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AgentRXI Text Embeddings (BGE-M3)
x402
AgentRXI Text Embeddings (BGE-M3) 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 multilingual BGE-M3 text embeddings (1024 dimensions) for up to 100 texts per call, optimized for semantic search and RAG pipelines.
Price / call$0.003
Success—
StatusUnknown
How this service works
Text embeddings for semantic search and RAG (BGE-M3, multilingual, 1024 dimensions). Up to 100 texts per call, one flat price. Priced by size: $0.003 + 3x the model token cost. Also callable as GET with the same fields as query parameters.
Output
Returns 1024-dimensional float embedding vectors for each input text, produced by the multilingual BGE-M3 model. Each text maps to one dense vector suitable for cosine similarity, nearest-neighbor search, or storage in a vector database.
Example use cases
RAG pipeline document ingestion
“I'm building a RAG system — can you embed these five document chunks so I can store them in my vector DB? Here they are separated by pipes: 'Quantum computing uses qubits|Classical computers use bits|Superposition allows multiple states|Entanglement links qubits|Decoherence is a key challenge'.”
Multilingual semantic search index
“I need to vectorize these product descriptions in English, Spanish, and French so users can search across languages — embed all of them: 'Wireless headphones with noise cancellation|Auriculares inalámbricos con cancelación de ruido|Casque sans fil avec réduction de bruit'.”
Sentence similarity scoring setup
“Can you get me the BGE-M3 embeddings for both of these sentences so I can measure how similar they are: 'How do I reset my password?' and 'I forgot my login credentials and need to get back into my account'?”
Technical details
Endpoint
GEThttps://agentrxi.com/v1/embed?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need production-ready multilingual embeddings via a simple pay-per-call API without managing GPU infrastructure. BGE-M3's 1024-dimensional output is well-suited for cross-lingual semantic search and RAG. Best when batching up to 100 texts at once to maximize cost efficiency at $0.003 base price.
Side effects
- Input exceeds 100 texts per call — request is rejected
- Individual text too long for model token limit — truncation or error
- Malformed pipe-separated texts field — parsing failure
- Payment not included or insufficient — 402 response
- Empty input texts — returns error or empty array
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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