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Citiview BGE-M3 Embeddings
x402
Citiview BGE-M3 Embeddings is a paid API for AI agents from agents.citiviewgroup.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-14).
Generates multilingual text embeddings (1024-dimensional BGE-M3 vectors) for up to 100 text inputs per call, using an OpenAI-compatible API shape.
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://agents.citiviewgroup.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 a JSON object in OpenAI /v1/embeddings response format: an array of embedding objects each containing an index, the embedding vector (as a float array or base64-encoded string depending on encoding_format), and object type 'embedding', plus a usage object with token counts and the model name 'bge-m3'.
Example use cases
RAG pipeline document indexing
“I need to embed these 50 product descriptions so I can store them in my vector database for semantic search — can you vectorize all of them using BGE-M3 and return the float arrays?”
Multilingual semantic similarity
“I have customer reviews in Spanish, French, and Japanese — embed each one so I can compare how semantically similar they are to a set of English reference sentences.”
No-key OpenAI SDK swap
“I want to switch my embedding calls from OpenAI to a pay-per-request service that uses BGE-M3 — just point my existing SDK at it and embed this text: 'summarize quarterly earnings trends'.”
Technical details
Endpoint
GEThttps://agents.citiviewgroup.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 coverage across 100+ languages with a single model, prefer pay-per-call pricing with no subscription, or need to swap in a drop-in replacement for /v1/embeddings in an existing OpenAI SDK integration. BGE-M3's 1024-dimensional output is well-suited for dense retrieval and reranking tasks.
Side effects
- Missing 'input' parameter returns a validation error
- More than 100 inputs in a single call may be rejected
- Invalid encoding_format value falls back to float silently or errors
- Payment not provided or insufficient USDC causes 402 Payment Required
- GET requests with very long input strings may hit URL length limits — use POST for large inputs
- Network timeout if the model is cold-starting
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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