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Agent Hub BGE-M3 Embeddings
x402
Agent Hub BGE-M3 Embeddings is a paid API for AI agents from agents.torbrooknews.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-13).
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.
Price / call$0.002
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
RAG pipeline document ingestion
“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.”
Multilingual semantic search indexing
“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?”
Sentence similarity scoring
“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'?”
Technical details
Endpoint
GEThttps://agents.torbrooknews.com/v1/embeddings?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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