{"uid":"cap_9G7H2tXh1lEL6rnL2qcKd","slug":"agent-hub-bge-m3-embeddings-e0ac2ee7","name":"Agent Hub BGE-M3 Embeddings","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://agents.torbrooknews.com/v1 and pay per request with x402. Also callable as GET with the same fields as query parameters.","url":"https://agents.torbrooknews.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":"0.002","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":"$0.002/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_P1rQru8fm5vglcomBpFAM","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates multilingual text embeddings using bge-m3 (1024 dimensions) via an OpenAI-compatible /v1/embeddings endpoint, supporting up to 100 inputs per call with no API key required.","exampleAgentPrompt":"Embed the following paragraph using bge-m3 so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog.' Return the float vector.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I'm building a retrieval-augmented generation pipeline — can you embed these 50 product description chunks into 1024-dim vectors so I can store them in Pinecone? Here they are as a list of strings."},{"title":"Multilingual semantic search indexing","prompt":"I have customer reviews in French, Spanish, and Japanese that I need to vectorize for cross-lingual semantic search — can you embed all of them using bge-m3 and return the float arrays?"},{"title":"Sentence similarity scoring","prompt":"Can you generate embeddings for these two sentences so I can compute their cosine similarity: 'The patient has a fever' and 'The individual is running a high temperature'?"}],"resultDescription":"Returns an OpenAI-compatible embeddings response object containing a list of embedding objects, each with an index, the embedding vector (as a float array or base64 string depending on encoding_format), and a usage field with prompt_token_count and total_token_count. Vectors are 1024-dimensional from bge-m3.","failureModes":["Missing required 'input' parameter returns a 400/422 validation error","Submitting more than 100 strings in a single call may exceed limits and return an error","Payment failure via x402 protocol results in a 402 Payment Required response","Invalid encoding_format value may return a 400 error","Network or upstream model unavailability may return a 500 or 503 error"],"whenToPreferThis":"Choose this endpoint when you need OpenAI SDK-compatible embeddings without managing an API key, need multilingual support across 100+ languages, want a flat per-call price, or need to embed up to 100 texts in a single call using bge-m3's 1024-dimensional vectors. Prefer this over OpenAI's own embeddings endpoint when cost predictability per call matters or when you want to avoid OpenAI account dependency for a self-contained agent workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T12:46:28.818Z","isFirstParty":false,"canonicalSlug":"agent-hub-bge-m3-embeddings-e0ac2ee7"}