{"uid":"cap_ZgKaKArAFTXKbVO7Os6n8","slug":"agentrxi-text-embeddings-bge-m3-multilingual-1ff189c5","name":"AgentRXI Text Embeddings (BGE-M3, Multilingual)","description":"OpenAI-compatible embeddings endpoint (bge-m3, 1024 dimensions, 100+ languages): same request and response shape as OpenAI /v1/embeddings, including encoding_format base64. Up to 100 inputs per call, flat price, no API key: point any OpenAI SDK at https://agentrxi.com/v1 and pay per request with x402. Priced by size and model: $0.003 + 3x the model token cost (bge-m3 is cheapest). Also callable as GET with the same fields as query parameters.","url":"https://agentrxi.com/v1/embeddings?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["input"],"properties":{"input":{"type":"string","description":"Text to embed (POST also accepts an array of up to 100 strings)"},"model":{"type":"string","description":"Ignored; always bge-m3"},"encoding_format":{"type":"string","description":"float (default) | base64"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","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":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_mpEZbm6bfeh3XNI5VDyhR","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates OpenAI-compatible text embeddings using the BGE-M3 model (1024 dimensions, 100+ languages) for up to 100 texts per call, payable per-request via x402.","exampleAgentPrompt":"Generate an embedding vector for the text 'quantum entanglement is a phenomenon in quantum physics' using the BGE-M3 model, returning the result as a float array.","exampleUseCases":[{"title":"Multilingual RAG document ingestion","prompt":"I need to embed these 50 product descriptions in Spanish and English so I can store them in my vector database for semantic search — can you batch them into a single embeddings call and return the float vectors?"},{"title":"Semantic similarity scoring","prompt":"Take these two sentences — 'the cat sat on the mat' and 'a feline rested on a rug' — and get their embedding vectors so I can compute how semantically similar they are."},{"title":"No-API-key embeddings for prototyping","prompt":"I don't have an OpenAI API key right now — can you call the AgentRXI embeddings endpoint to vectorize this customer review in base64 format so I can plug it into my classifier?"}],"resultDescription":"Returns an OpenAI-compatible JSON response with an array of embedding objects, each containing the embedding vector (as float array or base64-encoded string), the index of the input text, and the object type. Also includes a usage field with prompt_token_count and total_token_count. Fully compatible with OpenAI SDK parsing.","failureModes":["Missing required 'input' parameter returns a 400 error","Providing more than 100 input strings may be rejected or truncated","Invalid encoding_format value returns an error","Payment failure via x402 results in 402 Payment Required response","Very long texts may exceed token limits and fail","Model parameter is silently ignored (always uses bge-m3)"],"whenToPreferThis":"Choose this endpoint when you need OpenAI-compatible embeddings without an OpenAI API key, want multilingual support across 100+ languages with BGE-M3, need to batch up to 100 texts in a single call at a flat per-request price, or are building a pay-per-use agent pipeline using x402 micropayments. Prefer over OpenAI embeddings when cost predictability per call matters more than per-token billing, or when BGE-M3's multilingual strength is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:38:49.156Z","isFirstParty":false,"canonicalSlug":"agentrxi-text-embeddings-bge-m3-multilingual-1ff189c5"}