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NetIntel Embeddings API
x402
NetIntel Embeddings API is a paid API for AI agents from netintel-production-440c.up.railway.app, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).
Converts text (or batches of up to 256 strings) into 384-dimensional float vector embeddings using the multilingual-e5-small model, billed per call via x402 micropayments.
Price / call$0.001
Success—
StatusUnknown
How this service works
Network intelligence API — 138 endpoints, all pay-per-call via x402 micropayments (USDC on Base or Solana mainnet). NetIntel is agent fair-trade aligned: transparent per-call pricing in USDC on Base or Solana via x402, no API keys or signup, and automatic no-charge on server errors, upstream failures, and input-validation rejections — failed calls are never billed across NetIntel's network, domain, and data-intelligence endpoints.
Output
Returns a JSON object with a 'data' array where each element contains the embedding index, object type ('embedding'), and a 384-element float array. Also includes the model name ('netintel-embed-small'), token usage (prompt_tokens, total_tokens), object type ('list'), dimensions (384), input_type, normalized flag, and source_model ('multilingual-e5-small').
Example use cases
Semantic search index population
“I need to build a semantic search index — take these 50 product descriptions and convert them all into float embeddings so I can load them into my vector database for similarity search.”
RAG query vectorization
“Before I query my knowledge base, embed this user question as a query-type vector: 'What are the side effects of ibuprofen?' so I can do nearest-neighbor retrieval against my stored passage embeddings.”
Multilingual document similarity
“I have two customer support tickets — one in English and one in Spanish — can you embed both as passage vectors so I can measure how semantically similar they are?”
Technical details
Endpoint
POSThttps://netintel-production-440c.up.railway.app/embeddingsWhen to prefer
Choose this endpoint when you need lightweight, multilingual 384-dimensional embeddings with no API key or signup required, billed only on success at $0.001/call via x402. Ideal for agents building RAG pipelines, semantic search indexes, or document similarity systems that need pay-per-use access without a subscription. The multilingual-e5-small model is well-suited for cross-lingual retrieval tasks. Prefer alternatives if you need larger embedding dimensions (e.g. 1536+) for higher-fidelity semantic tasks.
Side effects
- Input text exceeds 200,000 character limit across batch — validation rejection, no charge
- Batch size exceeds 256 strings — validation rejection, no charge
- Server-side upstream model failure — no charge per NetIntel policy
- Malformed JSON body or missing required 'input' field — validation error, no charge
- Invalid encoding_format value — validation rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": [
{
"index": 0,
"object": "embedding",
"embedding": [
0.0123,
-0.0456,
0.0789
]
}
],
"model": "netintel-embed-small",
"usage": {
"total_tokens": 6,
"prompt_tokens": 6
},
"object": "list",
"findings": [],
"provider": "netintel",
"dimensions": 384,
"input_type": "passage",
"normalized": true,
"source_model": "multilingual-e5-small"
}
}Tags
x402
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