# NetIntel Multilingual Text Embeddings

> 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.

## Facts

- Endpoint: POST https://netintel.dev/embeddings
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/netintel-multilingual-text-embeddings-9fa7126c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1zbCm6m3M8ps_7MM68oj0

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability netintel-multilingual-text-embeddings-9fa7126c -d '<json body>'
```

Example prompt: Embed this passage using NetIntel's multilingual embeddings so I can store it in my vector database: 'Climate change is accelerating the melting of polar ice caps.' — use passage input type.

## When to prefer this

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.

## Known failure modes

- 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

## 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).

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "type": "object",
     "required": [
      "input"
     ],
     "properties": {
      "input": {
       "type": "string",
       "description": "Text to embed. Also accepts an array of up to 256 strings (200000 chars total). Aliases: text, inputs."
      },
      "input_type": {
       "type": "string",
       "description": "query or passage (default). Aliases: task, type."
      },
      "encoding_format": {
       "type": "string",
       "description": "float (default) or base64."
      }
     }
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "data": {
       "type": "array",
       "description": "[{object:'embedding', index, embedding:[…384 floats]}]"
      },
      "model": {
       "type": "string",
       "description": "Always 'netintel-embed-small'"
      },
      "usage": {
       "type": "object",
       "description": "prompt_tokens, total_tokens (input-only, estimated)"
      },
      "object": {
       "type": "string",
       "description": "Always 'list'"
      },
      "dimensions": {
       "type": "number",
       "description": "Always 384"
      }
     }
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "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"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/netintel-multilingual-text-embeddings-9fa7126c/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from netintel.dev](https://www.zero.xyz/host/netintel.dev/llms.txt)