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x402.tanship.dev Multilingual Text Embeddings
x402
x402.tanship.dev Multilingual Text Embeddings is a paid API for AI agents from x402.tanship.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Generates 1024-dimensional multilingual text embeddings using the BGE-M3 model via edge AI
Price / call$0.002
Success—
StatusUnknown
How this service works
Payment-gated AI and browser automation API using the x402 protocol. All /v1/* endpoints require USDC payment on Base (eip155:8453) via x402 v2 exact scheme.
Output
Returns a 1024-dimensional float vector (BGE-M3 embedding) representing the semantic content of the input text, suitable for cosine similarity, vector database storage, or downstream ML tasks.
Example use cases
Semantic search over multilingual documents
“Embed this English query so I can find semantically similar documents in my multilingual vector index: 'climate change effects on agriculture'”
Duplicate detection in support tickets
“Convert these two customer support messages into embeddings so I can measure how similar they are and decide if they should be merged: 'My order never arrived' and 'I never received my package'”
RAG pipeline document indexing
“Generate a BGE-M3 embedding for this Spanish text so I can add it to my retrieval-augmented generation knowledge base: 'El cambio climático afecta a la producción agrícola mundial'”
Technical details
Endpoint
POSThttps://x402.tanship.dev/v1/ai/embeddingsWhen to prefer
Choose this endpoint when you need high-quality multilingual text embeddings (1024 dims) from the BGE-M3 model without running your own GPU infrastructure. It is ideal for RAG pipelines, semantic search, and cross-lingual similarity tasks where language coverage matters. Prefer it over OpenAI embeddings when you want a pay-per-call USDC micropayment model via x402 with no monthly subscription, or when BGE-M3's multilingual capabilities are specifically required.
Side effects
- Empty or missing 'input' body returns a 400/422 validation error
- Oversized input text exceeding model context window may be truncated or rejected
- Network timeouts at edge node under high load
- Invalid bodyType enum value causes schema validation failure
- Non-UTF-8 encoded text may cause processing errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Tags
x402
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