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AgentRXI Text Embeddings (BGE-M3, Multilingual)
x402
AgentRXI Text Embeddings (BGE-M3, Multilingual) is a paid API for AI agents from agentrxi.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-15).
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.
Price / call$0.003
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://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.
Output
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.
Example use cases
Multilingual RAG document ingestion
“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?”
Semantic similarity scoring
“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.”
No-API-key embeddings for prototyping
“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?”
Technical details
Endpoint
GEThttps://agentrxi.com/v1/embeddings?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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