'fast'|'openai-compat') or full Venice embedding model name. Default 'default'."},"texts":{"type":"array","items":{"type":"string"},"description":"1 to 100 strings to embed; each up to 30,000 chars."}}},"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":{"tier":{"type":"string"},"count":{"type":"integer"},"model":{"type":"string"},"usage":{"type":"object","properties":{"total_tokens":{"type":"integer"},"prompt_tokens":{"type":"integer"}}},"source":{"type":"string"},"dimensions":{"type":"integer"},"embeddings":{"type":"array","items":{"type":"array","items":{"type":"number"}}}}}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"settled","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_j-c_JxNt2Ip9ennCQ-AtZ","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Embeds 1–100 strings into semantic vectors using Venice-hosted models (Gemini embedding-2-preview, BGE-M3, or text-embedding-3-small), returning float vectors aligned with input order.","exampleAgentPrompt":"Embed these 3 sentences for me using the newest recommended model — 'The cat sat on the mat', 'A feline rested on the rug', 'Dogs love to play fetch' — I want float vectors back so I can compare their semantic similarity.","exampleUseCases":null,"resultDescription":"A list of float vectors (one per input string), aligned with input order, representing the semantic content of each string in a high-dimensional space. The model used determines dimensionality and quality characteristics.","failureModes":["More than 100 strings submitted — batch size limit exceeded","Invalid or unrecognized model name passed — returns error","Empty input array — returns error or empty list","Network timeout on large batches — retry with smaller batch","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need fast, cheap semantic vector embeddings for RAG pipelines, semantic search, clustering, or similarity scoring, especially when you want access to cutting-edge models like Gemini embedding-2-preview or the multilingual BGE-M3 without managing your own model infrastructure. The tier shorthand system makes it easy to swap models without knowing full model names.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T06:30:22.803Z","isFirstParty":false}