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NetIntel Multilingual Text Embeddings
x402
NetIntel Multilingual Text Embeddings is a paid API for AI agents from netintel.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).
Converts text (or batches up to 256 strings) into 384-dimensional multilingual embeddings using a hosted e5 model, with no OpenAI dependency or API key required.
Price / call$0.001
Success—
StatusUnknown
How this service works
Multilingual text embeddings (384-dim) served in-house, no OpenAI dependency, no API key. NetIntel's cheapest embeddings at $0.001/call, batches up to 256 texts. Supports query/passage input types for retrieval (e5). Pairs with /text/chunk for a full chunk→embed RAG pipeline on one provider.
Output
Returns a JSON object with a 'data' array of embedding objects (each with index, object type, and a 384-float embedding array), the model name 'netintel-embed-small', dimensions (384), normalized flag, source model identifier, input_type used, and token usage counts (prompt_tokens, total_tokens).
Example use cases
RAG pipeline chunk embedding
“I've split my document into chunks using netintel's chunker — now embed all these chunks as passages so I can store them in my vector DB for retrieval. Here are the 50 text chunks.”
Multilingual semantic search index
“I need to embed these 200 product descriptions in multiple languages as passage vectors so users can search them semantically. Batch them all in one call and give me the 384-dim float arrays.”
Query vector for similarity lookup
“Convert this user search query — 'best noise-cancelling headphones under $200' — into a query embedding so I can find the closest matching passages in my vector store.”
Technical details
Endpoint
POSThttps://netintel.dev/embeddingsWhen to prefer
Choose this endpoint when you need cheap ($0.001/call), dependency-free multilingual text embeddings without an OpenAI account, especially when already using other NetIntel endpoints (e.g. /text/chunk) to build a self-contained RAG pipeline on a single provider. Prefer it for batch embedding up to 256 texts per call and for retrieval tasks requiring query/passage asymmetry via the e5 input_type parameter.
Side effects
- Input array exceeds 256 strings or 200,000 total characters — request rejected
- Invalid input_type value outside 'query'/'passage' — defaults or errors
- Payment not received or x402 handshake fails — 402 response with payment details
- Malformed JSON body or missing required 'input' field — 400 error
- Very long individual strings may be truncated or cause token limit errors
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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